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National Park Species

This week we're exploring species at the most visited National Parks in the USA! NPSpecies contains species listed by National Parks maintained by National Parks Service (NPS). Given the size of the dataset, we're focusing on the 15 most visited parks. The data comes from https://irma.nps.gov/NPSpecies/Search/SpeciesList.

The information in NPSpecies is available to the public. The exceptions to this are records for some sensitive, threatened, or endangered species, where widespread distribution of information could potentially put a species at risk.

An essential component of NPSpecies is evidence; that is, observations, vouchers, or reports that document the presence of a species in a park. Ideally, every species in a park that is designated as “present in park” will have at least one form of credible evidence substantiating the designation

If you are looking for more detailed information on the dataset, here is the glossary for column names, field options, and tag meanings: https://irma.nps.gov/content/npspecies/Help/docs/NPSpecies_User_Guide.pdf

To properly cite NPSpecies use the following: NPSpecies - The National Park Service biodiversity database. **https://irma.nps.gov/npspecies/. Accessed date/time. **

This data was accessed on September 2nd, 2024.

If you are interested in additional data, the curated dataset for all national parks is available at https://github.com/frankiethull/NPSpecies.

Thank you to f. hull for curating this week's dataset.

The Data

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2024-10-08')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 41)

most_visited_nps_species_data <- tuesdata$most_visited_nps_species_data

# Option 2: Read directly from GitHub

most_visited_nps_species_data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-10-08/most_visited_nps_species_data.csv')

How to Participate

  • Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
  • Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
  • Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
  • Submit your own dataset!

Data Dictionary

most_visited_nps_species_data.csv

variable class description
ParkCode character National Park Code.
ParkName character National Park Full Name.
CategoryName character Species Category.
Order character Species Order.
Family character Species Family.
TaxonRecordStatus character whether or not the taxon is active.
SciName character scientific name for the species.
CommonNames character common name of the species.
Synonyms list other names the species may go by.
ParkAccepted logical whether or not the park accepts this species.
Sensitive logical whether or not the species is 'sensitive'.
RecordStatus character whether or not nps approved the species.
Occurrence character The current status of existence or presence of each species in each park. Applicable only to scientific names with Park Accepted Status of "Accepted". Possible values reflect a combination of confidence, and availability and currency of verifiable evidence.
OccurrenceTags character additional sighting informational tag.
Nativeness character whether or not the species is native.
NativenessTags character additional native informational tag.
Abundance character how abundant is the species in the park.
NPSTags character NPSpecies system-wide attributes and tags are standard categories and designations that apply across all parks and species.
ParkTags character parks can create their own custom attributes, called “park tags,” and apply them to their park species records. For example, perhaps a park wants to set up a list of spring wildflowers, or identify the park subunits in which species occur.
References integer four columns that display the number of associated evidence records that substantiate the status of the species in the park: Observations, Vouchers, References, and External Links. A document, publication, article, database, or other information resource that contains information on one or more park species.
Observations integer four columns that display the number of associated evidence records that substantiate the status of the species in the park: Observations, Vouchers, References, and External Links. An observation is subjective evidence (no physical proof taken) as to the identity and the location of an organism.
Vouchers integer four columns that display the number of associated evidence records that substantiate the status of the species in the park: Observations, Vouchers, References, and External Links. Physical evidence used to confirm identity and prove an organism was found in a particular location. Forms of physical evidence include a voucher specimen at a museum or herbarium (including whole or piece of organism), or in some cases a photo image (i.e. digital or hardcopy)
ExternalLinks character four columns that display the number of associated evidence records that substantiate the status of the species in the park: Observations, Vouchers, References, and External Links.
TEStatus character indicates any FWS Threatened or Endangered species status.
StateStatus character Many states and US territories maintain their own lists of species of concern, or may have other status categories that are assigned to species within a state/territory.
OzoneSensitiveStatus character Plant species found within National Park boundaries that are known to have a negative response to high ozone exposure. Ground-level ozone can cause visible leaf injury (e.g. bleaching or dark stippling), growth and yield reductions, and altered sensitivity to stressors (e.g. pests, diseases, or drought).
GRank character Global ranks assess the level of rarity or abundance of a taxon throughout its range.
SRank character State ranks assess rarity or abundance of a taxon within a state.

Cleaning Script

# NPSpecies - The National Park Service biodiversity database.   
# https://irma.nps.gov/npspecies/       
# Accessed September 2nd, 2024   
devtools::install_github("frankiethull/NPSpecies")

# get rdb data from curated pkg ----
# uh-oh, it is 96MB, dslc notes that cannot exceed 20MB #
nps_species_data <- NPSpecies::species

# let's focus on most visited parks! ----
# note that I'm filtering on NPs and 2023: 
# https://irma.nps.gov/Stats/Reports/Park
# https://irma.nps.gov/Stats/SSRSReports/National%20Reports/Annual%20Park%20Ranking%20Report%20(1979%20-%20Last%20Calendar%20Year)

# top 10-20 parks but do not exceed 20MB
top_visited <- c("GRSM", "GRCA", "ZION", "YELL", "ROMO", 
                 "YOSE", "ACAD", "GRTE", "JOTR", "OLYM", 
                 "GLAC", "CUVA", "INDU", "HOSP", "BRCA")

most_visited_nps_species_data <-
nps_species_data |>
  dplyr::filter(ParkCode %in% top_visited) |>
  # removed a few columns, already lots of info
  dplyr::select(-c(CategorySort, TaxonCode, TSN))