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1. Stakeholder cooperation can be vital in managing conservation conflicts. Laboratory experiments show cooperation is less likely in the presence of uncertainty. Much less is known about how stakeholders in real‐life conservation conflicts respond to different types of uncertainty. 2. We tested the effects of different sources of uncertainty on cooperative behaviour using a framed field experiment and interviews. The experiment compared a baseline scenario of perfect certainty with scenarios including either: (a) scientific uncertainty about the effectiveness of a conflict‐reduction intervention; (b) administrative uncertainty about intervention funding; or (c) political uncertainty about the extent of community support. We applied these scenarios to a conservation conflict in the Outer Hebrides, Scotland, involving the management of geese to simultaneously meet both conservation and farming objectives. We asked 149 crofters (small‐scale farmers) if they would commit to cooperate with others by helping fund a goose management plan given the three sources of uncertainty. 3. On average, intention to cooperate was highest (99%) in scenarios without uncertainty, and lowest under administrative uncertainty (77%). Scientific uncertainty and political uncertainty both had less of an effect, with over 95% of crofters predicted to be willing to cooperate in these scenarios. Crofters who indicated concern for other crofters suffering the impact of geese were more likely to cooperate. The longer an individual had been a crofter, the less likely they were to cooperate. 4. Synthesis and applications. Crofters' intention to cooperate is high but lessened by uncertainty, especially over the commitment from other stakeholders such as government, to cooperate on goose management. Existing cooperation on goose management may be at risk if uncertainty is not reduced outright or commitments between parties are not strengthened. This has wide applicability, supporting the need for researchers and government advisers to: (a) determine how uncertainty will impact intention of stakeholders to cooperate; and (b) take steps (such as uncertainty reduction, communication or acceptance) to reduce the negative impact of uncertainty on cooperation.

In this article, we build on a growing literature of examples of transdisciplinary approaches to illustrate the catalysts and outcomes of a stakeholder‐driven process to conservation practice. We illustrate this using the case of one of Europe's most rapidly declining bird species, the Eurasian curlew (Numenius arquata ). As part of the response to its continuing decline, a stakeholder‐driven workshop was held in Ireland in November 2016, bringing together over 80 stakeholders from a range of governmental, non‐governmental, and private organizations responsible for or interested in curlew conservation and management. This innovative workshop sought to formulate ideas and support the implementation of actions from stakeholders themselves on how to halt further losses of curlews, within the current legislative framework. Four years on, many of the short‐ and medium‐term actions identified during the workshop have been implemented jointly by stakeholders. However, curlew recovery will require continued communication and meaningful engagement with all relevant stakeholders together with increased government support underpinned by increased public awareness and ownership of the curlew's plight. Ultimately, many stakeholders will measure the success of curlew conservation in Ireland by the long‐term viability of the breeding population.

Science-policy interface organizations and initiatives (SPIORG) are a key component of environmental governance designed to make links between science and society. However, the science­policy interface literature lacks a structured approach to explaining the impacts of context on and by these initiatives. To better understand these impacts on and interactions with governance, this paper uses the concept of the governance 'meshwork' to explore how dynamic processes – encompassing prior, current and anticipated interactions – co­produce knowledge and impact via processes, negotiation and networking activities at multiple governance levels. To illustrate the interactions between SPIORGs and governance meshwork we use five cases representing archetypal SPIORGs. These cases demonstrate how all initiatives and organizations link to their contexts in complex and unique ways, yet also identifies ten important aspects that connect the governance meshwork to SPIORGs. These aspects of the meshwork, together with the typology of organizations, provide a comprehensive framework that can help make sense how the SPIORGs are embedded in the surrounding governance contexts. We highlight that SPIORGs must purposively consider and engage with their contexts to increase their potential impact on knowledge co-­production and policy making.

