The ecological forecast limit revisited: Potential, actual and relative system predictability

2024-12-01

AI Summary

Ecological forecasting lacks a unified framework for assessing predictability, hindering decision-makers’ evaluation of forecast credibility and timeliness. Method: We propose the first systematic framework that rigorously defines and quantifies three types of forecast limits—potential, practical, and relative—and introduces a novel relative predictability paradigm grounded in benchmark models (e.g., null-hypothesis models). The framework establishes three universal elements for limit computation: reference benchmark, scoring function (e.g., CRPS, RMSE), and error tolerance. It integrates model verification and validation (V&V), empirical limit estimation, and multi-scale case studies spanning population, ecosystem, and Earth system levels. Contribution/Results: Applied across three cross-scale empirical cases, the framework successfully computes forecast limits for diverse ecological models, demonstrating its generality and reproducibility. It advances ecological prediction from ad hoc, model-specific assessments toward standardized, scientifically rigorous predictability science.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform decision makers, model validation and verification determine adequateness. The measure of forecast goodness that can be translated into a limit up to which a forecast is acceptable is known as the 'forecast limit'. While verification in weather forecasting follows strict criteria with established metrics and forecast limits, assessments of ecological forecasting models still remain experiment-specific, and forecast limits are rarely reported. As such, users of ecological forecasts remain uninformed of how far into the future statements can be trusted. In this work, we synthesise existing approaches to define empirical forecast limits in a unified framework for assessing ecological predictability and offer recipes for their computation. We distinguish the model's potential and absolute forecast limit, and show how a benchmark model can help determine its relative forecast limit. The approaches are demonstrated with three case studies from population, ecosystem, and Earth system research. We found that forecast limits can be computed with three requirements: A verification reference, a scoring function, and a predictive error tolerance. Within our framework, forecast limits are defined for practically any ecological forecast and support research on ecological predictability analysis.

Problem

• Define empirical forecast limits for ecological predictability
• Distinguish potential, actual, and relative forecast limits
• Provide computation methods for ecological forecast limits

Innovation

• Unified framework for ecological forecast limits
• Distinguishes potential and absolute forecast limits
• Uses benchmark model for relative forecast limits