The western and central Pacific Ocean supports the world’s largest tuna fishery, with skipjack tuna (Katsuwonus pelamis) constituting over 60% of the catch from this region. Stock assessment models that provide scientific advice for the management of skipjack and other pelagic species are typically Eulerian in nature. As such, they rely on assumptions of stock transfer rates between regions, mixing of individuals within a population, and homogeneity at fixed spatial scales. Altering these assumptions can have profound effects on the estimated abundance and spatial distribution of this heavily exploited species. Using Eulerian models, it is also non-trivial to represent the changes in mesoscale distribution due to ocean dynamics, range contraction due to population depletion, and interactions between tuna conspecifics or prey.
A Lagrangian, or individual-based, approach to modelling fish distribution allows many of these assumptions to be tested in a framework that is structured on the fundamental unit of ecology: the individual. Here, we detail such an approach using Pacific skipjack as an application. The model extends a newly developed Lagrangian ocean particle-tracking simulator by incorporating directed movement, non-directional kinesis, and random-walk behaviours. First replicating the spatial density evolution of a current advection-diffusion distribution model of skipjack tuna populations, we then discuss the effect on meso- and large-scale distribution of alternate behavioural scenarios at the individual level.
Individual-based models provide a valuable tool to analyse the assumptions behind our understanding of free-roaming animals that are difficult to observe. In the case of skipjack and other species of Pacific tuna, anticipated uses of this tool are the simulation of tagging experiments to aid design, the effect of changing habitat on mixing rates, and interaction with drifting fish aggregating devices.