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How to contact us?

D-10315 Berlin, Germany


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Contact

Coordination: Dr. Conny Landgraf
assist6[at]izw-berlin.de

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Address

Leibniz Institute for Zoo and Wildlife Research
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color: var(--hover-color, white); } - + diff --git a/docs/posts/posts.json b/docs/posts/posts.json index a2873bf3..36d1ab43 100644 --- a/docs/posts/posts.json +++ b/docs/posts/posts.json @@ -18,7 +18,7 @@ ], "contents": "\r\n\r\nContents\r\nSetup\r\nRaster Template\r\nExpand Raster\r\nExpand On All Sides\r\nExpand On Two Sides\r\nExpand On One Side With A Custom Extent\r\nCombined Plot\r\n\r\nRasterize Vector Data\r\nRasterized Plot\r\n\r\nExtract With Vector Data\r\nLine For Extraction\r\nPlot Of Raster And Line\r\nExtraction From Line\r\n\r\nVisualize Data With Tidyterra\r\nSummary\r\n\r\nSetup\r\nInstall the {d6geodata} library and load all necessary libraries.\r\n\r\n\r\n#remotes::install_github(\"EcoDynIZW/d6geodata\")\r\nlibrary(d6geodata)\r\nlibrary(terra)\r\nlibrary(ggplot2)\r\nlibrary(sf)\r\nlibrary(patchwork)\r\nlibrary(dplyr)\r\n\r\ntheme_set(\r\n theme(axis.text.x = element_text(size = 8),\r\n axis.text.y = element_text(size = 8))\r\n) \r\n\r\n\r\nRaster Template\r\nFirst get the template raster. The origin is the extent of Berlin with the projection LAEA (EPSG:3035).\r\n\r\n\r\ntemp <- d6geodata::temp_ras(1000) # resolution is 1000m\r\nvalues(temp) <- 1 # set a dummy value for visualization purposes \r\n\r\ntemp\r\n\r\nclass : SpatRaster \r\ndimensions : 37, 46, 1 (nrow, ncol, nlyr)\r\nresolution : 1000, 1000 (x, y)\r\nextent : 4531040, 4577040, 3253790, 3290790 (xmin, xmax, ymin, ymax)\r\ncoord. ref. : +proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs \r\nsource(s) : memory\r\nname : temp \r\nmin value : 1 \r\nmax value : 1 \r\n\r\nWe load Berlin districts data set from our cloud to show the borders of Berlin.\r\n\r\n\r\nberlin <- d6geodata::get_geodata(data_name = \"districs_berlin_2022_poly_03035_gpkg\",\r\n path_to_cloud = \"E:/PopDynCloud\",\r\n download_data = FALSE) %>% \r\n st_union() %>% # using `st_union()` to combine geometries\r\n st_cast(\"POLYGON\")\r\n\r\nReading layer `districs_berlin_2022_poly_03035' from data source \r\n `E:\\PopDynCloud\\GeoData\\data-raw\\berlin\\districs_berlin_2022_poly_03035_gpkg\\districs_berlin_2022_poly_03035.gpkg' \r\n using driver `GPKG'\r\nSimple feature collection with 97 features and 6 fields\r\nGeometry type: MULTIPOLYGON\r\nDimension: XY\r\nBounding box: xmin: 4531043 ymin: 3253864 xmax: 4576654 ymax: 3290795\r\nProjected CRS: ETRS89-extended / LAEA Europe\r\n\r\np_berlin <- geom_sf(data = berlin, alpha = 0, color = \"black\", lwd = 0.7)\r\n\r\n\r\nNow we plot the map.\r\n\r\n\r\np1 <- d6geodata::plot_qualitative_map(temp) # you can use this build-in plot function from the `{d6geodata}` package \r\n \r\np1 + p_berlin\r\n\r\n\r\n\r\nExpand Raster\r\nExpand On All Sides\r\nIf you want to expand the raster (similar to a buffer around a polygon) you can just add cells to all sides. You can do this by adding an integer number in the y argument in the function extend of the {terra} package.\r\n\r\n\r\n# expand the map\r\ntemp_exp_all <- terra::extend(x = temp, # template raster as input\r\n y = 10, # number of cells to add\r\n fill = 2) # value for new cells\r\n\r\n# plot the map again\r\np2 <- d6geodata::plot_qualitative_map(temp_exp_all)\r\np2 + p_berlin\r\n\r\n\r\n\r\nExpand On Two Sides\r\nYou can also expand the raster in one or two different directions only by adding cells on two opposite sides.\r\n\r\n\r\ntemp_exp_tb_sides <- terra::extend(x = temp,\r\n y = c(10, 0), # add 10 cells on top and below\r\n fill = 2,)\r\np3 <- d6geodata::plot_qualitative_map(temp_exp_tb_sides)\r\np3 + p_berlin\r\n\r\n\r\n\r\nExpand On One Side With A Custom Extent\r\nIn this case we use a workaround by using a custom extent. Here we are adding cells on the left (western) side by using the xmin of the larger extend. Xmax, ymin, ymax are from the given extent.\r\n\r\n\r\ntemp_exp_1_sides <- terra::extend(x = temp,\r\n y = ext(ext(temp_exp_all)[1], # xmin\r\n ext(temp)[2], # xmax\r\n ext(temp)[3], # ymin\r\n ext(temp)[4]),# ymax\r\n fill = 2)\r\n\r\np4 <- d6geodata::plot_qualitative_map(temp_exp_1_sides)\r\np4 + p_berlin\r\n\r\n\r\n\r\nCombined Plot\r\nHere you see a combination of all plots made to compare them directly.\r\n\r\n\r\n# arrange plots in a 2x2 grid\r\n(p1 + p_berlin + theme(legend.position=\"none\") + p2 + p_berlin) / (p3 + p_berlin + p4 + p_berlin) + \r\n plot_layout(guides = \"collect\") +\r\n plot_annotation(tag_levels = \"1\", tag_prefix = \"(P\", tag_suffix = \")\") # add tag to every plot\r\n\r\n\r\n\r\nRasterize Vector Data\r\nWith vect() you can make a spatVector (similar to a shapefile) from an extent object.\r\nAfterwards, we can use this spatVector to rasterize the data. This works similar with an sf object.\r\n\r\n\r\n# create spatVector\r\ntemp_vec <- terra::vect(ext(temp), crs(temp)) # spatVector out of smallest raster extent\r\n\r\n# rasterize spatVector\r\ntemp_rast <- rasterize(temp_vec, # vector data\r\n temp_exp_all %>% disagg(10), \r\n # raster data with larger extent, disaggregated to 100m \r\n background = 2) # set to 2 to visualize the difference\r\n\r\ntemp_rast\r\n\r\nclass : SpatRaster \r\ndimensions : 570, 660, 1 (nrow, ncol, nlyr)\r\nresolution : 100, 100 (x, y)\r\nextent : 4521040, 4587040, 3243790, 3300790 (xmin, xmax, ymin, ymax)\r\ncoord. ref. : +proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs \r\nsource(s) : memory\r\nname : layer \r\nmin value : 1 \r\nmax value : 2 \r\n\r\nRasterized Plot\r\nView the results.\r\n\r\n\r\nd6geodata::plot_qualitative_map(temp_rast)\r\n\r\n\r\n\r\nExtract With Vector Data\r\nImagine you have a track of an animal and you want to know what information lays in this track. In this case, you can use your line and extract the values from a raster below. There are several ways to this but the easiest way is to use the extract() function form the {terra} package to get the values laying under the vector data.\r\nFor this, you can use any vector data (points, lines, polygons) with the same projection and extent to extract values from a raster. The extract() function uses the raster as first input and the vector data as second. Depending on the type of data you are using you can define a function for the extraction like min, max or mean. ‘Cells’ gives you the call ID, ‘xy’ the coordinates and ‘ID’ the row number of your extraction.\r\nAs mentioned, it is important that both the vector and the raster have the same projection.\r\nLine For Extraction\r\nFor this we are just creating a dummy line with coordinates laying within the raster\r\n\r\n\r\n# create points\r\npts <- tibble(x = c(4528790, 4576890), # a point out of two coordinates from the raster\r\n y = c(3271740, 3272140))\r\n\r\n# create line from points\r\ndummy_line <- st_as_sf(pts, # a line made out of the point data\r\n coords = c(\"x\", \"y\"), # specify columns of xy coordinates\r\n crs = st_crs(temp)$wkt, # using the same projection as for the template raster\r\n sf_column_name = \"geometry\",\r\n remove = FALSE) %>% \r\n summarise(do_union = FALSE) %>% # this part is for creating the lines out of the points\r\n st_cast(\"LINESTRING\") %>% # this as well\r\n mutate(id = 1:n()) # add an id\r\n\r\n\r\nPlot Of Raster And Line\r\n\r\n\r\np5 <- d6geodata::plot_qualitative_map(temp_rast) + # first plot raster\r\n geom_sf(data = dummy_line) # plot line on top of the raster\r\n\r\np5\r\n\r\n\r\n\r\nExtraction From Line\r\nHere we finally extract the data and view the result with a table.\r\n\r\n\r\next_tab <- terra::extract(x = temp_rast, # template raster as first input\r\n y = dummy_line, # dummy line as second input\r\n fun=NULL, # here you can set a function to summarise the data directly\r\n method=\"simple\", \r\n cells=FALSE, # here you can get the cell number as well if cell = TRUE\r\n xy=FALSE, # here the xy coordinates if xy = TRUE\r\n ID=TRUE # and the id in front of each row\r\n )\r\n\r\n\r\nAs you already can see on the plot, the largest part of the line lays within the purple area (value = 1). This function can be used to know on which land use class your track lays, for example.\r\n\r\n\r\ntable(ext_tab$layer)\r\n\r\n\r\n 1 2 \r\n463 23 \r\n\r\nVisualize Data With Tidyterra\r\nThere are several ways to plot raster data with ggplot and one relatively new way is to do it with the package {tidyterra}. It has some well developed {ggplot} functions, but it cannot always be combined with the base functions of {ggplot}. If you use this package you may have to stay within the package functions.\r\n\r\n\r\nlibrary(tidyterra)\r\n\r\nggplot() + \r\n geom_spatraster(data = temp_rast) + # use geom_spatraster() to plot the raster data with {tidyterra}\r\n p_berlin # add vector data of Berlin\r\n\r\n\r\n# same a before, but add some color to the plot\r\nggplot() + \r\n geom_spatraster(data = temp_rast) +\r\n scale_fill_whitebox_c(palette = \"purple\") +\r\n p_berlin\r\n\r\n\r\n\r\nSummary\r\nYou learned how to extend a raster and how to extract data from a raster by using {sf} and {terra}. Of course there are several ways to do this, but this the most convenient way!!!\r\n\r\n\r\n\r\n", "preview": "posts/rastertemplate/rastertemplate_files/figure-html5/unnamed-chunk-5-1.png", - "last_modified": "2024-03-14T09:00:46+01:00", + "last_modified": "2024-03-11T10:46:05+01:00", "input_file": {}, "preview_width": 1248, "preview_height": 768 @@ -41,7 +41,7 @@ ], "contents": "\r\n\r\nContents\r\nFile Paths\r\nAbsolute Paths: Start from the Root Directory\r\nRelative Paths: Start from the Working Directory\r\n\r\nData Import\r\n{utils} aka “base R”\r\n{vroom} and {readr}\r\n{data.table}\r\n{readxl}\r\n{rio}\r\nWorking with R-specific Formats\r\n\r\nData Export\r\n\r\nThere is one essential step to make use of the power of the R programming language to wrangle, analyze, visualize, and communicate our data: getting the data into R. In this blog post, we will show you multiple approaches for various tabular, plain-text file formats. Note that this blog post is focusing on tabular data — if you need to import spatial data files, have a look here.\r\nFile Paths\r\nIn this blog post, we will make use of files stored remotely but all the workflows would be the same if the file is placed in a local directory. There are multiple ways how to specify the path to a local file:\r\nAbsolute Paths: Start from the Root Directory\r\nsomething like C:\\\\Username\\\\Documents\\\\...\\\\data.csv for Windows or /Users/Username/Documents/.../data.csv for MacOS and Linux\r\nfor Windows paths you have to change the backslashes to forward slashes or escape them by using two backslashes (as in the path above)\r\nwill always point to the correct file on your current machine with the current setup\r\nhowever, this is not recommended as it fails to find the file with any other setup\r\nRelative Paths: Start from the Working Directory\r\nsomething like ./data/data.csv or simply data/data.csv\r\nwill always point to the correct file if the working directory is set correctly and the folder structure is the same\r\nthe current working directory can be retrieved via getwd() and is displayed in Rstudio at the top of your console\r\nthe recommended workflow to ensure comparability across machines, operating systems, and collaborators\r\nR Projects\r\nTo simplify your life, make use of R projects. The .Rproj file can be used as a shortcut for opening the project directly from the filesystem with the working directory set to that path. This way, you do not need to set the working directory manually (which again can cause issues on different machines, systems etc.).\r\n{here} Package\r\nWhen you are working with nested directories and especially notebook formats such as Rmarkdown or Quarto, the {here} package is helpful to make sure that the same working directory is used, no matter if you run the code locally or if you render a notebook that is placed in a subfolder. Read more about the {here} package here.\r\nData Import\r\nFor the showcase of different I/O packages we use data on deforestation provided by OurWorldInData which is hosted on the TidyTuesday repository:\r\n\r\n\r\npath_to_data <- \"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-04-06/forest.csv\"\r\n\r\n\r\nUsually, you can specify the symbols used as separators (indicating columns), for quotation marks, for decimal points and more.\r\nIf the file would be stored locally, a relative path coudl look like this:\r\n\r\n\r\npath_to_data <- \"./data/raw/forest.csv\"\r\npath_to_data <- \"data/raw/forest.csv\"\r\n\r\n## with the {here} package\r\npath_to_data <- here::here(\"data\", \"raw\", \"forest.csv\")\r\n\r\n\r\n{utils} aka “base R”\r\nThe read.*() functions from the {utils} package return data frames.\r\nread.table() is a generic function that can be used for all kinds of tabular data formats; read.csv() is a short-hand wrapper that calls read.delim() with sensible defaults for CSV files.\r\n\r\n\r\ndat <- read.table(path_to_data, sep = \",\", quote = \"\\\"'\", dec = \".\")\r\ndat <- read.csv(path_to_data)\r\n\r\n\r\nOften (as in this case) we have column names that should be used. We can indicate that we want to use those variable names instead by setting header = TRUE:\r\n\r\n\r\ndat <- read.csv(path_to_data, header = TRUE)\r\n\r\n\r\nIf you have “German” CSV files which use a semicolon as column separator (so strictly speaking no CSV files at all) and a , as decimal separator, you can use read.csv2() which uses sep = \";\" and dec = \",\" as the defaults. There are also shorthand wrappers for tab-separated data formats (delimited files) named read.delim() and read.delim2().\r\nYou can directly specify the column types by passing a vector that contains the classes for all columns:\r\n\r\n\r\ndat <- \r\n read.csv(\r\n path_to_data, header = TRUE, \r\n colClasses = c(code = \"factor\", year = \"factor\")\r\n)\r\n\r\n\r\nIf you want to inspect your data, you can use the nrows argument to specify the maximum number of rows to import. If the file contains any rows that should be skipped before reading the data (often the case if the file contains also meta data as in governmental data or NetLogo outputs), you can specify the number of rows to skip via skip.\r\n\r\n\r\ndat <- read.csv(path_to_data, nrows = 5, skip = 3)\r\n\r\n\r\nAnother argument you might commonly see is stringsAsFactors. As of version 4.0.0, the default behaviour has changed; R treats strings in data frames as strings rather than factors now. You can activate that functionality by setting stringsAsFactors = TRUE but this is usually not advised. Ig is likely better to specify factor columns via the colClasses argument or to convert them at a later point.\r\n{vroom} and {readr}\r\nThe vroom() function from the {vroom} package and the read_*() functions from the {readr} package return tibbles (the “modern data frames” used within the tidyverse). They work mostly the same as the read.*() functions but are meant to be faster and come with several additional options to control imports. The {readr} functions call vroom() so they behave exactly the same (and might be merged into a single package at some point).\r\n\r\n\r\ndat <- vroom::vroom(path_to_data)\r\ndat <- readr::read_delim(path_to_data, delim = \",\")\r\ndat <- readr::read_csv(path_to_data)\r\n\r\n\r\nThere are shorthand wrappers for different file formats including:\r\nread_delim() for delimited files in general\r\nread_table() for whitespace-separated files\r\nread_csv() for comma-separated values (CSV)\r\nread_csv2() for semicolon-separated values with comma as the decimal mark\r\nread_tsv() for tab-separated values (TSV)\r\nread_fwf() for fixed-width files\r\nAgain, one can control the column types in the same step. vroom() and read_*() functions allow to specify the types via the col_*() functions which allow for additional arguments or as a very compact combination of shortcuts. The shortcuts are representing columns of type character c, double d, factor f, logical l, integer i, and date D.\r\n\r\n\r\nlibrary(vroom)\r\n\r\n## specify types for certain columns\r\ndat <- \r\n vroom(path_to_data, col_types = cols(\r\n code = col_factor(), \r\n year = col_factor(levels = c(\"1990\", \"2000\", \"2010\", \"2015\"))\r\n ))\r\n\r\n## specify types as shortcuts for all columns\r\ndat <- vroom(path_to_data, col_types = \"cffi\")\r\n\r\n\r\n{data.table}\r\nThe {data.table} package offers a “fast read” function that is especially recommended when working with large files with many rows.\r\n\r\n\r\ndat <- data.table::fread(path_to_data)\r\n\r\n\r\nDifferent file types can be imported by specifying the separator between columns via sep. Nested lists are also supported, then you have to specify sep2 to define the separator within columns.\r\nAs in the read.*() functions, one can specify the type of the columns right away. Multiple ways are possible:\r\n\r\n\r\n## specified as in read.*()\r\ndat <- data.table::fread(path_to_data, colClasses = c(code = \"factor\", year = \"factor\")) \r\n\r\n## columns specified by type with names\r\ndat <- data.table::fread(path_to_data, colClasses = list(factor = c(\"code\", \"year\")))\r\n\r\n## columns specified by type with numbers\r\ndat <- data.table::fread(path_to_data, colClasses = list(factor = 2:3))\r\n\r\n\r\n{readxl}\r\nExcel files have a very specific format and can contain multiple sheets. In general, it is advised to avoid Excel files whenever it is possible. If you have to work with XLS or XLSX files, you likely should not open the files with Excel (as it can introduce multiple issues as it may change column formats). Better import it into R to inspect and wrangle the data. You can use the {readxl} package to handle those files:\r\n\r\n\r\ndat <- readxl::read_excel(\"data/data.xlsx\")\r\n\r\ndat <- readxl::read_xls(\"data/data.xls\")\r\ndat <- readxl::read_xlsx(\"data/data.xlsx\")\r\n\r\n\r\nThe functions from the {readxl} package only work with paths to local files, not URLs. The example above uses some placeholder names, the files do not exists. If you know the type of your file, you may want to use read_xls() or read_xlsx(), respectively, as read_excel() is guessing the right format.\r\nBy default, the functions import the first sheet. You can specify the sheet to import by passing either a number or the name of the sheet:\r\n\r\n\r\n## open sheet by number\r\ndat <- readxl::read_xlsx(\"data/data.xlsx\", sheet = 2)\r\n\r\n## open sheet by name\r\ndat <- readxl::read_xlsx(\"data/data.xlsx\", sheet = \"Table 2\") \r\n\r\n\r\n{rio}\r\nThe {rio} package describes itself as “the Swiss army knife for data I/O”. The idea is that a single function can be used for any file format which is detected on the fly. The package is using several of the previously mentioned packages to achieve full flexibility with regard to file support.\r\n\r\n\r\ndata <- rio::import(path_to_data)\r\n\r\n\r\nWhen passing a CSV file as in this example, the import() function uses fread() from the {data.table} package by default. The import() functions supports all kind of tabular data and also other file format including SAS, SPSS, Stata, R Objects, json, geojson, Apache Arrow, Feather and more.\r\nWorking with R-specific Formats\r\nR provides two file formats for storing data: .RDS and .RData. RDS files, formerly known as RDA files which one should avoid nowadays, can store a only single R object while RData files can store multiple R objects. Another important difference is that RDS files need to be assigned while Rdata files are loaded and use the object name that has been picked by the person saving the file (for good or worse, as it may overwrite existing objects with the same name in your environment).\r\nYou can open a RDS file with readRDS() or readr::read_rds():\r\n\r\n\r\ndat <- readRDS(\"data/file.Rds\")\r\ndat <- readr::read_rds(\"data/file.Rds\")\r\n\r\n\r\nOpening RData files is even easier, simply run the function load() with the file:\r\n\r\n\r\nload(\"data/file.RData\")\r\n\r\n\r\nData Export\r\nAll the packages mentioned (except for {readxl}) also offer functions to write your objects to disk.\r\n\r\n\r\nutils::write.csv(dat, file = \"data/file.csv\")\r\nvroom::vroom_write(dat, file = \"data/file.csv\")\r\nreadr::write_csv(dat, file = \"data/file.csv\")\r\ndata.table::fwrite(dat, file = \"data/file.csv\")\r\nrio::export(dat, file = \"data/file.csv\")\r\nsave(dat, file = \"data/file.Rdata\")\r\nsaveRDS(dat, file = \"data/file.Rds\")\r\n\r\n\r\n\r\n\r\n\r\n", "preview": {}, - "last_modified": "2024-03-14T09:00:46+01:00", + "last_modified": "2024-02-20T11:07:59+01:00", "input_file": {} }, { @@ -63,7 +63,7 @@ ], "contents": "\r\n\r\nContents\r\nTypeface Choices\r\nLegend Position\r\nControl Grid\r\nModify Background Color\r\nSet Plot Margin\r\n\r\nRecently, I added a custom theme for {ggplot2} to our {d6} workflow package which makes it easy to create plots in a style that matches our lab identity used for our webpage, posters, and publications. Also, it hopefully simplifies your workflow by a ready-to-use theme with additional options that allow you to adjust the style of your ggplots.\r\n\r\nLet us know if there is a theme feature you often modify via the theme() function! We can add it as an argument to the complete theme function.\r\n\r\n\r\n\r\n## install (if needed) and load packages\r\nd6::simple_load(c(\"ggplot2\", \"EcoDynIZW/d6\"))\r\n\r\n\r\nThe default theme features a vertical and horizontal grid, our corporate sans font, and a transparent background. It also uses a larger default base_size of 14 pts that controls the size of text labels as well as line elements and borders (the ggplot2 default is 11 pts).\r\n\r\n\r\n## prepare data\r\ndata <- dplyr::filter(economics_long, !variable %in% c(\"pop\", \"pce\"))\r\n\r\n## create base plot\r\ng <- \r\n ggplot(data, aes(x = date, y = value01, color = variable)) +\r\n geom_line()\r\n\r\n## apply corporate theme\r\ng + d6::theme_d6()\r\n\r\n\r\n\r\nAs usual with complete themes, you can overwrite the base settings, namely base_family, base_size, base_size_line, and base_size_rect:\r\n\r\n\r\n## modify theme base setting\r\ng + d6::theme_d6(base_size = 18, base_rect_size = 2)\r\n\r\n\r\n\r\nTypeface Choices\r\nMain Typeface\r\nThe default typeface used is PT Sans. If you prefer a serif typeface, you can switch to PT Serif by setting serif = TRUE.\r\n\r\n\r\ng + d6::theme_d6(serif = FALSE) ## uses PT Sans (default)\r\ng + d6::theme_d6(serif = TRUE) ## uses PT Serif\r\n\r\n\r\n\r\n\r\n\r\nNote that the typefaces need to be installed on your machine. A warning will inform you if the relevant fonts are not installed.\r\nPlease use the {systemfonts} package to access font files as this is the most recent and best implementation to use non-default fonts in combination with {ggplot2}. Loading other font packages, especially the {showtext} package, likely cause problems.\r\nTabular Font Option\r\nFor numeric axis labels and legends, you might want to use a tabular typeface (i.e. one where the characters all have the same width). You can set the axis and legend text individually by combining x, y, and l (uppercase letters work as well).\r\n\r\n\r\n## all combinations of upper- and lowercase work\r\ng + d6::theme_d6(mono = \"xyl\")\r\ng + d6::theme_d6(mono = \"xy\")\r\ng + d6::theme_d6(mono = \"y\")\r\n\r\n\r\n\r\n\r\n\r\nLegend Position\r\nBy default, the legend is placed at the bottom. You can easily change the position via the legend argument. This is a shortcut to theme(legend.position) and thus you can pass either strings specifying the position or a vector defining the x and y position as usual:\r\n\r\n\r\ng + d6::theme_d6(legend = \"right\")\r\ng + d6::theme_d6(legend = \"none\")\r\ng + d6::theme_d6(legend = c(.65, .8))\r\n\r\n\r\n\r\n\r\n\r\nControl Grid\r\nThe default themes comes with no grid lines but you can easily add them by specifying “x” for vertical, “y” for horizontal, or “xy” for both, vertical and horizontal grid lines (uppercase letters work as well).\r\nAll theme styles do not feature minor grid lines to avoid cluttering and distractions.\r\n\r\n\r\ng + d6::theme_d6(grid = \"none\") ## \"\" works as well (default)\r\ng + d6::theme_d6(grid = \"xy\") ## \"XY\" works as well, and even \"xY\" or \"Xy\"\r\ng + d6::theme_d6(grid = \"x\") ## \"x\" works as well\r\ng + d6::theme_d6(grid = \"y\") ## \"Y\" works as well\r\n\r\n\r\n\r\n\r\n\r\nModify Background Color\r\nBy default the background color of all boxes (plot, panel, and legends) is transparent. You can adjust the colors by passing any color name or hex code to the bg argument:\r\n\r\n\r\ng + d6::theme_d6(bg = \"orange\")\r\ng + d6::theme_d6(bg = \"grey95\")\r\ng + d6::theme_d6(bg = \"beige\")\r\n\r\n\r\n\r\n\r\n\r\nIt is not possible to control individual boxes via theme_d6(). If you wish to, for example, color the background of the panel area in a different, you have to specify that via the theme() function as usual:\r\n\r\n\r\ng + d6::theme_d6(bg = \"orange\") + \r\n theme(panel.background = element_rect(fill = \"white\", color = \"transparent\"))\r\n\r\n\r\n\r\nSet Plot Margin\r\nDepending on the use case, you might want to adjust the margin around the plot. The default is related to the base_size and the same for all sides (top, right, bottom, left) specified as rep(base_size / 2, 4). You can modify the margins easily within the theme_d6().\r\n\r\n\r\ng + d6::theme_d6(margin = rep(0, 4), bg = \"grey95\") ## no margins\r\ng + d6::theme_d6(margin = rep(50, 4), bg = \"grey95\") ## same margins\r\ng + d6::theme_d6(margin = c(120, 40, 40, 10), bg = \"grey95\") ## different margins\r\n\r\n\r\n\r\n\r\n\r\n", "preview": "posts/d6-ggplot-theme/d6-ggplot-theme_files/figure-html5/theme-d6-default-1.png", - "last_modified": "2023-12-21T08:22:03+01:00", + "last_modified": "2023-12-08T13:17:48+01:00", "input_file": {}, "preview_width": 1820, "preview_height": 1118 @@ -87,7 +87,7 @@ ], "contents": "\r\n\r\nContents\r\nLoad {terra} package\r\nCreate example data\r\nAdd categorical column\r\nAdd numerical column\r\nMake the raster numerical\r\nCompare spatRasters\r\nCompare values\r\n\r\nCategorical rasters, such as land cover classes, can be tricky to deal with in R.\r\nImagine you get a raster (in {terra} the object is called spatRaster) with categories.\r\nIn {terra}, those categories are stored as labels. Additionally, a raster layer can have multiple labelling columns, in a way that we can activate the column with the information we want at the moment.\r\nFurthermore, we might want to operate with our rasters, for which we may need numerical values.\r\nFirst, we are going to see how to change or activate the labels to show in the categorical rasters.\r\nThen, we are going to see how to effectively transform this into numerical values.\r\nThe {Terra} changed the way how to do this compared to the {raster} package.\r\nHere are some tricks.\r\nLoad {terra} package\r\n\r\n\r\nlibrary(terra)\r\n\r\n\r\nCreate example data\r\nWe create a categorical raster as an example for our code\r\n\r\n\r\n# a simple spatRaster with 3 categories\r\nras <- rast(matrix(rep(c(\"forest\", \"farm\", \"urban\"), each = 3), nrow = 3, ncol = 3))\r\n\r\nras\r\n\r\nclass : SpatRaster \r\ndimensions : 3, 3, 1 (nrow, ncol, nlyr)\r\nresolution : 1, 1 (x, y)\r\nextent : 0, 3, 0, 3 (xmin, xmax, ymin, ymax)\r\ncoord. ref. : \r\nsource(s) : memory\r\ncategories : label \r\nname : lyr.1 \r\nmin value : farm \r\nmax value : urban \r\n\r\nplot(ras)\r\n\r\n\r\n\r\nOur new raster has the categories we gave it as the labels in the plot.\r\nHowever, internally this categories as associated with “Values”. To see how the internal “Values” relate to the label, we can call the table of categories for the raster\r\nvalue\r\n\r\n\r\n# this shows the categories and the numeric internal representative numeric value it automatically gets.\r\ncats(ras)\r\n\r\n[[1]]\r\n value label\r\n1 1 farm\r\n2 2 forest\r\n3 3 urban\r\n\r\nThis is something like a “master table” that tell the raster what to show.\r\nNow that we have access to it, we can transform it and add information as needed, for example reclassifying the classes of the data, and relink this information back to the raster object.\r\nAdd categorical column\r\nlets say you want to reclassify the data, you can add another column by using this:\r\n\r\n\r\nrecl_df <- cbind(as.data.frame(cats(ras)),\r\n data.frame(new_label = c(\"d\", \"f\", \"e\"))) # I shuffled the characters to show the difference in the end\r\n\r\n\r\n# the function `categories` links the new table we created to the raster and \"activates\" the column we want to use. \r\n# In this case the new added column called new_label. The column order is counted as the position of the column AFTER the Value column\r\nras_new_cat <- categories(ras, \r\n layer = 1, \r\n value = recl_df, \r\n active = 2) # column 2 (do not count the value column! It has to be numeric)\r\n\r\n# now our raster has two entries for the categories. We can select which one to show.\r\nras_new_cat\r\n\r\nclass : SpatRaster \r\ndimensions : 3, 3, 1 (nrow, ncol, nlyr)\r\nresolution : 1, 1 (x, y)\r\nextent : 0, 3, 0, 3 (xmin, xmax, ymin, ymax)\r\ncoord. ref. : \r\nsource(s) : memory\r\ncategories : label, new_label \r\nname : new_label \r\nmin value : d \r\nmax value : e \r\n\r\n\r\n\r\nplot(ras_new_cat)\r\n\r\n\r\n\r\nIf we want to go to the old labels, we just need to activate the old labels by indicating the column they are stored in {activeCat} function\r\n\r\n\r\nactiveCat(ras_new_cat) <- 1\r\nplot(ras_new_cat)\r\n\r\n\r\n\r\nAdd numerical column\r\nLet’s say we want to reclassify the spatRaster with a numeric value, we have to take one more step\r\nFirst, we add a column with the numerical value we want to our categorical raster “master table”\r\n\r\n\r\nrecl_df_num <- cbind(as.data.frame(cats(ras_new_cat)),\r\n new_value = c(5, 4, 6))\r\n\r\n# In this case the fourth (numeric) column will be activated\r\n\r\nras_new_cat_2 <- categories(ras, \r\n layer = 1, \r\n value = recl_df_num, \r\n active = 3) # column 4 (do not count the value column! It has to be numeric)\r\n\r\nplot(ras_new_cat_2)\r\n\r\n\r\n\r\nHere we see that the plot show us the numbers we just included, but the raster still reads then as categories\r\n\r\n\r\nras_new_cat_2\r\n\r\nclass : SpatRaster \r\ndimensions : 3, 3, 1 (nrow, ncol, nlyr)\r\nresolution : 1, 1 (x, y)\r\nextent : 0, 3, 0, 3 (xmin, xmax, ymin, ymax)\r\ncoord. ref. : \r\nsource(s) : memory\r\ncategories : label, new_label, new_value \r\nname : new_value \r\nmin value : 5 \r\nmax value : 6 \r\n\r\n# if we use a numerical call, like values for hte cells, we obtain an output that does not correspond with our new numbers\r\nvalues(ras_new_cat_2)\r\n\r\n new_value\r\n [1,] 2\r\n [2,] 1\r\n [3,] 3\r\n [4,] 2\r\n [5,] 1\r\n [6,] 3\r\n [7,] 2\r\n [8,] 1\r\n [9,] 3\r\n\r\nMake the raster numerical\r\nFor this we need the function called {catalyze} and the column in the “master table” that actually has the values we want to use as numeric.\r\nWe specify the column using the index parameter.\r\n\r\n\r\n# this function activates our desired column of the spatRaster\r\nras_new_num <- catalyze(ras_new_cat_2, index = 3) # column 3 for new_value\r\n\r\n# we select only the correct numerical column for the new raster\r\nras_new_num <- ras_new_num$new_value\r\n\r\n# only the numerical column is left\r\nras_new_num\r\n\r\nclass : SpatRaster \r\ndimensions : 3, 3, 1 (nrow, ncol, nlyr)\r\nresolution : 1, 1 (x, y)\r\nextent : 0, 3, 0, 3 (xmin, xmax, ymin, ymax)\r\ncoord. ref. : \r\nsource(s) : memory\r\nname : new_value \r\nmin value : 4 \r\nmax value : 6 \r\n\r\nplot(ras_new_num, type = \"continuous\") # with 'type' we decide to make numerical the legend\r\n\r\n\r\n\r\nCompare spatRasters\r\nLet’s compare the different spatRasters to see the differences\r\n\r\n\r\n\r\nAs you can see in the upper right plot that the categories got the internal count value from 1 to 3 instead of the given values.\r\nCompare values\r\nAnother way of checking if the correct numerical column is used is to show the categories in the spatRaster.\r\n\r\n\r\ncats(ras_new_cat_2)\r\n\r\n[[1]]\r\n value label new_label new_value\r\n1 1 farm d 5\r\n2 2 forest f 4\r\n3 3 urban e 6\r\n\r\ncats(ras_new_num)\r\n\r\n[[1]]\r\nNULL\r\n\r\nThe numeric spatRaster does not have any categories left, as expected.\r\n\r\n\r\n\r\n", "preview": {}, - "last_modified": "2023-11-16T14:14:44+01:00", + "last_modified": "2023-12-08T13:25:35+01:00", "input_file": {} }, { @@ -108,7 +108,7 @@ ], "contents": "\r\nIntroduction\r\nlink\r\nDiscussion\r\nlink\r\nResponse Letter\r\nlink\r\n\r\n\r\n\r\n", "preview": {}, - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {} }, { @@ -129,7 +129,7 @@ ], "contents": "\r\n\r\nContents\r\nExample PopDynCloud\r\nExample geoboundaries\r\n\r\nSometimes you will get a dataset that is larger than your study area and you want to clip it to your specific extent or boundaries. There are two ways to do that:\r\nthe crop function of the {terra} package: this will crop the dataset to the extent of the cropping mask\r\nthe mask function of the {terra} package: this will crop the dataset to the extent of the cropping mask and set everything outside of the mask boundaries to NA (or to a custom set value)\r\nIn this tutorial only the mask function is covered because the crop function is straightforward to use. The mask function gives the opportunity to only get the raster cells that are covered by another raster or spatial object.\r\nHere are two examples showing how to use data from the PopDynCloud and one with data from the geoboundaries website/package:\r\n\r\n\r\nlibrary(d6geodata)\r\nlibrary(sf)\r\nlibrary(terra)\r\nlibrary(dplyr)\r\n\r\n\r\nExample PopDynCloud\r\nIf you have access to the PopDynCloud you can use the districts_berlin_layer like this:\r\n\r\n\r\nberlin_mask <- get_geodata(data_name = \"districs_berlin_2022_poly_03035_gpkg\",\r\n path_to_cloud = \"E:/PopDynCloud\") # get_geodata function from the d6geodata package\r\n\r\nReading layer `districs_berlin_2022_poly_03035' from data source \r\n `E:\\PopDynCloud\\GeoData\\data-raw\\berlin\\districs_berlin_2022_poly_03035_gpkg\\districs_berlin_2022_poly_03035.gpkg' \r\n using driver `GPKG'\r\nSimple feature collection with 97 features and 6 fields\r\nGeometry type: MULTIPOLYGON\r\nDimension: XY\r\nBounding box: xmin: 4531043 ymin: 3253864 xmax: 4576654 ymax: 3290795\r\nProjected CRS: ETRS89-extended / LAEA Europe\r\n\r\nrast_example <- get_geodata(data_name = \"tree-cover-density_berlin_2018_10m_03035_tif\",\r\n path_to_cloud = \"E:/PopDynCloud\")\r\n\r\n\r\nplot_quantitative_map(tif = rast_example) # plot not masked layer\r\n\r\n\r\nrast_example_masked <- mask(rast_example, # input raster \r\n berlin_mask) # mask to be clipped on \r\n\r\nplot_quantitative_map(tif = rast_example_masked) # plot masked layer\r\n\r\n\r\n\r\nExample geoboundaries\r\nIf not, you can use the data from the geoboundaries website or using the rgeoboundaries package from github:\r\n\r\n\r\nremotes::install_github(\"dickoa/rgeoboundaries\")\r\n\r\n\r\n\r\n\r\nlibrary(rgeoboundaries)\r\nrgeob_mask_berlin <- rgeoboundaries::gb_adm2(\"Germany\") %>% # set Country name(s)\r\n filter(shapeName %in% \"Berlin\") %>% # filter for Berlin\r\n st_transform(3035) # reproject to 3035 (or desired crs) \r\n\r\nrast_example_rgeob_masked <- mask(rast_example, # input raster \r\n rgeob_mask_berlin) # mask to be clipped on\r\n\r\nplot_quantitative_map(tif = rast_example_rgeob_masked) # plot with function from d6geodata package\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "preview": "posts/clippingmaskberlin/clippingmaskberlin_files/figure-html5/example-1.png", - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:14+01:00", "input_file": {}, "preview_width": 1248, "preview_height": 768 @@ -155,7 +155,7 @@ ], "contents": "\r\n\r\nContents\r\nCreate a Blogpost\r\nStep 0 — Install the {d6} Package\r\nStep 1 — Use the D6 Blogpost Template\r\nStep 2 — Fill the YAML Header of the Script\r\nStep 3 — Add the Content\r\nStep 4 — Render the Blogpost\r\nStep 5 — Review Process\r\nStep 6 — Published!\r\n\r\nGeneral Tips for Posting\r\n\r\nYou have a nice piece of code? You have developed a cool package? You have something to share within (or even outside) our department?\r\nYou can share it with a blogpost on our EcoDynIZW Website by following these simple steps!\r\nCreate a Blogpost\r\nStep 0 — Install the {d6} Package\r\nIf not yet installed, install the {d6} package. It provides several functions for our department along with Rmarkdown templates, including the D6 blogpost template.\r\n\r\n\r\n# install.packages(remotes)\r\nremotes::install_github(\"EcoDynIZW/d6\")\r\n\r\n\r\nStep 1 — Use the D6 Blogpost Template\r\nIn RStudio, navigate to File > New File... > R Markdown... .\r\nIn the From Template section, choose the D6 Blogpost Template from the list.\r\n\r\n\r\nIf the template is not listed, please make sure that the latest version of {d6} is installed.\r\nStep 2 — Fill the YAML Header of the Script\r\nFill in a proper name, please use uppercase for the name.\r\nWrite a short description. Please have in mind that this description will appear on the blogpost listing page as well as in the post. Usually, we start these descriptions with “Learn how to learn x to do y.”.\r\nAdd some categories that relate to your article. For examples, browse through other posts featured in the wiki section of our web page.\r\nEnter your name and the date of the post in the given format.\r\nStep 3 — Add the Content\r\nDescribe briefly what you will show us and describe each chunk separately.\r\nLeave a line of space between text and chunk for separating text and chunk output.\r\nPlease use the chunk options to name chunks, hide them or ignore them in the knitting process. This will help later to find possible errors.\r\nStep 4 — Render the Blogpost\r\nKnit the post and check if the knitted document looks as desired.\r\nStep 5 — Review Process\r\nSend the Rmd file to the data manager for review.\r\nWait for feedback by the data manager. If changes are requested, update the article accordingly and send the corrected script.\r\nStep 6 — Published!\r\nThe data manager will publish your post as soon as possible on the website.\r\nGeneral Tips for Posting\r\nPlease use the spell check before knitting and pushing the post.\r\nPlease check if the code is running (in a reasonable time) as we have to rebuild the page from time to time.\r\nUse styling like `plot()` for function names in the text and `{pckg}` package names.\r\nPick examples that are simple enough in terms of file size and performance so that they can be easily reproduced within the script.\r\nMake sure that your code can be run by anybody reading the post. If you need data inputs, generate made-up data examples, use data sets from packages, or link to the data source (make sure it is publicly available as readers are not necessarily part of the department!)\r\n\r\n\r\n\r\n", "preview": {}, - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {} }, { @@ -177,7 +177,7 @@ ], "contents": "\r\n\r\nContents\r\nSetup\r\nTheming\r\nSet Themes Globally\r\nAdjust Theme Base Settings\r\nUpdate Theme Elements\r\nCustom Local Modifications\r\nSummary\r\n\r\nMulti-Panel Figures\r\nAdjust Layout\r\nAdd White Space\r\nNested Layouts\r\nMerge Legends\r\nAutomate Plot Tags\r\nInset Plots\r\nSummary\r\n\r\n\r\nSetup\r\nLet’s load the {ggplot2} library and create two basic ggplots, stored as g1 (scatter plot) and g2 (box-and-whisker plot) that can be used later.\r\n\r\n\r\nlibrary(ggplot2)\r\n\r\n\r\n\r\n\r\ng1 <- ggplot(mpg, aes(x = displ, y = hwy)) +\r\n geom_point(aes(color = class))\r\n\r\ng1\r\n\r\n\r\ng2 <- ggplot(mpg, aes(x = class, y = hwy)) +\r\n geom_boxplot()\r\n\r\ng2\r\n\r\n\r\n\r\n\r\n\r\nTheming\r\nThe resulting plots use the default gray theme: theme_gray() or theme_grey().\r\nWe can change the default theme by adding a complete theme, starting with theme_*(), and/or customizing single elements of the default theme via theme():\r\n\r\n\r\ng1 + \r\n ## apply light complete theme\r\n theme_light() + \r\n ## remove minor grid + modify typeface\r\n theme(panel.grid.minor = element_blank(),\r\n text = element_text(family = \"PT Sans\"))\r\n\r\n\r\ng2 + \r\n theme_light() + \r\n theme(panel.grid.minor = element_blank(), \r\n text = element_text(family = \"PT Sans\"))\r\n\r\n\r\n\r\n\r\n\r\nThis procedure involves a lot of copy-and-paste’ing, which makes it a tedious procedure especially in case you decide to make some general styling changes at a later point. It is also prone to mistakes as you might forget to set specific adjustments for single plots.\r\nCheck also our D6 corporate theme which is part of our {d6} R package and the respective blog post! The theme comes with a larger base size and several additional arguments to simplify customization with regard to typefaces, grid lines, margins and more.\r\n\r\n\r\ng1 + d6::theme_d6()\r\n\r\n\r\ng2 + d6::theme_d6(grid = \"y\", serif = TRUE)\r\n\r\n\r\n\r\n\r\n\r\nSet Themes Globally\r\nInstead of repeating the same code to change the appearance of your plots, it is more efficient and beneficial to overwrite the default global theme:\r\n\r\n\r\ntheme_set(theme_light())\r\n\r\n\r\nAfter setting the new theme, all plots created within the same environment are styled accordingly:\r\n\r\n\r\ng1\r\n\r\n\r\ng2\r\n\r\n\r\n\r\n\r\n\r\nAdjust Theme Base Settings\r\nComplete themes allow for some general modifications, no matter if added locally to your plot or if set globally. The setting include the typeface used for all text elements (base_family), the general base size (base_size) as well as dedicated relative sizes for line elements (base_line_size) and rect elements (base_rect_size).\r\n\r\n\r\ng1 + \r\n theme_light(\r\n base_family = \"PT Serif\", ## default: depends on OS\r\n base_size = 18, ## default: 11\r\n base_line_size = 3, ## default: base_size/22 -> 0.5\r\n base_rect_size = 10 ## default: base_size/22 -> 0.5\r\n )\r\n\r\n\r\n\r\nKnowing of this feature, we can already adjust the general size (which tends to be too small by default) as well as the typeface of our custom global theme:\r\n\r\n\r\ntheme_set(theme_light(base_size = 15, base_family = \"PT Sans\")) \r\n\r\n\r\n… which is then used for all following plots:\r\n\r\n\r\ng1\r\n\r\n\r\ng2\r\n\r\n\r\n\r\n\r\n\r\nUpdate Theme Elements\r\nComplete themes are great but in most circumstances we likely want to adjust a few things. Usually, you do that by adding the theme() function to your ggplot (as shown in the beginning). However, similarly to theme_set() we can apply the modifications globally:\r\n\r\n\r\ntheme_update(\r\n panel.grid.minor = element_blank(),\r\n axis.title = element_text(face = \"bold\"),\r\n legend.title = element_text(face = \"bold\")\r\n)\r\n\r\n\r\n\r\n\r\ng1\r\n\r\n\r\ng2\r\n\r\n\r\n\r\n\r\n\r\nCustom Local Modifications\r\nOf course, you can still either overwrite the global theme as before or modify specific elements for a single plot if needed:\r\n\r\n\r\ng1 + \r\n ## new complete theme\r\n theme_classic(base_family = \"PT Serif\", base_size = 15) + \r\n ## add grid lines\r\n theme(panel.grid.major = element_line(color = \"grey90\"))\r\n\r\n\r\ng2 + \r\n ## remove vertical grid lines + overwrite axis title styling\r\n theme(panel.grid.major.x = element_blank(), \r\n axis.title = element_text(color = \"red\", face = \"italic\")) \r\n\r\n\r\n\r\n\r\n\r\nSummary\r\nSetting and updating ggplot themes globally is efficient and avoids potential mistakes.\r\nAs a workflow routine, add a chunk that loads {ggplot2} and afterwards sets and updates your theme at the beginning of a script rather than adding the same code to each plot individually.\r\nMulti-Panel Figures\r\nWe often use multi-panel visualizations, i.e. several plots layed out in a single graphic. Instead of combining single figures manually, we make use of a coding-first approach.\r\nThere are many packages to combine ggplots such as {gridExtra}, {cowplot}, and {ggarrange}. The most recent, and IMHO the best in terms of functionality and simplicity, is the {patchwok} package by Thomas Lin Pedersen. For simple multi-panel graphics, mathematical operators can be used – easy to use and remember.\r\n\r\n\r\nlibrary(patchwork)\r\n\r\n\r\n\r\n\r\ng1 + g2\r\n\r\n\r\n\r\n\r\n\r\ng1 / g2\r\n\r\n\r\n\r\nAdjust Layout\r\nBy default, both plots take the same space. In case you want to adjust how the plots are laid out, use plot_layout() in combination with either widths or heights. These arguments take a vector with the relative width or height for each plot, respectively.\r\n\r\n\r\ng1 + g2 + \r\n plot_layout(widths = c(.5, 1))\r\n\r\n\r\n\r\nAdd White Space\r\n{patchwok} comes with a placeholder to add empty space between plots. Once can add a plot_spacer() similar to a regular plot:\r\n\r\n\r\ng1 + plot_spacer() + g2 + \r\n plot_layout(widths = c(.5, .1, 1))\r\n\r\n\r\n\r\nNested Layouts\r\nAlso more complex layouts can be created:\r\n\r\n\r\n\r\n\r\n\r\n(g1 + g3 + g4) / (g2 + g5)\r\n\r\n\r\n\r\nCode to create g3, g4, and g5\r\n\r\n\r\ng3 <- ggplot(mpg, aes(x = cyl, y = hwy)) +\r\n geom_point(aes(color = class))\r\n\r\ng4 <- ggplot(mpg, aes(x = cty, y = hwy)) +\r\n geom_point(aes(color = class))\r\n\r\ng5 <- ggplot(mpg, aes(x = class, y = hwy)) +\r\n stat_summary(fun.data = \"mean_sdl\", fun.args = list(mult = 1))\r\n\r\n\r\n\r\n\r\n\r\nAlternatively, you can also create a design layout to have full control:\r\n\r\n\r\nlayout <- \r\n \"\r\n ABBCCC#\r\n DDDD#EE\r\n \"\r\n\r\ng1 + g3 + g4 + g2 + g5 + \r\n plot_layout(design = layout)\r\n\r\n\r\n\r\nThe letters refer to the single plots (in the order you combine them later) and a hash # indicates empty space, similar to plot_spacer().\r\nMerge Legends\r\nDisplaying the legend three times makes no sense. {patchwork} offers the utility to “collect your guides” inside the plot_layout() function:\r\n\r\n\r\n(g1 + g3 + g4) / (g2 + g5) + \r\n plot_layout(guides = \"collect\")\r\n\r\n\r\n\r\nNow we may want to move it to the top so it is shown next to the relevant colored scatter plots, not in the middle. We can adjust the theme for all plots inside another {patchwork} function called plot_annotation()—or by updating your global theme 😉\r\n\r\n\r\n((g1 + g3 + g4) / (g2 + g5)) + \r\n plot_layout(guides = \"collect\") +\r\n plot_annotation(theme = theme(legend.justification = \"top\"))\r\n\r\n\r\n\r\nAutomate Plot Tags\r\nWhen preparing such multi-panel figures for publications, we usually want to add tags to be able to refer to subplots in the figure caption or main text. Again, we can do this inside R instead of adding them afterwards by hand (which either takes very long or results in irregularly aligned labels).\r\n\r\n\r\n((g1 + g3 + g4) / (g2 + g5)) + \r\n plot_layout(guides = \"collect\") +\r\n plot_annotation(tag_levels = \"A\", \r\n theme = theme(legend.justification = \"top\"))\r\n\r\n\r\n\r\n{patchwork} understands a range of numbering formats such as a for lowercase letters, 1 for numbers, or i and I for lowercase and uppercase Roman numerals, respectively. Furthermore we can style the tag by defining a pre- and/or suffix:\r\n\r\n\r\n((g1 + g3 + g4) / (g2 + g5)) + \r\n plot_layout(guides = \"collect\") +\r\n plot_annotation(tag_levels = \"i\", tag_prefix = \"(\", tag_suffix = \")\",\r\n theme = theme(legend.justification = \"top\"))\r\n\r\n\r\n\r\nInset Plots\r\nSimilar to other arrangement packages, we can use {patchwork} also to add inset plots. Inside the inside_element() function, we specify the plot to draw and then the outer bounds (left, bottom, right top).\r\n\r\n\r\ng4 + inset_element(g1 + guides(color = \"none\"), .5, 0, 1, .5)\r\n\r\n\r\n\r\nBy default, the inset plot is aligned with the panel of the main plot. If you want to modify the behavior, overwrite the default input of align_to.