Projecting the performance of conservation interventions. (November 2017)

• Record Type: Journal Article
• Authors: Law, Elizabeth A.; Ferraro, Paul J.; Arcese, Peter; Bryan, Brett A.; Davis, Katrina; Gordon, Ascelin; Holden, Matthew H.; Iacona, Gwenllian; Marcos Martinez, Raymundo; McAlpine, Clive A.; Rhodes, Jonathan R.; Sze, Jocelyne S.; Wilson, Kerrie A.
• Is Part Of: Biological conservation. Volume 215(2017)
• Page Start: 142
• Page End: 151
• Publication Date: 2017-11
• Subjects: Causal inference -- Evidence-based policy -- Policy evaluation -- Prediction -- Projection -- Transportability; Conservation of natural resources -- Periodicals; Nature conservation -- Periodicals; Ecology -- Periodicals; Environment -- Periodicals; Environmental Pollution -- Periodicals; Electronic journals; 333.9516
• DOI: 10.1016/j.biocon.2017.08.029
• Languages: English
• ISSNs: 0006-3207

Abstract

Abstract: Successful decision-making for environmental management requires evidence of the performance and efficacy of proposed conservation interventions. Projecting the future impacts of prospective conservation policies and programs is challenging due to a range of complex ecological, economic, social and ethical factors, and in particular the need to extrapolate models to novel contexts. Yet many extrapolation techniques currently employed are limited by unfounded assumptions of causality and a reliance on potentially biased inferences drawn from limited data. We show how these restrictions can be overcome by established and emerging techniques from causal inference, scenario analysis, systematic review, expert elicitation, and global sensitivity analysis. These technical advances provide avenues to untangle cause from correlation, evaluate and transfer models between contexts, characterize uncertainty, and address imperfect data. With more rigorous projections of prospective performance of interventions, scientists can deliver policy and program advice that is more scientifically credible. Highlights: Conservation policies and programs are hotly debated, with complex, uncertain impacts. To make informed decisions, reliable projections of the likely performance of interventions are required. Robust projections need to focus on model assumptions, bias and uncertainty. Clarifying causal assumptions will lead to better data and better use of data.