Predicting fish diet composition using a bagged classification tree approach: a case study using yellowfin tuna (Thunnus albacares)

We provided a classification tree modeling framework for investigating complex feeding relationships and illustrated the method using stomach contents data for yellowfin tuna (Thunnus albacares) collected by longline fishing gear deployed off eastern Australia between 1992 and 2006. The non-parametric method is both exploratory and predictive, can be applied to varying size datasets and therefore is not restricted to a minimum sample size. The method uses a bootstrap approach to provide standard errors of predicted prey proportions, variable importance measures to highlight important variables and partial dependence plots to explore the relationships between explanatory variables and predicted prey composition. Our results supported previous studies of yellowfin tuna feeding ecology in the region. However, the method provided a number of novel insights. For example, significant differences were noted in the prey of yellowfin tuna sampled north of 20°S in summer where oligotrophic waters dominate. The analysis also identified that sea-surface temperature, latitude and yellowfin size were the most important variables associated with dietary differences. The methodology is appropriate for delineating ecosystem-level trophic dynamics, as it can easily incorporate large datasets comprising multiple predators to explore trophic interactions among members of a community. Broad-scale relationships among explanatory variables (environmental, biological, temporal and spatial) and prey composition elucidated by this method then serve to focus and lend validity to subsequent fine-scale analyses of important parameters using standard diet methods and chemical tracers such as stable isotopes.