Beta diversity, species replacement and nestedness are often examined through pairwise comparisons of sites based on presence-absence data, and the relative importance of these ecological phenomena is evaluated by operations with dissimilarity coefficients. An example is the nestedness resultant dissimilarity (NRD) procedure recently proposed by Baselga (2010, Global Ecology andBiogeography 19: 134–143) to disentangle the nestedness fraction of beta diversity from species replacement. In our view, the component terms in this measure are not scaled uniformly and the nestedness fraction cannot be quantified properly without giving clear definitions for its measurement. We suggest to distinguish among three additive fractions of the species set of two sites: number of species shared (overlap), species replacement (=spatial turnover) and richness difference. Then, absolute beta diversity is obtained as a composite of the second two fractions (known as βWB), while nestedness is derived from the first and the third. To express beta diversity and nestedness in a relativized form, the respective sums are divided by the total number of species. These allow defining a new index to measure the fraction of beta diversity which is shared by nestedness as well, and is calculated as relativized richness difference with the condition that the two sites being compared have at least one species in common. It is called diversity-nestedness intersection coefficient (F). Baselga’s nestedness resultant dissimilarity and the diversity-nestedness intersection coefficient are compared graphically using artificial and actual examples. These functions follow a mathematical relationship for perfectly nested data, otherwise their results are divergent. Discrepancy increases when beta diversity is large, especially if richness differences override species replacement effects in shaping presence-absence data structures. An advantage of F is its compatibility with a general theoretical and methodological framework for revealing pattern in presence-absence data matrices.
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Madhur Anand, CAN (forest ecology, computational ecology, and ecological complexity)
S. Bagella, ITA (temporal dynamics, including succession, community level patterns of species richness and diversity, experimental studies of plant, animal and microbial communities, plant communities of the Mediterranean)
P. Batáry, HUN (landscape ecology, agroecology, ecosystem services)
P. A. V. Borges, PRT (community level patterns of species richness and diversity, sampling in theory and practice)
A. Davis, GER (supervised learning, multitrophic interactions, food webs, multivariate analysis, ecological statistics, experimental design, fractals, parasitoids, species diversity, community assembly, ticks, biodiversity, climate change, biological networks, cranes, olfactometry, evolution)
Z. Elek, HUN (insect ecology, invertebrate conservation, population dynamics, especially of long-term field studies, insect sampling)
T. Kalapos, HUN (community level plant ecophysiology, grassland ecology, vegetation-soil relationship)
G. M. Kovács, HUN (microbial ecology, plant-fungus interactions, mycorrhizas)
W. C. Liu,TWN (community-based ecological theory and modelling issues, temporal dynamics, including succession, trophic interactions, competition, species response to the environment)
L. Mucina, AUS (vegetation survey, syntaxonomy, evolutionary community ecology, assembly rules, global vegetation patterns, mediterranean ecology)
P. Ódor, HUN (plant communities, bryophyte ecology, numerical methods)
F. Rigal, FRA (island biogeography, macroecology, functional diversity, arthropod ecology)
D. Rocchini, ITA (biodiversity, multiple scales, spatial scales, species distribution, spatial ecology, remote sensing, ecological informatics, computational ecology)
F. Samu, HUN (landscape ecology, biological control, generalist predators, spiders, arthropods, conservation biology, sampling methods)