Bayesian Spatial Point Process Modeling of Line Transect Data
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This paper develops a Bayesian approach for spatial inference on animal density from line transect survey data. We model the spatial distribution of animals within a geographical area of interest by an inhomogeneous Poisson process whose intensity function incorporates both covariate effects and spatial smoothing of residual variation. Independently thinning the animal locations according to their estimated detection probabilities results into another spatial Poisson process for the sightings (the observations). Prior distributions are elicited for all unknown model parameters. Due to the sparsity of data in the application we consider, eliciting sensible prior distributions is important in order to get meaningful estimation results. A reversible jump Markov Chain Monte Carlo (MCMC) algorithm for simulation of the posterior distribution is developed. We present results for simulated data and a real data set of minke whale pods from Antarctic waters. The main advantages of our method compared to design-based analyses are that it can use data arising from sources other than specifically designed surveys and its ability to link covariate effects to variation of animal density. The Bayesian paradigm provides a coherent framework for quantifying uncertainty in estimation results.
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- Bayesian Spatial Point Process Modeling of Line Transect Data
Journal of Agricultural, Biological, and Environmental Statistics
Volume 15, Issue 3 , pp 327-345
- Cover Date
- Print ISSN
- Online ISSN
- Additional Links
- Animal density
- Distance sampling
- Reversible Jump MCMC
- Sightings survey
- Spatial covariate
- Spatial smoothing
- Author Affiliations
- 1. Department of Mathematics and Statistics, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
- 2. Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, 01301, Vantaa, Finland
- 3. Instituto Español de Oceanografía, Cabo Estai – Canido, Apdo. 1552, 36200, Vigo, Spain