Combining sale records of landings and fishers knowledge for predicting métiers in a small-scale, multi-gear, multispecies fishery

Abstract

Stock management should be guided by assessment models that, among others, need to be fed by reliable data of catch and effort. However, precise data are difficult to obtain in heterogeneous fisheries. Specifically, small-scale, multi-gear, multispecies fisheries are dynamic systems where fishers may lively change fishing strategy (i.e., métier) conditioned by multiple drivers. Provided that some stocks can be shared by several métiers, a precise categorization of métiers should be the first step toward métier-specific estimates of catch and effort, which in turn would allow a better understanding of the system dynamics. Here we propose an approach for predicting the métier of any given fishing trip from its landing records. This approach combines the knowledge of expert fishers with the existing sales register of landings in Mallorca (Western Mediterranean). It successfully predicts métiers for all the 162,815 small-scale fishery fishing trips from Mallorca between 2004 and 2015. The largest effort is invested in the métiers Cuttlefish/Fish and Spiny lobster, landings peak for Cuttlefish/Fish and Dolphinfish and revenues for Spiny lobster and Dolphinfish. Métier predictions also allowed us to describe the temporal (seasonal and between-year) trends experienced by each métier and to characterize the species (commercial categories) that are specific to each métier. Seasonal variability is by far more relevant than between-year variability, which confirms that at least some fishers are adopting a rotation cycle of métiers along the year. Effort (fishing trips), landings and gross revenues decreased in the last 12 years (2004–2015). The approach proposed is also applicable to any other fishery for which the métier for a fishing trip sample is known (e.g., on-board observers or logbooks), but relying on fishers expertise points more directly to fishers’ intention. Thus, métier predictions produced with the proposed approach are closer to the actual uses of fishers, providing better grounds for an improved management.