DOI: 10.5061/dryad.v15dv427w
title: Data from: Biotic velocity identifies potential climate refugia beyond climate velocity estimates

description: We developed a biotic velocity–based framework to identify three
 functionally distinct types of climate refugia with significant
 conservation value at the regional scale: (1) in-situ refugia, where
 species assemblage can persist locally without spatial displacement (i.e.,
 areas with mean forward or backward biotic velocity equal to 0), (2)
 stepping-stone refugia, where species can track shifting climates with
 relatively low forward biotic velocity, and (3) ex-situ refugia, which are
 areas that can be readily colonized by species from other regions due to
 low backward biotic velocity. This framework, which integrates species
 distribution models (SDMs), climate exposure metrics, and biotic velocity,
 was applied to four taxa (mammals, reptiles, birds, and amphibians) under
 two climate scenarios in the Middle and Lower Reaches of the Yangtze River
 (MLRYR), China.
description: # Data from: Biotic velocity identifies potential climate refugia beyond
 climate velocity estimates Dataset DOI:
 [10.5061/dryad.v15dv427w](10.5061/dryad.v15dv427w) This dataset supports
 the research titled "Biotic velocity identifies potential climate
 refugia beyond climate velocity estimates". It contains the data and
 scripts required to reproduce key analytical steps, including species
 distribution models (SDMs), climate exposure assessments, and biotic
 velocity calculations. ## Description of the data and file structure ####
 1.Data.zip * *Species List.xlsx*\ Provides detailed information for the 95
 species included in this study, encompassing: scientific name, taxonomy,
 IUCN Red List threatened categories, number of points generated from the
 Area of Habitat (AOH - referring to species distribution ranges refined by
 incorporating land cover and elevation information), number of
 pre-processed occurrence records obtained from GBIF and iDigBio, number of
 occurrence points after filtering using environmental subsampling methods,
 habitat type, and elevation. - *Analysis scripts|*\ A directory containing
 the main R scripts: * *Biotic velocity calculation.R:* Calculates the
 distance from each cell within a species’ projected current (or future)
 range to the nearest cell of future (or current) suitable habitat. *
 *Climate exposure assessment.R:* Identify regions where the degree of
 projected climate change is relatively low. * *Species distribution
 modelling.R:* Projects current and future suitable habitats for species by
 integrating environmental variables and species occurrence data (using the
 megaSDM R package). * *Principal component analysis.R:* Performs
 dimensionality reduction on 19 bioclimatic variables to extract
 independent principal component (PC) axes. * *Parameter tuning results|*\
 SDM parameter tuning results (.csv) from the ENMeval R package for eight
 representative species. For each species, 30 combinations of five feature
 classes (FCs: "L", "LQ", "LQH",
 "LQHP", "LQHPT") and six regularization multipliers
 (RMs: 0.5, 1, 2, 3, 4, 5) were tested. The results include information
 such as the corrected Akaike Information Criterion (AICc) and average
 validation AUC. * *Sample|*\ Contains complete modelling data for the
 example species *Panthera tigris*, provided to facilitate replication of
 the SDM. * *aoh_points|* *| - Panthera_tigris.csv* ``` Coordinates of
 Panthera tigris. ``` * *trainingarea|* ``` 10 Environmental variables
 (.bil) under current (1985s) climate conditions, including
 BIO3 (Isothermality, %), BIO8 (Mean Temperature of Wettest Quarter, °C),
 BIO9 (Mean Temperature of Driest Quarter, °C), BIO14 (Precipitation of
 Driest Month, mm), BIO15 (Precipitation Seasonality, Coefficient of
 Variation, %), BIO18 (Precipitation of Warmest Quarter, mm), land use
 (categorical, unitless), aspect (°), slope, (°), and the distance to the
 nearest water (m). ``` * *predictenv|* *| - SSP5-8.5|* *|   |- 2090|* ```
 Projections for the same 10 environmental variables (.bil) under future
 (2090s, SSP5-8.5) climate conditions. ``` \**Note:* MegaSDM requires
 environmental variables in .bil format. #### **2.SDM_outputs.zip** * The
 SDM outputs of 89 individual species are categorized into four
 subfolders—Amphibians, Birds, Mammals, and Reptiles—based on their
 taxonomic groups. Within the subfolder, distribution maps for each species
 are stored in separate folders named by their Latin names. Taking the
 species *Andrias davidianus* as an example, the structure of the folder
 named '*Andrias davidianus*' is as follows: *
 *Andrias_davidianus|* ``` [Current range maps] Including GRD files of
 continuous (0-1) and binary (0/1) habitat suitability maps for the current
 period (1985s). ``` *| - map_pdfs|* ``` Including preview images of
 continuous (0-1) and binary (0/1) habitat suitability maps for the 1985s
 (1970-2000) and 2090s (2081-2100, under SSP5-8.5). ``` *| - SSP585|* ```
 Including GRD files of continuous (0-1) and binary (0/1) habitat
 suitability maps for the future period (2090s). ``` ## **Sharing/Access
 information** #### **Data Sources** * Initial species distribution maps
 were derived from: *  IUCN Red List
 [https://www.iucnredlist.org/en](https://www.iucnredlist.org/en). *
 BirdLife
 International [https://www.birdlife.org/](https://www.birdlife.org/). * 19
 Bioclimatic variables were derived from: * WorldClim version 2.1
 [https://worldclim.org/](https://worldclim.org/). ## Code/Software All
 scripts were run using R version 4.3.1 and contain comprehensive
 annotations throughout. All code derived from external sources has been
 explicitly credited within the corresponding script comments.