Philippe Tschanz

Bees that nest in the soil in self‐excavated burrows comprise the majority of wild bee species and provide important pollination and soil functions, yet many species are threatened. Conservation efforts for ground‐nesting bees are often hindered by limited knowledge of their nesting habitat requirements, in part because nests are difficult to locate and efficient methods for monitoring nesting sites are lacking. Automated detection and monitoring of soil mounds (tumuli) produced by ground‐nesting bees, indicating nest presence, could provide new insights into bee nesting biology and population dynamics while also providing crucial data to support conservation and management. Image‐based methods, such as the automated acquisition of high‐resolution aerial imagery using drones combined with modern computer vision techniques, offer a promising path toward scalable systems for detecting and monitoring bee nest tumuli across large areas. Here, we evaluate the feasibility of integrating drone‐based image acquisition with deep learning to detect tumuli representing bee nests and to distinguish them from other soil surface deposits, such as earthworm casts. We demonstrate this approach on a 120 m ² area of a densely populated nesting aggregation of Lasioglossum malachurum on bare soil containing numerous earthworm casts. Our model reliably detected bee nest tumuli, achieving an F ₁ score of 0.90 (precision: 0.89, recall: 0.91). Misclassifications mainly arose from atypically shaped tumuli (e.g., new and incomplete or damaged), and from cases where tumuli overlapped, but no earthworm casts were confused for bee nest tumuli. This pilot study represents a step toward more efficient monitoring of ground‐nesting bees and demonstrates the potential of this approach under specific conditions. Future work could evaluate its applicability across additional habitats and species and explore alternative methods, such as image segmentation, which may be better suited for cases with less distinct tumuli or extensive overlap among soil mounds.

Ground‐nesting wild bees provide essential pollination services in agroecosystems, but they are jeopardized by intensive agricultural management. To mitigate such negative impacts, agri‐environment schemes have been implemented. While the success of enhancing floral food resources is relatively well studied, the role of agri‐environmental schemes in providing suitable nesting habitat remains underexplored. We studied the effectiveness of meadow extensification according to the Swiss agri‐environment scheme in promoting nesting of ground‐nesting bees. Using a paired design, we quantified their nests during four rounds (March–June) in pairs of nine randomly selected extensively (i.e. no fertilizer input, postponed first mowing) and nine intensively managed meadows with similar soil properties, slope, exposure and landscape context. Nest numbers and vegetation characteristics were surveyed in areas of 250 m². Vegetation properties were also assessed in 0.5 × 0.5 m plots around nest locations and randomly selected locations without nests within each meadow to assess their role as drivers of nesting incidence (nest presence/absence) at this plot scale. We found substantially higher nest numbers of ground‐nesting bees in extensively (mean ± SE per sampling round = 46.8 ± 14.2) compared to intensively managed meadows (0.8 ± 0.3; no nests in three of nine intensively managed meadows). Extensively managed meadows harboured nests of several dominant crop pollinator species, including aggregations of, for example, Lasioglossum malachurum contributing to high nest densities in some of them. Number of nests was negatively related to grass cover and vegetation height, which were lower in extensively compared to intensively managed meadows. Plot‐level nesting incidence increased with bare ground and moss cover, and decreased with grass cover. Synthesis and applications. Our study shows that extensively managed meadows are better nesting habitats for ground‐nesting bees than intensively managed meadows, if reduced management intensity is associated with altered vegetation characteristics such as reduced grass cover and vegetation height, and small‐scale availability of bare ground, driving these effects. This highlights that maintaining and promoting extensive management of meadows can promote ground‐nesting wild bees, including dominant crop pollinators, not only by enhancing floral resources but also by improving nesting opportunities in agroecosystems.

