62: A Bayesian Approach to Species Distribution Modeling with INLA

Music City Center 

Records of species data available on online portals, such as the Global Biodiversity Information Facility, are collected through the citizen science program. This kind of data is often referred to as presence-only data since only presence records are available. Species distribution models (SDMs) require presence-absence data. In previous studies, SDMs were developed using background data. This approach tends to favor nonparametric models intended for prediction. Thus, the effect of the parameters of interest in the SDMs is ignored.

We propose a Bayesian approach to modeling species distribution with R INLA. We incorporate uncertainty about the absence of species in places without records using the combination of missing data imputation and Bayesian model averaging. We recognize that misclassification can attenuate the estimated parameters. So an adjusted logit link function is used to correct the effect of measurement error on the estimated parameters. We present results for the parameters of interest using simulated and real data. Our approach performs better than the alternative parametric method and achieves satisfactory predictive accuracy.

Presence only

Misclassification

INLA

Bayesian

Model Averaging 

Section on Statistics and the Environment