hSDM R package
hSDM is an R package for estimating parameters of hierarchical Bayesian species distribution models. Such models allows 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).
Manual and vignette
Package manual and vignette
are available here:
The source code and development version are available on GitHub and can be cloned with the following command:
git clone https://github.com/ghislainv/hSDM.git
Tutorials on internet
- Tutorial on using opportunistic species occurrence data for occupancy modelling by Adam M. Wilson on GitHub.
- Tutorial on modelling spatial autocorrelation by Jérôme Guélat on Amazonaws.
Scientific papers using the hSDM R package
Wilson, A. M. & Jetz, W. 2016. Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions. PLoS Biology. http://dx.doi.org/10.1371/journal.pbio.1002415
Domisch, S.; Wilson, A. M. & Jetz, W. 2015. Model-based integration of observed and expert-based information for assessing the geographic and environmental distribution of freshwater species. Ecography. http://dx.doi.org/10.1111/ecog.01925
Scientific papers citing the hSDM R package
Bird, T. J., Bates, A. E., Lefcheck, J. S., Hill, N. A., Thomson, R. J., Edgar, G. J., … & Pecl, G. T. (2014). Statistical solutions for error and bias in global citizen science datasets. Biological Conservation. 173: 144-154. http://dx.doi.org/10.1016/j.biocon.2013.07.037
Lee, D. (2013). CARBayes: An R package for Bayesian spatial modeling with conditional autoregressive priors. Journal of Statistical Software. 55(13): 1-24. http://dx.doi.org/10.18637/jss.v055.i13
Mazerolle, M. J. (2015). Estimating Detectability and Biological Parameters of Interest with the Use of the R Environment. Journal of Herpetology. http://dx.doi.org/10.1670/14-075
Thuiller, W. (2014). Editorial commentary on “BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change”. Global Change Biology. 20: 3591-3592. http://dx.doi.org/10.1111/gcb.12728
Methodological related publications
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. pdf
Latimer A. M., Wu S. S., Gelfand A. E. and Silander J. A. 2006. Building statistical models to analyze species distributions. Ecological Applications. 16(1): 33-50. pdf
MacKenzie D. I., Nichols J. D., Lachman G. B., Droege S., Royle J. A. and Langtimm C. A. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology. 83: 2248-2255. pdf
Royle, J. A. 2004. N-Mixture Models for Estimating Population Size from Spatially Replicated Counts. Biometrics. 60: 108-115. pdf
Cirad, UPR BSEF
Campus de Baillarguet
34398 Montpellier cedex 5