Time to fly: A comparison of marginal value theorem approximations in an agent-based model of foraging waterfowl
Authored by Jeffrey C Schank, Matt L Miller, Kevin M Ringelman, John M Eadie
Date Published: 2017
DOI: 10.1016/j.ecolmodel.2017.02.013
Sponsors:
United States National Science Foundation (NSF)
Platforms:
MASON
Model Documentation:
ODD
Flow charts
Pseudocode
Mathematical description
Model Code URLs:
Model code not found
Abstract
One of the fundamental decisions foragers face is how long an individual
should remain in a given foraging location. Typical approaches to
modeling this decision are based on the marginal value theorem. However,
direct application of this theory would require omniscience regarding
food availability. Even with complete knowledge of the environment,
foraging with intraspecific competition requires resolution of
simultaneous circular dependencies. In response to these issues in
application, a number of approximating algorithms have been proposed,
but it remains to be seen whether these algorithms are effective given a
large number of foragers with realistic characteristics. We implemented
several algorithms approximating marginal value foraging in a
large-scale avian foraging model and compared the results. We found that
a novel reinforcement-learning algorithm that includes cost of travel is
the most effective algorithm that most closely approximates marginal
value foraging theory and recreates depletion patterns observed in
empirical studies. (C) 2017 Elsevier B.V. All rights reserved.
Tags
Simulation
behavior
Decision-Making
Optimal foraging theory
Prey
Algorithm
Animals
Foragers
Public information
Spatial-distribution
Predators
Individual decisions
Patchy
environment
Expectation