UBC Theses and Dissertations
Prey avoidance learning and the functional response of predators Dill, Lawrence Michael
The research attempts to determine the effect on the number of prey eaten by predators of the addition of the component "avoidance learning by prey" to a computer model of the predation process developed by Holling (1966). Generality was retained by concentrating upon only a few aspects of the prey's behaviour, particularly its distance of reaction to an approaching predator. The zebra danio (Brachydanio rerio), a small freshwater fish, was used as an analogue of a general vertebrate prey. Predators used were plexiglass models, films of approaching objects, and largemouth bass (Micropterus salmoides). It was shown that the reactive distance of zebra danios could be predicted from the equation: [See Thesis for Equation] where V = predator approach velocity S = predator diameter k = threshold rate of change of angle subtended at the eye of the prey by the predator (= .43 rad/sec). Thus, reactive distance increased with both predator size and velocity. Escape velocity was independent of these same parameters. The threshold rate of change of visual angle, and hence the reactive distance, was not affected by prey hunger. However, reactive distance increased with number of previous experiences, apparently because of secondary conditioning to other features of the predator, such as shape and color. The increased prey reactive distance due to experience was shown to increase predator pursuit time and hypothesized to decrease predator pursuit success. These relationships were expressed mathematically and built into Holling's model of the predation process, along with an equation causing reactive distance to increase following an unsuccessful attack. Simulation was used to explore the consequences of these additions. The capability of learning substantially increased the prey's probability of surviving subsequent attack. Addition of the avoidance learning component caused declines in the predator's functional responses to both prey and predator density. The new component was also suggested to decrease the predator's numerical response to prey density and to increase the probability of stability in a predator-prey interaction.
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