\r\n\r\n\r\ng4 + inset_element(g1 + guides(color = \"none\"), .5, 0, 1, .5, align_to = \"plot\")\r\ng4 + inset_element(g1 + guides(color = \"none\"), .5, 0, 1, .5, align_to = \"full\")\r\n\r\n\r\n\r\n\r\n\r\nSummary\r\n{patchwork} offers some great functionality to create basic and pretty complex layouts, add inset plots, merge repeated legends, and automate tag numbering. This makes it a powerful tool as you do not need to adjust tag labels, legends, and more for the individual ggplot.\r\n\r\n\r\n\r\n", "preview": "posts/ggplot-workflow/ggplot-workflow_files/figure-html5/basic-ggplots-plots-1.png", - "last_modified": "2023-12-21T08:22:03+01:00", + "last_modified": "2023-12-11T11:28:33+01:00", "input_file": {}, "preview_width": 3640, "preview_height": 1118 @@ -200,7 +200,7 @@ ], "contents": "\r\n\r\nContents\r\nHow to project Netlogo Turtle coordinates into a real map\r\n0. Load libraries\r\n1. Create data\r\n2. Get reference coordinates\r\n3. Transform turtle coordinates into map coordinates\r\n4. Quick plot with tmap\r\n5. Plot with ggplot2\r\n\r\n\r\nHow to project Netlogo Turtle coordinates into a real map\r\nWhen using spatial data in Netlogo, the coordinates of a raster get transformed to relative coordinates. This means, the cell in the bottom left gets coordinate (1,1), the one on top of it is (1,2), and so on.\r\nAfter running a model, usually we want to reproject the output back to the spatial data coordinates used, either for post-simulation analyses or for plotting.\r\nThis code shows how to project the turtles’ coordinates back into a map, when a raster was\r\nused to create the Netlogo landscape.\r\nFor this we need:\r\n- The raster used as netlogo input\r\n- The turtle coordinates in the output\r\n0. Load libraries\r\n\r\n\r\nlibrary(terra)\r\nlibrary(dplyr)\r\nlibrary(sf)\r\nlibrary(tmap)\r\nlibrary(ggplot2)\r\nlibrary(ggspatial)\r\n\r\n\r\n1. Create data\r\nIn this example we create a raster and some turtle data to use.\r\nWith real data, you will load your raster here and make sure it has a PROJECTED coordinate system.\r\nTurtle data will have different formats depending how it was created, the basic data we need for this is the identity of the turtle and the coordinates.\r\n\r\n\r\n## Create raster with 100 cells for the example\r\nmyraster <- rast(nrows = 100, ncols = 100, \r\n xmin = 4541100, xmax = 4542100, \r\n ymin = 3265800, ymax = 3266800)\r\n## give random values to the raster\r\nmyraster <- init(myraster, sample(1:1000))\r\n\r\n## assign projection\r\ncrs(myraster) <- \"epsg:3035\"\r\nplot(myraster)\r\n\r\n\r\n## turtle data - example data\r\nwho <- seq(1,10)\r\nxcoord <- sample(1:100, 10) # create random integers for x coordinate\r\nycoord <- sample(1:100, 10) # create random integers for y coordinate\r\n\r\nturtle_variables <- cbind.data.frame(who, xcoord, ycoord)\r\nhead(turtle_variables)\r\n\r\n who xcoord ycoord\r\n1 1 8 23\r\n2 2 51 97\r\n3 3 87 74\r\n4 4 11 24\r\n5 5 69 26\r\n6 6 68 81\r\n\r\nNow that we have our data, let’s extract the map coordinates as reference and transform the turtle ones. This process will work with any PROJECTED coordinate system.\r\n2. Get reference coordinates\r\nWe need the bottom left corner of the map as a reference point and the resolution of the map\r\n\r\n\r\n# we are going to trasnform the cell relative numbering to real coordinates, starting left down as this is where netlogo starts numbering the cells\r\nstart_left <- xmin(myraster)\r\nstart_down <- ymin(myraster)\r\nmy_res <- res(myraster)[1]\r\n\r\n\r\n3. Transform turtle coordinates into map coordinates\r\nNow we use the reference point to transform our coordinates into the projected coordinates and the resolution to correct for the size of the cells\r\n\r\n\r\nturtle_spatial <- turtle_variables %>% \r\n mutate(spatial_xcoord = start_left + ((xcoord * my_res) + my_res/2), #divided by 2 to locate in the center of the cell\r\n spatial_ycoord = start_down + ((ycoord * my_res) + my_res/2))\r\n\r\n\r\n## make spatial points\r\nturtle_sf <- st_as_sf(turtle_spatial, \r\n coords = c(\"spatial_xcoord\", \"spatial_ycoord\"), \r\n crs = crs(myraster))\r\n\r\n\r\n4. Quick plot with tmap\r\n\r\n\r\n\r\n5. Plot with ggplot2\r\n\r\n x y hs\r\n1 4541105 3266795 902\r\n2 4541115 3266795 698\r\n3 4541125 3266795 500\r\n4 4541135 3266795 492\r\n5 4541145 3266795 405\r\n6 4541155 3266795 151\r\n\r\n\r\n\r\n\r\n\r\n", "preview": {}, - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {} }, { @@ -223,7 +223,7 @@ ], "contents": "\r\n\r\nContents\r\nPreparation\r\n{rnaturalearth}\r\nCountry Data\r\nPhysical Data Sources\r\nCultural Data Sources\r\nRelief Data\r\n\r\n{rgeoboundaries}\r\n{osmdata}\r\n{elevatr}\r\n\r\nPreparation\r\nTo visualize the data sets, we use the {ggplot2} package. We will also use the {sf} and the {terra} packages to work and plot spatial data–vector and raster, respectively–in R. Make sure all packages are installed when running the code snippets.\r\n\r\n\r\n# install.packages(\"ggplot2\")\r\n# install.packages(\"sf\")\r\n# install.packages(\"terra\")\r\n\r\nlibrary(ggplot2)\r\n\r\n### set \"empty\" theme with centered titles for ggplot output\r\ntheme_set(theme_void())\r\ntheme_update(plot.title = element_text(face = \"bold\", hjust = .5))\r\n\r\n\r\n\r\n{rnaturalearth}\r\nNaturalEarth is a public domain map data set that features vector and raster data of physical and cultural properties. It is available at 1:10m, 1:50m, and 1:110 million scales.\r\n{rnaturalearth} is an R package to hold and facilitate interaction with NaturalEarth map data via dedicated ne_* functions. After loading the package, you can for example quickly access shapefiles of all countries–the resulting spatial object contains vector data that is already projected and can be stored as either sp or sf format:\r\n\r\n\r\n## install development version of {rnaturalearth} as currently the \r\n## download doesn't work in the CRAN package version\r\n# install.packages(\"remotes\")\r\n# remotes::install_github(\"ropensci/rnaturalearth\")\r\n\r\n# if downloading raster data such as 'MRS_50m' is not working try to install the developer version of 'rnaturalearth' by using\r\n# remotes::install_dev(\"rnaturalearth\")\r\n\r\n## for high resolution data, also install {rnaturalearthhires}\r\n# remotes::install_github(\"ropensci/rnaturalearthhires\")\r\n\r\nlibrary(rnaturalearth)\r\n\r\n## store as sf object (simple features)\r\nworld <- ne_countries(returnclass = \"sf\")\r\nclass(world)\r\n\r\n[1] \"sf\" \"data.frame\"\r\n\r\nsf::st_crs(world)[1]\r\n\r\n$input\r\n[1] \"WGS 84\"\r\n\r\nCountry Data\r\nThis country data set (which is actually not downloaded but stored locally by installing the package) already contains several useful variables, mostly referring to different naming conventions (helpful when joining with other data sets), to identify continents and regions, and also some information on population size, GDP, and economy:\r\n\r\n\r\nnames(world)\r\n\r\n [1] \"featurecla\" \"scalerank\" \"labelrank\" \"sovereignt\" \"sov_a3\" \r\n [6] \"adm0_dif\" \"level\" \"type\" \"tlc\" \"admin\" \r\n [11] \"adm0_a3\" \"geou_dif\" \"geounit\" \"gu_a3\" \"su_dif\" \r\n [16] \"subunit\" \"su_a3\" \"brk_diff\" \"name\" \"name_long\" \r\n [21] \"brk_a3\" \"brk_name\" \"brk_group\" \"abbrev\" \"postal\" \r\n [26] \"formal_en\" \"formal_fr\" \"name_ciawf\" \"note_adm0\" \"note_brk\" \r\n [31] \"name_sort\" \"name_alt\" \"mapcolor7\" \"mapcolor8\" \"mapcolor9\" \r\n [36] \"mapcolor13\" \"pop_est\" \"pop_rank\" \"pop_year\" \"gdp_md\" \r\n [41] \"gdp_year\" \"economy\" \"income_grp\" \"fips_10\" \"iso_a2\" \r\n [46] \"iso_a2_eh\" \"iso_a3\" \"iso_a3_eh\" \"iso_n3\" \"iso_n3_eh\" \r\n [51] \"un_a3\" \"wb_a2\" \"wb_a3\" \"woe_id\" \"woe_id_eh\" \r\n [56] \"woe_note\" \"adm0_iso\" \"adm0_diff\" \"adm0_tlc\" \"adm0_a3_us\"\r\n [61] \"adm0_a3_fr\" \"adm0_a3_ru\" \"adm0_a3_es\" \"adm0_a3_cn\" \"adm0_a3_tw\"\r\n [66] \"adm0_a3_in\" \"adm0_a3_np\" \"adm0_a3_pk\" \"adm0_a3_de\" \"adm0_a3_gb\"\r\n [71] \"adm0_a3_br\" \"adm0_a3_il\" \"adm0_a3_ps\" \"adm0_a3_sa\" \"adm0_a3_eg\"\r\n [76] \"adm0_a3_ma\" \"adm0_a3_pt\" \"adm0_a3_ar\" \"adm0_a3_jp\" \"adm0_a3_ko\"\r\n [81] \"adm0_a3_vn\" \"adm0_a3_tr\" \"adm0_a3_id\" \"adm0_a3_pl\" \"adm0_a3_gr\"\r\n [86] \"adm0_a3_it\" \"adm0_a3_nl\" \"adm0_a3_se\" \"adm0_a3_bd\" \"adm0_a3_ua\"\r\n [91] \"adm0_a3_un\" \"adm0_a3_wb\" \"continent\" \"region_un\" \"subregion\" \r\n [96] \"region_wb\" \"name_len\" \"long_len\" \"abbrev_len\" \"tiny\" \r\n[101] \"homepart\" \"min_zoom\" \"min_label\" \"max_label\" \"label_x\" \r\n[106] \"label_y\" \"ne_id\" \"wikidataid\" \"name_ar\" \"name_bn\" \r\n[111] \"name_de\" \"name_en\" \"name_es\" \"name_fa\" \"name_fr\" \r\n[116] \"name_el\" \"name_he\" \"name_hi\" \"name_hu\" \"name_id\" \r\n[121] \"name_it\" \"name_ja\" \"name_ko\" \"name_nl\" \"name_pl\" \r\n[126] \"name_pt\" \"name_ru\" \"name_sv\" \"name_tr\" \"name_uk\" \r\n[131] \"name_ur\" \"name_vi\" \"name_zh\" \"name_zht\" \"fclass_iso\"\r\n[136] \"tlc_diff\" \"fclass_tlc\" \"fclass_us\" \"fclass_fr\" \"fclass_ru\" \r\n[141] \"fclass_es\" \"fclass_cn\" \"fclass_tw\" \"fclass_in\" \"fclass_np\" \r\n[146] \"fclass_pk\" \"fclass_de\" \"fclass_gb\" \"fclass_br\" \"fclass_il\" \r\n[151] \"fclass_ps\" \"fclass_sa\" \"fclass_eg\" \"fclass_ma\" \"fclass_pt\" \r\n[156] \"fclass_ar\" \"fclass_jp\" \"fclass_ko\" \"fclass_vn\" \"fclass_tr\" \r\n[161] \"fclass_id\" \"fclass_pl\" \"fclass_gr\" \"fclass_it\" \"fclass_nl\" \r\n[166] \"fclass_se\" \"fclass_bd\" \"fclass_ua\" \"geometry\" \r\n\r\nWe can quickly plot it:\r\n\r\n\r\nggplot(world) + \r\n geom_sf(aes(fill = economy)) + \r\n coord_sf(crs = \"+proj=eqearth\")\r\n\r\n\r\n\r\nNOTE: Unfortunately, NaturalEarth is using weird de-facto and on-the-ground rules to define country borders which do not follow the borders the UN and most countries agree on. For correct and official borders, please use the {rgeoboundaries} package (see below).\r\nPhysical Data Sources\r\nYou can specify the scale, category, and type you want as in the examples below.\r\n\r\n\r\nglacier_small <- ne_download(type = \"glaciated_areas\", category = \"physical\", \r\n scale = \"small\", returnclass = \"sf\")\r\n\r\nglacier_large <- ne_download(type = \"glaciated_areas\", category = \"physical\", \r\n scale = \"large\", returnclass = \"sf\")\r\n\r\n\r\nNow we can compare the impact of different scales specified–there is a notable difference in detail here (and also in size of the object with 11 versus 1886 observations).\r\n\r\n\r\nggplot() + \r\n geom_sf(data = world, color = \"grey85\", fill = \"grey85\") +\r\n geom_sf(data = glacier_small, color = \"grey40\", fill = \"grey40\") + \r\n coord_sf(crs = \"+proj=eqearth\")\r\n\r\nggplot() +\r\n geom_sf(data = world, color = \"grey85\", fill = \"grey85\") +\r\n geom_sf(data = glacier_large, color = \"grey40\", fill = \"grey40\") +\r\n coord_sf(crs = \"+proj=eqearth\")\r\n\r\n\r\n\r\n\r\nlibrary(patchwork)\r\n\r\nsmall <- ggplot() + \r\n geom_sf(data = world, color = \"grey85\", fill = \"grey85\", lwd = .05) +\r\n geom_sf(data = glacier_small, color = \"grey40\", fill = \"grey40\") + \r\n coord_sf(crs = \"+proj=eqearth\") +\r\n labs(title = 'scale = \"small\"')\r\n\r\nlarge <- ggplot() +\r\n geom_sf(data = world, color = \"grey85\", fill = \"grey85\", lwd = .05) +\r\n geom_sf(data = glacier_large, color = \"grey40\", fill = \"grey40\") +\r\n coord_sf(crs = \"+proj=eqearth\") + \r\n labs(title = 'scale = \"large\"')\r\n\r\nsmall + large * theme(plot.margin = margin(0, -20, 0, -20))\r\n\r\n\r\n\r\nCultural Data Sources\r\nNaturalEarth also provides several cultural data sets, such as airports, roads, disputed areas. Let’s have a look at the urban areas across the world:\r\n\r\n\r\nurban <- ne_download(type = \"urban_areas\", category = \"cultural\", \r\n scale = \"medium\", returnclass = \"sf\")\r\n\r\nggplot() + \r\n geom_sf(data = world, color = \"grey90\", fill = \"grey90\") +\r\n geom_sf(data = urban, color = \"firebrick\", fill = \"firebrick\") + \r\n coord_sf(crs = \"+proj=eqearth\")\r\n\r\n\r\n\r\nRelief Data\r\nThe physical and cultural data sets showcased above are all vector data. NaturalEarth also provides raster data, namely gridded relief data:\r\n\r\n\r\nrelief <- ne_download(type = \"MSR_50M\", category = \"raster\",\r\n scale = 50, returnclass = \"sf\")\r\n\r\nterra::plot(relief)\r\n\r\n\r\n\r\n\r\n{rgeoboundaries}\r\nThe {rgeoboundaries} package uses the Global Database of Political Administrative Boundaries that provide generally accepted political borders. The data are licensed openly.\r\n\r\n\r\n## install package from GitHub as it is not featured on CRAN yet\r\n# install.packages(\"remotes\")\r\n# remotes::install_github(\"wmgeolab/rgeoboundaries\")\r\n\r\nlibrary(rgeoboundaries)\r\n\r\ngb_adm0()\r\n\r\nSimple feature collection with 218 features and 3 fields\r\nGeometry type: MULTIPOLYGON\r\nDimension: XY\r\nBounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.63339\r\nGeodetic CRS: WGS 84\r\nFirst 10 features:\r\n shapeGroup shapeType shapeName\r\n1 AFG ADM0 Afghanistan\r\n2 GBR ADM0 United Kingdom\r\n3 ALB ADM0 Albania\r\n4 DZA ADM0 Algeria\r\n5 USA ADM0 United States\r\n6 ATA ADM0 Antarctica\r\n7 ATG ADM0 Antigua & Barbuda\r\n8 ARG ADM0 Argentina\r\n9 AND ADM0 Andorra\r\n10 AGO ADM0 Angola\r\n geometry\r\n1 MULTIPOLYGON (((74.88986 37...\r\n2 MULTIPOLYGON (((33.01302 34...\r\n3 MULTIPOLYGON (((20.07889 42...\r\n4 MULTIPOLYGON (((8.641941 36...\r\n5 MULTIPOLYGON (((-168.1579 -...\r\n6 MULTIPOLYGON (((-60.06171 -...\r\n7 MULTIPOLYGON (((-62.34839 1...\r\n8 MULTIPOLYGON (((-63.83417 -...\r\n9 MULTIPOLYGON (((1.725802 42...\r\n10 MULTIPOLYGON (((11.7163 -16...\r\n\r\nggplot(gb_adm0()) + \r\n geom_sf(color = \"grey40\", lwd = .2) + \r\n coord_sf(crs = \"+proj=eqearth\") \r\n\r\n\r\n\r\nLower administrative levels are available as well, e.g. in Germany adm1 represents federal states (“Bundesländer”), adm2 districts (“Kreise”) and so on.\r\nLet’s plot the admin 1 levels for the DACH countries:\r\n\r\n\r\ndach <- gb_adm1(c(\"germany\", \"switzerland\", \"austria\"), type = \"sscgs\")\r\n\r\nggplot(dach) +\r\n geom_sf(aes(fill = shapeGroup)) +\r\n scale_fill_brewer(palette = \"Set2\")\r\n\r\n\r\n\r\n{osmdata}\r\nOpenStreetMap (https://www.openstreetmap.org) is a collaborative project to create a free editable geographic database of the world. The geodata underlying the maps is considered the primary output of the project and is accessible from R via the {osmdata} package.\r\nWe first need to define our query and limit it to a region. You can explore the features and tags (also available as information via OpenStreetMap directly).\r\n\r\n\r\n## install package\r\n# install.packages(\"osmdata\")\r\n\r\nlibrary(osmdata)\r\n\r\n## explore features + tags\r\nhead(available_features())\r\n\r\n[1] \"4wd_only\" \"abandoned\" \"abutters\" \"access\" \"addr\" \r\n[6] \"addr:city\"\r\n\r\nhead(available_tags(\"craft\"))\r\n\r\n# A tibble: 6 × 2\r\n Key Value \r\n \r\n1 craft agricultural_engines\r\n2 craft atelier \r\n3 craft bag_repair \r\n4 craft bakery \r\n5 craft basket_maker \r\n6 craft beekeeper \r\n\r\n## building the query, e.g. beekeepers\r\nbeekeeper_query <- \r\n ## you can automatically retrieve a boudning box (pr specify one manually)\r\n getbb(\"Berlin\") %>%\r\n ## build an Overpass query\r\n opq(timeout = 999) %>%\r\n ## access particular feature\r\n add_osm_feature(\"craft\", \"beekeeper\")\r\n \r\n## download data\r\nsf_beekeepers <- osmdata_sf(beekeeper_query)\r\n\r\n\r\nNow we can investigate beekeepers in Berlin:\r\n\r\n\r\nnames(sf_beekeepers)\r\n\r\n[1] \"bbox\" \"overpass_call\" \"meta\" \r\n[4] \"osm_points\" \"osm_lines\" \"osm_polygons\" \r\n[7] \"osm_multilines\" \"osm_multipolygons\"\r\n\r\nhead(sf_beekeepers$osm_points)\r\n\r\nSimple feature collection with 6 features and 27 fields\r\nGeometry type: POINT\r\nDimension: XY\r\nBounding box: xmin: 13.24443 ymin: 52.35861 xmax: 13.69093 ymax: 52.573\r\nGeodetic CRS: WGS 84\r\n osm_id name addr:city addr:country addr:housenumber\r\n358407135 358407135 \r\n358407138 358407138 \r\n417509803 417509803 \r\n417509805 417509805 \r\n597668310 597668310 \r\n597668311 597668311 \r\n addr:postcode addr:street addr:suburb check_date\r\n358407135 \r\n358407138 \r\n417509803 \r\n417509805 \r\n597668310 \r\n597668311 \r\n contact:email contact:phone contact:website craft email\r\n358407135 \r\n358407138 \r\n417509803 \r\n417509805 \r\n597668310 \r\n597668311 \r\n facebook instagram man_made opening_hours operator organic\r\n358407135 \r\n358407138 \r\n417509803 \r\n417509805 \r\n597668310 \r\n597668311 \r\n phone product shop source website wheelchair works\r\n358407135 \r\n358407138 \r\n417509803 \r\n417509805 \r\n597668310 \r\n597668311 \r\n geometry\r\n358407135 POINT (13.69068 52.35918)\r\n358407138 POINT (13.69093 52.35894)\r\n417509803 POINT (13.68991 52.35888)\r\n417509805 POINT (13.6902 52.35861)\r\n597668310 POINT (13.24445 52.573)\r\n597668311 POINT (13.24443 52.57295)\r\n\r\nbeekeper_locations <- sf_beekeepers$osm_points\r\n\r\n## Berlin borders via {geoboundaries}\r\nsf_berlin <- gb_adm1(c(\"germany\"), type = \"sscgs\")[6,] # the sixth element is Berlin\r\n\r\n## Berlin border incl. district borders via our {d6berlin}\r\n# remotes::install_github(\"EcoDynIZW/d6berlin\")\r\nsf_berlin <- d6berlin::sf_districts\r\n\r\nggplot(beekeper_locations) + \r\n geom_sf(data = sf_berlin, fill = \"grey10\", color = \"grey30\") +\r\n geom_sf(size = 4, color = \"#FFB000\", alpha = .3) +\r\n labs(title = \"Beekeepers in Berlin\",\r\n caption = \"© OpenStreetMap contributors\")\r\n\r\n\r\n\r\n{elevatr}\r\nThe {elevatr} (https://github.com/jhollist/elevatr/) is an R package that provides access to elevation data from AWS Open Data Terrain Tiles and the Open Topography Global data sets API for raster digital elevation models (DEMs).\r\nWe first need to define a location or bounding box for our elevation data. This can either be a data frame or a spatial object. We use an sf object which holds the projection to be used when assessing the elevation data:\r\n\r\n\r\n## install package\r\n# install.packages(\"elevatr\")\r\n\r\nlibrary(elevatr)\r\n\r\n## manually specify corners of the bounding box of the US\r\nbbox_usa <- data.frame(x = c(-125.0011, -66.9326), \r\n y = c(24.9493, 49.5904))\r\n\r\n## turn into spatial, projected bounding box\r\nsf_bbox_usa <- sf::st_as_sf(bbox_usa, coords = c(\"x\", \"y\"), crs = 4326)\r\n\r\n\r\nNow we can download the elevation data with a specified resolution z (ranging from 1 to 14 with 1 being very coarse and 14 being very fine).\r\n\r\n\r\nelev_usa <- get_elev_raster(locations = sf_bbox_usa, z = 5)\r\n\r\nterra::plot(elev_usa)\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "preview": "posts/r-spatial-data/r-spatial-data_files/figure-html5/plot-world-rnaturalearth-1.png", - "last_modified": "2024-03-14T09:00:46+01:00", + "last_modified": "2024-02-20T11:14:01+01:00", "input_file": {}, "preview_width": 1920, "preview_height": 768 @@ -246,7 +246,7 @@ ], "contents": "\r\nThe {d6geodata} package aims to access the data from the Geodata archive of the EcoDyn Department for members only!\r\nThe two main functions are:\r\ngeo_overview()\r\nget_geodata()\r\n\r\n\r\n## remotes::install_github(\"EcoDynIZW/d6geodata\")\r\n## library(d6geodata)\r\n\r\n\r\nIf you want to work with geodata that is already stored in our Geodata archive you have two options:\r\nGo to the EcoDynIZW Website, click on wikis and select Geodata. There you find several spatial data sets with respective metadaat and visualizations. In the metadata section, you’ll find the information . To donwload the data, cope the folder_name information provided in the metadata and use it as an input in the get_geodata() function from our {d6geodata} to get the data from our PopDynCloud. Another option is the function called geo_overview(). There you can select which data and from which location you want to have a list of data.\r\nIf you run the function geo_overview you have to decide if you want to see the raw or processed data by typing 1 for raw and 2 for processed data. Afterwards, you have to decide if you want to see the main (type 1) folders (the regions or sub-regions we have data from) or the sub (type 2) folders (the actually data we have in each region).\r\nExample 1: Main Folder\r\n\r\nd6geodata::geo_overview(path_to_cloud = \"E:/PopDynCloud\")\r\nRaw or processed data: \r\n\r\n1: raw\r\n2: processed\r\n\r\nAuswahl: 2\r\nchoose folder type: \r\n\r\n1: main\r\n2: sub\r\n\r\nAuswahl: 1\r\n[1] \"atlas\" \"BB_MV_B\" \"berlin\" \"europe\" \"germany\" \"world\"\r\n\r\nExample 2: Sub Folder\r\n\r\nd6geodata::geo_overview(path_to_cloud = \"E:/PopDynCloud\")\r\nRaw or processed data: \r\n\r\n1: raw\r\n2: processed\r\n\r\nAuswahl: 2\r\nchoose folder type: \r\n\r\n1: main\r\n2: sub\r\n\r\nAuswahl: 2\r\n$atlas\r\n[1] \"distance-to-human-settlements_atlas_2009_1m_03035_tif\"\r\n[2] \"distance-to-kettleholes_atlas_2022_1m_03035_tif\" \r\n[3] \"distance-to-rivers_atlas_2009_1m_03035_tif\" \r\n[4] \"distance-to-streets_atlas_2022_1m_03035_tif\" \r\n[5] \"landuse_atlas_2009_1m_03035_tif\" \r\n\r\n$BB_MV_B\r\n[1] \"_archive\" \"_old_not_verified\" \"dist_path_bb_agroscapelabs\"\r\n[4] \"scripts\" \r\n\r\n$berlin\r\n [1] \"_old_not_verified\" \r\n [2] \"corine_berlin_2015_20m_03035_tif\" \r\n [3] \"distance-to-paths_berlin_2022_100m_03035_tif\" \r\n [4] \"green-capacity_berlin_2020_10m_03035_tif\" \r\n [5] \"imperviousness_berlin_2018_10m_03035_tif\" \r\n [6] \"light-pollution_berlin_2021_100m_03035_tif\" \r\n [7] \"light-pollution_berlin_2021_10m_03035_tif\" \r\n [8] \"motorways_berlin_2022_100m_03035_tif\" \r\n [9] \"noise-day-night_berlin_2017_10m_03035_tif\" \r\n[10] \"population-density_berlin_2019_10m_03035_tif\"\r\n[11] \"template-raster_berlin_2018_10m_03035_tif\" \r\n[12] \"tree-cover-density_berlin_2018_10m_03035_tif\"\r\n\r\n$europe\r\n[1] \"imperviousness_europe_2018_10m_03035_tif\"\r\n\r\n$germany\r\n [1] \"_old_not_verified\" \r\n [2] \"distance-to-motorway-rural-road_germany_2022_100m_03035_tif\"\r\n [3] \"distance-to-motorways_germany_2022_100m_03035_tif\" \r\n [4] \"distance-to-paths_germany_2022_100m_03035_tif\" \r\n [5] \"distance-to-roads-paths_germany_2022_100m_03035_tif\" \r\n [6] \"distance-to-roads_germany_2022_100m_03035_tif\" \r\n [7] \"distance_to_paths_germany_2022_100m_03035_tif\" \r\n [8] \"motoroways_germany_2022_03035_osm_tif\" \r\n [9] \"motorway-rural-road_germany_2022_100m_03035_tif\" \r\n[10] \"motorways_germany_2022_100m_03035_tif\" \r\n[11] \"paths_germany_2022_100m_03035_tif\" \r\n[12] \"Roads-germany_2022_100m_03035_tif\" \r\n[13] \"roads_germany_2022_100m_03035_tif\" \r\n[14] \"tree-cover-density_germany_2015_100m_03035_tif\" \r\n\r\n$world\r\ncharacter(0)\r\n\r\nNow you can copy the name of one of the layers and paste it into the get_geodata() function\r\n\r\n\r\ncorine <-\r\n d6geodata::get_geodata(\r\n data_name = \"corine_berlin_2018_20m_03035_tif\",\r\n path_to_cloud = \"E:/PopDynCloud\",\r\n download_data = FALSE\r\n )\r\n\r\n\r\nIf you set download_data = TRUE the data will be download and copied to your data-raw folder. If the data-raw folder doesn’t exist, it will be created.\r\nIf you want to download more than one file, you can simply use lapply() and add multiple file names like this:\r\n\r\n\r\ndata_list <-\r\n lapply(\r\n c(\r\n \"corine_berlin_2018_20m_03035_tif\",\r\n \"motorways_berlin_2022_100m_03035_tif\"\r\n ),\r\n FUN = function(x) {\r\n d6geodata::get_geodata(\r\n data_name = x,\r\n path_to_cloud = \"E:/PopDynCloud\",\r\n download_data = FALSE\r\n )})\r\n\r\n\r\nAdditional functions\r\nThe three functions plot_binary_map(), plot_qualitative_map() and plot plot_quantitative_map() can be used to plot raster data with the respective color schemes we used for the Geodata wiki page (note that this function works only for raster data).\r\n\r\n\r\nplot_binary_map(tif = tif)\r\nplot_qualitative_map(tif = tif)\r\nplot_quantitative_map(tif = tif)\r\n\r\n\r\nExample plot\r\n\r\n\r\nlibrary(d6geodata)\r\nplot_qualitative_map(tif = corine)\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "preview": "posts/d6geodatapackage/d6geodatapackage_files/figure-html5/unnamed-chunk-1-1.png", - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 1248, "preview_height": 768 @@ -271,7 +271,7 @@ ], "contents": "\r\n\r\nContents\r\nThe FIS-Broker database\r\nWFS Data\r\nATOM Data\r\n\r\nThe {d6berlin} package provides several functions for the members of the Ecological Department of the IZW. Now two functions are added to the package: download_fisbroker_wfs() and download_fisbroker_atom().\r\n\r\n\r\ninstall.packages(\"remotes\")\r\nremotes::install_github(\"EcoDynIZW/d6berlin\")\r\ninstall.packages(\"rcartocolor\")\r\ninstall.packages(\"stars\")\r\n\r\n\r\n\r\n\r\nlibrary(d6berlin)\r\nlibrary(dplyr)\r\nlibrary(ggplot2)\r\n\r\n\r\nThe FIS-Broker database\r\nThe FIS-Broker database is hosted by the Berlin Senate and provides several geographical data sets. The file formats differ and some data sets have just one of the file formats to offer. The file formats are WMS (Web Media Service: just like a png or jpg), WFS (Web Feature Service: Shapefiles) and ATOM (xml format: raster layers data). This function is only looking for WFS files (shapefiles), because these are the polygons, lines or points that we are looking for.\r\nFor using these two functions you have to select the layer you aim to download from the online data base.\r\nWFS Data\r\nAs an example we will download the layer containing the districts of Berlin (“ALKIS Bezirke”):\r\n\r\n\r\n\r\nurl <- \"https://fbinter.stadt-berlin.de/fb/wfs/data/senstadt/s_wfs_alkis_bezirk\"\r\n\r\ndata_wfs <- d6berlin::download_fisbroker_wfs(link = url)\r\n\r\nReading layer `s_wfs_alkis_bezirk' from data source \r\n `https://fbinter.stadt-berlin.de/fb/wfs/data/senstadt/s_wfs_alkis_bezirk?service=wfs&version=2.0.0&request=GetFeature&typenames=s_wfs_alkis_bezirk&srsName=EPSG%3A25833' \r\n using driver `GML'\r\nSimple feature collection with 12 features and 6 fields\r\nGeometry type: MULTIPOLYGON\r\nDimension: XY\r\nBounding box: xmin: 370000.8 ymin: 5799521 xmax: 415786.6 ymax: 5837259\r\nProjected CRS: ETRS89 / UTM zone 33N\r\n\r\nglimpse(data_wfs)\r\n\r\nRows: 12\r\nColumns: 7\r\n$ gml_id \"s_wfs_alkis_bezirk.445\", \"s_wfs_alkis_bezirk.446\", \"…\r\n$ gem 3, 12, 8, 10, 6, 9, 4, 1, 11, 7, 5, 2\r\n$ namgem \"Pankow\", \"Reinickendorf\", \"Neukölln\", \"Marzahn-Helle…\r\n$ namlan \"Berlin\", \"Berlin\", \"Berlin\", \"Berlin\", \"Berlin\", \"Be…\r\n$ lan 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11\r\n$ name 11000003, 11000012, 11000008, 11000010, 11000006, 110…\r\n$ geom MULTIPOLYGON (((399003.5 58..., MULTIPOLYGON (((38999…\r\n\r\nggplot() +\r\n geom_sf(data = data_wfs, aes(fill = namgem)) +\r\n rcartocolor::scale_fill_carto_d(palette = \"Bold\")\r\n\r\n\r\n\r\nYou got a spatial layer which you can save to disk or to use it directly.\r\nATOM Data\r\nAs an example we will download a raster of vegetation heights (“Vegetationshöhen 2020 (Umweltatlas)”):\r\n\r\n\r\n\r\nurl <- \"https://fbinter.stadt-berlin.de/fb/atom/Vegetationshoehen/veghoehe_2020.zip\"\r\n\r\ndata_atom <-\r\n d6berlin::download_fisbroker_atom(\r\n zip_link = url,\r\n path = here::here(\"_posts\", \"d6berlin-fisbroker\", \"man\"),\r\n name = \"vegetation_heights\"\r\n )\r\n\r\nglimpse(data_atom)\r\n\r\nS4 class 'SpatRaster' [package \"terra\"]\r\n\r\ndata_atom_10 <- terra::aggregate(data_atom, 10)\r\n\r\n\r\n\r\n\r\nggplot() +\r\n stars::geom_stars(data = stars::st_as_stars(data_atom_10)) +\r\n coord_sf(expand = FALSE) + \r\n rcartocolor::scale_fill_carto_c(\r\n palette = \"Emrld\", name = NULL, \r\n guide = guide_legend(label.position = \"bottom\")\r\n ) + \r\n theme_void()\r\n\r\n\r\n\r\nA shortcut to plot this kind of data is the plot_qualitative_map() function from our dedicated {d6geodata} package. You can install this package with devtools::install_github(“EcoDynIZW/d6geodata”).\r\n\r\n\r\nd6geodata::plot_quantitative_map(tif = data_atom_10)\r\n\r\n\r\n\r\n\r\n\r\n", "preview": "posts/d6berlin-fisbroker/d6berlin-fisbroker_files/figure-html5/unnamed-chunk-1-1.png", - "last_modified": "2023-10-30T11:29:36+01:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 1248, "preview_height": 768 @@ -295,7 +295,7 @@ ], "contents": "\r\n\r\nContents\r\nLibraries and Folders\r\nSetting Up the Library to Work with Your NetLogo Model\r\nCreating an Experiment\r\nCreating a Simulation\r\nRunning the Experiment\r\nNLRX output handling\r\n\r\nCleaning the data\r\nExample plots\r\n\r\nThe NLRX package provides tools to setup and execute NetLogo simulations from R developed by Salecker et al. 2019. NetLogo is a free, open-source and cross-platform modelling environment for simulating natural and social phenomena.\r\nLibraries and Folders\r\n\r\n\r\n# libraries\r\n\r\nfor (pckg in c('dplyr', 'ggplot2', 'scico', 'nlrx'))\r\n{\r\n if (!require(pckg, character.only = TRUE))\r\n install.packages(pckg, dependencies = TRUE)\r\n require(pckg, character.only = TRUE)\r\n}\r\n\r\n# output folder\r\n\r\ncurrentDate <- gsub(\"-\", \"\", Sys.Date())\r\n# todaysFolder <- paste(\"Output\", currentDate, sep = \"_\")\r\n# dir.create(todaysFolder) #in case output folder is wanted\r\n\r\n#virtual-ram if needed\r\n#memory.limit(85000)\r\n\r\n\r\nSetting Up the Library to Work with Your NetLogo Model\r\n\r\n\r\n#NetLogo path\r\nnetlogoPath <- file.path(\"C:/Program Files/NetLogo 6.2.2/\")\r\n\r\n#Model location\r\nmodelPath <- file.path(\"./Model/myModel.nlogo\")\r\n\r\n#Output location (hardly ever used)\r\noutPath <- file.path(\"./\")\r\n\r\n#Java\r\nSys.setenv(JAVA_HOME=\"C:/Program Files/Java/jre1.8.0_331\") \r\n\r\n\r\nConfigure the NetLogo object:\r\n\r\n\r\nnl <- nlrx::nl(\r\n nlversion = \"6.2.2\",\r\n nlpath = netlogoPath,\r\n modelpath = modelPath,\r\n jvmmem = 1024 # Java virtual machine memory capacity\r\n)\r\n\r\n\r\nCreating an Experiment\r\n\r\n\r\nnl@experiment <- nlrx::experiment(\r\n expname = 'Exp_1_1',\r\n # name\r\n outpath = outPath,\r\n # outpath\r\n repetition = 1,\r\n # number of times the experiment is repeated with the !!!SAME!!! random seed\r\n tickmetrics = 'true',\r\n # record metrics at every step\r\n idsetup = 'setup',\r\n # in-code setup function\r\n idgo = 'go',\r\n # in-code go function\r\n runtime = 200,\r\n # soft runtime-cap\r\n metrics = c('population', # global variables to be recorded, can also use NetLogos 'count' e.g. count turtles\r\n 'susceptible', # but requires escaped quotation marks for longer commands when strings are involved\r\n 'infected', # functions similar for both patch and turtle variables (below)\r\n 'immune'),\r\n metrics.patches = c('totalINfectionsHere',\r\n 'pxcor',\r\n 'pycor'),\r\n # metrics.turtles = list(\r\n # \"turtles\" = c(\"xcor\", \"ycor\")\r\n # ),\r\n constants = list( # model parameters that are fixed. In theory all the constant values set in the UI before saving are the ones used\r\n 'duration' = 20, # however, I would always make sure to 'fix\" them though the constant input\r\n 'turtle-shape' = \"\\\"circle\\\"\",\r\n 'runtime' = 5\r\n ),\r\n variables = list( # model parameters you want to 'test' have several ways to be set\r\n 'number-people' = list(values = c(150)), # simple list of values\r\n 'infectiousness' = list( #stepwise value change\r\n min = 50,\r\n max = 100,\r\n step = 25,\r\n qfun = 'qunif'\r\n ),\r\n # string based inputs such as used in NetLogo's 'choosers' or inputs require escaped quotation marks\r\n # \"turtle-shape\" = list(values = c(\"\\\"circle\\\"\",\"\\\"person\\\"\")), \r\n 'chance-recover' = list(values = c(50, 75, 95))\r\n )\r\n)\r\n\r\n\r\nCreating a Simulation\r\nHere we have several choices of simulation types. The full factorial simdesign used below creates a full-factorial parameter matrix with all possible combinations of parameter values. There are however, other options to choose from if needed and the vignette provides a good initial overview.\r\nA bit counter intuitive but nseeds used in the simdesign is actually the number of repeats we want to use for our simulation. In case we set nseed = 10 and have set the the repetitions above to 1, we would run each parameter combination with 10 different random seeds i.e. 10 times per combination. If we would have set the repetitions to 2 we would run each random seed 2 times i.e. 20 times in total but twice for each seed so 2 results should be identical.\r\nHowever, if your model handles seeds internally such as setting a random seed every time the model is setup, repetitions could be used instead.\r\n\r\n\r\nnl@simdesign <- nlrx::simdesign_ff(nl=nl, nseeds=1) \r\n\r\nprint(nl)\r\n\r\nnlrx::eval_variables_constants(nl)\r\n\r\n\r\nRunning the Experiment\r\nSince the simulations are executed in a nested loop where the outer loop iterates over the random seeds of the simdesign, and the inner loop iterates over the rows of the parameter matrix. These loops can be executed in parallel by setting up an appropriate plan from the future package which is built into nlrx.\r\n\r\n\r\n#plan(multisession, workers = 12) # one worker represents one CPU thread\r\n\r\nresults<- nlrx::run_nl_all(nl = nl)\r\n\r\n\r\nto speed up this this tutorial we load a pre-generated simulation result\r\n\r\n\r\nresults <- readr::read_rds(\"example1.rds\")\r\n\r\ndplyr::glimpse(results)\r\n\r\nRows: 1,809\r\nColumns: 15\r\n$ `[run number]` 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…\r\n$ `number-people` 150, 150, 150, 150, 150, 150, 150, 150, 150…\r\n$ infectiousness 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,…\r\n$ `chance-recover` 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,…\r\n$ duration 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,…\r\n$ `turtle-shape` \"circle\", \"circle\", \"circle\", \"circle\", \"ci…\r\n$ runtime 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5…\r\n$ `random-seed` -662923684, -662923684, -662923684, -662923…\r\n$ `[step]` 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…\r\n$ population 0, 153, 155, 156, 156, 157, 158, 159, 160, …\r\n$ susceptible 0, 10, 10, 10, 11, 11, 13, 13, 16, 16, 17, …\r\n$ infected 0, 143, 145, 146, 145, 146, 145, 146, 144, …\r\n$ immune 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…\r\n$ metrics.patches [], []…\r\n$ siminputrow 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…\r\n\r\nNLRX output handling\r\nThere are several ways to use / analyze the output directly via nlrx (although I have not used them personally) detailed information can be found here: https://cran.r-project.org/web/packages/nlrx/nlrx.pdf\r\n\r\n\r\nnlrx::setsim(nl, \"simoutput\") <- results # attaching simpout to our NetLogo object\r\n\r\n#write_simoutput(nl) # having nlrx write the output into a file only works without patch or turtle metrics\r\n\r\nnlrx::analyze_nl(nl, metrics = nlrx::getexp(nl, \"metrics\"), funs = list(mean = mean))\r\n\r\n\r\nCleaning the data\r\nNetLogo really dislikes outputting nice and readable variable names so some renaming is in order:\r\n\r\n\r\nraw1 <- results\r\n\r\nraw2 <-\r\n raw1 %>% dplyr::rename(\r\n run = `[run number]`,\r\n recoveryChance = `chance-recover`,\r\n StartingPop = `number-people`,\r\n t = `[step]`\r\n )\r\n\r\n\r\nIn case of very large datasets we want to separate the patch (or turtle) specific data from the global data to speed up the analysis of the global data\r\n\r\n\r\nglobalData <- raw2 %>% dplyr::select(!metrics.patches)#, !metrics.turtles)\r\n\r\n\r\nand summarise like any other dataset\r\n\r\n\r\nsum_1 <-\r\n globalData %>%\r\n dplyr::group_by(t, recoveryChance, infectiousness) %>%\r\n dplyr::summarise(\r\n across(\r\n c('infected',\r\n 'immune',\r\n 'susceptible',\r\n 'population'),\r\n mean\r\n )\r\n ) %>%\r\n dplyr::filter(t < 201) %>%\r\n tidyr::pivot_longer(cols = -c(recoveryChance, infectiousness, t),\r\n names_to = \"EpiStat\") %>%\r\n dplyr::ungroup()\r\n\r\n\r\nExample plots\r\nWe use the global data to get an overview of the simulated SIR dynamics:\r\n\r\n\r\nggplot(sum_1) +\r\n geom_line(aes(x = t, y = value, colour = EpiStat), size = 1) +\r\n facet_grid(infectiousness ~ recoveryChance, labeller = label_both) +\r\n scale_colour_scico_d(\r\n palette = \"lajolla\",\r\n begin = 0.1,\r\n end = 0.9,\r\n direction = 1\r\n ) +\r\n theme_bw()\r\n\r\n\r\n\r\n… and use the patch specific data to create a heatmap to visualize where most infections have happened in one specific scenario:\r\n\r\n\r\nhmpRaw <- raw2 %>%\r\n dplyr::filter(infectiousness == 75 & recoveryChance == 75) %>%\r\n dplyr::select(metrics.patches, t) %>%\r\n dplyr::rename(pm = metrics.patches)\r\n\r\nhmpSplit <- hmpRaw %>% split(hmpRaw$t)\r\ntempDf <- hmpSplit[[1]]$pm \r\ntempDf <- tempDf %>% as.data.frame() %>%\r\n dplyr::select(totalINfectionsHere, pxcor, pycor)\r\n\r\nfor (i in 1:length(hmpSplit))\r\n{\r\n tmp1 <- as.data.frame(hmpSplit[[i]]$pm)\r\n colnames(tmp1) <- c(paste0(\"tab_\", i), 'pxcor', 'pycor', 'agent', 'breed')\r\n tmp1 <- tmp1 %>% \r\n dplyr::select(-c('pxcor', 'pycor', 'agent', 'breed'))\r\n tempDf <- cbind(tempDf, tmp1)\r\n}\r\n\r\ntempDf2 <- tempDf %>% \r\n dplyr::select(-c(pxcor, pycor)) \r\n\r\ntempDf$summ <- rowSums(tempDf2)\r\nhmpData <- tempDf %>% dplyr::select(summ, pxcor, pycor)\r\n\r\nggplot(hmpData)+\r\n geom_tile(aes(x = pxcor, y = pycor, fill = summ)) +\r\n scale_fill_scico(palette = 'roma', direction = -1, name= 'N infected') +\r\n ggtitle(\"N infections per cell\") +\r\n theme_bw()\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "preview": "posts/NLRX/nlrxpackage_files/figure-html5/spatial-plots-1.png", - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:14+01:00", "input_file": {}, "preview_width": 1248, "preview_height": 768 @@ -319,7 +319,7 @@ ], "contents": "\r\n\r\nContents\r\nFunctions and Loops\r\nWhat are they for?\r\nFunctions\r\nLoops\r\nA Classic, the ‘for’ Loop\r\n‘while’ Loops\r\n\r\nBonus Round: Conditionals\r\n\r\n\r\n\r\n\r\n\r\nFunctions and Loops\r\nWhat are they for?\r\nIn short: making you life easier. They are used to automate certain steps in your code to make the execution faster.\r\nFunctions\r\nA simple example: you have multiple data sets with a measurement in inches. To continue working with that data you need to convert it meter, but doing that manually would take hours. Solution: a function!!\r\nIn R:\r\n\r\n\r\ninch_to_meter <- #function name\r\n function(inch) #function input parameter(s)\r\n { # function body\r\n inM <- (inch * 0.0254) # in our case: transformation\r\n \r\n return(inM) # function reporter\r\n }\r\n\r\n\r\nNow lets apply that function to some random example data:\r\n\r\n\r\nmyOldData <- c(15,98,102,5,17)\r\n\r\nmyNewData <- inch_to_meter(myOldData)\r\n\r\n\r\nOur function is applied to all elements of the old data, giving us the converted measurements.\r\n\r\n\r\nmyOldData\r\n\r\n[1] 15 98 102 5 17\r\n\r\nmyNewData\r\n\r\n[1] 0.3810 2.4892 2.5908 0.1270 0.4318\r\n\r\nFor a more general approach we can use the following template for simple functions:\r\n\r\n\r\nmyFunction <-\r\n function(input1, input2, input3)\r\n {\r\n output <- input1 * input3 / input2 # example operation\r\n \r\n return(output)\r\n }\r\n\r\n\r\nIn C++:\r\n\r\n# the viableCell function of the class Grid returns a bool and takes two inputs\r\n# (x and y coordinates)\r\nbool Grid::viableCell(CellCount_t x, CellCount_t y) \r\n{\r\n return !m_grid.empty() && x < m_grid.cbegin() -> size() && y < m_grid.size();\r\n}\r\n\r\nIn NetLogo, the closest thing we have to functions are reporters:\r\n\r\nto-report Male_Alpha_Alive \r\n\r\n ifelse (any? turtles-here with [isAlpha = true AND isFemale = false])\r\n [report true]\r\n [report false] #else\r\n\r\nend\r\n\r\nLoops\r\nWhat is a loop? It a simply a piece of code we want to repeat. But wait, isn’t that exactly what a function does? Well, yes and no. A function (after it is declared) is an embodiment of a piece of code that we can run anytime just by calling it. A loop is a local repetition of code.\r\nLets stay in R and look at some examples:\r\nA Classic, the ‘for’ Loop\r\nIf you auto fill “for” in R, it will give you the following structure:\r\n\r\n\r\nfor (variable in vector)\r\n{\r\n #body\r\n}\r\n\r\n\r\nSo what is variable and what is vector? In this case the variable, you could also call iterator (often you will see the letter i used) is simply put a counter that tells our loop how many times it has repeated itself. The vector is determined by us and tells the loop how many repetitions we want before it stops.\r\n\r\n\r\nfor (i in 1:10)\r\n{\r\n #body\r\n}\r\n\r\n\r\nThe loop above will now repeat exactly 10 times and then stop. R is helpful in the sense that it automatically increments i after each repetition. Other language like C++ for example need a manual increment of i:\r\n\r\nfor (i = 0, i <= 10, ++i) #C++ \r\n{\r\n #body\r\n}\r\n\r\nWith the basics out of the way lets look at an example:\r\n\r\n\r\nmyData <- rnorm(30) # random numbers\r\nmyResults <- 0 # initializing myResults\r\n\r\nfor(i in 1:10)\r\n{\r\n # i-th element of `myData` squared into `i`-th position of `myResults`\r\n myResults[i] <- myData[i] * myData[i] \r\n print(i)\r\n}\r\n\r\n[1] 1\r\n[1] 2\r\n[1] 3\r\n[1] 4\r\n[1] 5\r\n[1] 6\r\n[1] 7\r\n[1] 8\r\n[1] 9\r\n[1] 10\r\n\r\nmyResults\r\n\r\n [1] 2.348470e+00 8.045558e-02 9.254717e+00 6.298287e-01 5.587004e-02\r\n [6] 5.496863e-01 2.161664e+00 1.326536e-05 2.026844e+00 2.065682e-01\r\n\r\nThis was of course a very simple loop and there is pretty much no limit to the level of complexity those loops can have and how many loops could be nested. But be warned there can be some pitfalls with loops. Exercise: Would the following loop work?\r\n\r\n\r\nfor (i in 1:length(summIdentSplit))\r\n{\r\n tmpSumm <- summIdentSplit[[i]]\r\n tmpName <- SummIdentVector[[i]]\r\n \r\n if (base::grepl(\"HD\", tmpName) == TRUE)\r\n {\r\n clearName <- \"Habitat-driven movement\"\r\n cn <- \"HD\"\r\n }\r\n \r\n combiMelt <- tmpSumm %>%\r\n dplyr::ungroup() %>%\r\n dplyr::select(t, Ninfected_mean, Nimmune_mean) %>%\r\n dplyr::rename(Infected = Ninfected_mean, Immune = Nimmune_mean)\r\n \r\n combiMelt_sd <- tmpSumm %>%\r\n dplyr::ungroup() %>%\r\n dplyr::select(t, Ninfected_sd, Nimmune_sd) %>%\r\n dplyr::rename(Inf_sd = Ninfected_sd, Imm_sd = Nimmune_sd)\r\n \r\n combiMelt_c <- dplyr::left_join(combiMelt, combiMelt_sd , by = \"t\")\r\n \r\n q <- 0\r\n combiMelt_c$quarter <- 0\r\n \r\n for (i in 1:nrow(combiMelt_c))\r\n {\r\n if (i %% 13 == 0)\r\n {\r\n q <- q + 1\r\n }\r\n combiMelt_c$quarter[i] <- q\r\n }\r\n}\r\n\r\n\r\nThe answer is no. There are two loops involved where one is nested inside the other. So far, that would not be an issue, however, both loops use the iterator (variable) i. To see what would happen:\r\n\r\nFirst iteration:\r\n i = 1\r\n\r\nfor (i in 1:length(summIdentSplit))\r\n{\r\n tmpSumm <- summIdentSplit[[i]]\r\n tmpName <- SummIdentVector[[i]]\r\n \r\n if (base::grepl(\"HD\", tmpName) == TRUE)\r\n {\r\n clearName <- \"Habitat-driven movement\"\r\n cn <- \"HD\"\r\n }\r\n \r\n combiMelt <- tmpSumm %>%\r\n dplyr::ungroup() %>%\r\n dplyr::select(t, Ninfected_mean, Nimmune_mean) %>%\r\n dplyr::rename(Infected = Ninfected_mean, Immune = Nimmune_mean)\r\n \r\n combiMelt_sd <- tmpSumm %>%\r\n dplyr::ungroup() %>%\r\n dplyr::select(t, Ninfected_sd, Nimmune_sd) %>%\r\n dplyr::rename(Inf_sd = Ninfected_sd, Imm_sd = Nimmune_sd)\r\n \r\n combiMelt_c <- dplyr::left_join(combiMelt, combiMelt_sd , by = \"t\")\r\n \r\n q <- 0\r\n combiMelt_c$quarter <- 0\r\n\r\nAt this point i is still 1 and lets say nrow(combiMelt_c) is also 10 (like in our outer loop)\r\n\r\n for (i in 1:nrow(combiMelt_c))\r\n {\r\n if (i %% 13 == 0)\r\n {\r\n q <- q + 1\r\n }\r\n combiMelt_c$quarter[i] <- q\r\n\r\nat this point i is iterated within the inner loop\r\n\r\n }\r\n\r\nonce we reach this point i is 10, so the for the next iteration of the outer\r\nloop, i = 10 so most iterations of the outer loop will be skipped!\r\n\r\n}\r\n\r\nSolution: make sure to use different iterators in nested loops. For example the outer loop uses i, the inner loop uses j and maybe that loop has also a nested loop which then uses k as its iterator.\r\n‘while’ Loops\r\nSimilar to for loops, while loops also repeat a certain block of code. The difference here is, that while loop repeat until a certain condition is fulfilled, potentially forever. R’s auto fill provides us the following code snippet:\r\n\r\n\r\nwhile (condition)\r\n{\r\n \r\n}\r\n\r\n\r\nA simple example:\r\n\r\n\r\nn <- 0\r\n\r\nwhile(n < 100)\r\n{\r\n n = n + 1\r\n}\r\n\r\nprint(n)\r\n\r\n[1] 100\r\n\r\nAs long as n is below 100 we increment n every repeat. Take note: if the condition is never fulfilled, the loop will run forever and the software might crash. In complex loops you could run a “escape timer” such as in this example plucked from an IBM:\r\n\r\n\r\nwhile(!viableCell())\r\n{\r\n turn(2 * (atan (( (1 - 0.5) / (1 + 0.9)) * tan ((randomDouble(1) - 0.5)) * 180)) + 1) \r\n}\r\n\r\n\r\nThis little loop runs on an individual and check’s the cell in front of the individual for its viability to move into. As long as viableCell() reports false, the individual turns. However, there are cases when they are no viable cells around so the individual would turn in a circle forever. Introducing a random timer (note, this is not an optimal solution just a quick fix).\r\n\r\nint timer = 0\r\n\r\nwhile(!viableCell() && timer < 50 )\r\n{\r\n turn(2 * (atan (( (1 - 0.5) / (1 + 0.9)) * tan ((randomDouble(1) - 0.5)) * 180)) + 1) \r\n \r\n ++timer #increment the timer\r\n}\r\n\r\nNow the individual turns a maximum of 50 times before the loop ends. Another option would be conditionals:\r\nBonus Round: Conditionals\r\nMost people already know what a conditional is. Basically, when a certain condition is fulfilled something happens (usually done via if and/or else).\r\n\r\nint timer = 0\r\n\r\nwhile(!viableCell())\r\n{\r\n turn(2 * (atan (( (1 - 0.5) / (1 + 0.9)) * tan ((randomDouble(1) - 0.5)) * 180)) + 1) \r\n \r\n ++timer #increment the timer\r\n \r\n if(timer == 50)\r\n {\r\n break # break is a function that for example ends a loop\r\n }\r\n}\r\n\r\nConditionals can be used in a variety of way within and outside of loops but have the advantage that they can be used stop loops under certain conditions. Lets say you are running a complex construct of multiple nested for loops to find a certain value in your data. You don’t need to always iterate through all the data if you can create certain logical stop conditions.\r\n\r\n\r\n\r\n", "preview": {}, - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:14+01:00", "input_file": {} }, { @@ -340,7 +340,7 @@ ], "contents": "\r\n\r\nContents\r\nGeneral:\r\nVariable Naming Conventions\r\nSnakecase:\r\nCamelcase:\r\nPascalcase:\r\nHungarian Notation:\r\nSidenote: Namespaces and Function Names\r\n\r\nIndentation\r\nCommenting Your Code\r\nDon’t Repeat Yourself\r\n\r\nGeneral:\r\nWrite as few lines as possible.\r\nUse appropriate naming conventions.\r\nSegment blocks of code in the same section into paragraphs.\r\nUse indentation to marks the beginning and end of control structures.\r\nDon’t repeat yourself.\r\n\r\n\r\n\r\nVariable Naming Conventions\r\nVariable naming is an important aspect in making your code readable. Create variables that describe their function and follow a consistent theme throughout your code. Separate words in a variable name without the use of whitespace and do not repeat variable names (unless in certain circumstances such as temporary variable within loops).\r\nSnakecase:\r\nWords are delimited by an underscore.\r\n\r\n\r\nvariable_one <- 1\r\n\r\nvariable_two <- 2\r\n\r\n\r\nCamelcase:\r\nWords are delimited by capital letters, except the initial word.\r\n\r\n\r\nvariableOne <- 1\r\n\r\nvariableTwo <- 2\r\n\r\n\r\nPascalcase:\r\nWords are delimited by capital letters.\r\n\r\n\r\nVariableOne <- 1\r\n\r\nVariableTwo <- 2\r\n\r\n\r\nHungarian Notation:\r\nThis notation describes the variable type or purpose at the start of the variable name, followed by a descriptor that indicates the variable’s function. The Camelcase notation is used to delimit words.