A new approach to evaluate and reduce uncertainty of model-based biodiversity projections for conservation policy formulation

BioScience
By: , and 

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Abstract

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.

Publication type Article
Publication Subtype Journal Article
Title A new approach to evaluate and reduce uncertainty of model-based biodiversity projections for conservation policy formulation
Series title BioScience
DOI 10.1093/biosci/biab094
Year Published 2021
Language English
Publisher Oxford Academic
Contributing office(s) National Climate Adaptation Science Center
Description biab094, 13 p.
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