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.

旱地生态系统修复是一项全球性挑战，系干扰的协同驱动因子与难以预测的环境条件共同作用所致。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）鲜少被量化，但这类物种中很可能广泛存在该现象，且其可能对修复结果产生影响。当前，跨大空间尺度重新引入繁殖体（propagules）以修复旱地生态系统的常规做法，使得我们必须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，以及预测修复工作的成效至关重要。然而，通过传统的同质园实验（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）领域的最新进展，为非模式物种（non-model species）提供了基于分子标记的方法，用以识别适应性遗传变异（adaptive genetic variability）的环境驱动因子，但目前仍缺乏能将这类方法与生态修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的低成本评估如何助力生态修复中不同种子采种策略（seed sourcing strategies）的落地实施。我们将这些方法应用于两类具有高修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：一类是长寿、风媒传粉的裸子植物内华达麻黄（Ephedra nevadensis），另一类是短命、虫媒传粉的被子植物荒蜀葵（Sphaeralcea ambigua）。年均温被确定为两类物种适应性遗传分化的重要驱动因子。内华达麻黄在降水波动维度上呈现出更强的适应性分化，而温度波动与降水均值则对荒蜀葵的适应性分化解释度更高。本研究阐述了用于插值绘制适应性分化空间格局的多元统计方法，同时考量了中性遗传结构（neutral genetic structure）可能带来的偏差。本研究还通过空间自助法（spatial bootstrapping procedure）可视化了模型不确定性的量级格局。最后，本研究提出了一种交互式、基于距离的制图方法，该方法可将基于分子标记的适应性分化模型与本地采种或混合采种（admixture seed sourcing）策略直接关联，从而助力原生植物的高效修复。