Analysis of habitat-selection rules using an individual-based model
Authored by Steven F Railsback, Bret C Harvey
Date Published: 2002
DOI: 10.2307/3071767
Sponsors:
Electric Power Research Institute (EPRI)
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Model Code URLs:
Model code not found
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 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 habitat-selection 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, population-level survival and biomass
accumulation were hi-hest for fish moving to maximize EM. These results
support the basic assumptions of state-based dynamic-model in g
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.
Tags
Atlantic salmon
Foraging behavior
Brown trout
Coho salmon
Rainbow-trout
Grayling thymallus-arcticus
Dominance hierarchies
Juvenile salmon
Life-history variation
Microhabitat use