B ecause environmental and natural resource stakeholders hold valuable knowledge about social-ecological dynamics, researchers often attempt to incorporate local knowledge into environmental models and resource management (Gray et al. 2018). This knowledge is considered valuable because many resource users routinely engage with the natural environment through activities like fishing or hunting (Arlinghaus and Krause 2013). In addition, natural resource users may share information about environmental, policy, or social changes across their social networks (Barnes et al. 2016), allowing them to accumulate and refine knowledge and observations across years and locations (eg anglers moving among lakes; Carruthers et al. 2018). Such local ecological knowledge is useful and can be integrated with expert scientific knowledge to provide a more comprehensive understanding of pressing environmental issues (Gray et al. 2012). At the same time, recent technological developments have vastly improved the way this information is not only collected from stakeholders but also provided through new, cyberenabled forms of participatory environmental assessments (Voinov et al. 2018). For example, the emergence of citizen science (Cooper et al. 2007; Bonney et al. 2009; Shirk et al. 2012) and the internet has changed the nature of data-sourcing by extending the geographic reach of ecological surveys considerably, and in some cases has shifted monitoring away from the consultation of a small minority of scientific experts to now include larger scale collaborations involving dozens to thousands of local stakeholders and scientists (Shirk et al. 2012). More recently, traditional citizen science has been expanded to include open online collaborative communities, leading to scientific discoveries that blend the strengths of humans and machines (Mugar et al. 2014; Trouille et al. 2019). By integrating both local and expert knowledge, citizen science and other crowdsourcing approaches can reduce the uncer

The concept of social-ecological knowledge diversity (SEKD) provides a novel way of examining coupled human-environment interactions-it acknowledges differences in knowledge, values, and beliefs of stakeholder groups within social-ecological systems (SES). Thus, understanding and measuring SEKD is an essential component of sustainable management with implications for conflict resolution, collective action and policymaking. However, methods to efficiently define and model knowledge diversity are still underdeveloped. Using a semiquantitative cognitive mapping approach, we collected and analyzed stakeholder-specific knowledge and perceptions of the Western Baltic cod fishery to model SEKD. Results demonstrate substantial variation in perceptions across different individuals and social groups. SEKD was evident in (a) distinctive meanings attached to social factors relative to ecological factors, (b) causal relationships underlying the understanding of SES dynamics, and (c) social impacts of ecological changes on ecosystems (and vice versa). By identifying and representing knowledge-specific disparities in SES frameworks, our model explicitly improves the understanding of human-environment interactions with implications that could help reduce conflicts and legitimize management plans.