Behavioral Assumptions in Models of Fish Movement and Their Influence on Population Dynamics

Abstract

This study investigates the movement and growth of cohorts in a coastal fish stock by simulating animal responses to spatial heterogeneity of biotic and abiotic conditions in a dynamic marine landscape. A coastal bay is modeled using spatial and temporal data on prey distribution, benthic habitat, depth, and salinity. Prey abundance and salinity vary daily through an annual cycle to create a spatiotemporally dynamic environment with seasonal fluctuations in the quality and distribution of habitats favoring growth. Three movement behaviors—random walk, kinesis, and gradient response via restricted-area search—simulate fish cohort movements in relation to environmental characteristics. A bioenergetic growth model is used to describe somatic growth by comparing spatiotemporally variable prey consumption rates and metabolic requirements. This facilitates evaluation of the way in which movement behavior influences the ability of cohorts to locate and occupy favorable habitats in a heterogeneous environment. Random movement behavior proved inefficient for locating preferable habitats and resulted in the lowest cohort growth trajectory and stock biomass per recruit. Kinesis and restricted-area search behaviors resulted in similar spatial distributions and characteristics of stock biomass when cohorts were initially distributed at random. However, the results from the restricted-area search simulations were highly sensitive to the initial positions of cohorts. The restricted-area search simulations also resulted in high variation in growth rates among cohorts, reflecting complex interactions between behavioral mechanisms and the structure of local heterogeneity. The results show that movement models reflecting similar density patterns can differ in their influence on cohort growth and mortality. In particular, the presence of local optima can bias the results of movement models employing directional responses to a gradient structure. These results underscore the importance of sound theoretical assumptions in movement model construction and suggest that minimalism be adopted in the absence of empirical support for behavioral assumptions concerning animal responses to environmental cues.