Summary: 1. A critical property of count data is its mean–variance relationship, yet this is rarely considered inmultivariate analysis in ecology. 2. This study considers what is being implicitly assumed about the mean–variance relationship indistance-based analyses – multivariate analyses based on a matrix of pairwise distances – and whatthe effect is of any misspecification of the mean–variance relationship. 3. It is shown that distance-based analyses make implicit assumptions that are typically out-of-stepwith what is observed in real data, which has major consequences. 4. Potential consequences of this mean–variance misspecification are: confounding location anddispersion effects in ordinations; misleading results when trying to identify taxa in which an effect isexpressed; failure to detect a multivariate effect unless it is expressed in high-variance taxa. 5. Data transformation does not solve the problem. 6. A solution is to use generalised linear models and their recent multivariate generalisations, whichis shown here to have desirable properties.