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

• Published in: Ecology (Durham) 2012-08, Vol.93 (8), p.1772-1778
• Main Authors: White, Ethan P, Thibault, Katherine M, Xiao, Xiao
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Animals; Biodiversity; Biological and medical sciences; birds; Birds - physiology; butterflies; Butterflies - physiology; citizen science; commonness; Communities; community structure; Computer Simulation; Datasets; Demography; Ecological modeling; Ecology; Ecosystem; Ecosystems; Entropy; Forest ecology; Fundamental and applied biological sciences. Psychology; General aspects; informatics; mammals; Mammals - physiology; maximum entropy; Maximum entropy method; Modeling; Models, Biological; Population Density; Population ecology; rare species; rarity; Species; species abundance distribution; species diversity; Synecology; Taxonomy; trees; Trees - physiology
• ISSN: 0012-9658; 1939-9170
• DOI: 10.1890/11-2177.1

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.