Ann Van Herzele

This interpretive qualitative study addresses publics’ sensemaking in relation to ecological changes that they had not known or experienced before. The context is the current recolonization by gray wolves in Europe, which led to the first wolf pack in the Flanders Region (Belgium) in two centuries. Ethnographic observation of the conversations that took place on government social media, after surprising events—from the arrival of a she-wolf to the raising of her young—reveals a recurring pattern of four sensemaking activities: (1) creating welcoming, protective sentiments, (2) assessing future survival chances for wolves, (3) anticipating disruptions by wolves, and (4) explaining raisons d’être of wolves. By elaborating on and discussing these activities, we highlight the self-triggering dynamics of the unfolding sensemaking process, and the diversity of knowledge sources and approaches involved. In this ongoing process, social media participants increasingly relied on abstract knowledge, analogies and past-time representations that, however, do not resolve the uncertainties and ambiguities encountered. Yet, despite the unprecedented circumstances, our findings suggest potential for place-based knowledge to support the publics’ sensemaking in ways that open up relevant experiential perspectives –including those of the wolves—to deal with the daily challenges of human-wolf coexistence on the ground.

As a large proportion of land is managed by professional family farms, agent-based models are of interest for simulating agricultural land use. This requires a deep understanding of the farm characteristics that influence land use decisions. We developed a methodology to identify a data-driven farm typology by combining participatory methods, multivariate statistical modeling and spatiotemporal parcel-based land cover analysis between 2000 and 2020. A formal questionnaire provided data on the farm characteristics, which were subjected to principal component analysis and k-means clustering. The resulting data-driven typology complemented a production-based approach to understanding land use decisions. The main influencing factors were farm size, share of private land, dominant crops and participation in European schemes such as NATURA2000 and agri-environment-climate measures. Overall, family tradition and a high return on investment were the most important motivations for maintaining current land use practices, while a higher income, income support and diversification were the most important reasons for pursuing new land use options. Differences between the farm characteristics highlighted the importance of the motivations for land use decisions between the farm types. This methodology can be used to generate data-driven typologies suitable for implementing agent-based models to explore sustainable land management options in a changing environment.

Agentschap voor Natuur en Bos Het Instituut voor Natuur-en Bosonderzoek (INBO) is het Vlaams onderzoeks-en kenniscentrum voor natuur en het duurzame beheer en gebruik ervan. Het INBO verricht onderzoek en levert kennis aan al wie het beleid voorbereidt, uitvoert of erin geïnteresseerd is. Het Agentschap voor Natuur en Bos (ANB) werkt elke dag aan het behoud, de bescherming en de ontwikkeling van natuur in Vlaanderen. Het agentschap staat binnen de Vlaamse overheid in voor het beleid, het duurzaam beheren en het versterken van natuur samen met alle partners.

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.