The impact of cumulative stressor effects on uncertainty and ecological risk

To enable environmental management actions to be more effectively prioritized, cumulative effects between multiple stressors need to be accounted for in risk-assessment frameworks. Ecological risk and uncertainty are generally high when multiple stressors occur. In the face of high uncertainty, transparent communication is essential to inform decision-making. The impact of stressor interactions on risk and uncertainty was assessed using generalized linear models for additive and multiplicative effect of six anthropogenic stressors on the abundance of estuarine macrofauna across New Zealand. Models that accounted for multiplicative stressor interactions demonstrated that non-additive effects dominated, had increased explanatory power (6 to 73 % relative increase between models), and thereby reduced the risk of unexpected ecological responses to stress. Secondly, 3D-plots provide important insights in the direction, magnitude and gradients of change, and aid transparency and communication of complex stressor effects. Notably, small changes in a stressor can cause a disproportionally steep gradient of change for a synergistic effect where the tolerance to stressors are lost, and would invoke precautionary management. 3D-plots were able to clearly identify directional shifts where the nature of the interaction changed from antagonistic to synergistic along increasing stressor gradients. For example, increased nitrogen load and exposure caused a shift from positive to negative effect on the abundance of a deposit-feeding polychaete (Magelona). Assessments relying on model coefficient estimates, which provide one effect term, could not capture the complexities observed in 3D-plots and are at risk of mis-identifying interaction types. Finally, visualising model uncertainty demonstrated that although error terms were higher for multiplicative models, they better captured the uncertainty caused by data availability. Together, the steep gradients of change identified in 3D-plots and the higher uncertainty in model predictions in multiplicative models urges more conservative limits to be set for management that account for risk and uncertainty in multiple stressor effects.