Univ. Grenoble Alpes, CNRS, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d'Écologie Alpine, F-38000, Grenoble, France.
Université de Lyon, F-69000, Lyon, France.
Ecol Lett. 2019 Apr;22(4):737-747. doi: 10.1111/ele.13221. Epub 2019 Jan 24.
Describing how ecological interactions change over space and time and how they are shaped by environmental conditions is crucial to understand and predict ecosystem trajectories. However, it requires having an appropriate framework to measure network diversity locally, regionally and between samples (α-, γ- and β-diversity). Here, we propose a unifying framework that builds on Hill numbers and accounts both for the probabilistic nature of biotic interactions and the abundances of species or groups. We emphasise the importance of analysing network diversity across different species aggregation levels (e.g. from species to trophic groups) to get a better understanding of network structure. We illustrate our framework with a simulation experiment and an empirical analysis using a global food-web database. We discuss further usages of the framework and show how it responds to recent calls on comparing ecological networks and analysing their variation across environmental gradients and time.
描述生态相互作用如何随空间和时间变化，以及它们如何受到环境条件的影响，对于理解和预测生态系统轨迹至关重要。然而，这需要有一个适当的框架来衡量网络多样性，包括本地、区域和样本之间的多样性（α-、γ-和β-多样性）。在这里，我们提出了一个统一的框架，该框架基于Hill 数，并考虑了生物相互作用的概率性质和物种或群体的丰度。我们强调了在不同的物种聚集水平（例如从物种到营养级群）分析网络多样性的重要性，以更好地理解网络结构。我们使用一个模拟实验和一个全球食物网数据库的实证分析来说明我们的框架。我们进一步讨论了该框架的其他用途，并展示了它如何响应最近关于比较生态网络和分析它们在环境梯度和时间上变化的呼吁。
