Species distribution modelling in fisheries science

Latest fisheries directives propose adopting an ecosystem approach to manage fisheries \citep{FAO-EAFM}. Such an approach aims to protect important ecosystems based on the principle that healthy ecosystems produce more and thus enhance sustainability. Unfortunately, quantifying the importance of an ecosystem is a difficult task to do due the immense number of interactions involved in marine systems. This PhD dissertation relies on the fact that good fisheries distribution maps could play a very important role as they allow a visual and intuitive assessment of different marine areas. Unfortunately, the limited amount of data available and the inherent difficulties of modelling fishery data has resulted in relatively low quality maps in the near past (see \citep{atlas} and \url{http://www.ices.dk/marine-data/maps/Pages/ICES-FishMap.aspx)}. As a result, the spatial fisheries management framework requires competent statistical approaches to quantify the importance of different marine areas with an appropriate measure of uncertainty associated to the estimates. The aim of this PhD is to provide competent spatial and spatio-temporal modelling approaches that allow us characterise different fishery processes that are relevant for their sustainable management

Modelling spatially sampled proportion processes​​

Many ecological processes are measured as proportions and are spatially sampled. In all these cases the standard procedure has long been the transformation of proportional data with the arcsine square root or logit transformation, without considering the spatial correlation in any way. This paper presents a robust regression model to analyse this kind of data using a beta regression and including a spatially correlated term within the Bayesian framework. As a practical example, we apply the proposed approach to a spatio-temporally sampled fishery discard dataset