Exploring Biodiversity Patterns in Climate SpaceSummary of Dataset ContentsThis dataset supports the manuscript "Biodiversity patterns redefined in environmental space," which explores biodiversity patterns in environmental space. The data encompasses various metrics and measurements that map species occurrences and richness in both climate and geographical spaces, offering a novel perspective on the mechanisms driving broad-scale biodiversity patterns. The dataset includes processed data in the form of tables across multiple tabs in an XLS file named “Full_Data_Set_Perspective_Piece.xlsx”, accompanied by R scripts for pattern exploration named “Script_Perspective_Piece.R”.Description of the Data and File StructureThe data is organized into several tabs within an XLS file named “Full_Data_Set_Perspective_Piece.xlsx”, each corresponding to different aspects of the study. Below is a detailed description of each tab and its columns:Tab: Fig1_G_space_mappingThis table maps species occurrences and richness in both climate and geographical spaces.ID: The ID of each geographical cell.ge_p_nc: The geographical occurrences of Panthera onca.g_p_jgr: The geographical occurrences of Panthera pardus.clm_p_n: The climatic occurrences of Panthera onca.clm_p_j: The climatic occurrence overlap among the two species.e_mmml_: The richness of mammal species in environmental space.Dlty_vr: Variable used to create a color code for duality plotting.Dlty_pl: Variable used to create a color code for duality plotting.geometry: Spatial information in Well-Known Text (WKT) format.Tabs: Fig2_Temperature_Space_20, Fig2_Temperature_Space_40, Fig2_Temperature_Space_60These tables define one-dimensional climate spaces at different resolutions, containing bird richness data.MinX: Minimum temperature of the bin.MaxX: Maximum temperature of the bin.Climate_Area: Geographical area occupied by that climate bin (scaled in km²).Richness: Number of bird species in the bin.Tabs: Fig_2D_E_Space_20_20, Fig_2D_E_Space_40_40, Fig_2D_E_Space_60_60These tables define two-dimensional climate spaces at different resolutions, containing bird richness data.x: X coordinate of a two-dimensional climate space.y: Y coordinate of a two-dimensional climate space.MinX: Minimum value of PC2 scores for the bin.MinY: Minimum value of PC2 scores for the bin.MaxX: Maximum value of PC1 scores for the bin.MaxY: Maximum value of PC1 scores for the bin.Richness: Number of bird species in the bin.Tab: Fig3_Area_and_IsolationThis table explores patterns of area and isolation in climate space at a resolution of 20 equal intervals.x: X coordinate of a two-dimensional climate space.y: Y coordinate of a two-dimensional climate space.MinX: Minimum value of PC2 scores for the bin.MinY: Minimum value of PC2 scores for the bin.MaxX: Maximum value of PC1 scores for the bin.MaxY: Maximum value of PC1 scores for the bin.Climate_Area: Geographical extent covered by the bin (km²).AverageDistAmongFrags: Distance among fragments of a climate.Tabs: Fig4_amphibians_RSFD, Fig4_birds_RSFDThese tables contain range size frequency distribution data for amphibians and birds.spp: Species identity.rs.g: Range size in geographical space.rs.e: Range size in environmental space.std.rs.g: Standardized geographical range size (Z standardization).std.rs.e: Standardized environmental range size (Z standardization).Tabs: Fig4_amphibian_RSFD_Density, Fig4_birds_RSFD_DensityThese tables contain range size frequency distribution density data for amphibians and birds.Space: Space where the range size was measured (G-Space or E-Space).Range_Size: Range size in geographical and environmental space.Tab: Fig5_Amphibian_Phylo_MeasuresThis table contains phylogenetic measures for amphibians in climate space.x: X coordinate of a two-dimensional climate space.y: Y coordinate of a two-dimensional climate space.MinX: Minimum value of PC2 scores for the bin.MinY: Minimum value of PC2 scores for the bin.MaxX: Minimum value of PC1 scores for the bin.MaxY: Maximum value of PC1 scores for the bin.Richness: Number of amphibian species in the bin.PD: Faith's Phylogenetic Diversity (PD) of the bin.STD.PD: Standardized PD controlling for species richness.Data was Derived from the Following Sources:Climate data: CHELSA (https://chelsa-climate.org/bioclim/) and CGIAR (https://cgiarcsi.community)Vector range maps of amphibians and mammals: International Union for Conservation of Nature (https://iucn.org)Bird range maps: BirdLife (version 2020.1, http://datazone.birdlife.org)Code/SoftwareThe dataset includes R scripts used for processing and analyzing the data, and generating the figures presented in the study. The scripts facilitate reproducibility and comprehension of the workflow:Required Libraries and Versions:ggplot2: For plotting (version 3.3.5)sf: For handling spatial data frames (version 1.0-3)dplyr: For data manipulation (version 1.0.7)wesanderson: For color palettes (version 0.3.6)MetBrewer: For additional color palettes (version 0.2.0)Main Script:Script_Perspective_Piece.R: This script includes functions to read data, process it, and explore all patterns described in the manuscript. Each section of the script corresponds to a different figure or analysis, with comments and documentation to guide the user through the process.

