Impact of increasing deployment of artificial floating objects on the spatial distribution of social fish species

Abstract

SummaryApproximately 300 pelagic fish species naturally aggregate around floating objects (FOBs) at the surface of the oceans. Currently, more than 50% of the world catch of tropical tuna comes from the industrial tuna fisheries around driftingFOBs. Greater understanding of the complex decision‐making processes leading to this aggregation pattern and the impact of the massive release of artificialFOBs by fishermen on the spatial distribution and management of tuna is needed.We analyse how the interplay between social (relationships between individuals) and non‐social (responses to the environment) behaviours may affect the spatial distribution of a population in a multi‐FOBenvironment. Taking the example of tropical tunas associating withFOBs and using differential equations and stochastic simulations, we examine how, when increasing the number ofFOBs, fish aggregation dynamics and the distribution of the population among patches are affected by the population size, level of sociality and the natural retentive and/or attractive forces ofFOBs on individual tuna.Our model predicts that, depending on the species' level of sociality, fish will be scattered amongFOBs or aggregated around a singleFOBbased on the number ofFOBs deployed in a homogeneous oceanic region.For social species, we demonstrated that the total fish catch is reduced with increasingFOBs number. Indeed, for each size of population, there are a number ofFOBs minimizing the total population of fish associated withFOBs and another number ofFOBs maximizing the total population of associated fish.Synthesis and applications. In terms of fisheries management, the total catch volume is directly linked to the total number of floating objects (FOBs) for non‐social species, and any limit on the number of sets would then result in a limit on the total catch. For social species (e.g. tuna), however, increasing the number ofFOBs does not necessarily lead to an increase in the total catch, which is a non‐intuitive result. Indeed, our model shows that, for specific values of the parameters, deploying a greater number ofFOBs in the water (all other parameters being constant) does not necessarily help fishermen to catch more tuna, but does increase the level of fishing effort and bycatch.