Multi-behavioral strategies in a predator–prey game: an evolutionary algorithm analysis
Version of Record online: 17 MAR 2009
© 2009 The Authors
Volume 118, Issue 7, pages 1073–1083, July 2009
How to Cite
Mitchell, W. A. (2009), Multi-behavioral strategies in a predator–prey game: an evolutionary algorithm analysis. Oikos, 118: 1073–1083. doi: 10.1111/j.1600-0706.2009.17204.x
- Issue online: 23 JUN 2009
- Version of Record online: 17 MAR 2009
- Manuscript Accepted 15 January 2009
Behavioral games between predators and prey often involve two sub-games: ‘pre-encounter’ games affecting the rate of encounter between predators and prey (e.g. predator–prey space games, Sih 2005), and ‘post-encounter’ games that influence the outcome of encounters (e.g. waiting games at prey refugia, Hugie 2003, and games of vigilance, Brown et al. 1999). Most models, however, focus on only one or the other of these two sub-games.
I investigated a multi-behavioral game between predators and prey that integrated both pre-encounter and post-encounter behaviors. These behaviors included landscape-scale movements by predators and prey, a type of prey vigilance that increases immediately after an encounter and then decays over time (‘ratcheting vigilance’), and predator management of prey vigilance. I analyzed the game using a computer-based evolutionary algorithm. This algorithm embedded an individual-based model of ecological interactions within a dynamic adaptive process of mutation and selection. I investigated how evolutionarily stable strategies (ESS) varied with the predators’ learning ability, killing efficiency, density and rate of movement. I found that when predators learn prey location, random prey movement can be an ESS. Increased predator killing efficiency reduced prey movement, but only if the rate of predator movement was low. Predators countered ratcheting vigilance by delaying their follow-up attacks; however, this delay was reduced in the presence of additional predators. The interdependence of pre-and post-encounter behaviors revealed by the evolutionary algorithm suggests an intricate co-evolution of multi-behavioral predator–prey behavioral strategies.