Reason for SelectionThe Southeast United States is a global biodiversity hotspot that supports many rare and endemic reptile and amphibian species (Barrett et al. 2014, EPA 2014). These species are experiencing dramatic population declines driven by habitat loss, pollution, invasive species, and disease (Sutherland and deMaynadier 2012, EPA 2014, CI et al. 2004). Amphibians provide an early signal of environmental change because they rely on both terrestrial and aquatic habitats, are sensitive to pollutants, and are often narrowly adapted to specific geographic areas and climatic conditions. As a result, they serve as effective indicators of ecosystem health (CI et al. 2004, EPA 2014). Their association with particular microhabitats and microclimates makes amphibians vulnerable to climate change, and Southeast amphibians are predicted to lose significant amounts of climatically suitable habitat in the future (Barrett et al. 2014). PARCAs also represent the condition and arrangement of embedded isolated wetlands. Many amphibians breed in temporary (i.e., ephemeral) wetlands surrounded by upland habitat, which are not well-captured by existing indicators in the Blueprint (Erwin et al. 2016).Input DataSoutheast Blueprint 2024 extent2023 U.S. Census TIGER/Line state boundaries, accessed 4-5-2024: download the data Southeast Priority Amphibian and Reptile Conservation Areas (PARCAs) PARCAs for all Southeast states except for Mississippi, Virginia, and Kentucky, shared by José Garrido with the Amphibian and Reptile Conservancy (ARC) on 3-5-2024PARCAs for Mississippi, shared by Luis Tirado with ARC on 4-26-2024 (these PARCAs were identified more recently and were not yet captured in ARC’s Southeast PARCAs dataset)South Atlantic PARCAs: Neuse Tar River PARCA (this PARCA was identified through a project funded by the South Atlantic Landscape Conservation Cooperative and is not yet captured in ARC’s Southeast PARCAs dataset; we added this PARCA after consultation with ARC staff) To view a map depicting some of the PARCAs provided, scroll to the bottom of the work page of the ARC website under the heading “PARCAs Nationwide”; to access the data, email info@ARCProtects.org. PARCA is a nonregulatory designation established to raise public awareness and spark voluntary action by landowners and conservation partners to benefit amphibians and/or reptiles. Areas are nominated using scientific criteria and expert review, drawing on the concepts of species rarity, richness, regional responsibility, and landscape integrity. Modeled in part after the Important Bird Areas program developed by BirdLife International, PARCAs are intended to be nationally coordinated but locally implemented at state or regional scales. Importantly, PARCAs are not designed to compete with existing landscape biodiversity initiatives, but to complement them, providing an additional spatially explicit layer for conservation consideration.
PARCAs are intended to be established in areas: capable of supporting viable amphibian and reptile populations, occupied by rare, imperiled, or at-risk species, and rich in species diversity or endemism. For example, species used in identifying the PARCAs in the Southeast include: alligator snapping turtle, Barbour’s map turtle, one-toed amphiuma, Savannah slimy salamander, Mabee’s salamander, dwarf waterdog, Neuse river waterdog, chicken turtle, spotted turtle, tiger salamander, rainbow snake, lesser siren, gopher frog, Eastern diamondback rattlesnake, Southern hognose snake, pine snake, flatwoods salamander, gopher tortoise, striped newt, pine barrens tree frog, indigo snake, and others. There are four major implementation steps: Regional PARC task teams or state experts can use the criteria and modify them when appropriate to designate potential PARCAs in their area of interest. Following the identification of all potential PARCAs, the group then reduces these to a final set of exceptional sites that best represent the area of interest. Experts and stakeholders in the area of interest collaborate to produce a map that identifies these peer-reviewed PARCAs. Final PARCAs are shared with the community to encourage the implementation of voluntary habitat management and conservation efforts. PARCA boundaries can be updated as needed. Mapping Steps Merge the three PARCA polygon datasets and convert from vector to a 30 m pixel raster using the ArcPy Feature to Raster function. Give all PARCAs a value of 1.Add zero values to represent the extent of the source data and to make it perform better in online tools. Convert to raster the TIGER/Line state boundaries for all SEAFWA states except for Virginia and Kentucky and assign them a value of 0. We excluded Virginia and Kentucky because PARCAs have not yet been identified for these states. Use the Cell Statistics “MAX” function to combine the two above rasters.As a final step, clip to the spatial extent of Southeast Blueprint 2024. