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.

Summary: 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.

Complex socio-economic, political and demographic factors have driven the increased conversion of Europe’s semi-natural grasslands to intensive pastures. This trend is particularly strong in some of the most biodiverse regions of the continent, such as Central and Eastern Europe. Intensive grazing is known to decrease species diversity and alter the composition of plant and insect communities. Comparatively little is known, however, about how intensive grazing influences plant functional traits related to pollination and the structure of plant-pollinator interactions. In traditional hay meadows and intensive pastures in Central Europe, we contrasted the taxonomic and functional group diversity and composition, the structure of plant-pollinator interactions and the roles of individual species in networks. We found mostly lower taxonomic and functional diversity of plants and insects in intensive pastures, as well as strong compositional differences among the two grassland management types. Intensive pastures were dominated by a single plant with a specialized flower structure that is only accessible to a few pollinator groups. As a result, intensive pastures have lower diversity and specificity of interactions, higher amount of resource overlap, more uniform interaction strength and lower network modularity. These findings stand in contrast to studies in which plants with more generalized flower traits dominated pastures. Our results thus highlight the importance of the functional traits of dominant species in mediating the consequences of intensive pasture management on plant-pollinator networks. These findings could further contribute to strategies aimed at mitigating the impact of intensive grazing on plant and pollinator communities.

Societal Impact Statement: Pollen relates to many aspects of human and environmental health, which protection and improvement are endorsed by the United Nations Sustainable Development Goals. By highlighting these connections in the frame of current challenges in monitoring and research, we discuss the need of more integrative and multidisciplinary pollen research related to societal needs, improving health of humans and our ecosystems for a sustainable future.Summary: Pollen is at once intimately part of the reproductive cycle of seed plants and simultaneously highly relevant for the environment (pollinators, vector for nutrients, or organisms), people (food safety and health), and climate (cloud condensation nuclei and climate reconstruction). We provide an interdisciplinary perspective on the many and connected roles of pollen to foster a better integration of the currently disparate fields of pollen research, which would benefit from the sharing of general knowledge, technical advancements, or data processing solutions. We propose a more interdisciplinary and holistic research approach that encompasses total environmental pollen diversity (ePD) (wind and animal and occasionally water distributed pollen) at multiple levels of diversity (genotypic, phenotypic, physiological, chemical, and functional) across space and time. This interdisciplinary approach holds the potential to contribute to pressing human issues, including addressing United Nations Sustainable Development Goals, fostering social and political awareness of these tiny yet important and fascinating particles.

Environmental monitoring involves the quantification of microscopic cells and particles such as algae, plant cells, pollen, or fungal spores. Traditional methods using conventional microscopy require expert knowledge, are time‐intensive and not well‐suited for automated high throughput. Multispectral imaging flow cytometry (MIFC) allows measurement of up to 5000 particles per second from a fluid suspension and can simultaneously capture up to 12 images of every single particle for brightfield and different spectral ranges, with up to 60x magnification. The high throughput of MIFC has high potential for increasing the amount and accuracy of environmental monitoring, such as for plant‐pollinator interactions, fossil samples, air, water or food quality that currently rely on manual microscopic methods. Automated recognition of particles and cells is also possible, when MIFC is combined with deep‐learning computational techniques. Furthermore, various fluorescence dyes can be used to stain specific parts of the cell to highlight physiological and chemical features including: vitality of pollen or algae, allergen content of individual pollen, surface chemical composition (carbohydrate coating) of cells, DNA‐ or enzyme‐activity staining. Here, we outline the great potential for MIFC in environmental research for a variety of research fields and focal organisms. In addition, we provide best practice recommendations.