Estimating community stability and ecological interactions from time-series data
Estimating community stability and ecological interactions from time-series data
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DOI：
10.1890/0012-9615(2003)073
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发表时间：
2003-05-01
影响因子：
6.1
通讯作者：
Carpenter, SR
中科院分区：
文献类型：
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作者：
Ives, AR;Dennis, B;Carpenter, SR
自然生态群落不断受到变化的环境的冲击，往往难以用需要存在平衡点的概念来衡量群落的稳定性。受环境随机性影响的群落的平衡状态不是平衡点，而是一种平稳分布，其特征在于均值、方差和其他统计矩。在这里，我们推导出三个属性的随机多物种社区，测量不同的特性与社区的稳定性。这些属性可以使用一阶多变量自回归（MAk（1））模型从多物种时间序列数据中估计。我们演示了如何估计MAR（1）模型的参数，并获得两个参数和稳定性措施的置信区间。我们还解决了存在观测（测量）误差时的估计问题。为了说明这些方法，我们比较了在三个湖泊中的营养负荷和食饵性鱼类丰度的稳定性。MAR（1）模型和我们提出的统计方法可以用来识别物种之间的动态重要的相互作用，并测试关于自然变化的生态群落的稳定性和其他动力学性质的假设。因此，它们可以用来整合社区动态的理论和实证研究。
Natural ecological communities are continuously buffeted by a varying environment, often making it difficult to measure the stability of communities using concepts requiring the existence of an equilibrium point. Instead of an equilibrium point, the equilibrial state of communities subject to environmental stochasticity is a stationary distribution, which is characterized by means, variances, and other statistical moments. Here, we derive three properties of stochastic multispecies communities that measure different characteristics associated with community stability. These properties can be estimated from multispecies time-series data using first-order multivariate autoregressive (MAk(1)) models. We demonstrate how to estimate the parameters of MAR(1) models and obtain confidence intervals for both parameters and the measures of stability. We also address the problem of estimation when there is observation (measurement) error. To illustrate these methods, we compare the stability of the planktonic communities in three lakes in which nutrient loading and planktivorous fish abundance were experimentally manipulated. MAR(1) models and the statistical methods we present can be used to identify dynamically important interactions between species and to test hypotheses about stability and other dynamical properties of naturally varying ecological communities. Thus, they can be used to integrate theoretical and empirical studies of community dynamics.