MODELING INTERFERENCE FROM BASIC FORAGING BEHAVIOR

1. We develop an individuals-based model that predicts the strength of interference between foraging animals from basic elements of their be haviour, The model is based on the same principles as previous behavio ur-based interference models, but extends and adds further realism to these models, One kev difference is that in our model the responses of animals to competitors are not fi;ed, as is assumed in previous model s, Instead, animals use optimal decision rules to determine responses which maximize their intake rate, 2. The general shape of interference function generated by the model is similar to that predicted by previ ous behaviour-based models, Interference is insignificant at low compe titor densities, but steadily increases in intensity as density rises, However, comparison with the observed level of interference between o ystercatchers. Haematopus ostralegus, feeding on mussels, Mytilus edul is, shows that the model's predictive power is substantially increased through the addition of optimal decision rules, When animals have a f ixed response to encounters. too much interference occurs because domi nant animals waste time avoiding subdominants and subdominants waste t ime attempting, but failing, to steal prey from dominants. When animal s use optimal decision rules, only subdominants avoid, and only domina nts initiate attacks, Interference is therefore reduced and is much cl oser to that observed, 3, The conditions under which optimal decision rules will lead to interference are described in terms of basic elemen ts of foraging behaviour. interference is predicted to occur when hand ling time and the probability of winning fights are high, and when pre y encounter rate and the duration of fights are low. These parameters are used to predict successfully the presence or absence of interferen ce in a range of shorebird-prey systems. 4. We suggest that behaviour- based interference models will need to incorporate optimal decision ru les if they are to predict accurately the strength of interference obs erved in real predator-prey systems.