Plant size inequality: How to analyse competing spatial dissimilarity indices?

• Spatial plant diversity indices help monitor diversity trends. • We proposed a framework for analysing competing diversity indices. • We tested the dissimilarity coefficient accounting for size inequality. • We identified higher test sensitivity for the dissimilarity coefficient. • The dissimilarity coefficient has a solid statistical foundation. • The dissimilarity coefficient principle can form spatial test functions. Worldwide biodiversity loss is perceived as a major threat and is likely to be enforced by ongoing climate change. Continued monitoring of biodiversity can assist in compensating for decreasing biodiversity by goal-oriented conservation management. Plant size diversity, also termed size inequality and size hierarchy, has often been neglected in studies of biodiversity, but plays a crucial role in many ecosystems, e.g. in forests. Several competing diversity indices and other characteristics of spatial plant size inequality have been proposed but to date no protocols or guidelines exist for evaluating their relative merits. In our study, we proposed a broad framework for such an analysis. In order to put this framework to a test, we revisited the dissimilarity coefficient, a somewhat ignored but promising spatial size inequality index published at the end of the 1990s, identified its nearest index competitors and analysed their performances in different mathematical-statistical contexts. We learned that the dissimilarity coefficient is more sensitive in statistical significance tests relating to three very different summary characteristics than its closest competitor, the size differentiation index. In addition the dissimilarity coefficient has a more solid foundation than the differentiation index, since it is based on the well-known coefficient of variation. The dissimilarity coefficient can be recommended in a wide range of plant diversity applications. The principle of the dissimilarity coefficient can also be used to define an effective mark correlation function. We recommend our evaluation framework for use in similar analyses of competing diversity characteristics.