Hierarchical Bayesian Species Distribution Models

User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer et al. 2006 ). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.

hSDM is an R package for estimating parameters of hierarchical Bayesian species distribution models. Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.

Installation

# Install release version from CRAN
install.packages("hSDM")
 
# Install development version from GitHub
devtools::install_github("ghislainv/hSDM")

Vignettes and manual

• Presentation at ISEC 2014: hSDM-ISEC2014.pdf
• Long vignette with several examples: hSDM-vignette.pdf
• Manual: hSDM-manual.pdf

In the wild

• Tutorial on using opportunistic species occurrence data for occupancy modelling by Adam M. Wilson on GitHub.
• Tutorial by Adam M. Wilson adapted by Marta A. Jarzyna on spatial-ecology.net
• Tutorial on modelling spatial autocorrelation by Jérôme Guélat on Amazonaws.

Related publications

Diez J. M. and Pulliam H. R. 2007. Hierarchical analysis of species distributions and abundance across environmental gradients. Ecology. 88(12): 3144-3152.

Gelfand A. E., Silander J. A., Wu S. S., Latimer A., Lewis P. O., Rebelo A. G. and Holder M. 2006. Explaining species distribution patterns through hierarchical modeling. Bayesian Analysis. 1(1): 41-92.

Latimer, A. M.; Wu, S. S.; Gelfand, A. E. & Silander, J. A. 2006. Building statistical models to analyze species distributions. Ecological Applications. 16(1): 33-50.

MacKenzie, D. I.; Nichols, J. D.; Lachman, G. B.; Droege, S.; Andrew Royle, J. and Langtimm, C. A. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology. 83: 2248-2255.

Royle, J. A. 2004. N-mixture models for estimating population size from spatially replicated counts. Biometrics. 60: 108-115.