Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Abstract

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Keywords: ecology; forecast; prediction.

Fig. 1.

Conceptual relationships between iterative ecological forecasting, adaptive decision-making, and basic science. Hypotheses (A) are embedded in models (B) that integrate over uncertainties in initial conditions (IC), inputs, and parameters to make probabilistic forecasts (Fx, C), often conditioned on alternative scenarios. New observations are then compared with these predictions (D) to update estimates of the current state of the system (Analysis) and assess model performance (E), allowing for the selection among alternative model hypotheses (Test and Refine). Analysis and partitioning of forecast uncertainties facilitates targeted data collection (dotted line) and adaptive monitoring (dashed line). In decision analysis, alternative decision scenarios are generated (2) based on an assessment of a problem (1). Since decisions are based on what we think will happen in the future, forecasts play a key role in assessing the trade-offs between decision alternatives (3). Adaptive decisions (4) lead to an iterative cycle of monitoring (5) and reassessment (1) that interacts continuously with iterate forecasts.

Fig. 2.

Forms of model–data integration (Top) and the availability of environmental data (Bottom) through time. Changes in relative data volume indicated by line width.