Laurent Dagorn

Simulation of large-scale tropical tuna movements in relation with daily remote sensing data: the artificial life approach

Tunas are known to be able to travel long distances. The aim of this paper is to propose new ethological models which reproduce some tuna movements using the dynamics of their environment. We use sea surface temperature animations (from remote sensing data) to model the South West Indian Ocean, and French purse seiners data are used to estimate movements of fish. The objective of the models will be to find a northern movement from the Mozambique Channel to the Seychelles Islands at the appropriate time (May-July). The initial model uses our ecological knowledge of tunas, i.e. the search behavior for high concentrations of food commonly associated with thermal fronts. In some cases, this simple model creates some northern movements from the Mozambique Channel, but it cannot be used to reproduce large-scale movements between the Mozambique Channel and the Seychelles Islands. The next generation model is created where tuna behaviors are modeled by an artificial neural network, using a genetic algorithm to adjust the connection weights. The tuna school-network receives daily information from its local environment and chooses the best actions in order to be able to pass from the Mozambique Channel to the Seychelles Islands at the appropriate time. One neural network emerges and represents an adaptive behavior able to interpret daily sea surface temperatures to mimic large-scale tuna movements. This artificial behavior can be generalized to each possible departure position from the Mozambique Channel. This modelling represents a new tool to study large-scale movements of pelagic fish, and is a first step towards real-time management of fisheries.

Improving our Understanding of Tropical Tuna Movements from Small to Large Scales

One tactical objective of behavioural studies in fisheries sciences is to understand the 3-D movements of fish, with the ambitious strategic objective of being able to explain some large-scale movements and distributions from knowledge of small-scale behaviour. The present paper reviews different possibilities to improve our understanding of tropical tuna movements, from small (days) to large scales (weeks and months). We propose some ideas for better observations of fine-scale movements of fish (sonic tags) and of the surrounding environment of the tagged fish. After determining behaviour rules, appropriate modelling should be developed in order to extrapolate results to larger-scale movements. This process can be achieved only if we can simultaneously observe the relevant factors of the environment at appropriate scales and the large-scale movements of fish (using pop-up archival tags), in order to force the models to reproduce the observed movements. This paper shows the importance of (i) small-scale studies, (ii) appropriate observations of large-scale movements and environment, and (iii) models of behaviour in order to extract relevant processes necessary to predict tuna dynamics.