DETRENDED CORRESPONDENCE-ANALYSIS - AN IMPROVED ORDINATION TECHNIQUE
DETRENDED CORRESPONDENCE-ANALYSIS - AN IMPROVED ORDINATION TECHNIQUE
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DOI：
10.1007/bf00048870
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发表时间：
1980-01-01
期刊：
影响因子：
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通讯作者：
GAUCH, HG
中科院分区：
文献类型：
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作者：
HILL, MO;GAUCH, HG
去势对应分析(DCA)是对倒数平均(RA)排序技术的改进。RA有两个主要缺陷：第二个轴通常是第一个轴的“拱形”或“马蹄形”变形，排序空间中的距离在成分变化方面没有一致的含义(特别是第一个RA轴两端的距离相对于中间被压缩)。DCA可以纠正这两个故障。用模拟和现场数据进行的测试表明，DCA在给出清晰、可解释的结果方面优于RA和非计量多维密封。DCA有几个优点。(A)在测试的排序技术中，它的表现是最好的，物种和样本排序都是同时进行的。(B)轴以具有明确含义的标准偏差单位进行标度，(C)正如在名为DECORANA的FORTRAN程序中实现的那样，计算时间仅随着分析的数据量线性增加，并且只有数据矩阵中的正条目被存储在存储器中，因此非常大的数据集不存在困难。然而，DCA有其局限性，因此最好在分析之前删除极端的异常值和不连续性。DCA始终提供最易解释的排序结果，但一如既往，结果的解释仍然是一个生态洞察力的问题，并通过实地经验和整合植被样点的补充环境数据而得到改进。
Detrended correspondence analysis (DCA) is an improvement upon the reciprocal averaging (RA) ordination technique. RA has two main faults: the second axis is often an ‘arch’ or ‘horseshoe’ distortion of the first axis, and distances in the ordination space do not have a consistent meaning in terms of compositional change (in particular, distances at the ends of the first RA axis are compressed relative to the middle). DCA corrects these two faults. Tests with simulated and field data show DCA superior to RA and to nonmetric multidimensional sealing in giving clear, interpretable results. DCA has several advantages. (a) Its performance is the best of the ordination techniques tested, and both species and sample ordinations are produced simultaneously. (b) The axes are scaled in standard deviation units with a definite meaning, (c) As implemented in a FORTRAN program called DECORANA, computing time rises only linearly with the amount of data analyzed, and only positive entries in the data matrix are stored in memory, so very large data sets present no difficulty. However, DCA has limitations, making it best to remove extreme outliers and discontinuities prior to analysis. DCA consistently gives the most interpretable ordination results, but as always the interpretation of results remains a matter of ecological insight and is improved by field experience and by integration of supplementary environmental data for the vegetation sample sites.