Partitioning prediction uncertainty in climate-dependent population models.

Area of Science:

• Complex systems science
• Ecological forecasting
• Climate change impact assessment

Background:

• Complex systems science is increasingly relied upon for climate change impact forecasting.
• Accurate prediction of future environmental changes necessitates effective communication of uncertainty.
• Understanding uncertainty components is crucial for reliable ecological predictions.

Purpose of the Study:

• To hierarchically decompose prediction uncertainty in animal population size changes.
• To quantify uncertainty components in forecasting the greater snow goose (Chen caeruslescens atlantica) population.
• To demonstrate methods for improving complex system forecasting through uncertainty analysis.

Main Methods:

• Hierarchical decomposition of uncertainty into climate scenarios, demographic model structure, climatic and environmental stochasticity, and sampling variance.
• Development and application of a process-based demographic model for the greater snow goose.
• Quantification of uncertainty components for population abundance predictions over a 40-year period.

Main Results:

• The demographic model predicts a slow population increase for the greater snow goose, accompanied by substantial prediction uncertainty.
• Sampling variance emerged as the dominant contributor to overall prediction uncertainty, surpassing process variance.
• Uncertainty from climate scenarios accounted for less than 3% of total prediction variance, significantly less than environmental stochasticity.

Conclusions:

• Effective forecasting of complex systems requires a thorough understanding and decomposition of uncertainty sources.
• Long-term studies and robust demographic data are essential for reducing sampling variance and improving prediction accuracy.
• Environmental stochasticity poses a greater challenge to population forecasting than climate scenario uncertainty for this species.