Distance‐Based Tests for Homogeneity of Multivariate Dispersions

By Marti J. Anderson. Biometrics, 2006

2,930 citations recorded by the source corpus.

Subfields: Ecology, Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/fa12dfd11be780c5a30fdd795179401a911391cd

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Citations in the corpus

Invertebrate assemblages of pools in arid‐land streams have high functional redundancy and are resistant to severe drying (2014, methodology): “We calculated community dispersion with a Sørensen distance measure (Sørensen, 1948) and compared dispersions between treatments using a permutation test with 999 permutations (Anderson, 2006).”
Fatty acid composition as biomarkers of freshwater microalgae: analysis of 37 strains of microalgae in 22 genera and in seven classes (2013, methodology): “Because within groups sample sizes were limited for certain groups, Monte Carlo p-values were used to assess significance of the PERMANOVA test statistic by random sampling of the asymptotic permutation distribution (Anderson et al. 2006).”
Influence of corn, switchgrass, and prairie cropping systems on soil microbial communities in the upper Midwest of the United States (2016, methodology): “Multivariate analysis of variance by permutation (PMANOVA) was used to test for significant differences in dispersion among groups and for differences between group centroids (Anderson, 2001, 2006) using vegan’s betadisper and adonis functions.”
Impacts of oil palm plantations on changes in environmental heterogeneity and Heteroptera (Gerromorpha and Nepomorpha) diversity (2017, methodology): “As a measure of β-diversity we used the dispersion among the samples and the centroid of each group (betadisper function, vegan package) through the PCoA, the difference between groups was assessed using 9999 permutations (Anderson 2006; Anderson et al.”
Spatial extent, regional specificity and metacommunity structuring in lake macrophytes (2013, methodology): “Second, we conducted tests of homogeneity of multivariate dispersion (PERMDISP; Anderson, 2006; Anderson et al., 2006) to examine the multivariate dispersions across the lakes within each subregion.”
Species invasions and the changing biogeography of Australian freshwater fishes (2007, methodology): “We tested the null hypothesis of no difference in beta-diversity among sampling units (Australian drainages, north-east coastal basins) before and after species introduction using a test for homogeneity of multivariate dispersions (Anderson, 2006).”
Dispersion operators and resistant second-order functional data analysis (2012, background): “…matrices cannot be directly extended to the functional case, as they often depend on the assumption of an invertible empirical covariance, which will by default be violated in the functional case for all sample sizes (Tiku & Balakrishnan, 1985; O’Brien, 1992; Zhang et al., 1991; Anderson, 2006).”
Predicting the effects of increasing temporal scale on species composition, diversity, and rank-abundance distributions (2010, methodology): “The significance of the change in dispersion is assessed with a test for homogeneity of multivariate dispersions (Anderson 2006; Anderson et al. 2006).”
Linking small-scale topography with microclimate, plant species diversity and intra-specific trait variation in an alpine landscape (2015, methodology): “We tested the null hypothesis of homogeneity of multivariate dispersions by applying permutation tests (Anderson 2006).”
Declining diversity and abundance of High Arctic fly assemblages over two decades of rapid climate warming (2018, methodology): “We performed this analysis with a test of multivariate homogeneity of group dispersions (Anderson 2006, Anderson et al.”
Distance‐based multivariate analyses confound location and dispersion effects (2012, methodology): “Usually location effects are of primary interest, however sometimes dispersion effects are of interest (Warwick & Clarke 1993; Anderson et al. 2011), and Anderson (2006) developed a method intended to test specifically for such dispersion effects.”
Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences (2014, methodology): “…variances in the sponge microbiome and human oral microbiome datasets explained by each method using the betadisper function in the vegan package (Anderson, 2006) for R (R Core Team, 2013) and reduced distances produced from Horn dissimilarity coefficients to principal coordinates, which embeds…”