Ground‐nesting wild bees are crucial for the pollination of wild plants and crops and thus human wellbeing. Arable land currently covers 14 million km² globally, but little is known about the role of arable fields as potential nesting habitats and how agricultural management, such as tillage system, affects nesting. We quantified nest density and nesting incidence (plot‐level nest presence/absence) of ground‐nesting bees in 12 conventionally tilled and 13 no‐till winter cereal fields in southwestern Switzerland. In each field, nests were quantified in eight belt transects at increasing distances from field edges within an area of 400 m², and vegetation cover and soil properties were measured at nest sites and sites without nests. Nest density ranged from 0 (32% of fields) to 16 nests (mean: 4.0 nests) per 400 m², corresponding to 0 to 400 nests ha⁻¹ (mean: 101 nests ha⁻¹). Fifteen nesting species were captured. Nest density was not significantly different between tillage systems. Nest density declined exponentially with distance from the field edge. Nest density and incidence were positively related to proportion of bare ground. Nests occurred across a wide range of soil textures and tended to increase with soil bulk density and sand content. Moreover, nest density tended to increase with the proportion of and proximity to areas under agri‐environment scheme in the surrounding landscape. Synthesis and applications. Our study shows that arable fields, irrespective of tillage system, are used as nesting sites by various ground‐nesting bee species, including important crop pollinators. The concentration of nests along field edges suggests that incentives to maintain small field sizes and to increase edge density have a great potential to support nesting of ground‐nesting bees in agricultural landscapes. Moreover, measures to reduce crop cover, for example, through increased row spacing, offer a promising way to promote nesting opportunities in arable fields, in particular, if floral‐rich agri‐environment scheme areas are locally available. Further studies are needed to better understand to what extent tilled arable fields are suitable nesting habitats for ground‐nesting bees or whether they act as ecological traps due to the adverse effects of tillage on bee offspring.

Airspace is a key but not well-understood habitat for many animal species. Enormous amounts of insects and birds use the airspace to forage, disperse, and migrate. Despite numerous studies on migration, the year-round flight activities of both birds and insects are still poorly studied. We used a 2 year dataset from a vertical-looking radar in Central Europe and developed an iterative hypothesis-testing algorithm to investigate the general temporal pattern of migratory and local movements. We estimated at least 3 million bird and 20 million insect passages over a 1 km transect annually. Most surprisingly, peak non-directional bird movement intensities during summer were of the same magnitude as seasonal directional movement peaks. Birds showed clear peaks in seasonally directional movements during day and night, coinciding well with the main migration period documented in this region. Directional insect movements occurred throughout the year, paralleling non-directional movements. In spring and summer, insect movements were non-directional; in autumn, their movements concentrated toward the southwest, similar to birds. Notably, the nocturnal movements of insects did not appear until April, while directional movements mainly occurred in autumn. This simple monitoring reveals how little we still know about the movement of biomass through airspace.

Each year, billions of birds migrate across the continents by day and night through airspaces increasingly altered by human activity, resulting in the deaths of millions of birds every year through collisions with man‐made structures. To reduce these negative impacts on wildlife, forecasts of high migration intensities are needed to apply mitigation actions. While existing weather radar networks offer a unique possibility to monitor and forecast bird migration at large spatial scales, forecasts at the fine spatial scale within a complex terrain, such as the mountainous Swiss landscape, require a small‐scale network of ornithological radars. Before attempting to build such a network, it is crucial to first investigate the consistency of the migratory flow across space and time. In this study, we simultaneously operated three ornithological radar systems across the Swiss lowlands to assess the spatio‐temporal consistency of diurnal and nocturnal bird movements during the spring and autumn migration season. The relative temporal course of migration intensities was generally consistent between sites during peak migration, in particular for nocturnal movements in autumn, but absolute intensities differed greatly between sites. Outside peak migration, bird movement patterns were much less consistent and, unexpectedly, some presumably non‐migratory bird activity achieved intensities close to peak migration intensities, but without spatial correlations. Only nocturnal migration intensity in autumn could be predicted with consistently high accuracy, but including parameters of atmospheric conditions in the model improved predictability of diurnal movements considerably. Predictions for spring were less reliable, probably because we missed an important part of the migration season. Our results show that reliable forecasts of bird movements within a complex terrain call for a network of year‐round bird monitoring systems, whereas accurate information of atmospheric conditions can help to limit the number of measurement points.