A New Approach to Evaluate and Reduce Uncertainty of Model-Based Biodiversity Projections for Conservation Policy Formulation
AuthorMyers, Bonnie J E
Weiskopf, Sarah R
Shiklomanov, Alexey N
Casey, Kimberly A
Jackson, Stephen T
Leidner, Allison K
Lenton, Timothy M
Rosa, Isabel M D
Ruane, Alex C
Senay, Gabriel B
Serbin, Shawn P
Tittensor, Derek P
Beard, T Douglas
AffiliationUniversity of Arizona
Essential Biodiversity Variables
MetadataShow full item record
PublisherOxford University Press (OUP)
CitationMyers, B. J. E., Weiskopf, S. R., Shiklomanov, A. N., Ferrier, S., Weng, E., Casey, K. A., Harfoot, M., Jackson, S. T., Leidner, A. K., Lenton, T. M., Luikart, G., Matsuda, H., Pettorelli, N., Rosa, I. M. D., Ruane, A. C., Senay, G. B., Serbin, S. P., Tittensor, D. P., & Douglas Beard, T. (2021). A New Approach to Evaluate and Reduce Uncertainty of Model-Based Biodiversity Projections for Conservation Policy Formulation. BioScience.
RightsPublished by Oxford University Press on behalf of the American Institute of Biological Sciences 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.
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AbstractBiodiversity 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.
NotePublic domain article
VersionFinal published version
Except where otherwise noted, this item's license is described as Published by Oxford University Press on behalf of the American Institute of Biological Sciences 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.