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Abstract AimTwo important environmental hazards for nocturnally migrating birds are artificial light at night (ALAN) and air pollution, with ambient fine particulate matter (PM_2.5) considered to be especially harmful. Nocturnally migrating birds are attracted to ALAN during seasonal migration, which could increase exposure to PM_2.5. Here, we examine PM_2.5concentrations and PM_2.5trends and the spatial correlation between ALAN and PM_2.5within the geographical ranges of the world’s nocturnally migrating birds. LocationGlobal. Time period1998–2018. Major taxa studiedNocturnally migrating birds. MethodsWe intersected a global database of annual mean PM_2.5concentrations over a 21‐year period (1998–2018) with the geographical ranges (breeding, non‐breeding and regions of passage) of 225 nocturnally migrating bird species in three migration flyways (Americas,n = 143; Africa–Europe,n = 36; and East Asia–Australia,n = 46). For each species, we estimated PM_2.5concentrations and trends and measured the correlation between ALAN and PM_2.5, which we summarized by season and flyway. ResultsCorrelations between ALAN and PM_2.5were significantly positive across all seasons and flyways. The East Asia–Australia flyway had the strongest ALAN–PM_2.5correlations within regions of passage, the highest PM_2.5concentrations across all three seasons and the strongest positive PM_2.5trends on the non‐breeding grounds and within regions of passage. The Americas flyway had the strongest negative air pollution trends on the non‐breeding grounds and within regions of passage. The breeding grounds had similarly negative air pollution trends within the three flyways. Main conclusionsThe combined threats of ALAN and air pollution are greatest and likely to be increasing within the East Asia–Australia flyway and lowest and likely to be decreasing within the Americas and Africa–Europe flyways. Reversing PM_2.5trends in the East Asia–Australia flyway and maintaining negative PM_2.5trends in the Americas and Africa–Europe flyways while reducing ALAN levels would likely be beneficial for the nocturnally migrating bird populations in each region. 

Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.