Having Fun with Fishpass: Agent-based Simulator for Assessing the Impact of Different Fishing Scenarios

In this work we extend FIShPASs (FIshing Stock Probabilistic Agent-Based Simulator) [1], a probabilistic agent-based model for the behavior of the common sole (Solea Solea), with capabilities for exploring different fishing scenarios. In order to assess and predict the availability of the fish stock under different fishing efforts, the simulator allows the user to specify fishing mortality rates (F) on a monthly basis. We simulated the following scenarios for a period of 10 years (120 months).: - Virgin Stock (F=0): the stock of common sole is not affected by fishing activities. This represents a sort of null model that indicates the ideal population dynamics; - moderate fishing effort F=0.2; overfishing F=1.2); - Optimal period of fishery closure: we simulated different periods of fishing ban for investigating the optimal policies that has the minimum impact on fish stock; - Recovery after overfishing: with this scenario we calculated the time that the fish stock takes to reach sustainable levels, after simulating a period of strong fishing effort; In addition the simulator has been integrated with SURBA (SURvey-Based Assessments)[2], a well- established model in the context of marine biology, in order to validate our model. SURBA is fed with the simulation outputs from FIShPASs and conversely SURBA outputs are shown to fit with the results obtained after 50 runs of the FIShPASs simulator.