Predicting food web responses to biomanipulation using Bayesian Belief Network: Assessment of accuracy and applicability using in-situ exclosure experiments

Ecological networks are useful for describing the complex trophic interactions within an ecosystem and hold great potential for ecosystem-based management. However, owing to the complexity and limited knowledge on the trophic interactions of natural food webs, it is challenging to make quantitative predictions about ecological community response to management interventions. Here, we use stable isotope mixing models in conjunction with Bayesian Belief Networks (BBN) to develop and examine the trophic interactions for six empirically determined aquatic food webs in tropical reservoirs. Using BBN, we predicted potential trophic cascade outcomes to predator removals, validated the predictions against data observed from in-situ biomanipulation experiments, and identified influential species using sensitivity analyses. Comparisons among various food web modelling frameworks demonstrated the importance of weighted connectance and network-centric approach for quantitative predictions, suggesting that the Bayesian Belief Network framework can play an important role in ecosystem-based management.