LAUSR

Abstract An effective management of trawl fishing should be based on the classification of strategies adopted by fishers in terms of fishing grounds selected and target species. In addition, the dynamics behind strategy selection should be investigated and understood, since they are likely to affect the status of the stocks. This study applies artificial neural networks to identify the main strategies characterizing the fishing activity of Italian trawlers operating in the Central Mediterranean during the period 2009–2016. Moreover, the rationale driving fishers' choice is modelled using General Additive Models (to investigate the role of external factors) and Conditional Logit (to investigate the interaction among fishers). Five strategies were identified together with the potential effects on some key stocks which, in turn, determine the fishers' preference towards alternative strategies. Results suggest that both external factors and interactions are relevant in driving fishers’ behavior, and provide some potentially useful insights for the set-up of management strategies in which the adaptation of fishers to biological and economic factors are explicitly considered.