Artificial evolution of life history and Behavior
Authored by Geir Huse, E Strand, J Giske
Date Published: 2002
DOI: 10.1086/339997
Sponsors:
Norwegian Research Council (NRF)
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Flow charts
Mathematical description
Model Code URLs:
Model code not found
Abstract
We present an individual-based model that uses artificial evolution to
predict fit behavior and life-history traits on the basis of
environmental data and organism physiology. Our main purpose is to
investigate whether artificial evolution is a suitable tool for studying
life history and behavior of real biological organisms. The evolutionary
adaptation is founded on a genetic algorithm that searches for improved
solutions to the traits under scrutiny. From the genetic algorithm's
``genetic code,{''} behavior is determined using an artificial neural
network. The marine planktivorous fish Muller's pearlside (Maurolicus
muelleri) is used as the model organism because of the broad knowledge
of its behavior and life history, by which the model's performance is
evaluated. The model adapts three traits: habitat choice, energy
allocation, and spawning strategy. We present one simulation with, and
one without, stochastic juvenile survival. Spawning pattern, longevity, and energy allocation are the life-history traits most affected by
stochastic juvenile survival. Predicted behavior is in good agreement
with field observations and with previous modeling results, validating
the usefulness of the presented model in particular and artificial
evolution in ecological modeling in general. The advantages, possibilities, and limitations of this modeling approach are further
discussed.
Tags
Genetic Algorithms
individual-based models
ecology
zooplankton
Environments
Neural-networks
Vertical-distribution
Maurolicus-muelleri
Pelagic planktivorous fish
Mesopelagic fish