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code.Final indicator valuesIndicator values are assigned as follows:1 = Priority Amphibian and Reptile Conservation Area (PARCA) 0 = Not a PARCA (excluding Kentucky and Virginia)Known IssuesThe mapping of this indicator is relatively coarse and doesn’t always capture differences in pixel-level quality in the outer edge of PARCAs. For example, some PARCAs include developed areas.This indicator is binary and doesn’t capture the full continuum of value across the Southeast.The methods of combining expert knowledge and data in this indicator may have caused some poorly known and/or under-surveyed areas to be scored too low.This indicator underprioritizes important reptile and amphibian habitat in Kentucky and Virginia because PARCAs have not yet been identified for these areas. ARC is working to expand PARCAs to more states in the future.Because of the state-by-state PARCA development and review process, sometimes PARCA boundaries stop at the state line, though suitable habitat for reptiles and amphibians does not always follow jurisdictional boundaries.This indicator excludes “protected” PARCAs maintained by ARC that are too small and spatially explicit to share publicly due to concerns about poaching. As a result, it underprioritizes some important reptile and amphibian habitat. However, these areas are, with a few exceptions in northwest Arkansas and Tennessee, generally well-represented in the Blueprint due to their value for other indicators.This indicator contains small gaps 1-2 pixels wide between some adjoining PARCAs that likely should be continuous, often on either side of a state line. These are represented in the source data as separate polygons with tiny gaps between them, and these translate into gaps in the resulting indicator raster. This results from the PARCA digitizing process and does not reflect meaningful differences in priority.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedAmphibian and Reptile Conservancy. Priority Amphibian and Reptile Conservation Areas (PARCAs). Revised February 7, 2024. Apodaca, Joseph. 2013. Determining Priority Amphibian and Reptile Conservation Areas (PARCAs) in the South Atlantic landscape, and assessing their efficacy for cross-taxa conservation: Geographic Dataset. [https://www.sciencebase.gov/catalog/item/59e105a1e4b05fe04cd000df]. Barrett, Kyle, Nathan P. Nibbelink, John C. Maerz; Identifying Priority Species and Conservation Opportunities Under Future Climate Scenarios: Amphibians in a Biodiversity Hotspot. Journal of Fish and Wildlife Management 1 December 2014; 5 (2): 282–297. [https://doi.org/10.3996/022014-JFWM-015]. Conservation International, International Union for the Conservation of Nature, NatureServe. 2004. Global Amphibian Assessment Factsheet. [https://www.natureserve.org/sites/default/files/amphibian_fact_sheet.pdf]. Environmental Protection Agency. 2014. Mean Amphibian Species Richness: Southeast. EnviroAtlas Factsheet. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/MeanAmphibianSpeciesRichness.pdf]. Erwin, K. J., Chandler, H. C., Palis, J. G., Gorman, T. A., & Haas, C. A. (2016). Herpetofaunal Communities in Ephemeral Wetlands Embedded within Longleaf Pine Flatwoods of the Gulf Coastal Plain. Southeastern Naturalist, 15(3), 431–447. [https://www.jstor.org/stable/26454722]. Sutherland and deMaynadier. 2012. Model Criteria and Implementation Guidance for a Priority Amphibian and Reptile Conservation Area (PARCA) System in the USA. Partners in Amphibian and Reptile Conservation, Technical Publication PARCA-1. 28 pp. [https://parcplace.org/wp-content/uploads/2017/08/PARCA_System_Criteria_and_Implementation_Guidance_FINAL.pdf]. U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch. TIGER/Line Shapefile, 2023, U.S. Current State and Equivalent National. 2023. [https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html].

