ANALYSIS OF HABITAT-SELECTION RULES USING ANINDIVIDUAL-BASED MODEL

Abstract

Despite their promise for simulating natural complexity, individual-based models (IBMs) are rarely used for ecological research or resource management. Few IBMs have been shown to reproduce realistic patterns of behavior by individual organisms. To test our IBM of stream salmonids and draw conclusions about foraging theory, we analyzed the IBM's ability to reproduce six patterns of habitat selection by real trout in simulations contrasting three alternative habitat-selection objectives: maximizing current growth rate, current survival probability, or ''expected maturity'' (EM). EM is the product of (1) pre­ dicted survival of starvation and other mortality risks over a future time horizon, and (2) the fraction of reproductive size attained over the time horizon. Minimizing the ratio of mortality risk to growth rate was not tested as a habitat-selection rule because it produces nonsensical results when any habitat yields negative growth rates. The IBM simulates habitat selection in response to spatial and temporal variation in mortality risks and food availability as fish compete for food. The model fish move each daily time step to maximize their habitat-selection objective with no other restrictions (e.g., territoriality) imposed. Simulations with habitat selected to maximize growth reproduced three of the six habitat- selection patterns; maximizing survival reproduced two patterns; and maximizing EM re- produced all six patterns. Two patterns (shifts in habitat with changes in temperature and food availability) were not reproduced by the objectives that consider only current growth and risk but were explained by the EM objective that considers how future starvation risk depends on current energy reserves and energy intake. In 75-d simulations, population- level survival and biomass accumulation were highest for fish moving to maximize EM. These results support the basic assumptions of state-based dynamic-modeling approaches to habitat selection. Our IBM appears successful because it avoids restrictive assumptions, incorporates competition for food, assumes salmonids make good habitat-selection decisions at a daily time step, and uses a habitat objective (EM) that provides reasonable trade-offs between growth and mortality risks.