Incorporating adaptive genomic variation into predictive models for invasion risk assessment

Area of Science:

• Ecology
• Genomics
• Climate Change Biology

Background:

• Global climate change is predicted to increase the rate of biological invasions.
• Current risk assessments often neglect adaptive genomic variation, crucial for invasive species success.
• Accurate forecasting is vital for managing invasive species and their ecological impacts.

Purpose of the Study:

• To assess the invasion risks of the ascidian Molgula manhattensis along Chinese coasts under climate change.
• To evaluate the role of adaptive genomic variation in predicting invasion risks.
• To integrate genomic offset and habitat suitability models for enhanced risk assessment.

Main Methods:

• Population genomics analyses to identify genetic clusters (north and south).
• Gradient forest modeling to calculate genomic offset.
• Species distribution modeling to assess future habitat suitability.

Main Results:

• Two distinct genetic clusters (north and south) were identified in Molgula manhattensis.
• Gradient forest and species distribution models provided contrasting predictions for the north cluster.
• Integrated analysis revealed higher invasion risks for the south cluster due to minor genome-niche disruptions.

Conclusions:

• Genomic offset and habitat suitability are complementary metrics for invasion risk assessment.
• Incorporating adaptive genomic variation significantly improves the accuracy of invasion risk predictions.
• Enhanced predictive models are essential for effective management of biological invasions under climate change.