\r\n\r\n\r\niVariableOne <- 1 # i - integer\r\n\r\nsVariableTwo <- \"two\" # s - string\r\n\r\nlVariableThree <- list() # l - list\r\n\r\n\r\nSidenote: Namespaces and Function Names\r\nOften when using for example R we will use libraries and many of them at the same time. Quite a few of those libraries are using the same names for their functions. This can be a big issues and cause code to suddenly not run anymore even though the only difference my be the order in which libraries are loaded. The last library loaded with a certain function will be the default one used by R. A prominent example would be the ‘raster’ package and ‘tidyverse’ (dplyr) who share the name ‘select’ for a function. So, when in doubt use namespaces to make sure to link a function to their library.\r\n\r\nuse the select function from the dplyr library\r\ndplyr::select(.....)\r\n\r\nIndentation\r\nI assume you already know that your code should have some sort of indentation. However, it’s also worth noting that its a good idea to keep your indentation style consistent.\r\nAs a quick reminder:\r\nBad:\r\nLong lines of text with no separation:\r\n\r\n\r\nmydata <- iris %>% dplyr::filter(Species == \"virginica\") %>% summarise_at(.vars = c(\"Sepal.Length\", \"Sepal.Width\"),.funs = \"mean\")\r\n\r\nfor (i in 1:length(hmpSplit)){ tmp1 <- as.data.frame(hmpSplit[[i]]$pm); colnames(tmp1) <- c(paste0(\"tab_\", i), 'pxcor', 'pycor', 'agent', 'breed'); tmp1 <- tmp1 %>% dplyr::select(-c('pxcor', 'pycor', 'agent', 'breed')); tempDf <- cbind(tempDf, tmp1)}\r\n\r\n\r\nGood:\r\n\r\n\r\nmydata <- iris %>%\r\n dplyr::filter(Species == \"virginica\") %>%\r\n summarise_at(.vars = c(\"Sepal.Length\", \"Sepal.Width\"),\r\n .funs = \"mean\")\r\n\r\n\r\nfor (i in 1:length(hmpSplit))\r\n{\r\n tmp1 <- as.data.frame(hmpSplit[[i]]$pm)\r\n \r\n colnames(tmp1) <- c(paste0(\"tab_\", i), 'pxcor', 'pycor', 'agent', 'breed')\r\n \r\n tmp1 <- tmp1 %>% \r\n dplyr::select(-c('pxcor', 'pycor', 'agent', 'breed'))\r\n \r\n tempDf <- cbind(tempDf, tmp1)\r\n}\r\n\r\n\r\nThe details on how to indent or use white spaces are up to individual styles with some guidelines, such as avoiding long lines of text, to keep in mind. Also fine would be something like the following.\r\n\r\n\r\nfor (i in 1:length(hmpSplit))\r\n{\r\n tmp1 <- as.data.frame(hmpSplit[[i]]$pm)\r\n colnames(tmp1) <- c(paste0(\"tab_\", i), 'pxcor', 'pycor', 'agent', 'breed')\r\n tmp1 <- tmp1 %>% dplyr::select(-c('pxcor', 'pycor', 'agent', 'breed'))\r\n tempDf <- cbind(tempDf, tmp1)\r\n}\r\n\r\n\r\nCommenting Your Code\r\nCommenting your code is fantastic but it can be overdone or just be plain redundant. Comments should add information or explanations to make your code understandable for people who didn’t write it or yourself in a year from now:\r\n\r\n\r\n# comment that adds information:\r\n\r\nwrite_simoutput(nl) # having nlrx write the output into a file - only works without patch or turtle metrics\r\n\r\n\r\n# redundant comments:\r\n\r\nif (col == \"blue\") # if colour is blue\r\n{\r\n print('colour is blue') # print that the colour is blue\r\n}\r\n\r\n\r\nA better solution (if a comment is absolutely necessary) would be:\r\n\r\n\r\n# display selected colour\r\nif (colour == \"blue\")\r\n{\r\n print('colour is blue')\r\n}\r\n\r\n\r\nDon’t Repeat Yourself\r\nAs a rule of thumb, if you have to do the same task multiple times in your code: automate it. A while back, I was decomposing many time series and I needed only part of the output, in this case the ‘trend’. Instead of running the same lines of code that remove all other components for each time series individually, a short function reduced the amount of needed code substantially.\r\n\r\n\r\nDecompTrend <- function(ts){\r\n \r\n temp1<-stats::decompose(ts)\r\n return(temp1$trend)\r\n}\r\n\r\n\r\n\r\n\r\n\r\n", "preview": {}, - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:14+01:00", "input_file": {} }, { @@ -361,7 +361,7 @@ ], "contents": "\r\n\r\nContents\r\nDensity distribution function\r\nR\r\nNetLogo\r\n\r\nCumulative density function\r\nR\r\nNetLogo\r\n\r\n\r\nDensity distribution\r\nfunction\r\nLet’s first take a look at the Weibull density distribution\r\nfunction:\r\n\\[\\begin{equation}\r\n\r\nf(x) = \\frac{\\gamma} {\\alpha} (\\frac{x-\\mu}\r\n{\\alpha})^{(\\gamma - 1)}\\exp{(-((x-\\mu)/\\alpha)^{\\gamma})}\r\n\\hspace{.3in} x \\ge \\mu; \\gamma, \\alpha > 0\r\n\r\n\\end{equation}\\]\r\nR\r\nIn R, this is already implemented:\r\n\r\n\r\nscale <- 3\r\nshape <- 1\r\n\r\ndweibull(scale, shape = shape)\r\n\r\n\r\n[1] 0.04978707\r\n\r\nNetLogo\r\nIn Netlogo we can simply translate the mathematical equation into a\r\nfunction:\r\nto-report weibull [a_scale a_shape x]\r\n\r\n let Wei (a_shape / a_scale ) * ((x / a_scale)^(a_shape - 1)) * exp( - ((x / a_scale)^ a_shape))\r\n\r\n report Wei\r\n\r\nend\r\nCumulative density function\r\nLet’s also take a look at the Weibull cumulative density\r\nfunction:\r\n\\[\\begin{equation}\r\n\r\nF(x) = 1 - e^{-(x^{\\gamma})} \\hspace{.3in} x \\ge 0; \\gamma > 0\r\n\r\n\\end{equation}\\]\r\nR\r\nAgain, fully implemented in R already:\r\n\r\n\r\nscale <- 3\r\nshape <- 1\r\nx <- 5\r\n\r\npweibull(q = x, scale = scale, shape = shape)\r\n\r\n\r\n[1] 0.8111244\r\n\r\nNetLogo\r\nIn NetLogo we can simply translate the mathematical equation into a\r\nfunction:\r\nto-report weibull_cumulative [a_scale a_shape x]\r\n\r\n let Wei_cumu 1 - exp( - ((x / a_scale)^ a_shape))\r\n\r\n report Wei_cumu\r\n\r\nend\r\n\r\n\r\n\r\n", "preview": {}, - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {} }, { @@ -378,7 +378,7 @@ "categories": [], "contents": "\r\nimageseg\r\nR package for deep learning image segmentation using the U-Net model architecture by Ronneberger (2015), implemented in Keras and TensorFlow. It provides pre-trained models for forest structural metrics (canopy density and understory vegetation density) and a workflow to apply these on custom images.\r\nIn addition, it provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on the U-net architecture. Model can be trained on grayscale or color images, and can provide binary or multi-class image segmentation as output.\r\nThe package can be found on CRAN.\r\nThe preprint of the paper describing the package is available on bioRxiv.\r\nInstallation\r\nFirst, install the R package “R.rsp” which enables the static vignettes.\r\n\r\n\r\n\r\nInstall the imageseg package from CRAN via:\r\n\r\n\r\n\r\nAlternatively you can install from GitHub (requires remotes package and R.rsp):\r\n\r\n\r\n\r\nUsing imageseg requires Keras and TensorFlow. See the vignette for information about installation and initial setup:\r\nTutorial\r\nSee the vignette for an introduction and tutorial to imageseg.\r\n\r\n\r\n\r\nThe vignette covers:\r\n- Installation and setup\r\n- Sample workflow for canopy density assessments\r\n- Training new models\r\n- Continued training of existing models\r\n- Multi-class image segmentation models\r\n- Image segmentation based on grayscale images\r\nForest structure model download\r\nThe models, example predictions, training data and R script for model training for both the canopy and understory model are available from Dryad as a single download.\r\nSee the “Usage Notes” section for details on the dataset.\r\nThe models and script (without the training data) are also hosted on Zenodo and can be downloaded individually from zenodo.\r\nThe pre-trained models for forest canopy density and understory vegetation density are available for download. The zip files contain the model (as .hdf5 files) and example classifications to give an impression of model performance and output:\r\nCanopy model\r\nUnderstory model\r\nPlease see the vignette for further information on how to use these models.\r\nTraining data download\r\nTraining data for both the canopy and understory model are included in the Dryad dataset download in the zip files:\r\nimageseg_canopy_training_data.zip\r\nimageseg_understory_training_data.zip\r\nFor details, please see the Usage Notes and the info.txt files contained in the zip files.\r\nThe training data are not required for users who only wish to use the pre-trained models on their own images.\r\n\r\n\r\n\r\n", "preview": "https://raw.githubusercontent.com/EcoDynIZW/EcoDynIZW.github.io/main/img/wiki/hex-imageseg.png", - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {} }, { @@ -395,7 +395,7 @@ "categories": [], "contents": "\r\nHow to download and install IsoriX?\r\nYou can download and install the stable version of IsoriX directly from within R by typing:\r\n\r\n\r\ninstall.packages(\"IsoriX\", dependencies = TRUE)\r\n\r\n\r\nNote: if you get into troubles due to gmp, magick, maps, maptools, RandomFields, rgeos, or rgl, retry using simply:\r\n\r\n\r\ninstall.packages(\"IsoriX\")\r\n\r\n\r\nThese packages offer additional functionalities but some of them are particularly difficult to install on some systems.\r\nIf you want the development version of IsoriX, you can download and install it by typing:\r\n\r\n\r\nremotes::install_github(\"courtiol/IsoriX/IsoriX\")\r\n\r\n\r\nMind that you need the R package remotes to be installed for that to work. Mind also that the development version, being under development, can sometimes be broken. So before downloading it make sure that the current build status is build passing. The current built status is provided at the top of this readme document.\r\nAlso, if you access the network via a proxy, you may experience troubles with install_github. In such case try something like:\r\n\r\n\r\nlibrary(httr)\r\nwith_config(use_proxy(\"192.168.2.2:3128\"), devtools::install_github(\"courtiol/IsoriX/IsoriX\"))\r\n\r\n\r\nOff course, unless you are in the same institute than some of us, replace the numbers with your proxy settings!\r\nWhere to learn about IsoriX?\r\nYou can start by reading our bookdown!\r\nThen, if may not be a bad idea to also have a look at our papers: here and there.\r\nAnother great source of help is our mailing list. First register for free (using your Google account) and then feel free to send us questions.\r\nFor specific help on IsoriX functions and objects, you should also check the documentation embedded in the package:\r\n\r\n\r\nhelp(package = \"IsoriX\")\r\n\r\n\r\nin R after having installed and attached (= loaded) the package.\r\nHow can you contribute?\r\nThere are plenty way you can contribute! If you are fluent in R programming, you can improve the code and develop new functions. If you are not so fluent, you can still edit the documentation files to make them more complete and clearer, write new vignettes, report bugs or make feature requests.\r\nSome useful links\r\n\r\n\r\n\r\n", "preview": "https://raw.githubusercontent.com/EcoDynIZW/EcoDynIZW.github.io/main/img/wiki/hex-isorix.png", - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {} }, { @@ -417,7 +417,7 @@ ], "contents": "\r\n\r\nContents\r\nInstallation\r\nExiftool\r\n\r\nThe {camtrapR} R package simplifies camera trap data management in R.\r\nInstallation\r\nYou can install the release version of {camtrapR} from CRAN:\r\n\r\n\r\ninstall.packages(\"camtrapR\")\r\n\r\n\r\nExiftool\r\nNumerous important {camtrapR} functions read EXIF metadata from JPG images (and videos). This is done via Exiftool, a free and open-source sofware tool developed by Phil Harvey and available for Windows, MacOS and Linux.\r\nTo make full use of {camtrapR}, you will need Exiftool on your system. You can download it from the Exiftool homepage and follow the installation instruction in vignette 1.\r\nYou may not need Exiftool if you do not work with image files, but only use {camtrapR} to create input for occupancy or spatial capture-recapture models from existing record tables.\r\nSee the article in Methods in Ecology and Evolution for an overview of the package. The five vignettes provide examples for the entire workflow.\r\nCitation\r\n\r\n\r\n\r\n", "preview": "https://raw.githubusercontent.com/EcoDynIZW/EcoDynIZW.github.io/main/img/wiki/hex-camtrapr.png", - "last_modified": "2023-10-30T10:34:49+01:00", + "last_modified": "2023-11-13T15:55:14+01:00", "input_file": {} }, { @@ -439,7 +439,7 @@ ], "contents": "\r\nThe {NLMR} R package to simulate neutral landscape models (NLM). Designed to be a generic framework like NLMpy, it leverages the ability to simulate the most common NLM that are described in the ecological literature.\r\nIf you want to learn more about the {NLMR} package and the accompanying {landscapetools} package, check the publication Sciaini, Fritsch, Scherer 6 Simpkins (2019) Methods in Ecology and Evolution.\r\nInstallation\r\nInstall the release version from CRAN:\r\n\r\n\r\ninstall.packages(\"NLMR\")\r\n\r\n\r\nor the development version from Github, along with two packages that are needed for the generation of random fields:\r\n\r\n\r\ninstall.packages(\"remotes\")\r\nremotes::install_github(\"cran/RandomFieldsUtils\")\r\nremotes::install_github(\"cran/RandomFields\")\r\nremotes::install_github(\"ropensci/NLMR\")\r\n\r\n\r\nUsage\r\nEach neutral landscape models is simulated with a single function (all starting with nlm_) in NLMR, e.g.:\r\n\r\n\r\nrandom_cluster <- NLMR::nlm_randomcluster(\r\n nrow = 100,\r\n ncol = 100,\r\n p = 0.5,\r\n ai = c(0.3, 0.6, 0.1),\r\n rescale = FALSE\r\n)\r\n\r\nrandom_curdling <- NLMR::nlm_curds(\r\n curds = c(0.5, 0.3, 0.6),\r\n recursion_steps = c(32, 6, 2)\r\n)\r\n\r\nmidpoint_displacement <- NLMR::nlm_mpd(\r\n ncol = 100,\r\n nrow = 100,\r\n roughness = 0.61\r\n)\r\n\r\n\r\n{NLMR} supplies 15 NLM algorithms, with several options to simulate derivatives of them. The algorithms differ from each other in spatial auto-correlation, from no auto-correlation (random NLM) to a constant gradient (planar gradients).\r\nThe package builds on the advantages of the raster package and returns all simulation as RasterLayer objects, thus ensuring a direct compatibility to common GIS tasks and a flexible and simple usage:\r\n\r\n\r\nclass(random_cluster)\r\n\r\n[1] \"RasterLayer\"\r\nattr(,\"package\")\r\n[1] \"raster\"\r\n\r\nrandom_cluster\r\n\r\nclass : RasterLayer \r\ndimensions : 100, 100, 10000 (nrow, ncol, ncell)\r\nresolution : 1, 1 (x, y)\r\nextent : 0, 100, 0, 100 (xmin, xmax, ymin, ymax)\r\ncrs : NA \r\nsource : memory\r\nnames : clumps \r\nvalues : 1, 3 (min, max)\r\n\r\nVisualization\r\nThe {landscapetools} package provides a function show_landscape that was developed to plot raster objects and help users to adhere to some standards concerning color scales and typography. This means for example that by default the viridis color scale is applied (and you can pick others from the {viridis} package, too).\r\n\r\n\r\n#install.packages(\"landscapetools\")\r\n\r\n## plotting continuous values\r\nlandscapetools::show_landscape(random_cluster)\r\n\r\n\r\n## plotting discrete values\r\nlandscapetools::show_landscape(random_curdling, discrete = TRUE)\r\n\r\n\r\n## using another viridis palette\r\nlandscapetools::show_landscape(midpoint_displacement, viridis_scale = \"magma\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "preview": "https://raw.githubusercontent.com/EcoDynIZW/EcoDynIZW.github.io/main/img/wiki/hex-nlmr.png", - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {} }, { @@ -464,7 +464,7 @@ ], "contents": "\r\n\r\nContents\r\nInstallation\r\nA Basic Template Map of Imperviousness\r\nBerlin Data Sets\r\nAdding Locations to the Map\r\nCustom Styling\r\nSave Map\r\n\r\nThe {d6berlin} package aims provide template maps for Berlin with carefully chosen and aesthetically pleasing defaults. Template maps include green spaces, imperviousness levels, water bodies, district borders, roads, and railways, plus the utility to add a globe with locator pin, a scalebar, and a caption to include the data sources.\r\nThere are two main functionalities:\r\nCreate a template map with imperviousness and green spaces with base_map_imp()\r\nProvide various ready-to-use Berlin data sets with sf_*\r\nFurthermore, the package provides utility to add a globe with locator pin, a scalebar, and a caption to include the data sources.\r\nInstallation\r\nYou can install the {d6berlin} package from GitHub:\r\n\r\n\r\n## install.packages(\"remotes\")\r\n## remotes::install_github(\"EcoDynIZW/d6berlin\")\r\nlibrary(d6berlin)\r\n\r\n\r\nNote: If you are asked if you want to update other packages either press “No” (option 3) and continue or update the packages before running the install command again.\r\nA Basic Template Map of Imperviousness\r\nThe basic template map shows levels of imperviousness and green areas in Berlin. The imperviousness raster data was derived from Geoportal Berlin (FIS-Broker) with a resolution of 10m. The vector data on green spaces was collected from data provided by the OpenStreetMap Contributors. The green spaces consist of a mixture of land use and natural categories (namely “forest”, “grass”, “meadow”, “nature_reserve”, “scrub”, “heath”, “beach”, “cliff”).\r\nThe map is projected in EPSG 4326 (WGS84).\r\n\r\n\r\nd6berlin::base_map_imp()\r\n\r\n#> Aggregating raster data.\r\n#> Plotting basic map.\r\n#> Styling map.\r\n\r\n\r\nYou can also customize the arguments, e.g. change the color intensity, add a globe with a locator pin, change the resolution of the raster, and move the legend to a custom position:\r\n\r\n\r\nbase_map_imp(color_intensity = 1, globe = TRUE, resolution = 500,\r\n legend_x = .17, legend_y = .12)\r\n\r\n\r\n\r\nIf you think the legend is not need, there is also an option called \"none\". (The default is \"bottom\". You can also use of the predefined setting \"top\" as illustrated below or a custom position as shown in the previous example.)\r\nBerlin Data Sets\r\nThe package contains several data sets for Berlin. All of them start with sf_, e.g. d6berlin::sf_roads. Here is a full overview of the data sets that are available:\r\n\r\n\r\n\r\nAdding Locations to the Map\r\nLet’s assume you have recorded some animal locations or you want to plot another information on to of this plot. For example, let’s visualize the Berlin metro stations by adding geom_sf(data = x) to the template map:\r\n\r\n\r\nlibrary(ggplot2)\r\nlibrary(sf)\r\n\r\nmap <- base_map_imp(color_intensity = .4, resolution = 500, legend = \"top\")\r\n\r\nmap + geom_sf(data = sf_metro) ## sf_metro is contained in the {d6berlin} package\r\n\r\n\r\n\r\nNote: Since the template map contains many filled areas, we recommend to add geometries with variables mapped to color|colour|col to the template maps.\r\nYou can, of course, style the appearance of the points as usual:\r\n\r\n\r\nmap + geom_sf(data = sf_metro, shape = 8, color = \"red\", size = 2)\r\n\r\n\r\n\r\nIt is also possible to filter the data inside the geom_sf function — no need to use subset:\r\n\r\n\r\nlibrary(dplyr) ## for filtering\r\nlibrary(stringr) ## for filtering based on name\r\n\r\nmap + \r\n geom_sf(data = filter(sf_metro, str_detect(name, \"^U\")), \r\n shape = 21, fill = \"dodgerblue\", size = 2) +\r\n geom_sf(data = filter(sf_metro, str_detect(name, \"^S\")), \r\n shape = 21, fill = \"forestgreen\", size = 2)\r\n\r\n\r\n\r\nYou can also use the mapping functionality of ggplot2 to address variables from your data set.\r\n\r\n\r\nmap + \r\n geom_sf(data = sf_metro, aes(color = type), size = 2) +\r\n scale_color_discrete(type = c(\"forestgreen\", \"dodgerblue\"), \r\n name = NULL) +\r\n guides(color = guide_legend(direction = \"horizontal\",\r\n title.position = \"top\", \r\n title.hjust = .5))\r\n\r\n\r\n\r\n(It looks better if you style the legend in the same horizontal layout.)\r\nCustom Styling\r\nSince the output is a ggplot object, you can manipulate the result as you like (but don’t apply a new theme, this will mess up the legend design):\r\n\r\n\r\nlibrary(systemfonts) ## for title font\r\n\r\nbase_map_imp(color_intensity = 1, resolution = 250, globe = TRUE,\r\n legend_x = .17, legend_y = .12) + \r\n geom_sf(data = sf_metro, shape = 21, fill = \"white\", \r\n stroke = .4, size = 4) +\r\n ggtitle(\"Metro Stations in Berlin\") + \r\n theme(plot.title = element_text(size = 30, hjust = .5, family = \"Bangers\"),\r\n panel.grid.major = element_line(color = \"white\", linewidth = .3),\r\n axis.text = element_text(color = \"black\", size = 8),\r\n plot.background = element_rect(fill = \"#fff0de\", color = NA),\r\n plot.margin = margin(rep(20, 4)))\r\n\r\n\r\n\r\nSave Map\r\nUnfortunately, the size of the text elements is fixed. The best aspect ratio to export the map is 12x9 and you can save it with ggsave() for example:\r\n\r\n\r\nggsave(\"metro_map.pdf\", width = 12, height = 9, device = cairo_pdf)\r\n\r\n\r\n\r\n\r\n\r\n", "preview": "https://raw.githubusercontent.com/EcoDynIZW/EcoDynIZW.github.io/main/img/wiki/hex-d6berlin.png", - "last_modified": "2023-10-19T12:55:42+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {} }, { @@ -486,7 +486,7 @@ ], "contents": "\r\n\r\nContents\r\nInstallation\r\nCreate Project Directory\r\n\r\n\r\n\r\n\r\nInstall Common Packages\r\nCorporate ggplot2 Theme\r\nTypefaces\r\nAdditional Utility Arguments\r\n\r\nUse Custom Rmarkdown Templates(\r\nRender Rmarkdown Files to Reports\r\nInstall and Load a Set of Packages\r\nAcknowledgements:\r\n\r\n\r\n\r\nThe {d6} package aims to simplify workflows of our D6 research projects by providing a standardized folder structure incl. version control, Rmarkdown templates, and other utilities.\r\nThere are five main functionalities:\r\nCreate standardized project directories with new_project()\r\nInstall a set of common packages with install_d6_packages()\r\nCreate figures that match our lab identity via theme_d6()\r\nProvide custom Rmarkdown templates via File > New File > Rmarkdown... > From Template\r\nRender all your Rmarkdown documents to ./docs/report with render_all_reports() or render_report()\r\nThe function simple_load() is a utility function that is currently in an experimental state. It allows you to install (if not yet) and load a set of packages—even a combination of CRAN and GitHub packages—in a single step.\r\n\r\n\r\nInstallation\r\nThe package is not on CRAN and needs to be installed from GitHub. To do\r\nso, open Rstudio and run the following two lines in the console. In case\r\nthe {remotes} package is already installed, skip that step.\r\n\r\n\r\ninstall.packages(\"remotes\")\r\nremotes::install_github(\"EcoDynIZW/d6\")\r\n\r\n\r\n(Note: If you are asked if you want to update other packages either\r\npress “No” (option 3) and continue or update the packages before running\r\nthe install command again.)\r\n\r\n\r\nCreate Project Directory\r\nRun the function new_project() to create a new project. This will\r\ncreate a standardized directory with all the scaffolding we use for all\r\nprojects in our department. It also add several files needed for\r\ndocumentation of your project.\r\nTo start a new project in the current working directory, simply run:\r\n\r\n\r\nd6::new_project(\"unicornus_wl_sdm_smith_j\")\r\n\r\n\r\nPlease give your project a unique and descriptive name:\r\nspecies_country_topic_name\r\nFor example, when John Smith is developing a species distribution models\r\nfor unicorns in Wonderland, a descriptive title could be:\r\nunicornus_wl_sdm_smith_j. Please use underscores and the\r\ninternational Alpha-2 encoding for\r\ncountries.\r\nThe main folders created in the root folder (here\r\nunicornus_wl_sdm_smith_j) are the following:\r\n\r\n.\r\n└── unicornus_wl_sdm_smith_j\r\n ├── data\r\n ├── docs\r\n ├── output\r\n ├── plots\r\n └── scripts\r\n\r\nThe full scaffolding structure including all sub directories and\r\nadditional files looks like this:\r\n\r\n. \r\n└── unicornus_wl_sdm_smith_j\r\n ├── .Rproj.user — Rproject files\r\n ├── data — main folder data\r\n │ ├── geo — main folder spatial data (if `geo = TRUE`)\r\n │ │ ├── processed — processed spatial data files\r\n │ │ └── raw — raw spatial data files\r\n │ ├── processed — processed tabular data files\r\n │ └── raw — raw tabular data files\r\n ├── docs — documents main folder\r\n │ ├── admin — administrative docs, e.g. permits \r\n │ ├── literature — literature used for parametrization + manuscript\r\n │ ├── manuscript — manuscript drafts (main + supplement)\r\n │ ├── presentations — talks and poster presentations\r\n │ └── reports — rendered reports\r\n ├── output — everything that is computed (except plots)\r\n ├── plots — plot output\r\n ├── scripts — script files (e.g. .R, .Rmd, .Qmd, .py, .nlogo)\r\n │ ├── 00_start.R — first script to run after project setup\r\n │ └── zz_submit.R — final script to run before submission\r\n ├── .gitignore — contains which files to ignore for version control\r\n ├── .Rbuildignore — contains which files to ignore for package builds\r\n ├── DESCRIPTION — contains project details and package dependencies\r\n ├── NAMESPACE — contains context for R objects\r\n └── project.Rproj — Rproject file: use to start your project\r\n\r\n\r\nUse A Custom Root Directory\r\nYou don’t need to change the working directory first—you can also\r\nspecify a path to a custom root folder in which the new project folder\r\nis created:\r\n\r\n\r\n## both work:\r\nd6::new_project(\"unicornus_wl_sdm_smith_j\", path = \"absolute/path/to/the/root/folder\")\r\n## or:\r\nd6::new_project(\"unicornus_wl_sdm_smith_j\", path = \"absolute/path/to/the/root/folder/\")\r\n\r\n\r\nThe resulting final directory of your project would be\r\nabsolute/path/to/the/root/folder/unicornus_wl_sdm_smith_j.\r\n\r\nUse Version Control\r\nIf you want to create a GitHub repository for the project at the same\r\ntime, use instead:\r\n\r\n\r\nd6::new_project(\"unicornus_wl_sdm_smith_j\", github = TRUE)\r\n\r\n\r\nBy default, the visibility of the GitHub repository is set to “private”\r\nbut you can also change that:\r\n\r\n\r\nd6::new_project(\"unicornus_wl_sdm_smith_j\", github = TRUE, private_repo = FALSE)\r\n\r\n\r\nNote that to create a GitHub repo you will need to have configured your\r\nsystem as explained\r\nhere.\r\n\r\nSetup without Geo Directories\r\nIf your project does not (or will not) contain any spatial data, you can\r\nprevent the creation of the directories geo-raw and geo-proc by\r\nsetting geo to FALSE:\r\n\r\n\r\nd6::new_project(\"unicornus_wl_sdm_smith_j\", geo = FALSE)\r\n\r\n\r\n\r\nAdd Documentation to Your Project\r\nAfter you have set up your project directory, open the file 00_start.R\r\nin the R folder. Add the details of your project, fill in the readme,\r\nadd a MIT license (if needed) and add package dependencies.\r\n\r\n\r\nInstall Common Packages\r\nYou can install the packages that are most commonly used in our\r\ndepartment via install_d6_packages():\r\n\r\n\r\nd6::install_d6_packages()\r\n\r\n\r\nNote that this function is going to check pre-installed versions and will\r\nonly install packages that are not installed with your current R\r\nversion.\r\nAgain, there is an argument geo so you can decide if you want to\r\ninstall common geodata packages as well (which is the default). If you\r\nare not intending to process geodata, set geo to FALSE:\r\n\r\n\r\nd6::install_d6_packages(geo = FALSE)\r\n\r\n\r\nThe default packages that are going to be installed are:\r\n\r\ntidyverse (tibble, dplyr, tidyr, ggplot2, readr, forcats, stringr, purrr), lubridate, here, vroom, patchwork, remotes\r\n\r\nThe following packages will be installed in case you specify\r\ngeo = TRUE:\r\n\r\nsf, terra, stars, tmap\r\n\r\n\r\n\r\nCorporate ggplot2 Theme\r\nThe package provides a ggplot2 theme with sensible defaults and additional\r\nutilities to simplify the process of creating a good-looking, clean look.\r\nFurthermore, we aim to have a consistent look across all our figures shown\r\nin manuscripts, presentations, and posters.\r\nThe theme can be added to a ggplot object as usual:\r\n\r\n\r\nlibrary(ggplot2)\r\nggplot(mpg, aes(x = displ, y = cty)) +\r\n geom_point() +\r\n d6::theme_d6()\r\n\r\n\r\nOr set as the new global theme by overwriting the current default:\r\n\r\n\r\ntheme_set(d6::theme_d6())\r\n\r\n\r\nTypefaces\r\nThe D6 corporate theme uses the PT font super family and will inform you to\r\ninstall the relevant files in case they are missing on your machine:\r\nPT Sans\r\nPT Serif\r\nPT Mono\r\nBy default, the theme uses PT Sans. If you prefer serif fonts or such a\r\ntypeface is required, you can set serif = TRUE inside the theme:\r\n\r\n\r\nggplot(mpg, aes(x = displ, y = cty)) +\r\n geom_point() +\r\n d6::theme_d6(serif = TRUE)\r\n\r\n\r\nAdditional Utility Arguments\r\nIn addition to the common arguments to specify the base settings\r\n(base_family, base_size, base_line_size, and base_rect_size),\r\nwe have added the following utility settings to simplify the modification\r\nof the theme:\r\ngrid: remove or add major grid lines (\"xy\" by default)\r\nlegend: control legend position (\"bottom\" by default)\r\nmono: use a tabular, mono spaced font for numeric scales such as x, y, and legends (\"none\" by default)\r\nbg: define background color (\"transparent\" by default)\r\nserif: set main typeface (FALSE by default; see above)\r\n\r\n\r\nggplot(mpg, aes(x = class, y = hwy, color = factor(year))) + \r\n geom_boxplot() +\r\n d6::theme_d6(\r\n grid = \"y\",\r\n legend = \"top\",\r\n mono = \"yl\",\r\n bg = \"cornsilk\"\r\n )\r\n\r\n\r\n\r\n\r\nUse Custom Rmarkdown Templates(\r\nThe package also provides several templates for your scripts. In\r\nRstudio, navigate to File > New File > RMarkdown... > Templates and\r\nchoose the template you want to use. All templates come with a\r\npre-formatted YAML header and chunks for the setup.\r\nThe following templates are available for now:\r\nEcoDynIZW Basic: Template for a basic Rmarkdown research report\r\nincluding bits of codes and comments to get started\r\nEcoDynIZW Minimal: Template for an Rmarkdown research report\r\n(almost empty)\r\n\r\n\r\nRender Rmarkdown Files to Reports\r\nThe render_*() functions take care of knitting your Rmarkdown files\r\ninto HTML reports. The functions assume that your .Rmd files are saved\r\nin the R directory or any sub directory, and will store the resulting\r\n.html files in the according directory, namely ./docs/reports/.\r\nYou can render all .Rmd files that are placed in the R directory and\r\nsub directories in one step:\r\n\r\n\r\nd6::render_all_reports()\r\n\r\n\r\nYou can also render single Rmarkdown documents via render_report():\r\n\r\n\r\nd6::render_report(\"my-report.Rmd\")\r\nd6::render_report(\"notsurewhybutIhaveasubfolder/my-report.Rmd\")\r\n\r\n\r\n\r\n\r\nInstall and Load a Set of Packages\r\nThe simple_load() function takes a vector of packages, checks if they\r\nare installed already, installs them if needed, and loads them via\r\nlibrary() afterward. You can provide both, CRAN and GitHub packages,\r\nat the same time. GitHub packages need to be specified as\r\n“owner/repository” without any spaces.\r\n\r\n\r\nd6::simple_load(pcks = c(\"dplyr\", \"ggplot2\", \"EcoDynIZW/d6berlin\"))\r\n\r\n\r\nYou can also force a re-installation of packages. CRAN and GitHub packages\r\nare controlled individually via update_cran and update_gh, respectively.\r\n\r\n\r\nd6::simple_load(pcks = c(\"dplyr\", \"ggplot2\", \"EcoDynIZW/d6berlin\"),\r\n update_cran = TRUE, update_gh = TRUE)\r\n\r\n\r\n\r\n\r\nAcknowledgements:\r\nThis package would not exist without the work of many great people!\r\nThe code to create the project folder is based on the template\r\npackage by Francisco\r\nRodriguez-Sanchez (and\r\nreferences therein)\r\nThe 00_start script is inspired by the {golem}\r\npackage\r\nRstudio for the development of Rmarkdown and all the great things\r\nthat come with it (knitting, templates, themes, …)\r\nAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "preview": "https://raw.githubusercontent.com/EcoDynIZW/EcoDynIZW.github.io/main/img/wiki/hex-d6.png", - "last_modified": "2023-12-21T08:22:03+01:00", + "last_modified": "2023-12-08T13:17:48+01:00", "input_file": {} } ] diff --git a/docs/posts_geodata/posts_geodata.json b/docs/posts_geodata/posts_geodata.json index 647fb67a..631f703e 100644 --- a/docs/posts_geodata/posts_geodata.json +++ b/docs/posts_geodata/posts_geodata.json @@ -18,7 +18,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ncorine-landcover_germany_2018_polygon_25832_gpkgname\r\ncorine-landcover_germany_2018_polygon_25832.gpkgepsg\r\n25832crs\r\n+proj=utm +zone=32 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defsyear_of_data\r\n2018units_of_data\r\ncorine landcover classesresolution\r\npolygontype_of_data\r\nordered_categoricaltype_of_file\r\n.gpkgdate_of_compile\r\n2024-05-28source\r\nbkgshort_description\r\nNAmodified\r\nNAlink_of_source\r\nhttps://gdz.bkg.bund.decopyright\r\n© GeoBasis-DE / BKG (2024)\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2024-05-29 15:00:50 CEST\"\r\nR version 4.3.3 (2024-02-29 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.2.0\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.1 sass_0.4.9 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.1 class_7.3-22 \r\n [7] xml2_1.3.6 KernSmooth_2.23-22 distill_1.6 \r\n[10] digest_0.6.35 magrittr_2.0.3 evaluate_0.23 \r\n[13] grid_4.3.3 fastmap_1.2.0 jsonlite_1.8.8 \r\n[16] e1071_1.7-14 DBI_1.2.2 purrr_1.0.2 \r\n[19] fansi_1.0.6 scales_1.3.0 textshaping_0.3.7 \r\n[22] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3 \r\n[25] units_0.8-5 munsell_0.5.1 withr_3.0.0 \r\n[28] cachem_1.1.0 yaml_2.3.8 tools_4.3.3 \r\n[31] memoise_2.0.1 dplyr_1.1.4 colorspace_2.1-0 \r\n[34] ggplot2_3.5.1 vctrs_0.6.5 R6_2.5.1 \r\n[37] proxy_0.4-27 lifecycle_1.0.4 classInt_0.4-10 \r\n[40] ragg_1.3.0 pkgconfig_2.0.3 pillar_1.9.0 \r\n[43] bslib_0.7.0 gtable_0.3.5 glue_1.7.0 \r\n[46] Rcpp_1.0.12 sf_1.0-16 systemfonts_1.0.6 \r\n[49] highr_0.11 xfun_0.44 tibble_3.2.1 \r\n[52] tidyselect_1.2.1 rstudioapi_0.15.0 knitr_1.46 \r\n[55] farver_2.1.2 htmltools_0.5.8.1 rmarkdown_2.27 \r\n[58] compiler_4.3.3 downlit_0.4.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/corine-landcover_germany_2018_polygon_25832_gpkg/corine-landcover_germany_2018_polygon_25832_gpkg_files/figure-html5/plot-1.png", - "last_modified": "2024-06-04T11:34:27+02:00", + "last_modified": "2024-05-29T15:00:54+02:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -42,7 +42,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nadministrativ-units_germany_2020_polygon_28532_gpkgname\r\nadministrativ-units_germany_2020_polygon_28532.gpkgepsg\r\n25832crs\r\n+proj=utm +zone=32 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defsyear_of_data\r\n2020units_of_data\r\nname of federal stateresolution\r\npolygontype_of_data\r\nbinary_categoricaltype_of_file\r\n.gpkgdate_of_compile\r\n2024-05-27source\r\nbkgshort_description\r\ncombined federal states with countiesmodified\r\nNAlink_of_source\r\nhttps://gdz.bkg.bund.decopyright\r\n© GeoBasis-DE / BKG (2024)\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2024-05-27 16:19:04 CEST\"\r\nR version 4.3.3 (2024-02-29 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.2.0\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.1 sass_0.4.9 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.1 class_7.3-22 \r\n [7] xml2_1.3.6 KernSmooth_2.23-22 distill_1.6 \r\n[10] digest_0.6.35 magrittr_2.0.3 evaluate_0.23 \r\n[13] grid_4.3.3 fastmap_1.2.0 jsonlite_1.8.8 \r\n[16] e1071_1.7-14 DBI_1.2.2 purrr_1.0.2 \r\n[19] fansi_1.0.6 scales_1.3.0 textshaping_0.3.7 \r\n[22] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3 \r\n[25] units_0.8-5 munsell_0.5.1 withr_3.0.0 \r\n[28] cachem_1.1.0 yaml_2.3.8 tools_4.3.3 \r\n[31] memoise_2.0.1 dplyr_1.1.4 colorspace_2.1-0 \r\n[34] ggplot2_3.5.1 vctrs_0.6.5 R6_2.5.1 \r\n[37] proxy_0.4-27 lifecycle_1.0.4 classInt_0.4-10 \r\n[40] ragg_1.3.0 pkgconfig_2.0.3 pillar_1.9.0 \r\n[43] bslib_0.7.0 gtable_0.3.5 glue_1.7.0 \r\n[46] Rcpp_1.0.12 sf_1.0-16 systemfonts_1.0.6 \r\n[49] highr_0.10 xfun_0.44 tibble_3.2.1 \r\n[52] tidyselect_1.2.1 rstudioapi_0.15.0 knitr_1.46 \r\n[55] farver_2.1.2 htmltools_0.5.8.1 rmarkdown_2.26 \r\n[58] compiler_4.3.3 downlit_0.4.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/administrativ-units_germany_2020_polygon_28532_gpkg/administrativ-units_germany_2020_polygon_28532_gpkg_files/figure-html5/plot-1.png", - "last_modified": "2024-06-04T11:34:27+02:00", + "last_modified": "2024-05-27T16:19:07+02:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -66,7 +66,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nvegetation-heights_berlin_2020_1m_25833_tifname\r\nvegetation-heights_berlin_2020_1m_25833.tifepsg\r\n25833crs\r\n+proj=utm +zone=33 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defsyear_of_data\r\n2020units_of_data\r\nheight of vegetation in cmresolution\r\n1mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifdate_of_compile\r\n2024-05-23source\r\nfisbroker - Umweltatlasshort_description\r\nNAmodified\r\nNAlink_of_source\r\nhttps://stadtentwicklung.berlin.de/geoinformation/fis-broker/copyright\r\nUmweltatlas Berlin / Vegetationshöhen 2020 (Umweltatlas)\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2024-05-24 10:40:56 CEST\"\r\nR version 4.3.3 (2024-02-29 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.2.0\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.1 sass_0.4.9 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.1 class_7.3-22 \r\n [7] xml2_1.3.6 KernSmooth_2.23-22 lattice_0.22-5 \r\n[10] distill_1.6 digest_0.6.35 magrittr_2.0.3 \r\n[13] timechange_0.3.0 evaluate_0.23 grid_4.3.3 \r\n[16] fastmap_1.2.0 jsonlite_1.8.8 e1071_1.7-14 \r\n[19] DBI_1.2.2 purrr_1.0.2 fansi_1.0.6 \r\n[22] scales_1.3.0 codetools_0.2-19 textshaping_0.3.7 \r\n[25] jquerylib_0.1.4 abind_1.4-5 cli_3.6.2 \r\n[28] rlang_1.1.3 units_0.8-5 munsell_0.5.1 \r\n[31] withr_3.0.0 cachem_1.1.0 yaml_2.3.8 \r\n[34] raster_3.6-26 parallel_4.3.3 tools_4.3.3 \r\n[37] memoise_2.0.1 dplyr_1.1.4 colorspace_2.1-0 \r\n[40] ggplot2_3.5.1 vctrs_0.6.5 R6_2.5.1 \r\n[43] lubridate_1.9.3 proxy_0.4-27 lifecycle_1.0.4 \r\n[46] classInt_0.4-10 ragg_1.3.0 pkgconfig_2.0.3 \r\n[49] terra_1.7-71 pillar_1.9.0 bslib_0.7.0 \r\n[52] gtable_0.3.5 glue_1.7.0 Rcpp_1.0.12 \r\n[55] sf_1.0-16 systemfonts_1.0.6 highr_0.10 \r\n[58] xfun_0.44 tibble_3.2.1 tidyselect_1.2.1 \r\n[61] rstudioapi_0.15.0 knitr_1.46 farver_2.1.2 \r\n[64] htmltools_0.5.8.1 labeling_0.4.3 rmarkdown_2.26 \r\n[67] d6geodata_0.0.0.9000 compiler_4.3.3 downlit_0.4.3 \r\n[70] sp_2.1-3 stars_0.6-5 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/vegetation-heights_berlin_2020_1m_25833_tif/vegetation-heights_berlin_2020_1m_25833_tif_files/figure-html5/plot-1.png", - "last_modified": "2024-06-04T11:34:28+02:00", + "last_modified": "2024-05-24T10:40:58+02:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -90,7 +90,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ntree-cover-density_berlin_2018_10m_03035_tifname\r\ntree-cover-density_berlin_2018_10m_03035.tifepsg\r\n3035crs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsyear_of_data\r\n2018units_of_data\r\npercentage tree coverresolution\r\n10mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifdate_of_compile\r\n2024-05-17source\r\ncopernicusshort_description\r\ncropped to berlin extentmodified\r\nNAlink_of_source\r\nhttps://land.copernicus.eu/copyright\r\n© European Union, Copernicus Land Monitoring Service 2024, European Environment Agency (EEA)\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2024-05-17 13:13:51 CEST\"\r\nR version 4.3.3 (2024-02-29 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.2.0\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.1 sass_0.4.9 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.1 class_7.3-22 \r\n [7] xml2_1.3.6 KernSmooth_2.23-22 distill_1.6 \r\n[10] digest_0.6.35 magrittr_2.0.3 timechange_0.3.0 \r\n[13] evaluate_0.23 grid_4.3.3 fastmap_1.2.0 \r\n[16] jsonlite_1.8.8 e1071_1.7-14 DBI_1.2.2 \r\n[19] purrr_1.0.2 fansi_1.0.6 scales_1.3.0 \r\n[22] codetools_0.2-19 textshaping_0.3.7 jquerylib_0.1.4 \r\n[25] abind_1.4-5 cli_3.6.2 rlang_1.1.3 \r\n[28] units_0.8-5 munsell_0.5.1 withr_3.0.0 \r\n[31] cachem_1.1.0 yaml_2.3.8 parallel_4.3.3 \r\n[34] tools_4.3.3 memoise_2.0.1 dplyr_1.1.4 \r\n[37] colorspace_2.1-0 ggplot2_3.5.1 vctrs_0.6.5 \r\n[40] R6_2.5.1 lubridate_1.9.3 proxy_0.4-27 \r\n[43] lifecycle_1.0.4 classInt_0.4-10 ragg_1.3.0 \r\n[46] pkgconfig_2.0.3 terra_1.7-71 pillar_1.9.0 \r\n[49] bslib_0.7.0 gtable_0.3.5 glue_1.7.0 \r\n[52] Rcpp_1.0.12 sf_1.0-16 systemfonts_1.0.6 \r\n[55] highr_0.10 xfun_0.44 tibble_3.2.1 \r\n[58] tidyselect_1.2.1 rstudioapi_0.15.0 knitr_1.46 \r\n[61] farver_2.1.2 htmltools_0.5.8.1 labeling_0.4.3 \r\n[64] rmarkdown_2.26 d6geodata_0.0.0.9000 compiler_4.3.3 \r\n[67] downlit_0.4.3 stars_0.6-5 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/tree-cover-density_berlin_2018_10m_03035_tif/tree-cover-density_berlin_2018_10m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2024-06-04T11:34:28+02:00", + "last_modified": "2024-05-17T13:13:55+02:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -114,7 +114,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nopen-green-areas_berlin_2024_polygon_3035_gpkgname\r\nopen-green-areas_berlin_2024_polygon_3035.gpkgepsg\r\n3035crs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsyear_of_data\r\n2024units_of_data\r\narea in [m2]resolution\r\nvectortype_of_data\r\ncontinuous_numerictype_of_file\r\n.gpkgdate_of_compile\r\n2024-05-13source\r\nfisbroker - Umweltatlasshort_description\r\nnamenr is the column with the name of the green areamodified\r\narea [m2] of each polygon was calculatedlink_of_source\r\nhttps://stadtentwicklung.berlin.de/geoinformation/fis-broker/copyright\r\nUmweltatlas Berlin / Grünanlagenbestand Berlin (einschließlich der öffentlichen Spielplätze)\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2024-05-13 16:23:15 CEST\"\r\nR version 4.3.3 (2024-02-29 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.2.0\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.1 sass_0.4.9 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.1 class_7.3-22 \r\n [7] xml2_1.3.6 KernSmooth_2.23-22 distill_1.6 \r\n[10] digest_0.6.35 magrittr_2.0.3 evaluate_0.23 \r\n[13] grid_4.3.3 fastmap_1.1.1 jsonlite_1.8.8 \r\n[16] e1071_1.7-14 DBI_1.2.2 purrr_1.0.2 \r\n[19] fansi_1.0.6 scales_1.3.0 textshaping_0.3.7 \r\n[22] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3 \r\n[25] units_0.8-5 munsell_0.5.1 withr_3.0.0 \r\n[28] cachem_1.0.8 yaml_2.3.8 tools_4.3.3 \r\n[31] memoise_2.0.1 dplyr_1.1.4 colorspace_2.1-0 \r\n[34] ggplot2_3.5.1 vctrs_0.6.5 R6_2.5.1 \r\n[37] proxy_0.4-27 lifecycle_1.0.4 classInt_0.4-10 \r\n[40] ragg_1.3.0 pkgconfig_2.0.3 pillar_1.9.0 \r\n[43] bslib_0.7.0 gtable_0.3.5 glue_1.7.0 \r\n[46] Rcpp_1.0.12 sf_1.0-16 systemfonts_1.0.6 \r\n[49] highr_0.10 xfun_0.43 tibble_3.2.1 \r\n[52] tidyselect_1.2.1 rstudioapi_0.15.0 knitr_1.46 \r\n[55] farver_2.1.2 htmltools_0.5.8.1 labeling_0.4.3 \r\n[58] rmarkdown_2.26 compiler_4.3.3 downlit_0.4.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/open-green-areas_berlin_2024_polygon_3035_gpkg/open-green-areas_berlin_2024_polygon_3035_gpkg_files/figure-html5/plot-1.png", - "last_modified": "2024-06-04T11:34:28+02:00", + "last_modified": "2024-05-13T16:23:18+02:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -133,7 +133,7 @@ "categories": [], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nimperviousness_berlin_2021_line_03035_gpkgname\r\nimperviousness_berlin_2021_line_03035.gpkgepsg\r\n3035crs\r\nNAyear_of_data\r\n2021units_of_data\r\nimperviousness in %resolution\r\nvectortype_of_data\r\ncontinuous_numerictype_of_file\r\n.gpkgdate_of_compile\r\n2024-04-16source\r\nfisbroker - Umweltatlasshort_description\r\nvg is the imperviousness columnmodified\r\nreprojected to epsg 3035link_of_source\r\nhttps://stadtentwicklung.berlin.de/geoinformation/fis-broker/copyright\r\nUmweltatlas Berlin / Versiegelung 2021 (Umweltatlas)\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2024-04-16 09:43:43 CEST\"\r\nR version 4.3.3 (2024-02-29 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.2.0\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.1 sass_0.4.9 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.1 class_7.3-22 \r\n [7] xml2_1.3.6 KernSmooth_2.23-22 distill_1.6 \r\n[10] digest_0.6.35 magrittr_2.0.3 evaluate_0.23 \r\n[13] grid_4.3.3 fastmap_1.1.1 jsonlite_1.8.8 \r\n[16] e1071_1.7-14 DBI_1.2.2 purrr_1.0.2 \r\n[19] fansi_1.0.6 scales_1.3.0 textshaping_0.3.7 \r\n[22] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3 \r\n[25] units_0.8-5 munsell_0.5.1 withr_3.0.0 \r\n[28] cachem_1.0.8 yaml_2.3.8 tools_4.3.3 \r\n[31] memoise_2.0.1 dplyr_1.1.4 colorspace_2.1-0 \r\n[34] ggplot2_3.5.0 vctrs_0.6.5 R6_2.5.1 \r\n[37] proxy_0.4-27 lifecycle_1.0.4 classInt_0.4-10 \r\n[40] ragg_1.3.0 pkgconfig_2.0.3 pillar_1.9.0 \r\n[43] bslib_0.7.0 gtable_0.3.4 glue_1.7.0 \r\n[46] Rcpp_1.0.12 sf_1.0-16 systemfonts_1.0.6 \r\n[49] highr_0.10 xfun_0.43 tibble_3.2.1 \r\n[52] tidyselect_1.2.1 rstudioapi_0.15.0 knitr_1.46 \r\n[55] farver_2.1.1 htmltools_0.5.8.1 labeling_0.4.3 \r\n[58] rmarkdown_2.26 compiler_4.3.3 downlit_0.4.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/imperviousness_berlin_2021_line_03035_gpkg/imperviousness_berlin_2021_line_03035_gpkg_files/figure-html5/plot-1.png", - "last_modified": "2024-05-02T12:46:23+02:00", + "last_modified": "2024-04-16T09:43:46+02:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -157,7 +157,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nimperviousness_berlin_2021_polygon_03035_gpkgname\r\nimperviousness_berlin_2021_polygon_03035.gpkgepsg\r\n3035crs\r\nNAyear_of_data\r\n2021units_of_data\r\nimperviousness in %resolution\r\nvectortype_of_data\r\ncontinuous_numerictype_of_file\r\n.gpkgdate_of_compile\r\n2024-04-15source\r\nfisbroker - Umweltatlasshort_description\r\nvg_2021 is the imperviousness columnmodified\r\nNAlink_of_source\r\nhttps://stadtentwicklung.berlin.de/geoinformation/fis-broker/copyright\r\nUmweltatlas Berlin / Versiegelung 2021 (Umweltatlas)\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2024-05-13 16:22:52 CEST\"\r\nR version 4.3.3 (2024-02-29 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.2.0\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.1 sass_0.4.9 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.1 class_7.3-22 \r\n [7] xml2_1.3.6 KernSmooth_2.23-22 distill_1.6 \r\n[10] digest_0.6.35 magrittr_2.0.3 evaluate_0.23 \r\n[13] grid_4.3.3 fastmap_1.1.1 jsonlite_1.8.8 \r\n[16] e1071_1.7-14 DBI_1.2.2 purrr_1.0.2 \r\n[19] fansi_1.0.6 scales_1.3.0 textshaping_0.3.7 \r\n[22] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3 \r\n[25] units_0.8-5 munsell_0.5.1 withr_3.0.0 \r\n[28] cachem_1.0.8 yaml_2.3.8 tools_4.3.3 \r\n[31] memoise_2.0.1 dplyr_1.1.4 colorspace_2.1-0 \r\n[34] ggplot2_3.5.1 vctrs_0.6.5 R6_2.5.1 \r\n[37] proxy_0.4-27 lifecycle_1.0.4 classInt_0.4-10 \r\n[40] ragg_1.3.0 pkgconfig_2.0.3 pillar_1.9.0 \r\n[43] bslib_0.7.0 gtable_0.3.5 glue_1.7.0 \r\n[46] Rcpp_1.0.12 sf_1.0-16 systemfonts_1.0.6 \r\n[49] highr_0.10 xfun_0.43 tibble_3.2.1 \r\n[52] tidyselect_1.2.1 rstudioapi_0.15.0 knitr_1.46 \r\n[55] farver_2.1.2 htmltools_0.5.8.1 labeling_0.4.3 \r\n[58] rmarkdown_2.26 compiler_4.3.3 downlit_0.4.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/imperviousness_berlin_2021_polygon_03035_gpkg/imperviousness_berlin_2021_polygon_03035_gpkg_files/figure-html5/plot-1.png", - "last_modified": "2024-06-04T11:34:27+02:00", + "last_modified": "2024-05-13T16:22:55+02:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -181,7 +181,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nlanduse_berlin_2021_polygon_03035_gpkgname\r\nlanduse_berlin_2021_polygon_03035.gpkgepsg\r\n3035crs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsyear_of_data\r\n2021units_of_data\r\nlanduse types in germanresolution\r\nPolygontype_of_data\r\nunordered_categoricaltype_of_file\r\n.gpkgdate_of_compile\r\n2024-04-15source\r\nfisbroker - Umweltatlasshort_description\r\nseveral columns to use. typklar is the fine categorial column. nutzung has a broader classification.modified\r\nTransformed to epsg 3035link_of_source\r\nhttps://stadtentwicklung.berlin.de/geoinformation/fis-broker/copyright\r\nUmweltatlas Berlin / Reale Nutzung 2021 (Umweltatlas)\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2024-04-15 16:42:53 CEST\"\r\nR version 4.3.3 (2024-02-29 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.2.0\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.1 sass_0.4.8 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.1 class_7.3-22 \r\n [7] xml2_1.3.6 KernSmooth_2.23-22 distill_1.6 \r\n[10] digest_0.6.35 magrittr_2.0.3 evaluate_0.23 \r\n[13] grid_4.3.3 fastmap_1.1.1 jsonlite_1.8.8 \r\n[16] e1071_1.7-14 DBI_1.2.2 purrr_1.0.2 \r\n[19] fansi_1.0.6 scales_1.3.0 textshaping_0.3.7 \r\n[22] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3 \r\n[25] units_0.8-5 munsell_0.5.0 withr_3.0.0 \r\n[28] cachem_1.0.8 yaml_2.3.8 tools_4.3.3 \r\n[31] memoise_2.0.1 dplyr_1.1.4 colorspace_2.1-0 \r\n[34] ggplot2_3.5.0 vctrs_0.6.5 R6_2.5.1 \r\n[37] proxy_0.4-27 lifecycle_1.0.4 classInt_0.4-10 \r\n[40] ragg_1.3.0 pkgconfig_2.0.3 pillar_1.9.0 \r\n[43] bslib_0.6.1 gtable_0.3.4 glue_1.7.0 \r\n[46] Rcpp_1.0.12 sf_1.0-15 systemfonts_1.0.6 \r\n[49] highr_0.10 xfun_0.42 tibble_3.2.1 \r\n[52] tidyselect_1.2.1 rstudioapi_0.15.0 knitr_1.45 \r\n[55] farver_2.1.1 htmltools_0.5.7 rmarkdown_2.26 \r\n[58] compiler_4.3.3 downlit_0.4.