Predicting effects of multiple interacting global change drivers across trophic levels

Area of Science:

• Ecology
• Environmental Science
• Conservation Biology

Background:

• Global change involves multiple anthropogenic drivers impacting ecological systems.
• Predicting biodiversity change under co-occurring drivers remains a significant challenge.
• Mechanistic understanding of how drivers affect species vital rates is lacking.

Purpose of the Study:

• To propose reaction norms as a framework for understanding community responses to multiple global change drivers.
• To illustrate how driver impacts can be scaled from population to community levels.
• To improve predictions of ecological interactions and biodiversity change.

Main Methods:

• Utilizing reaction norms (relationships between drivers and vital rates like growth, mortality, consumption).
• Applying a consumer-resource interaction framework and thermal performance curves.
• Developing a proof-of-concept model to predict community-level driver interactions.
• Outlining methods for inferring multidimensional reaction norms (response surfaces).

Main Results:

• Reaction norms provide mechanistic insights into community responses to multiple drivers.
• A model demonstrates that vital rate reaction norms can predict driver interaction prevalence.
• The proposed reaction-norm approach is currently underutilized in multiple driver research.

Conclusions:

• Reaction norms offer a powerful tool to understand and predict community and food web responses to multiple global change drivers.
• This approach can enhance ecological understanding and improve predictions of interactive effects.
• Further development and application of response surfaces are crucial for addressing key ecological challenges.