A Multi-Stage Approach to Fit Bayesian Spatial Point Process Models

Music City Center 

Bayesian point process models are commonly used to analyze presence-only data in ecology. Current methods for fitting these models are computationally expensive because they require numerical quadrature and algorithm supervision. We propose a flexible and efficient multi-stage Bayesian approach to fitting point process models that leverages parallel computing resources to estimate coefficients and predict total abundance. We show how this method can be extended to study designs with compact observation windows and allows for posterior prediction in unobserved areas, which can be used for downstream analyses. We demonstrate this approach using a simulation study and on imagery data from aerial surveys to learn spatially explicit abundance of harbor seals in Johns Hopkins Inlet, an important glacial fjord in Alaska.

spatial point process

recursive Bayes

species distribution modeling

presence-only data

parallel processing

Markov Chain Monte Carlo