Improving multiple stressor-response models through the inclusion of nonlinearity and interactions among stressor gradients.

Stressor-response models are used to detect and predict changes within ecosystems in response to anthropogenic and naturally occurring stressors. While nonlinear stressor-response relationships and interactions between stressors are common in nature, predictive models often do not account for them due to perceived difficulties in the interpretation of results. We used Irish river monitoring data from 177 river sites to investigate if multiple stressor-response models can be improved by accounting for nonlinearity, interactions in stressor-response relationships and environmental context dependencies. Out of the six models of distinct biological responses, five models benefited from the inclusion of nonlinearity while all six benefited from the inclusion of interactions. The addition of nonlinearity means that we can better see the exponential increase in Trophic Diatom Index (TDI3) as phosphorus increases, inferring ecological conditions deteriorating at a faster rate with increasing phosphorus. Furthermore, our results show that the relationship between stressor and response has the potential to be dependent on other variables, as seen in the interaction of elevation with both siltation and nutrients in relation to Ephemeroptera, Plecoptera and Trichoptera (EPT) richness. Both relationships weakened at higher elevations, perhaps demonstrating that there is a decreased capacity for resilience to stressors at lower elevations due to greater cumulative effects. Understanding interactions such as this is vital to managing ecosystems. Our findings provide empirical support for the need to further develop and employ more complex modelling techniques in environmental assessment and management.