Science-policy interface organizations and initiatives (SPIORG) are a key component of environmental governance designed to make links between science and society. However, the science­policy interface literature lacks a structured approach to explaining the impacts of context on and by these initiatives. To better understand these impacts on and interactions with governance, this paper uses the concept of the governance 'meshwork' to explore how dynamic processes – encompassing prior, current and anticipated interactions – co­produce knowledge and impact via processes, negotiation and networking activities at multiple governance levels. To illustrate the interactions between SPIORGs and governance meshwork we use five cases representing archetypal SPIORGs. These cases demonstrate how all initiatives and organizations link to their contexts in complex and unique ways, yet also identifies ten important aspects that connect the governance meshwork to SPIORGs. These aspects of the meshwork, together with the typology of organizations, provide a comprehensive framework that can help make sense how the SPIORGs are embedded in the surrounding governance contexts. We highlight that SPIORGs must purposively consider and engage with their contexts to increase their potential impact on knowledge co-­production and policy making. ; Peer reviewed

Multiple anthropogenic challenges threaten nature's contributions to human well-being. Agricultural expansion and conventional intensification are degrading biodiversity and ecosystem functions, thereby undermining the natural foundations on which agriculture is itself built. Averting the worst effects of global environmental change and assuring ecosystem benefits, requires a transformation of agriculture. Alternative agricultural systems to conventional intensification exist, ranging from adjustments to efficiency (e.g. sustainable intensification) to a redesign (e.g. ecological intensification, climate-smart agriculture) of the farm management system. These alternatives vary in their reliance on nature or technology, the level of systemic change required to operate, and impacts on biodiversity, landscapes and agricultural production. Different socio-economic, ecological and political settings mean there is no universal solution, instead there are a suite of interoperable practices that can be adapted to different contexts to maximise efficiency, sustainability and resilience. Social, economic, technological and demographic issues will influence the form of sustainable agriculture and effects on landscapes and biodiversity. These include: (1) the socio-technical-ecological architecture of agricultural and food systems and trends such as urbanisation in affecting the mode of production, diets, lifestyles and attitudes; (2) emerging technologies, such as gene editing, synthetic biology and 3D bioprinting of meat; and (3) the scale or state of the existing farm system, especially pertinent for smallholder agriculture. Agricultural transformation will require multifunctional landscape planning with cross-sectoral and participatory management to avoid unintended consequences and ultimately depends on people's capacity to accept new ways of operating in response to the current environmental crisis.

A better, more effective dialogue is needed between biodiversity science and policy to underpin the sustainable use and conservation of biodiversity. Many initiatives exist to improve communication, but these largely conform to a 'linear' or technocratic model of communication in which scientific "facts" are transmitted directly to policy advisers to "solve problems". While this model can help start a dialogue, it is, on its own, insufficient, as decision taking is complex, iterative and often selective in the information used. Here, we draw on the literature, interviews and a workshop with individuals working at the interface between biodiversity science and government policy development to present practical recommendations aimed at individuals, teams, organisations and funders. Building on these recommendations, we stress the need to: (a) frame research and policy jointly; (b) promote inter- and trans-disciplinary research and "multi-domain" working groups that include both scientists and policy makers from various fields and sectors; (c) put in place structures and incentive schemes that support interactive dialogue in the long-term. These are changes that are needed in light of continuing loss of biodiversity and its consequences for societal dependence on and benefits from nature.

The number of collaborative initiatives between scientists and volunteers (i.e., citizen science) is increasing across many research fields. The promise of societal transformation together with scientific breakthroughs contributes to the current popularity of citizen science (CS) in the policy domain. We examined the transformative capacity of citizen science in particular learning through environmental CS as conservation tool. We reviewed the CS and social-learning literature and examined 14 conservation projects across Europe that involved collaborative CS. We also developed a template that can be used to explore learning arrangements (i.e., learning events and materials) in CS projects and to explain how the desired outcomes can be achieved through CS learning. We found that recent studies aiming to define CS for analytical purposes often fail to improve the conceptual clarity of CS; CS programs may have transformative potential, especially for the development of individual skills, but such transformation is not necessarily occurring at the organizational and institutional levels; empirical evidence on simple learning outcomes, but the assertion of transformative effects of CS learning is often based on assumptions rather than empirical observation; and it is unanimous that learning in CS is considered important, but in practice it often goes unreported or unevaluated. In conclusion, we point to the need for reliable and transparent measurement of transformative effects for democratization of knowledge production.