A MANOVA STATISTIC IS JUST AS POWERFUL AS DISTANCE-BASED STATISTICS, FOR MULTIVARIATE ABUNDANCES

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Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices (2008, background): “This is a typical property of this type of data (e.g., Warton and Hudson 2004).”
Many zeros does not mean zero inflation: comparing the goodness‐of‐fit of parametric models to multivariate abundance data (2005, unknown): “However, such methods are not currently used, although recent work ( Warton and Hudson, 2004 ) demonstrated that test statistics based on least squares methods were at least as powerful as current methods, across 20 multivariate abundance datasets.”
Distance‐based multivariate analyses confound location and dispersion effects (2012, methodology): “This reduces power (Warton & Hudson 2004), because the taxa in which the strongest effects are observed are the ones that tend to be most heavily down-weighted in standardisation.”
Session-effects in the laboratory (2011, unknown): “Experimental data in ecology (Warton and Hudson 2004), biology (Williams 2000), medicine (Altman and Bland 1997) and other areas have repeated measurements per treatment which induce similar correlations, and the analysis of genes and their comparison across subpopulations generates the same type of”