Australian Centre for Ancient DNA, School of Biological Sciences, Environment Institute, University of Adelaide, Adelaide, South Australia, Australia.
Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH, USA.
Mol Ecol. 2020 Jan;29(2):218-246. doi: 10.1111/mec.15315. Epub 2019 Dec 17.
Genetic time-series data from historical samples greatly facilitate inference of past population dynamics and species evolution. Yet, although climate and landscape change are often touted as post-hoc explanations of biological change, our understanding of past climate and landscape change influences on evolutionary processes is severely hindered by the limited application of methods that directly relate environmental change to species dynamics through time. Increased integration of spatiotemporal environmental and genetic data will revolutionize the interpretation of environmental influences on past population processes and the quantification of recent anthropogenic impacts on species, and vastly improve prediction of species responses under future climate change scenarios, yielding widespread revelations across evolutionary biology, landscape ecology and conservation genetics. This review encourages greater use of spatiotemporal landscape genetic analyses that explicitly link landscape, climate and genetic data through time by providing an overview of analytical approaches for integrating historical genetic and environmental data in five key research areas: population genetic structure, demography, phylogeography, metapopulation connectivity and adaptation. We also include a tabular summary of key methodological information, suggest approaches for mitigating the particular difficulties in applying these techniques to ancient DNA and palaeoclimate data, and highlight areas for future methodological development.
来自历史样本的遗传时间序列数据极大地促进了对过去种群动态和物种进化的推断。然而，尽管气候和景观变化通常被吹捧为生物变化的事后解释，但我们对过去气候和景观变化对进化过程影响的理解，由于缺乏将环境变化与物种动态直接相关联的方法的应用而受到严重阻碍。通过时空环境和遗传数据的集成，可以彻底改变对过去种群过程中环境影响的解释，以及对物种近期人为影响的量化，并大大提高对未来气候变化情景下物种反应的预测，从而在进化生物学、景观生态学和保护遗传学等领域产生广泛的启示。这篇综述鼓励更多地使用时空景观遗传分析，通过提供五个关键研究领域中整合历史遗传和环境数据的分析方法概述，通过时间明确地将景观、气候和遗传数据联系起来：种群遗传结构、人口动态、系统地理学、复合种群连通性和适应性。我们还包括一个关键方法信息的表格摘要，提出了减轻将这些技术应用于古 DNA 和古气候数据的特殊困难的方法，并强调了未来方法发展的领域。
