A New Approach to Evaluate and Reduce Uncertainty of Model-Based Biodiversity Projections for Conservation Policy Formulation. (13th October 2021)

• Record Type: Journal Article
• Authors: Myers, Bonnie J E; Weiskopf, Sarah R; Shiklomanov, Alexey N; Ferrier, Simon; Weng, Ensheng; Casey, Kimberly A; Harfoot, Mike; Jackson, Stephen T; Leidner, Allison K; Lenton, Timothy M; Luikart, Gordon; Matsuda, Hiroyuki; Pettorelli, Nathalie; Rosa, Isabel M D; Ruane, Alex C; Senay, Gabriel B; Serbin, Shawn P; Tittensor, Derek P; Beard, T Douglas
• Is Part Of: BioScience. Volume 71:Number 12(2021)
• Page Start: 1261
• Page End: 1273
• Publication Date: 2021-10-13
• Subjects: biodiversity conservation -- predictive modeling -- biodiversity policy -- remote sensing -- Essential Biodiversity Variables; Life sciences -- Periodicals; Life sciences -- Research -- Periodicals; Life sciences -- United States -- Periodicals; Life sciences -- Government policy -- United States -- Periodicals; Biology -- Periodicals; Biotechnology industries -- Periodicals; 570
• DOI: 10.1093/biosci/biab094
• Languages: English
• ISSNs: 0006-3568

Abstract

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.