Despite the importance of climate‐adjusted provenancing to mitigate the effects of environmental change, climatic considerations alone are insufficient when restoring highly degraded sites. Here we propose a comprehensive landscape genomic approach to assist the restoration of moderately disturbed and highly degraded sites. To illustrate it we employ genomic data sets comprising thousands of single nucleotide polymorphisms from two plant species suitable for the restoration of iron‐rich Amazonian Savannas. We first use a subset of neutral loci to assess genetic structure and determine the genetic neighbourhood size. We then identify genotype‐phenotype‐environment associations, map adaptive genetic variation, and predict adaptive genotypes for restoration sites. Whereas local provenances were found optimal to restore a moderately disturbed site, a mixture of genotypes seemed the most promising strategy to recover a highly degraded mining site. We discuss how our results can help define site‐adjusted provenancing strategies, and argue that our methods can be more broadly applied to assist other restoration initiatives.

中文翻译：

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结合基因型、表型和环境数据来描绘生态恢复的场地调整来源策略。

尽管气候调整来源对减轻环境变化的影响很重要，但在恢复高度退化的地点时，仅考虑气候因素是不够的。在这里，我们提出了一种全面的景观基因组方法，以帮助恢复中度干扰和高度退化的地点。为了说明这一点，我们使用了基因组数据集，该数据集包含来自两种适合恢复富含铁的亚马逊热带稀树草原的植物物种的数千个单核苷酸多态性。我们首先使用中性位点的子集来评估遗传结构并确定遗传邻域大小。然后，我们确定基因型-表型-环境关联，绘制适应性遗传变异图，并预测恢复地点的适应性基因型。而当地出处被发现最适合恢复中度扰动的地点，基因型的混合似乎是恢复高度退化的采矿场的最有希望的策略。我们讨论了我们的结果如何帮助定义现场调整出处策略，并认为我们的方法可以更广泛地应用于协助其他恢复计划。