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Predicting the strength of interference more quickly using behaviour‐based models
Author(s) -
Stillman Richard A.,
Poole Alison E.,
GossCustard John D.,
Caldow Richard W. G.,
Yates Michael G.,
Triplet Patrick
Publication year - 2002
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1046/j.1365-2656.2002.00621.x
Subject(s) - interference (communication) , cerastoderma edule , foraging , target strength , statistics , mathematics , biology , ecology , computer science , fishery , telecommunications , bivalvia , channel (broadcasting) , mollusca , fish <actinopterygii>
Summary1 Interference between foraging animals can be quantified directly only through intensive studies. A quicker alternative is to predict the strength of interference using behaviour‐based models. We describe a field method to parameterize an interference model for shorebirds, Charadrii. 2 Kleptoparasitic attack distance is the main factor affecting the strength of interference but has rarely been measured. Attack distance is related to handling time, a frequently measured parameter, allowing the model to be parameterized for systems in which attack distance has not been measured. 3 The model accurately predicts the strength of interference between oystercatchers Haematopus ostralegus L. feeding on cockles Cerastoderma edule L. and the absence of interference between bar‐tailed godwits Limosa lapponica L. feeding on lugworms Arenicola marina L. at low competitor densities. 4 We predict the strength of interference in black‐tailed godwit Limosa limosa L. and oystercatcher systems in which it has not been measured previously. The strength of interference is almost entirely determined by attack distance; interference is stronger in systems with longer attacks. Interference is usually weaker in black‐tailed godwits because handling time is generally shorter and this limits attack distance. 5 The interference model can be parameterized much more quickly than the alternative of measuring interference directly. Behaviour‐based models have the potential to be a valuable tool for predicting the strength of interference.