Looking at the big picture: defining a method to incorporate multiple pressure into fisheries management considerations

Abstract

In the Mediterranean Sea the scientific advice aimed to maintain the long-term productivity of fish stocks is achieved by single species-stock assessment. Ecosystem-oriented advice requires knowledge on the relations between species biology and the environment that surrounds and a method to forecast the biological response to future scenarios. Taking as a case study the Common cuttlefish in the Adriatic Sea, we collected knowledge and we implemented a probabilistic Risk Assessment to describe the sources of error associated to the single species stock assessment. We observe that Bayesian Belief Networks can be used to summarize outputs of ecological models and to link them to expert based conceptual models. We gathered the knowledge on the ecosystem and anthropic pressures and their relationship with biological process by the means of literature review, single species stock assessment, machine learning models and bayesian meta analysis. We then implement a semi-quantitative extension of a risk assessment based on a hierarchical composite indicator describing stock assessment considerations and a Bayesian belief network to model population dynamic and environmental/ecosystem considerations. The proposed approach combines Risk Table, model weighing, ecological models results and Bayesian Belief Network to identify which is the most relevant source of uncertainty in the single species stock assessment. The Bayesian Belief Network is used to model management and environmental scenarios tracking the risk probability that growth performance of common cuttlefish is impaired. Food web, and to a less extent temperature, can impact the growth performance of cuttlefish. Furter research is needed to explicitly model the biomass dynamic as a function of alternative biological parameters accounting for food web status