DOI: 10.5061/dryad.hx3ffbgms
title: Mapping the missing: assessing amphibian sampling completeness and overlap with global protected areas

description: The aim of the study was to assess amphibian sampling completeness and the
 overlap of sampling completeness categories with natural protected areas
 (NPAs) and key biodiversity areas (KBAs) at global scale. We evaluated
 amphibian sampling completeness across six of the earth's eight
 biogeographic realms to identify well‐sampled, under‐sampled, and data‐gap
 areas in the context of global amphibian distribution. Additionally, we
 examined the spatial overlap of each sampling category with NPAs and KBAs.
 The Nearctic and Australasian realms had the highest number of records and
 well‐sampled areas. Significant data gaps were identified, particularly in
 the Afrotropical, Indo‐Malayan, Neotropical, and Palearctic realms. We
 found low levels of spatial match (&lt; 35%) between classified areas
 and NPAs/KBAs. Amphibian distribution data are largely incomplete, with
 the most extensive gaps in the most species‐rich realms: Neotropic,
 Indo‐Malayan, and Afrotropical. The low overlap between under‐sampled and
 data‐gap areas with NPAs and KBAs suggests that these regions, critical
 for amphibian diversity, are insufficiently represented within established
 conservation priorities. Given the urgent threats to biodiversity from
 global change, rapid responses are essential to enhance our understanding
 of species distributions and community structures in amphibians. This
 study provides spatial insights to help identify key data‐gap areas for
 amphibian research and conservation prioritization.
description: # **Mapping amphibian data gaps: global assessment of sampling
 completeness and overlap with protected and key biodiversity areas** This
 dataset is part of the Data Availability Statement of the article Mapping
 the Missing: Assessing Amphibian Sampling Completeness and Overlap With
 Global Protected Areas (DOI: 10.1002/ECE3.71137), published in Ecology and
 Evolution. The article provides detailed citations for each dataset used,
 along with a full description of the processing steps applied. To respect
 the usage rights of publicly available datasets, we do not share the
 original files but instead provide simplified versions that retain only
 the essential structure required to replicate our analyses. We strongly
 encourage users to access the original datasets for a more comprehensive
 source of information. The most important datasets to run through the
 available scripts are: Global amphibian distribution data: Retrieved from
 the Global Biodiversity Information Facility (GBIF) and available at DOI:
 https://doi.org/10.15468/dl.gv57xr. We provide a filtered and simplified
 version that preserves only the necessary information for analysis
 replication. This modified version is distributed under the CC BY-NC 4.0
 license, aligning with the original dataset’s licensing and our
 statement’s non-commercial focus. Key Biodiversity Areas (KBAs): We do not
 have permission to distribute the original KBA dataset, nor modified
 versions of it, therefore, we ask that in cases where KBAs are needed, you
 download the file on request from:
 https://www.keybiodiversityareas.org/kba-data/request and include it in
 the file path marked in the script. Natural Protected Areas (NPAs): We do
 not have permission to distribute the original NPAs dataset, nor modified
 versions of it, therefore, we ask that in cases where NPAs are needed, you
 download the file on request from:
 https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA and
 include it in the file path marked in the script. Usage Guidelines: This
 material contains files of various types. Ideally, all files and folders
 should be managed directly in R via the RStudio graphical interface by
 opening the project "R_manuscrito.Rproj", which is included in
 the compressed file. You can download R from r-project.org and RStudio
 from posit.co. While most files can also be opened with other software,
 their compatibility depends on the file format. Below are recommendations
 for suitable programs to open different file types: • .rda – R data files
 that should be loaded in R using the command load(file.rda). We recommend
 accessing these files only within the provided scripts (scripts folder). •
 .gpkg / .shp – Vector-based spatial data files that can be opened in QGIS
 or in R using the sf or terra packages, as demonstrated in the scripts. •
 .txt / .csv – Plain text files that can be viewed with standard text
 editors (e.g., Gedit on Debian-based Linux systems) or office suites like
 LibreOffice, OpenOffice, or Microsoft Office. • .png / .tiff – Image files
 that can be opened with default image viewers (e.g., GNOME Image Viewer on
 Debian-based Linux systems), office software, or image editors such as
 GIMP (gimp.org). • .R – R script files containing executable code for the
 R console. It is recommended to open them in RStudio. For a structured
 workflow, we recommend installing R and RStudio, opening the project
 "R_manuscrito.Rproj", and running the scripts sequentially (01,
 02, 03, etc.). Each script includes detailed explanations of the
 analytical workflow. Below, we describe the contents of the dataset: •
 data: Corresponds to the input data used as the basis for the analyses. In
 cases where they are made available, they are modified and simplified
 versions intended solely for use in running this script. In each case, the
 user should refer to the article mentioned at the beginning of this README
 to access the complete datasets through the citations. • output: Refers to
 the processed data generated through the application of the different
 scripts. These contain the results presented in the article mentioned at
 the beginning. • scripts: Contains the scripts in R language used to
 process the data (data) and obtain the results (output). Detailed
 description of the dataset: data: • Amphinom: This file contains the
 taxonomic database for amphibian species, sourced from the Amphinom
 package: Liedtke (2019). AmphiNom: an amphibian systematics tool.
