Advancing understanding and prediction in multiple stressor research through a mechanistic basis for null models.

Global environmental change is driven by multiple anthropogenic stressors. Conservation and restoration require understanding the individual and joint action of these stressors to evaluate and prioritize management measures. To date, most studies on multiple stressor effects have sought to identify potential stressor interactions, defined as deviations from null models, and related meta-analyses have focused on quantifying the relative proportion of stressor interactions across studies. These studies have provided valuable insights about the complexity of multiple stressor effects, but remain largely devoid of a theoretical framework for null model selection and prediction of effects. We suggest that multiple stressor research would benefit by (1) integrating and developing additional null models and (2) selecting null models based on their mechanistic assumptions of the stressor mode of action and organism sensitivities as well as stressor-effect relationships for individuals and populations. We present a range of null models and outline their underlying assumptions and application in multiple stressor research. Moving beyond mere description requires multiple stressor research to shift its focus from identifying statistically significant interactions to the use and development of mechanistic (null) models. Justified selection of the appropriate null model is a first step to achieve this.