description: The Birds of the Americas Original Grids of the Gridded Species Distribution, Version 1are converted 1- kilometer grid cell data in the Geographic Coordinate System (GCS) that cover North America, South America and the Caribbean Islands. The input vector data were created by a consortium led by Natureserve. The grids are produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a gridded version of existing vector maps of bird species distribution at 1- kilometer spatial resolution, which in turn can be used in modeling efforts, wildlife conservation planning, natural resource management, policy-making, biodiversity studies and human-environment interactions.); abstract: The Birds of the Americas Original Grids of the Gridded Species Distribution, Version 1are converted 1- kilometer grid cell data in the Geographic Coordinate System (GCS) that cover North America, South America and the Caribbean Islands. The input vector data were created by a consortium led by Natureserve. The grids are produced by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a gridded version of existing vector maps of bird species distribution at 1- kilometer spatial resolution, which in turn can be used in modeling efforts, wildlife conservation planning, natural resource management, policy-making, biodiversity studies and human-environment interactions.)

Reason for Selection Islands provide important habitat for many species of birds, reptiles, amphibians, mammals, insects, and plants. Due to their isolation, islands tend to be ecologically unique, making them hotspots for species diversity. In addition, the relative isolation of islands from disturbance and mainland predators can make them important breeding habitat for coastal birds and sea turtles. However, these factors also increase islands’ vulnerability to invasive species (IUCN 2018).

According to Bernie 2015, “islands warrant a unique level of attention for biodiversity conservation because they make up only a small percentage of land area but are known for their many endemic species.” This rich diversity is also disproportionately threatened, with “61% of all extinct species and 37% of all critically endangered species confined to islands” (Bernie 2015).

The critical habitat included in this indicator refers to areas with specific physical or biological features that are essential to conserving a federally threatened or endangered species and may require special management or protection. Input Data

Southeast Blueprint 2023 subregions: Caribbean
Caribbean island extent and size
Southeast Blueprint 2023 extent
Critical habitat provided by the U.S. Fish and Wildlife Survey, accessed 6-23-2022; download the data
Threatened Island Biodiversity (TIB) Database, accessed 11-29-2022 

This database includes terrestrial vertebrate species that breed on islands and appear on the IUCN Red List as either Critically Endangered or Endangered. It also includes seabird species listed as Vulnerable and terrestrial vertebrates listed as Extinct in the Wild.

Puerto Rico Gap Analysis Project (GAP) predicted vertebrate species distributions: data provided by Dr. Bill Gould with the Caribbean Climate Hub on 4-4-2022 (contact william.a.gould@usda.gov for more information); read the final report 
U.S. Virgin Islands GAP predicted vertebrate species distributions: data and report appendices provided by Dr. Bill Gould with the Caribbean Climate Hub on 2-6-2023 (contact william.a.gould@usda.gov for more information); read the final report

Mapping Steps

From the TIB Database, in the “Invasive Species on Island” sheet, select islands where “Region_Archipelago” is “Greater Antilles (Puerto Rican Islands)”, “US Virgin Islands”, “US Virgin Islands (St. Croix Islands)”, or “US Virgin Islands (St. Thomas Islands)”.
Count the number of invasives species records per island, ignoring any row where “Common_Name” is “NONE”.
From the TIB Database, in the “Threatened Species on Island” sheet, select islands where “Region_Archipelago” is “Greater Antilles (Puerto Rican Islands)”, “US Virgin Islands”, “US Virgin Islands (St. Croix Islands)”, or “US Virgin Islands (St. Thomas Islands)”.
Count the number of threated species records per island where “Present_Breeding_Status” is “Confirmed” or “Potential Breeding”.
Combine the invasive and threatened species data using “Island_GID_Code” and fill in invasive or threatened species counts with 0 if islands didn’t have a record in either of the sheets.
Include only islands < 50 sq km (smaller than St. Thomas) using the “Corrected_Area_KM2” field. The input data on imperiled species and invasive animals did not include species distributions within individual islands. That was less of an issue on smaller islands but made the source data less informative for larger islands.
Extract “Corrected_Latitude” and “Corrected_Longitude” of islands from the “Invasive Species on Islands” sheet, then create shapefile of the summarized data.
Spatially join the summarized data from TIB Database to the Caribbean island extent layer.
Many species in the island biodiversity database have habitat on large islands, but the database doesn’t provide sufficiently detailed species distribution information within those islands to be used as an indicator. To address this, use predicted habitat models from Puerto Rico and U.S. Virgin Islands GAP for species listed in the TIB Database that live on large islands (i.e., >50 sq km). These species were: Puerto Rican parrot, Culebra Island giant anole, Puerto Rican nightjar, Mottled coqui, Cricket coqui, Hedrick’s coqui, Golden coqui, Locust coqui, Forest coqui, Richmond’s coqui, Wrinkled coqui, Virgin Island tree boa, and Desecheo dwarf gecko.
To prepare the GAP data, begin by fixing issues in two species layers. For Puerto Rican nightjar (Caprimulgus noctitherus), convert NoData to 0 to fill in a square of NoData in the center of Puerto Rico. For forest coqui (Eleutherodactylus portoricensis), add 1 to the values to make them consistent with the values used in the other layers for presence and absence.
Sum the values of the species models and subtract by the total number of species to get species counts for each pixel. GAP uses 2 to indicate presence and 1 to indicate absence.
Combine the Puerto Rico and U.S. Virgin Islands GAP species counts into a single raster. Then snap and reproject based on the Caribbean Blueprint subregion.
Clip the critical habitat to the Caribbean islands extent layer and convert to raster. Include all critical habitat in the area except for yellow-shoulder blackbird. The large area of poor quality habitat covered by critical habitat for this species was causing significant overprioritization in this indicator and local reviewers recommended removing it. This still results in important areas for the species being included based on models for other species, but greatly reduces overall overprioritization in many places. 
Combine data from the TIB database, critical habitat, and Puerto Rico and U.S. Virgin Islands GAP as shown in the final indicator values below.
Clip to the Caribbean Blueprint 2023 subregion.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 7 = Island area with critical habitat for a threatened or endangered species 6 = Island area with no invasive animals and 2+ imperiled species 5 = Island area with no invasive animals and 1 imperiled species 4 = Island area with no invasive animals 3 = Island area with invasive animals and 2+ imperiled species 2 = Island area with invasive animals and 1 imperiled species 1 = Island area with invasive animals 0 = Not an island Known Issues

