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Positive associations among rare species and their persistence in ecological assemblages

Nature Ecology & Evolution volume 4, pages 40–45 (2020)Cite this article

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Abstract

According to the competitive exclusion principle, species with low competitive abilities should be excluded by more efficient competitors; yet, they generally remain as rare species. Here, we describe the positive and negative spatial association networks of 326 disparate assemblages, showing a general organization pattern that simultaneously supports the primacy of competition and the persistence of rare species. Abundant species monopolize negative associations in about 90% of the assemblages. On the other hand, rare species are mostly involved in positive associations, forming small network modules. Simulations suggest that positive interactions among rare species and microhabitat preferences are the most probable mechanisms underpinning this pattern and rare species persistence. The consistent results across taxa and geography suggest a general explanation for the maintenance of biodiversity in competitive environments.

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Fig. 1: Approaching assembly mechanisms through the lens of positive and negative association networks.
Fig. 2: The contrasting patterns of positive and negative association networks.
Fig. 3: Positive interactions among weak competitors alone or together with habitat preferences reproduce realized association patterns.
Fig. 4: The organization of association networks remains invariant across the globe and regardless of taxa.

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Data availability

The dataset used in this study is freely available at https://doi.org/10.6084/m9.figshare.9906092.

Code availability

The R scripts used in this study are freely available at https://doi.org/10.6084/m9.figshare.9906092.

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Acknowledgements

We thank J. Hortal and S. Allesina for their critical comments on an early version of the manuscript. The simulations were performed on resources provided by the Swedish National Infrastructure for Computing at HPC2N. J.C. is supported by the Carl Tryggers Foundation for Scientific Research (no. CTS 16:384). E.A. is supported by a postdoctoral grant (no. CT39/17) funded by the Universidad Complutense de Madrid. C.J.M. is supported by the Swiss National Science Foundation (grant no. SNSF-31003A-144162). R.B.-M. is supported by the Spanish Ministry of Science and Innovation predoctoral fellowship no. BES-2013-065753. M.S., J.A.B.-C. and J.M.-G. acknowledge support from the University of Geneva (project: C-CIA; no. 309354). X.A. is supported by a Ramón y Cajal research contract by the Spanish Ministry of Economy and Competitiveness (MINECO, no. RYC-2015-18448). M.R. is supported by the Swedish Research Council grant no. 2016-00796. J.A.N. was supported by a Colombian COLCIENCIAS doctoral scholarship (no. 617-2013). F.A.-M. is grateful to CAPES for a doctoral scholarship (no. 120147/2016-01). A.L., P.F. and J.M.-G. were funded by the AGORA Project (MINECO, no. CGL2016-77417-P). C.M.-M. was supported by an IdEx Bordeaux Postdoctoral Fellowship (VECLIMED project). A.H. was supported by the University of Alcalá own research programme 2018 postdoctoral grant and Basque Country Government funding support to FisioClimaCO2 (IT1022-16) research group. L.J. received productivity grants from of CNPq (process no. 307597/2016-4).