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/landuse_berlin_2021_polygon_03035_gpkg/landuse_berlin_2021_polygon_03035_gpkg_files/figure-html5/plot-1.png", - "last_modified": "2024-05-02T12:46:24+02:00", + "last_modified": "2024-04-15T16:42:56+02:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -205,7 +205,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ntri_europe_2016_100m_03035_tifname\r\ntri_europe_2016_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2016units_of_data\r\nmean of the absolute differences between the value of a cell and its 8 surrounding cellsresolution\r\n100mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\ndlrlink_of_source\r\nhttps://download.geoservice.dlr.de/TDM90/date_of_compile\r\n2023-11-08copyright\r\n[1] Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., Roth, A.(2018): Accuracy Assessment of the Global TanDEM-X Digital Elevation Model with GPS Data. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 139, pp. 171-182. [2] Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Borla Tridon, D., Bräutigam, B., Bachmann, M., Schulze, D., Fritz, T., Huber, M., Wessel, B., Krieger, G., Zink, M., and Moreira, A. (2017): Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS Journal of Photogrammetry and Remote Sensing, Vol 132, pp. 119-139.short_description\r\nmade from dem europe 100mmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-11-08 10:37:16 CET\"\r\nR version 4.3.1 (2023-06-16 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.3\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.0 sass_0.4.7 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.0 class_7.3-22 \r\n [7] xml2_1.3.5 KernSmooth_2.23-21 lattice_0.21-8 \r\n[10] distill_1.6 digest_0.6.33 magrittr_2.0.3 \r\n[13] timechange_0.2.0 evaluate_0.22 grid_4.3.1 \r\n[16] fastmap_1.1.1 jsonlite_1.8.7 e1071_1.7-13 \r\n[19] DBI_1.1.3 purrr_1.0.2 fansi_1.0.5 \r\n[22] scales_1.2.1 codetools_0.2-19 textshaping_0.3.7 \r\n[25] jquerylib_0.1.4 abind_1.4-5 cli_3.6.1 \r\n[28] rlang_1.1.1 units_0.8-4 munsell_0.5.0 \r\n[31] withr_2.5.2 cachem_1.0.8 yaml_2.3.7 \r\n[34] raster_3.6-26 parallel_4.3.1 tools_4.3.1 \r\n[37] memoise_2.0.1 dplyr_1.1.3 colorspace_2.1-0 \r\n[40] ggplot2_3.4.4 vctrs_0.6.4 R6_2.5.1 \r\n[43] lubridate_1.9.3 proxy_0.4-27 lifecycle_1.0.3 \r\n[46] classInt_0.4-10 ragg_1.2.6 pkgconfig_2.0.3 \r\n[49] terra_1.7-55 pillar_1.9.0 bslib_0.5.1 \r\n[52] gtable_0.3.4 glue_1.6.2 Rcpp_1.0.11 \r\n[55] sf_1.0-14 systemfonts_1.0.5 highr_0.10 \r\n[58] xfun_0.40 tibble_3.2.1 tidyselect_1.2.0 \r\n[61] rstudioapi_0.15.0 knitr_1.45 farver_2.1.1 \r\n[64] htmltools_0.5.6.1 labeling_0.4.3 rmarkdown_2.25 \r\n[67] d6geodata_0.0.0.9000 compiler_4.3.1 downlit_0.4.3 \r\n[70] sp_2.1-1 stars_0.6-4 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/tri_europe_2016_100m_03035_tif/tri_europe_2016_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-11-16T14:14:44+01:00", + "last_modified": "2023-11-20T17:06:10+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -229,7 +229,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndem_europe_2016_100m_03035_tifname\r\ndem_europe_2016_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2016units_of_data\r\nheigth in mresolution\r\n100mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\ndlrlink_of_source\r\nhttps://download.geoservice.dlr.de/TDM90/date_of_compile\r\n2023-11-01copyright\r\n[1] Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., Roth, A.(2018): Accuracy Assessment of the Global TanDEM-X Digital Elevation Model with GPS Data. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 139, pp. 171-182. [2] Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Borla Tridon, D., Bräutigam, B., Bachmann, M., Schulze, D., Fritz, T., Huber, M., Wessel, B., Krieger, G., Zink, M., and Moreira, A. (2017): Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS Journal of Photogrammetry and Remote Sensing, Vol 132, pp. 119-139.short_description\r\nseveral tiles where combined to one filemodified\r\nreprojected and resampled to 3035 and 100m resolution\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-11-07 14:19:05 CET\"\r\nR version 4.3.1 (2023-06-16 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.3\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.0 sass_0.4.7 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.0 class_7.3-22 \r\n [7] xml2_1.3.5 KernSmooth_2.23-21 lattice_0.21-8 \r\n[10] distill_1.6 digest_0.6.33 magrittr_2.0.3 \r\n[13] timechange_0.2.0 evaluate_0.22 grid_4.3.1 \r\n[16] fastmap_1.1.1 jsonlite_1.8.7 e1071_1.7-13 \r\n[19] DBI_1.1.3 purrr_1.0.2 fansi_1.0.5 \r\n[22] scales_1.2.1 codetools_0.2-19 textshaping_0.3.7 \r\n[25] jquerylib_0.1.4 abind_1.4-5 cli_3.6.1 \r\n[28] rlang_1.1.1 units_0.8-4 munsell_0.5.0 \r\n[31] withr_2.5.2 cachem_1.0.8 yaml_2.3.7 \r\n[34] raster_3.6-26 parallel_4.3.1 tools_4.3.1 \r\n[37] memoise_2.0.1 dplyr_1.1.3 colorspace_2.1-0 \r\n[40] ggplot2_3.4.4 vctrs_0.6.4 R6_2.5.1 \r\n[43] lubridate_1.9.3 proxy_0.4-27 lifecycle_1.0.3 \r\n[46] classInt_0.4-10 ragg_1.2.6 pkgconfig_2.0.3 \r\n[49] terra_1.7-55 pillar_1.9.0 bslib_0.5.1 \r\n[52] gtable_0.3.4 glue_1.6.2 Rcpp_1.0.11 \r\n[55] sf_1.0-14 systemfonts_1.0.5 highr_0.10 \r\n[58] xfun_0.40 tibble_3.2.1 tidyselect_1.2.0 \r\n[61] rstudioapi_0.15.0 knitr_1.45 farver_2.1.1 \r\n[64] htmltools_0.5.6.1 labeling_0.4.3 rmarkdown_2.25 \r\n[67] d6geodata_0.0.0.9000 compiler_4.3.1 downlit_0.4.3 \r\n[70] sp_2.1-1 stars_0.6-4 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/dem_europe_2016_100m_03035_tif/dem_europe_2016_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-11-14T08:52:47+01:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -253,7 +253,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nsmall-woody-features_europe_2018_100m_03035_tifname\r\nsmall-woody-features_europe_2018_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2018units_of_data\r\npercentage of small woody features per cellresolution\r\n100mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\ncopernicuslink_of_source\r\nhttps://land.copernicus.eu/date_of_compile\r\n2023-11-03copyright\r\n© European Union, Copernicus Land Monitoring Service 2023, European Environment Agency (EEA)short_description\r\nNAmodified\r\nactivated the numeric column\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-11-07 11:59:02 CET\"\r\nR version 4.3.1 (2023-06-16 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19044)\r\n\r\nMatrix products: default\r\n\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8 \r\n[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\ntime zone: Europe/Berlin\r\ntzcode source: internal\r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.3\r\n\r\nloaded via a namespace (and not attached):\r\n [1] gt_0.10.0 sass_0.4.7 utf8_1.2.4 \r\n [4] generics_0.1.3 tidyr_1.3.0 class_7.3-22 \r\n [7] xml2_1.3.5 KernSmooth_2.23-21 distill_1.6 \r\n[10] digest_0.6.33 magrittr_2.0.3 timechange_0.2.0 \r\n[13] evaluate_0.22 grid_4.3.1 fastmap_1.1.1 \r\n[16] jsonlite_1.8.7 e1071_1.7-13 DBI_1.1.3 \r\n[19] purrr_1.0.2 fansi_1.0.5 scales_1.2.1 \r\n[22] codetools_0.2-19 textshaping_0.3.7 jquerylib_0.1.4 \r\n[25] abind_1.4-5 cli_3.6.1 rlang_1.1.1 \r\n[28] units_0.8-4 munsell_0.5.0 withr_2.5.2 \r\n[31] cachem_1.0.8 yaml_2.3.7 parallel_4.3.1 \r\n[34] tools_4.3.1 memoise_2.0.1 dplyr_1.1.3 \r\n[37] colorspace_2.1-0 ggplot2_3.4.4 vctrs_0.6.4 \r\n[40] R6_2.5.1 lubridate_1.9.3 proxy_0.4-27 \r\n[43] lifecycle_1.0.3 classInt_0.4-10 ragg_1.2.6 \r\n[46] pkgconfig_2.0.3 terra_1.7-55 pillar_1.9.0 \r\n[49] bslib_0.5.1 gtable_0.3.4 glue_1.6.2 \r\n[52] Rcpp_1.0.11 sf_1.0-14 systemfonts_1.0.5 \r\n[55] highr_0.10 xfun_0.40 tibble_3.2.1 \r\n[58] tidyselect_1.2.0 rstudioapi_0.15.0 knitr_1.45 \r\n[61] farver_2.1.1 htmltools_0.5.6.1 labeling_0.4.3 \r\n[64] rmarkdown_2.25 d6geodata_0.0.0.9000 compiler_4.3.1 \r\n[67] downlit_0.4.3 stars_0.6-4 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/small-woody-features_europe_2018_100m_03035_tif/small-woody-features_europe_2018_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-11-14T08:52:47+01:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -277,7 +277,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nlandcover_germany_2023_10m_04326_tifname\r\nlandcover_germany_2023_10m_04326.tifcrs\r\n+proj=longlat +datum=WGS84 +no_defsepsg\r\n4326year_of_data\r\n2021units_of_data\r\nlandcover categories: 10 - Tree cover, 20 - Shrubland, 30 - Grassland, 40 - Cropland, 50 - Built-up, 60 - Bare / sparse vegetation, 70 - Snow and ice, 80 - Permanent water bodies, 90 - Herbaceous wetland, 100 - Mangroves, 110 - Moss and lichenresolution\r\n10mtype_of_data\r\nordered_categoricaltype_of_file\r\n.tifsource\r\nterrascpopelink_of_source\r\nhttps://viewer.terrascope.bedate_of_compile\r\n2023-06-22copyright\r\n© ESA WorldCover project 2021short_description\r\ndownloaded several tiles, merged them together and cropped them to german bordersmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-06-22 12:49:40 CEST\"\r\nR version 4.2.3 (2023-03-15 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] terra_1.7-29 tidyselect_1.2.0 xfun_0.39 \r\n [4] bslib_0.5.0 purrr_1.0.1 sf_1.0-13 \r\n [7] colorspace_2.1-0 vctrs_0.6.2 generics_0.1.3 \r\n[10] htmltools_0.5.5 stars_0.6-1 yaml_2.3.7 \r\n[13] utf8_1.2.3 rlang_1.1.1 e1071_1.7-13 \r\n[16] jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.3 ragg_1.2.5 \r\n[25] codetools_0.2-19 memoise_2.0.1 evaluate_0.21 \r\n[28] knitr_1.43 fastmap_1.1.1 parallel_4.2.3 \r\n[31] class_7.3-21 fansi_1.0.4 highr_0.10 \r\n[34] Rcpp_1.0.10 KernSmooth_2.23-20 scales_1.2.1 \r\n[37] classInt_0.4-9 lwgeom_0.2-13 cachem_1.0.8 \r\n[40] d6geodata_0.0.0.9000 jsonlite_1.8.5 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.2 digest_0.6.31 \r\n[49] dplyr_1.1.2 grid_4.2.3 cli_3.6.1 \r\n[52] tools_4.2.3 magrittr_2.0.3 sass_0.4.6 \r\n[55] rcartocolor_2.1.1 proxy_0.4-27 tibble_3.2.1 \r\n[58] tidyr_1.3.0 pkgconfig_2.0.3 downlit_0.4.2 \r\n[61] xml2_1.3.4 timechange_0.2.0 lubridate_1.9.2 \r\n[64] gt_0.9.0 rmarkdown_2.22 rstudioapi_0.14 \r\n[67] R6_2.5.1 units_0.8-2 compiler_4.2.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/landcover_germany_2021_10m_04326_tif/landcover_germany_2021_10m_04326_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -301,7 +301,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nndvi_brandenburg_2023_30m_32633_tifname\r\nndvi_brandenburg_2023_30m_32633.tifcrs\r\n+proj=utm +zone=33 +datum=WGS84 +units=m +no_defsepsg\r\n32633year_of_data\r\n2023units_of_data\r\nndviresolution\r\n30mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nusgslink_of_source\r\nhttps://www.usgs.gov/date_of_compile\r\n2023-06-16copyright\r\nLandsat-8 image courtesy of the U.S. Geological Surveyshort_description\r\nvalues devided by 10000 to get values between -1 and 1modified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-06-16 14:03:01 CEST\"\r\nR version 4.2.3 (2023-03-15 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] terra_1.7-29 tidyselect_1.2.0 xfun_0.39 \r\n [4] bslib_0.5.0 purrr_1.0.1 sf_1.0-13 \r\n [7] colorspace_2.1-0 vctrs_0.6.2 generics_0.1.3 \r\n[10] htmltools_0.5.5 stars_0.6-1 yaml_2.3.7 \r\n[13] utf8_1.2.3 rlang_1.1.1 e1071_1.7-13 \r\n[16] jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.3 ragg_1.2.5 \r\n[25] codetools_0.2-19 memoise_2.0.1 evaluate_0.21 \r\n[28] labeling_0.4.2 knitr_1.43 fastmap_1.1.1 \r\n[31] parallel_4.2.3 class_7.3-21 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20 \r\n[37] scales_1.2.1 classInt_0.4-9 lwgeom_0.2-13 \r\n[40] cachem_1.0.8 d6geodata_0.0.0.9000 jsonlite_1.8.5 \r\n[43] abind_1.4-5 farver_2.1.1 systemfonts_1.0.4 \r\n[46] textshaping_0.3.6 distill_1.5 ggplot2_3.4.2 \r\n[49] digest_0.6.31 dplyr_1.1.2 grid_4.2.3 \r\n[52] cli_3.6.1 tools_4.2.3 magrittr_2.0.3 \r\n[55] sass_0.4.6 proxy_0.4-27 tibble_3.2.1 \r\n[58] tidyr_1.3.0 pkgconfig_2.0.3 downlit_0.4.2 \r\n[61] xml2_1.3.4 timechange_0.2.0 lubridate_1.9.2 \r\n[64] gt_0.9.0 rmarkdown_2.22 rstudioapi_0.14 \r\n[67] R6_2.5.1 units_0.8-2 compiler_4.2.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/ndvi_brandenburg_2023_30m_32633_tif/ndvi_brandenburg_2023_30m_32633_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -325,7 +325,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nndvi_world_2023_1km_04326_tifname\r\nndvi_world_2023_1km_04326.tifcrs\r\n+proj=longlat +datum=WGS84 +no_defsepsg\r\n4326year_of_data\r\n2023units_of_data\r\nndvi*10000resolution\r\n1kmtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nusgslink_of_source\r\nhttps://www.usgs.gov/date_of_compile\r\n2023-06-16copyright\r\ndoi: 10.5067/VIIRS/VNP13A3.001short_description\r\nndvi values should be devided by 10000 to get values between -1 and 1modified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-06-16 14:04:30 CEST\"\r\nR version 4.2.3 (2023-03-15 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] terra_1.7-29 tidyselect_1.2.0 xfun_0.39 \r\n [4] bslib_0.5.0 purrr_1.0.1 sf_1.0-13 \r\n [7] colorspace_2.1-0 vctrs_0.6.2 generics_0.1.3 \r\n[10] htmltools_0.5.5 stars_0.6-1 yaml_2.3.7 \r\n[13] utf8_1.2.3 rlang_1.1.1 e1071_1.7-13 \r\n[16] jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.3 ragg_1.2.5 \r\n[25] codetools_0.2-19 memoise_2.0.1 evaluate_0.21 \r\n[28] labeling_0.4.2 knitr_1.43 fastmap_1.1.1 \r\n[31] parallel_4.2.3 class_7.3-21 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20 \r\n[37] scales_1.2.1 classInt_0.4-9 lwgeom_0.2-13 \r\n[40] cachem_1.0.8 d6geodata_0.0.0.9000 jsonlite_1.8.5 \r\n[43] abind_1.4-5 farver_2.1.1 systemfonts_1.0.4 \r\n[46] textshaping_0.3.6 distill_1.5 ggplot2_3.4.2 \r\n[49] digest_0.6.31 dplyr_1.1.2 grid_4.2.3 \r\n[52] cli_3.6.1 tools_4.2.3 magrittr_2.0.3 \r\n[55] sass_0.4.6 proxy_0.4-27 tibble_3.2.1 \r\n[58] tidyr_1.3.0 pkgconfig_2.0.3 downlit_0.4.2 \r\n[61] xml2_1.3.4 timechange_0.2.0 lubridate_1.9.2 \r\n[64] gt_0.9.0 rmarkdown_2.22 rstudioapi_0.14 \r\n[67] R6_2.5.1 units_0.8-2 compiler_4.2.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/ndvi_world_2023_1km_04326_tif/ndvi_world_2023_1km_04326_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -349,7 +349,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ntree-cover-density_germany_2015_100m_03035_tifname\r\ntree-cover-density_germany_2015_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2015units_of_data\r\nmresolution\r\n100type_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\ncopernicuslink_of_source\r\nNAdate_of_compile\r\n2022-07-25short_description\r\ncreated from TreeCoverDensity_germany_2015_100m_03035_Copernicus_tifmodified\r\nmasked to germany borders\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-05-23 12:35:15 CEST\"\r\nR version 4.2.3 (2023-03-15 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 xfun_0.37 bslib_0.4.2 \r\n [4] purrr_1.0.1 sf_1.0-12 colorspace_2.1-0 \r\n [7] vctrs_0.6.1 generics_0.1.3 htmltools_0.5.4 \r\n[10] stars_0.6-1 yaml_2.3.7 utf8_1.2.3 \r\n[13] rlang_1.1.0 e1071_1.7-13 jquerylib_0.1.4 \r\n[16] pillar_1.9.0 glue_1.6.2 withr_2.5.0 \r\n[19] DBI_1.1.3 lifecycle_1.0.3 munsell_0.5.0 \r\n[22] gtable_0.3.3 ragg_1.2.5 memoise_2.0.1 \r\n[25] evaluate_0.20 labeling_0.4.2 knitr_1.42 \r\n[28] fastmap_1.1.1 parallel_4.2.3 class_7.3-21 \r\n[31] fansi_1.0.4 highr_0.10 Rcpp_1.0.10 \r\n[34] KernSmooth_2.23-20 scales_1.2.1 classInt_0.4-9 \r\n[37] lwgeom_0.2-11 cachem_1.0.7 d6geodata_0.0.0.9000\r\n[40] jsonlite_1.8.4 abind_1.4-5 farver_2.1.1 \r\n[43] systemfonts_1.0.4 textshaping_0.3.6 distill_1.5 \r\n[46] ggplot2_3.4.2 digest_0.6.31 dplyr_1.1.1 \r\n[49] grid_4.2.3 cli_3.6.0 tools_4.2.3 \r\n[52] magrittr_2.0.3 sass_0.4.5 proxy_0.4-27 \r\n[55] tibble_3.2.1 tidyr_1.3.0 pkgconfig_2.0.3 \r\n[58] downlit_0.4.2 xml2_1.3.3 gt_0.9.0 \r\n[61] rmarkdown_2.21 rstudioapi_0.14 R6_2.5.1 \r\n[64] units_0.8-1 compiler_4.2.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/tree-cover-density_germany_2015_100m_03035_tif/tree-cover-density_germany_2015_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -373,7 +373,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nhuman-modification-index_world_2016_1km_mollweide_tifname\r\nhuman-modification-index_world_2016_1km_mollweide.tifcrs\r\nNAepsg\r\n0year_of_data\r\n2016units_of_data\r\nproportion of a landscape modifiedresolution\r\n1kmtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nhttps://sedac.ciesin.columbia.edu/link_of_source\r\nhttps://sedac.ciesin.columbia.edu/date_of_compile\r\n2023-05-08copyright\r\nCopyright © 1997–2023. The Trustees of Columbia University in the City of New York.short_description\r\nThe Global Human Modification of Terrestrial Systems data set provides a cumulative measure of the human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified, based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global data sets with a median year of 2016. https://doi.org/10.1111/gcb.14549.modified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-05-08 15:40:53 CEST\"\r\nR version 4.2.3 (2023-03-15 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] terra_1.7-23 tidyselect_1.2.0 xfun_0.37 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-12 \r\n [7] colorspace_2.1-0 vctrs_0.6.1 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-1 yaml_2.3.7 \r\n[13] utf8_1.2.3 rlang_1.1.0 e1071_1.7-13 \r\n[16] jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.3 ragg_1.2.5 \r\n[25] codetools_0.2-19 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.1 \r\n[31] parallel_4.2.3 class_7.3-21 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20 \r\n[37] scales_1.2.1 classInt_0.4-9 lwgeom_0.2-11 \r\n[40] cachem_1.0.7 d6geodata_0.0.0.9000 jsonlite_1.8.4 \r\n[43] abind_1.4-5 farver_2.1.1 systemfonts_1.0.4 \r\n[46] textshaping_0.3.6 distill_1.5 ggplot2_3.4.2 \r\n[49] digest_0.6.31 dplyr_1.1.1 grid_4.2.3 \r\n[52] cli_3.6.0 tools_4.2.3 magrittr_2.0.3 \r\n[55] sass_0.4.5 proxy_0.4-27 tibble_3.2.1 \r\n[58] tidyr_1.3.0 pkgconfig_2.0.3 downlit_0.4.2 \r\n[61] xml2_1.3.3 timechange_0.2.0 lubridate_1.9.2 \r\n[64] gt_0.9.0 rmarkdown_2.21 rstudioapi_0.14 \r\n[67] R6_2.5.1 units_0.8-1 compiler_4.2.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/human-modification-index_world_2016_1km_mollweide_tif/human-modification-index_world_2016_1km_mollweide_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -397,7 +397,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ngreen-capacity_berlin_2020_10m_03035_tifname\r\ngreen-capacity_berlin_2020_10m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2020units_of_data\r\nqm3/qm2resolution\r\n10mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nfisbrokerlink_of_source\r\nhttps://stadtentwicklung.berlin.de/geoinformation/fis-broker/date_of_compile\r\n2023-04-24copyright\r\nAmt für Statistik Berlin-Brandenburg 2023short_description\r\ncreated from green-capacity_berlin_2020_poly_03035_gpkg. Values are from column vegvol2020.modified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-04-24 17:02:00 CEST\"\r\nR version 4.2.3 (2023-03-15 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] terra_1.7-23 tidyselect_1.2.0 xfun_0.37 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-12 \r\n [7] colorspace_2.1-0 vctrs_0.6.1 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-1 yaml_2.3.7 \r\n[13] utf8_1.2.3 rlang_1.1.0 e1071_1.7-13 \r\n[16] jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.3 ragg_1.2.5 \r\n[25] codetools_0.2-19 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.1 \r\n[31] parallel_4.2.3 class_7.3-21 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20 \r\n[37] scales_1.2.1 classInt_0.4-9 lwgeom_0.2-11 \r\n[40] cachem_1.0.7 d6geodata_0.0.0.9000 jsonlite_1.8.4 \r\n[43] abind_1.4-5 farver_2.1.1 systemfonts_1.0.4 \r\n[46] textshaping_0.3.6 distill_1.5 ggplot2_3.4.2 \r\n[49] digest_0.6.31 dplyr_1.1.1 grid_4.2.3 \r\n[52] cli_3.6.0 tools_4.2.3 magrittr_2.0.3 \r\n[55] sass_0.4.5 proxy_0.4-27 tibble_3.2.1 \r\n[58] tidyr_1.3.0 pkgconfig_2.0.3 downlit_0.4.2 \r\n[61] xml2_1.3.3 timechange_0.2.0 lubridate_1.9.2 \r\n[64] gt_0.9.0 rmarkdown_2.21 rstudioapi_0.14 \r\n[67] R6_2.5.1 units_0.8-1 compiler_4.2.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/green-capacity_berlin_2020_10m_03035_tif/green-capacity_berlin_2020_10m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -421,7 +421,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ncorine_berlin_2018_20m_03035_tifname\r\ncorine_berlin_2018_20m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2018units_of_data\r\nmresolution\r\n20type_of_data\r\nunordered_categoricaltype_of_file\r\n.tifsource\r\nbkglink_of_source\r\nNAdate_of_compile\r\n2022-11-22short_description\r\ncreated from Corine_Landcover_germany_2018_25832_shpmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-04-21 11:50:10 CEST\"\r\nR version 4.2.3 (2023-03-15 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] terra_1.7-23 tidyselect_1.2.0 xfun_0.37 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-12 \r\n [7] colorspace_2.1-0 vctrs_0.6.1 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-1 yaml_2.3.7 \r\n[13] utf8_1.2.3 rlang_1.1.0 e1071_1.7-13 \r\n[16] jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.3 ragg_1.2.5 \r\n[25] codetools_0.2-19 memoise_2.0.1 evaluate_0.20 \r\n[28] knitr_1.42 fastmap_1.1.1 parallel_4.2.3 \r\n[31] class_7.3-21 fansi_1.0.4 highr_0.10 \r\n[34] Rcpp_1.0.10 KernSmooth_2.23-20 scales_1.2.1 \r\n[37] classInt_0.4-9 lwgeom_0.2-11 cachem_1.0.7 \r\n[40] d6geodata_0.0.0.9000 jsonlite_1.8.4 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.2 digest_0.6.31 \r\n[49] dplyr_1.1.1 grid_4.2.3 cli_3.6.0 \r\n[52] tools_4.2.3 magrittr_2.0.3 sass_0.4.5 \r\n[55] rcartocolor_2.0.0 proxy_0.4-27 tibble_3.2.1 \r\n[58] tidyr_1.3.0 pkgconfig_2.0.3 downlit_0.4.2 \r\n[61] xml2_1.3.3 timechange_0.2.0 lubridate_1.9.2 \r\n[64] gt_0.9.0 rmarkdown_2.21 rstudioapi_0.14 \r\n[67] R6_2.5.1 units_0.8-1 compiler_4.2.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/corine_berlin_2018_20m_03035_tif/corine_berlin_2018_20m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -445,7 +445,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nmammal-richness_world_2015_1km_04326_tifname\r\nmammal-richness_world_2015_1km_04326.tifcrs\r\n+proj=longlat +datum=WGS84 +no_defsepsg\r\n4326year_of_data\r\n2015units_of_data\r\nnumber of speciesresolution\r\n1kmtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nearthdatalink_of_source\r\nhttps://doi.org/10.7927/H4N014G5date_of_compile\r\n2023-03-15copyright\r\nCopyright © 1997–2023. The Trustees of Columbia University in the City of New York.short_description\r\nNAmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-04-21 12:01:25 CEST\"\r\nR version 4.2.3 (2023-03-15 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] terra_1.7-23 tidyselect_1.2.0 xfun_0.37 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-12 \r\n [7] colorspace_2.1-0 vctrs_0.6.1 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-1 yaml_2.3.7 \r\n[13] utf8_1.2.3 rlang_1.1.0 e1071_1.7-13 \r\n[16] jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.3 ragg_1.2.5 \r\n[25] codetools_0.2-19 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.1 \r\n[31] parallel_4.2.3 class_7.3-21 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20 \r\n[37] scales_1.2.1 classInt_0.4-9 lwgeom_0.2-11 \r\n[40] cachem_1.0.7 d6geodata_0.0.0.9000 jsonlite_1.8.4 \r\n[43] abind_1.4-5 farver_2.1.1 systemfonts_1.0.4 \r\n[46] textshaping_0.3.6 distill_1.5 ggplot2_3.4.2 \r\n[49] digest_0.6.31 dplyr_1.1.1 grid_4.2.3 \r\n[52] cli_3.6.0 tools_4.2.3 magrittr_2.0.3 \r\n[55] sass_0.4.5 proxy_0.4-27 tibble_3.2.1 \r\n[58] tidyr_1.3.0 pkgconfig_2.0.3 downlit_0.4.2 \r\n[61] xml2_1.3.3 timechange_0.2.0 lubridate_1.9.2 \r\n[64] gt_0.9.0 rmarkdown_2.21 rstudioapi_0.14 \r\n[67] R6_2.5.1 units_0.8-1 compiler_4.2.3 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/mammal-richness_world_2015_1km_04326_tif/mammal-richness_world_2015_1km_04326_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -469,7 +469,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nlanduse_atlas_2009_1m_03035_tifname\r\nlanduse_atlas_2009_1m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2009units_of_data\r\nlanduse categoriesresolution\r\n1mtype_of_data\r\nordered_categoricaltype_of_file\r\n.tifsource\r\nmetaverlink_of_source\r\nhttps://metaver.de/trefferanzeige?docuuid=B57B9F35-AFFF-49F2-BA32-618D1A1CD412#detail_overviewdate_of_compile\r\n2023-01-04copyright\r\n© Landesbetrieb Geoinformation und Vermessung. Alle Rechte vorbehalten.short_description\r\ncreated from landuse_brandenburg_2009_polygons_25833_gpkg and landuse_brandenburg_2009_lines_25833_gpkgmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-03-13 11:21:07 CET\"\r\nR version 4.2.2 (2022-10-31 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] terra_1.7-18 tidyselect_1.2.0 xfun_0.37 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.3 rlang_1.0.6 e1071_1.7-13 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] knitr_1.42 fastmap_1.1.1 parallel_4.2.2 \r\n[31] class_7.3-20 fansi_1.0.4 highr_0.10 \r\n[34] Rcpp_1.0.10 KernSmooth_2.23-20 scales_1.2.1 \r\n[37] classInt_0.4-9 cachem_1.0.7 lwgeom_0.2-11 \r\n[40] d6geodata_0.0.0.9000 jsonlite_1.8.4 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.1 digest_0.6.31 \r\n[49] dplyr_1.1.0 grid_4.2.2 cli_3.6.0 \r\n[52] tools_4.2.2 magrittr_2.0.3 sass_0.4.5 \r\n[55] rcartocolor_2.0.0 proxy_0.4-27 tibble_3.2.0 \r\n[58] tidyr_1.3.0 pkgconfig_2.0.3 downlit_0.4.2 \r\n[61] timechange_0.2.0 lubridate_1.9.2 gt_0.8.0 \r\n[64] rmarkdown_2.20 rstudioapi_0.14 R6_2.5.1 \r\n[67] units_0.8-1 compiler_4.2.2 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/landuse_atlas_2009_1m_03035_tif/landuse_atlas_2009_1m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -493,7 +493,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndistance-to-waterbodies_atlas_2009_1m_03035_tifname\r\ndistance-to-waterbodies_atlas_2009_1m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2009units_of_data\r\nmresolution\r\n1mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nmetaverlink_of_source\r\nhttps://metaver.de/trefferanzeige?docuuid=B57B9F35-AFFF-49F2-BA32-618D1A1CD412#detail_overviewdate_of_compile\r\n2023-03-01copyright\r\n© Landesbetrieb Geoinformation und Vermessung. Alle Rechte vorbehalten.short_description\r\ncreatet from landuse_brandenburg_2009_lines_25833_gpkg and landuse_brandenburg_2009_polygons_25833_gpkgmodified\r\nonly water classes where selected to rasterize the polygon and lines layer\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-03-02 09:31:30 CET\"\r\nR version 4.2.2 (2022-10-31 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] Rcpp_1.0.10 lubridate_1.9.2 lattice_0.20-45 \r\n [4] tidyr_1.3.0 class_7.3-20 digest_0.6.31 \r\n [7] utf8_1.2.3 R6_2.5.1 evaluate_0.20 \r\n[10] e1071_1.7-13 ggplot2_3.4.1 highr_0.10 \r\n[13] pillar_1.8.1 rlang_1.0.6 rstudioapi_0.14 \r\n[16] raster_3.6-14 jquerylib_0.1.4 rmarkdown_2.20 \r\n[19] textshaping_0.3.6 labeling_0.4.2 rgdal_1.6-4 \r\n[22] d6geodata_0.0.0.9000 munsell_0.5.0 proxy_0.4-27 \r\n[25] compiler_4.2.2 xfun_0.36 pkgconfig_2.0.3 \r\n[28] systemfonts_1.0.4 htmltools_0.5.4 downlit_0.4.2 \r\n[31] tidyselect_1.2.0 tibble_3.1.8 codetools_0.2-18 \r\n[34] fansi_1.0.4 dplyr_1.1.0 withr_2.5.0 \r\n[37] sf_1.0-9 grid_4.2.2 jsonlite_1.8.4 \r\n[40] lwgeom_0.2-11 gtable_0.3.1 lifecycle_1.0.3 \r\n[43] DBI_1.1.3 magrittr_2.0.3 units_0.8-1 \r\n[46] scales_1.2.1 KernSmooth_2.23-20 cli_3.6.0 \r\n[49] cachem_1.0.6 farver_2.1.1 sp_1.6-0 \r\n[52] bslib_0.4.2 ragg_1.2.5 generics_0.1.3 \r\n[55] vctrs_0.5.2 distill_1.5 tools_4.2.2 \r\n[58] glue_1.6.2 purrr_1.0.1 abind_1.4-5 \r\n[61] parallel_4.2.2 fastmap_1.1.0 yaml_2.3.6 \r\n[64] timechange_0.2.0 colorspace_2.1-0 terra_1.7-3 \r\n[67] stars_0.6-0 gt_0.8.0 classInt_0.4-8 \r\n[70] memoise_2.0.1 knitr_1.42 sass_0.4.5 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/distance-to-waterbodies_atlas_2009_1m_03035_tif/distance-to-waterbodies_atlas_2009_1m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -517,7 +517,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nlight-pollution_berlin_2021_100m_03035_tifname\r\nlight-pollution_berlin_2021_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2021units_of_data\r\nradiance 10e-9 W/ cme2 * srresolution\r\n100mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nhttps://www.lightpollutionmap.infolink_of_source\r\nhttps://www.lightpollutionmap.infodate_of_compile\r\n2023-01-25copyright\r\nhttps://www.lightpollutionmap.infoshort_description\r\nmanually downloaded, reprojected and resampled to 3035 and 100m resolutionmodified\r\nreprojected and resampled, raw data in _archive folder\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-26 13:30:52 CET\"\r\nR version 4.2.2 (2022-10-31 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 xfun_0.36 bslib_0.4.2 \r\n [4] purrr_1.0.1 sf_1.0-9 colorspace_2.0-3 \r\n [7] vctrs_0.5.1 generics_0.1.3 htmltools_0.5.4 \r\n[10] stars_0.6-0 yaml_2.3.6 utf8_1.2.2 \r\n[13] rlang_1.0.6 e1071_1.7-12 jquerylib_0.1.4 \r\n[16] pillar_1.8.1 glue_1.6.2 withr_2.5.0 \r\n[19] DBI_1.1.3 lifecycle_1.0.3 stringr_1.5.0 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] memoise_2.0.1 evaluate_0.20 labeling_0.4.2 \r\n[28] knitr_1.41 fastmap_1.1.0 parallel_4.2.2 \r\n[31] class_7.3-20 fansi_1.0.4 highr_0.10 \r\n[34] Rcpp_1.0.10 KernSmooth_2.23-20 scales_1.2.1 \r\n[37] classInt_0.4-8 lwgeom_0.2-11 cachem_1.0.6 \r\n[40] jsonlite_1.8.4 abind_1.4-5 farver_2.1.1 \r\n[43] systemfonts_1.0.4 textshaping_0.3.6 distill_1.5 \r\n[46] ggplot2_3.4.0 digest_0.6.31 stringi_1.7.12 \r\n[49] dplyr_1.0.10 grid_4.2.2 cli_3.6.0 \r\n[52] tools_4.2.2 magrittr_2.0.3 sass_0.4.4 \r\n[55] proxy_0.4-27 tibble_3.1.8 tidyr_1.2.1 \r\n[58] pkgconfig_2.0.3 downlit_0.4.2 ellipsis_0.3.2 \r\n[61] assertthat_0.2.1 gt_0.8.0 rmarkdown_2.20 \r\n[64] rstudioapi_0.14 R6_2.5.1 units_0.8-1 \r\n[67] compiler_4.2.2 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/light-pollution_berlin_2021_100m_03035_tif/light-pollution_berlin_2021_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -541,7 +541,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nnoise-day-night_berlin_2017_10m_25833_tifname\r\nnoise-day-night_berlin_2017_10m_25833.tifcrs\r\n+proj=utm +zone=33 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defsepsg\r\n25833year_of_data\r\n2017units_of_data\r\nnoise mean of 24 hours in dezibelresolution\r\n10mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nfisbrokerlink_of_source\r\nhttps://stadtentwicklung.berlin.de/geoinformation/fis-broker/date_of_compile\r\n2023-01-25copyright\r\nAmt für Statistik Berlin-Brandenburg 2023short_description\r\ndownloaded from fisborker, original name Strat. Lärmkarte Gesamtlärmindex L_DEN (Tag-Abend-Nacht) Raster 2017 (Umweltatlas)modified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-26 13:53:37 CET\"\r\nR version 4.2.2 (2022-10-31 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 xfun_0.36 bslib_0.4.2 \r\n [4] purrr_1.0.1 sf_1.0-9 colorspace_2.0-3 \r\n [7] vctrs_0.5.1 generics_0.1.3 htmltools_0.5.4 \r\n[10] stars_0.6-0 yaml_2.3.6 utf8_1.2.2 \r\n[13] rlang_1.0.6 e1071_1.7-12 jquerylib_0.1.4 \r\n[16] pillar_1.8.1 glue_1.6.2 withr_2.5.0 \r\n[19] DBI_1.1.3 lifecycle_1.0.3 stringr_1.5.0 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] memoise_2.0.1 evaluate_0.20 labeling_0.4.2 \r\n[28] knitr_1.41 fastmap_1.1.0 parallel_4.2.2 \r\n[31] class_7.3-20 fansi_1.0.4 highr_0.10 \r\n[34] Rcpp_1.0.10 KernSmooth_2.23-20 scales_1.2.1 \r\n[37] classInt_0.4-8 lwgeom_0.2-11 cachem_1.0.6 \r\n[40] jsonlite_1.8.4 abind_1.4-5 farver_2.1.1 \r\n[43] systemfonts_1.0.4 textshaping_0.3.6 distill_1.5 \r\n[46] ggplot2_3.4.0 digest_0.6.31 stringi_1.7.12 \r\n[49] dplyr_1.0.10 grid_4.2.2 cli_3.6.0 \r\n[52] tools_4.2.2 magrittr_2.0.3 sass_0.4.4 \r\n[55] proxy_0.4-27 tibble_3.1.8 tidyr_1.2.1 \r\n[58] pkgconfig_2.0.3 downlit_0.4.2 ellipsis_0.3.2 \r\n[61] assertthat_0.2.1 gt_0.8.0 rmarkdown_2.20 \r\n[64] rstudioapi_0.14 R6_2.5.1 units_0.8-1 \r\n[67] compiler_4.2.2 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/noise-day-night_berlin_2017_10m_25833_tif/noise-day-night_berlin_2017_10m_25833_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -565,7 +565,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nimperviousness_berlin_2018_10m_03035_tifname\r\nimperviousness_berlin_2018_10m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2018units_of_data\r\npercentage imperviousnessresolution\r\n10mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\ncopernicuslink_of_source\r\nhttps://land.copernicus.eu/date_of_compile\r\n2023-01-17copyright\r\n© European Union, Copernicus Land Monitoring Service 2023, European Environment Agency (EEA)short_description\r\ncropped from imperviousness_europe_raster_10m_2018_3035modified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-26 13:57:01 CET\"\r\nR version 4.2.2 (2022-10-31 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.6-53 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.0-3 vctrs_0.5.1 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.6 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] stringr_1.5.0 munsell_0.5.0 gtable_0.3.1 \r\n[25] ragg_1.2.5 codetools_0.2-18 memoise_2.0.1 \r\n[28] evaluate_0.20 labeling_0.4.2 knitr_1.41 \r\n[31] fastmap_1.1.0 parallel_4.2.2 class_7.3-20 \r\n[34] fansi_1.0.4 highr_0.10 Rcpp_1.0.10 \r\n[37] KernSmooth_2.23-20 classInt_0.4-8 scales_1.2.1 \r\n[40] lwgeom_0.2-11 cachem_1.0.6 jsonlite_1.8.4 \r\n[43] abind_1.4-5 farver_2.1.1 systemfonts_1.0.4 \r\n[46] textshaping_0.3.6 distill_1.5 ggplot2_3.4.0 \r\n[49] digest_0.6.31 stringi_1.7.12 dplyr_1.0.10 \r\n[52] grid_4.2.2 cli_3.6.0 tools_4.2.2 \r\n[55] magrittr_2.0.3 sass_0.4.4 proxy_0.4-27 \r\n[58] tibble_3.1.8 tidyr_1.2.1 pkgconfig_2.0.3 \r\n[61] downlit_0.4.2 ellipsis_0.3.2 assertthat_0.2.1 \r\n[64] gt_0.8.0 rmarkdown_2.20 rstudioapi_0.14 \r\n[67] R6_2.5.1 units_0.8-1 compiler_4.2.2 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/imperviousness_berlin_2018_10m_03035_tif/imperviousness_berlin_2018_10m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -589,7 +589,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\npopulation-density_berlin_2019_10m_03035_tifname\r\npopulation-density_berlin_2019_10m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2019units_of_data\r\npopulation density in haresolution\r\n10mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nfisbrokerlink_of_source\r\nhttps://stadtentwicklung.berlin.de/geoinformation/fis-broker/date_of_compile\r\n2023-01-17copyright\r\nAmt für Statistik Berlin-Brandenburg 2023short_description\r\ncreated from human_population_density_berlin_2019_3035modified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-26 13:58:08 CET\"\r\nR version 4.2.2 (2022-10-31 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.6-53 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.0-3 vctrs_0.5.1 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.6 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] stringr_1.5.0 munsell_0.5.0 gtable_0.3.1 \r\n[25] ragg_1.2.5 codetools_0.2-18 memoise_2.0.1 \r\n[28] evaluate_0.20 labeling_0.4.2 knitr_1.41 \r\n[31] fastmap_1.1.0 parallel_4.2.2 class_7.3-20 \r\n[34] fansi_1.0.4 highr_0.10 Rcpp_1.0.10 \r\n[37] KernSmooth_2.23-20 classInt_0.4-8 scales_1.2.1 \r\n[40] lwgeom_0.2-11 cachem_1.0.6 jsonlite_1.8.4 \r\n[43] abind_1.4-5 farver_2.1.1 systemfonts_1.0.4 \r\n[46] textshaping_0.3.6 distill_1.5 ggplot2_3.4.0 \r\n[49] digest_0.6.31 stringi_1.7.12 dplyr_1.0.10 \r\n[52] grid_4.2.2 cli_3.6.0 tools_4.2.2 \r\n[55] magrittr_2.0.3 sass_0.4.4 proxy_0.4-27 \r\n[58] tibble_3.1.8 tidyr_1.2.1 pkgconfig_2.0.3 \r\n[61] downlit_0.4.2 ellipsis_0.3.2 assertthat_0.2.1 \r\n[64] gt_0.8.0 rmarkdown_2.20 rstudioapi_0.14 \r\n[67] R6_2.5.1 units_0.8-1 compiler_4.2.2 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/population-density_berlin_2019_10m_03035_tif/population-density_berlin_2019_10m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -613,7 +613,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndistance-to-human-settlements_atlas_2009_1m_03035_tifname\r\ndistance-to-human-settlements_atlas_2009_1m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2009units_of_data\r\ndistances in meterresolution\r\n1mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nmetaverlink_of_source\r\nhttps://metaver.de/trefferanzeige?docuuid=B57B9F35-AFFF-49F2-BA32-618D1A1CD412#detail_overviewdate_of_compile\r\n2023-01-04copyright\r\n© Landesbetrieb Geoinformation und Vermessung. Alle Rechte vorbehalten.short_description\r\ncreated from landuse_brandenburg_2009_polygons_25833_gpkgmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 08:53:31 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.0 \r\n[31] parallel_4.2.1 class_7.3-20 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20\r\n[37] scales_1.2.1 classInt_0.4-8 lwgeom_0.2-11 \r\n[40] cachem_1.0.6 jsonlite_1.8.4 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.0 digest_0.6.31 \r\n[49] dplyr_1.0.10 grid_4.2.1 cli_3.6.0 \r\n[52] tools_4.2.1 magrittr_2.0.3 sass_0.4.5 \r\n[55] proxy_0.4-27 tibble_3.1.8 tidyr_1.3.0 \r\n[58] pkgconfig_2.0.3 downlit_0.4.2 assertthat_0.2.1 \r\n[61] gt_0.8.0 rmarkdown_2.20 rstudioapi_0.14 \r\n[64] R6_2.5.1 units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/distance-to-human-settlements_atlas_2009_1m_03035_tif/distance-to-human-settlements_atlas_2009_1m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -637,7 +637,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndistance-to-kettleholes_atlas_2022_1m_03035_tifname\r\ndistance-to-kettleholes_atlas_2022_1m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2022units_of_data\r\ndistances in meterresolution\r\n1mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nunknownlink_of_source\r\nNAdate_of_compile\r\n2023-01-04copyright\r\nNAshort_description\r\ncreated from Soelle_all_25833.shpmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 09:04:22 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.0 \r\n[31] parallel_4.2.1 class_7.3-20 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20\r\n[37] scales_1.2.1 classInt_0.4-8 lwgeom_0.2-11 \r\n[40] cachem_1.0.6 jsonlite_1.8.4 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.0 digest_0.6.31 \r\n[49] dplyr_1.0.10 grid_4.2.1 cli_3.6.0 \r\n[52] tools_4.2.1 magrittr_2.0.3 sass_0.4.5 \r\n[55] proxy_0.4-27 tibble_3.1.8 tidyr_1.3.0 \r\n[58] pkgconfig_2.0.3 downlit_0.4.2 assertthat_0.2.1 \r\n[61] gt_0.8.0 rmarkdown_2.20 rstudioapi_0.14 \r\n[64] R6_2.5.1 units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/distance-to-kettleholes_atlas_2022_1m_03035_tif/distance-to-kettleholes_atlas_2022_1m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -661,7 +661,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndistance-to-motorway-rural-road_germany_2022_100m_03035_tifname\r\ndistance-to-motorway-rural-road_germany_2022_100m_03035.tifcrs\r\n+proj=utm +zone=33 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defsepsg\r\n25833year_of_data\r\n2022units_of_data\r\nmresolution\r\n100type_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nosmlink_of_source\r\nNAdate_of_compile\r\n2022-08-02short_description\r\ncreated from motorway-rural-road_germany_2022_100m_03035_tifmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 09:09:51 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.0 \r\n[31] parallel_4.2.1 class_7.3-20 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20\r\n[37] scales_1.2.1 classInt_0.4-8 lwgeom_0.2-11 \r\n[40] cachem_1.0.6 jsonlite_1.8.4 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.0 digest_0.6.31 \r\n[49] dplyr_1.0.10 grid_4.2.1 cli_3.6.0 \r\n[52] tools_4.2.1 magrittr_2.0.3 sass_0.4.5 \r\n[55] proxy_0.4-27 tibble_3.1.8 tidyr_1.3.0 \r\n[58] pkgconfig_2.0.3 downlit_0.4.2 assertthat_0.2.1 \r\n[61] gt_0.8.0 rmarkdown_2.20 rstudioapi_0.14 \r\n[64] R6_2.5.1 units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/distance-to-motorway-rural-road_germany_2022_100m_03035_tif/distance-to-motorway-rural-road_germany_2022_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -685,7 +685,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndistance-to-rivers_atlas_2009_1m_03035_tifname\r\ndistance-to-rivers_atlas_2009_1m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2009units_of_data\r\ndistances in meterresolution\r\n1mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nmetaverlink_of_source\r\nhttps://metaver.de/trefferanzeige?docuuid=B57B9F35-AFFF-49F2-BA32-618D1A1CD412#detail_overviewdate_of_compile\r\n2023-01-04copyright\r\n© Landesbetrieb Geoinformation und Vermessung. Alle Rechte vorbehalten.short_description\r\ncreated from landuse_brandenburg_2009_lines_25833_gpkgmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 09:11:33 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.0 \r\n[31] parallel_4.2.1 class_7.3-20 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20\r\n[37] scales_1.2.1 classInt_0.4-8 lwgeom_0.2-11 \r\n[40] cachem_1.0.6 jsonlite_1.8.4 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.0 digest_0.6.31 \r\n[49] dplyr_1.0.10 grid_4.2.1 cli_3.6.0 \r\n[52] tools_4.2.1 magrittr_2.0.3 sass_0.4.5 \r\n[55] proxy_0.4-27 tibble_3.1.8 tidyr_1.3.0 \r\n[58] pkgconfig_2.0.3 downlit_0.4.2 assertthat_0.2.1 \r\n[61] gt_0.8.0 rmarkdown_2.20 rstudioapi_0.14 \r\n[64] R6_2.5.1 units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/distance-to-rivers_atlas_2009_1m_03035_tif/distance-to-rivers_atlas_2009_1m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -709,7 +709,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndistance-to-roads-paths_germany_2022_100m_03035_tifname\r\ndistance-to-roads-paths_germany_2022_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2022units_of_data\r\ndistancesresolution\r\n100mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nbkglink_of_source\r\nhttps://gdz.bkg.bund.dedate_of_compile\r\n2023-01-04copyright\r\n© GeoBasis-DE / BKG (2023)short_description\r\ncreated from paths_germany_2022_100m_03035_tif and roads_germany_2022_100m_03035_tifmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 09:17:25 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.0 \r\n[31] parallel_4.2.1 class_7.3-20 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20\r\n[37] scales_1.2.1 classInt_0.4-8 lwgeom_0.2-11 \r\n[40] cachem_1.0.6 jsonlite_1.8.4 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.0 digest_0.6.31 \r\n[49] dplyr_1.0.10 grid_4.2.1 cli_3.6.0 \r\n[52] tools_4.2.1 magrittr_2.0.3 sass_0.4.5 \r\n[55] proxy_0.4-27 tibble_3.1.8 tidyr_1.3.0 \r\n[58] pkgconfig_2.0.3 downlit_0.4.2 assertthat_0.2.1 \r\n[61] gt_0.8.0 rmarkdown_2.20 rstudioapi_0.14 \r\n[64] R6_2.5.1 units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/distance-to-roads-paths_germany_2022_100m_03035_tif/distance-to-roads-paths_germany_2022_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -733,7 +733,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nmotorway-rural-road_germany_2022_100m_03035_tifname\r\nmotorway-rural-road_germany_2022_100m_03035.tifcrs\r\n+proj=utm +zone=33 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defsepsg\r\n25833year_of_data\r\n2022units_of_data\r\nmresolution\r\n100type_of_data\r\nbinary_categoricaltype_of_file\r\n.tifsource\r\nosmlink_of_source\r\nNAdate_of_compile\r\n2022-08-02short_description\r\ncreated from osm_germany_2022_4326.gpkg. filtered from column highway with \\motorway\\, \\motorway_link\\, \\primary\\, \\primary_link\\, \\secondary\\, \\secondary_link\\modified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 10:25:25 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] knitr_1.42 fastmap_1.1.0 parallel_4.2.1 \r\n[31] class_7.3-20 fansi_1.0.4 highr_0.10 \r\n[34] Rcpp_1.0.10 KernSmooth_2.23-20 scales_1.2.1 \r\n[37] classInt_0.4-8 lwgeom_0.2-11 cachem_1.0.6 \r\n[40] jsonlite_1.8.4 abind_1.4-5 farver_2.1.1 \r\n[43] systemfonts_1.0.4 textshaping_0.3.6 distill_1.5 \r\n[46] ggplot2_3.4.0 digest_0.6.31 dplyr_1.0.10 \r\n[49] grid_4.2.1 cli_3.6.0 tools_4.2.1 \r\n[52] magrittr_2.0.3 sass_0.4.5 proxy_0.4-27 \r\n[55] tibble_3.1.8 tidyr_1.3.0 pkgconfig_2.0.3 \r\n[58] downlit_0.4.2 assertthat_0.2.1 gt_0.8.0 \r\n[61] rmarkdown_2.20 rstudioapi_0.14 R6_2.5.1 \r\n[64] units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/motorway-rural-road_germany_2022_100m_03035_tif/motorway-rural-road_germany_2022_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -757,7 +757,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nmotorways_germany_2022_100m_03035_tifname\r\nmotorways_germany_2022_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2022units_of_data\r\nmresolution\r\n100type_of_data\r\nbinary_categoricaltype_of_file\r\n.tifsource\r\nosmlink_of_source\r\nNAdate_of_compile\r\n2022-08-01short_description\r\ncreated from osm_germany_2022_4326.gpkgmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 10:29:00 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] knitr_1.42 fastmap_1.1.0 parallel_4.2.1 \r\n[31] class_7.3-20 fansi_1.0.4 highr_0.10 \r\n[34] Rcpp_1.0.10 KernSmooth_2.23-20 scales_1.2.1 \r\n[37] classInt_0.4-8 lwgeom_0.2-11 cachem_1.0.6 \r\n[40] jsonlite_1.8.4 abind_1.4-5 farver_2.1.1 \r\n[43] systemfonts_1.0.4 textshaping_0.3.6 distill_1.5 \r\n[46] ggplot2_3.4.0 digest_0.6.31 dplyr_1.0.10 \r\n[49] grid_4.2.1 cli_3.6.0 tools_4.2.1 \r\n[52] magrittr_2.0.3 sass_0.4.5 proxy_0.4-27 \r\n[55] tibble_3.1.8 tidyr_1.3.0 pkgconfig_2.0.3 \r\n[58] downlit_0.4.2 assertthat_0.2.1 gt_0.8.0 \r\n[61] rmarkdown_2.20 rstudioapi_0.14 R6_2.5.1 \r\n[64] units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/motorways_germany_2022_100m_03035_tif/motorways_germany_2022_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -781,7 +781,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\npaths_germany_2022_100m_03035_tifname\r\npaths_germany_2022_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2022units_of_data\r\nbinaryresolution\r\n100mtype_of_data\r\nbinary_categoricaltype_of_file\r\n.tifsource\r\nbkglink_of_source\r\nhttps://gdz.bkg.bund.dedate_of_compile\r\n2023-01-04copyright\r\n© GeoBasis-DE / BKG (2023)short_description\r\ncreated from digital_base_dlm_germany_2022_25832_BKG_shpmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 10:33:54 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] knitr_1.42 fastmap_1.1.0 parallel_4.2.1 \r\n[31] class_7.3-20 fansi_1.0.4 highr_0.10 \r\n[34] Rcpp_1.0.10 KernSmooth_2.23-20 scales_1.2.1 \r\n[37] classInt_0.4-8 lwgeom_0.2-11 cachem_1.0.6 \r\n[40] jsonlite_1.8.4 abind_1.4-5 farver_2.1.1 \r\n[43] systemfonts_1.0.4 textshaping_0.3.6 distill_1.5 \r\n[46] ggplot2_3.4.0 digest_0.6.31 dplyr_1.0.10 \r\n[49] grid_4.2.1 cli_3.6.0 tools_4.2.1 \r\n[52] magrittr_2.0.3 sass_0.4.5 proxy_0.4-27 \r\n[55] tibble_3.1.8 tidyr_1.3.0 pkgconfig_2.0.3 \r\n[58] downlit_0.4.2 assertthat_0.2.1 gt_0.8.0 \r\n[61] rmarkdown_2.20 rstudioapi_0.14 R6_2.5.1 \r\n[64] units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/paths_germany_2022_100m_03035_tif/paths_germany_2022_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -805,7 +805,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\nroads_germany_2022_100m_03035_tifname\r\nroads_germany_2022_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2022units_of_data\r\nbinaryresolution\r\n100mtype_of_data\r\nbinary_categoricaltype_of_file\r\n.tifsource\r\nbkglink_of_source\r\nhttps://gdz.bkg.bund.dedate_of_compile\r\n2023-01-04copyright\r\n© GeoBasis-DE / BKG (2023)short_description\r\ncreated from digital_base_dlm_germany_2022_25832_BKG_shpmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 10:38:05 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] knitr_1.42 fastmap_1.1.0 parallel_4.2.1 \r\n[31] class_7.3-20 fansi_1.0.4 highr_0.10 \r\n[34] Rcpp_1.0.10 KernSmooth_2.23-20 scales_1.2.1 \r\n[37] classInt_0.4-8 lwgeom_0.2-11 cachem_1.0.6 \r\n[40] jsonlite_1.8.4 abind_1.4-5 farver_2.1.1 \r\n[43] systemfonts_1.0.4 textshaping_0.3.6 distill_1.5 \r\n[46] ggplot2_3.4.0 digest_0.6.31 dplyr_1.0.10 \r\n[49] grid_4.2.1 cli_3.6.0 tools_4.2.1 \r\n[52] magrittr_2.0.3 sass_0.4.5 proxy_0.4-27 \r\n[55] tibble_3.1.8 tidyr_1.3.0 pkgconfig_2.0.3 \r\n[58] downlit_0.4.2 assertthat_0.2.1 gt_0.8.0 \r\n[61] rmarkdown_2.20 rstudioapi_0.14 R6_2.5.1 \r\n[64] units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/roads_germany_2022_100m_03035_tif/roads_germany_2022_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -829,7 +829,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndistance-to-paths_germany_2022_100m_03035_tifname\r\ndistance-to-paths_germany_2022_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2022units_of_data\r\nmeterresolution\r\n100mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nbkglink_of_source\r\nhttps://gdz.bkg.bund.dedate_of_compile\r\n2023-01-03copyright\r\n© GeoBasis-DE / BKG (2023)short_description\r\ncreated from paths_germany_2022_100m_03035_tifmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 09:23:01 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.0 \r\n[31] parallel_4.2.1 class_7.3-20 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20\r\n[37] scales_1.2.1 classInt_0.4-8 lwgeom_0.2-11 \r\n[40] cachem_1.0.6 jsonlite_1.8.4 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.0 digest_0.6.31 \r\n[49] dplyr_1.0.10 grid_4.2.1 cli_3.6.0 \r\n[52] tools_4.2.1 magrittr_2.0.3 sass_0.4.5 \r\n[55] proxy_0.4-27 tibble_3.1.8 tidyr_1.3.0 \r\n[58] pkgconfig_2.0.3 downlit_0.4.2 assertthat_0.2.1 \r\n[61] gt_0.8.0 rmarkdown_2.20 rstudioapi_0.14 \r\n[64] R6_2.5.1 units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/distance-to-paths_germany_2022_100m_03035_tif/distance-to-paths_germany_2022_100m_03035_tif_files/figure-html5/plot-1.png", - 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"last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:16+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -925,7 +925,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndistance-to-motorways_germany_2022_100m_03035_tifname\r\ndistance-to-motorways_germany_2022_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2022units_of_data\r\nmresolution\r\n100mtype_of_data\r\ncontinuous_numerictype_of_file\r\n.tifsource\r\nosmlink_of_source\r\nNAdate_of_compile\r\n2022-07-29short_description\r\nbuild from motorways_germany_2022_100m_03035_tifmodified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 09:51:32 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 xfun_0.36 bslib_0.4.2 \r\n [4] purrr_1.0.1 sf_1.0-9 colorspace_2.1-0 \r\n [7] vctrs_0.5.2 generics_0.1.3 htmltools_0.5.4 \r\n[10] stars_0.6-0 yaml_2.3.7 utf8_1.2.2 \r\n[13] rlang_1.0.6 e1071_1.7-12 jquerylib_0.1.4 \r\n[16] pillar_1.8.1 glue_1.6.2 withr_2.5.0 \r\n[19] DBI_1.1.3 lifecycle_1.0.3 munsell_0.5.0 \r\n[22] gtable_0.3.1 ragg_1.2.5 memoise_2.0.1 \r\n[25] evaluate_0.20 labeling_0.4.2 knitr_1.42 \r\n[28] fastmap_1.1.0 parallel_4.2.1 class_7.3-20 \r\n[31] fansi_1.0.4 highr_0.10 Rcpp_1.0.10 \r\n[34] KernSmooth_2.23-20 scales_1.2.1 classInt_0.4-8 \r\n[37] lwgeom_0.2-11 cachem_1.0.6 jsonlite_1.8.4 \r\n[40] abind_1.4-5 farver_2.1.1 systemfonts_1.0.4 \r\n[43] textshaping_0.3.6 distill_1.5 ggplot2_3.4.0 \r\n[46] digest_0.6.31 dplyr_1.0.10 grid_4.2.1 \r\n[49] cli_3.6.0 tools_4.2.1 magrittr_2.0.3 \r\n[52] sass_0.4.5 proxy_0.4-27 tibble_3.1.8 \r\n[55] tidyr_1.3.0 pkgconfig_2.0.3 downlit_0.4.2 \r\n[58] assertthat_0.2.1 gt_0.8.0 rmarkdown_2.20 \r\n[61] rstudioapi_0.14 R6_2.5.1 units_0.8-1 \r\n[64] compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/distance-to-motorways_germany_2022_100m_03035_tif/distance-to-motorways_germany_2022_100m_03035_tif_files/figure-html5/plot-1.png", - "last_modified": "2023-10-19T12:55:43+02:00", + "last_modified": "2023-11-13T15:55:15+01:00", "input_file": {}, "preview_width": 4500, "preview_height": 3000 @@ -949,7 +949,7 @@ ], "contents": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncolumn\r\n input\r\n folder_name\r\ndistance-to-roads_germany_2022_100m_03035_tifname\r\ndistance-to-roads_germany_2022_100m_03035.tifcrs\r\n+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defsepsg\r\n3035year_of_data\r\n2022units_of_data\r\nmresolution\r\n100mtype_of_data\r\ncontinual_numerictype_of_file\r\n.tifsource\r\nbkglink_of_source\r\nhttps://gdz.bkg.bund.dedate_of_compile\r\n2022-05-13short_description\r\ncreated from roads_germany_2022_100m_03035modified\r\nNA\r\n\r\n\r\nSession Info\r\n\r\n[1] \"2023-01-31 09:56:25 CET\"\r\nR version 4.2.1 (2022-06-23 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 17763)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 \r\n[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C \r\n[5] LC_TIME=C \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods \r\n[7] base \r\n\r\nother attached packages:\r\n[1] patchwork_1.1.2\r\n\r\nloaded via a namespace (and not attached):\r\n [1] tidyselect_1.2.0 terra_1.7-3 xfun_0.36 \r\n [4] bslib_0.4.2 purrr_1.0.1 sf_1.0-9 \r\n [7] colorspace_2.1-0 vctrs_0.5.2 generics_0.1.3 \r\n[10] htmltools_0.5.4 stars_0.6-0 yaml_2.3.7 \r\n[13] utf8_1.2.2 rlang_1.0.6 e1071_1.7-12 \r\n[16] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 \r\n[19] withr_2.5.0 DBI_1.1.3 lifecycle_1.0.3 \r\n[22] munsell_0.5.0 gtable_0.3.1 ragg_1.2.5 \r\n[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.20 \r\n[28] labeling_0.4.2 knitr_1.42 fastmap_1.1.0 \r\n[31] parallel_4.2.1 class_7.3-20 fansi_1.0.4 \r\n[34] highr_0.10 Rcpp_1.0.10 KernSmooth_2.23-20\r\n[37] scales_1.2.1 classInt_0.4-8 lwgeom_0.2-11 \r\n[40] cachem_1.0.6 jsonlite_1.8.4 abind_1.4-5 \r\n[43] farver_2.1.1 systemfonts_1.0.4 textshaping_0.3.6 \r\n[46] distill_1.5 ggplot2_3.4.0 digest_0.6.31 \r\n[49] dplyr_1.0.10 grid_4.2.1 cli_3.6.0 \r\n[52] tools_4.2.1 magrittr_2.0.3 sass_0.4.5 \r\n[55] proxy_0.4-27 tibble_3.1.8 tidyr_1.3.0 \r\n[58] pkgconfig_2.0.3 downlit_0.4.2 assertthat_0.2.1 \r\n[61] gt_0.8.0 rmarkdown_2.20 rstudioapi_0.14 \r\n[64] R6_2.5.1 units_0.8-1 compiler_4.2.1 \r\n\r\n\r\n\r\n\r\n", "preview": "posts_geodata/distance-to-roads_germany_2022_100m_03035_tif/distance-to-roads_germany_2022_100m_03035_tif_files/figure-html5/plot-1.png", - 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2024


