Abstract

In an effort to address challenges arising out of traditional forms of public participation, several alternative strategies for encouraging stakeholder engagement in conservation have been implemented. The role of social learning in addressing these ecological complexities has gained increasing attention in natural resource management over the last decade. Social learning forms the backbone of any collaborative conceptual modeling of complex human–environmental systems. Collaborative modeling, grounded in social learning theory, is the practice of building models with rather than for stakeholders. By focusing on stakeholder interests rather than positions, collaborative modeling enables joint action through interactive, iterative, and reflective processes. Using a case study conducted in East Sikkim, India, we hypothesize that in situations where opportunities for formal environmental education are limited, and avenues for meaningful stakeholder engagement are few, collaborative modeling can help create a platform where stakeholders have meaningful opportunities to engage, learn, share, and (re)negotiate with a focus on joint problem solving. In particular, we engaged stakeholders in an iterative collaborative modeling process in an attempt to enable them to (1) openly interact with each other, (2) build trust, (3) unfold mutual interdependencies through social learning and thus see themselves as part of a complex human-dominated ecosystem, and (4) build and translate these shared visions toward collaborative forest management in the region. Using Senecah’s Trinity of Voice as an analytical framework, we further examined how participation in the collaborative modeling process provided stakeholders with access, standing, and influence—the three critical dimensions of “voice”. Our results demonstrate that the collaborative modeling process enabled key stakeholders to unfold mutual interdependencies, and opened spaces for knowledge (re)creation, sharing, trust building, and power sharing. By moving beyond narrow technical strategies for conservation, the collaborative modeling process helped create a platform that provided stakeholders with voice—a chance to share their environmental knowledge and joint visions toward formulating effective conservation plans that address the growing uncertainties of managing complex human-dominated systems.

Highlights

  • In a growing effort to address challenges arising out of traditional forms of public participation, several alternative and adaptive strategies for encouraging stakeholder engagement in conservation have been implemented (Bousset et al, 2005; Peterson and Feldpausch-Parker, 2013; Basco-Carrera et al, 2017)

  • Keeping in mind Sprain’s (2017) claim that such participatory spaces should not be taken as intrinsically positive, leading to sustainable outcomes or improved ecological standards (Sprain et al, 2011; Fritsch and Newig, 2012), this paper focuses on social learning that emerged through the collaborative modeling process rather than on products such as sustainable outcomes and better standards

  • As a nodal agency for programs related to forests, land use, and environment, Joint Forest Management Committees (JFMCs) consist of a General Body comprising a member from each household in the village ward and an elected Executive Committee

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Summary

Introduction

In a growing effort to address challenges arising out of traditional forms of public participation, several alternative and adaptive strategies for encouraging stakeholder engagement in conservation have been implemented (Bousset et al, 2005; Peterson and Feldpausch-Parker, 2013; Basco-Carrera et al, 2017). Building on this broader adaptive management and governance approaches, Wyborn et al (2016) call for a “future oriented” conservation that moves beyond technical approaches to conservation by acknowledging the inherent sociocultural and political underpinnings of complex ecological change and adaptations. Social learning, according to Voinov and Bousquet (2010), forms the backbone of any collaborative conceptual modeling of complex environmental systems

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