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We perform a Bayesian analysis on abundance data for ten species of NorthAmerican duck, using the results to investigate the evidence in favour ofbiologically motivated hypotheses about the causes and mechanisms of densitydependence in these species. We explore the capabilities of our methods todetect density dependent effects, both by simulation and through analyzes ofreal data. The effect of the prior choice on predictive accuracy is alsoexamined. We conclude that our priors, which are motivated by considering thedynamics of the system of interest, offer clear advances over the priors usedby previous authors for the duck data sets. We use this analysis as amotivating example to demonstrate the importance of careful parameter priorselection if we are to perform a balanced model selection procedure. We alsopresent some simple guidelines that can be followed in a wide variety ofmodelling frameworks where vague parameter prior choice is not a viable option.These will produce parameter priors that not only greatly reduce bias inselecting certain models, but improve the predictive ability of the resultingmodel-averaged predictor.

The Importance of Prior Choice in Model Selection: a Density Dependence Example