Abstract

Rewilding can be defined as the reorganisation or regeneration of wildness in an ecologically degraded landscape with minimal ongoing intervention. While proposals for rewilding are increasingly common, they are frequently controversial and divisive amongst stakeholders. If implemented, rewilding initiatives may alter the social-ecological systems within which they are situated and thus generate sudden and unforeseen outcomes. So far, however, much of the discourse on the planning and implementation of rewilding has focused on identifying and mitigating ecological risks. There has been little consideration of how rewilding could alter the human components of the social-ecological systems concerned, nor governance arrangements that can manage these dynamics. This paper addresses this gap by proposing a generic adaptive governance framework tailored to the characteristics of rewilding, based on principles of managing complex social-ecological systems. We integrate two complementary natural resource governance approaches that lend themselves to the contentious and unpredictable characteristics of rewilding. First, adaptive co-management builds stakeholder adaptive capacity through iterative knowledge generation, collaboration and power-sharing, and cross-scale learning networks. Second, social licence to operate establishes trust and transparency between project proponents and communities through new public-private partnerships. The proposed framework includes structural and process elements which incorporate a boundary organisation, a decision-into-practise social learning exercise for planning and design, and participatory evaluation. The latter assesses rewilding outcomes and pre-conditions for the continuation of adaptive governance and conservation conflict resolution.

Highlights

  • Rewilding, defined in this paper as the reorganisation or regeneration of wildness in an ecologically degraded landscape with minimal ongoing intervention, is a novel and rapidly developing conservation concept, with a burgeoning number of initiatives proposed or implemented (Pettorelli et al, 2019) in diverse social and ecological contexts (Butler et al, 2019)

  • Given the experimental nature of rewilding, and its potentially contentious and unpredictable influences on social-ecological system dynamics, we argue that adaptive governance should be central to both its planning and implementation

  • We propose the integration of adaptive co-management (ACM) and social licence to operate (SLO) in a generic governance framework for rewilding initiatives

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Summary

INTRODUCTION

Rewilding, defined in this paper as the reorganisation or regeneration of wildness in an ecologically degraded landscape with minimal ongoing intervention, is a novel and rapidly developing conservation concept, with a burgeoning number of initiatives proposed or implemented (Pettorelli et al, 2019) in diverse social and ecological contexts (Butler et al, 2019). Armitage et al (2009) identified further pre-conditions for the continuation of effective ACM These frameworks, and methods for applying them have since been trialled in different natural resource management (e.g., Cundill and Fabricius, 2010), protected area (e.g., Plummer et al, 2017), climate adaptation (e.g., Butler et al, 2016b) and conservation conflict contexts (e.g., Butler et al, 2015a; Cox et al, 2020). Considering the complementarities between ACM and SLO’s themes, an indicator framework is suggested which assesses institutional, process, wellbeing, livelihoods and ecosystem outcomes, and pre-conditions for adaptive governance to continue (Table 1) This adapts an approach originally designed for evaluating the ACM of conservation conflict by Butler et al (2015a). To evaluate conflict transformation, which capitalises on the identification of the root socio-political sources of conflict, the outcome indicator “questioning of routines, values and governance” could examine stakeholders’ underlying perceptions of the drivers of conflict, and whether the process has succeeded in altering them

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