Simulating fishery dynamics by combining empirical data and behavioral theory

Understanding human decision-making in the context of complex fisheries socio-ecological systems remains one of the key challenges for ecosystem-based management. Agent-based models (ABM) are increasingly seen as one of the most promising methods to simulate human decision-making. In many fishery models, human behavior is highly simplified and reduced to an economic motivation, although scientific literature suggests that it is more multi‑facetted. Here, we present FISHCODE a spatio-temporal ABM for German fisheries in the southern North Sea. Our decision‑making submodel combines different behavioral motivations, i.e. habitual behavior, profit-maximization, competition, conformism, and planning insecurity. Using highly resolved information on fishing trips, we parameterized model parameters either straight from data or through pattern‑oriented modelling. Model validation showed that model outputs were in realistic ranges when compared to observed data. We applied FISHCODE to assess scenarios of two growing challenges to fisheries in the North Sea: expansions of offshore wind farms and increasing fuel prices.