  • Becker J, Liess M, Kramer-Schadt S, Franz M, Jager T (2024): Critical Evaluation of Effect Models for the Risk Assessment of Plant Protection Products. UBA Report 41/2024, ISSN 1862-4804, 596 pages. https://www.umweltbundesamt.de/sites/default/files/medien/11850/publikationen/41_2024_texte_critical_evaluation.pdf

    • -

  • Biersteker L, Planillo A, Lammertsma DR, van der Sluis T, Knauer F, Kramer-Schadt S, van der Grift EA, van Eupen M, Jansman HAH (2024): Habitatgeschiktheid voor de wolf in Nederland: een modelanalyse. Wageningen Environmental Research, Rapport 3350, 87 pages, ISSN: 1566-7197; https://research.wur.nl/en/publications/habitatgeschiktheid-voor-de-wolf-in-nederland-een-modelanalyse

    • +

  • Biersteker L, Planillo A, Lammertsma DR, van der Sluis T, Knauer F, Kramer-Schadt S, van der Grift EA, van Eupen M, Jansman HAH (2024): Habitatgeschiktheid voor de wolf in Nederland: een modelanalyse. Wageningen Environmental Research, Rapport 3350, 87 pages, ISSN: 1566-7197; https://research.wur.nl/en/publications/habitatgeschiktheid-voor-de-wolf-in-nederland-een-modelanalyse

  • Thonicke K, Rahner E, Arneth A, Bonn A, Borchard N, Chaudhary A, Darbi M, Dutta T, Eberle U, Eisenhauer N, Farwig N, Flocco CG, Freitag J, Grobe P, Grosch R, Grossart HP, Grosse A, Grützmacher K, Hagemann N, Hansjürgens B, Hartman Scholz A, Hassenrück C, Häuser C, Hickler T, Hölker F, Jacob U, Jähnig S, Jürgens K, Kramer-Schadt S, Kretsch C, Krug C, Lindner JP, Loft L, Mann C, Matzdorf B, Mehring M, Meier R, Meusemann K, Müller D, Nieberg M, Overmann J, Peters RS, Pörtner L, Pradhan P, Prochnow A, Rduch V, Reyer C, Roos C, Scherber C, Scheunemann N, Schroer S, Schuck A, Sioen GB, Sommer S, Sommerwerk N, Tanneberger F, Tockner K, van der Voort H, Veenstra T, Verburg P, Voss M, Warner B, Wende W, Wesche K (2024): 10 Must-Knows aus der Biodiversitätsforschung 2024. https://zenodo.org/records/10794362

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color: var(--hover-color, white); } - + diff --git a/docs/search.json b/docs/search.json index 71dbe4e6..1602918c 100644 --- a/docs/search.json +++ b/docs/search.json @@ -6,14 +6,14 @@ "description": "", "author": [], "contents": "\r\n\r\n\r\n\r\nWe are scientists of the Department of Ecological Dynamics at the Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany. Our research is focused on understanding ecological dynamics in space and time, at different levels of organisation, from individuals to communities, and across gradients of human altered environments. We investigate how fitness consequences of processes acting at the individual level, such as social behaviour or movements, competition, predator-prey or host-pathogen interactions, shape population and community dynamics, also at evolutionary scales.\r\n\r\nHow to contact us?\r\nEmail: assist6[at]izw-berlin.de\r\nAddress:\r\nLeibniz Institute for Zoo and Wildlife Research\r\nDepartment of Ecological Dynamics\r\nAlfred-Kowalke-Str. 17\r\nD-10315 Berlin, Germany\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:02+02:00" + "last_modified": "2024-06-05T15:50:12+02:00" }, { "path": "coding.html", "title": "Coding", "author": [], "contents": "\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:03+02:00" + "last_modified": "2024-06-05T15:50:13+02:00" }, { "path": "doctoral-theses.html", @@ -21,14 +21,14 @@ "description": "", "author": [], "contents": "\r\n\r\nSuccessfully Defended Theses\r\n\r\nAna Patricia Calderon Quinonez: Ecology and conservation of the jaguar (Panthera onca) in Central America. 01.11.2023. Institut für Biochemie und Biologie der Universität Potsdam. Kramer-Schadt S, Grimm V.\r\nMorgane Gicquel: Early-life conditions and their long-term consequences in spotted hyenas (Crocuta Crocuta). 07.02.2023. Department of Biology, Chemistry and Pharmacy Freie Universität Berlin. Benhaiem S, East ML, Hofer H.\r\nTobias Kürschner: Disease transmission and persistence in dynamic landscapes. 23.09.2022. Institut für Biochemie und Biologie der Universität Potsdam. Kramer-Schadt S, Grimm V, Berger U.\r\nThanh Van Nguyen: Unravelling the mysteries of the Annamites: First insights in ecology, distribution, and genetic diversity of Annamite mammals. 24.06.2022. Mathematisch-Naturwissenschaftliche Fakultät\r\nInstitut für Biochemie und Biologie der Universität Potsdam. Fickel J, Wilting A.\r\nJoseph Premier: Research for Eurasian lynx conservation. Inclusion of genetic processes in an individual-based spatially explicit population model and updating the ecological and demographic basis to support evidence-based conservation management of Eurasian lynx. 25.05.2022. Faculty of Environment and Natural Resources, Albert-Ludwigs-Universität, Freiburg im Breisgau. Heurich M, Kramer-Schadt S.\r\nCarolin Scholz: The ecology of red foxes (Vulpes vulpes) in anthropogenic landscapes. 28.01.2021. Department of Biology, Chemistry and Pharmacy Freie Universität Berlin. Hofer H, Kramer-Schadt S.\r\nAndrew Tilker: Assessing defaunation in the central Annamites ecoregion of Vietnam and Laos. 19.11.2020. Biologie Freie Universität Berlin. Hofer H, Wilting A.\r\nSusana Carolina Martins Ferreira: Intrinsic and extrinsic determinants of parasite infection in spotted hyenas in the Serengeti National Park. 23.09.2019. Veterinärmedizin Freie Universität Berlin. Hofer H, East ML.\r\nJohn Mathai: Distribution and conservation of small carnivores focussing on the Bornean endemic Hose’s civet. 17.04.2019. Biologie Freie Universität Berlin. Hofer H, Wilting A.\r\nPhilipp Cédric Scherer: Infections on the move: Individual host movement drives disease persistence in spatially structured landscapes. 25.03.2019. Institut für Biochemie und Biologie der Universität Potsdam. Kramer-Schadt S, Jeltsch F.\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:04+02:00" + "last_modified": "2024-06-05T15:50:14+02:00" }, { "path": "geodata.html", "title": "Geodata", "author": [], "contents": "\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:06+02:00" + "last_modified": "2024-06-05T15:50:15+02:00" }, { "path": "guidelines.html", @@ -36,7 +36,7 @@ "description": "The guidelines for studies in the Department of Ecological Dynamics are meant to ease your start in the department and to secure high quality standards for the analysis of your data.", "author": [], "contents": "\r\nTL;DR: Guidelines in a Nutshell\r\n\r\nGeneral Information\r\nImportant lab information (server access, responsibilities,…) can be found on our cloud in the folder Lab_Orga/Important_Information.\r\nWhen sharing documents, please use use the following syntax for the file name: lastname_topic_YYYYMMDD.docx. Do not use v1, latest or similar suffixes.\r\nAn overview of geo-spatial data sets is available in our geodata wiki.\r\nTo fulfill the requirement of a retention period of 10 years for all projects, follow our rules how to set up, maintain, and archive projects (see below).\r\nProjects\r\nCreate a new project for every study (i.e. publication).\r\nName project folder using our unified syntax: species_where_what_who.→ e.g. sciurus_vulgaris_de_camtrap_kramer_s\r\nComply with our obligatory project folder structure.→ easily to set up with our d6 R package: d6::new_project().\r\nTrack and control changes of source codes: set up version control.\r\nFor a corporate chart style, please use our d6 ggplot2 theme: d6::theme_d6().\r\nTransfer your repository to our GitHub organization at the end of a project.\r\nAsk Moritz Wenzler-Meya (wenzler[at]izw-berlin.de) for help if needed.\r\nAdvice for Students (BSc | MSc | PhD)\r\nPlan regular supervisory meetings at least 3 months ahead.\r\nPrepare for the meeting! Collect recent achievements, potential problems, and open questions. Provide a timeline for upcoming tasks.\r\nWrite the minutes of the meeting and send it to your supervisor(s).\r\n\r\nStart writing your thesis early enough and attend our “write now” meetings.\r\nRegularly send drafts to your supervisor(s) to get feedback.\r\nAllow for sufficient time to read and respond (2-4 weeks).\r\n\r\nTable of Content\r\nGeneral Information\r\nConducting BSc/MSc/PhD Projects\r\nOrganizing Workflows\r\nProject Folders\r\nData Backup\r\nManuscript Submissions/Revisions\r\nGitHub repository\r\nAppendices\r\nScripting\r\nSpatial Data Information\r\nWhat Nature Says…\r\n\r\n\r\n\r\n\r\n\r\n\r\nGeneral Information\r\nThe department is composed of three teams on (1) Individual Dynamics, (2) Population Dynamics and (3) Biodiversity Dynamics.\r\nStephanie Kramer-Schadt is the department head and leader of the Population Dynamics team. Conny Landgraf is the assistant of the department head; she can assist with administrative duties (e.g. contracts, travel sheets, etc.). Moritz Wenzler-Meya and Jan Axtner are responsible for data management, data storage and the working group’s R code collection. As each department member has a specific area of expertise, we created an expert list (see 1.3) if you need help on specific topics. PhD students and scientists are obliged to update this expert list with their own skills and responsibilities as well.\r\nFor information on the Department, check our webpage or subscribe to our Twitter account (@EcoDynIZW) for news about papers, helpful R-code, courses or scientific positions.\r\nTeams of the Department of Ecological Dynamics:\r\nIndividual Dynamics – PI: Sarah Benhaiem & Sonja Metzger (field coordination)\r\nPopulation Dynamics – PI: Stephanie Kramer-Schadt & Viktoriia Radchuk\r\nBiodiversity Dynamics – PI: Andreas Wilting & Rahel Sollmann\r\nAdministrative support\r\nConny Landgraf\r\nTel: -466\r\nEmail: assist6[at]izw-berlin.de or landgraf[at]izw-berlin.de\r\nData Managers\r\nDr. Jan Axtner\r\nTel: -342\r\nEmail: axtner[at]izw-berlin.de\r\nMoritz Wenzler-Meya\r\nTel: -342\r\nEmail: wenzler[at]izw-berlin.de\r\nIn-house experts / scientists / responsibilities\r\nWho does what in the department? Tasks and expertise as well as meeting dates and protocols can be found in the IZW Wolke.\r\n↑ Jump back to top.\r\nConducting BSc/MSc/PhD Projects\r\nFor PhD students: please read the IZW PhD guidelines (\\\\izw-daten-8\\Alle\\GUEST\\Doktorand(inn)en\\IZW-PhD-rules) carefully and follow the instructions (e.g. when you have to give an introduction talk). For any question about these guidelines, contact the PhD coordinators Gábor Czirják (czirjak[at]izw-berlin.de) and Sarah Benhaiem (benhaiem[at]izw-berlin.de).\r\nMeetings\r\nRegular meetings with your supervisor often help to avoid a waste of time (see also Appendix C). It is in the responsibility of the student to organize and schedule regular meeting with his/ her supervisor or the team.\r\nYou can either arrange a fixed date with your supervisor (e.g. first Monday of every month) or schedule them on demand. Please find an agreement with your supervisor how to handle this best. Please keep in mind that any non-regular meeting should be planned at least two weeks ahead.\r\nUse the IZW outlook calendar to schedule meetings with supervisors and colleagues (if you have an IZW Email address). That is: create the entry in the calendar and ‘invite’ the others, so that the date/ location appears in each calendar, with sufficient notice time for the automated reminder (i.e. 1 day/ 1 hr).\r\nPrepare the meetings: Think carefully about your problems/questions and write your agenda (what you want to discuss) before the meeting, make suggestions for your solution(s) and ask the supervisors/collaborators to comment on your solution(s).\r\nWrite the minutes of the meeting and save it in the project folder (we give instructions about this project folder below 3.1.). Minimum information needed: (1) date of meeting, (2) name of participants, (3) questions or problems discussed, (4) main solutions suggested and (5) aims or results to prepare for the next meeting. Write down agreements of the responsibilities of all collaborators.\r\nPresent your project progress regularly in the department meetings, also to get feedback and discuss problems you ran into.\r\nThesis writing\r\nStart writing the thesis early enough. Write simple and concise sentences based on what is known in the literature. Join a ‘pub club’ (i.e. retreats for writing), e.g. Team 2 has regular meetings on jointly discussing drafts of each other. Please also read the instructions (Under construction) for how to write good scientific papers.\r\nArrange with your supervisor how and in what form you report progress. Getting regular feedback for chapters or even just paragraphs might keep you from running into dead ends. When asking for revisions plan in sufficient time for the reviewers to read and respond (1-3 weeks, depending on the amount of text).\r\nFor PhD students: A first complete manuscript draft should be ready after ~ 1 year after starting time.\r\nSend the final version of your manuscript/thesis to all supervisors/colleagues at least 4 weeks before the submission date and keep in mind that you might have to revise it.\r\n↑ Jump back to top.\r\nOrganizing Workflows\r\nTo store data and results, the IZW follows the DFG-guidelines for good scientific practice. In our department, we often use “scripting” (written code of different programming languages) to process and analyze data, and make results reproducible. Work is structured and organized in projects and each project will get a project ID from the data administrators of the department. We consider a project to be a self-contained topic, e.g. analyses for a manuscript, thesis chapter, etc. For each project a separate folder is created and all relevant scripts, results, the lab-book, documents, literature, minutes of meetings etc. related to this project are to be kept in that main folder. For documents (not for scripts!) use your surname, type of document, project name and in the end the date (YYYYMMDD) as version numbers for file names (kramer_manuscript_lynxibm_20201231.docx) and do not use names like doc_final.docx, doc_finalfinal.docx, doc_lastversion.docx etc. It should contain all information needed to repeat the study and conduct follow-up studies. Whenever possible use the standardized project/folder structure shown in section 3.1. Each project or subproject should have a concise but meaningfully electronic lab-book (see section X.Z) that allows to follow the workflow and the decisions made in it.\r\nProject workflow\r\nContact the Data Managers (Jan Axtner or Moritz Wenzler-Meya) of the respective teams to organize your project ID, get server access and how to arrange your workspace best. To setup a project folder, we ask you to follow the setup:\r\nUse the standardized project folder structure! (see Project Folder Structure, d6 R package and GitHub)\r\nMake an outline of the structure separately in your electronic lab-book (see 3.3.1), to indicate what to find in each folder and keep it updated.\r\nPlease hand over the raw data to the data manager before starting your project analysis, to avoid accidental data losses.\r\nStore the raw data, e.g. data from field work, in the folder named data-raw. Use a folder named output to store data created from the raw data (e.g. after data cleaning or editing). This should also be the place where the master table is located, i.e. the data set of all subsequent analyses. If needed you can have a temporary subfolder in the “output” folder for everyday work and analysis trials. Empty the temporary folder regularly. Plots and figures should be stored in the plots folder. Scripts should be stored in the R folder. Documents like manuscripts, proposals, etc. should be stored in the docs folder. Save important, computational or labour intensive interim results or final results. Use appropriate subfolders such as interim-results_ or final-results_.\r\nReproducibility\r\nTo ensure reproducibility and for your own sake always try to script your work! If possible use R, Python, SQL, etc. for analyses and avoid manual / mouse commands (e.g. ‘clicking’ in ArcGIS, QGIS etc.). If you cannot use scripts, use tools such as the model-builder in ArcGIS or QGIS and save the drag & drop model builder scripts.\r\nDocumentation\r\nProject documentation is not a final task, but an ongoing process starting from day one. DFG guidelines for Safeguarding and Storing of Primary Data states ‘Primary data as the basis for publications shall be securely stored for ten years in a durable form in the institution of their origin.’ Hence, at the end of a research project, you have to hand over a single well-documented folder per project (usually a paper on the respective subject, or the BSc thesis) along with all original data. The project folder should contain all project related documents and data (e.g. simulation model codes, simulation results, scripts for statistical analysis, scripts for tables and figures, manuscripts, important work documents, applications, permits, reports, etc.) as well as a the final version of a thesis or paper. Unfinished work (data from conducted experiments that were not analyzed or published) should contain the above documents as far as possible, including a very detailed method description. For the ease of documentation effort please follow the section (3.3.1).\r\nElectronic lab-book\r\nAt the start of each new project, create an electronic lab-book to document your workflow and decisions therein. It is of major importance that you keep this lab-book updated. For the ease of use we recommend simple .doc files (MS Word, Libre Office etc.) to keep record of our work. Write down important thoughts, things you have tried, also failed experiments. Try to use clear and comprehensible notes. Although this generates some additional effort you will realize soon that it will help you and others to track your work and make it reproducible. We highly recommend to use GitHub as version control of your project. You have to connect your GitHub account to the EcoDynIZW organization on GitHub to share code with colleagues. Please create repositories as part of this organization. It is mandatory to hand over the electronic lab-book to the institute as part of the ‘Safeguarding and Storing of Primary Data’ regulation of the German Research Council DFG after finishing your project.\r\nTo document scripting we recommend to use R Markdown (.rmd) files if possible. Between your code chunks you should annotate the script with everything that is necessary to understand and follow the code. Additionally, if you push new code to GitHub for version control, you have to comment on your committed code as well. It may be necessary to have an additional word document.\r\n↑ Jump back to top.\r\nProject Folder Structure\r\nA new project can be started by installing the d6 R-package. Running new_project() with a unique and descriptive name for your project (see below) will create a full scaffolding structure for all your future analysis steps. If you like, you can also specify a path and the project will be created there.\r\nPlease create repositories on the EcoDynIZW account and not on your private\r\naccount!\r\nRoot project folder\r\nName it like:\r\nspecies|topic_country|simu_method|approach_surname_firstletterofgivenname\r\ne.g. unicornus_wl_sdm_smith_j (unicornus project in wonderland, species distribution model from John Smith) or stability_simu_rpackage_smith_j\r\nEverything should be written in small letters.\r\nAvoid spaces or hyphen in the path to the file.\r\nSpecies name should be in latin and please use your surname and the first letter of your given name at the end.\r\nCountry should be the international abbreviation.\r\nThe root project folder should hold:\r\nRproj file: your main file for your R project (if used) — do not confuse this with R-scripts!\r\nWe highly recommend to use R, but if it is necessary to use another programming language and/or program please use the same structure as described in the following:\r\n\r\ndata: A folder that contains the complete data, with sub folders for raw (e.g. telemetry data, experiment results, electrophoresis images, vegetation survey plots, species lists, etc.) and processed data. If spatial data is used, placde them in a dedicated geo folder.\r\nDo not use MS Excel files use .csv or .txt files to store/ save data.\r\nMetadata belonging to the raw data should be provided in a separate file but it should be given the same name with an additional suffix as the raw data file, e.g. area1_specieslist.txt or area1_specieslist_metadata.txt\r\n\r\ndocs: A folder that contains anything related with project administration e.g. applications, permits, grants, timeline, research proposal, the electronic lab-book.\r\nresults: A folder that contains results from your data wrangling and analyses.\r\nplots: A folder that contains all produced images for the data exploration, presentations, and publications.\r\nscripts: A folder that contains all scripts that are used within the project.\r\nIf you need additional subfolders add them. Please use also second-level subfolders inside the subfolders to keep a clear and tidy structure.\r\nThe main folder structure created in the root directory should look like this:\r\n.\r\n└── unicornus_wl_sdm_smith_j\r\n ├── data\r\n ├── docs\r\n ├── results\r\n ├── plots\r\n └── scripts\r\nThe full scaffolding structure including all subdirectories and\r\nadditional files looks like this:\r\n. \r\n└── unicornus_wl_sdm_smith_j\r\n ├── .Rproj.user — Rproject files\r\n ├── data — main folder data\r\n │ ├── processed — processed tabular data files\r\n │ │ └── geo — processed geospatial data files\r\n │ └── raw — raw tabular data files\r\n │ │ └── geo — raw geospatial data files\r\n ├── docs — documents main folder\r\n │ ├── admin — administrative docs, e.g. permits \r\n │ ├── literature — literature used for parametrization + manuscript\r\n │ ├── manuscript — manuscript drafts (main + supplement)\r\n │ ├── presentations — talks and poster presentations\r\n │ └── reports — rendered reports\r\n ├── results — explorative plots, tables etc. (except final figures)\r\n ├── plots — final figures for manuscript and supplementary material\r\n ├── scripts — script files (e.g. .R, .Rmd, .Qmd, .py, .nlogo)\r\n │ └── zz_submit.R — final script to run before submission\r\n ├── .gitignore — contains which files to ignore for version control\r\n ├── README.md — contains project details and package dependencies\r\n └── project.Rproj — Rproject file: use to start your project\r\n↑ Jump back to top.\r\nData Backup\r\nImportant: backup your work! Make copies of raw data/files and scripts and save it in another location if you work on private computers. PhD students should work on the M-drive or the U-drive, never directly on the C-drive of the computer.\r\nManuscript Submissions/Revisions\r\nWhen you are submitting a manuscript create a copy of the related subproject folder and add a suffix submission_1_. This copy shall be stored and never changed—we recommend to zip this folder! When conducting a revisions or resubmission, copy the folder to submission_2_ and revise the paper.\r\nPlease find additional information on ‘how to write a scientific paper’, how to avoid statistical pitfalls or how to organize workflows here.\r\nGitHub repository\r\nThe final step after your paper gets published is to make the GitHub repository public (or create a repository if you haven’t before). The repository should contain clean code and data that runs properly. If you are using R, please make sure to use a R-Project with the given folder structure (see appendix A).\r\nThe title of the repository should contain the last name of the first author, the year, and the abbreviation of the journal (e.g. Smith_2023_Science). Please add the title of the paper as the description (in quotation marks). Please provide the reference of the paper, including the DOI as a hyperlink, and the abstract in the README file. Add further notes on the files contained and the scripts if needed. Please use this template:\r\n# Smith et al. (2023) *Science*\r\n\r\n> **Richard Smith**, Don Joe Radchuk & **Jane Head** (2023): Study title. \r\n*Science* 1-2. DOI: [doi-goes-here](https://doi.org/doi-goes-here)\r\n\r\nAbstract text.\r\n\r\n## Scripts\r\n\r\n... more notes if needed\r\n\r\nAdd the title in quotation marks as the description and the DOI as link to the repository about (right upper corner). For examples see study repositories on our GitHub).\r\nFinally, make sure to turn the visibility to public and transfer the ownership to the EcoDynIZW GitHub organization. If you have need help or have questions please ask the data manager for help.\r\n↑ Jump back to top.\r\nAppendices\r\nAppendix A) Scripting\r\nGeneral recommendations\r\nWrite the author, name and date in a header (you can use the d6 package templates for this as well).\r\nKeep code in scripts as concise as possible.\r\nUse in-line comments to explain your code that others can follow your code.\r\nIf possible, use dynamic paths (i.e. see R-package here).\r\nAlways set variables/ parameters at the top of your script; do not set values somewhere inside the code as this is error prone, because these values tend to be forgotten to get changed when updating a script. The same applies to loading packages. Please also make sure to only load packages that are really needed and remove those that you are not using for the final analysis.\r\nSave the output of a script as .rds file to smoothly load it in the next script. Additionally, save the output in a file format usable beyond R, e.g. use .csv or .txt if you want to save tabular data or GeoTiff if you want to save a raster map.\r\nUse separate scripts to create figures of publishable quality (700 dpi) from the results. Use a vector graphics format like pdf.\r\nFor simulation studies: Include all scripts, e.g. additional model code, batch files with simulated parameters, simulated landscapes and simulation results, and files used for the analyses.\r\nR\r\nStick to the Rstudio style guide to ensure readability of code.\r\nIf you are using RStudio, make sure to set the following under Tools > Global Options > General > Basic:\r\nuntick “Restore .RData into workspace at startup”\r\nset “Save workspace to .RData on exit:” to “Never”\r\nuntick “Always save history (even when not saving .RData)” (recommended)\r\n\r\nA screenshot of the RStudio general settingsWe recommend using R-Studio projects and R Markdown documents when scripting in R. If you use another “integrated development environment” (IDE) make sure you can provide versioned plain R scripts using a relative path to the used data files.\r\nImplement version control by using GitHub. If you install packages or use scripts and code chunks from GitHub provided by others, be aware to acknowledge the code developer prior to publication of scripts.\r\nDo not save the R history or the R environment. Always start a fresh and an empty R session to avoid artifacts by using objects of functions that were not defined in your script. More details here: https://www.tidyverse.org/blog/2017/12/workflow-vs-script/.\r\nIf possible use log files to document messages and errors.\r\nPython\r\nIn general we recommend using Jupyter notebooks.\r\n[under construction]\r\nNetlogo\r\n[under construction]\r\nQGIS\r\nIn general we recommend doing GIS analyses in R. If you have to use QGIS use Python as scripting language. However, the RQGIS package provides a great opportunity to script complete workflows in QGIS by using R. Moreover, you can include R scripts in the QGIS toolbox.\r\n[under construction]\r\n↑ Jump back to top.\r\nAppendix B) Spatial Data\r\nIf you are working with spatial data please have the following in mind:\r\nAlways check the projection (cartographic reference system (CRS)) of your data. Depending on what you want to do it could be necessary to change the CRS system, i.e. from an angular unit (like GPS data) to a projected unit in meters. BUT sometimes you cannot easily change it or it will drastically change your data.\r\nIf you want to save you spatial data, please save it as Geopackage (.gpkg) or raster data GeoTiff (.tif). Please give a clear name, document the filename in your lab book and add the EPSG code of your CRS to the file name. Always use utf-8 as file encoding.\r\nIf you are not sure how to handle your spatial data please contact Moritz Wenzler-Meya, the GIS manager, for help.\r\nWhen using spatial data give clear names for maps ≤ 13 characters without special characters or spaces.\r\nWrite down the cartographic reference system (CRS) at the end of the file name, e.g. filename_4326.\r\nAlways use utf-8 encoding, NEVER use the system setting. If you have to use different character encoding settings, then name the used encoding in the filename.\r\nSave the data as geopackage (.gpkg, recommended but cannot be used via ArcGis) or shapefile. Geopackages should be the first choice as the format is much quicker in every task (loading, saving, changing and computing), less limited and OGC-Standard (Open Geospatial Consortium). Be aware, that shapefiles have many limitations e.g. max 7 characters for column names. Further information is listed in Appendix B.\r\nCrosscheck your scripts and results. If you have calculated data/processed maps, make a plot and check for consistency. E.g. there should be no data outliers, terrestrial species should not occur in the sea etc. If results are valid, mark it as a milestone in your project documentation.\r\nBe aware: if you want to change the coordinate reference system (CRS) you have to transform / project the data. Never just specify a new one, because this doesn’t change the CRS!\r\nAlways include the Coordinate Reference System as EPSG code (e.g. 4326 for log/lat WGS84 Coordinates) at the end of a file name, e.g. my_env_variable_4326.gpkg.\r\nPlease save all your geo data into geopackage files (.gpkg) format to save and process your data. In general, if you export data from R please save it as an R Studio file (.rds). It will be saved without any losses.\r\nStore all point/line/polygon data as WGS84 (EPSG 4326).\r\nIf you do not understand this paragraph, contact the geodata-manager (Moritz Wenzler-Meya) before doing anything involving geodata.\r\n↑ Jump back to top.\r\nAppendix C) What Nature Says…\r\nNature 561, 277 (2018)\r\nWhy you need an agenda for meetings with your principal investigator\r\nA list of talking points can help with navigating potentially difficult topics and sticky negotiations.\r\nAs PhD students, we often find ourselves discussing our interactions with our principal investigators (PIs) and swapping advice for improving our mentoring meetings. We have found three practices to be consistently helpful:\r\nasking our PIs about all aspects of their job;\r\npreparing an agenda for each meeting;\r\nnegotiating new experiments without explicitly saying ‘no’.\r\nRead the full text: https://doi.org/10.1038/d41586-018-06619-3\r\n↑ Jump back to top.\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:07+02:00" + "last_modified": "2024-06-05T15:50:16+02:00" }, { "path": "impressum.html", @@ -44,7 +44,7 @@ "description": "", "author": [], "contents": "\r\nAngaben gemäß § 5 TMG\r\nStephanie Kramer-Schadt\r\nBerlin\r\nKontakt\r\nE-Mail: kramer[at]izw-berlin.de\r\nVerantwortlich für den Inhalt nach § 55 Abs. 2 RStV\r\nStephanie Kramer-Schadt\r\nBerlin\r\nEU-Streitschlichtung\r\nDie Europäische Kommission stellt eine Plattform zur Online-Streitbeilegung (OS) bereit: https://ec.europa.eu/consumers/odr. Unsere E-Mail-Adresse finden Sie oben im Impressum.\r\nVerbraucher­streit­beilegung/Universal­schlichtungs­stelle\r\nWir sind nicht bereit oder verpflichtet, an Streitbeilegungsverfahren vor einer Verbraucherschlichtungsstelle teilzunehmen.\r\nHaftung für Inhalte\r\nAls Diensteanbieter sind wir gemäß § 7 Abs.1 TMG für eigene Inhalte auf diesen Seiten nach den allgemeinen Gesetzen verantwortlich. Nach §§ 8 bis 10 TMG sind wir als Diensteanbieter jedoch nicht verpflichtet, übermittelte oder gespeicherte fremde Informationen zu überwachen oder nach Umständen zu forschen, die auf eine rechtswidrige Tätigkeit hinweisen.\r\nVerpflichtungen zur Entfernung oder Sperrung der Nutzung von Informationen nach den allgemeinen Gesetzen bleiben hiervon unberührt. Eine diesbezügliche Haftung ist jedoch erst ab dem Zeitpunkt der Kenntnis einer konkreten Rechtsverletzung möglich. Bei Bekanntwerden von entsprechenden Rechtsverletzungen werden wir diese Inhalte umgehend entfernen.\r\nHaftung für Links\r\nUnser Angebot enthält Links zu externen Websites Dritter, auf deren Inhalte wir keinen Einfluss haben. Deshalb können wir für diese fremden Inhalte auch keine Gewähr übernehmen. Für die Inhalte der verlinkten Seiten ist stets der jeweilige Anbieter oder Betreiber der Seiten verantwortlich. Die verlinkten Seiten wurden zum Zeitpunkt der Verlinkung auf mögliche Rechtsverstöße überprüft. Rechtswidrige Inhalte waren zum Zeitpunkt der Verlinkung nicht erkennbar.\r\nEine permanente inhaltliche Kontrolle der verlinkten Seiten ist jedoch ohne konkrete Anhaltspunkte einer Rechtsverletzung nicht zumutbar. Bei Bekanntwerden von Rechtsverletzungen werden wir derartige Links umgehend entfernen.\r\nUrheberrecht\r\nDie durch die Seitenbetreiber erstellten Inhalte und Werke auf diesen Seiten unterliegen dem deutschen Urheberrecht. Die Vervielfältigung, Bearbeitung, Verbreitung und jede Art der Verwertung außerhalb der Grenzen des Urheberrechtes bedürfen der schriftlichen Zustimmung des jeweiligen Autors bzw. Erstellers. Downloads und Kopien dieser Seite sind nur für den privaten, nicht kommerziellen Gebrauch gestattet.\r\nSoweit die Inhalte auf dieser Seite nicht vom Betreiber erstellt wurden, werden die Urheberrechte Dritter beachtet. Insbesondere werden Inhalte Dritter als solche gekennzeichnet. Sollten Sie trotzdem auf eine Urheberrechtsverletzung aufmerksam werden, bitten wir um einen entsprechenden Hinweis. Bei Bekanntwerden von Rechtsverletzungen werden wir derartige Inhalte umgehend entfernen.\r\nQuelle: eRecht24\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:08+02:00" + "last_modified": "2024-06-05T15:50:16+02:00" }, { "path": "index.html", @@ -52,7 +52,7 @@ "description": "Department 6 at Leibniz Institute for Zoo and Wildlife Research (IZW)", "author": [], "contents": "\r\nWelcome to D6!\r\nWe are scientists of the Department of Ecological Dynamics at the Leibniz Institute for Zoo and Wildlife Research in Berlin, Germany. Our research is focused on understanding ecological dynamics in space and time, at different levels of organisation, from individuals to communities, and across gradients of human altered environments.\r\nWe investigate how fitness consequences of processes acting at the individual level, such as social behaviour or movements, competition, predator-prey or host-pathogen interactions, shape population and community dynamics, also at evolutionary scales. To ensure transparency and reproducibility, we follow the FAIR principles and provide code and data of published papers in our GitHub repositories.\r\n→ Follow us on Twitter and GitHub.\r\n\r\nOur Vision\r\n\r\nOur ultimate goal is to predict the future viability of wildlife populations, species, and communities faced with accelerating environmental change in the anthropocene and to improve landscape scale planning for conservation.\r\n\r\n\r\n\r\nOur Mission\r\n\r\nWe advance theory and concepts in ecology and evolution as well as the use of computational toolkits by developing and using a wide range of modern field and lab techniques and dynamic and simulation modeling.\r\n\r\n\r\n\r\nOur Teams\r\n\r\nWe are organized in three teams spanning all levels from individuals to populations to communities. Check out the three team pages for an overview of our current projects, group activities, and our latest publications!\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nContact\r\nDepartment Lead: Prof. Dr. S. Kramer-Schadt\r\nDeputy Lead: Dr. Andreas Wilting\r\nCoordination: Dr. Conny Landgrafassist6[at]izw-berlin.de\r\n\r\n\r\n\r\nAddress\r\nLeibniz Institute for Zoo and Wildlife Research\r\nDepartment of Ecological Dynamics\r\nAlfred-Kowalke-Str. 17\r\nD-10315 Berlin, Germany\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:09+02:00" + "last_modified": "2024-06-05T15:50:17+02:00" }, { "path": "msc-bsc-theses-2018.html", @@ -60,7 +60,7 @@ "description": "", "author": [], "contents": "\r\n2018\r\nLife-history consequences of snare injuries in female spotted hyenas: A long term study in the Serengeti National Park, Tanzania. Sara Kaidatzi. 02.05.2018. Diploma thesis. Biologie. Freie Universität Berlin. Benhaiem S, East ML, Hofer H.\r\nUsage of landscape metrics to predict spatial distribution of mammals in urban areas. Jessica Thimian. 04.08.2018. BSc. Biologie. Freie Universität Berlin. Supervision: Gras P, Kramer-Schadt S, Hofer H.\r\nMonthly home ranges and habitat preferences of raccoons (Procyon lotor) in the Uckermark, Germany – a estimation method comparison. Pedro Mello Rose. 2018. BSc. Biologie. Freie Universität Berlin. Supervision: Scholz C, Hofer H.\r\nAn occupancy analysis of the ocelot (Leopardus pardalis) and its potential preys in Eastern Panama. Angelica Maria Moreno Sosa. 09.10.2018. MSc. Universität Bremen. Supervision: Abrams J, Kramer-Schadt S, Marko Rohlfs (U Bremen).\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:10+02:00" + "last_modified": "2024-06-05T15:50:18+02:00" }, { "path": "msc-bsc-theses-2019.html", @@ -68,7 +68,7 @@ "description": "", "author": [], "contents": "\r\n2019\r\nDemography and population viability of the Northern Bald Ibis (Geronticus eremita), a reintroduced bird species. Sinah Drenske. 04.02.2019. Bsc. Ökologie und Umweltplanung. Technische Universität Berlin. Supervision: Kramer-Schadt S, Radchuk V, Kowarik I (TUB).\r\nWolfmanagement in Deutschland – Vergleich der Wolfmanagementpläne der Bundesländer. Patrick Enders. 26.09.2019. Bsc. Landschaftsarchitektur und Landschaftsplanung. TU Berlin. Supervision: Heiland S (TUB), Kramer-Schadt S.\r\nInwiefern beeinflussen anthropogene Faktoren die Streifgebietsgrößen sowie täglichen Distanzen von Wildschweinen (Sus scrofa)? Elena Wernitz. 08.08.2019. Bsc. Ökologie und Umweltplanung. TUB. Supervision: Stillfried M, Kramer-Schadt S.\r\nPredicting community structure in a changing world – a new dynamic agent-based model to simulate multi-species home range behavior. Marie-Sophie Rohwäder. 26.02.2019. Msc. Ökologie, Evolution und Naturschutz. Universität Potsdam. Supervision: Kramer-Schadt S, Jeltsch F (UP).\r\nAssessing patterns of livestock attacks caused by wolf (Canis lupus) using sightings and habitat predictions. Moritz Wenzler. 26.11.2019. Msc. Global Change Geography. Humboldt-Universität zu Berlin. Kramer-Schadt S, Planillo A, Kümmerle T (HU).\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:11+02:00" + "last_modified": "2024-06-05T15:50:18+02:00" }, { "path": "msc-bsc-theses-2020.html", @@ -76,7 +76,7 @@ "description": "", "author": [], "contents": "\r\n2020\r\nA population viability and connectivity analysis for the alpine lynx (Lynx lynx) population. Eva Sanchez Arribas. 19.11.2020. MSc. Uppsala University, Sweden. Supervision: Kramer-Schadt S, Planillo A, Molinari-Jobin A (KORA).\r\nInfluence of roost site availability on activity of forest-dwelling bats above coniferous forests. Franziska Röpke. 15.10.2020. BSc. Ökologie und Umweltplanung. TU Berlin. Supervision: Volker Kelm (K&S Umweltgutachten), Kramer-Schadt S.\r\nSpatial, temporal and interindividual determinants of wildlife-vehicle collision mortality in spotted hyenas in the Serengeti National Park between 1989 and 2020. Marwan Naciri. 25.06.2020. Msc. Master Biosciences École Normale Supérieure (ENS) Lyon, Frankreich. Supervision: Benhaiem S, Gicquel M, Planillo A, East M, Hofer H.\r\nUsing social network analysis on a population of reintroduced Northern Bald Ibis (Geronticus eremita) to understand decisions of migratory association. Sinah Drenske. 03.08.2020. Msc. Ökologie und Umweltplanung. Technische Universität Berlin. Supervision: Kramer-Schadt S, Radchuk V, Benhaiem S, Landgraf C. → Prize for best Master thesis!\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:12+02:00" + "last_modified": "2024-06-05T15:50:19+02:00" }, { "path": "msc-bsc-theses-2021.html", @@ -84,7 +84,7 @@ "description": "", "author": [], "contents": "\r\n2021\r\nModelling Eurasian red squirrel (Sciurus vulgaris) occurrence along urban gradients in Berlin. Marius Grabow. 29.09.2021. MSc. TU Berlin. Ecology and Environmental Planning. Supervision: Louvrier J (IZW), Kramer-Schadt S. → Prize for best Master thesis!\r\nAccuracy of a new high throughput wildlife movement tracking system in comparison with GPS tracking. Johannes Marold. 16.09.2021. BSc. TU Berlin. Ecology and Environmental Planning. Supervision: Radchuk V (IZW), Kramer-Schadt S.\r\nDensity and life-history of the Sunda clouded leopard Neofelis diardi in the Deramakot Forest Complex, Sabah, Malaysia. Katharina Kasper. 30.08.2021. MSc. Goethe-Universität Frankfurt am Main. Supervision: Thomas Müller (SBiK-F), Andreas Wilting (IZW).\r\nEvaluating the habitat suitability of tiger in Bardia National Park and Buffer Zone areas, Nepal. Kamal Ghimire. 18.08.2021. MSc. TU Dresden. Tropical Forestry. Supervision: Berger U (TUD), Kramer-Schadt S.