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September 2015 Population counts along elliptical habitat contours: Hierarchical modeling using Poisson-lognormal mixtures with nonstationary spatial structure
Alexandra M. Schmidt, Marco A. Rodríguez, Estelina S. Capistrano
Ann. Appl. Stat. 9(3): 1372-1393 (September 2015). DOI: 10.1214/15-AOAS838


Ecologists often interpret variation in the spatial distribution of populations in terms of responses to environmental features, but disentangling the effects of individual variables can be difficult if latent effects and spatial and temporal correlations are not accounted for properly. Here, we use hierarchical models based on a Poisson-lognormal mixture to understand the spatial variation in relative abundance (counts per standardized unit of effort) of yellow perch, Perca flavescens, the most abundant fish species in Lake Saint Pierre, Quebec, Canada. The mixture incorporates spatially varying environmental covariates that represent local habitat characteristics, and random temporal and spatial effects that capture the effects of unobserved ecological processes. The sampling design covers the margins but not the central region of the lake. We fit spatial generalized linear mixed models based on three different prior covariance structures for the local latent effects: a single Gaussian process (GP) over the lake, a GP over a circle, and independent GP for each shore. The models allow for independence, isotropy, or nonstationary spatial effects. Nonstationarity is dealt with using two different approaches, geometric anisotropy and the inclusion of covariates in the correlation structure of the latent spatial process. The proposed approaches for specification of spatial domain and choice of Gaussian process priors may prove useful in other applications that involve spatial correlation along an irregular contour or in discontinuous spatial domains.


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Alexandra M. Schmidt. Marco A. Rodríguez. Estelina S. Capistrano. "Population counts along elliptical habitat contours: Hierarchical modeling using Poisson-lognormal mixtures with nonstationary spatial structure." Ann. Appl. Stat. 9 (3) 1372 - 1393, September 2015.


Received: 1 September 2014; Revised: 1 May 2015; Published: September 2015
First available in Project Euclid: 2 November 2015

zbMATH: 06525990
MathSciNet: MR3418727
Digital Object Identifier: 10.1214/15-AOAS838

Keywords: Bayesian inference , covariate-in-correlation function , Gaussian process , geometric anisotropy , lake shorelines , Perca flavescens , spatial confounding

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.9 • No. 3 • September 2015
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