Understanding the effects of neonicotinoid insecticides on bees is vital because of reported declines in bee diversity and distribution and the crucial role bees have as pollinators in ecosystems and agriculture. Neonicotinoids are suspected to pose an unacceptable risk to bees, partly because of their systemic uptake in plants, and the European Union has therefore introduced a moratorium on three neonicotinoids as seed coatings in flowering crops that attract bees. The moratorium has been criticized for being based on weak evidence, particularly because effects have mostly been measured on bees that have been artificially fed neonicotinoids. Thus, the key question is how neonicotinoids influence bees, and wild bees in particular, in real-world agricultural landscapes. Here we show that a commonly used insecticide seed coating in a flowering crop can have serious consequences for wild bees. In a study with replicated and matched landscapes, we found that seed coating with Elado, an insecticide containing a combination of the neonicotinoid clothianidin and the non-systemic pyrethroid β-cyfluthrin, applied to oilseed rape seeds, reduced wild bee density, solitary bee nesting, and bumblebee colony growth and reproduction under field conditions. Hence, such insecticidal use can pose a substantial risk to wild bees in agricultural landscapes, and the contribution of pesticides to the global decline of wild bees may have been underestimated. The lack of a significant response in honeybee colonies suggests that reported pesticide effects on honeybees cannot always be extrapolated to wild bees.
Pollen analysis is an important tool in many fields, including pollination ecology, paleoclimatology, paleoecology, honey quality control, and even medicine and forensics. However, labour‐intensive manual pollen analysis often constrains the number of samples processed or the number of pollen analysed per sample. Thus, there is a desire to develop reliable, high‐throughput, automated systems. We present an automated method for pollen analysis, based on deep learning convolutional neural networks (CNN). We scanned microscope slides with fuchsine stained, fresh pollen and automatically extracted images of all individual pollen grains. CNN models were trained on reference samples (122,000 pollen grains, from 347 flowers of 83 species of 17 families). The models were used to classify images of different pollen grains in a series of experiments. We also propose an adjustment to reduce overestimation of sample diversity in cases where samples are likely to contain few species. Accuracy of a model for 83 species was 0.98 when all samples of each species were first pooled, and then split into a training and a validation set (splitting experiment). However, accuracy was much lower (0.41) when individual reference samples from different flowers were kept separate, and one such sample was used for validation of models trained on remaining samples of the species (leave‐one‐out experiment). We therefore combined species into 28 pollen types where a new leave‐one‐out experiment revealed an overall accuracy of 0.68, and recall rates >0.90 in most pollen types. When validating against 63,650 manually identified pollen grains from 370 bumblebee samples, we obtained an accuracy of 0.79, but our adjustment procedure increased this to 0.85. Validation through splitting experiments may overestimate robustness of CNN pollen analysis in new contexts (samples). Nevertheless, our method has the potential to allow large quantities of real pollen data to be analysed with reasonable accuracy. Although compiling pollen reference libraries is time‐consuming, this is simplified by our method, and can lead to widely accessible and shareable resources for pollen analysis.
Bees often focus their foraging effort on a few or even a single flower species, even if other equally rewarding flower species are present. Although this phenomenon—called flower constancy—has been widely documented during single foraging trips, it is largely unknown if the behavior persists over longer time periods, especially under field conditions with large temporal variations of resources. We studied the pollen diet of individuals from nine different Bombus terrestris colonies for up to 6 weeks, to investigate flower constancy and pollen diversity of individuals and colonies, and how these change over time. We expected high degrees of flower constancy and foraging consistency over time, based on foraging theory and previous studies. Instead, we found that only 23% of the pollen foraging trips were flower constant. The fraction of constant pollen samples did not change over the study period, although repeatedly sampled individuals that were flower constant once often showed different preferences at other sampling occasions. The similarity of pollen composition in samples collected by the same individuals at different occasions dropped with time. This suggests that the flower preferences change in response to shifting floral resources. The average diversity of pollen from single foraging trips was around 2.5 pollen types, while the colony-level pollen diversity was about three times higher. How rapidly preferences change in response to shifting resources, and if this differs between and within bee species depending on factors such as size, should be the focus of future research.
Ongoing pollinator declines threaten the production of many entomophilous crops. Recent reports that yields of animal-pollinated crops in India are increasing less than pollinator-independent ones suggest the occurrence of pollen limitation. We experimentally evaluated if production of the common food crop chilli benefits from insect pollination and if crop production is constrained by lack of pollinators under field conditions. Experiments were conducted in eleven chilli fields distributed across a semi-arid agricultural landscape in Andhra Pradesh, India. The experimental treatments included open controls, open pollen-supplemented flowers, and bagged flowers for pollinator exclusion. The fruit set from the two open treatments (control and pollen supplementation) was about three times higher than that from the exclusion treatment, suggesting strong dependence on insect pollination. Control supplementation treatments did not differ, which suggests that there normally is sufficient pollination for chilli production in the area. Bees contributed 98% of flower visits. Flower visitor abundance correlated with higher fruit set, but only significantly so in the pollen supplemented treatment. While previous studies that are mostly conducted in greenhouse settings suggest that chilli reproduction does not depend much on animal pollination, our field study confirms that presence of animal pollinators increases fruit set. Future research should establish if this also applies to fruit quality and total yield. Our study highlights the importance of field-realistic experiments and warrants research on pollinator dependencies of other crops. The results have implications for crop production in an area where pollinator levels may be sufficiently high for crop pollination today but possibly not in the future due to environmental change.
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