\r\nSmall-Scale Habitat Selection of the European Wildcat Felis silvestris silvestris in the Bernese Seeland, Switzerland. Johanna Bellack. 26.06.2021. BSc. TU Berlin. Ecology and Environmental Planning. Supervision: Maronde L (KORA), Kramer-Schadt S.\r\nEffect of artificial light at night (ALAN) on behaviour of the European hedgehog (Erinaceus europaeus Linnaeus, 1758) in the urban area of Berlin. Briseida Lozano Granados. 15.05.2021. MSc. TU Berlin. Urban Ecology. Supervision: Berger A, Kramer-Schadt S.\r\nWhat contribution can non-standardized Citizen Science data make in biodiversity monitoring? Lena Fiechter. 21.01.2021. MSc. TU Berlin. Ecology and Environmental Planning. Supervision: Planillo A, Kramer-Schadt S, Heucke-Voigt S (MfN).\r\nA habitat suitability model for Grey Crowned Cranes (Balearica regulorum) in Rwanda based on sightings. Ann-Marie Attenberger. 04.01.2021. MSc. TU Berlin. Ecology and Environmental Planning. Supervision: Olivier Nsengimana (RWCA), Kramer-Schadt S, vd Lippe M (TUB).\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:13+02:00" + "last_modified": "2024-06-05T15:50:20+02:00" }, { "path": "msc-bsc-theses-2022.html", @@ -92,7 +92,7 @@ "description": "", "author": [], "contents": "\r\n2022\r\n\r\nPilot study for the use of tick surveys and blood meal analysis to monitor wildlife populations and disease risk in the Southern Black Forest. Sarah Evelyn Hollis. 18.11.2022. MSc. Albert-Ludwigs-Universität Freiburg , Faculty of Environmental and Natural Sciences. Supervision: Prof. Dr. Gernot Segelbacher (ALUF), S Kramer-Schadt.\r\nRetracing the wolf colonization of Germany using an individual-based model. Paul Ritter. 19.10.2022. BSc. Rheinische Friedrich-Wilhelms-Universität Bonn, Geographisches Institut. Supervision: Prof. Dr. Jürgen Löffler (UB), S Kramer-Schadt, Cedric Scherer, Aimara Planillo (all IZW).\r\nControlled habitat degradation as a mitigation measure for interventions in habitats of Lacerta agilis. Magdalena Sophia Engl. 16.10.2022. MSc. TU Berlin. Urban Ecosystem Sciences. Supervision: S Kramer-Schadt, PD Dr. M.-O. Rödel (MfN).\r\nEinfluss von Witterungsbedingungen auf den Nachweis von Zauneidechsen (Lacerta agilis) in Berlin und Brandenburg. Maximilian Schwenke 18.07.2022. BSc. TU Berlin. Ecology and Environmental Planning. Supervision: S Kramer-Schadt, M von der Lippe (TUB).\r\nEvaluating the effects of blackberry (Rubus niveus) abundance on land bird diversity on Santiago Island, Galapagos. Mateo Reyes. 23.05.2022. BSc. TU Berlin. Ecology and Environmental Planning. Supervision: S Kramer-Schadt, M von der Lippe (TUB).\r\nZur Verbreitung des Rotfuchses (Vulpes vulpes) in deutschen Nationalparken auf Grundlage von Kamerafallendaten. Clara Heinrich. 20.05.2022. BSc. TU Berlin. Ecology and Environmental Planning. Supervision: S Kramer-Schadt, C Fiderer (ALUF).\r\nWildtiere in deutschen Großstädten: ein Vergleich zwischen Medienpräsenz und Wahrnehmung von Stadtbewohnern. Henry Karsch. 15.05.2022. MSc. FU Berlin. Fachbereich Biologie/Chemie/Pharmazie. Supervision: J Jeschke (FU), T Straka (TUB), S Kramer-Schadt\r\nSocial status driven epigenetic differences in female spotted hyenas in the Serengeti National Park. Nick Mewes. 14.06.2022. MSc. Universität Potsdam. Ecology, Evolution and Conservation. Supervision: A Weyrich, S Benhaiem\r\nBirds in the city: understanding direct and indirect effects of human disturbance and vegetation structure on functional diversity in Berlin. Estelle Solem. 06.04.2022. BSc. Humboldt Universität zu Berlin. Geographie. Supervision: T Kümmerle (HU), A Planillo, S Kramer-Schadt\r\nDifferential DNA methylation between dominant and subordinate spotted hyena twins. Lena Ruf. 05.04.2022. MSc. University of Potsdam. Ecology, Evolution and Conservation. Supervision: A Weyrich, S Benhaiem, J Fickel (UP)\r\nUrban planning for animals and humans: An empirical investigation of planning approaches and their perception by Berlin citizens. Lisa Jäger. 23.02.2022. MSc. TU Berlin. Ecology and Environmental Planning. Supervision: T Straka (TUB), S Kramer-Schadt\r\nEinfluss einer Unterrichtseinheit über Wildbienen auf die Einstellungen, die Emotionen und das Wissen von Schülern. Sarah Festl. 25.02.2022. BSc. TU Berlin. Ecology and Environmental Planning. Supervision: T Straka (TUB), S Kramer-Schadt\r\nSpatial use and population development of the Eurasian otter Lutra lutra in Sielmanns Naturlandschaft Groß Schauen and the state of Brandenburg. Yvonne Rychlak. 02.02.2022. MSc. Ecology, Evolution and Nature Conservation. University of Potsdam. Supervision: N Blaum (UP), S Kramer-Schadt.\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:14+02:00" + "last_modified": "2024-06-05T15:50:21+02:00" }, { "path": "msc-bsc-theses-202x-dummy.html", @@ -100,7 +100,7 @@ "description": "", "author": [], "contents": "\r\n20xx\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:16+02:00" + "last_modified": "2024-06-05T15:50:21+02:00" }, { "path": "msc-bsc-theses-amphibian_radchuk.html", @@ -108,7 +108,7 @@ "description": "", "author": [], "contents": "\r\n(contact radchuk[at]izw-berlin.de)\r\nDue to their dam-building activity, beavers affect other species in diverse ways, ranging from positive to negative effects. Although effects of beaver activity on several taxa were studied, our understanding of their effects on amphibian assemblages is still incomplete. Especially little is understood how effects of beaver activity on amphibians may be moderated by the gradient of anthropogenic disturbance (represented by human population density). However, such knowledge is important in our human-dominated world. In this study we will sample amphibian diversity (both compositional and functional) along the gradient of human population density, in ponds with and without beavers. The study will take place in North Rhine-Westfalia, in Eifel region, where beaver presence is monitored along the gradient of human population density over several decades.\r\nProfile: interest in community and functional ecology, previous field work experience is an advantage, driver’s license, basic knowledge of R\r\nSupervisors: Viktoriia Radchuk radchuk@izw-berlin.de, Lutz Dalbeck, Stephanie Kramer-Schadt\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:17+02:00" + "last_modified": "2024-06-05T15:50:22+02:00" }, { "path": "msc-bsc-theses-bacteria_starlings_swallows_grabow.html", @@ -116,7 +116,7 @@ "description": "", "author": [], "contents": "\r\nM.Sc. Marius Grabow/ Dr. Conny Landgraf\r\n(contact grabow[at]izw-berlin.de or landgraf[at]izw-berlin.de)\r\nGeneral background\r\nParasites are ubiquitous, shaping host traits and behaviours such as movement. While parasite dynamics and host movements are often studied at large scales, e.g. migratory behaviour, our knowledge of the implications at fine scales, e.g. local movements during breeding, remains scarce. We hypothesize that parasites are proximate drivers of individual movement decisions, altering the way hosts allocate their energy, and ultimately influencing foraging, competition and fitness.\r\nThe overarching project aims to understand how fitness consequences of parasites are reflected by individual movement decisions and the surrounding environment. By accounting for a broad range of parasites, high resolution animal movement data and individual trait measures of passerines (swallows and starlings) we follow an integrative approach to model effects of host–parasite interactions in agricultural landscapes.\r\nFeather degrading bacteria (FDB)\r\nFDB are a group of bacteria with the ability to to degrade β-keratin, the principal building block of feathers, imposing substantial selection pressures on host plumage. These bacterial species colonize the plumage of most wild birds with ground-foraging and water birds having a higher prevalence. Although there is no experimental evidence about a direct link between FDB and changes in feather condition in free-living birds, we hypothesize that reduced feather quality associated with higher FDB load might affect flight performance and thus, movement behaviours in temperate breeding birds. To investigate this we collected feather samples from different passerine species including common starling (Sturnus vulgaris), barn swallow (Hirundo rustica) and house martin (Delichon urbica) at their breeding colonies, which will used to quantify the FDB loads. Additionally, for each bird we have information on individual bird condition (body measures and infection status), breeding behaviour and reproductive success as well as movement data which can be analysed using GLM/ GLMM (optional depending on candidate).\r\nJob description\r\nThe successful applicant will perform classic bacteriological analysis to quantify the total and feather-degrading bacterial loads on feather samples collected from each bird. This involves work under sterile conditions, from handling the samples, extract both free-living and attached microorganisms, dilute the obtained bacterial solutions, prepare bacteriological growth media, weigh the quantity of feather used, to count the visible colony-forming units on each plate. If the candidate is motivated statistical analyses (GLM/ GLMM) can be performed after lab work.\r\nProfile:\r\nEnrolled student (Bachelor / Master) in Biology, Ecology, Veterinary Medicine, etc.\r\nPrevious general/ basic laboratory experience (e.g. pipetting, dilution) and skills in bacteriology (work under sterile conditions, preparing growth media) is advantageous\r\nHigh motivation & sense of responsibility to work in an academic environment\r\nGood knowledge of English\r\nDate/ duration: starting time November 2022 (but flexible), minimum duration 3 month (internship), preferably longer (e.g. Master thesis)\r\nApplications and working environment:\r\nThe position preferably starts on 14. November 2022 and will last for at least 3 months. The place of employment is the IZW Berlin. Enquiries or questions should be directed to Dr. Conny Landgraf / M.Sc. Marius Grabow.\r\nPlease upload complete application documents including a letter of motivation, CV, proof of university enrolment and contact details (email address and telephone). Submit your application as soon as possible but no later than 21st October, 2022 via the Leibniz-IZW’s online-job-market.\r\nInterviews will take place 7.-11. November 2022.\r\nWe are looking forward to your application!\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:18+02:00" + "last_modified": "2024-06-05T15:50:23+02:00" }, { "path": "msc-bsc-theses-flying-insect_kramer.html", @@ -124,7 +124,7 @@ "description": "", "author": [], "contents": "\r\n(contact wiebke.ullmann[at]uni-potsdam.de or kramer[at]izw-berlin.de)\r\nShort Abstract:\r\nDie reduzierte Abundanz und Diversität von Insekten bedeutet eine Verringerung der Nahrungsverfügbarkeit für insektivore Räuber. Dadurch steigt die Konkurrenz zwischen Arten welche sich von ähnlichen Insekten ernähren. Dies kann zum Konkurrenzausschlussprinzip und somit zum Verlust von Biodiversität führen. Um die Auswirkungen des Insektensterbens auf das Konkurrenzverhalten insektivorer Tierarten zu bestimmen, wollen wir Rauch- und Mehlschwalben in insektenarmen Agrarlandschaften besendern und die Bewegungen der Tiere mit der lokalen Abundanz und Diversität der Insekten in Zusammenhang bringen. Da die fliegenden Insektivoren jedoch in unterschiedlichen Flughöhen ihre Beute jagen, müssen die Insekten in unterschiedlichen Luftschichten gefangen werden. Dazu wollen wir eine Drohne mit einem Insektenfangnetz ausstatten. Die insektenfangende Drohne soll dann in unterschiedlichen Luftschichten mit dem Netz fliegen und Insekten fangen. Mit dieser Methode wollen wir die Insektendiversität und –abundanz in den unterschiedlichen Luftschichten quantifizieren und diese mit den Flugpfaden der Schwalben korrelieren. Gleichzeitig soll ein Vergleich verschiedener Insektenfangmethoden unternommen werden: Befinden sich die gleichen Insekten im Netz der Drohne wie in der Malaisefalle?\r\n\r\nFreilanduntersuchungen und Laborarbeit:\r\n- Erstellen einer Arbeitsroutine zum Abfliegen verschiedener Luftschichten mit Drohnen und Insektenfangnetzen\r\n- Durchführung des Insektenfangs an verschiedenen Orten im Untersuchungsgebiet\r\n- Hilfe bei der Ausstattung der Drohne mit dem Fangnetz\r\n- Insektenbestimmung und Quantifizierung, der mit den Drohnen und den Malaisefallen gefangenen Insekten (möglichst bis zur Familie)\r\nUntersuchungsgebiet/Zeitraum:\r\nDie Masterarbeit beginnt im März 2023 mit den Vorbereitungen (Annerkennung als Vertiefungsmodul möglich). Im Mai 2023 und Juni/Juli 2023 werden für jeweils ca. eine Woche täglich Insektenfänge durchgeführt. Das Untersuchungsgebiet befindet sich in der Nordwestuckermark (ca. 15km westlich von Prenzlau). In der ZALF-Forschungsstation in Dedelow stehen Zimmer als Unterkunftsmöglichkeit bereit.\r\nVoraussetzungen\r\nFührerschein Klasse B, selbständige Arbeit im Feld und im Team mit Masterstudenten, HiWis und Praktikanten, Drohnenführerschein von Vorteil\r\nKontakt:\r\nWiebke Ullmann\r\nUniversität Potsdam, Vegetationsökolgie & Naturschutz\r\nAm Mühlenberg 3, 14476 PotsdamUllmann\r\nTel.: 01715453029\r\nProf. Dr. Stephanie Kramer-Schadt\r\nLeibniz-Institute für Zoo- und Wild-tierforschung\r\nAlfred-Kowalke-Str. 17, 10315 Berlin\r\nKramer-Schadt\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:19+02:00" + "last_modified": "2024-06-05T15:50:24+02:00" }, { "path": "msc-bsc-theses-offers.html", @@ -132,7 +132,7 @@ "description": "", "author": [], "contents": "\r\nHow do starlings adapt feeding effort to disturbances at the nesting site?\r\nGeneral background\r\nDer Reproduktionserfolg bei Singvögeln hängt zu großen Teilen von dem elterlichen Investment ab, also deren Fütterungsraten und Erfolg während der Nahrungssuche. Durch Landnutzungswandel, Insektensterben und Klimawandel wird die Eigenschaft zur effektiven Nahrungssuche zunehmend wichtiger, da Störungen in Ökosystemen vermehrt auftreten. Um den Einfluss solcher Störungen zu bestimmen, nutzen wir ein natürliches Studiensystem in dem natürliche (z.B. invasive Prädatoren) und anthropogene Störungen (z.B. regelmäßige Brutkastenkontrollen durch Forscher) auf verminderte Nahrungs-vorkommen in hochindustrialisierten Agrarlandschaften treffen. Um beobachten zu können, wie sich europäische Stare (Sturnus vulgaris) während der Brutzeit auf Nahrungssuche begeben, werden wir die Stare mit Hochdurchsatztelemetrie (ATLAS – Advanced Tracking of Animals in Real Life Systems) besendern um ihr Bewegungsverhalten in real time zu studieren. Parallel werden wir Kamerafallen und Soundrekorder in der Nähe der Brutkästen ausbringen um durchgehend mögliche Störungen innerhalb des Systems zu dokumentieren, was eine genaue Klassifikation von Störungsevents ermöglicht. Durch regelmäßige Kontrollen der Körpermaße (z.B. Gewicht, Körpermaße) von Jungtieren in einer etablierten Brutkolonie können wir Rückschlüsse auf den Erfolg der Eltern bei der Jungenaufzucht (Anzahl und Zustand der Küken) feststellen und prüfen, ob dieser mit den Fütterungsraten bzw. dem Nahrungsangebot im Habitat erklärbar wird. Anschließend können wir zeitliche Störungsdynamiken innerhalb des Brutzeitraums quantifizieren, um zu überprüfen, ob diese die Fitness (= Reproduktionserfolg) zusätzlich beeinflussen.\r\n → read more\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:20+02:00" + "last_modified": "2024-06-05T15:50:24+02:00" }, { "path": "msc-bsc-theses-prey_abundance_diversity_kramer.html", @@ -140,7 +140,7 @@ "description": "", "author": [], "contents": "\r\n(contact wiebke.ullmann[at]uni-potsdam.de or kramer[at]izw-berlin.de)\r\nShort Abstract:\r\nDie reduzierte Abundanz und Diversität von Insekten bedeutet eine Verringerung der Nahrungsverfügbarkeit für insektivore Räuber. Dadurch steigt die Konkurrenz zwischen Arten welche sich von ähnlichen Insekten ernähren. Dies kann zum Konkurrenzausschlussprinzip und somit zum Verlust von Biodiversität führen. Um die Auswirkungen des Insektensterbens auf das Konkurrenzverhalten insektivorer Tierarten zu bestimmen, wollen wir Rauch- und Mehlschwalben in insektenarmen Agrarlandschaften besendern und die Bewegungen der Tiere sowie deren Reproduktionserfolg mit der lokalen Abundanz und Diversität der Insekten in Zusammenhang bringen. Dazu werden die Schwalben mit hochauf-lösenden Telemetriesendern ausgestattet und Insekten mit Malaisefallen und Drohnen im Untersuchungsgebiet gefangen.\r\nIn den Gebieten in denen die Insektivoren nach Nahrung suchen werden 18 Malaisefallen aufgestellt und gleichzeitig unterschiedliche Luftschichten mit insektenfangenden Drohnen abgeflogen. So soll die Nahrungsverfügbarkeit im Unterschungsgebiete überprüft werden. Für die Mitarbeit im Drohnenprojekt, der Betreuung der Insektenfallen vor Ort und eventuell der Bestimmung der Insekten im Labor benötigen wir Hilfe und vergeben Praktikanten-Stellen.\r\n\r\nFreilanduntersuchungen und eventuelle Laborarbeit:\r\n- Hilfe bei der Durchführung des Insektenfangs mit Drohnen und Malaisefallen im Untersuchungsgebiet\r\n- Eventuell Insektenbestimmung und Quantifizierung, der mit den Drohnen und den Malaisefallen gefangenen Insekten (möglichst bis zur Familie)\r\nUntersuchungsgebiet/Zeitraum:\r\nDas Feldarbeit findet statt vom 30.04. bis zum 12.05.2023 und vom 24.06. bis 06.07.2023. Wenn ein längeres Praktikum erwünscht wird, kann bei der Insektenbestimmung im Labor geholfen werden. Die Laborarbeit findet zwischen dem 12.05. und dem 24.06.2023 statt. Das Untersuchungsgebiet befindet sich in der Nordwestuckermark (ca. 15km westlich von Prenzlau). In der ZALF-Forschungsstation in Dedelow stehen Zimmer als Unterkunfts-möglichkeit bereit. Die Insektenbestimmungen können zuhause oder an der Uni Potsdam im Labor durchgeführt werden.\r\nVoraussetzungen\r\nFührerschein Klasse B (wenn möglich, eigenes Auto), selbständige Arbeit im Feld und im Team mit Masterstudenten, HiWis und Praktikanten\r\nKontakt:\r\nWiebke Ullmann\r\nUniversität Potsdam, Vegetationsökolgie & Naturschutz\r\nAm Mühlenberg 3, 14476 PotsdamUllmann\r\nTel.: 01715453029\r\nProf. Dr. Stephanie Kramer-Schadt\r\nLeibniz-Institute für Zoo- und Wild-tierforschung\r\nAlfred-Kowalke-Str. 17, 10315 Berlin\r\nKramer-Schadt\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:21+02:00" + "last_modified": "2024-06-05T15:50:26+02:00" }, { "path": "msc-bsc-theses-prey_abundance_kramer.html", @@ -148,7 +148,7 @@ "description": "", "author": [], "contents": "\r\n(contact wiebke.ullmann[at]uni-potsdam.de or kramer[at]izw-berlin.de)\r\nShort Abstract:\r\nDie reduzierte Abundanz und Diversität von Insekten bedeutet eine Verringerung der Nahrungsverfügbarkeit für insektivore Räuber. Dadurch steigt die Konkurrenz zwischen Arten welche sich von ähnlichen Insekten ernähren. Dies kann zum Konkurrenzausschlussprinzip und somit zum Verlust von Biodiversität führen. Um die Auswirkungen des Insektensterbens auf das Konkurrenzverhalten insektivorer Tierarten zu bestimmen, wollen wir Rauch- und Mehlschwalben in insektenarmen Agrarlandschaften besendern und die Bewegungen der Tiere sowie deren Reproduktionserfolg mit der lokalen Abundanz und Diversität der Insekten in Zusammenhang bringen. Dazu werden die Schwalben mit hochauf-lösenden Telemetriesendern ausgestattet und Insekten mit Malaisefallen im Untersuchungsgebiet gefangen. Um den Reproduktionserfolg verzeichnen zu können, müssen die Nester der besenderten Schwalben identifiziert werden. Die Nestidentifikation wird während des Schwalbenfangs mit Sichtbeobachtungen durchgeführt und später mit der Telemetrietechnick überprüft bzw. vervollständigt. Der Nachwuchs soll mit Kameraauf-zeichnungen und/oder mit einer Endoskopkamera gezählt werden. Die Insekten aus den Malaisefallen sollen möglichst bis zur Familie bestimmt werden und in Größenklassen unterteilt und gewogen werden.\r\n\r\nFreilanduntersuchungen und Laborarbeit:\r\n- Erstellen einer Arbeitsroutine zur Nestidentifikation und zum Zählen des Nachwuches\r\n- Nestidentifikation und Zählen des Nachwuchses der besenderten Tiere\r\n- Mitbetreuung der Malaisefallen im Untersuchungsgebiet\r\n- Insektenbestimmung und Quantifizierung, der mit Malaisefallen gefangenen Insekten (möglichst bis zur Familie)\r\nUntersuchungsgebiet/Zeitraum:\r\nDie Masterarbeit beginnt im März 2023 mit den Vorbereitungen (Annerkennung als Vertiefungsmodul möglich). Im Mai 2023 und Juni/Juli 2023 findet die Feldarbeit statt (ca. 10-14 Tage im Mai und dann noch mal 10-14 Tage ab ca. Ende Juni). Das Untersuchungsgebiet befindet sich in der Nordwestuckermark (ca. 15km westlich von Prenzlau). In der ZALF-Forschungsstation in Dedelow stehen Zimmer als Unterkunftsmöglichkeit bereit.\r\nVoraussetzungen\r\nFührerschein Klasse B, selbständige Arbeit im Feld und im Team mit weiteren Masterstudenten, HiWis und Praktikanten\r\nKontakt:\r\nWiebke Ullmann\r\nUniversität Potsdam, Vegetationsökolgie & Naturschutz\r\nAm Mühlenberg 3, 14476 PotsdamUllmann\r\nTel.: 01715453029\r\nProf. Dr. Stephanie Kramer-Schadt\r\nLeibniz-Institute für Zoo- und Wild-tierforschung\r\nAlfred-Kowalke-Str. 17, 10315 Berlin\r\nKramer-Schadt\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:22+02:00" + "last_modified": "2024-06-05T15:50:26+02:00" }, { "path": "msc-bsc-theses-resilience_radchuk.html", @@ -156,7 +156,7 @@ "description": "", "author": [], "contents": "\r\n(contact radchuk[at]izw-berlin.de)\r\nAssessing demographic resilience is crucial to assist the conservation of populations and species. A recently proposed method to quantify demographic resilience is based on using average population matrices over time to calculate a single demographic resilience metric. This neglects the fact that the resilience of a population may change over time and is driven by population responses to disturbances, which may also change over time. We will use the COMADRE database to assess demographic resilience of many animal species by considering time explicitly and compare this measure to a single demographic resilience metric calculated over the whole period. The results will be of high importance for revealing the factors affecting demographic resilience and, in turn, for designing appropriate management measures.\r\nProfile: interest in population ecology, basic knowledge of R\r\nSupervisors: Viktoriia Radchuk radchuk@izw-berlin.de, Stephanie Kramer-Schadt, Oliver Höner, Sarah Benhaiem\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:23+02:00" + "last_modified": "2024-06-05T15:50:28+02:00" }, { "path": "msc-bsc-theses-rodents_radchuk.html", @@ -164,7 +164,7 @@ "description": "", "author": [], "contents": "\r\n(contact radchuk[at]izw-berlin.de)\r\nAlthough population cycles received much research interest, we still poorly understand how the demographic structure of the population, i.e. sex ratio and age structure, are changing along the cycle. This is partly because in rodents, which became models for studying population cycles, the field data are typically collected at the coarse yearly resolution. Another question that remains unanswered is whether the dynamics of demographic structure contributes to the emergence of population cycles, or, the other way around, demographic structure is changing because of the population cycling. To address these research gaps we are using a previously developed agent-based model that depicts the mechanisms of predator-prey population dynamics at fine weekly temporal resolution. This model allows obtaining both population abundance and demographic data (sex, age, reproductive state) at a weekly resolution. We will analyse these data with the advanced methods in time series analyses to answer our research questions.\r\nProfile: interest in population ecology, good command of R\r\nSupervisors: Viktoriia Radchuk radchuk@izw-berlin.de, Stephanie Kramer-Schadt\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:24+02:00" + "last_modified": "2024-06-05T15:50:28+02:00" }, { "path": "msc-bsc-theses-starlings_grabow.html", @@ -172,7 +172,7 @@ "description": "", "author": [], "contents": "\r\n(contact grabow[at]izw-berlin.de and landgraf[at]izw-berlin.de)\r\nHintergrund:\r\nDer Reproduktionserfolg bei Singvögeln hängt zu großen Teilen von dem elterlichen Investment ab, also deren Fütterungsraten und Erfolg während der Nahrungssuche. Durch Landnutzungswandel, Insektensterben und Klimawandel wird die Eigenschaft zur effektiven Nahrungssuche zunehmend wichtiger, da Störungen in Ökosystemen vermehrt auftreten. Um den Einfluss solcher Störungen zu bestimmen, nutzen wir ein natürliches Studiensystem in dem natürliche (z.B. invasive Prädatoren) und anthropogene Störungen (z.B. regelmäßige Brutkastenkontrollen durch Forscher) auf verminderte Nahrungs-vorkommen in hochindustrialisierten Agrarlandschaften treffen. Um beobachten zu können, wie sich europäische Stare (Sturnus vulgaris) während der Brutzeit auf Nahrungssuche begeben, werden wir die Stare mit Hochdurchsatztelemetrie (ATLAS – Advanced Tracking of Animals in Real Life Systems) besendern um ihr Bewegungsverhalten in real time zu studieren. Parallel werden wir Kamerafallen und Soundrekorder in der Nähe der Brutkästen ausbringen um durchgehend mögliche Störungen innerhalb des Systems zu dokumentieren, was eine genaue Klassifikation von Störungsevents ermöglicht. Durch regelmäßige Kontrollen der Körpermaße (z.B. Gewicht, Körpermaße) von Jungtieren in einer etablierten Brutkolonie können wir Rückschlüsse auf den Erfolg der Eltern bei der Jungenaufzucht (Anzahl und Zustand der Küken) feststellen und prüfen, ob dieser mit den Fütterungsraten bzw. dem Nahrungsangebot im Habitat erklärbar wird. Anschließend können wir zeitliche Störungsdynamiken innerhalb des Brutzeitraums quantifizieren, um zu überprüfen, ob diese die Fitness (= Reproduktionserfolg) zusätzlich beeinflussen.\r\nFeldarbeit und Datenanalyse:\r\nHilfe beim Fang, Beringung, Besenderung und Probenentnahme bei adulten Staren\r\nKontrolle von Nistkästen, Vermessung von Jungtieren\r\nAusbringung von Kamerafallen, Analyse und Klassifizierung von Stör-Events (z.B. Prädatoren)\r\nAnalyse der Ergebnisse in R (gemeinsam mit WissenschaftlerInnen des Leibniz IZW)\r\n\r\nUntersuchungsgebiet und Studienzeitraum:\r\nDie Feldarbeit beginnt Mitte April 2024 (spätestens Anfang Mai). Das Untersuchungsgebiet befindet sich in der Nordwestuckermark (ca. 15 km westlich von Prenzlau). In der ZALF-Forschungsstation in Dedelow stehen Zimmer gratis als Unterkunftsmöglichkeit bereit.\r\nAnforderungen:\r\nInteresse am Forschen im Team, Beantwortung eigener Fragestellungen\r\nmehrtägige Forschungsaufenthalte auf der Feldstation (teilweise am Wochenende)\r\nBelastbarkeit (frühes Aufstehen, Feldarbeit auch bei schwierigem Wetter!)\r\neigenständiges Einarbeiten in den Umgang mit Kamerafallen\r\neigenständige Klassifizierung von Fotofallen-daten (Software wird zur Verfügung gestellt)\r\nR Kenntnisse erwünscht\r\n\r\nBei Interesse melden Sie sich bitte zeitnah an Marius Grabow grabow[at]izw-berlin.de (verantwortlicher Projekt-Doktorand) und Dr. Conny Landgraf landgraf[at]izw-berlin.de (Feldkoordinatorin).\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:25+02:00" + "last_modified": "2024-06-05T15:50:29+02:00" }, { "path": "msc-bsc-theses.html", @@ -180,7 +180,7 @@ "description": "", "author": [], "contents": "\r\n\r\nWe are looking for motivated students interested in questions about wildlife dynamics and distributions under global change, animal behavioral ecology or advancing theory in ecology and evolution. Students should have a strong background in (or the will to learn) R, statistics and modelling. Please contact us for possible thesis subjects and state your skills and interests (e.g., CV, certificates,…) along with the name of one or two references.\r\nHere you’ll find a list of offers for Bachelor and Master theses.\r\nFor TU Berlin students: Please consider the Reader for the steps necessary to conduct a thesis. Successful attendance of our courses ‘biodiversity dynamics I + II’ , where we teach spatial R, distribution modelling and occupancy modelling, is of advantage.\r\n\r\nBelow, you will find an overview over currently running theses as well as a list of completed theses for your information.\r\nFor older Bachelor and Master theses please check here:2022 2021 2020 2019 2018\r\n\r\nCompleted Theses in 2023\r\n\r\nQuantifying demographic resilience across animal species. Malte Ben Kurreck, 10.10.2023 BSc. FU Berlin, Department of Biology, Chemistry and Farmacy. Supervisors: V Radchuk (IZW), J Louvrier (IZW), U Steiner (FU).\r\nThe acoustic communication of West African sabre-toothed frogs of the genus Odontobatrachus (Anura, Odontiobatrachidae). Saskia Piorecki, 03.11.2023 MSc. Fakultät VI Planen Bauen Umwelt, Institut für Ökologie der TU Berlin. Supervisors: S Kramer-Schadt, M.-O. Rödel (Museum für Naturkunde).\r\nLarger tree diameter and high urbanisation level increases capture rates of red squirrels in Berlin. Alina Doreen Stemmer, 06.11.2023 MSc. FU Berlin, Department of Biology, Chemistry, Pharmacy. Supervisors: S Kramer-Schadt, Sinah Drenske (IZW), Jonathan Jeschke (FU).\r\nFine-scale movements and fitness: Feedback between foraging decisions and reproductive success of Common Starlings (Sturnus vulgaris). Johannes Till, 19.10.2023 BSc.\r\nFU Berlin, Department of Biology, Chemistry and Pharmacy. Supervisors S Kramer-Schadt, M Grabow, Prof. Jens Rolff (FU).\r\nWildtiere in der Stadt: Die komplexe beziehung von Urbanisierung, menschlicher Wahrnehmung und Sozialökonomie. Titus M. Mußhoff, 30.08.2023 MSc.\r\nUniversität Freiburg. Fakultät für Umwelt und Natürliche Ressourcen. Supervision: Prof. Ilse Storch (ALUF) & S Kramer-Schadt (TUB/ IZW).\r\nFood or parasite? Classification of fecal DNA based on taxonomic profiling of interaction data. Milena Luke, 11.07.2023 BSc. Humboldt-Universität zu Berlin. Lebenswissenschaftliche Fakultät Institut für Biologie. Supervision: Prof. E. Heitlinger (HU) & S Kramer-Schadt (TUB/ IZW).\r\nThe effect of natural haemosporidian infections on host fitness and movement patterns in passerines. Sirkka Mang. 24.04.2023 MSc. Universität Koblenz Landau, Fachbereich 3 Mathematik/ Naturwissenschaften. Supervision: S Kramer-Schadt (TUB/ IZW), Conny Landgraf (IZW).\r\nChanging climates, shifting phenologies. Analysing long-term data of juvenile bat occurrences for signals of climate change impacts in Bavaria, Germany. Alec Paul Christoph. 05.02.2023 MSc. Urban Ecosystem Studies, TU Berlin. Supervison: S Kramer-Schadt (IZW&TUB) & Tanja Straka (TUB).\r\nAre acoustic measurements at wind masts an effective additional method to evaluate the mortality risk for bats at wind turbines before their construction? Corinna Seidel. 17.01.2023 MSc. Urban Ecosystem Studies, TU Berlin. Supervison: S Kramer-Schadt (IZW&TUB) & Volker Kelm (Umweltgutachten K&S).\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:26+02:00" + "last_modified": "2024-06-05T15:50:29+02:00" }, { "path": "publications.html", @@ -188,7 +188,7 @@ "description": "", "author": [], "contents": "\r\nFind all our publication as PDFs also at ResearchGate.\r\nFor older publications please check here:2023 | 2022 | 2021 | 2020 | 2019 | 2018\r\n\r\n2024\r\n\r\nPeer-Reviewed Publications\r\n\r\nAchter S, Borit M, Cottineau C, Meyer M, Polhill J, Radchuk V (2024): How to conduct more systematic reviews of agent-based models and foster theory development - Taking stock and looking ahead. ENVIRON MODEL SOFTW, 173 ,105867. doi:10.1016/j.envsoft.2023.105867\r\nBerger U, Bell A, Barton CM, Chappin E, Dreßler G, Filatova T, Fronville T, Lee A, van Loon E, Lorscheid I, Meyer M, Müller B, Piou C, Radchuk V, Roxburgh N, Schüler L, Troost C, Wijermans N, Williams TG, Wimmler M-C, Grimm V (2024): Towards reusable building blocks for agent-based modelling and theory development. ENVIRON MODEL SOFTW, 175. doi:10.1016/j.envsoft.2024.106003\r\nBurton AC, Beirne C, Gaynor KM, …, Kramer-Schadt S, …, Louvrier J et al. (2024): Mammal responses to global changes in human activity vary by trophic group and landscape. NAT ECOL EVOL. doi:10.1038/s41559-024-02363-2\r\nCalderón AP, Landaverde-Gonzalez P, Wultsch C, Foster R, Harmsen B, Figueroa O, Garcia-Anleu R, Castañeda F, Amato G, Grimm V, Kramer-Schadt S, Zeller AK (2024): Modelling jaguar gene flow in fragmented landscapes offers insights into functional population connectivity. LANDSC ECOL, 39, 12. doi:10.1007/s10980-024-01795-2\r\nCaro T, Rashid RS, Zeltman J, Gierse L-M and Sollmann R (2024): Meta- and subpopulation estimation with disparate data: coconut crabs in the Western Indian Ocean. ANIM CONSERV, 27, 184-195.\r\ndoi:10.1111/acv.12896\r\nFronville T, Blaum N, Kramer-Schadt S, Schlägel U, Radchuk V (2024): Performance of five statistical methods to infer interactions among moving individuals in a predator–prey system. METHODS ECOL EVOL, 00, 1–16. doi:10.1111/2041-210X.14323\r\nJarquín-Díaz VH, Ferreira Martins SC, Balard A, Ďureje Ľ, Macholán M, Piálek J, Bengtsson-Palme J, Kramer-Schadt S, Forslund-Startceva SK, Heitlinger E (2024): Aberrant microbiomes are associated with increased antibiotic resistance gene load in hybrid mice, ISME COMMUN, 1, 4, ycae053. doi:10.1093/ismeco/ycae053\r\nKürschner T, Scherer C, Radchuk V, Blaum N, Kramer-Schadt S (2024): Resource asynchrony and landscape homogenization as drivers of virulence evolution: The case of a directly transmitted disease in a social host. ECOL EVOL, 14, e11065. doi:10.1002/ece3.11065\r\nMendes CP, Wido RA, Zachary A, …, Wilting A, et al. (2024): CamTrapAsia: A Dataset of Tropical Forest Vertebrate Communities from 239 Camera Trapping Studies. ECOLOGY, e4299. doi:10.1002/ecy.4299\r\nMilles A, Bielcik M, Banitz T, Gallagher CA, Jeltsch F, Jepsen JU, Oro D, Radchuk V, Grimm V (2024): Defining ecological buffer mechanisms should consider diverse approaches. TREE, 39. doi:10.1016/j.tree.2023.12.008\r\nMugerwa B, Niedballa J, Planillo A, Sheil D, Kramer-Schadt S, Wilting A (2024): Global disparity of camera trap research allocation and defaunation risk of terrestrial mammals. REMOTE SENS ECOL CONS, 10, 121-136. doi:10.1002/rse2.360\r\nNguyen A, Tilker A, Le D, Niedballa J, Pflumm L, Pham XH, Le VS, Luu HT, Tran VB, Kramer-Schadt S, Sollmann R, Wilting A (2024): Ground-dwelling mammal and bird diversity in the southern Annamites: Exploring complex habitat associations and the ghost of past hunting pressure. CONSERV SCI PRACT, 6, e13093. doi:10.1111/csp2.13093\r\nPlanillo A, Wenzler-Meya M, Reinhardt I, Kluth G, Michler F-U, Stier N, Louvrier J, Steyer K, Gillich B, Rieger S, Knauer F, Kuemmerle T, Kramer-Schadt S (2024): Understanding habitat selection of range-expanding populations of large carnivores: 20 years of grey wolves (Canis lupus) recolonizing Germany. DIVERS DISTRIB, 30, 71–86. doi:10.1111/ddi.13789\r\nSchmied née Stommel C, Hofer H, Scherer C, Kramer-Schadt S, East ML (2024): Effect of human induced surface water scarcity on herbivore distribution during the dry season in Ruaha National Park, Tanzania. WILDL BIOL, e01131. doi:10.1002/wlb3.01131\r\nScholz C, Jarquín-Díaz VH, Planillo A, Radchuk V, Scherer C, Schulze C, Ortmann S, Kramer-Schadt S, Heitlinger E (2024): Host weight, seasonality and anthropogenic factors contribute to parasite community differences between urban and rural foxes. SCI TOTAL ENVIRON, 936. doi:10.1016/j.scitotenv.2024.173355\r\nSinovas P, Alexiou I, Roberts O, Holden J, Chantha N, Tilker A (2024): Status and conservation implications of a newly discovered large-antlered muntjac population in Cambodia. ENDANG SPECIES RES, 53, 493-498. doi:10.3354/esr01316\r\nSollmann R (2024): Estimating the temporal scale of lagged responses in species abundance and occurrence. ECOSPHERE, 15(1), e4704. doi:10.1002/ecs2.4704\r\nSollmann R (2024): Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models. PEERJ, 4, e1. doi:10.24072/pcjournal.357\r\nVullioud C, Benhaiem S, Meneghini D, Szyf M, Shao Y, Hofer H, East ML, Fickel J, Weyrich A (2024): Epigenetic signatures of social status in wild female spotted hyenas (Crocuta crocuta). COMMUN BIOL, 7, 1-12. doi:10.1038/s42003-024-05926-y\r\n\r\n<\r\n\r\nReports and other scientific outlets\r\n\r\nBecker J, Liess M, Kramer-Schadt S, Franz M, Jager T (2024): Critical Evaluation of Effect Models for the Risk Assessment of Plant Protection Products. UBA Report 41/2024, ISSN 1862-4804, 596 pages. https://www.umweltbundesamt.de/sites/default/files/medien/11850/publikationen/41_2024_texte_critical_evaluation.pdf\r\nBiersteker L, Planillo A, Lammertsma DR, van der Sluis T, Knauer F, Kramer-Schadt S, van der Grift EA, van Eupen M, Jansman HAH (2024): Habitatgeschiktheid voor de wolf in Nederland: een modelanalyse. Wageningen Environmental Research, Rapport 3350, 87 pages, ISSN: 1566-7197; https://research.wur.nl/en/publications/habitatgeschiktheid-voor-de-wolf-in-nederland-een-modelanalyse\r\nThonicke K, Rahner E, Arneth A, Bonn A, Borchard N, Chaudhary A, Darbi M, Dutta T, Eberle U, Eisenhauer N, Farwig N, Flocco CG, Freitag J, Grobe P, Grosch R, Grossart HP, Grosse A, Grützmacher K, Hagemann N, Hansjürgens B, Hartman Scholz A, Hassenrück C, Häuser C, Hickler T, Hölker F, Jacob U, Jähnig S, Jürgens K, Kramer-Schadt S, Kretsch C, Krug C, Lindner JP, Loft L, Mann C, Matzdorf B, Mehring M, Meier R, Meusemann K, Müller D, Nieberg M, Overmann J, Peters RS, Pörtner L, Pradhan P, Prochnow A, Rduch V, Reyer C, Roos C, Scherber C, Scheunemann N, Schroer S, Schuck A, Sioen GB, Sommer S, Sommerwerk N, Tanneberger F, Tockner K, van der Voort H, Veenstra T, Verburg P, Voss M, Warner B, Wende W, Wesche K (2024): 10 Must-Knows aus der Biodiversitätsforschung 2024. https://zenodo.org/records/10794362\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:27+02:00" + "last_modified": "2024-06-05T15:50:30+02:00" }, { "path": "pubs-2018.html", @@ -196,7 +196,7 @@ "description": "", "author": [], "contents": "\r\n2024 | 2023 | 2022 | 2021 | 2020 | 2019\r\n\r\nPeer-Reviewed Publications\r\n\r\nAbrams JF, Hohn S, Rixen T & Merico A (2018). Sundaland peat carbon dynamics and its contribution to the Holocene atmospheric CO2 concentration. Global Biogeochemical Cycles 32:704-719. DOI: 10.1002/2017GB005763.\r\nAsad S, Siku J, Shabrani A, Wilting A & Rödel MO (2018). Naja sumatrana (Sumatran spitting cobra) diet. Herpetological Review 49:134-135.\r\nAsad S, Siku J, Guharajan R, Wilting A & Rödel MO (2018). Alcalus baluensis (Dwarf Mountain Frog); Predation. Herpetological Review 49:727.\r\nAsad S, Siku J, Loader C, Gordon M, Siku J, Wilting A & Rödel MO (2018). Rhacophorus nigropalmatus (Wallace’s Flying Frog) and Polypedates leucomystax (Four-lined Treefrog); Interspecific amplexus. Herpetological Review 49:728-729.\r\nBenhaiem S, Marescot L, Hofer H, East ML, Lebreton J-D, Kramer-Schadt S & Gimenez O (2018). Robustness of eco-epidemiological capture-recapture parameter estimates to variation in infection state uncertainty. Frontiers in Veterinary Science 5:197. DOI: 10.3389/fvets.2018.00197.\r\nBenhaiem S, Marescot L, East ML, Kramer-Schadt S, Gimenez O, Lebreton JD & Hofer H (2018). Slow recovery from a disease epidemic in the spotted hyena, a keystone social carnivore. Communications Biology 1:201. DOI: 10.1038/s42003-018-0197-1.\r\nBrozovic R, Abrams JF, Mohamed A, Wong ST, Niedballa J, Bhagwat T, Sollmann R, Mannan S, Kissing J & Wilting A (2018). Effects of forest degradation on the moonrat Echinosorex gymnura in Sabah, Malaysian Borneo. Mammalan Biology 93:135-143. DOI: 10.1016/j.mambio.2018.10.003.\r\nBudiharta S, Meijaard E, Gaveau DLA, Struebig MJ, Wilting A, Kramer-Schadt S, Niedballa J, Raes N, Maron M & Wilson KA (2018). Restoration to offset the impacts of developments at a landscape scale reveals opportunities, challenges and tough choices. Global Environmental Change 52:152-161. DOI: 10.1016/j.gloenvcha.2018.07.008.\r\nCostantini D, Seeber PA, Soilemetzidou SE, Azab W, Bohner J, Buuveibaatar B, Czirják GA, East ML, Greunz EM, Kaczensky P, Lamglait B, Melzheimer J, Uiseb K, Ortega A, Osterrieder N, Sandgreen DM, Simon M, Walzer C & Greenwood AD (2018). Physiological costs of infection: herpesvirus replication is linked to blood oxidative stress in equids. Global Environmental Change 8:10347. DOI: 10.1016/j.gloenvcha.2018.07.008.\r\nEgli L, Weise H, Radchuk V, Seppelt R, Grimm V (2018). Exploring resilience with agent-based models: State of the art, knowledge gaps and recommendations for coping with multidimensionality. Ecological Complexity 40. DOI: 10.1016/j.ecocom.2018.06.008.\r\nFrigerio D, Pipek P, Kimmig S, Winter S, Melzheimer J, Diblikova L, Wachter B & Richter A (2018). Citizen science and wildlife biology: synergies and challenges. Ethology 124:365-377. DOI: 10.1111/eth.12746.\r\nFlemming D, Cress U, Kimmig S, Brandt M, Kimmerle J (2018). Emotionalization in Science Communication: The Impact of Narratives and Visual Representations on Knowledge Gain and Risk Perception. FRONT COMM 3(3), 1-9. DOI: 10.3389/fcomm.2018.00003.\r\nFranz M, Kramer‐Schadt S, Greenwood AD & Courtiol A (2018). Sickness‐induced lethargy can increase host contact rates and pathogen spread in water‐limited landscapes. Funcitonal Ecology 32:2194–2204. DOI: 10.1111/1365-2435.13149.\r\nGras P, Knuth S, Börner K, Marescot L, Benhaiem S, Aue A, Wittstatt U, Kleinschmit B & Kramer-Schadt S (2018). Landscape structures affect risk of canine distemper in urban wildlife. Frontiers in Ecology and Evolution 6:136. DOI: 10.3389/fevo.2018.00136.\r\nHeim O, Lenski J, Schulz J, Jung K, Kramer-Schadt S, Eccard JA & Voigt CC (2018). The relevance of vegetation structure and smaller water bodies for bats foraging above farmland. Basic and Applied Ecology 27:9-19. DOI: 10.1016/j.baae.2017.12.001.\r\nHernández MC, Navarro-Castilla A, Planillo A, Sánchez-González B & Barja I (2018). The landscape of fear: why some free-ranging rodents choose repeated live-trapping over predation risk and how it is associated with the physiological stress response. Behavioural Processes 157:125–132. DOI: 10.1016/j.beproc.2018.09.007.\r\nHeurich M, Schultze-Naumburg J, Piacenza N, Magg N, Cerveny J, Engleder T, Herdtfelder M, Sladova M & Kramer-Schadt S (2018). Illegal hunting as a major driver of the source-sink dynamics of a reintroduced lynx population in Central Europe. Biological Conservation 224:355-365. DOI: 10.1016/j.biocon.2018.05.011.\r\nJerosch S, Kramer-Schadt S, Götz M & Roth M (2018). The importance of small-scale structures in an agriculturally dominated landscape for the European wildcat (Felis silvestris silvestris) in central Europe and implications for its conservation. Journal for Nature Conservation 41:88-96. DOI: 10.1016/j.jnc.2017.11.008.\r\nKarakoc C, Radchuk V, Hauke H & Chatzinotas A (2018). Interactions between predation and disturbances shape prey communities. Scientific Reports:2968. DOI: 10.1038/s41598-018-21219-x.\r\nKuemmerle T, Levers C, Bleyhl B, Olech W, Perzanowski K, Reusch C & Kramer-Schadt S (2018). One size does not fit all: European bison habitat selection across herds and spatial scales. Landscape Ecology 33:1559-1572. DOI: 10.1007/s10980-018-0684-2.\r\nLehnert LS, Kramer-Schadt S, Teige T, Hoffmeister U, Popa-Lisseanu A, Bontadina F, Ciechanowski M, Dechmann DKN, Kravchenko K, Presetnik P, Starrach M, Straube M, Zöphel U & Voigt CC (2018). Variability and repeatability of noctule bat migration in Central Europe: evidence for partial and differential migration. Proc R Soc B 285:-20182174. DOI: 10.1098/rspb.2018.2174.\r\nMarescot L, Benhaiem S, Gimenez O, Hofer H, Lebreton JD, Olarte-Castillo XA, Kramer-Schadt S & East ML (2018). Social status mediates the fitness costs of infection with canine distemper virus in a social carnivore. Functional Ecology 32:1237-1250. DOI: 10.1111/1365-2435.13059.\r\nMartins RF, Schmidt A, Lenz D, Wilting A, Fickel J (2018). Human- mediated introduction of introgressed deer across Wallace’s line: Historical biogeography of Rusa unicolor and R. timorensis. Ecology and Evolution 8:1465-1479. DOI: 10.1111/2041-210X.13076.\r\nSchnell IB, Bohmann K, Sebastian E, Schultze SE, Richter SR, Murray DC, Sinding MHS, Bass D, Cadle JE, Campbell MJ, Dolch R, Edwards DP, Gray TNE, Hansen T, Hoa ANQ, Lehmkuhl Noer C, Heise-Pavlov S, Pedersen AFS, Ramamonjisoa JC, Siddall ME, Tilker A, Traeholt C, Wilkinson N, Paul Woodcock P, Yu DW, Bertelsen MF, Bunce M & Gilbert MTP (2018). Debugging diversity – a pan-continental exploration of the potential of terrestrial blood-feeding leeches as a vertebrate monitoring tool. Molecular Ecology Resources 2018:1-17. DOI: 10.1111/1755-0998.12912.\r\nSciaini M, Fritsch M, Scherer C & Simpkins CE (2018). NLMR and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R. Methods in Ecology and Evolution 9:2240-2248. DOI: 10.1111/2041‐210X.13076.\r\nSeeber PA, Franz M, Dehnhard M, Ganswindt A, Greenwood AD & East ML (2018). Plains zebra (Equus quagga) adrenocortical activity increases during times of large aggregations in the Serengeti ecosystem. Hormones and Behavior 102:1-9. DOI: 10.1016/j.yhbeh.2018.04.005. Data DOI: 10.17632/zpt32w3k39.1.\r\nTeckentrup L, Grimm V, Kramer-Schadt S & Jeltsch F (2018). Community consequences of foraging under fear. Ecological Modelling 383:80-90. DOI: 10.1016/j.ecolmodel.2018.05.015.\r\nTilker A, Nguyen A, Abrams JF, Bhagwat T, Le M, Nguyen TV, Nguyen AT, Niedballa J, Solllmann R & Wilting A (2020). A little-known endemic caught in the South-east Asian extinction crisis: the Annamite striped rabbit Nesolagus timminsi. Oryx 54:178-187. DOI: 10.1017/S0030605318000534.\r\nUllmann W, Fischer C, Pirhofer-Walzl K, Kramer-Schadt S & Blaum N (2018). Spatiotemporal variability in resources affects herbivore home range formation in structurally contrasting and unpredictable agricultural landscapes. Landscape Ecology volume 33:1505-1517. DOI: 10.1007/s10980-018-0676-2.\r\nWietzkea A, Westphal C, Gras P, Kraft M, Pfohl K, Karlovsky P, Pawelzik E, Tscharntke T & Smit I (2018). Insect pollination as a key factor for strawberry physiology and marketable fruit quality. Agriculture, Ecosystems & Environment 258:197-204. DOI: 10.1016/j.agee.2018.01.036.\r\nWong ST, Belant JL, Sollmann R, Mohamed A, Niedballa J, Mathai J, Meijaard E, Street GM, Kissing J, Mannan S & Wilting A (2018). Habitat associations of the Sunda stink-badger Mydaus javanensis in three forest reserves in Sabah, Malaysian Borneo. Mammalian Biology 88:75-80. DOI: 10.1016/j.mambio.2017.11.010.\r\n\r\nBook Chapters & Reports\r\n\r\nEast ML (2018). Reproductive behavior in the Hyaenidae. In: Encyclopedia of Animal Behavior (2nd ed.). Choe JC (ed), Vol. 4, pp. 539–546, Elsevier, Academic Press. ISBN: 9780128132517. DOI: 10.1016/B978-0-12-809633-8.90127-4.\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:28+02:00" + "last_modified": "2024-06-05T15:50:31+02:00" }, { "path": "pubs-2019.html", @@ -204,7 +204,7 @@ "description": "", "author": [], "contents": "\r\n2024 | 2023 | 2022 | 2021 | 2020 | 2018\r\n\r\nPeer-Reviewed Publications\r\n\r\nAbrams JF, Vashishtha A, Wong ST, Nguyen A, Mohamed A, Wieser S, Kuijper A, Wilting A & Mukhopadhyay A (2019). Habitat-Net: segmentation of habitat images using deep learning. Ecological Informatics 51:121-128. DOI: 10.1016/j.ecoinf.2019.01.009.\r\nAbrams JF, Hörig LA, Brozovic R, Axtner J, Crampton-Platt A, Mohamed A, Wong ST, Sollmann R, Yu DW & Wilting A (2019). Shifting up a gear with iDNA: from mammal detection events to standardised surveys. Journal of Applied Ecology 56:1637-1648. DOI: 10.1111/1365-2664.13411.\r\nAxtner J, Crampton-Platt A, Hörig LA, Mohamed A, Xu CCY, Yu DW & Wilting A (2019). An efficient and robust laboratory workflow and tetrapod database for larger scale environmental DNA studies. GigaScience 8:giz025. DOI: 10.1093/gigascience/giz029.\r\nDalleau M, Kramer‐Schadt S, Gangat Y, Bourjea J, Lajoie G & Grimm V (2019). Modeling the emergence of migratory corridors and foraging hot spots of green sea turtle. Ecology and Evolution 9;10317-10342. DOI: 10.1002/ece3.5552.