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For beta diversity analysis, we normalized OTU matrices with a cumulative sum scaling (CSS) approach [16].
For beta diversity, the difference between plots in structural richness and in cover of thick tussocks grasses and lawns were the best predictors (r2 = 0.45).
Finally, applications of the species area relationship often assume that the area of interest is contiguous while in practice it seldom is, and so calculations using the species area relationship need to account for beta diversity between disjunctive areas.
Similarly, mean Bray-Curtis distances among the four microhabitats along the bedforms, another robust measure for beta diversity, significantly increased with flow heterogeneity (r2 = 0.89, P = 0.005).
In addition, we used the average Bray-Curtis distance between samples from one flume at a given time as a complementing measure for beta diversity.
For beta diversity metrics, means and ANOVA were performed on the subsampled data and Tukey's honest significance test was used for all post hoc comparisons.
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Permutational multivariate analysis of variance (PERMANOVA) was used for significance test of beta diversity difference between sample groups.
A neighbor-joining phylogeny was reconstructed using FastTree for UniFrac analysis of beta diversity [ 64].
For case control comparisons of beta diversity, the sum of UniFrac values across all pairings of the 13 cases (13 × 12/2=78 summands) was compared with the corresponding sum in 1000 random selections of 13 individuals.
This is perhaps best demonstrated by the higher estimates of beta diversity between plots for larger organisms, which is also suggestive of a link with scaling.
The Simple Matching, Soerensen, and multi-plot similarity coefficient and distance decay analyses were applied for examining beta diversity.
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