Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore 117558, Singapore.
Department of Physical and Natural Sciences, FLAME University, Pune, Maharashtra 412115, India.
Math Biosci. 2024 Mar;369:109131. doi: 10.1016/j.mbs.2023.109131. Epub 2023 Dec 17.
Research into the processes governing species richness has often assumed that the environment is fixed, whereas realistic environments are often characterised by random fluctuations over time. This temporal environmental stochasticity (TES) changes the demographic rates of species populations, with cascading effects on community dynamics and species richness. Theoretical and applied studies have used process-based mathematical models to determine how TES affects species richness, but under a variety of frameworks. Here, we critically review such studies to synthesise their findings and draw general conclusions. We first provide a broad mathematical framework encompassing the different ways in which TES has been modelled. We then review studies that have analysed models with TES under the assumption of negligible interspecific interactions, such that a community is conceptualised as the sum of independent species populations. These analyses have highlighted how TES can reduce species richness by increasing the frequency at which a species becomes rare and therefore prone to extinction. Next, we review studies that have relaxed the assumption of negligible interspecific interactions. To simplify the corresponding models and make them analytically tractable, such studies have used mean-field theory to derive fixed parameters representing the typical strength of interspecific interactions under TES. The resulting analyses have highlighted community-level effects that determine how TES affects species richness, for species that compete for a common limiting resource. With short temporal correlations of environmental conditions, a non-linear averaging effect of interspecific competition strength over time gives an increase in species richness. In contrast, with long temporal correlations of environmental conditions, strong selection favouring the fittest species between changes in environmental conditions results in a decrease in species richness. We compare such results with those from invasion analysis, which examines invasion growth rates (IGRs) instead of species richness directly. Qualitative differences sometimes arise because the IGR is the expected growth rate of a species when it is rare, which does not capture the variation around this mean or the probability of the species becoming rare. Our review elucidates key processes that have been found to mediate the negative and positive effects of TES on species richness, and by doing so highlights key areas for future research.
关于物种丰富度的形成过程,人们通常假设环境是固定不变的,而实际上真实环境会随时间随机波动。这种时间上的环境随机性(TES)会改变物种种群的繁殖率,从而对群落动态和物种丰富度产生级联效应。理论和应用研究已经使用基于过程的数学模型来确定 TES 如何影响物种丰富度,但这些模型是在各种框架下建立的。在这里,我们批判性地回顾了这些研究,以综合它们的发现并得出一般性结论。我们首先提供了一个广泛的数学框架,涵盖了 TES 建模的不同方式。然后,我们回顾了那些在忽略种间相互作用的假设下,用 TES 分析模型的研究,即把群落看作是独立的物种种群的总和。这些分析强调了 TES 如何通过增加物种变得稀有并因此容易灭绝的频率来降低物种丰富度。接下来,我们回顾了那些放松了忽略种间相互作用的假设的研究。为了简化相应的模型并使其具有可分析性,这些研究使用平均场理论来推导出表示 TES 下种间相互作用典型强度的固定参数。由此产生的分析强调了决定 TES 如何影响物种丰富度的群落水平效应,适用于竞争共同限制资源的物种。当环境条件的时间相关性较短时,种间竞争强度随时间的非线性平均效应会导致物种丰富度增加。相反,当环境条件的时间相关性较长时,在环境条件变化之间,强烈的选择有利于最适应的物种,从而导致物种丰富度降低。我们将这些结果与入侵分析进行了比较,后者直接检查入侵增长率(IGR)而不是物种丰富度。由于 IGR 是物种稀少时的预期增长率,它没有捕捉到平均值周围的变化或物种变得稀少的概率,因此有时会出现定性差异。我们的综述阐明了介导 TES 对物种丰富度的负面影响和正面影响的关键过程,并通过这种方式突出了未来研究的关键领域。