Evaluating probabilistic ecological forecasts.

Area of Science:

• Ecology
• Forecasting Science
• Environmental Science

Background:

• Probabilistic forecasting is crucial for ecological decision-making and societal impact.
• Many ecologists lack familiarity with established probabilistic forecast evaluation tools.
• A gap exists in applying cross-disciplinary forecasting evaluation methods to ecology.

Purpose of the Study:

• To bridge the knowledge gap by reviewing probabilistic forecast evaluation methods.
• To adapt and present established practices for ecological forecasting applications.
• To facilitate the use of robust evaluation techniques in ecological modeling.

Main Methods:

• Literature review of probabilistic forecast evaluation from climatology, economics, and epidemiology.
• Presentation of data selection (end-sample hold out) and graphical evaluation techniques (time-series plots, probability integral transform plots).
• Explanation of quantitative evaluation using scoring rules (log, quadratic, spherical, ranked probability scores) and model comparison (skill score, Diebold-Mariano test).

Main Results:

• Established practices for selecting evaluation data, graphical evaluation, and quantitative scoring are presented.
• Methods for comparing forecast scores across different models are detailed.
• An illustrative application to a rodent population time series demonstrates the practical use of these methods.

Conclusions:

• Ecology can benefit significantly from adopting and adapting probabilistic forecast evaluation tools from other disciplines.
• Understanding and applying these methods will enhance the reliability and impact of ecological forecasts.
• This work promotes a cross-disciplinary approach to advance the field of forecasting science within ecology.