Many forest tree species have characteristics that make the study of their evolutionary ecology complex. For example, they are long-lived and thus have long generation times, and their often large, complex genomes have hampered establishing genomic resources. One way to tackle this challenge is to access multiple complementary data sources and analytical approaches when attempting to infer patterns of adaptive evolution. In the cover article of this issue of Molecular Ecology, Depardieu et al. (2021) combine large amounts of environmental, genomic, dendrochronological, and gene expression data in a common garden to explore the polygenic basis of drought resistance in white spruce (Picea glauca), a long-lived conifer. They identify candidate genes involved in growth and resistance to extreme drought events and show how multiple data sets may deliver complementary evidence to circumvent the manifold challenges of the research field.

中文翻译：

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用多数据方法应对进化森林研究的挑战

许多森林树种的特征使得对其进化生态学的研究变得复杂。例如，它们寿命长，因此世代时间长，而且它们通常庞大而复杂的基因组阻碍了基因组资源的建立。应对这一挑战的一种方法是在尝试推断适应性进化模式时访问多个互补的数据源和分析方法。在本期Molecular Ecology的封面文章中，Depardieu 等人。(2021) 结合公共花园中的大量环境、基因组、树木年代学和基因表达数据，探索白云杉 ( Picea glauca)抗旱性的多基因基础)，一种长寿的针叶树。他们确定了与极端干旱事件的生长和抗性有关的候选基因，并展示了多个数据集如何提供补充证据以规避研究领域的多重挑战。