Red-shifts and red herrings in geographical ecology
Red-shifts and red herrings in geographical ecology
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DOI：
10.1111/j.1600-0587.2000.tb00265.x
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发表时间：
2000-02
期刊：
影响因子：
5.9
通讯作者：
J. Lennon
中科院分区：
文献类型：
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作者：
J. Lennon
我提请注意，生态学家需要在假设检验中更认真地考虑空间结构。如果忽略空间自相关，那么根据环境因素分析生态格局可能会产生非常误导性的结果。这是用已知空间属性的合成但现实的空间模式来证明的，这些空间模式受到经典相关和多元回归分析的影响。自相关响应变量与一组解释变量之间的相关性强烈偏向于那些高度自相关的解释变量——即使空间模式完全独立，相关系数的预期幅度也会随着自相关而增加。类似地，多元回归分析发现高度自相关的解释变量“显著”的频率比它应该的要高得多。如果使用经典回归，在自相关模式中错误识别“显著”斜率的机会非常高。因此，在这些情况下，文献中报道的与生态模式相关的强自相关环境因素实际上可能并不重要。很可能这些被错误地描述为重要的因素构成了一组潜在解释的红移子集，而更多的空间不连续因素（那些具有更蓝光谱的因素）实际上比它们目前的状态显示的相对更重要。生态学家可以做很多事情来改善这种状况。我从文献中讨论了解决空间自相关问题的各种方法，并提出了一种随机化测试，用于两种空间模式的关联，它比目前可用的方法有优势。
I draw attention to the need for ecologists to take spatial structure into account more seriously in hypothesis testing. If spatial autocorrelation is ignored, as it usually is, then analyses of ecological patterns in terms of environmental factors can produce very misleading results. This is demonstrated using synthetic but realistic spatial patterns with known spatial properties which are subjected to classical correlation and multiple regression analyses. Correlation between an autocorrelated response variable and each of a set of explanatory variables is strongly biased in favour of those explanatory variables that are highly autocorrelated - the expected magnitude of the correlation coefficient increases with autocorrelation even if the spatial patterns are completely independent. Similarly, multiple regression analysis finds highly autocorrelated explanatory variables “significant” much more frequently than it should. The chances of mistakenly identifying a “significant” slope across an autocorrelated pattern is very high if classical regression is used. Consequently, under these circumstances strongly autocorrelated environmental factors reported in the literature as associated with ecological patterns may not actually be significant. It is likely that these factors wrongly described as important constitute a red-shifted subset of the set of potential explanations, and that more spatially discontinuous factors (those with bluer spectra) are actually relatively more important than their present status suggests. There is much that ecologists can do to improve on this situation. I discuss various approaches to the problem of spatial autocorrelation from the literature and present a randomisation test for the association of two spatial patterns which has advantages over currently available methods.