Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

Author:

Urban Mark C1ORCID,Travis Justin M J2,Zurell Damaris3ORCID,Thompson Patrick L4,Synes Nicholas W2,Scarpa Alice2,Peres-Neto Pedro R5,Malchow Anne-Kathleen3,James Patrick M A6,Gravel Dominique7,De Meester Luc8,Brown Calum9,Bocedi Greta2,Albert Cécile H10,Gonzalez Andrew11,Hendry Andrew P11

Affiliation:

1. University of Connecticut, Storrs, Connecticut, United States

2. University of Aberdeen, Aberdeen, Scotland, United Kingdom

3. University of Potsdam, Potsdam, Germany

4. University of British Columbia, Vancouver, British Columbia, Canada

5. Department of Biology at Concordia University, Montreal, Quebec, Canada

6. University of Toronto, Toronto, Canada

7. Université de Sherbrooke, Sherbrooke, Quebec, Canada

8. Laboratory of Aquatic Ecology, Evolution, and Conservation, KU Leuven, Leuven, Belgium, with the Leibniz-Institut für Gewässerökologie und Binnenfischerei, Berlin, Germany, and with the Institute of Biology, Freie Universität Berlin, Berlin, Germany

9. IMK-IFU, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

10. Aix Marseille Univ, CNRS, Univ Avignon, IRD, IMBE, Marseille, France

11. McGill University, Montreal, Quebec, Canada

Abstract

Abstract Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences

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