How well do food distributions predict spatial distributions of shorebirds with different degrees of self-organization?
Article first published online: 19 MAR 2010
© 2010 The Authors. Journal compilation © 2010 British Ecological Society
Journal of Animal Ecology
Volume 79, Issue 4, pages 747–756, July 2010
How to Cite
Folmer, E. O., Olff, H. and Piersma, T. (2010), How well do food distributions predict spatial distributions of shorebirds with different degrees of self-organization?. Journal of Animal Ecology, 79: 747–756. doi: 10.1111/j.1365-2656.2010.01680.x
- Issue published online: 7 JUN 2010
- Article first published online: 19 MAR 2010
- Received: 28 July 2009; accepted 16 February 2010Handling Editor: Tim Coulson
- conspecific attraction;
- low-tide shorebird monitoring;
- predictive power of resource-based models;
- resource distribution;
- Wadden Sea
1. Habitat selection models usually assume that the spatial distributions of animals depend positively on the distributions of resources and negatively on interference. However, the presence of conspecifics at a given location also signals safety and the availability of resources. This may induce followers to select contiguous patches and causes animals to cluster. Resource availability, interference and attraction therefore jointly lead to self-organized patterns in foraging animals.
2. We analyse the distribution of foraging shorebirds at landscape level on the basis of a resource-based model to establish, albeit indirectly, the importance of conspecific attraction and interference.
3. At 23 intertidal sites with a mean area of 170 ha spread out over the Dutch Wadden Sea, the spatial distribution of six abundant shorebird species was determined. The location of individuals and groups was mapped using a simple method based on projective geometry, enabling fast mapping of low-tide foraging shorebird distributions. We analysed the suitability of these 23 sites in terms of food availability and travel distances to high tide roosts.
4. We introduce an interference sensitivity scale which maps interference as a function of inter-individual distance. We thus obtain interference-insensitive species, which are only sensitive to interference at short inter-individual distances (and may thus pack densely) and interference-sensitive species which interfere over greater inter-individual distances (and thus form sparse flocks).
5. We found that interference-insensitive species like red knot (Calidris canutus) and dunlins (Calidris alpina) are more clustered than predicted by the spatial distribution of their food resources. This suggests that these species follow each other when selecting foraging patches. In contrast, curlew (Numenius arquata) and grey plover (Pluvialis squatarola), known to be sensitive to interference, form sparse flocks. Hence, resource-based models have better predictive power for interference-sensitive species than for interference-insensitive species.
6. It follows from our analysis that monitoring programmes, habitat selection models and statistical analyses should also consider the mechanisms of self-organization.