A method of predicting ecological community structure

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. 1. Species inventories are the most basic form of ecological data. They provide information both about species richness and about community assembly rules. Fitting species abundance distribution models yields such information. Previous distributions either fit the data badly, assume that all species are equivalent, or ignore sampling processes. A distribution called the compound exponential-geometric series (CEGS) assumes that species vary randomly in their underlying abundances and that inventories are random draws reflecting this variation. 2. The predictive power of CEGS and of four rival distributions is tested in two ways. First, richness estimates for entire inventories are used to predict recomputed estimates after randomly winnowing of individuals. Second, counts for local inventories are used to predict counts for matched samples that represent the same ecological groups and biogeographic realms. 3. CEGS yields the best count predictions and is rarely rejected by the data. Its richness estimates are precise and nearly unbiased, so it outperforms not only other theoretical distributions but the benchmark Chao 1 extrapolation index. 4. Because of its solid performance, simple theoretical basis, and ability to yield absolute species richness estimates that are not lower bounds, CEGS may solve the twin problems of describing abundance distributions and estimating diversity. https://doi.org/10.32942/X2WH0V Ecology and Evolutionary Biology compound exponential-geometric series distribution, coverage-based rarefaction, Fisher's alpha, log series, Poisson log normal, shareholder quorum subsampling, Weibull distribution Published: 2025-04-22 12:07 Last Updated: 2025-04-22 12:07 CC BY Attribution 4.0 International Conflict of interest statement: The author declares no conflicts of interest. Data and Code Availability Statement: The empirical data used in this study are available via the Dryad Digital Repository at https://datadryad.org/stash/dataset/doi:10.5061/dryad.brv15dvdc. Language: English