Author information

Authors and Affiliations

  1. Integrated Science Laboratory, Department of Physics, Umeå University, Umeå, Sweden

    Joaquín Calatayud, Martin Rosvall & Magnus Neuman

  2. Departamento de Biogeografía y Cambio Global, Museo Nacional de Ciencias Naturales, Madrid, Spain

    Joaquín Calatayud, Jorge Ari Noriega & Fernanda Alves-Martins

  3. Departamento de Ciencias de la Vida, Edificio de Ciencias, Universidad de Alcalá, Madrid, Spain

    Enrique Andivia & Asier Herrero

  4. Departamento de Biodiversidad, Ecología y Evolución, Universidad Complutense de Madrid, Madrid, Spain

    Enrique Andivia

  5. Departamento de Biología, Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos, Madrid, Spain

    Adrián Escudero & Arantzazu Luzuriaga

  6. Department of Fish Ecology and Evolution, Eawag, Kastanienbaum, Switzerland

    Carlos J. Melián

  7. Departamento de Biología de la Conservación, Estación Biológica de Doñana-CSIC, Seville, Spain

    Rubén Bernardo-Madrid

  8. Dendrolab, Department of Earth Sciences, University of Geneva, Geneva, Switzerland

    Markus Stoffel & Juan Antonio Ballesteros-Cánovas

  9. Department F.-A. Forel for Environmental and Aquatic Sciences, University of Geneva, Geneva, Switzerland

    Markus Stoffel

  10. Climate Change Impacts and Risks in the Anthropocene, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland

    Markus Stoffel, Juan Antonio Ballesteros-Cánovas & Jaime Madrigal-González

  11. Plant Science, University of Melbourne, Burnley Campus, Richmond, Victoria, Australia

    Cristina Aponte

  12. Department of Botany, Faculty of Sciences, University of South Bohemia, České Budějovice, Czech Republic

    Nagore G. Medina

  13. Departamento de Biología (Botánica), Facultad de Ciencias, Universidad Autónoma de Madrid, Madrid, Spain

    Nagore G. Medina, Isabel Draper & Luciano Pataro

  14. Plant Ecology, Institute of Plant Sciences, University of Bern, Bern, Switzerland

    Rafael Molina-Venegas

  15. Centre de Recerca Ecològica i Aplicacions Forestals Campus de Bellaterra (UAB), Cerdanyola del Vallès, Spain

    Xavier Arnan

  16. UMR CNRS 5805 EPOC, OASU, Université de Bordeaux Site de Talence-Pessac-Gradignan, Pessac, France

    César Morales-Molino

  17. Institute of Plant Sciences and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    César Morales-Molino

  18. Higher Technical School of Agricultural and Forestry Engineering and Botanical Institute, University of Castilla-La Mancha, Albacete, Spain

    Pablo Ferrandis

  19. Department of Plant Biology and Ecology, Pharmacy Faculty, University of Basque Country, Vitoria-Gasteiz, Spain

    Asier Herrero

  20. Laboratótio do Ecologia e Zoologia de Invertebrados, Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém do Pará, Brazil

    Leandro Juen

  21. Departamento de Biología, Facultad de Ciencias, Universidad de La Serena, La Serena, Chile

    Alex Cea

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  1. Joaquín Calatayud
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Contributions

J.C. and J.M.-G. conceived the study. J.C. and J.M.-G. designed the analyses with contributions from E.A., A.E., C.J.M. and R.B.-M. J.C., E.A., R.B.-M., M.S., C.A., X.A., N.G.M., J.A.N., F.A.-M., I.D., A.L., J.A.B.-C., C.M.-M., P.F., A.H., L.P., L.J., A.C. and J.M.-G. collected the data. J.C. analysed the data with assistance from C.J.M., M.R. and M.N. J.C., E.A. and J.M.-G. led the writing in close collaboration with A.E., C.J.M., R.B.-M., M.S., C.A. and R.M.-V. All authors contributed to the development and writing of the paper.

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Correspondence to Joaquín Calatayud.

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Extended data

Extended Data Fig. 1 The differences between positive and negative network properties were in general unaffected by sampling effort, null model degrees of freedom, species richness, latitude, longitude or taxa.

Generalized linear model summary statistics including explained deviance (Dev. expl.) for each model. Connectivity (P < N): Probability of negative networks to be more densely connected than their positive pairs. Abundance-degree (P < N): Probability of dominant species to monopolizing negative links but not positive ones (that is, a stronger positive abundance-degree relationship in negative networks). Abundance (P < N): probability of positive networks tending to be composed of less abundant species. Modularity (P > N): probability of positive networks being more modular than their negative pairs.

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Supplementary Appendices 1–4, Figs. 1–6 and Tables 1–2.

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Calatayud, J., Andivia, E., Escudero, A. et al. Positive associations among rare species and their persistence in ecological assemblages. Nat Ecol Evol 4, 40–45 (2020). https://doi.org/10.1038/s41559-019-1053-5

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