This package can efficiently create and project species distribution models using the MaxEnt framework and parallel processing. It can find and download occurrence data for a list of species on GBIF (Global Biodiversity Information Facility), environmentally subsample the occurrences to mitigate spatial bias, generate background (pseudo-absence) points, train the model and project it to different times (incorporating dispersal rate of each species and intermediate range fluctuations), and create species richness maps for each time period and taxon.
Benjamin Shipley
Renee Bach
Younje Do
Heather Strathearn
Jenny McGuire
Bistra Dilkina
maxent.jar file (may be downloaded at https://github.com/mrmaxent/Maxent)
dplyr (1.1.4)
gtools (3.9.5)
plotfunctions (1.4)
rgbif (3.7.9)
terra (1.7-71)
We have provided an example vignette (brshipley/megaSDM/megaSDM_vignette.html) using occurrence data from GBIF for 6 North American mammal species. Environmental data are from the WorldClim database (Hijmans et al. 2005; https://www.worldclim.org/), and data on dispersal rate (in km/year) were collected by HS. The html file (and acossiated R Markdown file within the package) displays the entire functionality of megaSDM in a cohesive workflow, from data collection to the analysis and presentation of results.
In R, use
devtools::install_github("brshipley/megaSDM", build_vignettes = TRUE)
to install the package with the vignette (see above) included. To access
the vignette itself, run ??megaSDM_vignette, and the vignette will be
loaded in the HTML viewer.
For more information about the methods employed in the package and highlighted features, refer to https://doi.org/10.1111/ecog.05450.
Please cite this package as: Shipley, B. R., Bach, R., Do, Y., Strathearn, H., McGuire, J. L., & Dilkina, B. (2022). megaSDM: integrating dispersal and time‐step analyses into species distribution models. Ecography, 2022: e05450.