A New Approach to Evaluate and Reduce Uncertainty of Model-Based Biodiversity Projections for Conservation Policy Formulation

Bonnie J.E. Myers, Sarah R. Weiskopf, Alexey N. Shiklomanov, Simon Ferrier, Ensheng Weng, Kimberly A. Casey, Mike Harfoot, Stephen T. Jackson, Allison K. Leidner, Timothy M. Lenton, Gordon Luikart, Hiroyuki Matsuda, Nathalie Pettorelli, Isabel M.D. Rosa, Alex C. Ruane, Gabriel B. Senay, Shawn P. Serbin, Derek P. Tittensor, T. Douglas Beard

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8 Citas (Scopus)

Resumen

Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities.

Idioma originalEnglish
Páginas (desde-hasta)1261-1273
Número de páginas13
PublicaciónBioScience
Volumen71
N.º12
DOI
EstadoPublished - dic. 1 2021

Nota bibliográfica

Publisher Copyright:
© 2021 Published by Oxford University Press on behalf of American Institute of Biological Sciences.

ASJC Scopus Subject Areas

  • General Agricultural and Biological Sciences

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