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

• Published in: Ecology and Evolution
• Main Authors: Sadykova, Dinara, Scott, Beth E., De Dominicis, Michela, Wakelin, Sarah L., Sadykov, Alexander, Wolf, Judith
• Other Authors: Engineering and Physical Sciences Research Council
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2017
• Subjects: Nature and Landscape Conservation; Ecology; Ecology, Evolution, Behavior and Systematics; Black-legged Kittiwake; common guillemot; harbor seal
• Online Access: http://dx.doi.org/10.1002/ece3.3081; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fece3.3081; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.3081; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.3081

Abstract 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.