This is a guide for using the scripts to reproduce results in John & Post 2022 Ecology and Evolution. Please cite the manuscript as needed.
The following workflow will reproduce the results and figures presented in our manuscript, as well as contribute to discussion that arose during the review process. You will need to full directory structure included here. To reproduce the analyses, follow the code in the /scripts/ folder in order; fill in check boxes as you go.
[x] Download worldclim v2 data using 01_getWC21.py
[x] Download and tidy North American Artiodactyla occurrences using 02_rgbif.R
[x] Spatially thin the occurrence records using 03_thinBovids.R\
[x] Resample present bioclimatic raster data using 04a_bioclResample.py and 04b_cmip6Resample.py
[x] Generate terrain covariates and resample using 04c_demOperations.py
[x] Prep and organize GCAM land cover data using 04d_gcamCdfToTif.R, 04e_gcamResample.py, and 04f_gcamCombine.R
[x] Prep ice sheet data using 04g_cavResample.R
[x] Visualize dynamic predictor data using 05_visualizeWorldclim.R
[x] Center present predictor variables; compile present worldclim + terrain into single rasters using 06_presentPredictorPrep.R
[x] Center future predictor variables in same fashion as above using 07_futurePredictorPrep.R\
[x] Generate a sampling bias grid (see Phillips et al 2009) using 08_biasMatrix.R
[x] Generate a set of background data for each species using 09_generateBG.R
[x] Visualize covariation among predictor variables 10_predPairsPlots.R\
[x] Fit MaxEnt models using 11_maxEnt.R
[x] Compute MESS grids using 12_MESS.R
[x] Visualize individual MESS grids usign 13a_visualizeMESS.R
[x] Visualize combined mess grids by climate scenario using 13b_combineMESS.R
[x] Generate prediction rasters from maxent models using 14_predictionRasters.R
[x] Visualize prediction rasters using 15_predictionPlots.R
[x] Generate comparisons of range shift estimates among species using 16*.R
[x] Compare results of models using 5000 and 10000 background points using 17_compareBGresults.R\