A novel method to improve estimates of predator diet compositions

Background/Question/Methods

Food web studies require estimating relative contributions of prey to predator diets.  Diet estimates provide knowledge of not only the links between species in a system, but how important each prey group is to the predator’s diet. In aquatic ecology, a typical source of this information is stomach content data, which often involves lack of independence, as individuals collected in a single sampling event, such as a trawl, are not statistically independent samples, as well as covariance between consumption of prey and prey type. Existing statistical methods to analyze stomach content data do not currently address these challenges, nor do they create an appropriate likelihood function to permit formal model selection, likelihood-based parameter estimation and foster Bayesian analyses. We are developing improved statistical methods that address these challenges to quantitatively estimate prey contributions to predators.

Results/Conclusions

Our Bayesian model can account for covariance between prey type and consumption rates, and can be fitted to data using standard numerical methods. By having the expected proportion of prey in a stomach conditional upon stomach mass, we allow for covariance between prey type and level of consumption. Simulation testing of the model indicates that it is robust to the model assumptions. We apply our model to multiple stomach content datasets to compare the resulting prey contribution estimates to traditional diet estimation methods.