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From ”tidy” evidence to ”messy” experience and expertise: a case study of the knowledge mix needed to enable nature restoration

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Abstract

This paper explores how evidence and other knowledges are used to enable nature restoration. We explore the case study of ‘Riverwoods’, an initiative to restore riparian woodlands across Scotland. Their strategy is notable for including explicit attention to enabling the use of knowledge, including through the compilation of an Evidence Review. We interviewed ten Riverwoods partner representatives to understand how different forms of knowledge were valued and used in planning and implementing restoration.
The Evidence Review was perceived as a credible and legitimate source of “tidy” information about the multiple benefits of river woodlands. However, other types of knowledge were often invoked as relevant or useful in justifying or planning specific interventions. These knowledges were more “messy”, for example localised site-based knowledge, or experience-based understandings of nature finance. This dualism - between an overt focus on tidy knowledge but an implicit appreciation of messy knowledge – was reflected in views on knowledge use. It was generally agreed that knowledge use is enhanced by refining its packaging and presentation, however when considering their own experiences of using knowledge, participants emphasised the value of interactions and relationships – thus a combination of curational and relational approaches.
This study offers new empirical insight into the many and varied knowledge types and the diverse knowledge use processes relevant to nature restoration. It also highlights the continuing assumed dominance of certain types of knowledge and knowledge use; finding ways to value and use more varied and plural knowledges remains an ongoing challenge for complex multi-objective nature restoration.
Original languageEnglish
JournalEnvironmental Science and Policy.
Publication statusSubmitted - 2 Apr 2026

Keywords

  • restoration
  • evidence
  • experiential
  • knowledges
  • knowledge-use
  • complexity

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