 Systematics and Biodiversity, 17(1), 1-6. In .rda format. • Borders: A
 shapefile and a .gpkg outlining the continents, primarily used for mapping
 purposes. • Donerstein_2017: These .gpkg includes the biogeographic
 domains defined by Olson et al. (2001) and Dinestein et al. (2017). The
 original dataset can be accessed at https://doi.org/10.1093/biosci/bix014.
 • extent: The biogeographic extent used in this study, described in detail
 in the methodology section of the article referenced at the beginning of
 this README. The format is .gpkg. • Holt_2013: This .gpkg contains the
 biogeographic domains from Holt et al. (2013), available at
 https://doi.org/10.1126/science.1228282. • IUCN: A modified and simplified
 .gpkg version of the spatial outline created by overlapping individual
 amphibian species polygons with IUCN spatial data. This file is used
 exclusively to define the study extent. • KBAs and NPAs: These folders are
 empty except for a README file containing instructions for downloading the
 Key Biodiversity Areas and Natural Protected Areas datasets. The README
 also specifies the directory where these polygon files should be stored,
 as we do not have permission to distribute the original data. Output: •
 Biogeographic_prop: Results from the comparison of the biogeographic
 frameworks proposed by Holt et al. (2013) and Dinestein et al. (2017).
 These findings are discussed in detail in the supplementary material of
 the article cited at the beginning of this README. This material contains
 two .png files illustrating a Similarity Analysis (ANOSIM) and a Cluster
 Analysis, comparing different biogeographic classifications. To fully
 grasp the analytical process and the results, we recommend reviewing the
 script "06_choose_biogeographic.R". • completeness: Contains the
 results of the sampling completeness analysis, which is examined and
 discussed in depth in the article cited at the beginning of this README.
 This folder contains two identical images, available in .png and .tiff
 formats, depicting variation in sampling completeness across different
 biogeographic realms. Additionally, it includes three .csv tables with the
 following variables: ◦ estimators2.csv / se2.csv: ▪ Area: Hexagon
 identifier ▪ Records: Number of records within each hexagon ▪
 Observed.richness: Observed species richness ▪ Richness: Estimated species
 richness ▪ Slope: Slope of the species accumulation curve ▪ Completeness:
 Estimated completeness ▪ Ratio: Ratio of records to species richness ▪ SE:
 Standard error of the richness estimate ▪ R²: Coefficient of determination
 of the accumulation curve ◦ species_per_site2.csv: ▪ Species name ▪
 Longitude (X-coordinate) ▪ Latitude (Y-coordinate) ▪ Number of records per
 species To fully understand the logic behind these files and their
 generation process, refer to the script
 "03_Completeness_estimation.R". • effect_size: Stores the
 results of the analysis assessing the impact of hexagon sizes on
 completeness estimates. These results are thoroughly discussed in the
 supplementary material of the article cited at the beginning of this
 README. This folder contains three subfolders and an image. Each subfolder
 includes tables with units that follow the same interpretation as those in
 the "completeness" folder described earlier. The .png image
 illustrates the effect of hexagon size on species richness and sampling
 completeness. To understand the data generation process, refer to the
 script "05_effect_size.R". • hexagons: A dataset of hexagons
 generated based on our defined extent, serving as the foundation for
 subsequent analyses. All files in this folder are in .gpkg format. Their
 origin and generation process are thoroughly explained in the script
 "03_Completeness_estimation.R". • maps: Includes the maps
 generated and presented as part of the results in the article cited at the
 beginning of this README. This folder contains image files in .tiff and
 .png formats, displaying the manuscript results. To understand their
 origin and the process behind their creation, please refer to the scripts
 "03_Completeness_estimation.R" and "07_maps.R". •
 occurrence_records: A modified version of occurrence data retrieved from
 GBIF, along with various sub-versions used throughout the analysis. This
 folder contains all the species tables used in the analysis, in both table
 format (.csv) and spatial format (.gpkg). Below is a description of the
 variables in each table: • data_KnowBR_format.csv – Species: Species name,
 Longitude: x-coordinates, Latitude: y-coordinates, Counts: number of
 independent records (see script "03_Completeness_estimation.R").
