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  • Coding for Life: Designing ...
    Urban, Mark C; Travis, Justin M J; Zurell, Damaris; Thompson, Patrick L; Synes, Nicholas W; Scarpa, Alice; Peres-Neto, Pedro R; Malchow, Anne-Kathleen; James, Patrick M A; Gravel, Dominique; De Meester, Luc; Brown, Calum; Bocedi, Greta; Albert, Cécile H; Gonzalez, Andrew; Hendry, Andrew P

    Bioscience, 01/2022, Letnik: 72, Številka: 1
    Journal Article

    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.