Improving the forecast for biodiversity under climate change.

Area of Science:

• Ecology
• Climate Change Biology
• Conservation Biology

Background:

• Modern biological models increasingly incorporate realistic processes for predicting species' responses to climate change and human disturbances.
• However, these advanced models demand extensive mechanistic data, which is unavailable for the majority of Earth's species.
• Current biodiversity monitoring primarily tracks changes rather than collecting the mechanistic data crucial for predictive modeling.

Purpose of the Study:

• To describe and prioritize essential biological information for improving species response projections under climate change.
• To highlight the utility of trait-based approaches and adaptive modeling in leveraging sparse data for broader ecological predictions.
• To outline a coordinated global initiative for collecting critical data to enhance understanding and mitigation of climate change effects on biodiversity.

Main Methods:

• Prioritization of key biological information required for mechanistic modeling of species' responses.
• Application of trait-based frameworks to synthesize and extrapolate ecological data.
• Development of adaptive modeling strategies to address data limitations.
• Proposal for a global data collection effort focused on mechanistic traits.

Main Results:

• Identification of critical data gaps hindering accurate predictions of species' responses to environmental change.
• Demonstration that trait-based and adaptive modeling approaches can effectively utilize limited data for wider predictive power.
• A framework for a global data collection initiative is proposed to address these knowledge gaps.

Conclusions:

• There is an urgent need to shift from simple biodiversity monitoring to collecting mechanistic data for predictive ecological modeling.
• Trait-based and adaptive modeling offer promising avenues for making robust predictions despite data scarcity.
• A concerted global effort is essential to gather the necessary biological information to anticipate and mitigate climate change impacts on biodiversity.