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Adaptive management is a growing trend within environment and natural resource management efforts in the United States. While many proponents of adaptive management emphasize the need for collaborative, iterative governance processes to facilitate adaptive management, legal scholars note that current legal requirements and processes in the United States often make it difficult to provide the necessary institutional support and flexibility for successful adaptive management implementation. Our research explores this potential disconnect between adaptive management theory and practice by interviewing practitioners in the field. We conducted a survey of individuals associated with the Collaborative Adaptive Management Network (CAMNet), a nongovernmental organization that promotes adaptive management and facilitates in its implementation. The survey was sent via email to the 144 participants who attended CAMNet Rendezvous during 2007–2011 and yielded 48 responses. We found that practitioners do feel hampered by legal and institutional constraints: >70% of respondents not only believed that constraints exist, they could specifically name one or more examples of a legal constraint on their work implementing adaptive management. At the same time, we found that practitioners are generally optimistic about the potential for institutional reform.

This Special Feature ofEcology and Societyseeks to communicate a practitioner’s perspective on the application of collaborative adaptive management (CAM) to contemporary natural resource management problems. One goal is to create an ongoing mechanism for dialogue that can connect practitioners, researchers, and policy makers. The core 15 papers are grouped into 3 categories that: (1) describe lessons learned through the practice of applying CAM principles to a specific project or generalizing principles from outcomes of a specific project; (2) summarize lessons learned from the author’s extensive CAM experiences; and (3) seek to be instructive of one or more CAM principles through a survey, evaluation, or comparison of multiple projects. Follow-up questions were submitted by authors to the online discussion section ofEcology and Societyto stimulate interactive communication among readers and authors about their papers and CAM in general.

The Anthropocene is characterized by rapid global change, necessitating adaptive governance. But how can such adaptive governance be operationalized? The article offers a three-point argument to approach this question. First, people and environment need to be considered together, as social (human) and ecological (biophysical) subsystems are linked by mutual feedbacks, and are interdependent and co-evolutionary. These integrated systems of humans and environment (social-ecological systems) provide an appropriate unit of analysis. Second, the resilience approach deals with change in multilevel complex systems, and has stimulated much of the adaptive governance literature by addressing uncertainty and adaptation to unforeseen future changes. Third, there is a need to foster collaborative approaches to improve social and institutional learning, as for example in adaptive management, collaborative learning networks, and knowledge co-production. Collaborative learning is perhaps where further research, experimentation, and application might make a difference for operationalizing adaptive governance, with a focus on institutions, at all levels from local to international.

Collaborative approaches to complex water quality problems can facilitate collective action across large watersheds with multiple, overlapping political jurisdictions, including Indigenous territories. Indigenous nations are increasingly engaging in collaborative water governance, in part, as a response to colonial legacies that have excluded Indigenous peoples from watershed management. This study uses social network analysis to explore emerging Klamath water governance networks. We seek to understand ongoing system transformation in contemporary water governance through tribal engagement in multi-jurisdictional water governance networks, from a system of Indigenous dispossession and exclusion (late 1800s-1980s) toward a yet unrealized system that centers Indigenous peoples. To envision the meaningful inclusion of Indigenous peoples in adaptive water governance, we first draw on criteria established by Indigenous water governance scholars. Then, we examine a snapshot of Indigenous participation in water quality governance in the Klamath Basin that focuses on the Karuk Tribe from 2018-2019. Specifically, Karuk tribal managers identified 21 different science-policy coalitions that they worked with on a range of water quality issues. We then used social network analysis methods to generate a network in which 210 different organizations were linked through co-membership in one or more coalitions. Our findings indicated that the Karuk and other Klamath Basin tribes play a central role in Klamath water quality governance and were not relegated to \"stakeholder status.\" Using a community detection algorithm, we found that tribes were key players in the central technical working group that emerged through network connections. Applying a log-linear statistical model, we also observed a high level of mixing in the network across all types of organizations, including tribes. Finally, through a multi-membership model, we found that tribes were more strongly connected to influential network actors than NGOs, despite environmental NGOs being more numerous. These analyses demonstrate how tribal engagement can activate key mechanisms for water quality governance transformation, e.g., shifting information flows and changing system structures to more effectively center Indigenous nations. In addition to insights from social network analysis, we also highlight the limitations of technical water management in supporting the deep connections held between Indigenous peoples and their waters that are central to Indigenous water governance.

Humanity faces increasingly intractable environmental problems characterized by high uncertainty, complexity, and swift change. Natural resource governance must therefore involve continuous production and use of new knowledge to adapt to highly complex, rapidly changing social-ecological systems to ensure long-term sustainable development. Bridging and boundary organizations have been proposed as potentially powerful means of achieving these aims by promoting cooperation among actors from the science, policy, and management sectors. However, despite substantial investments of time, capital, and human resources, little agreement exists about definitions and measures of knowledge production and how this is achieved in bridging organizations and there is only meager understanding of how knowledge production and its use are shaped by social interactions, socio-political environments, and power relations. New concepts, methods, and metrics for conceptualizing and measuring learning in support of natural resource governance and testing the conditions under which it can be achieved are therefore badly needed. This paper presents an attempt at a holistic framework to address this, drawing on theory, methods, and metrics from three research areas: knowledge utilization, boundary organizations, and stakeholder theory. Taken together, these provide a solid conceptual and methodological toolkit for conducting cross-case comparisons aimed at understanding the social environmental conditions under which learning in such organizations does and does not occur. We use empirical data to show how the framework can be applied and discuss some of the practical considerations and important challenges that emerge. We close with a general discussion and an agenda for future research to promote discussion around the topic of how to erect systematic comparisons of learning in support of adaptive natural resource governance as it occurs in bridging organizations.

