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

Area of Science:

• Environmental Science
• Ecology
• Conservation Biology

Background:

• Global environmental change results from numerous human-caused stressors.
• Effective conservation and restoration depend on understanding how these stressors interact.
• Current research often focuses on identifying stressor interactions but lacks a strong theoretical framework for null model selection.

Purpose of the Study:

• To propose a shift in multiple stressor research from identifying interactions to utilizing and developing mechanistic null models.
• To provide a framework for selecting null models based on mechanistic assumptions.
• To enhance the prediction of multiple stressor effects for improved management.

Main Methods:

• Review and presentation of various null models used in multiple stressor research.
• Outline of the underlying assumptions and applications of different null models.
• Discussion on selecting null models based on mechanistic understanding of stressor action and organism responses.

Main Results:

• Existing meta-analyses on stressor interactions offer valuable insights but lack theoretical grounding for null model selection.
• A framework is proposed for selecting null models based on mechanistic assumptions of stressor-effect relationships.
• The study emphasizes the need for mechanistic (null) models over mere statistical interaction detection.

Conclusions:

• Multiple stressor research requires a theoretical framework for null model selection to move beyond descriptive analyses.
• Mechanistic null models are essential for predicting the effects of multiple stressors on individuals and populations.
• Justified selection of appropriate null models is a critical first step for advancing multiple stressor research and informing conservation efforts.