Prediction in ecology: a first-principles framework.

Area of Science:

• Ecology
• Ecological Forecasting
• Quantitative Ecology

Background:

• Quantitative predictions are common in ecology, but the nature of prediction and its uncertainties are not well-defined.
• Existing ecological studies often lack a unified framework for analyzing the sources of uncertainty limiting predictive power.

Purpose of the Study:

• To derive a general quantitative framework for analyzing and partitioning uncertainty in ecological predictions.
• To link this framework to fundamental ecological questions and guide experimental design and hypothesis testing.
• To advocate for a comparative approach to studying predictability across diverse ecological systems.

Main Methods:

• Development of a general quantitative framework for uncertainty analysis.
• Conceptual assessment of the framework's implications for ecological theory.
• Quantitative application to a short-term forecast of net ecosystem exchange (NEE).

Main Results:

• The framework provides a method to partition sources of uncertainty controlling predictability.
• Novel predictions are generated, and approaches to experimental design, model selection, and hypothesis testing are reframed.
• The framework's application to NEE forecasting demonstrates its utility in partitioning uncertainties.

Conclusions:

• A robust framework for understanding and quantifying ecological predictability is established.
• The framework offers new insights into factors like density-dependence, stability, and spatial scaling.
• Future research should adopt a comparative approach to predictability, exploring its spatial and temporal scaling limits.