A framework for predicting the effects of chemical contaminants on biodiversity and ecosystem functions
Chemical contaminants are causing biodiversity losses and shifts in ecosystems functions. Consequently, there is an interest in evaluating the risk contaminants pose to biodiversity and ecosystem services. However, this task is made challenging by daunting number of chemicals, species, and community compositions. Scientists and risk assessors need a predictive framework that can offer a null expectation for contaminant-biodiversity-ecosystem-function relationships and handle extreme heterogeneity in chemical mixtures and species compositions. I submit that an integration of phylogenetic, food web, and biodiversity-ecosystem function theories offers such a predictive framework.
I will present data from a series of mesocosm and field experiments that support the hypothesis that the effects of single contaminants and mixtures of contaminants on biodiversity-ecosystem-function relationships can be predicted by integrating information on each functional group’s 1) sensitivity to chemical classes (i.e. direct effects), 2) reproductive rates (recovery rates and chemical half-lives), 3) strong interactions with other groups in food web (i.e. indirect effects), and 4) links with ecosystem functions. To demonstrate the value of this framework, I will apply it to a single chemical, across 12 chemicals, to chemical mixtures, and finally to the control of an emerging human infectious disease.