Finding Common Ground between Adaptive Management and Evidence-Based Approaches to Biodiversity Conservation. (January 2019)

• Record Type: Journal Article
• Authors: Gillson, Lindsey; Biggs, Harry; Smit, Izak P.J.; Virah-Sawmy, Malika; Rogers, Kevin
• Is Part Of: Trends in ecology & evolution. Volume 34:Number 1(2019)
• Page Start: 31
• Page End: 44
• Publication Date: 2019-01
• Subjects: Complexity -- uncertainty -- conservation decisions -- adaptive management cycle; Ecology -- Periodicals; Evolution (Biology) -- Periodicals; 576.8
• DOI: 10.1016/j.tree.2018.10.003
• Languages: English
• ISSNs: 0169-5347

Abstract

Abstract : Adaptive management (AM) and evidence-based conservation (EBC) have emerged as major decision-making frameworks for conservation management. AM deals with complexity and the importance of local context in making conservation decisions under conditions of high variability, uncertainty, and rapid environmental and social change. EBC seeks for generality from empirical data and aims to develop and enhance best practice. The goal of this review is to explore opportunities for finding common ground between AM and EBC. We propose a framework for distinguishing the subset of conservation problems that are amenable to an evidence-based approach, based on levels of uncertainty, complexity, and social agreement. We then suggest ways for combining multiple lines of evidence and developing greater opportunities for iteration and co-learning in EBC. Highlights: Adaptive management (AM) and evidence-based conservation (EBC) have emerged as leading approaches to conservation management. AM allows for exploration of complexity under conditions of high uncertainty, while EBC allows managers to pool experience of the outcomes of conservation intervention in order to decide on 'best practice'. We explore a framework for defining the subset of conservation problems to which EBC could be applied, using certainty and agreement as guiding principles. We develop a modified adaptive management cycle that includes an evidence base alongside iterative learning feedbacks. We consider tools and methods that allow the integration of multiple types of evidence alongside empirical scientific data and explore the possibilities for databases that are capable of learning.