Outstanding Challenges in the Transferability of Ecological Models

Area of Science:

• Ecology
• Environmental Science
• Predictive Modeling

Background:

• Predictive models are essential tools in science and management, especially in a changing world.
• The accuracy and precision of models when applied to new situations (transferability) are often poorly understood, reducing confidence in their predictions.

Purpose of the Study:

• To identify critical knowledge gaps that would most improve the transferability of predictive models.
• To outline challenges and research priorities for enhancing ecological predictability and model transfer best practices.

Main Methods:

• Convened 50 experts to identify priority knowledge gaps for improving model transferability.
• Synthesized expert input into fundamental and technical challenges.

Main Results:

• Identified 12 key challenges (six fundamental, six technical) impacting model transferability.
• Highlighted the need for research into determinants of ecological predictability, including species traits and data quality.

Conclusions:

• Urgent need to intensify research on factors influencing ecological predictability and develop best practices for model transfer.
• Development of widely applicable transferability metrics and tools to quantify prediction uncertainty under novel conditions is essential.