title: Dynamic state-dependent modelling predicts optimal usage patterns of responsive defences.
abstract: Chemical defences against predation often involve responses to specific predation events where the prey expels fluids, such as haemolymph or gut contents, which are aversive to the predator. The common link is that each predation attempt that is averted results in an energetic cost and a reduction in the chemical defences of the prey, which might leave the prey vulnerable if the next predation attempt occurs soon afterwards. Since prey appear to be able to control the magnitude of their responses, we should expect them to trade-off the need to repel the current threat against the need to preserve defences against future threats and conserve energy for other essential activities. Here we use dynamic state-dependent models to predict optimal strategies of defence deployment in the juvenile stage of an animal that has to survive to maturation. We explore the importance of resource level, predator density, and the costs of making defences on the magnitude of the responses and optimal age and size at maturation. We predict the patterns of investment and the magnitude of the deployment of defences to potentially multiple attacks over the juvenile period, and show that responses should be smaller when the costs of defences and/or predation risk are higher. The model enables us to predict that animals in which defences benefit the adult stage will employ different strategies than those that do not use the same defences as adults, and thereby experience a smaller reduction in body size as a result of repeated attacks. We also explore the effect of the importance of adult size, and find that the sex and mating system of the prey should also affect defensive strategies. Our work provides the first predictive theory of the adaptive use of responsive defences across taxa.
authors.affiliation: Division of Ecology & Evolutionary Biology, Faculty of Biomedical & Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK. A.Higginson@bio.gla.ac.uk
authors.forename: A D
authors.initials: AD
authors.lastname: Higginson
authors.forename: G D
authors.initials: GD
authors.lastname: Ruxton
mesh_terms: D000367:Age Factors; D000818:Animals; D001362:Avoidance Learning / Q000502:physiology*; D049628:Body Size; D003198:Computer Simulation; D020387:Food Chain; D008954:Models, Biological; D011156:Population Density; D012726:Sexual Behavior, Animal / Q000502:physiology