Despite increases in funding for scientific research on climate change impacts, mitigation, and adaptation, challenges remain for closing the gap between science research products and the public’s use of those products to achieve outcomes (Raaphorst et al. 2020; Cooke et al. 2021; Fischer et al. 2021). While scientists often perceive that they generate information intended to improve resource management decisions, resource managers often wonder why science does not provide the information they need to make decisions (Cooke 2019). Past reliance on top down, unidirectional science research that segregates academic disciplines and the research process from public involvement (Steelman et al. 2021) has inadvertently led to what has been called the science–policy divide (Steelman et al. 2019; Newcomb et al. 2021), knowledge–action gap (Knutti 2019), or theory–practice gap (Cooke et al. 2021). To help remedy this problem, transdisciplinary collaborative science research approaches have emerged to intentionally engage decision-makers and other stakeholders in the research process (Dekker et al. 2021; Steelman et al. 2021) with the goal of co-producing new knowledge that can inform and support actionable change on the ground (Caniglia et al. 2021). Ultimately, because neither scientists nor decision-makers alone can solve the kinds of complex resource management problems that climate change presents, collaboration between these groups is necessary (Vincent et al. 2018)
Evidence to date suggests that by engaging stakeholders in its production, scientific research becomes more trans-parent and legitimate to stakeholders (Singletary and Sterle 2020; Djenontin and Meadow 2018). In contributing to research, stakeholders acquire a sense of ownership of the research processes and outcomes (Norström et al. 2020). The resulting co-produced knowledge is perceived to be more useful at a relevant place-based scale, easier to integrate within an existing decision framework, and thus more likely to be used to make decisions (Dilling and Lemos 2011; Lemos et al. 2019; Tobias et al. 2019). Moreover, information exchange between scientists and stakeholders facilitates social learning and can identify areas of common ground in multi-party natural resource disputes (Singletary and Sterle 2018). Stakeholder engagement toward these ends can occur at one or multiple phases of research, including during the development of the research design, model specification, data collection, data analysis, and validation and distribution of research outcomes (Bremer and Meisch 2017).
While there are many documented benefits of engaging stakeholders in scientific research, such collaborations not only demand substantive time and resources to undertake, but little is known about how to maximize their effectiveness. Consequently, examples of best practices and metrics for empirically assessing what constitutes effective stake-holder engagement are evolving (Cronan et al. 2022; Harvey et al. 2019; Durose et al. 2018; Rigolot 2020). Robust systematic knowledge about engagement processes and outcomes is needed so that funding agencies, stakeholders, and researchers avoid wasting resources and potentially damaging relationships crucial to managing complex socio-environmental problems (Eaton et al. 2022, 2021). Recent analyses of stakeholder engagement in collaborative research suggest that key factors underlying success include researchers having a clear understanding of who, why, when, and how to engage (Muhar and Penker 2018, p. 6)-factors that should be determined by the research question(s), political context of the research problem, and the available time, resources, and capacities of the science team (Kliskey et al. 2021; Harvey et al. 2019; Klink et al. 2017). Additionally, while iterative engagement is thought to increase knowledge co-production and science utility (Lemos and Morehouse 2005), the optimal number of iterations, or engagement modality, remains less well understood (Eaton et al. 2021; Church et al. 2021; Bremer et al. 2019), and evaluations of outcomes such as increased adaptive capacity remain mixed (Mach et al. 2020; Church et al. 2022). In fact, the added time, resources, and skills required for engagement have been cited as an obstacle to the broader use of collaborative research, along with warnings of engagement fatigue and burnout for scientists and stakeholders alike (Dilling and Berggren 2015; Roux et al. 2021).
To advance empirical research on best practices for stakeholder engagement in collaborative research toward knowledge co-production, this paper outlines a collaborative research framework (CRF) grounded in Reed et al.’s (2018, pp. 13–18) theory of participation. We describe the initial implementation of the stakeholder engagement portion of our CRF in the Walker River Basin, California-Nevada, USA, as part of a project funded by the U.S. Department of Agriculture entitled Synthesizing kNowledge to Optimize Water Policy for Agriculture under Changing Snowpack (SNOWPACS), which centered on individual, semi-structured interviews with diverse stakeholders. We formatively evaluate the engagement experience through an online survey assessing how stakeholders perceived the engagement experience. Formatively evaluating and adapting engagement practices can improve the likelihood of knowledge co-production (Louder et al. 2021; Mach et al. 2020; Patton 2017) and help to ensure that engagement is structured at optimal frequency, duration, and modality at pivotal research stages (Louder et al. 2021; Dekker et al. 2021). The survey results reported here help us better under-stand what constitutes an effective engagement process and how such processes affect collaborative research outcomes. Thus, they can be used to adapt and improve the collaborative research process, especially when coupled with other formative evaluation mechanisms built into the CRF.
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