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Frugivores enhance potential carbon recovery in fragmented landscapes

Author

Listed:
  • Carolina Bello

    (Department of Environmental Systems Science)

  • Thomas W. Crowther

    (Department of Environmental Systems Science)

  • Danielle Leal Ramos

    (University of Exeter
    Universidade Estadual Paulista)

  • Teresa Morán-López

    (Universidad de Oviedo–CSIC–Principado de Asturias
    Universidad Nacional del Comahue)

  • Marco A. Pizo

    (Universidade Estadual Paulista)

  • Daisy H. Dent

    (Department of Environmental Systems Science
    Smithsonian Tropical Research Institute
    Max Planck Institute for Animal Behavior)

Abstract

Forest restoration is fundamental to overcoming biodiversity crises and climate change. In tropical forests, animals can improve forest recovery as they disperse >70% of tree species. However, representing animals in restoration and climate change policies remains challenging because a quantitative assessment of their contribution to forest and carbon recovery is lacking. Here we used individual-based models to assess frugivore-mediated seed rain in open areas along a fragmentation gradient. Movements of large birds were limited in landscapes with 40% is essential to optimizing animals’ contribution to restoration success. Active restoration (for example, planting trees) is required in more fragmented landscapes to achieve carbon and biodiversity targets.

Suggested Citation

  • Carolina Bello & Thomas W. Crowther & Danielle Leal Ramos & Teresa Morán-López & Marco A. Pizo & Daisy H. Dent, 2024. "Frugivores enhance potential carbon recovery in fragmented landscapes," Nature Climate Change, Nature, vol. 14(6), pages 636-643, June.
  • Handle: RePEc:nat:natcli:v:14:y:2024:i:6:d:10.1038_s41558-024-01989-1
    DOI: 10.1038/s41558-024-01989-1
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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Andrew Gelman & Ben Goodrich & Jonah Gabry & Aki Vehtari, 2019. "R-squared for Bayesian Regression Models," The American Statistician, Taylor & Francis Journals, vol. 73(3), pages 307-309, July.
    3. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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