How to test the significance of the relation between spatially autocorrelated data at the landscape scale: A case study using fire and forest maps
How to test the significance of the relation between spatially autocorrelated data at the landscape scale: A case study using fire and forest maps
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DOI：
10.1080/11956860.2002.11682707
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发表时间：
2002-01-01
期刊：
影响因子：
1.3
通讯作者：
Payette, S
中科院分区：
文献类型：
--
作者：
Fortin, MJ;Payette, S
为了更好地了解野火和森林更新之间的关系，在北方森林，我们量化的关系程度的相关性。考虑到北方森林中的野火可以覆盖大面积，还需要计算大面积的这种相关性（即，北方魁北克33，000 km（2））。在这个景观尺度上，这两个变量（火灾和森林）表现出强烈的和显着的正空间自相关。然而，这两个变量的空间自相关性的存在会影响其相关程度的统计显著性和解释。在本文中，我们比较了不同的方法，已经提出来解决这个问题：一个参数测试，纠正存在的自相关调整有效样本大小（Dutilleul改良t检验）;完全随机化检验;基于环形移位的限制性随机化检验;控制采样之间相对空间位置的Mantel检验：以及控制采样点间空间距离的部分Mantel检验。经参数检验和完全随机化检验，两个变量之间存在显著正相关。但当使用限制性随机化检验、Dutilleul改良t检验和Mantel检验时，差异不显著。相反，通过部分Mantel检验发现负相关。因此，为了控制空间自相关的存在，建议使用限制性随机化检验或Dutilleul方法，而为了控制数据的空间相对位置，应使用Mantel和部分Mantel检验。对这些统计检验及其各自关于数据空间结构的假设的坚定理解对于任何有效的生态理解和解释都至关重要。
To better understand the relationship between wildfire and forest regeneration in the boreal forest, we quantify their degree of relationship by means of correlation. Given that wildfires in the boreal forest can cover large areas, such correlation needs also to be computed for large areas (i.e., 33,000 km(2) in northern Quebec). At this landscape scale, both variables (fire and forest) show strong and significant positive spatial autocorrelation. The presence of spatial autocorrelation in the two variables, however, can affect the statistical significance and the interpretation of their degree of correlation. In this paper, we compare different approaches that have been proposed to solve this problem: a parametric test that corrects for the presence of autocorrelation by adjusting the effective sample size (Dutilleul's modified t test); a complete randomization test; a restricted randomization test based on a toroidal shift; the Mantel test that controls for the relative spatial locations among sampling: and the partial Mantel test that controls for the spatial distances among sampling sites. A positive correlation between the two variables was found significant by the parametric test and complete randomization test. but not significant when the restricted randomization test, Dutilleul's modified t test, and the Mantel test were used. Conversely, a negative correlation was found by the partial Mantel test. Hence, to control for the presence of spatial autocorrelation, either a restricted randomization test or the Dutilleul method is recommended, while to control for the spatial relative position of the data, the Mantel and partial Mantel tests should be used. A firm understanding of these statistical tests and their respective assumptions regarding the spatial structure of the data is crucial to any valid ecological understanding and interpretation.