Department of Landscape and Biodiversity, Norwegian Institute of Bioeconomy Research (NIBIO), Trondheim, Norway.
Department of Mathematics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Ecology. 2022 Sep;103(9):e3742. doi: 10.1002/ecy.3742. Epub 2022 Jun 23.
Understanding the mechanisms of ecological community dynamics and how they could be affected by environmental changes is important. Population dynamic models have well known ecological parameters that describe key characteristics of species such as the effect of environmental noise and demographic variance on the dynamics, the long-term growth rate, and strength of density regulation. These parameters are also central for detecting and understanding changes in communities of species; however, incorporating such vital parameters into models of community dynamics is challenging. In this paper, we demonstrate how generalized linear mixed models specified as intercept-only models with different random effects can be used to fit dynamic species abundance distributions. Each random effect has an ecologically meaningful interpretation either describing general and species-specific responses to environmental stochasticity in time or space, or variation in growth rate and carrying capacity among species. We use simulations to show that the accuracy of the estimation depends on the strength of density regulation in discrete population dynamics. The estimation of different covariance and population dynamic parameters, with corresponding statistical uncertainties, is demonstrated for case studies of fish and bat communities. We find that species heterogeneity is the main factor of spatial and temporal community similarity for both case studies.
理解生态群落动态的机制以及它们可能如何受到环境变化的影响是很重要的。种群动态模型具有众所周知的生态参数，这些参数描述了物种的关键特征，例如环境噪声和人口方差对动态、长期增长率和密度调节强度的影响。这些参数对于检测和理解物种群落的变化也很重要；然而，将这些重要参数纳入群落动态模型是具有挑战性的。在本文中，我们展示了如何使用广义线性混合模型作为仅包含截距的模型，并使用不同的随机效应来拟合动态物种丰度分布。每个随机效应都具有生态意义上的解释，要么描述了物种对时间或空间上的环境随机性的一般和特定反应，要么描述了物种之间增长率和承载能力的变化。我们使用模拟来表明，估计的准确性取决于离散种群动态中密度调节的强度。我们还为鱼类和蝙蝠群落的案例研究展示了不同协方差和种群动态参数的估计及其相应的统计不确定性。我们发现，对于这两个案例研究，物种异质性是空间和时间社区相似性的主要因素。
