Department of Biosciences, University of Helsinki, Viikinkaari 1, FI-00014 University of Helsinki, Finland.
Ecology. 2011 Feb;92(2):289-95. doi: 10.1890/10-1251.1.
Community ecologists and conservation biologists often work with data that are too sparse for achieving reliable inference with species-specific approaches. Here we explore the idea of combining species-specific models into a single hierarchical model. The community component of the model seeks for shared patterns in how the species respond to environmental covariates. We illustrate the modeling framework in the context of logistic regression and presence-absence data, but a similar hierarchical structure could also be used in many other types of applications. We first use simulated data to illustrate that the community component can improve parameterization of species-specific models especially for rare species, for which the data would be too sparse to be informative alone. We then apply the community model to real data on 500 diatom species to show that it has much greater predictive power than a collection of independent species-specific models. We use the modeling approach to show that roughly one-third of distance decay in community similarity can be explained by two variables characterizing water quality, rare species typically preferring nutrient-poor waters with high pH, and common species showing a more general pattern of resource use.
社区生态学家和保护生物学家经常处理数据，这些数据过于稀疏，无法通过特定于物种的方法进行可靠推断。在这里，我们探讨了将特定于物种的模型组合成单个层次模型的想法。模型的社区部分旨在寻找物种对环境协变量的响应中存在的共享模式。我们在逻辑回归和存在-缺失数据的背景下说明了建模框架，但类似的层次结构也可以用于许多其他类型的应用。我们首先使用模拟数据来说明社区部分可以改进特定于物种模型的参数化，特别是对于那些数据过于稀疏而无法单独提供信息的稀有物种。然后，我们将社区模型应用于 500 种硅藻物种的实际数据，以表明它比一组独立的特定于物种的模型具有更大的预测能力。我们使用建模方法表明，群落相似性的距离衰减大约有三分之一可以用两个描述水质的变量来解释，稀有物种通常更喜欢高 pH 值的贫营养水，而常见物种则表现出更普遍的资源利用模式。
