Líffræðifélag Íslands
Líffræðiráðstefnan 2015
Erindi/veggspjald / Talk/poster E9
W.E. Butler (1), C. Jørgensen (2), A.F. Opdal (2), N. Fouzai (2), G. Marteinsdóttir (1), Ø. Fiksen (2)
1. MARICE, Faculty of Life and Environmental Sciences, University of Iceland, 2. Department of Biology, University of Bergen.
Kynnir / Presenter: William Butler
Tengiliður / Corresponding author: William Butler (willb2306@hotmail.com)
"To capture adaptive behavior, individual-based models (IBMs) require an algorithm or a set of rules specifying individual responses to available information. How this is formulated can have major implications for the emergent system properties. Ideally, such an algorithm would be evolutionarily consistent, reflecting the flexible nature of behaviors such as habitat selection and foraging activity, and preferably also their dependence on individual state. An algorithm should be constrained by the individualʼs limited ability to sense and predict, yet still contain enough information to perform well in a dynamic environment. Using a state-dependent optimality model as a benchmark, we examined a variety of simple myopic rules that evaluate the tradeoff between energetic gain and mortality to determine the behavior of a larval fish. We show that increasing the complexity of the algorithm always improved its performance. This was achieved by sequentially incorporating the following into the strategy: (1) multiple behavioral traits; (2) proximate stimuli; (3) length-dependence. Whilst the best proximate rule failed to fully capture optimal behavior, with each step, key facets of the optimal strategy were uncovered. By combining the interplay between proximate stimuli and ontogeny into a fitness seeking objective, the rule serves as a useful template for IBMs that aim to capture adaptive foraging and the distribution of organisms in complex spatial gradients."