This indicator underestimates habitat values for some small islands due to a lack of data in the Threatened Island Biodiversity Database. For some areas on large islands, like Vieques National Wildlife Refuge, it underestimates habitat values due to a lack of detailed distribution models for some species. 
This indicator likely underpredicts important habitat for federally listed species. Not all federally listed species have critical habitat designated, and not all of the critical habitat data designated by the U.S. Fish and Wildlife Service is available from the data source used.

Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited Bernie R. Tershy, Kuo-Wei Shen, Kelly M. Newton, Nick D. Holmes, Donald A. Croll, The Importance of Islands for the Protection of Biological and Linguistic Diversity, BioScience, Volume 65, Issue 6, June 2015, Pages 592–597. [https://doi.org/10.1093/biosci/biv031]. Gould, William A.; Alarcón, Caryl; Fevold, Brick; Jiménez, Michael E.; Martinuzzi, Sebastián; Potts, Gary; Quiñones, Maya; Solórzano, Mariano; Ventosa, Eduardo. 2008. The Puerto Rico Gap Analysis Project. Volume 1: Land cover, vertebrate species distributions, and land stewardship. Gen. Tech. Rep. IITF-GTR-39. Río Piedras, PR: U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry. 165 p. [https://data.fs.usda.gov/research/pubs/iitf/iitf_gtr39.pdf].

Gould WA, Solórzano MC, Potts GS, Quiñones M, Castro-Prieto J, Yntema LD. 2013. U.S. Virgin Islands Gap Analysis Project – Final Report. USGS, Moscow ID and the USDA FS International Institute of Tropical Forestry, Río Piedras, PR. 163 pages and 5 appendices. [https://www.thinkamap.com/share/IndividualGISdata/PDFs/USVI_FINAL_REPORT.pdf].

IUCN (2018). Guidelines for invasive species planning and management on islands. Cambridge, UK and Gland, Switzerland: IUCN. viii + 40pp. [https://portals.iucn.org/library/sites/library/files/documents/2018-030-En.pdf].

Sayre, R., S. Noble, S. Hamann, R. Smith, D. Wright, S. Breyer, K. Butler, K. Van Graafeiland, C. Frye, D. Karagulle, D. Hopkins, D. Stephens, K. Kelly, Z, basher, D. Burton, J. Cress, K. Atkins, D. van Sistine, B. Friesen, B. Allee, T. Allen, P. Aniello, I Asaad, M. Costello, K. Goodin, P. Harris, M. Kavanaugh, H. Lillis, E. Manca, F. Muller-Karger, B. Nyberg, R. Parsons, J. Saarinen, J. Steiner, and A. Reed. 2018. A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized global ecological coastal units. Journal of Operational Oceanography–A Special Blue Planet Edition. [https://doi.org/10.1080/1755876X.2018.1529714].

Threatened Island Biodiversity Database Partners. 2018. The Threatened Island Biodiversity Database: developed by Island Conservation, University of California Santa Cruz Coastal Conservation Action Lab, BirdLife International and IUCN Invasive Species Specialist Group. Version 2018. Downloaded on November 2022. [https://tib.islandconservation.org/].

U.S. Fish and Wildlife Service. Critical Habitat.