\r\nFerreira SCM, Torelli F, Klein S, Fyumagwa R, Karesh WB, Hofer H, Seeber F & East ML (2019). Evidence of high exposure to Toxoplasma gondii in free-ranging and captive African carnivores. International Journal for Parasitology: Parasites and Wildlife 8:111-117. DOI: 10.1016/j.ijppaw.2018.12.007.\r\nFerreira SCM, Hofer H, Madeira de Carvalho L & East ML (2019). Parasite infections in a social carnivore: evidence of their fitness consequences and factors modulating infection load. Ecology and Evolution 9:8783-8799. DOI: 10.1002/ece3.5431.\r\nFischer M, Di Stefano J, Gras P, Kramer‐Schadt S, Sutherland DR, Coulson G & Stillfried M (2019). Circadian rhythms enable efficient resource selection in a human‐modified landscape. Ecology and Evolution 9:7509-7527. DOI: 10.1002/ece3.5283.\r\nKanagaraj R, Araujo MB, Barman R, Davidar P, De R, Digal DK, Gopi GV, Johnsingh AJT, Kakati K, Kramer‐Schadt S, Lamichhane BR, (…), Goyal SP (2019):.Predicting range shifts of Asian elephants under global change. Diversity and Distributions 25:822-838. DOI: 10.1111/ddi.12898.\r\nKhwaja H, Buchan C, Wearn OR, Bahaa-el-din L, Bantlin D, Bernard H, Bitariho R, Bohm T, Borah J, Brodie J, Chutipong W, du Preez B, Ebang-Mbele A, Edwards S, Fairet E, Frechette JL, Garside A, Gibson L, Giordano A, Veeraswami Gopi G, Granados A, Gubbi S, Harich F, Haurez B, Havmøller RW, Helmy O, Isbell LA, Jenks K, Kalle R, Kamjing A, Khamcha D, Kiebou-Opepa C, Kinnaird M, Kruger C, Laudisoit A, Lynam A, Macdonald SE, Mathai J, Metsio Sienne J, Meier A, Mills D, Mohd-AzlanJ, Nakashima Y, Nash HC, Ngoprasert D, Nguyen A, O’Brien T, Olson D, Orbell C, Poulsen J, Ramesh T, Reeder D, Reyna R, Rich LN, Rode-Margono J, Rovero F, Sheil D, Shirley MH, Stratford K, Sukumal N, Suwanrat S, Tantipisanuh N, Tilker A, Van Berkel T, Van der Weyde LK, Varney M, Weise F, Wiesel I, Wilting A, Wong ST, Waterman C, Challender DWS (2019). Pangolins in global camera trap data: Implications for ecological monitoring.\r\nGlobal Ecology and Conservation 20:e00769. DOI: 10.1016/j.gecco.2019.e00769.\r\nMaas B, Heath S, Grass I, Cassano C, Classen A, Faria D, Gras P, Williams-Guillén K, Johnson M, Karp D, Linden V, Martínez Salinas MA, Schmack J, Kross S (2019): Experimental field exclosure of birds and bats in agricultural systems – methodological insights, potential improvements, and cost-benefit trade-offs. Basic and Applied Ecology 351-12. DOI: 10.1016/j.baae.2018.12.002.\r\nMathai J, Niedballa J, Radchuk V, Sollmann R, Heckmann I, Brodie J, Struebig M, Hearn AJ, Ross J, Macdonald DW, Hin J & Wilting A (2019). Identifying refuges for Borneo’s elusive Hose’s civet. Global Ecology and Conservation 17:e00531. DOI: 10.1016/j.gecco.2019.e00531.\r\nNiedballa J, Wilting A, Sollmann R, Hofer H & Courtiol A (2019). Assessing analytical methods for detecting spatiotemporal interactions between species from camera trapping data. Remote Sensing in Ecology and Conservation 5:272-285. DOI: 10.1002/rse2.107.\r\nNguyen A, Tran VB, Huang MD, Nguyen TAM, Nguyen DT, Tran VT, Long B, Meijaard E, Holland J, Wilting A & Tilker A (2019). Camera-trap evidence that the silver-backed chevrotain Tragulus versicolor remains in the wild in Vietnam. Nature Ecology & Evolution 3:1650–1654. DOI: 10.1038/s41559-019-1027-7.\r\nPrinz C, Stillfried M, Neubert LK & Denner J (2019). Detection of PCV3 in German wild boars. Virology Journal 16:25. DOI: 10.1186/s12985-019-1133-9.\r\nRadchuk V, Reed T, Teplitsky C, van de Pol M, Charmantier A, Hassall C, Adamík P, Adriaensen F, Ahola, Markus, Arcese P, Avilés JM, Balbontin J, Blanckenhorn W, Borras A, Burthe S, Clobert J, Dehnhard N, de Lope F, Dhondt AA, Dingemanse NJ, Doi H, Eeva T, Fickel J, Filella I, Fossøy F, Goodenough AE, Hall SJG, Hansson B, Harris M, Hasselquist D, Hickler T, Joshi J, Kharouba H, Martínez JG, Mihoub J-B, Mills JA, Molina-Morales M, Moksnes A, Ozgul A, Parejo D, Pilard P, Poisbleau M, Rousset F, Rödel MO, Scott D, Senar JC, Stefanescu C, Stokke BG, Tamotsu K, Tarka M, Tarwater C, Thonicke K, Thorley J, Wilting A, Tryjanowski P, Merilä J, Sheldon B, Møller AP, Matthysen E, Janzen F, Dobson S, Visser ME, Beissinger SR, Courtiol A & Kramer-Schadt S (2019). Adaptive responses of animals to climate change: not universal, likely insufficient. Nature Communications 10:3109. DOI: 10.1038/s41467-019-10924-4.\r\nRadchuk V, Kramer-Schadt S & Grimm V (2019). Transferability of mechanistic ecological models is about emergence. Trends in Ecology & Evolution 34:487-488. DOI: 10.1016/j.tree.2019.01.010.\r\nRadchuk V, Kramer-Schadt S, Fickel J & Wilting A (2019). Distributions of mammals in Southeast Asia: the role of the legacy of climate and species body mass. Journal of Biogeography 46:2350-2362. DOI: 10.1111/jbi.13675. Data DOI: 10.5061/dryad.qp44619.\r\nRadchuk V, De Laender F, Sarmento Cabral J, Boulangeat I, Crawford M, Bohn F, De Raedt J, Scherer C, Svenning JC, Thonicke K, Schurr FM, Grimm V & Kramer‐Schadt S (2019). The dimensionality of stability depends on disturbance type. Ecology Letters 22:674-684. DOI: 10.1111/ele.13226.\r\nRenner IW, Louvrier J & Gimenez O (2019). Combining multiple data sources in species distribution models while accounting for spatial dependence and overfitting with combined penalized likelihood maximization. Methods in Ecology and Evolution 10:2118-2128. DOI: 10.1111/2041-210X.13297\r\nRunting RK, Ruslandi G, Bronson W, Struebig MJ, Satar M, Meijaard E, Burivalova Z, Cheyne SM, Deere NJ, Game ET, Putz FE, Wells JA, Wilting A, Ancrenaz M, Ellis P, Khan FA, Leavitt SM, Marshall AJ, Possingham HP, Watson JEM & Venter OPY (2019). Larger gains from improved management over sparing–sharing for tropical forests. NAT SUSTAIN 2:53-61. DOI: 10.1038/s41893-018-0203-0.\r\nScherer C, Radchuk V, Staubach C, Müller S, Blaum N, Thulke HH & Kramer-Schadt S (2019). Seasonal host life-history processes fuel disease dynamics at different spatial scales. Journal of Animal Ecology 88:1812-1824. DOI: 10.1111/1365-2656.13070.\r\nSeeber PA, Franz M, Greenwood AD & East ML (2019). Life history stage and extrinsic factors affect behavioural time allocation in plains zebras (Equus quagga) in the Serengeti ecosystem. Behavioral Ecology and Sociobiology 73:126. DOI: 10.1007/s00265-019-2738-0.\r\nSeeber PA, McEwen GK, Löber U, Förster DW, East ML, Melzheimer J & Greenwood AD (2019). Terrestrial mammal surveillance using hybridization capture of environmental DNA from African waterholes. Molecular Ecology Resources 19:1486-1496. DOI: 10.1111/1755-0998.13069.\r\nStraka T, Wolff M, Gras P, Buchholz S & Voigt CC (2019). Tree cover mediates the effect of artificial light on urban bats. Frontiers in Ecology and Evolution, DOI: 10.3389/fevo.2019.00091.\r\nTeckentrup L, Kramer-Schadt S & Jeltsch F (2019). The risk of ignoring fear: Underestimating the effects of habitat loss and fragmentation on biodiversity. Landscape Ecology 34:2851–2868. DOI: 10.1007/s10980-019-00922-8.\r\nTilker A, Abrams JF, Mohamed A, Nguyen A, Wong ST, Sollmann R, Niedballa J, Bhagwat T, Gray TNE, Rawson BM, Guegan F, Kissing J, Wegmann M & Wilting A (2019). Habitat degradation and indiscriminate hunting differentially impact faunal communities in the Southeast Asian tropical biodiversity hotspot. Communications Biology 2:396. DOI: 10.1038/s42003-019-0640-y.\r\nWong ST, Belant JL, Sollmann R, Mohamed A, Niedballa J, Mathai J, Street GM & Wilting A (2019). Influence of body mass, sociality, and movement behavior on improved detection probabilities when using a second camera trap.\r\nGlobal Ecology and Conservation 20:e00791. DOI: 10.1016/j.gecco.2019.e00791.\r\n\r\nBook Chapters & Reports\r\n\r\nAbrams RW & Abrams JF (2019). Why should we care so much about old world tropical rainforests? In: Reference Module in Earth Systems and Environmental Sciences. Scott A. Elias (ed), Elsevier, ISBN 9780124095489. DOI: 10.1016/B978-0-12-409548-9.11969-4\r\nCourtiol A, Rousset F, Rohwäder MS, Soto DX, Lehnert LS, Voigt CC, Hobson KA, Wassenaar LI & Kramer-Schadt S (2019). Isoscape computation and inference of spatial origins with mixed models using the R package IsoriX. In: Tracking Animals with stable isotopes. Hobson KA & Wassenaar L (eds), pp. 207-236, Elsevier Academic Press, Cambridge, USA.\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:30+02:00" + "last_modified": "2024-06-05T15:50:32+02:00" }, { "path": "pubs-2020.html", @@ -212,7 +212,7 @@ "description": "", "author": [], "contents": "\r\n2024 | 2023 | 2022 | 2021 | 2019 | 2018\r\n\r\nPeer-Reviewed Publications\r\n\r\nAsad S, Abrams JF, Guharajan R, Sikui J, Wilting A & Rödel MO (2020). Stream amphibian detectability and habitat associations in a reduced impact logging concession in Malaysian Borneo. Journal of Herpetology 54:385-392. DOI: 10.1670/19-136.\r\nAsad S, Wilting A & Rödel MO (2020): Possible spatial separation at macro-habitat scales between two congeneric Psammodynastes species, including observations of fishing behaviour in Psammodynastes pictus. Salamandra 56:411-415. ISSN 0036-3375.\r\nBarnett G, Westbury MV, Sandoval-Velasco M, Vieira FG, Jeon S, Zazula G, Martin MD, Ho SYW, Mather N, Gopalakrishnan S, Ramos-Madrigal J, de Manuel M, Zepeda-Mendoza ML, Antunes A, Baez AC, De Cahsan B, Larson G, O’Brien SJ, Eizirik E, Johnson WE, Koepfli KP, Wilting A, Fickel J, Dalén L, Lorenzen ED, Marques-Bonet T, Hansen AJ, Zhang G, Bhak J, Yamaguchi N & Gilbert MTP (2020). Genomic adaptations and evolutionary history of the extinct scimitar-toothed cat, Homotherium latidens. Current Biology 30:5018-5025.E5. DOI: 10.1016/j.cub.2020.09.051.\r\nBerger A, Barthel LMF, Rast W, Hofer H & Gras P (2020). Urban hedgehog behavioural responses to temporary habitat disturbance versus permanent fragmentation. Animals 10:2109. DOI: 10.3390/ani10112109.\r\nChero G, Pradel R, Derville S, Bonneville C, Gimenez O & Garrigue C (2020). Reproductive capacity of an endangered and recovering population of humpback whales in the Southern Hemisphere. Marine Ecology Progress Series 643:219-227. DOI: 10.3354/meps13329.\r\nGicquel M, Sand H, Månsson J, Wallgren M & Wikenros C (2020): Does recolonization of wolves affect moose browsing damage on young Scots pine? Forest Ecology and Management 473:118298. DOI: 10.1016/j.foreco.2020.118298.\r\nGuerrero TP, Fickel J, Benhaiem S & Weyrich A (2020). Epigenomics and gene regulation in mammalian social systems. Current Zoology 66:307–319. DOI: 10.1093/cz/zoaa005.\r\nGrimm V, Railsback SF, Vincenot CE, Berger U, Gallagher C, DeAngelis DL, Edmonds B, Ge J, Giske J, Groeneveld J, Johnston ASA, Milles A, Nabe-Nielsen J, Polhill JG, Radchuk V, Rohwäder M-S, Stillman RA, Thiele JC, and D Ayllón (2020). The ODD protocol for describing agent-based and other simulation models: a second update to improve clarity, replication, and structural realism. JASSS 23:7. DOI: 10.18564/jasss.4259.\r\nHagen R & Suchant R (2020). Evidence of a spatial auto-correlation in the browsing level of four major European tree species. Ecology and Evolution 10:8517-8527. DOI: 10.1002/ece3.6577.\r\nHeger T, Aguilar-Trigueros AG, Bartram I, Braga RR, Dietl GP, Enders M, Gibson DJ, Gomez Aparicio L, Gras P, Jax K, Lokatis S, Lortie CJ, Mupepele AC, Schindler S, Starrfelt J, Synodinos AD & Jeschke JM (2020). The hierarchy-of-hypotheses approach: a synthesis method for enhancing theory development in ecology and evolution. BioScience 71:337–349. DOI: 10.1093/biosci/biaa130.\r\nHohn S, Acevedos-Trejos E, Abrams JF, Fulgencio de Moura J, Spranz R & Merico A (2020). The long term legacy of plastic mass production.\r\nScience of The Total Environment 746:141115. DOI: 10.1016/j.scitotenv.2020.141115.\r\nHorn J, Becher MA, Johst K, Kennedy P, Osborne JL, Radchuk V & Grimm V (2021). Honeybee colony performance affected by crop diversity and farmland structure: a modelling framework. Ecological Applications 31:e02216. DOI: 10.1002/eap.2216.\r\nKimmig SE, Beninde J, Brandt, M, Schleimer A, Kramer-Schadt S, Hofer H, Boerner K, Schulze C, Wittstatt U, Heddergott M, Halczok T, Staubach C & Frantz A (2020). Beyond the landscape: resistance modelling infers physical and behavioural gene flow barriers to a mobile carnivore across a metropolitan area. Molecular Ecology 29:466-484. DOI: 10.1111/mec.15345.\r\nKimmig SE, Flemming D, Kimmerle J, Cress U & Brandt M (2020). Elucidating the socio-demographics of wildlife tolerance using the example of the red fox (Vulpes vulpes) in Germany. Conservation Science and Practice 2:e212. DOi: 10.1111/csp2.212.\r\nKoenig H, Kiffner C, Kramer-Schadt S, Fuerst C, Keuling O & Ford A (2020). Human-wildlife coexistence in a changing world. Conservation Biology 34:786-794. DOI: 10.1111/cobi.13513.\r\nKrüger L, Stillfried M, Prinz C, Schröder V, Neubert LK & Denner J (2020). Copy number and prevalence of porcine endogenous retroviruses (PERVs) in German wild boars. Viruses 12:419. DOI: 10.3390/v12040419.\r\nKunde MN, Martins RF, Premier J, Fickel J & Förster D (2020). Population and landscape genetic analysis of the Malayan sun bear Helarctos malayanus. Conservation Genetics 21:23–135. DOI: 10.1007/s10592-019-01233-w.\r\nLigmann-Zielinska A, Siebers PO, Maglioccia N, Parker D, Grimm V, Jing Du E, Cenek M, Radchuk V, Arbab NN, Li S, Berger U, Paudel R, Robinson DT, Jankowski P, An L & Ye X (2020). One size does not fit all: a roadmap of purpose-driven mixed-method pathways for sensitivity analysis of agent-based models. JASSS 23:6. DOI: 10.18564/jasss.4201.\r\nMeijaard E, Abrams J, Juffe-Bignoli D, Voigt M & Sheil D (2020). Coconut Oil, Conservation and the Conscientious Consumer. Current Biology 30:2419-2650. DOI: 10.1016/j.cub.2020.05.059.\r\nMeijaard E, Brooks TM, Carlson KM, Slade EM, Garcia-Ulloa J, Gaveau DLA, Lee JSH, Santika T, Juffe-Bignoli D, Struebig MJ, Wich SA, Ancrenaz M, Koh LP, Zamira N, Abrams JF, Prins HHT, Sendashonga CN, Murdiyarso D, Furumo PR, Macfarlane N, Hoffmann R, Persio M, Descaks A, Szantoi Z & Sheil D (2020). The environmental impacts of palm oil in context. Nature Plants 6:1418-1426. DOI: 10.1038/s41477-020-00813-w.\r\nNguyen AT, Nguyen TV, Timmins R, Mcgowan P, Van Hoang T & Le MD (2020) Efficacy of camera traps in detecting primates in Hue Saola Nature Reserve. Primates 61:697–705. DOI: 10.1007/s10329-020-00823-4.\r\nPecoraro C, Zudaire I, Galiberti G, Romeo M, Murua H, Fruciano C, Scherer C, Tinti F, Diaha NC, Bodin N & Chassot E (2020). When size matters: the gonads of larger female yellowfin tuna (Thunnus albacares) have different fatty acid profiles compared to smaller individuals. Fisheries Research 232:105726. DOI: 10.1016/j.fishres.2020.105726\r\nPietzsch B, Fiedler S, Mertens KG, Richter M, Scherer C, Widyastuti K, Wimmler MC, Zakharova L & Berger U (2020). Metamodels for evaluating, valibrating and applying agent-based models: a review. JASSS 23:9. DOI: 10.18564/jasss.4274.\r\nPremier J, Fickel J, Heurich M & Kramer-Schadt S (2020). The boon and bane of boldness: movement syndrome as saviour and sink for population genetic diversity. Movement Ecology 8:16. DOI: 10.1186/s40462-020-00204-y.\r\nRast W, Kimmig SE, Giese L & Berger A (2020). Machine learning goes wild: Using data from captive individuals to infer wildlife behaviours. PLoS ONE 15:e0227317. DOI: 10.1371/journal.pone.0227317.\r\nScherer C, Radchuk V, Franz M, Thulke H, Lange M, Grimm V & Kramer–Schadt S (2020). Moving infections: individual movement decisions drive disease persistence in spatially structured landscapes. OIKOS 129:651-667. DOI: 10.1111/oik.07002.\r\nSchlägel UE, Grimm V, Blaum N, Colangeli P, Dammhahn M, Eccard J, Hausmann SL, Herde A, Hofer H, Joshi J, Kramer-Schadt S, Litwin M, Lozada Gobilard SD, Müller MEH, Müller T, Nathan R, Petermann JS, Pirhofer-Walzl K, Radchuk V, Rillig MC, Roeleke M, Schäfer M, Scherer C, Schiro G, Scholz C, Teckentrup L, Tiedemann R, Ullmann W, Voigt C, Weithoff G & Jeltsch F (2020). Movement-mediated community assembly and coexistence. Biological Reviews 94:1073-1096. DOI: 10.1111/brv.12600.\r\nSeeber AP, Morrison T, Ortega A, East ML, Greenwood AD & Czirják GA (2020). Immune differences in captive and free-ranging zebras (Equus zebra and E. quagga). Mammalian Biology 100:155–164. DOI: 10.1007/s42991-020-00006-0.\r\nScholz C, Firozpoor J, Kramer‐Schadt S, Gras P, Schulze C, Kimmig SE, Voigt CC & Ortmann S (2020). Individual dietary specialization in a generalistic predator: a stable isotope analysis of urban and rural red foxes. Ecolofy and Evolution 10:8855-8870. DOI: 10.1002/ece3.6584.\r\nTilker A, Abrams JF, Nguyen A, Hörig L, Axtner J, Louvrier J, Rawson BM, Nguyen HAQ, Guegan F, Nguyen TV, Le M, Sollmann R & Wilting A (2020). Identifying conservation priorities in a defaunated tropical biodiversity hotspot. Diversity and Distributions 26:426-440. DOI: 10.1111/ddi.13029.\r\nTilker A, Nguyen A, Abrams JF, Bhagwat T, Le M, Nguyen TV, Nguyen AT, Niedballa J, Sollmann R & Wilting A (2020). A little-known endemic caught in the South-east Asian extinction crisis: the Annamite striped rabbit Nesolagus timminsi. Oryx 54:178-187. DOI: 10.1017/S0030605318000534.\r\nTilker A, Nguyen A, Timmins RJ & Gray TNE (2020). No longer Data Deficient: recategorizing the Annamite striped rabbit Nesolagus timminsi as Endangered. ORYX 54:151-151. DOI: 10.1017/S0030605319001078.\r\nUllmann W, Fischer C, Kramer-Schadt S, Pirhofer-Walzl K, Glemnitz M & Blaum N (2020). How do agricultural practices affect the movement behaviour of European brown hares (Lepus europaeus)? Agriculture, Ecosystems & Environment 292:106819. DOI: 10.1016/j.agee.2020.106819.\r\nVoigt CC, Scholl JM, Bauer J, Teige T, Yovel Y, Kramer-Schadt S & Gras P (2020). Movement responses of common noctule bats to the illuminated urban landscape. Landscape Ecology 35:189–201. DOI: 10.1007/s10980-019-00942-4.\r\nWeise H, Auge H, Baessler C, Bärlund I, Bennett EM, Berger U, Bohn F, Bonn A, Borchardt D, Brand F, Chatzinotas A, Corstanje R, Laender FD, Dietrich P, Dunker S, Durka W, Fazey I, Groeneveld J, Guilbaud CSE, Harms H, Harpole S, Harris J, Jax K, Jeltsch F, Johst K, Joshi J, Klotz S, Kühn I, Kuhlicke C, Müller B, Radchuk V, Reuter H, Rinke K, Schmitt–Jansen M, Seppelt R, Singer A, Standish RJ, Thulke HH, Tietjen B, Weitere M, Wirth C, Wolf C & Grimm V (2020). Resilience trinity: safeguarding ecosystem functioning and services across three different time horizons and decision contexts. OIKOS 129:445-456. DOI: 10.1111/oik.07213.\r\n\r\nPreprints\r\n\r\nAbrams JF, Sollmann R, Mitchell SL, Struebig MJ & Wilting A (2020). Capturing biodiversity complexities while accounting for imperfect detection: the application of occupancy-based diversity profiles. bioRxiv DOI: 10.1101/2020.09.07.285510 → see Abrams et al. (2021) Ecography.\r\nAlfano N, Dayaram A, Axtner J, Tsangaras K, Kampmann ML, Mohamed A, Wong ST, Gilbert MTP, Wilting A & Greenwood AD (2020). Non-invasive surveys of mammalian viruses using environmental DNA. bioRxiv. DOI: 10.1101/2020.03.26.009993 → see Alfano et al. (2021) Methods in Ecology and Evolution.\r\nMarescot L, Franz M, Benhaiem S, Hofer H, East ML & Kramer-Schadt S (2020). Keeping the kids at home can limit the persistence of contagious pathogens in social animals. bioRXiv. DOI: 10.1101/2020.04.11.036806 → see Marescot, Franz, Benhaiem et al. (2021) Journal of Animal Ecology.\r\n\r\nBook Chapters & Reports\r\n\r\nHagen R, Kühl N, Kröschel M & Suchant R (2020). Verbiss an Tanne und Eiche in Baden-Württemberg: Ein Vergleich zwischen nadelbaum- und laubbaumdominierten Waldbeständen. Allgemeine Forst- und Jagdzeitung, Heft 7-8 der AFJZ Band 190.\r\nKramer-Schadt S, Wenzler M, Gras P & Knauer F (2020). Habitatmodellierung und Abschätzung der potenziellen Anzahl von Wolfsterritorien in Deutschland. BfN Skripten 556:7-30. DOI: 10.19217/skr556.\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:31+02:00" + "last_modified": "2024-06-05T15:50:32+02:00" }, { "path": "pubs-2021.html", @@ -220,7 +220,7 @@ "description": "", "author": [], "contents": "\r\n2024 | 2023 | 2022 | 2020 | 2019 | 2018\r\n\r\nPeer-Reviewed Publications\r\n\r\nAbrams JF, Sollmann R, Mitchel SL, Struebig MJ, Wilting A (2021). Occupancy-based diversity profiles: capturing biodiversity complexities while accounting for imperfect detection. ECOGRAPHY 44(7), 975-986. DOI: 10.1111/ecog.05577\r\nAlfano N, Dayaram A, Axtner J, Tsangaras K, Kampmann ML, Mohamed A, Wong ST, Gilbert MTP, Wilting A & Greenwood AD (2021). Non-invasive surveys of mammalian viruses using environmental DNA. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.13661\r\nAsad S, Abrams JF, Guharajan R, Lagan P, Kissing J, Sikui J, Wilting A, Rödel MO (2021). Amphibian responses to conventional and reduced impact logging. FOR ECOL MANAG 484, 118949. DOI: 10.1016/J.foreco.2021.118949\r\nAyllón D, Augusiak J, Baveco H, Berger U, Charles S, Martin R, Focks A, Galic N, Gallagher C, Liu C, van Loon EE, Nabe-Nielsen J, Piou C, Polhill JG, Preuss TG, Radchuk V, Schmolke A, Stadnicka-Michalak J, Thorbek P, Railsback SF & Grimm V (2021). Keeping modelling notebooks with TRACE: good for you and good for environmental research and management support. Environmental Modelling & Software 136:104932. DOI: 10.1016/j.envsoft.2020.104932\r\nBastianelli ML, Premier J, Herrmann M, Anile S, Monterroso P, Kuemmerle T, …, & Heurich M (2021). Survival and cause-specific mortality of European wildcat (Felis silvestris) across Europe. Biological Conservation 261:109239. DOI: j.biocon.2021.109239\r\nBueno de Mesquita CP, Nichols LM, Gebert MJ, Vanderburgh C, Bocksberger G, Lester JD, Kalan AK, Dieguez P, McCarthy MS, Agbor A, Álvarez Varona P, Ayimisin AE, Bessone M, Chancellor R, Cohen H, Coupland C, Deschner T, Egbe VE, Goedmakers A, Granjon A-C, Grueter CC, Head J, R. Hernandez-Aguilar A, Jeffery KJ, Jones S, Kadam P, Kaiser M, Lapuente J, Larson B, Marrocoli S, Morgan D, Mugerwa B, Mulindahabi F, Neil E, Niyigaba P, Pacheco L, Piel AK, Robbins MM, Rundus A, Sanz CM, Sciaky L, Sheil D, Sommer V, Stewart FA, Ton E, van Schijndel J, Vergnes V, Wessling EG, Wittig RM, Yuh YG, Yurkiw K, Zuberbühler K, Gogarten JF, Heintz-Buschart A, Muellner-Riehl AN, Boesch C, Kühl HS, Fierer N, Arandjelovic M, Dunn RR. 2021. Structure of Chimpanzee Gut Microbiomes across Tropical Africa. MSYSTEMS 6(3), e01269-20. DOI: 10.1128/mSystems.01269-20\r\nBruckermann T, Greving H, Schumann A, Stillfried M, Börner K, Kimmig SE, Hagen R, Brandt M, Harms U. (2021). To know about science is to love it? Unraveling cause–effect relationships between knowledge and attitudes toward science in citizen science on urban wildlife ecology. Journal of Research in Science Teaching 58, 1179-1202. DOI: 10.1002/tea.21697\r\nChakravarty, R., Mohan, R., Voigt, C.C. , Krishnan, A., Radchuk, V. (2021). Functional diversity of Himalayan bat communities declines at high elevation without the loss of phylogenetic diversity. Sci Rep 11, 22556. DOI: 10.1038/s41598-021-01939-3\r\nClark AT, Arnoldi J‐F, Zelnik YR, Barabas G, Hodapp D, Karakoç C, König S, Radchuk V, Donohue I, Huth A, Jacquet C, de Mazancourt C, Mentges A, Nothaaß D, Shoemaker LG, Taubert F, Wiegand T, Wang S, Chase JM, Loreau M, Harpole S (2021). General statistical scaling laws for stability in ecological systems. ECOL LETT. DOI: 10.1111/ele.13760\r\nDayaram AS, Seeber P, Courtiol A, Soilemetzidou S, Tsangaras K, Franz M, McEwen G, Azab W, Kaczensky P, Melzheimer J, East ML, Ganbaatar O, Walzer C, Osterrieder N, Greenwood AD (2021). Seasonal host and ecological drivers may promote restricted water as a viral vector. SCI TOTAL ENVIRON 773, 145446. DOI: 10.1016/j.scitotenv.2021.145446\r\nFerreira SCM, Veiga MM, Hofer H, East ML, Czirják GÁ (2021). Noninvasively measured immune responses reflect current parasite infections in a wild carnivore and are linked to longevity. ECOL EVOL, early view 11(12), 7685-7699. DOI: 10.1002/ece3.7602\r\nFischer L, Möller Palau-Ribes F, Kipper S, Weiss M, Landgraf C, Lierz M (accepted). Absence of Mycoplasma spp. in nightingales (Luscinia megarhynchos) and blue (Cyanistes caeruleus) and great tits (Parus major) in Germany and its potential implication for evolutionary studies in birds. EUR J WILDL RES 68, 2. DOI: 10.1007/s10344-021-01554-7\r\nFischer M, Sutherland D, Coulson G, Stillfried M, Kramer-Schadt S, di Stefano J (2021). Spatial and temporal responses of swamp wallabies to roads in a human-modified landscape. WILDL BIOL 2021(2), wlb.00691. DOI: 10.2981/wlb.00691\r\nGray TNE, Belecks M, O’Kelly HJ, Rao M, Roberts O, Tilker A, Signs M, Yoganand K (2021). Understanding and solving the South-East Asian snaring crisis. THE ECOLOGICAL CITIZEN 4(2), 129-141. https://www.ecologicalcitizen.net\r\nGuharajan R, Mohamed A, Wong ST, Niedballa J, Petrus A, Jubili J, Lietz R, Clements GR, Wong WM, Kissing J, Lagan P, Wilting A (2021). Sustainable forest management is vital for the persistence of sun bear Helarctos malayanus populations in Sabah, Malaysian Borneo. FOR ECOL MANAG 493, 119270. DOI: 10.1016/j.foreco.2021.119270\r\nHagen R, Ortmann S, Elliger A & Arnold J (2021). Advanced roe deer (Capreolus capreolus) parturition date in response to climate change. Ecosphere 12(11), e03819. DOI: 10.1002/ecs2.3819\r\nKrause T, Tilker A (2021). Defaunation jeopardizes the SDG’s – How the loss of forest fauna undermines the achievements of the SDG’s. AMBIO. DOI: 10.1007/s13280-021-01547-5\r\nKrücken J, Czirják GÁ, Ramünke S, Maria Serocki, Heinrich SK, Melzheimer J, Costa MC, Hofer H, Aschenborn OHK, Barker NA, Capodanno S, Madeira de Carvalho L, von Samson-Himmelstjerna G, East ML & Wachter B (2021). Genetic diversity of vector-borne pathogens in spotted and brown hyenas from Namibia and Tanzania relates to ecological conditions rather than host taxonomy. PARASITES VECTORS 14, 328. DOI: 10.1186/s13071-021-04835-x\r\nKürschner T, Scherer C, Radchuk V, Blaum N, Kramer-Schadt S. (2021) Movement can mediate temporal mismatches between resource availability and biological events in host-pathogen interactions. ECOL EVOL 11(10), 5728-41. DOI: 10.1002/ece3.7478\r\nMalishev M and Kramer-Schadt S (2021). Movement, Models, and Metabolism: Individual-based energy budget models as next-generation extensions for predicting animal movement outcomes across scales. ECOL MODELL 441, 109413. DOI: 10.1016/j.ecolmodel.2020.109413\r\nMarescot L, Franz M, Benhaiem S, Hofer H, Scherer C, East ML & Kramer-Schadt S (2021). Keeping the kids at home can limit the persistence of contagious pathogens in social animals. Journal of Animal Ecology. DOI: 10.1111/1365-2656.13555.\r\nMohamed A, Sollmann R, Wong ST, Niedballa J, Abrams JF, Kissing J, Wilting A (2021): Counting Sunda clouded leopards with confidence: incorporating individual heterogeneity in density estimates. ORYX 55(1), 56-65. DOI: 10.1017/S0030605318001503\r\nNguyen A, Tilker A, Le D, Le HV, Le SV, Luu TH, Tran BV, Wilting A (2021). New records and southern range extension of the Annamite striped rabbit Nesolagus timminsi in Vietnam. MAMMALIA, online first. DOI: 10.1515/mammalia-2020-0189\r\nNguyen AT, Tilker A, Nguyen TV & Le M (2021). Camera-trap records of muntjac in the lowlands of Hue Saola Nature Reserve, central Vietnam. DSG Newsletter 32.\r\nNguyen TV, Tilker A, Nguyen A, Hörig L, Axtner J, Schmidt A, Le M, Nguyen AHQ, Rawson BM, Wilting A, Fickel J (2021). Using terrestrial leeches to assess the genetic diversity of an elusive species: the Annamite striped rabbit Nesolagus timminsi. ENVIRONMENTAL DNA 3, 780-791. DOI: 10.1002/edn3.182\r\nOlarte-Castillo XA, Dos Remédios JF, Heeger F, Hofer H, Karl S, Greenwood AD, East ML (2021). The virus-host interface: Molecular interactions of Alphacoronavirus-1 variants from wild and domestic hosts with mammalian aminopeptidase N. MOL ECOL. 2021;30(11):2607-2625. DOI: 10.1111/mec.15910\r\nPlanillo A, Kramer-Schadt S, Buchholz S, Gras P, von der Lippe M, Radchuk V. (2021) Arthropod abundance modulates bird community responses to urbanization. DIV DISTRIB 27(1), 34-49. DOI: 10.1111/ddi.13169\r\nPlanillo A, Fiechter L, Sturm U, Heucke-Voigt S, Kramer-Schadt S (2021) Citizen science data for urban planning: Comparing different sampling schemes for modelling urban bird distribution. LAND URB PLAN 211. DOI: 10.1016/j.landurbplan.2021.104098\r\nPremier J, Gahbauer M, Leibl F, Heurich M (2021). In-situ feeding as a new management tool to conserve orphaned Eurasian lynx (Lynx lynx). ECOL EVOL, accepted. DOI: 10.1002/ece3.7261\r\nPretzlaff I, Radchuk V, Turner JM, Dausmann KH (2021) Flexibility in thermal physiology and behaviour allows body mass maintenance in hibernating hazel dormice. J ZOOL, 314(1), 1-11. DOI: 10.1111/jzo.12862\r\nTran DV, Viet DP, Tien TV, Nguyen A, Van CP & Tilker A (2021). New records of the forest musk deer Moschus berezovskii in Viet Nam revealed by camera-traps. ORYX 55(4) , 494-495. DOI: 10.1017/S0030605321000569\r\nWikenros C, Gicquel M, Zimmermann B, FlagstaO, Akesson M (2021). Age at first reproduction in wolves: different patterns of density dependence for females and males. PRoc R SOC B 288, 2021020. DOI: 10.1098/rspb.2021.0207\r\n\r\nPreprints\r\n\r\nDrenske S, Radchuk V, Scherer C, Esterer C, Kowarik I, Fritz J & Kramer-Schadt S (2021). Halfway to self-sustainability: reintroduced migratory European Northern Bald Ibises (Geronticus eremita) still need management interventions for population viability. bioRxiv. DOI: 10.1101/2021.04.03.438331\r\nHermanns K, Marklewitz M, Zirkel F, Kopp K, Kramer-Schadt S & Junglen S (2021). Mosquito community composition shapes virus prevalence patterns along anthropogenic disturbance gradients. bioRXiv. DOI: 10.1101/2021.02.04.429754\r\n\r\nBook Chapters & Reports\r\n\r\nHeurich M, Premier J, Schultze-Naumburg J, Herdtfelder M, Oeser J & Kramer-Schadt S (2021). Erforschung der Populations- und Bewegungsökologie des Luchses als Grundlage eines Metapopulationsmanagements der kontinentaleuropäischen Luchspopulationen (Lynx lynx). Natur und Landschaft 96 (1). DOI: 10.17433/1.2021.50153867.1118\r\nRadchuk V, Kramer-Schadt S, Berger U, Scherer C, Backmann P & Grimm V (2021). Individual-based models. In: Demographic Methods across the Tree of Life, Salguero-Gomez R & Gamelon M (eds.), Oxford University Press https://global.oup.com/academic/product/demographic-methods-across-the-tree-of-life-9780198838609?cc=de&lang=en&.\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:32+02:00" + "last_modified": "2024-06-05T15:50:33+02:00" }, { "path": "pubs-2022.html", @@ -228,7 +228,7 @@ "description": "", "author": [], "contents": "\r\nFind all our publication as PDFs also at ResearchGate.\r\nFor older publications please check here:2024 | 2023 | 2021 | 2020 | 2019 | 2018\r\n\r\n\r\nPeer-Reviewed Publications\r\n\r\nAlexiou I, Abrams JF, Coudrat CNZ, Nanthavong C, Nguyen A, Niedballa J, Wilting A , Tilker A (2022): Camera-trapping reveals new insights in the ecology of three sympatric muntjacs in an overhunted biodiversity hotspot. MAMM BIOL 102, 489–500. doi:10.1007/s42991-022-00248-0\r\nAntunes AC, Montanarin A, Gräbin DM, Erison Carlos dos Santos Monteiro, Fernando Ferreira de Pinho, Guilherme Costa Alvarenga, Jorge Ahumada, …, Sollmann R, …, Ribeiro MC (2022): AMAZONIA CAMTRAP: A dataset of mammal, bird, and reptile species recorded with camera traps in the Amazon forest. ECOLOGY 103(9), e3738. doi:10.1002/ecy.3738\r\nAppleton MR, Courtiol A, Emerton L, …, Tilker A, et al. (2022): Protected area personnel and ranger numbers are insufficient to deliver global expectations. NAT SUSTAIN 5, 1100–1110. doi:10.1038/s41893-022-00970-0\r\nAsad S, Vitalis V, Guharajan R, Abrams JF, Lagan P, Kissing J, Sikui J, Wilting A, Rödel MO (2022): Variable species but similar amphibian community responses across habitats following reduced impact logging. GLOB ECOL CONSERV 35, e02061. doi:10.1016/j.gecco.2022.e02061\r\nBitariho R, Akampurira E, Mugerwa B (2022): Long-term funding of community projects has contributed to mitigation of illegal activities within a premier African protected area, Bwindi impenetrable National Park, Uganda. CONSERV SCI PRACT 4, e12761. doi:10.1111/csp2.12761\r\nBolas EC, Sollmann R, Crooks KR, Boydston EE, Shaskey L, Boser CL, Dillon A, Van Vuren DH (2022): Role of microhabitat and temporal activity in facilitating coexistence of endemic carnivores on the California Channel Islands. J MAMMAL 103, 18-28. doi:10.1093/jmammal/gyab125\r\nBrieger F, Kämmerle JL, Hagen R, Suchant R (2022): Behavioural reactions to oncoming vehicles as a crucial aspect of wildlife-vehicle collision risk in three common wildlife species. ACCID ANAL PREV 168, 106564. doi:10.1016/j.aap.2021.106564\r\nBruckermann T, Greving H, Stillfried M, Schumann A, Brandt M, Harms U (2022): I’m fine with collecting data: Engagement profiles differ depending on scientific activities in an online community of a citizen science project. PLOS ONE 17, e0275785. doi:10.1371/journal.pone.0275785\r\nBruckermann T, Stillfried M, Straka TM, Harms U (2022): Citizen science projects require agreement: A Delphi study to identify which knowledge on urban ecology is considered relevant from scientists’ and citizens’ perspectives. INT J SCI EDUC B COMMUN PUBLIC ENGAGEM 12, 75–92. doi:10.1080/21548455.2022.2028925\r\nCalderon Quinonez AP, Louvrier J, Planillo AP, Araya-Gamboa, D, Arroyo-Arce S, Barrantes-Núñez M, Carazo-Salazar J, Corrales-Gutiérrez D, Doncaster CP, Foster R, García MJ, Garcia-Anleu R, Harmsen B, Hernández-Potosme S, Leonardo R, Trigueros DM, McNab R, Meyer N, Moreno R, Salom-Pérez R, Sauma Rossi A, Thomson I, Thornton D, Urbina Y, Grimm V, Kramer-Schadt S (2022): Occupancy models reveal potential of conservation prioritization for Central American jaguars. ANIM CONSERV 25, 680–691. doi:10.1111/acv.12772\r\nCardador L, Tella JL, Louvrier J, Anadón JD, Abellán P, Carrete M (2022): Climate matching, anthropogenic factors, and dispersal contribute differentially to the colonisation and extinction of local populations during dynamic avian invasions. DIVERS DISTRIB 28, 1908– 1921. doi:10.1111/ddi.13591\r\nCulhane K, Sollmann R, White AM, Tarbill GL, Cooper SD, Young HS (2022): Small mammal responses to fire severity mediated by vegetation characteristics and species traits. ECOL EVOL 12(5), e8918. doi:10.1002/ece3.8918\r\nEast ML, Thierer D, Benhaiem S, Metzger S, Hofer H (2022): Infanticide by adult females causes sexual conflict in a female-dominated social mammal. FRONT ECOL EVOL 10, 860854. doi:10.3389/fevo.2022.860854\r\nFigueiredo L, Scherer C, Cabral JS (2022): Computational notebooks to facilitate writing and improve reproducibility of (ecological) research. PLOS COMPUT BIOL 18, e1010356. doi:10.1371/journal.pcbi.1010356\r\nFischer L, Möller Palau-Ribes F, Kipper S, Weiss M, Landgraf C, Lierz M (2022): Absence of Mycoplasma spp. in nightingales (Luscinia megarhynchos) and blue (Cyanistes caeruleus) and great tits (Parus ma-jor) in Germany and its potential implication for evolutionary studies in birds. EUR J WILDL RES 68, 2. doi:10.1007/s10344-021-01554-7\r\nFogarty FA, Yen JDL, Fleishman E, Sollmann R, Ke A (2022): Multiple-region, N-mixture community models to assess associations of riparian area, fragmentation, and species richness. ECOL APPL 32(8), e2698. doi:10.1002/eap.2698\r\nGicquel M, East ML, Hofer H, Benhaiem S (2022): Early-life adversity predicts performance and fitness in a wild social carnivore. J ANIM ECOL 91, 2074–2086. doi:10.1111/1365-2656.13785\r\nGicquel M, East ML, Hofer H, Cubaynes S, Benhaiem S (2022): Climate change does not decouple interactions between a central place foraging predator and its migratory prey. ECOSPHERE 13, e4012. doi:10.1002/ecs2.4012\r\nGorczynski D, Hsieh C, Ahumad J, Akampurira E, Andrianarisoa MH, Espinosa S, Johnson S, Kayijamahe C, Lima MGM, Mugerwa B, Rovero F, Salvador J, Santos F, Sheil D, Uzabah, E, Beaudrot L (2022): Human density modulates spatial associations among tropical forest terrestrial mammal species. GLOB CHANG BIOL 28, 7205–7216. doi:10.1111/gcb.16434\r\nGrabow M, Louvrier JLP, Planillo A, Kiefer S, Drenske S, Börner K, Stillfried M, Hagen R, Kimmig S, Straka TM, Kramer-Schadt S (2022): Data-integration of opportunistic species observations into hierarchical modeling frameworks improves spatial predictions for urban red squirrels. FRONT ECOL EVOL 10, 881247. doi:10.3389/fevo.2022.881247\r\nGünther T, Kramer-Schadt S, Fuhrmann M, Belik V (2022): Environmental factors associated with the prevalence of ESBL/AmpC-producing Escherichia coli in wild boar (Sus scrofa).\r\nFRONT VET SCI 9, 980554. doi:10.3389/fvets.2022.980554\r\nHagemann J, Conejero C, Stillfried M, Mentaberre G, Castillo-Contreras R, Fickel J, López-Olvera JR (2022): Genetic population structure defines wild boar as an urban exploiter species in Barcelona, Spain. SCI TOTAL ENVIRONM 833, 155126. doi:10.1016/j.scitotenv.2022.155126\r\nHagen R, Ortmann S, Elliger A, & Arnold J (2022): Evidence for a male-biased sex ratio in the offspring of a large herbivore: The role of environmental conditions in the sex ratio variation. ECOL EVOL 12, e8938. doi:10.1002/ece3.8938\r\nHering R, Hauptfleisch M, Jago M, Smith T, Kramer-Schadt S, Stiegler J, Blaum N (2022): Don’t stop me now: Managed fence gaps could allow migratory ungulates to track dynamic resources and reduce fence related energy loss. FRONT ECOL EVOL 10, 907079. doi:10.3389/fevo.2022.907079\r\nHering R, Hauptfleisch M, Kramer-Schadt S, Stiegler J, Blaum N (2022): Effects of fences and fence gaps on the movement behavior of three southern African antelope species. FRONT CONSERV SCI 3, 959423. doi:10.3389/fcosc.2022.959423\r\nKalyahe MM, Hofer H, East ML (2022): Do anthropogenic sources of food increase livestock predation in the area surrounding Ruaha National Park? ENVIRON CONSERV 49, 105-113. doi:10.1017/S037689292200008X\r\nKe A, Sollmann R, Frishkoff LO, Karp DS (2022): A hierarchical N-mixture model to estimate behavioral variation and a case study of Neotropical birds. ECOL APPL 32, e2632. doi:10.1002/eap.2632\r\nKitchener A, Hoffmann M, Yamaguchi N, Breitenmoser-Würsten C, Wilting A (2022): A system for designating taxonomic certainty in mammals and other taxa. MAMM BIOL 102, 251-261. doi:10.1007/s42991-021-00205-3\r\nKitchener AC, Simo FT, Mugerwa B, Sanderson JG (2022): Evidence that Temminck described Felis aurata in 1825, not 1827. ARCH NAT HIST 49, 78-85. doi:10.3366/anh.2022.0759\r\nKrause T, Tilker A (2022): How the loss of forest fauna undermines the achievments of the SDG’s. AMBIO 51, 103–113. doi:10.1007/s13280-021-01547-5\r\nLouvrier J; Planillo A; Stillfried M; Hagen R; Boerner K; Kimmig S; Ortmann S; Schumann A; Brandt M; Kramer-Schadt S (2021). Spatiotemporal interactions of a novel mesocarnivore community in an urban environment before and during SARS-CoV-2 lockdown. J ANIM ECOL 91, 367–380. DOI: 10.1111/1365-2656.13635\r\nMcLaughlin JP, Schroeder J, White AM, Culhane K, Mirts HE, Tarbill GL, Sire L, Page M, Baker E, Moritz M, Brashares J, Young HS, Sollmann R (2022): Food webs for three burn severities after wildfire in the Eldorado National Forest, California. NAT SCI DATA 9, 384. doi:10.1038/s41597-022-01220-w\r\nMirts HE, McLaughlin JP, Weller TJ, White AM, Young HS, Sollmann R (2022): Bats in the megafire: assessing species‘ site use in a postfire landscape in the Sierra Nevada. J MAMMAL 103(1), 111-123. doi:10.1093/jmammal/gyab129\r\nMoreno-Sosa AM, Yacelga M, Craighead K, Kramer-Schadt S, Abrams JF (2022): Can prey occupancy act as a surrogate for mesopredator occupancy? A case study of ocelot (Leopardus pardalis). MAMM BIOL 102, 163-175, doi:10.1007/s42991-022-00232-8\r\nNguyen TV, Wilting A, Niedballa J, Nguyen A, Rawson BM, Nguyen AQH, Cao TT, Wearn OR, Dao AC, Tilker A (2022): Getting the big picture: Landscape-scale occupancy patterns of two Annamite endemics among multiple protected areas. CONS SCI PRACT 4, e620. doi:10.1111/csp2.620\r\nNiedballa J, Axtner J, Döbert TF, Tilker A, Nguyen A, Wong ST, Fiderer C, Heurich M, & Wilting A (2022): imageseg: An R package for deep learning-based image segmentation. METHODS ECOL EVO 13, 2363– 2371, doi:10.1111/2041-210X.13984\r\nReusch C, Lozar M, Kramer-Schadt S, Voigt CC (2022): Coastal onshore wind turbines lead to habitat loss for bats in Northern Germany. J ENV MANAGE 310. doi:10.1016/j.jenvman.2022.114715\r\nRipari L & Premier J, Belotti E, Bluhm H, Breitenmoser-Würsten C, Bufka L, Červený J, Drouet-Hoguet N, Fuxjäger C, Jędrze-jewski W, Kont R, Koubek P, Kowalczyk R, Krofel M, Krojerová-Prokešová J, Molinari-Jobin A, Okarma H, Oliveira T, Remm J, Schmidt K, Zimmermann F, Kramer-Schadt S, Heurich M (2022): Human disturbance is the most limiting factor driving habitat selection of a large carnivore throughout Continental Europe. BIOL CONS 266, 109446. doi:10.1016/j.biocon.2021.109446\r\nSemper-Pascual A, Bischof R, Milleret C, Beaudrot L, Vallejo-Vargas AF, Ahumada JA, Bitariho R, Jansen PA, Moreira Lima MG, Mugerwa B, Rovero F, Santos F, Sheil D (2022): Occupancy winners in tropical protected forests: a pantropical analysis. PROC R SOC B 289. doi:10.1098/rspb.2022.0457\r\nStiegler J, Lins A, Dammhahn M, Kramer-Schadt S, Ortmann S, Blaum N (2022): Personality drives activity and space use in a mammalian herbivore. MOV ECOL 10, 33. doi:10.1016/j.biocon.2021.109446\r\nTemu SE, Nahonyo CL, East ML, Moehlman PD (2022): Diet of the Golden Jackal (Canis aureus) and Sil-ver-Backed Jackal (Canis mesomelas) in the Southern Part of the Serengeti Ecosystem, Tanzania: A Comparative Study. TANZ J SCI 48, 773–784. doi:10.4314/tjs.v48i4.5\r\nVallejo-Vargas AF, Sheil D, Semper-Pascual A, Beaudrot L, Ahumada JA, Akampurira E, Bitariho R, Espinosa S, Estienne V, Jansen PA, Kayijamahe C, Martin EH, Lima MGM, Mugerwa B, Rovero F, Salvador J, Santos F, Spironello WR, Uzabaho E, Bischof R (2022): Consistent diel activity patterns of forest mammals among tropical regions. NAT COMMUN 13, 7102. doi:10.1038/s41467-022-34825-1\r\nVlaschenko A, Kravchenko K, Yatsiuk Y, Hukov V, Kramer-Schadt S, Radchuk V (2022): Bat assemblages are shaped by land cover types and forest age: A case study from eastern Ukraine. FORESTS 13, 1732. doi:10.3390/f13101732\r\nVoigt CC, Scherer C, Runkel V (2022). Modelling the power of acoustic monitoring to predict bat fatalities at wind turbines. CONSERV SCI PRACT 4, e12841. doi:10.1111/csp2.12841\r\nWilting A, Nguyen TV, Axtner J, Nguyen A, Schmidt A, Le M, Nguyen AHQ, Rawson BM, Tilker A, Fickel J (2022). Creating genetic reference datasets: Indirect sampling of target species using terrestrial leeches as sample “collectors”. EDNA 4(2), 311-325. DOI: 10.1002/edn3.256\r\nWong S, Guharajan R, Petrus A, Jubili J, Lietz R, Abrams J, Hon J, Alen L, Ting N, Wong G, Tchin L, Bijack N, Kramer-Schadt S, Wilting A, Sollmann R (2022): How do terrestrial wildlife communities respond to small-scale Acacia plantations embedded in harvested tropical forest? ECOL EVOL 12, e9337. doi:10.1002/ece3.9337\r\n\r\nPreprints\r\n\r\nMugerwa B, Niedballa J, Planillo A, Sheil D, Kramer-Schadt S, Wilting A (2022): Global disparity of research allocation and the Aichi biodiversity conservation targets. BIORXIV 2022.04.07.486958, doi:10.1101/2022.04.07.486958\r\n\r\nBook Chapters & Reports\r\n\r\nGimenez O, Louvrier J, Lauret V, Santostasi N (2022): Studying Species Demography and Distribution in Natural Conditions: Hidden Markov Models. In Statistical Approaches for Hidden Variables in Ecology (eds N. Peyrard and O. Gimenez). https://doi.org/10.1002/9781119902799.ch3\r\nPapaïx J, Soubeyrand S, Bonnefon O, Walker E, Louvrier J, Klein E, Roques L (2022): Inferring Mechanistic Models in Spatial Ecology Using a Mechanistic-Statistical Approach. In Statistical Approaches for Hidden Variables in Ecology (eds N. Peyrard and O. Gimenez). https://doi.org/10.1002/9781119902799.ch4\r\nThonicke K, Rahner E, Arneth A, Bartkowski B, Bonn A, Döhler C, Finger R, Freitag J, Grosch R, Grossart H.-P, Grützmacher K, Hartman Scholz A, Häuser C, Hickler T, Hölker F, Jähnig S C, Jeschke J, Kasen R, Kastner T, Kramer-Schadt S, Krug C, Lakner S, Loft L, Matzdorf B, Meakins F, De Meester L, Monaghan M T, Müller D, Overmann J, Quaas M, Radchuk V, Reyer C, Roos C, Scholz I, Schroer S, Sioen G B, Sommer S, Sommerwerk N, Tockner K, Turk Z, Warner B, Wätzold F, Wende W, Veenstra, van der Voort H (2022): 10 Must-Knows aus der Biodiversitätsforschung 2022 | 10 Must knows from biodiversity science 2022. Leibniz-Forschungsnetzwerk Biodiversität, Potsdam, Deutschland. doi:10.5281/zenodo.6257476 | zenodo.org/record/6257527\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:33+02:00" + "last_modified": "2024-06-05T15:50:34+02:00" }, { "path": "pubs-2023.html", @@ -236,7 +236,7 @@ "description": "", "author": [], "contents": "\r\nFind all our publication as PDFs also at ResearchGate.\r\nFor older publications please check here:2024 | 2022 | 2021 | 2020 | 2019 | 2018\r\n\r\n\r\nPeer-Reviewed Publications\r\n\r\nBenhaiem S, Kaidatzi S, Hofer H, East ML (2023): Long-term reproductive costs of snare injuries in a keystone terrestrial by-catch species. ANIM CONSERV 26: 61-71. doi:10.1111/acv.12798\r\nBonenfant C, Rutschmann A, Burton J, Boyles R, García F, Tilker A, Schütz E (2023): Cast away on Mindoro island: lack of space limits population growth of the endangered tamaraw. ANIM CONSERV 26, 546–557. doi:10.1111/acv.12842\r\nBrodie JF, Mohd-Azlan J, Chen C, Wearn OR, Deith MCM, Ball JGC, Slade EM, Burslem DFRP, Teoh SW, Williams PJ, Nguyen A, Moore JH, Goetz SJ, Burns P, Jantz P, Hakkenberg CR, Kaszta ZM, Cushman S, Coomes D, Helmy OE, Reynolds G, Rodríguez JP, Jetz W, Luskin MS (2023): Landscape-scale benefits of protected areas for tropical biodiversity. NATURE 620, 807–812. doi:10.1038/s41586-023-06410-z\r\nBruckermann T, Greving H, Schumann A, Stillfried M, Börner K, Kimmig SE, Hagen R, Brandt M, Harms U (2023): Scientific reasoning skills predict topic-specific knowledge after participation in a citizen science project on urban wildlife ecology. J RES SCI TEACH 60(9), 1915–1941. doi:10.1002/tea.21835\r\nBubnicki JW, Norton B, Baskauf SJ, Bruce T, Cagnacci F, Casaer J, Churski M, Cromsigt JPGM, Farra SD, Fiderer C, Forrester TD, Hendry H, Heurich M, Hofmeester TR, Jansen PA, Kays R, Kuijper DPJ, Liefting Y, Linnell JDC, Luskin MS, Mann C, Milotic T, Newman P, Niedballa J, Oldoni D, Ossi F, Robertson T, Rovero F, Rowcliffe M, Seidenari L, Stachowicz I, Stowell D, Tobler MW, Wieczorek J, Zimmermann F and Desmet P (2023): Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data. REMOTE SENS ECOL CONSERV. doi:10.1002/rse2.37\r\nChakravarty R, Radchuk V, Managave S, Voigt CC (2023): Increasing species richness along elevational gradients is associated with niche packing in bat assemblages. J ANIM ECOL 92, 863– 874. doi:10.1111/1365-2656.13897\r\nCouturier T, Bauduin S, Astruc G, Blanck A, Canonne C, Chambert T, Chiffard J, Cosquer A, Cubaynes S, Curtet L, Dortel E, Drouet-Hoguet N, Duchamp C, Francesiaz C, Grente O, Jailloux A, Kervellec M, Lauret V, Lebreton J-D, Louvrier J, Marescot L, Mathevet R, Navas ML, Perrot C, Poulet N, Quenete P-Y, Salas M, Souchay G, Vanpé C, Besnard A,Gimenez, O. (2023): Building spaces of interactions between researchers and managers: Case studies with wildlife monitoring and conservation in France. ECOL SOLUT EVID 4(2), e12245. doi:10.1002/2688-8319.12245\r\nDanabalan R, Planillo A, Butschkau S, Deeg S, Gras P, Thion C, Calvignac-Spencer S, Kramer-Schadt S, Mazzoni C (2023): Comparison of mosquito and fly derived DNA as a tool for sampling vertebrate biodiversity in suburban forests in Berlin, Germany. EDNA 5, 476–487. doi:10.1002/edn3.398\r\nDickey JWE, Liu C, Briski E, Wolter C, Moesch S, Jeschke JM (2023): Identifying potential emerging in-vasive non-native species from the freshwater pet trade. PEOPLE NAT 5, 1948–1961. doi:10.1002/pan3.10535\r\nDrenske S, Radchuk V, Scherer C, Esterer C, Kowarik I, Fritz J, Kramer-Schadt S (2023): On the road to self-sustainability: Reintroduced migratory European northern bald ibises Geronticus eremita still need management interventions for population viability. ORYX 57, 637–648. doi:10.1017/S0030605322000540\r\nFesta F, AncillottoL, Santini L, Pacifici M, Rocha R, Toshkova N, Amorim F, Benítez-5 López A, Domer A, Hamidović D, Kramer-Schadt S, Mathews F, Radchuk V, Rebelo H, Ruczynski I, Solem E, Tsoar A, Russo D, Razgour O (2023): Bat responses to climate change: a systematic review. BIOL REV 98, 19–33. doi:10.1111/brv.12893\r\nGreving H, Bruckermann T, Schumann A, Stillfried M, Börner K, Hagen R, Kimmig SE, Brandt M, Kimmerle J (2023): Attitudes Toward Engagement in Citizen Science Increase Self-Related, Ecology-Related, and Motivation-Related Outcomes in an Urban Wildlife Project. BIOSCIENCE 73, 206–219. doi:10.1093/biosci/biad003\r\nGuharajan R, Abrams JF, Abram NK, Lim HY, Clements GR, Deere NJ, Struebig MJ, Goossens B, Gardner PC, Brodie JF, Granados A, Teoh SW, Hearn AJ, Ross J, Macdonald DW, Mohamed A, Wong ST, Hastie AYL, Wong WM, Kretzschmar P, Wong ST, Koh SPH, Wilting A (2023): Determinants of sun bear Helarctos malayanus habitat use in Sabah, Malaysian Borneo and its predicted distribution under future forest degradation and loss. BIODIVERS CONSERV 32, 297–317. doi:10.1007/s10531-022-02503-9\r\nHermanns K, Marklewitz M, Zirkel F, Kopp A, Kramer-Schadt S, Junglen S (2023): Mosquito community composition shapes virus prevalence patterns along anthropogenic disturbance gradients.