 • Gbif_records_00.csv – Simplified table of GBIF records. Species: species
 name, Genus: genus, SpecificEpithet: specific epithet, Family: family,
 Order: order, Class: class, GbifID: GBIF record ID, DecimalLatitude:
 y-coordinates, DecimalLongitude: x-coordinates, Country: country of the
 record, Year: year of the record, VerbatimEventDate: record date,
 IndividualCount: individual count, IUCNRedListCategory: IUCN species
 category (see "00_Download_GBIF_records.R"). ◦
 Gbif_records_01.csv – Simplified and cleaned GBIF records table, with the
 same interpretation as the previous table (see
 "01_Clean_records.R"). ◦ Gbif_records_02_full.csv /
 Gbif_records_02_reduced.csv – GBIF records with updated and curated
 taxonomy, with the same interpretation as the previous table (see
 "02_Update_the_taxonomy_of_records.R"). ◦ sinonimos.csv /
 sinonimos_updated.csv – Contain taxonomic updates of the species used in
 the analysis. Query: species name in GBIF, Stripped: encoded name, Status:
 taxonomic status of the name, Warnings: warnings, ASW_names: species name
 in Amphibian Species of the World (see
 "02_Update_the_taxonomy_of_records.R"). • tables: The tables
 summarizing key results, as presented and discussed in the article cited
 at the beginning of this README. This folder contains three .csv tables,
 corresponding to the tables presented in the manuscript. Below is a
 description of the variables in each dataset: ◦ tab2.csv – This table
 provides information on sampling completeness per biogeographic domain.
 Variables include realm (biogeographic domain), area (total domain area),
 well_sampled (well-sampled area), wsp (percentage of the domain classified
 as well-sampled), under_sampled (under-sampled area), usp (percentage of
 the domain classified as under-sampled), no_inf (area with information
 gaps), and nip (percentage of the domain classified as having information
 gaps). All areas are expressed in million hectares (Mha). ◦ tab3.csv –
 This table focuses on the overlap between sampling completeness categories
 and natural protected areas (NPAs). It includes realm (biogeographic
 domain), npa.area (total area of NPAs within the domain),
 well_sampled_area (total well-sampled area), well_sampled_ov (overlapping
 area between NPAs and well-sampled areas), wsp (percentage of well-sampled
 areas overlapping with NPAs), under_sampled_area (total under-sampled
 area), under_sampled_ov (overlapping area between NPAs and under-sampled
 areas), usp (percentage of under-sampled areas overlapping with NPAs), dd
 (total data-deficient area), ddov (overlapping area between NPAs and
 data-deficient areas), and ddp (percentage of data-deficient areas
 overlapping with NPAs). All areas are in million hectares (Mha). ◦
 tab4.csv – This table examines the overlap between sampling completeness
 categories and Key Biodiversity Areas (KBAs). Variables include realm
 (biogeographic domain), kba.area (total area of KBAs), well_sampled_ov.kba
 (overlapping area between well-sampled areas and KBAs), wsp.kba
 (percentage of well-sampled areas overlapping with KBAs), usp.kba
 (overlapping area between under-sampled areas and KBAs),
 under_sampled_ov.kba (percentage of under-sampled areas overlapping with
 KBAs), ddov.kba (overlapping area between data-deficient areas and KBAs),
 and ddp.kba (percentage of data-deficient areas overlapping with KBAs).
 All areas are in million hectares (Mha). Scripts: • 00 - Download GBIF
 records: Downloads global amphibian occurrence data from GBIF. • 01 -
 Clean records: Cleans and preprocesses species occurrence records
 retrieved from GBIF. • 02 - Update taxonomy: Updates species taxonomy
 following the classification of the American Museum of Natural History
 (Frost, 2023). • 03 - Completeness estimation: Computes amphibian sampling
 completeness at the global scale using hexagonal grids. • 04 - Human
 population and sampling completeness correlation: Assesses the
 relationship between human population density and sampling completeness by
 analyzing the mean population per hexagon and its associated completeness
 value. • 05 - Effect size analysis: Evaluates the impact of grid cell size
 on completeness estimates. • 06 - Biogeographic framework selection:
 Compares the biogeographic classifications proposed by Holt et al. (2013)
 and Dinerstein et al. (2017). • 07 - Map generation: Produces the maps
 presented in the article. • 08 - Overlap with conservation areas:
 Calculates the spatial overlap between different sampling quality
 categories and NPAs (Natural Protected Areas) and KBAs (Key Biodiversity
 Areas). • 09 - Cartogram maps: Generates area-distorted cartogram
 representations. For any inquiries about the use of these data and code,
 please contact the authors of the article.