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Rescuing ecosystems from extinction cascades through compensatory perturbations

Author

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  • Sagar Sahasrabudhe

    (Northwestern University)

  • Adilson E. Motter

    (Northwestern University
    Northwestern Institute on Complex Systems, Northwestern University)

Abstract

Food-web perturbations stemming from climate change, overexploitation, invasive species and habitat degradation often cause an initial loss of species that results in a cascade of secondary extinctions, posing considerable challenges to ecosystem conservation efforts. Here, we devise a systematic network-based approach to reduce the number of secondary extinctions using a predictive modelling framework. We show that the extinction of one species can often be compensated by the concurrent removal or population suppression of other specific species, a counterintuitive effect not previously tested in complex food webs. These compensatory perturbations frequently involve long-range interactions that are not evident from local predator–prey relationships. In numerous cases, even the early removal of a species that would eventually go extinct is found to significantly reduce the number of cascading extinctions. These compensatory perturbations only exploit resources available in the system, and illustrate the potential of human intervention combined with predictive modelling for ecosystem management.

Suggested Citation

  • Sagar Sahasrabudhe & Adilson E. Motter, 2011. "Rescuing ecosystems from extinction cascades through compensatory perturbations," Nature Communications, Nature, vol. 2(1), pages 1-8, September.
  • Handle: RePEc:nat:natcom:v:2:y:2011:i:1:d:10.1038_ncomms1163
    DOI: 10.1038/ncomms1163
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    Cited by:

    1. Yao, Jia & Cheng, Ziyi & Chen, Anthony, 2023. "Bibliometric analysis and systematic literature review of the traffic paradoxes (1968–2022)," Transportation Research Part B: Methodological, Elsevier, vol. 177(C).
    2. Shen, Yi & Song, Guohao & Xu, Huangliang & Xie, Yuancheng, 2020. "Model of node traffic recovery behavior and cascading congestion analysis in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    3. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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