Forecasting biodiversity in breeding birds using best practices.

Area of Science:

• Ecology
• Conservation Biology
• Ecological Modeling

Background:

• Biodiversity forecasts are vital for conservation and management.
• Current biodiversity forecasts often lack rigorous evaluation, fail to account for uncertainty, and do not utilize time-series data.

Purpose of the Study:

• To evaluate the performance of different biodiversity forecasting models using best practices.
• To assess the ability to forecast breeding bird species richness in North America.

Main Methods:

• Employed hindcasting to evaluate six different modeling approaches for predicting species richness.
• Evaluated annual forecasts for a decade at 1,237 sites across the continental United States.
• Incorporated best practices such as accounting for uncertainty and observer effects.

Main Results:

• All models explained over 50% of the variance in richness, but none consistently outperformed a baseline model predicting constant richness.
• Accounting for uncertainty and observer effects significantly impacted model performance and rankings.
• Prediction accuracy decreased across all models as the forecast horizon increased.

Conclusions:

• Rigorous evaluation using best practices is essential for improving biodiversity forecasts.
• Even sophisticated models may not consistently outperform simpler baselines when evaluated comprehensively.
• Future research should focus on implementing and refining forecasting best practices for ecological predictions.