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Explaining the excess of rare species in natural species abundance distributions

Author

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  • Anne E. Magurran

    (University of St Andrews)

  • Peter A. Henderson

    (Pisces Conservation Ltd, IRC House, The Square)

Abstract

The observation that a few species in ecological communities are exceptionally abundant, whereas most are rare, prompted the development of species abundance models1,2,3. Nevertheless, despite the large literature on the commonness and rarity of species inspired by these pioneering studies, some widespread empirical patterns of species abundance resist easy explanation4. Notable among these is the observation5 that in large assemblages there are more rare species than the log normal model predicts6,7. Here we use a long-term (21-year) data set, from an estuarine fish community, to show how an ecological community can be separated into two components. Core species, which are persistent, abundant and biologically associated with estuarine habitats, are log normally distributed. Occasional species occur infrequently in the record, are typically low in abundance and have different habitat requirements; they follow a log series distribution. These distributions are overlaid, producing the negative skew that characterizes real data sets.

Suggested Citation

  • Anne E. Magurran & Peter A. Henderson, 2003. "Explaining the excess of rare species in natural species abundance distributions," Nature, Nature, vol. 422(6933), pages 714-716, April.
    • Handle: RePEc:nat:nature:v:422:y:2003:i:6933:d:10.1038_nature01547
    DOI: 10.1038/nature01547
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    Cited by:

    1. Fung, Tak & Chisholm, Ryan A., 2023. "Improving the realism of neutral ecological models by incorporating transient dynamics with temporal changes in community size," Theoretical Population Biology, Elsevier, vol. 149(C), pages 12-26.
    2. Vilenkin, Boris & Chikatunov, Vladimir I. & Pavlíček, Tomáš, 2009. "The pattern of species turnover resulting from stochastic population dynamics: The model and field data," Ecological Modelling, Elsevier, vol. 220(5), pages 657-661.
    3. Eliyajrj Kujur & Manoj Kumar Jhariya & Dhiraj Kumar Yadav & Arnab Banerjee, 2022. "Phytosociological attributes and regeneration potential of riparian vegetation in Northern Chhattisgarh, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2861-2886, February.
    4. Emma Schwager & Himel Mallick & Steffen Ventz & Curtis Huttenhower, 2017. "A Bayesian method for detecting pairwise associations in compositional data," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-21, November.
    5. Gretchen J A Hansen & Cayelan C Carey, 2015. "Fish and Phytoplankton Exhibit Contrasting Temporal Species Abundance Patterns in a Dynamic North Temperate Lake," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    6. Ai, Dexiecuo & Chu, Chengjin & Ellwood, M.D. Farnon & Hou, Rui & Wang, Gang, 2013. "Migration and niche partitioning simultaneously increase species richness and rarity," Ecological Modelling, Elsevier, vol. 258(C), pages 33-39.
    7. Cazzolla Gatti, Roberto & Amoroso, Nicola & Monaco, Alfonso, 2020. "Estimating and comparing biodiversity with a single universal metric," Ecological Modelling, Elsevier, vol. 424(C).

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