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Through the jungle of methods quantifying multiple-site resemblance

机译:通过定量多个站点相似性的方法丛林

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Methods that quantify multiple-site resemblance are basic toolkits of ecology for studying community variation in space and time. Although both pairwise and multiple-site coefficients have received increasing attention in the past decade, the high variety of methodologies combined with the absence of a systematic review prevents full understanding and comprehension. To illuminate the situation, we compare and classify methods that use incidence data and propose a unified terminology. The methods can be grouped according to families, approaches and forms. The examination of algebraic expressions and analyses of artificial and actual data sets suggest that inference drawn about communities strongly depends on the methodology applied. We found that the impact of mimicking the original pairwise indices (i.e. the impact of families) was stronger than the impact of components used in formulating the coefficients (i.e. the impact of approach). Our findings suggest that the measures examined quantify drastically different facets of multiple-site resemblance and therefore they have to be selected with care in community studies.
机译:量化多站点相似性的方法是用于研究空间和时间的社区变异的生态学的基本工具包。虽然过去十年成对和多站点系数都受到了越来越长的关注,但与没有系统评价的缺失相结合的高种类方法可以防止完全理解和理解。为了照亮这种情况,我们比较和分类使用发病资料并提出统一术语的方法。这些方法可以根据家庭,方法和形式进行分组。对人工和实际数据集的代数表达和分析的检查表明,关于社区的推断强烈取决于所应用的方法。我们发现模仿原始成对索引的影响(即家庭的影响)比制定系数(即方法的影响)的组分的影响更强。我们的研究结果表明,检验的措施量化了多个站点相似的众所周知,因此必须在社区研究中进行选择。

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