Evaluating Spatial Representativity in a Stakeholder-Driven Honeybee Monitoring Network Across Italy

Stakeholder participation is increasingly promoted in ecological monitoring programmes, yet it raises critical questions about the spatial representativity and scientific robustness of resulting datasets. This study evaluates the representativeness of BeeNet, Italy’s national honeybee monitoring network (2019–2025), in depicting the agricultural landscape despite the non-randomised placement of selected apiaries. Apiaries were selected from voluntary beekeepers, balancing stakeholder participation with the objectives of the project. The distribution of over 300 workstations was assessed across Italian regions in relation to surface area and agricultural land-use composition, using Corine Land Cover (CLC) data aggregated into macro-categories. The analysis revealed that, although regional imbalances persist, particularly in mountainous areas or regions with challenging climatic conditions, the network broadly reflects the agricultural landscape in accordance with project objectives. Agricultural categories such as “orchards,” “meadows,” and “complex agricultural surfaces” are often well represented, though limitations in CLC classification likely lead to underestimation in mosaic agroecosystems, such as mixed olive groves and vineyards. An overrepresentation of “anthropic” areas indicated a tendency to situate apiaries in rural yet accessible locations. By combining spatial analyses with field observations and apiary-level data, a refined categorisation of land types and explicit consideration of beekeeping practices, such as nomadism, could strengthen the interpretative capacity of such network. The results underline the importance of spatial validation of stakeholder-driven monitoring to ensure ecological datasets are reliable, policy-relevant, and scientifically robust.