Journal article
Determinants of reproductive success in dominant pairs of clownfish: a boosted regression tree analysis
Peter M Buston, Jane Elith
Journal of Animal Ecology | WILEY-BLACKWELL | Published : 2011
Abstract
1. Central questions of behavioural and evolutionary ecology are what factors influence the reproductive success of dominant breeders and subordinate nonbreeders within animal societies? A complete understanding of any society requires that these questions be answered for all individuals. 2. The clown anemonefish, Amphiprion percula, forms simple societies that live in close association with sea anemones, Heteractis magnifica. Here, we use data from a well-studied population of A. percula to determine the major predictors of reproductive success of dominant pairs in this species. 3. We analyse the effect of multiple predictors on four components of reproductive success, using a relatively ne..
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Awarded by NSF
Awarded by Australian Research Council
Funding Acknowledgements
We thank John Drake, Bill Langford, John Leathwick, Dragos Margineantu, Andrew Russell, Philip Stephens and Daniel Stouffer for helpful comments and discussion; Stephen Emlen, Paul Sherman, Andrew Bass, Amy McCune and Kern Reeve for consistent advice and support; John Mizeu, Mike Black, Claire Norris and Mike Moore for field assistance; the staff at the Christensen Research Institute and Jais Aben Resort for logistical support; and the land-owners of Riwo village, the Madang Provincial Government and Papua New Guinea Government for permission to work in Madang Lagoon. Field portion of this project is a product of Buston's Ph.D. dissertation. Financial support for the field portion came from Diane Christensen and the Christensen Fund; National Science Foundation, Dissertation Improvement Grant; Andrew W. Mellon Student Research Grant; Cornell, Graduate Research Travel Grant; Cornell and National Chapters of Sigma Xi, Grants-in-Aid of Research; International Women's Fishing Association, Scholarship; Percy Sladen Memorial Fund; Cornell, Department of Neurobiology and Behaviour and Department of Ecology and Evolutionary Biology. Analytical portion of the project is a product of the Ecological Machine Learning Working Group at NCEAS, a center funded by NSF grant #EF-0553768, the University of California and the Santa Barbara campus (USA). JE was supported by Australian Research Council grants DP0772671 and FT0991640. PB was supported by the Department of Biology at Boston University.