An evaluation of randomization models for nested species subsets analysis
An evaluation of randomization models for nested species subsets analysis
复制标题
DOI：
10.1007/s004420050412
复制
发表时间：
1998-02-01
期刊：
影响因子：
2.7
通讯作者：
Quinn, JF
中科院分区：
文献类型：
--
作者：
Cook, RR;Quinn, JF
随机化模型，通常被称为“空”模型，自20世纪70年代以来已被广泛用于物种群落和地理格局的研究，最近，它们已被用来测试嵌套物种子集模式（或嵌套性）之间的物种聚集占据空间细分的栖息地，如岛屿群岛和陆地栖息地斑块。当物种占据小的或物种贫乏的网站有一个强烈的倾向，形成更丰富的物种组合的真子集时，发生嵌套。在本文中，我们研究了几个已发表的模拟模型检测的能力，在一个公正的方式，嵌套的子集模式从一个简单的矩阵的网站由物种的存在-不存在的数据。每一种方法都试图建立在生物现实主义的假设，即生态过程，产生的模式观察到的性质，如果他们可以重复多次使用相同的物种螺柱景观配置，产生岛屿与相同数量的物种和物种存在于相同数量的岛屿观察。用数学术语来说，许多模拟矩阵的平均边缘总和（列和行总和）将与观察到的矩阵的平均边缘总和相匹配。模型模拟的结果表明，一个物种占据任何给定的网站的真实概率不能明确估计。几乎所有测试的模型都显示出模拟矩阵偏向低嵌套水平，增加了I型统计错误的概率。此外，所需的边际总数只能通过对计算出的概率进行特别处理来获得。奇怪的是，当实现这样的结果，该模型被证明有很少的统计能力来检测嵌套。这是因为嵌套性很大程度上取决于矩阵本身的边际总和，正如Wright和Reeves之前所建议的那样。我们的结论是，在目前的时间，嵌套子集模式的最佳空模型可能是一个基于所有物种的发生概率相等。这种模型的例子在文献中很容易找到。
Randomization models, often termed "null" models, have been widely used since the 1970s in studies of species community and biogeographic patterns, More recently they have been used to test for nested species subset patterns (or nestedness) among assemblages of species occupying spatially subdivided habitats, such as island archipelagoes and terrestrial habitat patches. Nestedness occurs when the species occupying small or species-poor sites have a strong tendency to form proper subsets of richer species assemblages. In this paper, we examine the ability of several published simulation models to detect, in an unbiased way, nested subset patterns from a simple matrix of site-by-species presence-absence data. Each approach attempts to build in biological realism by following the assumption that the ecological processes that generated the patterns observed in nature would, if they could be repeated many times over using the same species stud landscape configuration, produce islands with the same number of species and species present on the same number of islands as observed. In mathematical terms, the mean marginal totals (column and row sums) of many simulated matrices would match those of the observed matrix. Results of model simulations suggest that the true probability of a species occupying any given site cannot be estimated unambiguously. Nearly all of the models tested were shown to bias simulation matrices toward low levels of nestedness, increasing the probability of a Type I statistical error. Further, desired marginal totals could be obtained only through ad-hoc manipulation of the calculated probabilities. Paradoxically, when such results are achieved, the model is shown to have little statistical power to detect nestedness. This is because nestedness is determined largely by the marginal totals of the matrix themselves, as suggested earlier by Wright and Reeves. We conclude that at the present time, the best null model for nested subset patterns may be one based on equal probabilities of occurrence for all species. Examples of such models are readily available in the literature.