Chambers E Anne, Bishop Anusha P, Wang Ian J
Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, California, USA.
Museum of Vertebrate Zoology, University of California Berkeley, Berkeley, California, USA.
Mol Ecol Resour. 2023 Oct 26. doi: 10.1111/1755-0998.13884.
Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non-model systems has also enabled a shift away from population-based sampling to individual-based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual-based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population-based sampling to individual-based sampling schemes. Here, we discuss the benefits of individual-based sampling for conservation and describe how landscape genomic methods, paired with individual-based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user-friendly, open-source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R (algatr). The algatr package includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology.
景观基因组学可以利用环境和遗传数据，通过深入了解景观如何塑造生物多样性，为保护决策提供重要见解。基因组时代带来的遗传数据的大量增加，为回答关键的保护遗传学问题提供了卓越的分辨率。非模式系统基因组数据的可获取性，也促使从基于种群的采样转向基于个体的采样，现在这种采样方式能够提供准确且可靠的遗传变异估计，可用于研究基因组多样性的空间结构、种群连通性以及环境适应的本质。然而，在保护遗传学中采用基于个体的采样在很大程度上有所放缓，原因在于人们担心如何将为基于种群的采样开发的方法应用于基于个体的采样方案。在此，我们讨论基于个体的采样对保护的益处，并描述景观基因组学方法与基于个体的采样相结合如何能够回答基本的保护问题。我们已将关键的景观基因组学方法整理成一个用户友好的开源工作流程，并将其作为一个新的R包——R语言中的景观基因组学分析工具包（algatr）提供。algatr包为所有纳入的方法增添了新颖的功能，还配有大量的 vignette，其主要目的是使景观基因组学方法更易于理解，并能明确应用于保护生物学。
