Abstract

There is an increasing interest in dynamical food web modeling, and recent advances of computational biology provide new algorithms and tools for modern systems ecology. In this work stochastic individual-based models are used for simulating food web dynamics in two case studies: the Kelian river, in Borneo, Indonesia and a marine ecosystem in Gulf of Guinea. The two cases present effects from human perturbations. In both cases, we constructed food webs, based on real databases. We parameterized the stochastic dynamical model for the system models and performed sensitivity analysis (and community response indicators) in order to quantify the relative importance of system components. The main aims are to understand the importance of functional diversity of aquatic ecosystems and relations between the dynamics of species and the whole community in perturbed ecosystems due to human activities (pollution due to gold mining activity and human settlements in the case of the Kelian river ecosystem and the impact of Fish Aggregation Devices on skipjack tuna communities in the case of the Gulf of Guinea ecosystem). In Kelian river case, our results suggest that invertebrate shredders are indicators of human impact on the river. In downstream sites our results show that the river is more polluted and the relative importance of grazers and shredders decrease. The primary producers disappear downstream as consequence of mine activity and human waste. In the marine system case study, we analyzed the effects of association between tunas and FADs, and compared this to the behavior of free tuna individuals. The results suggest that skipjack tuna is affected by the use of FADs as fishing strategy on them. Some individual species show more sensibility to variation of population size of tuna. These two studies contribute to better understand how functional diversity is related to human impact. This kind of approach may help in shaping systems-based conservation and marine fisheries management strategies. Keywords: food web, aquatic ecosystems, stochastic model, sensitivity analysis