Incorporating adaptive genomic variation into predictive models for invasion risk assessment

Yiyong Chen; Yangchun Gao; Xuena Huang; Shiguo Li; Zhixin Zhang; Aibin Zhan

doi:10.1016/j.ese.2023.100299

Incorporating adaptive genomic variation into predictive models for invasion risk assessment

Environ Sci Ecotechnol. 2023 Jul 11:18:100299. doi: 10.1016/j.ese.2023.100299. eCollection 2024 Mar.

Authors

Yiyong Chen¹, Yangchun Gao², Xuena Huang¹, Shiguo Li^{1

3}, Zhixin Zhang^{4

5}, Aibin Zhan^{1

3}

Affiliations

¹ Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China.
² Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Institute of Zoology, Guangdong Academy of Science, Guangzhou, 510260, China.
³ University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100049, China.
⁴ CAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510275, China.
⁵ Global Ocean and Climate Research Center, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510275, China.

Abstract

Global climate change is expected to accelerate biological invasions, necessitating accurate risk forecasting and management strategies. However, current invasion risk assessments often overlook adaptive genomic variation, which plays a significant role in the persistence and expansion of invasive populations. Here we used Molgula manhattensis, a highly invasive ascidian, as a model to assess its invasion risks along Chinese coasts under climate change. Through population genomics analyses, we identified two genetic clusters, the north and south clusters, based on geographic distributions. To predict invasion risks, we employed the gradient forest and species distribution models to calculate genomic offset and species habitat suitability, respectively. These approaches yielded distinct predictions: the gradient forest model suggested a greater genomic offset to future climatic conditions for the north cluster (i.e., lower invasion risks), while the species distribution model indicated higher future habitat suitability for the same cluster (i.e, higher invasion risks). By integrating these models, we found that the south cluster exhibited minor genome-niche disruptions in the future, indicating higher invasion risks. Our study highlights the complementary roles of genomic offset and habitat suitability in assessing invasion risks under climate change. Moreover, incorporating adaptive genomic variation into predictive models can significantly enhance future invasion risk predictions and enable effective management strategies for biological invasions in the future.

Keywords: Adaptive genomic variation; Climate change; Genomic offset; Habitat suitability; Invasion risk.