Understanding ecological dynamics has been a central topic in ecology since its origins. Yet, identifying dynamic regimes remains a research frontier for modern ecology. The concept of ecological dynamic regime (EDR) emerged to emphasize the dynamic property of steady states in nature and refers to the fluctuations of ecosystems around some trend or average. Identifying and characterizing EDRs is of utmost importance in the current context of global change since they form the reference against which post-disturbance dynamics must be compared to assess ecological resilience. However, the implementation of EDRs in empirical science is still challenging given the high dimensionality and stochasticity of ecological data and the large volume of data required to distinguish stochastic dynamics from general and predictable dynamics. The era of big data and the recent advances in quantitative ecology and data science offer an opportunity to study dynamic regimes using empirical approaches from a new perspective. This paper presents a novel methodological framework to describe EDRs from a set of ecological trajectories defined by the temporal changes of state variables in a multidimensional state space. In our framework, we formally define EDRs and include analytical tools to identify, characterize, and compare EDRs based on their geometric characteristics. More specifically, we propose different ways to identify EDRs from empirical data, develop a new algorithm to identify representative trajectories summarizing the main dynamic patterns, propose a set of metrics to describe the internal distribution of ecological trajectories, and define a dissimilarity index to compare two or more dynamic regimes based on their shape and position in the state space. We used artificial data to illustrate the different elements of our framework and applied our analyses to real data, using permanent sampling plots of Canadian boreal forests as an example. Overall, our framework contributes to filling the gap between theoretical and empirical ecology by providing robust analytical tools to assess ecological resilience and study ecosystem dynamics from a multidimensional perspective and considering the variability of natural systems.

中文翻译：

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生态动态机制：识别、表征和比较

自生态学起源以来，理解生态动力学一直是生态学的中心话题。然而，识别动态机制仍然是现代生态学的研究前沿。生态动态状态（EDR）概念的出现是为了强调自然界稳态的动态特性，指的是生态系统围绕某种趋势或平均值的波动。在当前全球变化的背景下，识别和描述 EDR 至关重要，因为它们构成了必须与扰动后动态进行比较以评估生态恢复力的参考。然而，鉴于生态数据的高维度和随机性以及区分随机动力学与一般和可预测动力学所需的大量数据，EDR 在实证科学中的实施仍然具有挑战性。大数据时代以及定量生态学和数据科学的最新进展为从新的角度使用实证方法研究动态机制提供了机会。本文提出了一种新颖的方法框架，用于根据多维状态空间中状态变量的时间变化定义的一组生态轨迹来描述 EDR。在我们的框架中，我们正式定义了 EDR，并包含了分析工具，用于根据 EDR 的几何特征来识别、表征和比较 EDR。更具体地说，我们提出了从经验数据中识别 EDR 的不同方法，开发了一种新算法来识别总结主要动态模式的代表性轨迹，提出了一组度量来描述生态轨迹的内部分布，并定义了一个相异指数来比较两个生态轨迹或更动态的机制基于其在状态空间中的形状和位置。我们使用人工数据来说明框架的不同要素，并将我们的分析应用于实际数据，以加拿大北方森林的永久采样点为例。总体而言，我们的框架通过提供强大的分析工具来评估生态恢复力并从多维角度研究生态系统动态并考虑自然系统的可变性，有助于填补理论生态学和实证生态学之间的空白。