Species co-occurrence analysis predicts management outcomes for multiple threats

Area of Science:

• Ecology
• Conservation Biology
• Network Analysis

Background:

• Global anthropogenic change poses significant threats to species.
• Predicting species' responses to conservation actions is crucial but challenging due to incomplete data.

Purpose of the Study:

• To develop a predictive approach for community restructuring under threat management.
• To assess the role of species co-occurrence in predicting recovery.
• To validate the approach using a threatened bird community dataset.

Main Methods:

• Combined models of species responses to threats with network analyses of co-occurrence.
• Utilized species' contributions to network co-occurrence to predict recovery.
• Trained a community-wide response model using data from a few key species.

Main Results:

• Species' contributions to network co-occurrence accurately predict recovery under threat reduction.
• Highly connected species show greater recovery benefits from threat management.
• Demonstrated positive feedbacks in occurrence and co-occurrence during ecosystem recovery due to shared management responses.

Conclusions:

• The developed approach effectively predicts community restructuring under threat management.
• Network properties of species are key indicators of their response to conservation interventions.
• Targeted data from a few species can inform management for entire communities, enhancing ecosystem recovery.