Trust management has been shown to be an effective technique for protecting networks from malicious nodes and ensuring wireless sensor network (WSN) security. A number of trust systems have been proposed, but most of them are not adaptative to the current state of network security and the intensity of the attacks to which they are subjected, especially in the case of collaborative attacks. They employ fixed trust metrics derived from expert opinion rather than the objective method based on the network’s current security level. Furthermore, they are complex trust systems designed for a specific application with a high attack probability. Thus, even with a low attack rate, they consume a lot of energy. This paper proposes an adaptive trust system that considers both the network’s risk level and the trust values of sensor nodes at the same time. To match the situation in the network, the proposed system employs various trust policies. In risky situations where the WSN environment remains untrustworthy, the proposed system adjusts its trust metrics based on the network attack intensity. When the attacks are eliminated and the misbehavior rate is low, the system switches to an energy efficient policy and adjusts its trust metrics to conserve sensor node energy. Simulation results show that a zero-tolerance policy achieves 95% of the detection rate and conserves 50% of nodes’ energy under the presence of 35% of malicious nodes in the network. Energy efficient policy achieves 90% of detection rate and conserves 95% of nodes’ energy under the existence of 10% of malicious nodes in the network. Normal policy achieves up to 90% of detection rate between 15 and 25% of malicious nodes while conserving 70% and 80% of energy under these percentages.

The search for strategies to address ‘super wicked problems’ such as climate change is gaining urgency, and a collaborative governance approach, and adaptive co-management in particular, is increasingly recognized as one such strategy. However, the conditions for adaptive co-management to emerge and the resulting network structures and relational patterns remain unclear in the literature. To address these identified needs, this study examines social relationships from a network perspective while initiating a collaborative multiactor initiative aimed to develop into adaptive co-management for climate change adaptation, an action research project undertaken in the Niagara region of Canada. The project spanned 1 year, and a longitudinal analysis of participants’ networks and level of participation in the process was performed. Evidence of support for climate change adaptation from the process included the development of deliberative and adaptive responses to opportunities presented to the group and the development of a strong subgroup of participants where decision-making was centered. However, the complexity of the challenge of addressing climate change, funding constraints, competing initiatives, and the lack of common views among participants may have contributed to the group, highlighting the finding that beneficial network structural features and relational patterns are necessary but not sufficient condition for the development of an adaptive co-management process. The context of climate change adaptation may require a different social network structure and processes than other contexts for adaptive co-management to occur, and there may be limitations to adaptive co-management for dealing with super wicked problems.

This paper aims to better understand the complex interplay and co-shaping of relationships among quadruple helix actors, i.e. universities, firms, institutions, and civil society, underlying the dynamics of knowledge exchange networks for the entrepreneurial valorization of academic research, and related impacts. A longitudinal co-evolutionary analysis of a successful case study regarding a network among quadruple helix actors operating in Italy (Start Cup Lazio) has been conducted by adopting participatory action research. The co-evolutionary lens adopted in the paper looks at the relationship between actors of the network, the network itself, and the external environment as circular, mutually influential, and dialectical, considering them as forces that co-determine organizational adaptation. Results draw attention to the dialectical nature of the relationships among quadruple helix actors involved, the reciprocal influence between them and eight factors, internal and external to universities, in shaping the network dynamics, leading to societal impacts. The latter are comprehensively assessed through a set of proposed indicators. Findings suggest conceiving positive societal impacts generated by such networks as a result of multilevel co-evolutionary adaptations among quadruple helix actors and between the network itself and the rest of society. Systemic approach is identified as the main determinant of effective co-adaptations. Results extend prior research by providing a novel co-evolutionary explanation of network dynamics and related societal impacts in the context of complex adaptive systems. Practical implications for quadruple helix actors suggest how to engage in a network of relationships to co-create innovative solutions from entrepreneurial valorization of research that address societal needs.

Regional agroecological systems are examples of complex adaptive systems, where sustainability is promoted by social networks that facilitate information sharing, cooperation, and connectivity among specialized components of the system. Much of the existing literature on social capital fails to recognize how networks support multiple social processes. Our paper overcomes this problem by analyzing how the social networks of wine grape growers exhibit structural features related to multiple social processes: ties to central actors that build bridging social capital and facilitate the diffusion of innovations, ties that close triangles and build bonding social capital to solve cooperation dilemmas, and ties to individuals that span community boundaries to connect specialized components of the system. We use survey data to measure the communication networks of growers in three viticulture regions in California. A combination of descriptive statistics, conditional uniform random graph tests, and exponential random graph models provides empirical support for our hypotheses. The findings reflect regional differences in geography and institutional histories, which may influence the capacity to respond to regional environmental change.

We present SmartCrowd, a framework for optimizing task assignment in knowledge-intensive crowdsourcing (KI-C). SmartCrowd distinguishes itself by formulating, for the first time, the problem of worker-to-task assignment in KI-C as an optimization problem, by proposing efficient adaptive algorithms to solve it and by accounting for human factors, such as worker expertise, wage requirements, and availability inside the optimization process. We present rigorous theoretical analyses of the task assignment optimization problem and propose optimal and approximation algorithms with guarantees, which rely on index pre-computation and adaptive maintenance. We perform extensive performance and quality experiments using real and synthetic data to demonstrate that the SmartCrowd approach is necessary to achieve efficient task assignments of high-quality under guaranteed cost budget.