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ABSTRACT Many species of swallows and martins congregate in large nonbreeding aggregations throughout the Americas. These roosts typically occur for several days to weeks in the same place during the same time of the year and disappear suddenly as the birds continue their migratory journeys. In the Amazon Rainforest, however, there are reports of large communal roosts of varying species composition throughout the year. Due to the high biomass density of these aggregations, we can systematically observe these tropical roosts using data collected by the operational S‐band Doppler weather radar located in Manaus (3°08′56.0″ S, 59°59′29.1″ W) Using data collected by this radar over 2 years (2014, 2015), we describe the temporal and spatial patterns of roost size in the Amazon Rainforest, and compare it to a similar dataset collected in the Great Lakes region of North America, where swallows and martins form pre‐migratory roosts. Our findings confirm that roosting activity occurs throughout the year in the region around Manaus, and thus likely gather multiple species of swallows and martins. The peak of roosting activity in both years occurred from January to May, when observations on the ground suggest that roosts are predominantly Purple Martin aggregations. We found that the average daily number of birds in roosts in Manaus in 2015 is up to 7 times larger than what was observed in the Great Lakes, even though the area of the latter is 8.7 times larger than the area sampled around Manaus. Our findings highlight the significance of the Amazon Rainforest for swallow and martin populations. Because this region hosts migrating individuals from the Nearctic‐Neotropical and the Austral migratory systems, resident species may share these roosts with populations from both systems during separate times of the year, creating an indirect link between the two poles of the Americas. 

Abstract Migrating landbirds adjust their flight and stopover behaviors to efficiently cross inhospitable geographies, such as the Gulf of Mexico and the Sahara Desert. In addition to these natural barriers, birds may increasingly encounter anthropogenic barriers created by large‐scale changes in land use. One such barrier could be the Corn Belt in the Midwest United States, where 76.4% of precolonial vegetation (forest and grassland combined) has been replaced by agricultural and urban areas, primarily corn fields. We used 5 years of data from 47 weather radar stations in the United States to compare the population‐level flight patterns of migrating landbirds crossing the Corn Belt and the forested landscapes south and north of it in spring and autumn. We also examined the impacts of the Corn Belt relative to the Gulf of Mexico on the stopover behavior of migrating birds by comparing changes in the proportion of migrants that stop to rest (stopover‐to‐passage ratio [SPR]) relative to distance from both barriers. Birds showed increased meridional airspeeds and stronger selection for tailwinds when crossing the Corn Belt compared with forested landscapes. For birds crossing the Gulf of Mexico, the highest proportion of migrants stopped to rest after crossing the Gulf, and SPR decreased sharply as distance from the shoreline increased. We did not find this pattern after migrants crossed the Corn Belt, although the SPR increased in the Corn Belt as birds approached the down‐route forest boundary in both seasons. This weaker pattern for stopover propensity after crossing the Corn Belt is likely due to its narrower width, the availability of small forest patches throughout the Corn Belt, and the subset of species affected, compared with the gulf. We recommend restoring stepping stones of forest in the Corn Belt and protecting woodlands along the Gulf Coast to help landbirds successfully negotiate both barriers. 

Abstract The exodus of flying animals from their roosting locations is often visible as expanding ring‐shaped patterns in weather radar data. The NEXRAD network, for example, archives more than 25 years of data across 143 contiguous US radar stations, providing opportunities to study roosting locations and times and the ecosystems of birds and bats. However, access to this information is limited by the cost of manually annotating millions of radar scans. We develop and deploy an AI‐assisted system to annotate roosts in radar data. We build datasets with roost annotations to support the training and evaluation of automated detection models. Roosts are detected, tracked, and incorporated into our developed web‐based interface for human screening to produce research‐grade annotations. We deploy the system to collect swallow and martin roost information from 12 radar stations around the Great Lakes spanning 21 years. After verifying the practical value of the system, we propose to improve the detector by incorporating both spatial and temporal channels from volumetric radar scans. The deployment on Great Lakes radar scans allows accelerated annotation of 15 628 roost signatures in 612 786 radar scans with 183.6 human screening hours, or 1.08 s per radar scan. We estimate that the deployed system reduces human annotation time by ~7×. The temporal detector model improves the average precision at intersection‐over‐union threshold 0.5 (AP^{IoU = .50}) by 8% over the previous model (48%→56%), further reducing human screening time by 2.3× in its pilot deployment. These data contain critical information about phenology and population trends of swallows and martins, aerial insectivore species experiencing acute declines, and have enabled novel research. We present error analyses, lay the groundwork for continent‐scale historical investigation about these species, and provide a starting point for automating the detection of other family‐specific phenomena in radar data, such as bat roosts and mayfly hatches. 

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 The overuse and expansion of artificial light at night (ALAN) has emerged from complex social, economic, and political factors, making it a societal problem that negatively impacts wildlife and people. We propose that a convergence research approach combining ecological forecasting with community engagement and public policy is needed to address this diverse societal problem. To begin this convergence research approach, we hosted a workshop to strengthen connections among key biodiversity‐oriented ALAN stakeholders and to better understand how stakeholder groups function across the United States through facilitated discussions. We have prioritized the input of stakeholders early in our research design by including them in the formulation of a national survey on public perceptions surrounding ALAN and received their input on existing ecological forecasting tools to improve those research products for their future use.