A central goal in ecology is to develop theories that explain the diversity and distribution of species. The evolutionary history of species and their functional traits may provide mechanistic links between community assembly and the environment. Such links may be hierarchically structured such that the strength of environmental filtering decreases in a step‐wise manner from regional conditions through landscape heterogeneity to local habitat conditions. We sampled the wild bee species assemblages in power‐line strips transecting forests in south‐eastern Norway. We used altitude, landscape diversity surrounding sites and plant species composition, together with total plant cover as proxies for regional, landscape and local environmental filters, respectively. The species richness and abundance of wild bees decreased with altitude. The reduction in species richness and abundance was accompanied by a phylogenetic clustering of wild bee individuals. Furthermore, regional filters followed by local filters best explained the structure of the functional species composition. Sites at high altitudes and sites with Ericaceae‐dominated plant communities tended to have larger bees and a higher proportion of social and spring‐emerging bees. When Bombus species were excluded from the analysis, the proportion of pollen specialists increased with the dominance of Ericaceae. Furthermore, we also found that the taxonomic, phylogenetic and functional compositional turnover between sites was higher in the northern region than in the southern part of the study region. Altogether, these results suggest that regional filters drive the species richness and abundance in trait‐groups whereas local filters have more descrete sorting effects. We conclude that the model of multi‐level environmental filters provides a good conceptual model for community ecology. We suggest that future studies should focus on the relationship between the biogeographical history of species and their current distribution, and on the assumption that closely related species do indeed compete more intensely than distantly related species.

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Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

A recurrent concern in nature conservation is the potential competition for forage plants between wild bees and managed honey bees. Specifically, that the highly sophisticated system of recruitment and large perennial colonies of honey bees quickly exhaust forage resources leading to the local extirpation of wild bees. However, different species of bees show different preferences for forage plants. We here summarize known forage plants for honey bees and wild bee species at national scale in Denmark. Our focus is on floral resources shared by honey bees and wild bees, with an emphasis on both threatened wild bee species and foraging specialist species. Across all 292 known bee species from Denmark, a total of 410 plant genera were recorded as forage plants. These included 294 plant genera visited by honey bees and 292 plant genera visited by different species of wild bees. Honey bees and wild bees share 176 plant genera in Denmark. Comparing the pairwise niche overlap for individual bee species, no significant relationship was found between their overlap and forage specialization or conservation status. Network analysis of the bee-plant interactions placed honey bees aside from most other bee species, specifically the module containing the honey bee had fewer links to any other modules, while the remaining modules were more highly inter-connected. Despite the lack of predictive relationship from the pairwise niche overlap, data for individual species could be summarized. Consequently, we have identified a set of operational parameters that, based on a high foraging overlap (>70%) and unfavorable conservation status (Vulnerable+Endangered+Critically Endangered), can guide both conservation actions and land management decisions in proximity to known or suspected populations of these species.

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