Theoretical and Computational Ecology, Center for Advanced Studies of Blanes (CEAB-CSIC), Spanish Council for Scientific Research, Acces Cala St. Francesc 14, Blanes, E-17300, Spain.
Complex Systems Group, Department of Applied Mathematics, Universidad Politécnica de Madrid, Avenida Juan de Herrera, 6, Madrid, E-28040, Spain.
Ecology. 2021 Feb;102(2):e03247. doi: 10.1002/ecy.3247. Epub 2021 Jan 16.
A simple description of temporal dynamics of ecological communities may help us understand how community assembly proceeds, predict ecological responses to environmental disturbances, and improve the performance of biological conservation actions. Although community changes take place at multiple temporal scales, the variation of species composition and richness over time across communities and habitats shows general patterns that may potentially reveal the main drivers of community dynamics. We used the simplest stochastic model of island biogeography to propose two quantities to characterize community dynamics: the community characteristic time, as a measure of the typical time scale of species-richness change, and the characteristic Jaccard index, as a measure of temporal β diversity, that is, the variation of community composition over time. In addition, the community characteristic time, which sets the temporal scale at which null, noninteracting species assemblages operate, allowed us to define a relative sampling frequency (to the characteristic time). Here we estimate these quantities across microbial and macroscopic species assemblages to highlight two related results. First, we illustrated both characteristic time and Jaccard index and their relation with classic time-series in ecology, and found that the most thoroughly sampled communities, relative to their characteristic time, presented the largest similarity between consecutive samples. Second, our analysis across a variety of habitats and taxa show that communities span a large range of species turnover, from potentially very fast (short characteristic times) to rather slow (long characteristic times) communities. This was in agreement with previous knowledge, but indicated that some habitats may have been sampled less frequently than required. Our work provides new perspectives to explore the temporal component in ecological studies and highlights the usefulness of simple approximations to the complex dynamics of ecological communities.
对生态群落的时间动态进行简单描述,可能有助于我们理解群落组装的过程,预测生态对环境干扰的响应,并改进生物保护措施的效果。尽管群落变化发生在多个时间尺度上,但群落间和生境间的物种组成和丰富度随时间的变化表现出一般模式,这些模式可能揭示了群落动态的主要驱动因素。我们使用最简单的岛屿生物地理学随机模型,提出了两个数量来描述群落动态:群落特征时间,作为衡量物种丰富度变化典型时间尺度的度量;特征雅可比指数,作为衡量时间β多样性的度量,即群落组成随时间的变化。此外,群落特征时间设定了零相互作用物种组合作用的时间尺度,使我们能够定义相对采样频率(相对于特征时间)。在这里,我们估计了微生物和宏观物种组合中的这些数量,以突出两个相关的结果。首先,我们说明了特征时间和雅可比指数及其与生态学中经典时间序列的关系,并发现与特征时间相比,采样最彻底的群落,其连续样本之间的相似性最大。其次,我们对各种生境和类群的分析表明,群落的物种周转率范围很大,从潜在的非常快(特征时间短)到相当慢(特征时间长)的群落。这与之前的知识是一致的,但表明一些生境的采样频率可能低于要求。我们的工作为探索生态学研究中的时间成分提供了新的视角,并突出了简单近似方法在生态群落复杂动态中的有用性。