Statistical interpretation of species composition
Statistical interpretation of species composition
复制标题
DOI：
10.1198/016214501753381850
复制
发表时间：
2001-12-01
影响因子：
3.7
通讯作者：
Fagan, WF
中科院分区：
文献类型：
--
作者：
Billheimer, D;Guttorp, P;Fagan, WF
不同物种的相对丰度是生物群落结构的特征。我们分析了一个实验，解决杂食性喂养的联系和社区稳定性之间的关系。我们的目标是确定不同的捕食者组成的社区是否对环境干扰做出类似的反应。为了评估这些数据，我们开发了一个分层统计模型，该模型将艾奇森的逻辑正态分布与条件多项式观察分布相结合。此外，我们提出了一个代数的组合物，其中包括除了，标量乘法，并在组合物的差异度量。代数有助于解释治疗效应、治疗相互作用和协变量，马尔可夫链蒙特卡罗（MCMC）用于贝叶斯框架中的推断。我们的实验结果表明，高度的杂食性可以帮助稳定社区动态，防止社区组成的根本转变。这一结果与经典的食物网预测不一致，但与最近的理论公式一致。
The relative abundance of different species characterizes the structure of a biological community. We analyze an experiment addressing the relationship between omnivorous feeding linkages and community stability. Our goal is to determine whether communities with different predator compositions respond similarly to environmental disturbance. To evaluate these data, we develop a hierarchical statistical model that combines Aitchison's logistic normal distribution with a conditional multinomial observation distribution. In addition, we present an algebra for compositions that includes addition, scalar multiplication, and a metric for differences in compositions. The algebra aids interpretation of treatment effects, treatment interactions, and covariates, Markov chain Monte Carlo (MCMC) is used for inference in a Bayesian framework. Our experimental results indicate that a high degree of omnivory can help to stabilize community dynamics and prevent radical shifts in community composition. This result is at odds with classical food-web predictions, but agrees with recent theoretical formulations.