Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.
Department of Environmental Conservation, University of Massachusetts Amherst, Amherst, Massachusetts, USA.
Ecology. 2024 Sep;105(9):e4378. doi: 10.1002/ecy.4378. Epub 2024 Jul 26.
Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with the most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal the underlying abiotic and biotic drivers of community dynamics. Here, we present a model-based predictive framework to quantify the multidimensional trait distributions of communities (community trait spaces), which we use to identify ecological selection types shaping communities along environmental gradients. We apply the framework to over 3600 boreal forest understory plant communities with results indicating that directional, stabilizing, and divergent selection all modify community trait distributions and that the selection type acting on individual traits may change over time. Our results provide novel and rare empirical evidence for divergent selection within a natural system. Our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.
理解群落组装的驱动因素对于预测生物多样性和生态系统服务的未来至关重要。生态选择通过选择具有最合适的性状组合的个体，普遍地塑造群落。在环境梯度和时间上检测关键性状的选择类型，有可能揭示群落动态的潜在非生物和生物驱动因素。在这里，我们提出了一个基于模型的预测框架，用于量化群落的多维性状分布（群落性状空间），我们利用该框架来识别沿环境梯度塑造群落的生态选择类型。我们将该框架应用于超过 3600 个北方森林林下植物群落，结果表明，定向选择、稳定选择和分歧选择都改变了群落性状的分布，并且作用于单个性状的选择类型可能随时间而变化。我们的结果为自然系统内的分歧选择提供了新颖和罕见的经验证据。我们的方法为识别受选择影响的关键性状提供了一个框架，并有助于检测群落动态的潜在过程。
