Bayesian joint models with INLA exploring marine mobile predator-prey and competitor species habitat overlap

Sadykova, Dinara; Scott, Beth E.; De Dominicis, Michela ORCID: https://orcid.org/0000-0003-0544-7939; Wakelin, Sarah L. ORCID: https://orcid.org/0000-0002-2081-2693; Sadykov, Alexander; Wolf, Judith ORCID: https://orcid.org/0000-0003-4129-8221. 2017 Bayesian joint models with INLA exploring marine mobile predator-prey and competitor species habitat overlap. Ecology and Evolution, 7 (14). 5212-5226. 10.1002/ece3.3081

Before downloading, please read NORA policies.

Preview

Text (Open Access paper)
Sadykova_et_al-2017-Ecology_and_Evolution.pdf - Published Version
Available under License Creative Commons Attribution 4.0.
Download (1MB) | Preview

Official URL: https://doi.org/10.1002/ece3.3081

Abstract/Summary

Understanding spatial physical habitat selection driven by competition and/or predator–prey interactions of mobile marine species is a fundamental goal of spatial ecology. However, spatial counts or density data for highly mobile animals often (1) include excess zeros, (2) have spatial correlation, and (3) have highly nonlinear relationships with physical habitat variables, which results in the need for complex joint spatial models. In this paper, we test the use of Bayesian hierarchical hurdle and zero-inflated joint models with integrated nested Laplace approximation (INLA), to fit complex joint models to spatial patterns of eight mobile marine species (grey seal, harbor seal, harbor porpoise, common guillemot, black-legged kittiwake, northern gannet, herring, and sandeels). For each joint model, we specified nonlinear smoothed effect of physical habitat covariates and selected either competing species or predator–prey interactions. Out of a range of six ecologically important physical and biologic variables that are predicted to change with climate change and large-scale energy extraction, we identified the most important habitat variables for each species and present the relationships between these bio/physical variables and species distributions. In particular, we found that net primary production played a significant role in determining habitat preferences of all the selected mobile marine species. We have shown that the INLA method is well-suited for modeling spatially correlated data with excessive zeros and is an efficient approach to fit complex joint spatial models with nonlinear effects of covariates. Our approach has demonstrated its ability to define joint habitat selection for both competing and prey–predator species that can be relevant to numerous issues in the management and conservation of mobile marine species.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1002/ece3.3081
ISSN:	20457758
Additional Keywords:	Besag models; bio-physical habitats; hurdle models; integrated nested Laplace approximation; mobile marine species; spatial joint modeling; spatial niche selection; stochastic partial differential equations; zero-inflated models
Date made live:	18 Aug 2017 15:57 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/517643

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1002/ece3.3081

ISSN:

20457758

Additional Keywords:

Besag models; bio-physical habitats; hurdle models; integrated nested Laplace approximation; mobile marine species; spatial joint modeling; spatial niche selection; stochastic partial differential equations; zero-inflated models

Date made live:

18 Aug 2017 15:57 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/517643