Whole-genome sequencing is revolutionizing our understanding of organismal biology, including adaptations likely to influence demographic performance in different environments. Excitement over the potential of genomics to inform population dynamics has prompted multiple conservation applications, including genomics-based decision-making for translocation efforts. Despite interest in applying genomics to improve translocations, there is a critical research gap: we lack an understanding of how genomic differences translate into population dynamics in the real world. We review how genomics and genetics data could be used to inform organismal performance, including examples of how adaptive and neutral loci have been quantified in a translocation context, and future applications. Next, we discuss three main drivers of population dynamics: demographic structure, spatial barriers to movement, and introgression, and their consequences for translocations informed by genomic data. Finally, we provide a practical guide to different types of models, including size-structured and spatial models, that could be modified to include genomics data. We then propose a framework to improve translocation success by repeatedly developing, selecting, and validating forecasting models. By integrating lab-based and field-collected data with model-driven research, our iterative framework could address long-standing challenges in restoration ecology, such as when selecting locally adapted genotypes will aid translocation of plants and animals.

中文翻译：

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在种群模型中整合基因组学以预测易位成功

全基因组测序正在彻底改变我们对有机体生物学的理解，包括可能影响不同环境中人口表现的适应性。对基因组学为种群动态提供信息的潜力的兴奋促使了多种保护应用，包括基于基因组学的易位工作决策。尽管有兴趣应用基因组学来改善易位，但存在一个关键的研究空白：我们对基因组差异如何转化为现实世界中的种群动态缺乏了解。我们回顾了基因组学和遗传学数据如何用于告知生物体性能，包括如何在易位环境中量化适应性和中性位点的示例，以及未来的应用。接下来，我们讨论人口动态的三个主要驱动因素：人口结构、运动的空间障碍和基因渗入，以及它们对基因组数据告知的易位的影响。最后，我们为不同类型的模型提供了实用指南，包括大小结构模型和空间模型，可以修改这些模型以包含基因组数据。然后，我们提出了一个框架，通过反复开发、选择和验证预测模型来提高易位成功率。通过将基于实验室和现场收集的数据与模型驱动的研究相结合，我们的迭代框架可以解决恢复生态学中长期存在的挑战，例如选择适合当地的基因型将有助于植物和动物的易位。我们为不同类型的模型提供了实用指南，包括大小结构模型和空间模型，可以修改这些模型以包含基因组数据。然后，我们提出了一个框架，通过反复开发、选择和验证预测模型来提高易位成功率。通过将基于实验室和现场收集的数据与模型驱动的研究相结合，我们的迭代框架可以解决恢复生态学中长期存在的挑战，例如选择适合当地的基因型将有助于植物和动物的易位。我们为不同类型的模型提供了实用指南，包括大小结构模型和空间模型，可以修改这些模型以包含基因组数据。然后，我们提出了一个框架，通过反复开发、选择和验证预测模型来提高易位成功率。通过将基于实验室和现场收集的数据与模型驱动的研究相结合，我们的迭代框架可以解决恢复生态学中长期存在的挑战，例如选择适合当地的基因型将有助于植物和动物的易位。