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首页> 外文期刊>Journal of applied statistics >An empirical comparison of Canonical Correspondence Analysis and STATICO in the identification of spatio-temporal ecological relationships
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An empirical comparison of Canonical Correspondence Analysis and STATICO in the identification of spatio-temporal ecological relationships

机译：典型对应分析与STATICO在时空生态关系识别中的经验比较

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摘要

The wide-ranging and rapidly evolving nature of ecological studies mean that it is not possible to cover all existing and emerging techniques for analyzing multivariate data. However, two important methods enticed many followers: the Canonical Correspondence Analysis (CCA) and the STATICO analysis. Despite the particular characteristics of each, they have similarities and differences, which when analyzed properly, can, together, provide important complementary results to those that are usually exploited by researchers. If on one hand, the use of CCA is completely generalized and implemented, solving many problems formulated by ecologists, on the other hand, this method has some weaknesses mainly caused by the imposition of the number of variables that is required to be applied (much higher in comparison with samples). Also, the STATICO method has no such restrictions, but requires that the number of variables (species or environment) is the same in each time or space. Yet, the STATICO method presents information that can be more detailed since it allows visualizing the variability within groups (either in time or space). In this study, the data needed for implementing these methods are sketched, as well as the comparison is made showing the advantages and disadvantages of each method. The treated ecological data are a sequence of pairs of ecological tables, where species abundances and environmental variables are measured at different, specified locations, over the course of time.
机译：生态学研究的广泛性和迅速发展的特性意味着不可能涵盖所有现有的和新兴的分析多元数据的技术。但是，两种重要的方法吸引了许多追随者：规范对应分析（CCA）和STATICO分析。尽管它们各自具有特定的特征，但是它们具有相似性和差异性，如果对其进行正确的分析，它们可以共同为研究人员通常利用的结果提供重要的补充结果。如果一方面可以完全推广和实施CCA，从而解决了生态学家提出的许多问题，另一方面，该方法存在一些弱点，主要是由于强加了需要应用的变量数量（很多与样本相比更高）。同样，STATICO方法没有这种限制，但是要求在每个时间或空间中变量（物种或环境）的数量都相同。但是，STATICO方法可以提供更详细的信息，因为它可以可视化组内（时间或空间）的可变性。在这项研究中，草绘了实现这些方法所需的数据，并进行了比较，以显示每种方法的优缺点。处理过的生态数据是一系列成对的生态表，其中随时间推移在不同的指定位置测量物种丰度和环境变量。

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