An Individual-based Probabilistic Model for Fish Stock Simulation

TL;DR

This paper introduces a novel probabilistic model for simulating fish behavior, specifically soles, using a new formalism called EPDTA, which can be validated against real stock data and adapted for other species.

Contribution

It presents the EPDTA formalism for individual-based probabilistic modeling and demonstrates its application in simulating fish populations with validation against real data.

Findings

The EPDTA formalism effectively models sole behavior.

Simulation results align with real stock data.

The model can be adapted to other species.

Abstract

We define an individual-based probabilistic model of a sole (Solea solea) behaviour. The individual model is given in terms of an Extended Probabilistic Discrete Timed Automaton (EPDTA), a new formalism that is introduced in the paper and that is shown to be interpretable as a Markov decision process. A given EPDTA model can be probabilistically model-checked by giving a suitable translation into syntax accepted by existing model-checkers. In order to simulate the dynamics of a given population of soles in different environmental scenarios, an agent-based simulation environment is defined in which each agent implements the behaviour of the given EPDTA model. By varying the probabilities and the characteristic functions embedded in the EPDTA model it is possible to represent different scenarios and to tune the model itself by comparing the results of the simulations with real data about the sole stock in the North Adriatic sea, available from the recent project SoleMon. The simulator is presented and made available for its adaptation to other species.