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Cluster analysis successfully grouped sampling sites into five clusters.
In order to identify groups of populations based on genetic differences, we grouped sampling localities to maximise the among-group variance component (Φct).
CA grouped sampling sites into clusters (called zones in this study) on the basis of similarities within a zone and dissimilarities between different zones.
We grouped sampling sites in two categories (dichotomous categorical predictor size): small = drainage ditches, small streams and ponds smaller than approximately one hectare; large = lakes larger than one hectare.
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Division of the area into strata makes it easier to group sampling sites and interpret the data collected from them.
A PCA of the plant morphological and physiological characteristics also grouped samples according to their field and inoculation status.
Principal coordinates analysis, based on the similarity of CAZy gene family relative abundance profiles, grouped samples predominantly by soil layer and ecozone (Fig. 3a).
Precision is a number of relevant samples retrieved divided by the total number of grouped samples.
Therefore, we grouped samples 4′ 8′ for analysis and compared the results with sample 1′.
Our results showed that clustering PEFs grouped samples according to their biological similarity.
For grouped samples, ANOVA tests were utilised.
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