Modelling spatial patterns of distribution and abundance of mussel seed using structured additive regression models

Abstract

As mussel farming depends on sources of natural mussel seed, knowledge of factors is required to regulate both the spatial distribution and abundance of this resource. These spatial patterns were modelled using Bayesian STructured Additive Regression (STAR) models for categorical data, based on a mixed-model representation. We used Bayesian penalized splines for modelling the continuous covariate effects and a Markov random field prior for estimating the spatial effects.