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Current analyses of metacommunity data largely focus on global attributes across the entire metacommunity, such as mean alpha, beta, and gamma diversity, as well as the partitioning of compositional variation into single estimates of contributions of space and environmental effects and, more recently, possible contributions of species interactions. However, this view neglects the fact that different species and sites in the landscape can vary widely in how they contribute to these metacommunity‐wide attributes. We argue for a new conceptual framework with matched analytics with the goals of studying the complex and interactive relations between process and pattern in metacommunities that is focused on the variation among species and among sites which we call the 'internal structure' of the metacommunity. To demonstrate how the internal structure could be studied, we create synthetic data using a process‐based colonization–extinction metacommunity model. We then use joint species distribution models to estimate how the contributions of space, environment, and biotic interactions driving metacommunity assembly differ among species and sites. We find that this approach to the internal structure of metacommunities provides useful information about the distinct ways that different species and different sites contribute to metacommunity structure. Although it has limitations, our work points at a more general approach to understand how other possible complexities might affect internal structure and might thus be incorporated into a more cohesive metacommunity theory. [ABSTRACT FROM AUTHOR]

Surveying ecological communities often means the tedious work of collecting detailed information on each species within each sampling unit (e.g. trap, transect, quadrat). In this paper, we first argue that presence-absence and abundance data are the two extremes of a spectrum of data formats. By counting individuals of each species within a sampling unit until either a predefined (user-defined) number of individuals is reached or all individuals of the species are counted, all intermediate cases can be generated. By independently correlating each intermediate case with the complete abundance data, we show that it is not necessary to count all individuals to recover the patterns of variation characterizing a community data table. When the same procedure is applied in combination with different distance coefficients such as the Hellinger, chord, χ2, percentage difference or modified Gower, or the distance between species profiles, an even lower number of individuals per species need to be counted within a sampling unit for the patterns of variation defining a community to be recovered. By applying the same counting procedure to data collected during a pilot study, we show that the maximum number of individuals that need to be counted within a sampling unit for a species can be estimated from a pilot study containing as little as 3% of randomly selected sampling units throughout the complete survey area. An example of how to apply this new counting method is presented, using data from a boreal forest Carabidae community sampled in northwestern Alberta, Canada. [ABSTRACT FROM AUTHOR]