Modeling the habitat associations and spatial distribution of benthic macroinvertebrates: A hierarchical Bayesian model for zero-inflated biomass data

• Published in: Ecological modelling Vol. 265; pp. 74 - 84
• Main Authors: Lecomte, J.B., Benoît, H.P., Etienne, M.P., Bel, L., Parent, E.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 10.09.2013; Elsevier
• Subjects: Agricultural sciences; Animal, plant and microbial ecology; Bayesian hierarchical modeling; Biological and medical sciences; biomass; conservation areas; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Habitat associations; habitats; humans; Life Sciences; linear models; Macro-invertebrates; macroinvertebrates; Methods and techniques (sampling, tagging, trapping, modelling...); sediments; Spatial dependencies; surveys; temperature; temporal variation; uncertainty; Zero-inflated data; Canada; Gulf of Saint Lawrence; Bayesian hierarchical modeling; Zero-inflated data; Habitat associations; Spatial dependencies; Macro-invertebrates; Spatial distribution; Habitat; Biomass; Invertebrata; Modeling; Benthic zone; MANAGEMENT; RARE; ENVIRONMENT; MACROFAUNA; INFERENCE; ABUNDANCE; CHANNEL; CATCH; ECOLOGY
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2013.06.017

•Present a modeling approach for spatially correlated zero-inflated continuous data.•Habitat associations were modeled for the three marine invertebrate taxa.•Models were fit and predictive abilities were evaluated using survey data.•Approach is useful for producing distribution maps for marine spatial planning. Biomass samples from marine scientific surveys are commonly used to investigate spatial and temporal variations in stock abundances. Biomass records are often characterized by a high proportion of zeros on the one hand, and occasional large catches on the other. These features induce a modeling challenge when trying to understand the state of populations and their ecological associations with one another and with habitat. We develop a hierarchical Bayesian model to represent the spatial structure of biomass and analyze the spatial distribution and habitat associations of three species of macro-invertebrates sampled in the southern Gulf of St. Lawrence (Canada). A zero-inflated distribution based on a compound Poisson with Gamma marks is used for the observation layer, and a linear model with spatial correlated errors accounts for the role of habitat variables (temperature, depth and sediment type) in the process layer. Maps of quantities of interest (e.g. probability of presence, quantity of biomass) are produced, taking into account the uncertainty of the estimated parameters and observation errors. This hierarchical Bayesian modeling approach provides a useful tool for spatial management of human activities that may affect living resources that may affect living resources, such as marine protected areas.