Predicting important fishing grounds for the small-scale fishery, based on Automatic Identification System records, catches, and environmental data

Abstract

Effective and sustainable management of small-scale fisheries (SSF) is challenging. We describe a novel approach to identify important fishing grounds for SSF, by implementing a habitat modelling approach, using environmental predictors and Automatic Identification System (AIS)-B data coupled with logbook and First Sales Notes data, within the SE Bay of Biscay. Fishing activity patterns and catches of longliners and netters are used to determine the main environmental characteristics of the fishing grounds, and a habitat modelling approach is implemented to predict the zones that fulfil similar environmental characteristics across a larger geographical extent. Generalized additive mixed models (GAMMs) were built for 24 fish species, and to identify other zones that fulfil similar characteristics and, thus, could be considered relevant for the species targeted by each gear type. Most of the models showed a good prediction capacity. The models included between one and four predictor variables. `Depth of mixing layer' and `benthic rocky habitat' were the variables included more frequently for fish species captured by netter's fleet. For longliners, the `seafloor slope' and `benthic rocky habitat' were the two most important variables. The predictive maps provide relevant information to assist in management and marine spatial planning.