\r\nELIFE 12 ,e66550. doi:10.7554/eLife.66550\r\nKappeler PM, Benhaiem S, Fichtel C, Fromhage L, Höner OP, Jennions M, Kaiser S, Krüger O, Schneider JM, Tuni C, van Schaik J, Goymann W (2023): Sex roles and sex ratios in animals. BIOL REV 98, 462–480. doi:10.1111/brv.12915\r\nLi J, Seeber P, Axtner J, et al. (2023): Monitoring terrestrial wildlife by combining hybridization capture and metabarcoding data from waterhole environmental DNA. BIOL CONSERV 284, 110168. doi:10.1016/j.biocon.2023.110168\r\nLokatis S, Jeschke JM, Bernard-Verdier M, Buchholz S, Grossart H-P, Havemann F, Hölker F, Itescu Y, Kowarik I, Kramer-Schadt S, Mietchen D, Musseau CL, Planillo A, Schittko C, Straka TM and Heger T (2023): Hypotheses in urban ecology: building a common knowledge base. BIOL REV 98, 1530–1547. doi:10.1111/brv.12964\r\nMilles A, Banitz T, Bielcik M, Frank K, Gallagher CA, Jeltsch F, Jepsen JU, Oro D, Radchuk V, Grimm V (2023): Local buffer mechanisms for population persistence. TRENDS ECOL EVOL 38, 1051–1059. doi:10.1016/j.tree.2023.06.006\r\nNaciri M, Planillo A, Gicquel M, East ML, Hofer H, Metzger S, Benhaiem S (2023): Three decades of wildlife-vehicle collisions in a protected area: main roads and long-distance commuting trips to migratory prey increase spotted hyena roadkills in the Serengeti. BIOL CONSERV 279, 109950. doi:10.1016/j.biocon.2023.109950\r\nNguyen AT, Tilker A, Le Khac Q, Le M (2023): New records of the Annamite striped rabbit in Ngoc Linh, Quang Nam and Kon Tum provinces, Vietnam. MAMMALIA 87, 374–378. doi:10.1515/mammalia-2023-0005\r\nOeser J, Heurich M, Kramer-Schadt S Andrén H, Bagrade G, Belotti E, Bufka L, Breitenmoser-Würsten C, Černe R, Duľa M, Fuxjäger C, Gomerčić T, Jędrzejewski W, Kont R, Koubek P, Kowalczyk R, Krofel M, Krojerová-Prokešová J, Kubala J, Kusak J, Kutal M, Linnell JDC, Mattisson J, Molinari-Jobin A, Männil P, Odden J, Okarma H, Oliveira T, Pagon N, Persson J, Remm J, Schmidt K, Signer S, Tám B, Vogt K, Zimmermann F, Kuemmerle T (2023): Prerequisites for coexistence: human pressure and refuge habitat availability shape continental-scale habitat use patterns of a large carnivore. LANDSC ECOL 38, 1713–1728. doi:10.1007/s10980-023-01645-7\r\nOeser J, Heurich M, Kramer-Schadt S, Mattisson J, Krofel M, Krojerová-Prokešová J, Zimmermann F, Anders O, Andrén H, Bagrade G, Belotti E, Breitenmoser-Würsten C, Bufka L, Černe R, Drouet-Hoguet N, Duľa M, Fuxjäger C, Gomerčić T, Jędrze-jewski W, Kont R, Koubek P, Kowalczyk R, Kusak J, Kubala J, Kutal M, Linnell JDC, Molinari-Jobin A, Män-nil P, Middelhoff TL, Odden J, Okarma H, Oliveira T, Pagon N, Persson J, Remm J, Schmidt K, Signer S, Tám B, Vogt K, Kuemmerle T (2023): Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat. DIVERS DISTRIB, 29, 1546–1560. doi:10.1111/ddi.13784\r\nPalmero S, Premier J, Kramer-Schadt S, Monterroso P and Heurich M (2023): Sampling variables and their thresholds for the precise estimation of wild felid population density with camera traps and spatial capture–recapture methods. MAMM REV 53, 223–237. doi:10.1111/mam.12320\r\nPlanillo A, Viñuela J, Malo JE, García JT, Acebes P, Santamaría AE, Domínguez JC, Olea PP (2023): Addressing phase of population cycle and spatial scale is key to understand vole abundance in crop field margins: Implications for managing a cyclic pest species. AGRIC ECOSYST ENVIRON, 345, 108306. doi:10.1016/j.agee.2022.108306\r\nRasmussen SL, Schrøder BT, Berger A, Sollmann R, Macdonald DW, Pertoldi C, Alstrup AKO (2023): Testing the Impact of Robotic Lawn Mowers on European Hedgehogs (Erinaceus europaeus) and Desig-ning a Safety Test. ANIMALS 14, 122. doi:10.3390/ani14010122\r\nReusch R, Paul AA, Fritze M, Kramer-Schadt S, Voigt C (2023): Wind energy production in forests conflicts with tree-roosting bats. CURR BIOL, 33 737–743.e3. doi:10.1016/j.cub.2022.12.050\r\nRevilla-Martín N, Giralt D, Sanz-Pérez A, Bota G, Sardà-Palomera F (2023): Disentangling the effects of management, field characteristics of fallows, and surrounding landscape to promote steppe bird conservation. AGRIC ECOSYST ENVIRON 357, 108657. doi:10.1016/j.agee.2023.108657\r\nRocha DG and Sollmann R (2023): Habitat use patterns suggest that climate-driven vegetation changes will negatively impact mammal communities in the Amazon. ANIM CONSERV 26, 663–674. doi:10.1111/acv.12853\r\nRostro-García S, Kamler JF, Sollmann R, Balme G, Augustine BC, Kéry M, Crouthers R, Gray TNE, Groenenberg M, Prum S, Macdonald DW (2023): Population dynamics of the last leopard population of eastern Indochina in the context of improved law enforcement. BIOL CONSERV 283, 110080. doi:10.1016/j.biocon.2023.110080\r\nRusman M, Sikhounmeuang S, Phommachak A, Pathoummavan S, Ngonephetsy K, Valao M, Yoganand K, Tilker A (2023): A recent record of the Annamite striped rabbit Nesolagus timminsi in a local market in southern Lao P.D.R. MAMMALIA 87, 615-618. doi:10.1515/mammalia-2023-0038\r\nSchmitz OJ, Sylvén M, Atwood TB, Bakker ES, Berzaghi F, Brodie JF, Cromsigt JPGM, Davies AB, Leroux SJ, Schepers FJ, Smith FA, stark S, Svenning J-C, Tilker A, Ylänne H (2023): Trophic rewilding can expand natural climate solutions. NAT CLIM CHANG, 13, 324–333. doi:10.1038/s41558-023-01631-6\r\nSemper-Pascual A, Sheil D, Beaudrot L, Dupont P, Dey S, Ahumada J, Akampurira E, Bitariho R, Espinosa S, Jansen PA, Lima MGM, Martin EH, Mugerwa B, Rovero F, Santos F, Uzabaho E, Bischof R (2023): Occurrence dynamics of mammals in protected tropical forests respond to human presence and activities. NAT ECOL EVOL 7, 1092–1103. doi:10.1038/s41559-023-02060-6\r\nSvendsen NA, Radchuk V, Morel-Journel T, Thuillier V, Schtickzelle N (2023): Complexity vs linearity: relations between functional traits in a heterotrophic protist. BMC ECOL EVOL 23, 1. doi:10.1186/s12862-022-02102-w\r\nSynodinos AD, Karnatak R, Aguilar-Trigueros CA, Gras P, Heger T, Ionescu D, Maaß S, Musseau CL, Onandia G, Planillo A, Weiss L, Wollrab S, Ryo M (2023): The rate of environmental change as an important driver across scales in ecology. OIKOS 4, e09616. doi:10.1111/oik.09616\r\nTarbill GL, White AM & Sollmann R (2023): Response of pollinator taxa to fire is consistent with historic fire regimes in the Sierra Nevada and mediated through floral richness. ECOL EVOL 13, e10761. doi:10.1002/ece3.10761\r\nTilker A, Sinovas P (2023): Reading the signs: Camera-trapping provides new insights on scent marking in the large-antlered muntjac (Muntiacus vuquangensis). ECOL EVOL 13, e9692. doi:10.1002/ece3.9692\r\nTourani M, Sollmann R, Kays R, Karp DS (2023): Maximum temperatures determine the habitat affiliations of North American mammals. PNAS, 120(50), e2304411120. doi:10.1073/pnas.2304411120\r\nUllmann W, Fischer C, Kramer-Schadt S, Pirhofer Walzl K, Eccard JA, Wevers JP, Hardert A, Sliwinski K, Crawford MS, Glemnitz M, Blaum N (2023): The secret life of wild animals revealed by accelerometer data: how landscape diversity and seasonality influence the behavioural types of European hares. LANDSC ECOL 38, 3081–3095. doi:10.1007/s10980-023-01765-0\r\nVan Moorsel SJ, Thébault E, Radchuk V, Narwani A, Montoya JM, Dakos V, Holmes M, De Laender F, Pennekamp F (2023): Predicting effects of multiple interacting global change drivers across trophic levels. GLOB CHANG BIOL 29, 1223– 1238. doi:10.1111/gcb.16548\r\nZelnik YR, Clark AT, Radchuk V, Hodapp D and Dominguez-Garcia V (2023): Editorial: Stability across spatial and temporal scales. FRONT ECOL EEVOL 11, 1201269. doi:10.3389/fevo.2023.1201269\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:34+02:00" + "last_modified": "2024-06-05T15:50:35+02:00" }, { "path": "pubs-202x-dummy.html", @@ -244,7 +244,7 @@ "description": "", "author": [], "contents": "\r\n\r\nPeer-Reviewed Publications\r\n\r\n\r\nPreprints\r\n\r\n\r\nBook Chapters & Reports\r\n\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:35+02:00" + "last_modified": "2024-06-05T15:50:36+02:00" }, { "path": "repositories.html", @@ -252,7 +252,7 @@ "description": "", "author": [], "contents": "\r\n\r\n\r\nAxtner et al. 2019\r\n\r\n\r\nAn efficient and robust laboratory workflow and tetrapod database for larger scale environmental DNA studies. GIGASCIENCE, 8:giz029.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nBenhaiem et al. 2018\r\n\r\n\r\nSlow recovery from a disease epidemic in the spotted hyena, a keystone social carnivore. COMMUN BIOL, 1:201.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nCalderon et al. 2022\r\n\r\n\r\nOccupancy models reveal potential of conservation prioritization for Central American jaguars. ANIM CONSERV.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nCaro et al. 2023\r\n\r\n\r\nMeta- and subpopulation estimation with disparate data: coconut crabs in the Western Indian Ocean. ANIM CONSERV.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nDalleau et al. 2019\r\n\r\n\r\nModeling the emergence of migratory corridors and foraging hot spots of the green sea turtle. ECOL EVOL, 9:10317–1034.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nDanabalan et al. 2023\r\n\r\n\r\nComparison of mosquito and fly derived DNA as a tool for sampling vertebrate biodiversity in suburban forests in Berlin, Germany. ENVIRON DNA.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nDrenske et al. 2023\r\n\r\n\r\nOn the road to self-sustainability: reintroduced migratory European northern bald ibises Geronticus eremita still need management interventions for population viability. ORYX, 1-12.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nGrabow et al. 2022\r\n\r\n\r\nData-integration of opportunistic species observations into hierarchical modeling frameworks improves spatial predictions for urban red squirrels. FRONT ECOL EVOL, 10:881247.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nKürschner et al. 2021\r\n\r\n\r\nMovement can mediate temporal mismatches between resource availability and biological events in host–pathogen interactions. ECOL EVOL, 11:5728–5741.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nLouvrier et al. 2021\r\n\r\n\r\nSpatiotemporal interactions of a novel mesocarnivore community in an urban environment before and during SARS-CoV-2 lockdown. J ANIM ECOL, 91:367–380.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nMarescot et al. 2020\r\n\r\n\r\n‘Keeping the kids at home’ can limit the persistence of contagious pathogens in social animals. J ANIM ECOL, 90:2523–2535.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nNguyen et al. 2021\r\n\r\n\r\nGetting the big picture: Landscape-scale occupancy patterns of two Annamite endemics among multiple protected areas. CONS SCI PRACT, 4:e620.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nPlanillo et al. 2021\r\n\r\n\r\nArthropod abundance modulates bird community responses to urbanization. DIV DIST, 27:34-49.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nPlanillo et al. 2021\r\n\r\n\r\nCitizen science data for urban planning: Comparing different sampling schemes for modelling urban bird distribution. LAND URB PLAN, 211:104098.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nPlanillo et al. 2024\r\n\r\n\r\nUnderstanding habitat selection of range-expanding populations of large carnivores: 20 years of grey wolves (Canis lupus) recolonizing Germany. DIVDIST, 00, 1–16.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nRadchuk et al. 2016\r\n\r\n\r\nFrom individuals to population cycles: the role of extrinsic and intrinsic factors in rodent populations. ECOLOGY, 97:720-732.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nRadchuk et al. 2019\r\n\r\n\r\nAdaptive responses of animals to climate change are most likely insufficient. NAT COM, 10:3109.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nRocha & Sollmann 2023\r\n\r\n\r\nHabitat use patterns suggest that climate-driven vegetation changes will negatively impact mammal communities in the Amazon. ANIM CONSERV.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nScherer et al. 2020\r\n\r\n\r\nMoving infections: individual movement decisions drive disease persistence in spatially structured landscapes. OIKOS, 129:651–667.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nSchmied et al. 2024\r\n\r\n\r\nEffect of human induced surface water scarcity on herbivore distribution during the dry season in Ruaha National Park, Tanzania. WILDL BIOL, e01131\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nScholz 2024\r\n\r\n\r\nHost weight, seasonality and anthropogenic factors contribute to parasite community differences between urban and rural foxes. STOTEN 936.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nSollmann 2024\r\n\r\n\r\nMt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models. PEERJ 4, e1.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nVoigt et al. 2022\r\n\r\n\r\nModelling the power of acoustic monitoring to predict bat fatalities at wind turbines. CONSER SCI PRACT, 4:e12841.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:36+02:00" + "last_modified": "2024-06-05T15:50:37+02:00" }, { "path": "teaching.html", @@ -260,7 +260,7 @@ "description": "", "author": [], "contents": "\r\nCourse biodiversity dynamics\r\nWelcome to the course collection we have developed at our Department. Below you will find the compilation of our courses. These are hosted within one repository, so that data can be shared between the single course blocks The course has a strong focus on ecological data analysis and modelling, especially spatio-temporal data of animals. It covers the whole pipeline from observation and sampling → data analysis → model prediction → conservation concept.\r\nCourse pipelineThe single course blocks follow a logical order. The first two introductory courses give an overview over handling data in R. These two course blocks are mandatory for all other courses; we then introduce statistical analyses for various types of data, e.g. from spatial distribution models for single species via biodiversity analyses of community counts, towards analyses of population trends. To do the courses, please follow the code step by step in the respective html-files of each course (see below).\r\n\r\nEach year, we teach a block course (in March) held in English, which comprises 6 ECTS (approx. 180 hrs, of which 60 hrs are preparation, 60 hrs are in presence, and 60 hrs are for preparing a project which will be graded). We will lecture the steps in data analysis, and you will prepare a little spatial conservation concept with data we provide; you are welcome to bring your own data. Drop us a line if you are interested in joining ( assist6[at]izw-berlin.de ).\r\n\r\nCourse announcements\r\nCourses take place in March. Please contact kramer[at]izw-berlin.de\r\nPrior steps\r\nProgram installation\r\nRStudio Desktop (https://posit.co/download/rstudio-desktop/) \r\ncurrent R-version (https://cran.r-project.org/bin/windows/base/) \r\nRTools (https://cran.r-project.org/bin/windows/Rtools/) or you are using the {installr} package by following the examples (https://search.r-project.org/CRAN/refmans/installr/html/install.Rtools.html) \r\nIf you are not familiar with R and RStudio, open RStudio and follow the steps shown here of how to write scripts in R: Intro to RStudio\r\n\r\nIf you are familiar with scripting in R in RStudio, here is Cedric’s tip of the day of a cool feature in RStudio: use the rainbow parentheses option. For this, go to Tools → Global Options → Code →Display: tick rainbow parentheses. It helps you visually with closing brackets correctly for function calls and loops.\r\nChange decimal separator\r\n…from comma to point in your computer settings if you are working from a German PC, e.g. in Windows → Settings → Time and Region → Region → Advanced Settings → decimal separator: change here to point.\r\nDownload course\r\nSimply copy our course github repository directly here. This will download the whole folder structure with some basic data. However, we still recommend that you make a copy of the R-scripts so that you can write your own comments and code into the scripts. \r\nWe chose the following folder structure for our courses:\r\n\r\n└── d6_teaching_collection # root folder \r\n ├── data # data folder\r\n │ └── data_borneo # e.g., the Borneo data\r\n │ ├── geo_raster_current_asc # geo data, raster ascii format, as in data_borneo\r\n │ └── animal_data # animal observation data in borneo\r\n ├── output # storage for files created during course\r\n ├── R # store here all your scripts, i.e.\r\n │ ├── script_course1.R # r-files or rmd-files with codes\r\n │ └── script_course2.R\r\n ├── R_exercises # Exercises \r\n └── d6_teaching_collection.Rproj # the R-project\r\n\r\n\r\nOptional - use own course folder setup\r\nOptional - use own course folder setup\r\nOnly do this if you haven’t done the prior step of downloading the whole course and want to set up your own course folder structure or download only a part of the course. In order to ease access to data and script-functionality, we recommend that you use a similar folder structure as we do (see above). You will find the github repository here: https://github.com/EcoDynIZW/d6_teaching_collection.\r\nIf you are familiar with R and want to do the steps ‘by hand’, create a main (root) folder named d6_teaching_collection, then create the subfolders (data, output, R) relative to this root-folder (= d6_teaching_collection). Create an R-project within this folder (e.g. d6_teaching_collection.Rproj. For this, open RStudio → File → New Project → Existing directory: and then link it with the root folder).\r\n\r\nUsers familiar with R and RMarkdown could directly implement our d6 workflow package to make project setups easier and to handle code and projects in a reproducible way. If you chose this option, please read the ReadMe that appears on the github website and install the d6 package (with the options github= FALSE). This has the advantage that you have automatically created a root directory and an R-project. You can then load the data (e.g. data_borneo, provided in zip-files, see below) directly under the root directory. In the d6-package there are already start-rmd-scripts (= RMarkdown-scripts with yaml header → open and execute with the symbol knitR), which make the start easier. But you can also create your own R-scripts, e.g. a new script for each course unit, which you put into the folder R.\r\n\r\nFor both options, a) and b), you would need to download the data separately (link to zip.file below)\r\n\r\nFollow the courses\r\n…when you have downloaded the course folder repository (see above). In the respective course R folder, you will find html-files and rmd-files. Open the html-file in a browser by double-clicking. Open RStudio in parallel by double-clicking on the d6_teaching_collection.Rproj-file. Under Files (lower right window pane) , you can open the R-scripts.\r\n\r\nThen, either create your own R-script and follow the steps in the html-file (you can copy-paste the commands from the html-file), or save a copy of the rmd-file under your own name and run the code chunks step by step in the RStudio console. But you should always have the html-file opened in parallel to RStudio. Each course comes with exercises as a learning control.\r\nCourses 1 and 2: Basic concepts in R & Spatial R\r\n\r\nThese courses contain the minimum knowledge for handling, manipulating and visualizing various data types in R (Course1: vectors, data.frames, lists; Course2: spatial data like raster and vector files). The second course builds on the concepts introduced in course 1. It is therefore recommended to start with Course 1.\r\n\r\nYou can run Course 1 without downloading any additional data. Please note that course 1 is not meant as a beginners course - a lot of great in-depth courses are available online (see links provided in Course 1). Course 1 is rather a ‘refresher’ of the basic R concepts that are needed for all subsequent courses. In a nutshell, it goes through the base R cheatsheet. It is recommended to have the cheatsheet downloaded and to make your own notes into a copy of our script. You will find the course material for Course 1 in the repository under ./R/Course1_R_Intro.\r\n\r\nFor Course 2, you need to have the repository downloaded for access to the spatial data. Please open the .html file in ./R/Course2_R_Spatial.\r\n\r\nCourses 3_a and 3_b Species distribution models\r\n\r\nIn these courses, you will learn how to establish a functional relationship between the (relative) occurrence probability of species and (environmental) variables. This functional relationship – or species distribution/ occupancy model – can be extrapolated to other similar areas and used as an assessment of the availability of potentially suitable habitat for the respective species.\r\npresence only / MaxEnt\r\nPlease download MaxEnt here: https://biodiversityinformatics.amnh.org/open_source/maxent/\r\nFor MaxEnt you may still need to install a Java version. If you double-click the file maxent.jar, an interface should appear.\r\n\r\nDownload course data: For conducting course 3, we will provide you with additional data. Drag and drop the folders “data_borneo” and “data_berlin” from the provided zip files into the main folder (here: d6_teaching_collection).\r\n\r\nLink to data_borneo and data_berlin will be provided.\r\nPlease open the .html file in ./R/Course3_a_presence_only_sdm.\r\nrepeated presence absence / occupancy models\r\nPlease open the .html file in ./R/Course3_b_presence_absence_occupancy.\r\n\r\nCourse 4 Movement analysis\r\n\r\nThis course is an overview over the first steps for analysing relocation (telemetry) data. Using our collared red fox (Vulpes vulpes) ‘Q von Stralau’ as an example of movement data with fixes every 4 minutes, we will show how to clean your data and make first analyses of the tracks (step length, turning angles) and home range analyses.\r\nPlease open the .html file in ./R/Course4_telemetry_movement.\r\n\r\nCourse 5 Biodiversity metrics\r\n\r\nIn this part of the course we are going to explore more in detail how we measure and compare biodiversity among different sites. We will focus mainly of alpha diversity, which refers to the diversity at a specific site; this is the group of metrics we use to compare diversity among sites. We will explore the classic and current alpha diversity indices for both presence/absence data and abundance data. Then, we will learn which conditions the diversity samples need to meet to be comparable and how to analyse the effects of environmental variables in the diversity metric values. Finally, we will apply all this to statistically analyse urban Berlin bird diversity and create a spatial “map of diversity” for the city.\r\nPlease open the .html file in ./R/Course5_biodiversity_abundance_metrics.\r\n\r\nCourse 6 Population analyses\r\n\r\nPlease open the .html file in ./R/Course6_demography_population_models.\r\n\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:37+02:00" + "last_modified": "2024-06-05T15:50:37+02:00" }, { "path": "team-biodiversity.html", @@ -268,7 +268,7 @@ "description": "Lead: Dr. Andreas Wilting & Dr. Rahel Sollmann<\/b> \n Assistance: Dr. Jan Axtner<\/b>", "author": [], "contents": "\r\n\r\n\r\n\r\n\r\n\r\nGlobally mammalian biodiversity is declining at an alarming rate as a result of widespread habitat loss and degradation, and unsustainable hunting. We study how these anthropogenic drivers impact the distribution and abundance of ground dwelling mammals. In our field projects we use camera-traps and environmental DNA (eDNA) and invertebrate-derived DNA (iDNA) metabarcoding to assess species occurrences and we employ statistical models that investigate how anthropogenic drivers affect species distribution. We develop standardized methods and strategies for rigorous biodiversity assessments in order to provide robust scientific baseline data that allow monitoring species-specific and community-wide trends over time. In our work we regularly collaborate closely with local stakeholders and decision makers in order to integrate our data and results directly into wildlife and conservation management policy.\r\n\r\n\r\nDr. Andreas Wilting, Deputy Department and Team Lead — ecologist and evolutionary biologist with strong interest in species conservation, interested in methods and tools to study species distributions and to use these biodiversity data to target conservation efforts.\r\nDr. Rahel Sollmann, Team Lead — ecologist and quantitative biologist has specialised in the analysis of camera trap data with Bayesian methods for wildlife conservation.\r\nDr. Jan Axtner, Data Management — data manager, ecologist and evolutionary geneticist, finds ways how to produce, store and handle large amounts of data of modern high-throughput biodiversity assessments.\r\n\r\n\r\n\r\n\r\nRunning Projects\r\n\r\nField Projects\r\n\r\nMalaysia; Borneo\r\n\r\nPeople involved: Roshan Guharajan, Jürgen Niedballa, Seth T Wong\r\nLocal partners: Sabah Forestry Department, Ta Ann Holdings Berhad, WWF Malaysia (Sarawak)\r\nIn Malaysian Borneo we study how different anthropogenic drivers affect the occurrence and abundance of ground-dwelling mammal and bird communities. Since 2008, we have monitored mammalian communities over time in forest sites in various stages of recovering from severe logging impacts and under different forest management strategies. We compare the impacts of these strategies (i.e. mixed land-uses with industrial tree plantation and natural forest management, conventionally selective logging and reduced impact logging) at different spatial and temporal scales.\r\nFinancial support: Federal Ministry of Education and Research (BMBF), Panthera, Point Defiance Zoo and Aquarium, Point Defiance Zoo Society, Mohamed bin Zayed Species Conservation Fund, International Association for Bear Research and Management\r\n\r\nKey Publications:\r\n\r\nGuharajan et al. (2021) FOR ECOL MANAG\r\n\r\n\r\nMathai et al. (2019) GLOB ECOL EVOL\r\n\r\n\r\nBrozovic et al. (2018) MAMMALIAN BIOLOGY\r\n\r\n\r\nWong et al. (2018) MAMMALIAN BIOLOGY\r\n\r\n\r\nMathai et al. (2017) MAMMALIAN BIOLOGY\r\n\r\n\r\nSollmann et al. (2017) DIVERS DISTRIB\r\n\r\n\r\n\r\nViet Nam, Laos; Annamite region\r\n\r\nPeople involved: Andrew Tilker, An The Troung Nguyen, Thanh Van Nguyen, Jürgen Niedballa\r\nLocal partners Viet Nam: WWF Vietnam, Save Vietnam’s Wildlife, Fauna & Flora International, Re:wild, Southern Institute of Ecology SIE, Central Institute for Natural Resources and Environmental Studies, GreenViet\r\nLocal partners Laos: WWF Laos, Association Anoulak\r\nWe want to learn more about the ecology of the little known Annamite endemics and understand what anthropogenic predictors drive the current distribution of these threatened species. In addition to habitat loss, unsustainable hunting is emerging as an increasingly important threat to tropical wildlife biodiversity. Due to pervasive hunting even many protected areas face massive species losses today. This widespread defaunation has been particularly severe in Indochina, where ‘industrial’ snaring has decimated wildlife populations drastically and driven many species to local extinction. Since 2014 we have implemented systematic camera-trapping and iDNA surveys across protected and non-protected areas within the Annamite ecoregion. Using modern species distribution models we aim to identify and predict areas of particular conservation concern in order to support better conservation efforts by our partners.\r\nFinancial support: Federal Ministry of Education and Research (BMBF), Point Defiance Zoo & Aquaria, National Geographic, Ocean Park Conservation Foundation, Manfred-Hermsen-Stiftung, Mohamed bin Zayed Species Conservation Fund, Eva Mayr-Stihl Stiftung\r\n\r\nKey Publications:\r\n\r\nTilker et al. (2020) DIVERS DISTRIB\r\n\r\n\r\nNguyen et al. (2019) NAT ECOL EVOL\r\n\r\n\r\nTilker et al. (2019) COMMS BIOL\r\n\r\n\r\nTilker et al. (2017) SCIENCE\r\n\r\n\r\n\r\nConcepts & Methodology\r\n\r\nHierarchical statistical modeling for wildlife research.\r\n\r\n\r\nPeople involved: Ana Sanz, Jürgen Niedballa\r\nCollaborators: Prof. Dr. Beth Gardner and many others, depending on specific projects\r\nWildlife survey data are fraught with challenges: often sparse, spatially and/or temporally limited or biased due to logistic constraints, and imperfectly reflecting ecological states and processes due to imperfect detection (i.e., failing to observe species or individuals even though they are present). Hierarchical statistical modeling has emerged as the prime tool to deal with these challenges, by describing separate sub-models for the underlying ecological and the detection process. We employ such models throughout our research projects. But we also modify existing and develop new hierarchical models, and make these more accessible to end users, to improve our ability to study, monitor, and ultimately, protect wildlife. Modeling approaches we work with range from occupancy models for species occurrence to N-mixture, distance sampling and other count-based models for species abundance and population dynamics, to traditional and spatial capture-recapture models for abundance, density and demographics.\r\n\r\nKey Publications:\r\n\r\nKe et al. (2022) ECOL APPL\r\n\r\n\r\nSollmann et al. (2021) ECOL APPL\r\n\r\n\r\nGardner et al. (2018) ECOL EVOL\r\n\r\n\r\nSollmann et al. (2016) MEE\r\n\r\n\r\nRoyle et al. (2014) Spatial Capture Recapture. Academic Press, Waltham, MA.\r\n\r\n\r\n\r\nDeveloping standardized and reliable survey tools to monitor mammals in tropical rainforests.\r\n\r\n\r\nPeople involved: Jan Axtner, Roshan Guharajan, Thanh Van Nguyen, Jürgen Niedballa, Rahel Sollmann, Badru Mugerwa, Andrew Tilker, Seth T Wong, Andreas Wilting\r\nCollaborators: Prof. Dr. Douglas Yu (University of East Anglia, Norwich, UK & Kunming University, Kunming, China), Dr. Jesse F Abrams (University of Exeter, Exeter, UK)\r\nStudying biodiversity on a larger scale is essential to support political and conservation decisions. However, combining and integrating different biodiversity datasets into larger scale analyses is often challenging and associated with a loss of data accuracy and detail. We improve, develop and establish standardized methods and protocols to study and monitor biodiversity. Hereby we focus mainly on high-throughput methods that are applicable in tropical rainforests.\r\nScreenForBio user guide\r\n\r\nKey Publications:\r\n\r\nAbrams et al. (2021) ECOGRAPHY\r\n\r\n\r\nNguyen et al. (2021) ENVIRONMENTAL DNA\r\n\r\n\r\nWong et al. (2019) GLOBAL ECOL AND EVOL\r\n\r\n\r\nAbrams et al. (2019) J APPL ECOL\r\n\r\n\r\nAxtner et al. (2019) GIGASCIENSE\r\n\r\n\r\nNiedballa et al. (2019) REMOTE SENS ECOL CONSERV\r\n\r\n\r\nAbrams et al. (2019) ECOL INFORM\r\n\r\n\r\nBush et al. (2017) NAT ECOL EVOL\r\n\r\n\r\nMohd Salleh et al. (2017) GIGASCIENSE\r\n\r\n\r\nNiedballa et al. (2016) METHODS ECOL EVOL\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:39+02:00" + "last_modified": "2024-06-05T15:50:38+02:00" }, { "path": "team-individual.html", @@ -276,7 +276,7 @@ "description": "Lead: Dr. Sarah Benhaiem & Dr. Sonja Metzger (field coordination)<\/b> \nAssistance: Dagmar Thierer & Stephan Karl<\/b> \nIn collaboration with Dr. Marion L. East & Prof. Heribert Hofer (co-founders of the Serengeti Hyena Project)", "author": [], "contents": "\r\n\r\n\r\n\r\n\r\n\r\nWelcome! We study the behaviour, ecology and health of three clans of spotted hyenas (Crocuta crocuta) in the Serengeti National Park in Tanzania since 1987. We are currently interested in the impact of early life conditions, infections and human activities on individual performance and fitness at all life stages. With our collaborators we also study the role of epigenetic mechanisms in mediating the effects of the social environment on life history trade-offs, hormones and immunity, and the demographic consequences of disturbances. Our interdisciplinary research applies non-invasive or minimally invasive methods and to further this aim we have developed and verified several faecal assays for spotted hyenas.\r\n\r\n\r\nFigure: M. Gicquel (from Gicquel et al. 2022 Ecosphere)Why are spotted hyenas so interesting to study?\r\nThis highly social mammal has several unusual traits: female social dominance, an erectile ‘pseudopenis’ in females (similar to that of the male penis), an exceptionally long lactation period and intense sibling competing. It is a keystone carnivore in the ecosystem, which both hunts and scavenges. All these aspects make hyenas an interesting model species to study social behaviour, sexual conflict, maternal effects, host-pathogen interactions, immunology or endocrinology.\r\nOur study population experiences extreme and unpredictable fluctuations in prey abundance throughout the year, because of the migratory movements of its main prey (wildebeest, zebras and Thompson’s gazelles) and the low abundance of resident herbivores. We discovered that Serengeti hyenas solve this problem by commuting long distances to forage throughout the year. Individuals leave their clan territory and travel to areas up to 70 km to locate areas with large aggregations of migratory herbivores where they feed before returning to their clan territory. In the context of global change, the commuting system of Serengeti hyenas is particularly valuable to study how animals cope with a variable and uncertain resource.\r\nUnfortunately, commuting hyenas in the Serengeti can get killed by wire snares set illegally by bushmeat hunters along some borders of the Park or become victims of road accidents. These anthropogenic threats are likely to intensify with the expected increases in local human populations and traffic volume associated with tourism. Changes in rainfall patterns driven by global warming may also have cascading effects on this large slow-reproducing mammal.\r\n\r\n\r\n\r\n\r\nRunning Projects\r\n\r\nBehavioural Ecology, Ecology, and Demography\r\n\r\nEcological, social and maternal effects on individual performance, population dynamics and resilience\r\n\r\n\r\nCurrent team & collaborators: Morgane Gicquel, Dagmar Thierer, Stephan Karl, Sonja Metzger, Sarah Benhaiem, Marion L. East, Heribert Hofer, Viktoriia Radchuk (Dept. 6, IZW), Oliver Höner (Dept. 1, IZW), Adam Clark (University of Graz), Stephanie Kramer-Schadt (Dept. 6, IZW)\r\nWe investigate how ecological and social conditions (e.g. rainfall, maternal rank, litter size), behaviours (e.g. infanticides), and disturbances (e.g. epidemics, droughts) influence the reproductive performance and survival prospects of spotted hyenas at different life stages, and their demographic resilience.\r\n\r\nKey Publications:\r\n\r\nBenhaiem et al. (2018) COMMUN BIOL\r\n\r\n\r\nBenhaiem et al. (2018) FRONT VET SCI\r\n\r\n\r\nEast et al. (2022) FRONT ECOL EVOL\r\n\r\n\r\nGicquel et al. (2022) J ANIM ECOL\r\n\r\n\r\nMarescot et al. (2018) FUNCT ECOL\r\n\r\n\r\n\r\nDisease ecology\r\n\r\nDisease ecology: consequences of infections on allostatic load and fitness\r\n\r\n\r\nCurrent team & collaborators: Miguel Veiga, Susana Soares, Dagmar Thierer, Stephan Karl, Sonja Metzger, Joshua Dalijono (student helper), Marion L. East, Heribert Hofer, Gábor Á. Czirják (Dept. 3, IZW), Sarah Benhaiem, Jella Wauters (Dept. 4, IZW)\r\nWe investigate the interactions between spotted hyenas and their pathogens, including their gastrointestinal parasitic community and viruses. We are particularly interested in the causes and consequences of infections in individuals, immunosenescence and the interactions between allostatic load (“stress”), immunity and infections.\r\n\r\nKey Publications:\r\n\r\nDavidian et al. (2015) METHODS ECOL EVOL\r\n\r\n\r\nFerreira et al. (2019) ECOL EVOL\r\n\r\n\r\nFerreira et al. (2021) ECOL EVOL\r\n\r\n\r\nMarescot et al. (2021) J ANIM ECOL\r\n\r\n\r\nOlarte‐Castillo et al. (2021) MOL ECOL\r\n\r\n\r\n\r\nConservation\r\n\r\nHuman-wildlife conflicts and climate change in the Serengeti\r\n\r\n\r\nCurrent team & collaborators: Marwan Naciri (former MSc student), Montan Kalyahe, Morgane Gicquel, Dagmar Thierer, Stephan Karl, Sonja Metzger, Sarah Benhaiem, Marion L. East, Heribert Hofer, Aimara Planillo (Dept. 6, IZW), Sarah Cubaynes (CEFE, Montpellier)\r\nWe are interested in assessing the effects of illegal bushmeat hunting (snaring), roadkills and changes in rainfall patterns driven by global warming on spotted hyenas. We also investigate the factors that influence the likelihood of predation on livestock and consumption of discarded livestock ‘waste’.\r\n\r\nKey Publications:\r\n\r\nBenhaiem et al. (2022) ANIM CONSERV\r\n\r\n\r\nGicquel et al. (2022) ECOSPHERE\r\n\r\n\r\nKalyahe et al. (2022) ENVIRON CONSERV\r\n\r\n\r\nNaciri et al. (2023) BIOL CONSERV\r\n\r\n\r\n\r\nSocial Epigenetics\r\n\r\nEpigenetic stability and plasticity of social environmental effects\r\n\r\n\r\nCurrent team & collaborators: Alexandra Weyrich (Dept. 2, IZW, PI), Colin Vullioud (Dept. 2, IZW), Jörns Fickel (Dept. 2, IZW), Lena Ruf (Dept. 2, IZW), Nick Mewes (Dept. 2, IZW), Sarah Benhaiem, Marion L. East, Heribert Hofer, Gábor Á. Czirják (Dept. 2, IZW), Emmanuel Heitlinger (Humboldt University), Jerzy Adamski (Helmholtz-Center Munich), Alexander Cecil (Helmholtz-Center Munich), Moshe Szyf (McGill University), Yoav Soen (Weizmann Institute of Science)\r\nWe investigate if DNA methylation is a main mechanism through which an individual’s social environment regulates gene expression and physiological responses. We hypothesize that changes in social status will led to changes in epigenetic patterns. To test our hypotheses, we study the DNA methylation patterns in the spotted hyena.\r\n\r\nKey Publications:\r\n\r\nGuerrero et al. (2020) CURR ZOOL\r\n\r\n\r\nHeitlinger et al. (2017) FRONT CELL INFECT MICROBIOL\r\n\r\n\r\nVullioud et al. (2024) COMMUN BIOL\r\n\r\n\r\n\r\n\r\nGeneral Information\r\nGet our information leaflet about the Serengeti Spotted Hyaena Project!\r\n\r\n\r\n\r\n\r\n\r\n → English version\r\n → Deutsche Version (in German)\r\n → Toleo la Kiswahili (in Swahili)\r\n → Version française (in French)\r\n\r\n\r\nHere is an overview of our research interests, methods and partners since the establishment of the project in 1987:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:40+02:00" + "last_modified": "2024-06-05T15:50:39+02:00" }, { "path": "team-population.html", @@ -284,7 +284,7 @@ "description": "Lead: Dr. Stephanie Kramer-Schadt & Dr. Viktoriia Radchuk<\/b> \nAssistance: Dr. Conny Landgraf & Moritz Wenzler<\/b>", "author": [], "contents": "\r\n\r\n\r\n\r\n\r\n\r\nWildlife has to cope with many challenges in the Anthropocene, but data are often too scarce and messy to predict the future fate of populations and communities under global change. To disentangle processes and drivers behind ecological and evolutionary dynamics of wildlife populations and communities, we combine field work with advanced data analysis and the development of concepts with designing stochastic simulation models. We use forecasting techniques to project scenarios of population and community change under disturbances. With this, we contribute to applied and theoretic ecology and biodiversity conservation under global change.\r\n\r\n\r\nDr. Stephanie Kramer-Schadt, Department and Team Lead — applied ecologist, population and disease dynamics at the landscape scale, passionate about wildlife per se, carnivores in particular, D6 and movement ecology, uses models as tools to communicate management issues.\r\nDr. Viktoriia Radchuk, Team Lead — quantitative ecologist, stability of populations and communities under global change, interested in theory, synthesis, and integration of data with models to assist conservation.\r\nDr. Conny Landgraf, Coordination — organizes us, behavioral ecologist, interested in sensory and acoustic cues of animals.\r\nMoritz Wenzler-Meya, GIS-Lab— geodata analyst, responsible for the GIS lab, providing geodata and supporting coding.\r\n\r\n\r\n\r\n\r\nRunning Projects\r\n\r\nTheory and Synthesis\r\n\r\nStability under global change and across levels of organization\r\n\r\n\r\nDr. Cédric Scherer, Thibault Fronville\r\nTo understand how populations and communities react to global change we study how their stability is affected by disturbances. To this end we model disturbances of different types and intensity and measure several stability metrics.\r\nAnimal responses to climate change\r\nEnvironmental variation effects on stability of populations and communities\r\n\r\nKey Publications:\r\n\r\nRadchuk et al. (2019) ECOL LETT\r\n\r\n\r\nRadchuk et al. (2019) NAT COMMUN\r\n\r\n\r\n\r\nWildlife disease dynamics: Linking host and pathogen traits\r\n\r\n\r\nDr. Cédric Scherer, Tobias Kürschner, Marius Grabow\r\nPathogens are an integral part of biodiversity, influencing population dynamics of their hosts and playing an important functional role in shaping community structure. Here, our aim is to understand the effect that species as ‘mobile pathogen links’ with their different movement types and life-history strategies have on disease distribution, spread, persistence and evolution.\r\n → see also BioMove Graduate School\r\nMovement effects on pathogen spread and disease persistence\r\nEvolution of pathogenic strains in dynamic landscapes\r\nPathogen-induced movement strategies and fitness consequences\r\n\r\nKey Publications:\r\n\r\nKürschner et al. (2021) ECOL EVOL\r\n\r\n\r\nScherer et al. (2020) OIKOS\r\n\r\n\r\nScherer et al. (2019) J ANIM ECOL\r\n\r\n\r\nMarescot et al. (2018) FUNCT ECOL\r\n\r\n\r\nKramer-Schadt et al. (2009) OIKOS\r\n\r\n\r\n\r\nApplied Ecology\r\n\r\nUrban wildlife ecology: How do animals respond to novel environments?\r\n\r\nDr. Aimara Planillo, Dr. Julie Louvrier, Sinah Drenske, Simon Moesch\r\nUrbanisation poses risks and opportunities for wildlife. We investigate how species cope with these everyday challenges by analysing the spatial factors and species interactions that underlie their distributions along a rural to urban gradient and by making inference on their behavioral plasticity.\r\n → see also BIBS — rural-urban coupling\r\n → see also WT Impact\r\nBridging spatial data in community distribution models\r\nEffects of species interactions and human disturbance on community compositions\r\nEcology of red foxes (Vulpes vulpes) in anthropogenic landscapes\r\nHuman perceptions of urban wildlife\r\n\r\nKey Publications:\r\nPlanillo et al. (2021) LANDSC URBAN PLA\r\n\r\n\r\nPlanillo, Kramer-Schadt, et al. (2020) DIVERS DISTRIB\r\n\r\n\r\nGras et al. (2018) FRONT ECOL EVOL\r\n\r\n\r\nStillfried et al. (2017) FRONT ECOL EVOL\r\n\r\n\r\n\r\nWildlife distributions, population dynamics, and conservation\r\n\r\nDr. Julie Louvrier, Dr. Aimara Planillo, Dr. Cédric Scherer, Dr. Joe Premier, Ana Patricia Calderon, Eva Sánchez Arribas\r\nWildlife faces big challenges persisting in human-dominated landscapes. We model their population dynamics, viability and connectivity using individual-based models on a spatially-explicit basis, with the aim of supporting wildlife management and conservation.\r\nWolf population dynamics and establishment in Germany\r\nJaguar connectivity and conservation prioritization in Central America\r\nModelling genetic processes to support the conservation management of Eurasian lynx\r\n\r\nKey Publications:\r\n\r\nPremier et al. (2020) MOV ECOL\r\n\r\n\r\nHeurich, Schultze-Naumburg et al. (2018) BIOL CONSERV\r\n\r\n\r\nRadchuk, Ims & Andreassen (2016) ECOLOGY\r\n\r\n\r\nStruebig et al. (2015) CURR BIOL\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", - "last_modified": "2024-06-04T13:06:41+02:00" + "last_modified": "2024-06-05T15:50:40+02:00" } ], "collections": ["posts/posts.json", "posts_geodata/posts_geodata.json"] diff --git a/docs/site_libs/leaflet-binding-2.2.0/leaflet.js b/docs/site_libs/leaflet-binding-2.2.1/leaflet.js similarity index 100% rename from docs/site_libs/leaflet-binding-2.2.0/leaflet.js rename to docs/site_libs/leaflet-binding-2.2.1/leaflet.js diff --git a/docs/site_libs/leaflet-providers-plugin-2.2.0/leaflet-providers-plugin.js b/docs/site_libs/leaflet-providers-plugin-2.2.1/leaflet-providers-plugin.js similarity index 100% rename from docs/site_libs/leaflet-providers-plugin-2.2.0/leaflet-providers-plugin.js rename to docs/site_libs/leaflet-providers-plugin-2.2.1/leaflet-providers-plugin.js diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 882f0c3f..2438c3dc 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -2,394 +2,394 @@ https://ecodynizw.github.io/about.html - 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color: var(--hover-color, white); } - + diff --git a/publications.Rmd b/publications.Rmd index 1f79c050..ce69abf6 100644 --- a/publications.Rmd +++ b/publications.Rmd @@ -57,7 +57,7 @@ doi:[10.1111/acv.12896](https://doi.org/10.1111/acv.12896) * Becker J, Liess M, **Kramer-Schadt S**, Franz M, Jager T (2024): Critical Evaluation of Effect Models for the Risk Assessment of Plant Protection Products. UBA Report **41/2024**, ISSN 1862-4804, 596 pages. [https://www.umweltbundesamt.de/sites/default/files/medien/11850/publikationen/41_2024_texte_critical_evaluation.pdf](https://www.umweltbundesamt.de/sites/default/files/medien/11850/publikationen/41_2024_texte_critical_evaluation.pdf) -* Biersteker L, Planillo A, Lammertsma DR, van der Sluis T, Knauer F, Kramer-Schadt S, van der Grift EA, van Eupen M, Jansman HAH (2024): Habitatgeschiktheid voor de wolf in Nederland: een modelanalyse. Wageningen Environmental Research, Rapport 3350, 87 pages, ISSN: 1566-7197; [https://research.wur.nl/en/publications/habitatgeschiktheid-voor-de-wolf-in-nederland-een-modelanalyse](https://research.wur.nl/en/publications/habitatgeschiktheid-voor-de-wolf-in-nederland-een-modelanalyse) +* Biersteker L, Planillo A, Lammertsma DR, van der Sluis T, Knauer F, **Kramer-Schadt S**, van der Grift EA, van Eupen M, Jansman HAH (2024): Habitatgeschiktheid voor de wolf in Nederland: een modelanalyse. Wageningen Environmental Research, Rapport 3350, 87 pages, ISSN: 1566-7197; [https://research.wur.nl/en/publications/habitatgeschiktheid-voor-de-wolf-in-nederland-een-modelanalyse](https://research.wur.nl/en/publications/habitatgeschiktheid-voor-de-wolf-in-nederland-een-modelanalyse) * Thonicke K, Rahner E, Arneth A, Bonn A, Borchard N, Chaudhary A, Darbi M, Dutta T, Eberle U, Eisenhauer N, Farwig N, Flocco CG, Freitag J, Grobe P, Grosch R, Grossart HP, Grosse A, Grützmacher K, Hagemann N, Hansjürgens B, Hartman Scholz A, Hassenrück C, Häuser C, Hickler T, Hölker F, Jacob U, Jähnig S, Jürgens K, **Kramer-Schadt S**, Kretsch C, Krug C, Lindner JP, Loft L, Mann C, Matzdorf B, Mehring M, Meier R, Meusemann K, Müller D, Nieberg M, Overmann J, Peters RS, Pörtner L, Pradhan P, Prochnow A, Rduch V, Reyer C, Roos C, Scherber C, Scheunemann N, Schroer S, Schuck A, Sioen GB, Sommer S, Sommerwerk N, Tanneberger F, Tockner K, van der Voort H, Veenstra T, Verburg P, Voss M, Warner B, Wende W, Wesche K (2024): 10 Must-Knows aus der Biodiversitätsforschung 2024. [https://zenodo.org/records/10794362](https://zenodo.org/records/10794362)