Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model

Abstract

Abstract. The species abundance distribution (SAD) is one of the most studied patterns in ecology due to its potential insights into commonness and rarity, community assembly, and patterns of biodiversity. It is well established that communities are composed of a few common and many rare species, and numerous theoretical models have been proposed to explain this pattern. However, no attempt has been made to determine how well these theoretical characterizations capture observed taxonomic and global-scale spatial variation in the general form of the distribution. Here, using data of a scope unprecedented in community ecology, we show that a simple maximum entropy model produces a truncated log-series distribution that can predict between 83% and 93% of the observed variation in the rank abundance of species across 15 848 globally distributed communities including birds, mammals, plants, and butterflies. This model requires knowledge of only the species richness and total abundance of the community to predict the full abundance distribution, which suggests that these factors are sufficient to understand the distribution for most purposes. Since geographic patterns in richness and abundance can often be successfully modeled, this approach should allow the distribution of commonness and rarity to be characterized, even in locations where empirical data are unavailable.

An experimental test of the response of macroecological patterns to altered species interactions

“…If state variables determine macroecological patterns, then any model will do well at predicting those patterns if the model also predicts realistic values of state variables (McGill 2010, White et al 2012). More broadly, if the indirect effect of biotic interactions on macroecological patterns is general, then these patterns may be unsuitable for determining the detailed biological processes operating in specific ecosystems.…”

“…The potential value of macroecological patterns being determined only indirectly by specific biological processes is that it makes it easier and more generalizable to use them for building ecological theories, and apply them to accomplish important tasks like scaling diversity estimates for reserve design, hotspot analysis, and future climate scenarios (e.g., Brummitt and Lughadha 2003, Thomas et al 2004, Diniz-Filho et al 2005, Harte 2009) and estimating abundance from occupancy (e.g., Gaston 2000, Harte 2011). Because only the impacts of biological processes on S and N are important, and not the details of the biological interactions themselves, the same approaches can potentially be applied across diverse ecosystems and taxonomic groups (McGill 2010, Harte 2011, White et al 2012.…”

Scaling laws predict global microbial diversity

“…Our macrobial datasets comprised 14,862 different sites of mammal, tree, and bird communities. We used a compilation of data that included species abundance data for communities distributed across all continents, except Antarctica (40). This compilation is based, in part, on five continental-to global-scale surveys: United States Geological Survey (USGS) North American Breeding Bird Survey (41) We used 20,376 sites of communities of bacteria, archaea, and microscopic fungi; 14,615 of these were from the EMP (1) obtained on August 22, 2014.…”

An extensive comparison of species-abundance distribution models

“…Which model or models provide the best fit to the data, and the resulting implications for the processes structuring ecological systems, is an active area of research (e.g., McGill, 2003; Volkov et al, 2003; Ulrich, Ollik & Ugland, 2010; White, Thibault & Xiao, 2012; Connolly et al, 2014). However, most comparisons of the different models: (1) use only a small subset of available models (typically two; e.g., McGill, 2003; Volkov et al, 2003; White, Thibault & Xiao, 2012; Connolly et al, 2014); (2) focus on a single ecosystem or taxonomic group (e.g., McGill, 2003; Volkov et al, 2003); or (3) fail to use the most appropriate statistical methods (e.g., Ulrich, Ollik & Ugland, 2010, see Matthews & Whittaker, 2014 for discussion of best statistical methods for fitting SADs).…”

“…However, most comparisons of the different models: (1) use only a small subset of available models (typically two; e.g., McGill, 2003; Volkov et al, 2003; White, Thibault & Xiao, 2012; Connolly et al, 2014); (2) focus on a single ecosystem or taxonomic group (e.g., McGill, 2003; Volkov et al, 2003); or (3) fail to use the most appropriate statistical methods (e.g., Ulrich, Ollik & Ugland, 2010, see Matthews & Whittaker, 2014 for discussion of best statistical methods for fitting SADs). This makes it difficult to draw general conclusions about which, if any, models provide the best empirical fit to species abundance distributions.…”