Global abundance estimates for 9,700 bird species
- PMID: 34001610
- PMCID: PMC8166167
- DOI: 10.1073/pnas.2023170118
Global abundance estimates for 9,700 bird species
Abstract
Quantifying the abundance of species is essential to ecology, evolution, and conservation. The distribution of species abundances is fundamental to numerous longstanding questions in ecology, yet the empirical pattern at the global scale remains unresolved, with a few species' abundance well known but most poorly characterized. In large part because of heterogeneous data, few methods exist that can scale up to all species across the globe. Here, we integrate data from a suite of well-studied species with a global dataset of bird occurrences throughout the world-for 9,700 species (∼92% of all extant species)-and use missing data theory to estimate species-specific abundances with associated uncertainty. We find strong evidence that the distribution of species abundances is log left skewed: there are many rare species and comparatively few common species. By aggregating the species-level estimates, we find that there are ∼50 billion individual birds in the world at present. The global-scale abundance estimates that we provide will allow for a line of inquiry into the structure of abundance across biogeographic realms and feeding guilds as well as the consequences of life history (e.g., body size, range size) on population dynamics. Importantly, our method is repeatable and scalable: as data quantity and quality increase, our accuracy in tracking temporal changes in global biodiversity will increase. Moreover, we provide the methodological blueprint for quantifying species-specific abundance, along with uncertainty, for any organism in the world.
Keywords: SADs; abundance; data integration; global biodiversity; rarity.
Copyright © 2021 the Author(s). Published by PNAS.
Conflict of interest statement
The authors declare no competing interest.
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Comment in
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Reply to Robinson et al.: Data integration will form the basis of future abundance estimates.Proc Natl Acad Sci U S A. 2022 Mar 8;119(10):e2117920119. doi: 10.1073/pnas.2117920119. Epub 2022 Mar 1. Proc Natl Acad Sci U S A. 2022. PMID: 35238636 Free PMC article. No abstract available.
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Extreme uncertainty and unquantifiable bias do not inform population sizes.Proc Natl Acad Sci U S A. 2022 Mar 8;119(10):e2113862119. doi: 10.1073/pnas.2113862119. Epub 2022 Mar 1. Proc Natl Acad Sci U S A. 2022. PMID: 35238655 Free PMC article. No abstract available.
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