Hierarchical Bayesian Species Distribution Models
N-mixture model with CAR process
N-mixture model with K, the maximal theoretical abundance
N-mixture model
Poisson log regression model with CAR process
Poisson log regression model
Site-occupancy model with CAR process
Site occupancy model
hierarchical Bayesian species distribution models
Binomial logistic regression model with CAR process
Binomial logistic regression model
ZIB (Zero-Inflated Binomial) model with CAR process taking into accoun...
ZIB (Zero-Inflated Binomial) model with CAR process
ZIB (Zero-Inflated Binomial) model
ZIP (Zero-Inflated Poisson) model with CAR process taking into account...
ZIP (Zero-Inflated Poisson) model with CAR process
ZIP (Zero-Inflated Poisson) model
Generalized logit and inverse logit function
Predict method for models fitted with hSDM
User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer and others 2006 <doi:10.1890/04-0609>). 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.
Useful links