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Modelling state-dependent interference in common cranes
Article first published online: 6 SEP 2002
DOI: 10.1046/j.1365-2656.2002.00652.x
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How to Cite
Stillman, R. A., Bautista, L. M., Alonso, J. C. and Alonso, J. A. (2002), Modelling state-dependent interference in common cranes. Journal of Animal Ecology, 71: 874–882. doi: 10.1046/j.1365-2656.2002.00652.x
Publication History
- Issue published online: 6 SEP 2002
- Article first published online: 6 SEP 2002
- Received 22 January 2002; revision received 28 May 2002
- Abstract
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Keywords:
- dominance;
- Grus grus;
- kleptoparasitism;
- injury risk;
- starvation risk
Summary
- 1Interference is a key component of food competition, but is difficult to measure in natural animal populations. Using data from a long-term study, we show that interference between common cranes Grus grus L., feeding on patches of cereal seeds, reduces intake rates at high competitor densities, and that the strength of interference is unrelated to food abundance.
- 2An alternative to measuring interference directly is to predict its strength using behaviour-based models. We test an interference model, originally developed for shorebirds feeding on invertebrate prey, for cranes. We compare the predictions of a rate-maximizing model, in which animals steal food if this increases intake rate, and a state-dependent model, in which they only rate-maximize if their intake rate is below a target value, otherwise they minimize injury risk by not stealing food. State-dependent aggression occurs in cranes.
- 3The state-dependent model predicts more accurately the relative aggression rates of cranes of different dominance. However, both models predict accurately the observed strength of interference, that the strength of interference is unrelated to food abundance, at least within the observed range of crane and seed densities, and that cranes of a higher dominance have a higher intake rate than those of lower dominance.
- 4This paper shows how state-dependent behaviour can be incorporated into an interference model, and that the model can produce accurate predictions for a system quite different to that for which it was developed.