The densification of urban landscapes reshuffles plant--pollinator interactions and affects the provisioning of pollination services. Improving local-scale habitat quality, e.g. planting more flowers in urban greenspaces such as gardens, has been proposed to compensate, but effective pollination also depends on trait-matching between pollinators and flowers. In an experimental study, we used four phytometer species with differing flower-visitor specificities to assess pollinator visitation, richness, and pollination success along independent gradients of landscape-scale densification and local-scale floral richness. Pollinator visitation and richness declined both with increased densification and lower floral richness. Flower-rich gardens supported more small solitary and large social bees, but not hoverflies, beetles, or small social bees. Pollination success declined with densification but was compensated by floral richness only in phytometers with more specialised pollinators. While increasing local floral richness may support pollination success of plants with specialised visitors in dense urban landscapes, generalists may require additional conservation measures.

Bees are exposed to various stressors, including pesticides and lack of flowering resources. Despite potential interactions between these stressors, the impacts of pesticides on bees are generally assumed to be consistent across bee-attractive crops, and regulatory risk assessments of pesticides neglect interactions with flowering resources. We assessed the interactive impacts of the globally used azoxystrobin-based fungicide Amistar and three types of flowering resources (purple tansy, buckwheat, and a floral mix) on Bombus terrestris colonies in a full-factorial semi-field experiment with 39 large flight cages. Fungicide exposure through purple tansy monocultures reduced population (colony) growth, production of males, and adult worker body mass, while Amistar had no impact on colonies in buckwheat or floral mix cages. Furthermore, buckwheat monocultures hampered survival and fecundity irrespective of fungicide exposure. This shows that flowering resources modulate pesticide impacts and that B. terrestris requires access to complementary flowers to gain both fitness and fungicide tolerance.

Manual assessment of flower abundance of different flowering plant species in grasslands is a time consuming process. We present an automated approach to determine the flower abundance in grasslands from drone images using a deep learning (Faster R-CNN) object detection approach, which is trained and evaluated on data of five flights and two sites. Our deep learning network is able to identify and classify individual flowers. The novel method allows generating spatially explicit maps of flower abundance that meets or exceeds the accuracy of the manually counted extrapolation method and is less labor intensive. The results are very good for some types of flowers with precision and recall being close to or higher than $90\ \%$. Other flowers are detected poorly due to reasons such as lack of enough training data, appearance changes due to phenology or flowers being too small to be reliably distinguishable on the aerial images. The method is able to give precise estimates of the abundance of many flowering plant species. The collection of more training data will allow better predictions in the future for the flowers that are not well predicted yet. The developed pipeline can be applied to any sort of aerial object detection problems.

1. The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radio-frequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honey bees, behavioral studies on solitary bees still largely depend on direct observations, which is very time-consuming. 2. Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity-nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by a machine learning based software. This Bee Tracker software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees’ thorax as well as the IDs of their nests according to their positions in the nesting unit. 3. The software is able to identify each nest of each individual nesting bee, which permits to measure individual-based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters. 4. The software could be adapted to various experimental setups by training it to an according set of videos. The presented method allows to efficiently collect large amounts of data on cavity-nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi-field and field conditions.

Floral plantings are promoted to foster ecological intensification of agriculture through provisioning of ecosystem services. However, a comprehensive assessment of the effectiveness of different floral plantings, their characteristics and consequences for crop yield across global regions is lacking. Here we quantified the impacts of flower strips and hedgerows on pest control and pollination services in adjacent crops using a global dataset of 529 sites. Flower strips, but not hedgerows, enhanced pest control services in adjacent fields by 16% on average. However, effects on crop pollination and yield were more variable. Our synthesis identifies several important drivers of variability in effectiveness of plantings: pollination services declined exponentially with distance from plantings, and perennial and older flower strips with higher flowering plant diversity enhanced pollination more effectively. These findings provide promising pathways to optimize floral plantings to more effectively contribute to ecosystem service delivery and ecological intensification of agriculture in the future.