Species distribution models are widely used in ecology for conservation management of species and their environments. This paper demonstrates how to fit a log-Gaussian Cox process model to predict the intensity of sloth occurrence in Costa Rica, and assess the effect of climatic factors on spatial patterns using the R-INLA package. Species occurrence data are retrieved using spocc, and spatial climatic variables are obtained with raster. Spatial data and results are manipulated and visualized by means of several packages such as raster and tmap. This paper provides an accessible illustration of spatial point process modeling that can be used to analyze data that arise in a wide range of fields including ecology, epidemiology and the environment.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-017.zip
spocc, raster, tmap, ggplot2, leaflet, mapview, sp, rnaturalearth, rgeos
Spatial, SpatioTemporal, Graphics, OfficialStatistics, Phylogenetics, TeachingStatistics
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For attribution, please cite this work as
Moraga, "The R Journal: Species Distribution Modeling using Spatial Point Processes: a Case Study of Sloth Occurrence in Costa Rica", The R Journal, 2021
BibTeX citation
@article{RJ-2021-017,
author = {Moraga, Paula},
title = {The R Journal: Species Distribution Modeling using Spatial Point Processes: a Case Study of Sloth Occurrence in Costa Rica},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-017},
doi = {10.32614/RJ-2021-017},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {293-310}
}