Data from: Ecological genomics meets community-level modeling of biodiversity: mapping the genomic landscape of current and future environmental adaptation

Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism–environment relationships that combines genomic data and community-level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA-5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community-level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species-level assessments of climate change vulnerability.局部适应（local adaptation）是栖息于空间异质环境的多数物种的核心特征，且在物种应对环境变化的过程中发挥关键作用。然而，当前绝大多数模拟物种应对气候变化响应的研究，均忽略了由局部适应所引发的种内变异。在此，我们提出一种全新的物种-环境关系空间建模视角：该方法整合基因组数据与群落水平建模技术，以构建物种在环境变化下基因组变异的地理分布情景。与传统的群落内物种建模思路不同，本研究利用上述技术对全基因组范围内的大量基因座（locus）进行建模。本研究以香脂杨（balsam poplar，Populus balsamifera）为案例对象，展示了该建模框架如何适配基因座对环境梯度的非线性响应。我们在生物钟基因GIGANTEA-5（GI5）中检测到对温度的阈值响应模式，暗示该基因已针对温度产生了显著的局部适应性分化。此外，本研究还展示了如何通过基因组数据映射生态适应性，包括预测当前与未来气候情境下种群遗传组成的差异。基因组变异的群落水平建模是景观基因组学（landscape genomics）与生物多样性空间建模领域的一项重要进展，其突破了仅从物种水平评估气候变化脆弱性的局限。