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Reproductive pair correlations and the clustering of organisms

Author

Listed:
  • W. R. Young

    (Scripps Institution of Oceanography, University of California at San Diego)

  • A. J. Roberts

    (University of Southern Queensland)

  • G. Stuhne

    (Scripps Institution of Oceanography, University of California at San Diego)

Abstract

Clustering of organisms can be a consequence of social behaviour, or of the response of individuals to chemical and physical cues1. Environmental variability can also cause clustering: for example, marine turbulence transports plankton2,3,4,5,6,7,8 and produces chlorophyll concentration patterns in the upper ocean9,10,11. Even in a homogeneous environment, nonlinear interactions between species12,13,14 can result in spontaneous pattern formation. Here we show that a population of independent, random-walking organisms (‘brownian bugs’), reproducing by binary division and dying at constant rates, spontaneously aggregates. Using an individual-based model, we show that clusters form out of spatially homogeneous initial conditions without environmental variability, predator–prey interactions, kinesis or taxis. The clustering mechanism is reproductively driven—birth must always be adjacent to a living organism. This clustering can overwhelm diffusion and create non-poissonian correlations between pairs (parent and offspring) or organisms, leading to the emergence of patterns.

Suggested Citation

  • W. R. Young & A. J. Roberts & G. Stuhne, 2001. "Reproductive pair correlations and the clustering of organisms," Nature, Nature, vol. 412(6844), pages 328-331, July.
    • Handle: RePEc:nat:nature:v:412:y:2001:i:6844:d:10.1038_35085561
    DOI: 10.1038/35085561
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    Cited by:

    1. Ricardo Martínez-García & Clara Murgui & Emilio Hernández-García & Cristóbal López, 2015. "Pattern Formation in Populations with Density-Dependent Movement and Two Interaction Scales," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-14, July.
    2. Cianelli, Daniela & Sabia, Luciana & d’Alcalà, Maurizio Ribera & Zambianchi, Enrico, 2009. "An individual-based analysis of the dynamics of two coexisting phytoplankton species in the mixed layer," Ecological Modelling, Elsevier, vol. 220(19), pages 2380-2392.
    3. Vilar, J.M.G. & Solé, R.V. & Rubı́, J.M., 2003. "On the origin of plankton patchiness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 317(1), pages 239-246.
    4. Susanne Menden-Deuer, 2010. "Inherent High Correlation of Individual Motility Enhances Population Dispersal in a Heterotrophic, Planktonic Protist," PLOS Computational Biology, Public Library of Science, vol. 6(10), pages 1-10, October.
    5. El Saadi, N. & Bah, A., 2007. "An individual-based model for studying the aggregation behavior in phytoplankton," Ecological Modelling, Elsevier, vol. 204(1), pages 193-212.
    6. Juanico, Dranreb Earl & Monterola, Christopher & Saloma, Caesar, 2003. "Allelomimesis as a generic clustering mechanism for interacting agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 320(C), pages 590-600.
    7. Navidad Maeso, David & Patriarca, Marco & Heinsalu, Els, 2022. "Influence of invasion on natural selection in dispersal-structured populations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    8. Convertino, M., 2011. "Neutral metacommunity clustering and SAR: River basin vs. 2-D landscape biodiversity patterns," Ecological Modelling, Elsevier, vol. 222(11), pages 1863-1879.
    9. Omelyan, Igor, 2020. "Spatial population dynamics: Beyond the Kirkwood superposition approximation by advancing to the Fisher–Kopeliovich ansatz," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 544(C).
    10. Ross, Robert J.H. & Yates, C.A. & Baker, R.E., 2017. "The effect of domain growth on spatial correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 334-345.

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