Data from: Landscape genetic approaches to guide native plant restoration in the Mojave Desert

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea. We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.

恢复旱地生态系统是一项全球性挑战，这源于多重扰动因子的协同作用，加之环境条件变幻莫测。旱地植物物种已演化出复杂的生活史策略，以应对资源波动与极端气候。尽管本地适应（local adaptation）极少被量化，但这类物种中很可能广泛存在该现象，并会对恢复成效产生潜在影响。当前恢复旱地生态系统的常规做法是重新引入繁殖体（propagules），且往往涉及大范围空间尺度，这就要求对这些物种内的适应性分化（adaptive divergence）展开评估。这类评估对于理解大规模基因流调控的后果，以及预测恢复工作的成败至关重要。然而，借助传统的同质园实验（common garden experiments）获取目标物种的遗传信息，往往难度大且成本高昂。景观遗传学（landscape genetics）的最新进展为非模式物种提供了基于分子标记的方法，以识别适应性遗传变异的环境驱动因子，但目前仍缺乏能将这些方法与生态恢复实践环节相结合的工具。本研究将空间显式景观遗传学模型与灵活的可视化工具相结合，用以展示：对适应性分化开展高性价比评估，可如何助力生态恢复中不同种源策略的落地实施。我们将这些方法应用于两种具有极高恢复价值的莫哈韦沙漠灌木物种的扩增片段长度多态性（Amplified Fragment Length Polymorphism, AFLP）标记基因分型数据：分别是长寿、风媒传粉的裸子植物Ephedra nevadensis，以及短命、虫媒传粉的被子植物Sphaeralcea ambigua。年平均温度被确定为两个物种适应性遗传分化的重要驱动因子。Ephedra nevadensis对降水波动的适应性分化更为显著，而Sphaeralcea ambigua的适应性分化则更多由温度波动与降水均值所解释。我们提出了多元统计方法，用于插值生成适应性分化的空间格局，同时校正中性遗传结构带来的潜在偏差。我们还通过空间自举（spatial bootstrapping）程序，可视化了模型不确定性程度的分布格局。最后，我们提出了一种交互式、基于距离的绘图方法，该方法可将基于分子标记的适应性分化模型与本地种源或混合种源策略直接关联，从而助力高效的本土植物恢复工作。