Chen Yiyong, Gao Yangchun, Huang Xuena, Li Shiguo, Zhang Zhixin, Zhan Aibin
Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China.
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.
Environ Sci Ecotechnol. 2023 Jul 11;18:100299. doi: 10.1016/j.ese.2023.100299. eCollection 2024 Mar.
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 , 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.
预计全球气候变化将加速生物入侵，因此需要准确的风险预测和管理策略。然而，当前的入侵风险评估往往忽视了适应性基因组变异，而这种变异在入侵种群的存续和扩张中起着重要作用。在此，我们以一种高度入侵性的海鞘为模型，评估其在气候变化下沿中国海岸的入侵风险。通过群体基因组学分析，我们根据地理分布确定了两个遗传簇，即北部簇和南部簇。为了预测入侵风险，我们分别采用梯度森林模型和物种分布模型来计算基因组偏移和物种栖息地适宜性。这些方法得出了不同的预测结果：梯度森林模型表明，北部簇对未来气候条件的基因组偏移更大（即入侵风险较低），而物种分布模型则显示同一簇未来的栖息地适宜性较高（即入侵风险较高）。通过整合这些模型，我们发现南部簇在未来表现出较小的基因组-生态位不匹配，表明其入侵风险较高。我们的研究突出了基因组偏移和栖息地适宜性在评估气候变化下入侵风险中的互补作用。此外，将适应性基因组变异纳入预测模型可以显著提高对未来入侵风险的预测，并为未来生物入侵制定有效的管理策略。
