DOI: 10.5061/dryad.h18931zsb
title: The past, present, and future of predator-prey interactions in a warming world: using species distribution modeling to forecast ectotherm-endotherm niche overlap

description: Climate change has the potential to disrupt species interactions across
 global ecosystems. Ectotherm-endotherm interactions may be especially
 prone to this risk due to the possible mismatch between the species in
 physiological response and performance. However, few studies have examined
 how changing temperatures might differentially impact species’ niches or
 available suitable habitat when they have very different modes of
 thermoregulation. An ideal system for studying this interaction is the
 predator-prey system. In this study, we used ecological niche modeling to
 characterize the niche overlap and examine biogeography in past and future
 climate conditions of prairie rattlesnakes (Crotalus viridis) and Ord’s
 kangaroo rats (Dipodomys ordii), an endotherm-ectotherm pair typifying a
 predator-prey species interaction. 
description: We downloaded occurrence records for both prairie rattlesnakes
 and Ord’s kangaroo rats from GBIF and VertNet using R (R Core Team, 2023).
 Point databases were cleaned by removing all incomplete records (missing
 key information, such as latitude or longitude), removing all records that
 were of subspecies, and removing duplicates. Records that were in the
 NAD27 projection were converted into the WGS84 projection. We initially
 defined 22 environmental predictors for use in building our ENMs. This
 included the 19 bioclimatic predictors (1970-2000) in the WorldClim 1.4
 database (Hijmans et al. 2005) as well as elevation, terrain ruggedness
 index (TRI), and topsoil sand content. We included elevation due to the
 large variation of this metric throughout both species’ ranges. The
 elevation layers for both species were GTOPO30 tiles which we downloaded
 from the USGS Earth Explorer (USGS, 2000). We then merged tiles together
 using QGIS. We also included terrain ruggedness index as increased
 ruggedness has been shown to increase microhabitat variation and therefore
 areas available for snake refuge (Kirk et al., 2021). TRI was calculated
 from the previously downloaded elevation layer using the terrain
 ruggedness raster analysis function in QGIS. Lastly, we included topsoil
 sand content (percent sand) because Ord’s kangaroo rats are more abundant
 in areas with sparsely vegetated, sandy soils where they can construct
 their burrow systems (Gummer, 1997; Kissner et al., 2009). Topsoil sand
 content was downloaded from the Unified North American Soil Map (Liu et
 al. 2014). All environmental layers were at a 30 arc second (1 km)
 resolution, projected to WGS84, and were masked to the buffered range of
 the appropriate species for each model. 
description: # The Past, Present, and Future of Predator-Prey Interactions in a Warming
 World: Using Species Distribution Modeling to Forecast Ectotherm-Endotherm
 Niche Overlap
 [https://doi.org/10.5061/dryad.h18931zsb](https://doi.org/10.5061/dryad.h18931zsb) ## Description of the data and file structure There are four data files - two sets of occurrence data and two rasterstacks of environmental variables. One of each per focal species (Prairie rattlesnakes and Ord's kangaroo rats). Prairie rattlesnakes:  * viridiscleanedoccurence.csv * viridisenvnew.tif Ord's kangaroo rats: * ordscleanedoccurence.csv * ordsenvnew.tif ## Sharing/Access information Data was derived from the following sources: * Global Biodiversity Information Database (GBIF) * Vertnet * USGS Earth Explorer * Unified North American Soil Database ## Code/Software All analyses were performed in RStudio (R Core Team, 2023). Further information about the workflow can be found in the ODMAP (Zurrell et al., 2020) associated with the journal article. Included scripts: * Get points: used to perform initial downloads of occurrence data. * Cleaning scripts: used to prepare and clean occurrence data. * Convert NAD points: used to convert NAD27 projected points into WGS84. * Psuedo-absence generation scripts: used to generate 10,000 psuedo-absences per model using a target specific approach. Environmental Variables scripts: used to download and process bioclim data. * Sand_processing: used to do a small amount of post processing on the percent sand data. * Maxent scripts: used to produce maxent models of current distributions. * ENMeval scripts: used to run ENMeval (Kass et al.,2021) on models to determine best regularization multiplier. * Past_Real scripts: used to project current models into the mid-holocene and last glacial maximum. * Predict_Real scripts: used to project current models into future climate scenarios.