The emergent interactions that govern biodiversity change.

Area of Science:

• Ecology
• Environmental Science
• Mathematical Biology

Background:

• Observational studies struggle to link environmental variables to nonlinear species abundance patterns.
• These patterns may arise from indirect species interactions (e.g., competition), which are difficult to isolate at large scales.
• Existing models often only capture direct environmental responses, neglecting indirect effects.

Purpose of the Study:

• To develop and apply a biophysical approach for quantifying environment-species interactions (ESI) from field data.
• To measure indirect ecological responses induced by species interactions.
• To analyze how nonlinear community responses emerge even with linear direct environmental responses.

Main Methods:

• A novel biophysical framework was developed to quantify dynamic ESI, integrating data across different scales.
• The approach incorporates probabilistic uncertainty in parameters, model specification, and data.
• Simulations were used to validate the necessity of ESI for accurate ecological interpretation.

Main Results:

• ESI are crucial for accurately interpreting community dynamics and nonlinear responses to environmental gradients.
• Analysis revealed nonlinear responses arise from interactions, even when direct environmental responses are linear.
• Contrasting ESI were found in experimental lakes (strong interactions) versus the Breeding Bird Survey (weak interactions).

Conclusions:

• Interactions between species are essential drivers of nonlinear responses to environmental change.
• The probabilistic framework provides a robust method for ecological analysis and conservation planning.
• Ecological stability differs between closed systems (lakes) and open systems with high species mobility (BBS).