Predictive entomology: a causal framework for detecting and attributing insect population change.

Area of Science:

• Ecology
• Environmental Science
• Conservation Biology

Background:

• Widespread insect declines raise concerns about ecosystem stability.
• Population trends are heterogeneous, shaped by interacting drivers, not single stressors.
• Predicting insect responses to environmental change requires understanding underlying mechanisms.

Purpose of the Study:

• To outline predictive entomology, an integrative framework for understanding and forecasting insect population dynamics.
• To link environmental drivers, ecological mechanisms, detection processes, and causal inference for predictive modeling.
• To guide proactive conservation strategies under global change.

Main Methods:

• Synthesizing data from long-term ecological datasets.
• Utilizing mechanistic experiments to understand physiological and behavioral responses.
• Employing emerging sensing technologies for insect monitoring.
• Applying causal inference tools to disentangle driver effects and detection artifacts.

Main Results:

• Demonstrating how multiple drivers interact across spatial and temporal scales.
• Illustrating the utility of causal inference in separating true effects from confounding factors.
• Highlighting the importance of mechanistic understanding in ecological forecasting.

Conclusions:

• Predictive entomology provides a framework for forecasting ecological change and identifying emerging risks.
• Integrating mechanistic and causal understanding into monitoring is crucial for effective insect conservation.
• Proactive conservation requires a robust understanding of the complex drivers shaping insect populations.