Models and model selection uncertainty in estimating growth rates of endangered freshwater mussel populations

Abstract

Appropriate inference of population status for endangered species is extremely important. Using a single model for estimating population growth rates is typically inadequate for assessing endangered species because inferences based on only one “best” model ignore model uncertainty. In this study, the endangered dromedary pearlymussel ( Dromus dromas ) in the Clinch and Powell rivers of eastern Tennessee, USA, was used as an example to demonstrate the importance of multiple models, with consideration of environmental noises for evaluating population growth. Our results showed that more than one model deserves consideration in making inferences of population growth rate. A Bayesian model averaging approach was used to make inferences by weighting each model using the deviance information criterion. To test the uncertainty resulting from model selection and the efficiency of the Bayesian averaging approach, a simulation study was conducted on the dromedary pearlymussel populations, which showed that model selection uncertainty is very high. The results of these tests lead us to recommend using Bayesian model averaging to assess population growth status for endangered species, by balancing goodness-of-fit and selection uncertainty among alternate models.