Identifying and monitoring locally adaptive genetic variation can have direct utility for
conserving species at risk, especially when management may include actions such as …

Continuing advances in whole genome scale approaches integrated with other
'omic'technologies promise to revolutionise understanding about the relevance of genetic …

Gradient Forests (GF) is a machine learning algorithm that is gaining in popularity for
studying the environmental drivers of genomic variation and for incorporating genomic …

Climate change is increasingly impacting ecosystems globally. Understanding adaptive
genetic diversity and whether it will keep pace with projected climatic change is necessary to …

Understanding the genetic basis of climatic adaptation is essential for predicting species'
responses to climate change. However, intraspecific variation of these responses arising …

Understanding how the environment shapes genetic variation provides critical insight about
the evolution of local adaptation in natural populations. At multiple spatial scales and …

Local adaptation is a critical evolutionary process that allows plants to grow better in their
local compared to non‐native habitat and results in species‐wide geographic patterns of …

Despite the importance of climate‐adjusted provenancing to mitigate the effects of
environmental change, climatic considerations alone are insufficient when restoring highly …

Delineating conservation units (CUs, eg, evolutionarily significant units, ESUs, and
management units, MUs) is critical to the recovery of declining species because CUs inform …

Climate change is impacting locally adapted species such as the keystone tree species cork
oak (Quercus suber L.). Quantifying the importance of environmental variables in explaining …