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.
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.
The last stable version of the hSDM R package is officially
available for several operating systems (Unix, Windows and Mac OSX) on
the Comprehensive R Archive Network
(CRAN).
We gratefully acknowledge Shanshan Wu for the original C++ code used in Latimer et al. 2006.
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.
Ghislain Vieilledent Cirad, UPR BSEF Campus de Baillarguet TA C-105/D 34398 Montpellier cedex 5 FRANCE
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