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Multiple species effects and spatial autocorrelation in detecting species associations
Author(s) -
Dale M. R. T.,
Blundon D. J.,
MacIsaac D. A.,
Thomas A. G.
Publication year - 1991
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.2307/3236174
Subject(s) - contingency table , pairwise comparison , quadrat , statistics , spatial analysis , mathematics , statistic , table (database) , autocorrelation , econometrics , sampling (signal processing) , test statistic , statistical hypothesis testing , computer science , ecology , data mining , biology , transect , filter (signal processing) , computer vision
. The traditional approach to the analysis of species association within a community, based upon co‐occurrence in sampling units such as quadrats, has been to test all pairs of species, using a 2 × 2 contingency table for each pair. It has long been recognised that all these tests are not independent of each other, but there is an additional problem in that the association between any particular pair may depend on the combination of the other species that are present or on the environmental factors that determine that combination. We use a 2 k contingency table to examine this problem and find that pairwise associations are not independent of the other species. The second problem that we consider is the effect of spatial autocorrelation in the data which makes the statistical tests too liberal. In the absence of a derived solution for a deflation factor to correct the test statistic calculated from a 2 k table, we describe a Monte Carlo approach that provides an approximate solution to this problem. In our data the amount of deflation that is necessary for a 2 k table is small compared to the amount required for the 2 × 2 tables used to test pairwise association.