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.