Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensics). Researchers are exploring alternative methods to automate these tasks but, for several reasons, manual microscopy is still the gold standard. In this study, we present a new method for pollen analysis using multispectral imaging flow cytometry in combination with deep learning. We demonstrate that our method allows fast measurement while delivering high accuracy pollen identification. A dataset of 426 876 images depicting pollen from 35 plant species was used to train a convolutional neural network classifier. We found the best-performing classifier to yield a species-averaged accuracy of 96%. Even species that are difficult to differentiate using microscopy could be clearly separated. Our approach also allows a detailed determination of morphological pollen traits, such as size, symmetry or structure. Our phylogenetic analyses suggest phylogenetic conservatism in some of these traits. Given a comprehensive pollen reference database, we provide a powerful tool to be used in any pollen study with a need for rapid and accurate species identification, pollen grain quantification and trait extraction of recent pollen.

National and local governments need to step up efforts to effectively implement the post‐2020 global biodiversity framework of the Convention on Biological Diversity to halt and reverse worsening biodiversity trends. Drawing on recent advances in interdisciplinary biodiversity science, we propose a framework for improved implementation by national and subnational governments. First, the identification of actions and the promotion of ownership across stakeholders need to recognize the multiple values of biodiversity and account for remote responsibility. Second, cross‐sectorial implementation and mainstreaming should adopt scalable and multifunctional ecosystem restoration approaches and target positive futures for nature and people. Third, assessment of progress and adaptive management can be informed by novel biodiversity monitoring and modeling approaches handling the multidimensionality of biodiversity change.

Anthropogenic environmental change disrupts interactions between plants and their animal pollinators. To assess the importance of different drivers, baseline information is needed on interaction networks and plant reproductive success around the world. We conducted a systematic literature review to determine the state of our knowledge on plant–pollinator interactions and the ecosystem services they provide for European ecosystems. We focussed on studies that published information on plant–pollinator networks, as a community-level assessment of plant–pollinator interactions and pollen limitation, which assesses the degree to which plant reproduction is limited by pollinator services. We found that the majority of our knowledge comes from Western Europe, and thus there is a need for baseline assessments in the traditional landscapes of Eastern Europe. To address this data gap, we quantified plant–pollinator interactions and conducted breeding system and pollen supplementation experiments in a traditionally managed mountain meadow in the Western Romanian Carpathians. We found the Romanian meadow to be highly diverse, with a healthy plant–pollinator network. Despite the presence of many pollinator-dependent plant species, there was no evidence of pollen limitation. Our study is the first to provide baseline information for a healthy meadow at the community level on both plant–pollinator interactions and their relationship with ecosystem function (e.g. plant reproduction) in an Eastern European country. Alongside the baseline data, we also provide recommendations for future research, and the methodological information needed for the continued monitoring and management of Eastern European meadows.