Analysis of Habitat-Selection Rules Using an Individual-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) predicted 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 habitatselection patterns; maximizing survival reproduced two patterns; and maximizing EM reproduced 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, populationlevel 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.

Responses to the Foraging/Predation Risk Trade-Off and Individual Variability in Population-Level Fitness Correlates

“…With predation-sensitive foraging, a forager's state changes over time, and improved or reduced condition will affect the decision at the next step (e.g., [12,20,21]). Taking risks to offset depleted reserves exposes foragers in poor condition to predation [8,23,44].…”

“…A forager's present condition (e.g., [4,[12][13][14]) and the density-dependent distribution of predators [15] affect the amount of risk taken to obtain food, leading to variation in that condition through time (e.g., [12,16]), in turn affecting subsequent foraging decisions [17][18][19][20][21]. One prediction of state-dependent foraging is the "asset-protection principle" [22]; specifically, foragers in a good state (as an indicator of high potential future fitness) assume fewer risks and occupy a safer, but less productive patch where condition might decline due to limited foraging.…”

Adverse outcome pathways and ecological risk assessment: Bridging to population‐level effects

“…Individual-based models (IBMs), as the name suggests, model the survival, productivity, and movement of each individual in the population during its entire life span [31,32]. These models can be highly specific with regard to site and demographic rates and can include physiological states of each individual.…”

Response of Gilthead Seabream (Sparus aurata L., 1758) Larvae to Nursery Odor Cues as Described by a New Set of Behavioral Indexes

“…Indeed, habitat selection is the outcome of a non-random use of space, driven by environmental stimuli and behavioral choices under changing resource conditions (Kramer et al, 1997;Railsback and Harvey, 2002) and balanced by the risks inherent in each behavioral decision (Lima and Dill, 1990). Habitat choice is a mechanism resulting from a coevolutionary process allowing individuals to choose the highest quality habitat available to acquire the greatest fitness benefit (Kristan, 2003), and thus putatively enabling metapopulations with increased stability and resilience.…”