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Abstract During their nonbreeding period, many species of swallows and martins (family: Hirundinidae) congregate in large communal roosts. Some of these roosts are well-known within local birdwatching communities; however, monitoring them at large spatial scales and with day-to-day temporal resolution is challenging. Community-science platforms such as the Purple Martin Conservation Association’s project MartinRoost and eBird have addressed some of these challenges by centralizing data collected from regional communities. Additionally, due to the high densities of birds within these aggregations, their early morning dispersals are systematically detected by weather radars, which have also been used to collect data about roost timing and location. An important issue, however, limits spatiotemporal scope of previous radar-based studies: finding the roost signatures on millions of rendered reflectivity images is extremely time-consuming. Recent advances in computer vision, however, have allowed us to reduce this effort. The rise of this technology makes it necessary that we assess whether our biological definition of a roost matches what the machine-learning models are capturing. We do so by comparing eBird detections of roosts in the Great Lakes region with those obtained by a human-supervised machine-learning model from 2000 to 2022. With more than two decades of data, we assess the ability of these two tools to detect roosts on a day-to-day basis, and we compare the phenology of dispersals to investigate whether radar detections correspond to swallow and martin roosts or if they are associated with other well-known birds that form large aggregations. Our comparison of these datasets strongly suggests that swallows and martins are responsible for the dispersals we observe on the radars from July to late September; however, the alternative species we examined could be causing some of the detections in October. 

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the Corn Belt in the Midwest United States, where 76.4% of precolonial vegetation (forest and grassland combined) has been replaced by agricultural and urban areas, primarily corn fields.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 Our ability to forecast the spatial and temporal patterns of ecological processes at continental scales has drastically improved over the past decade. Yet, predicting ecological patterns at broad scales while capturing fine-scale processes is a central challenge of ecological forecasting given the inherent tension between grain and extent, whereby enhancing one often diminishes the other. We leveraged 10 years of terrestrial and atmospheric data (2012–2021) to develop a high-resolution (2.9 × 2.9 km), radar-driven bird migration forecast model for a highly active region of the Mississippi flyway. Based on the suite of candidate models we examined, adding terrestrial predictors improved model performance only marginally, whereas spatially distant atmospheric predictors, particularly air temperature and wind speed from focal and distant regions, were major contributors to our top model, explaining 56% of variation in regional migration activity. Among terrestrial predictors, which ranked considerably lower than atmospheric predictors in terms of variable importance, vegetation phenology, artificial light at night, and percent of forest cover were the most important predictors. Furthermore, we scale this model to demonstrate the capacity to generate real-time, high-resolution forecasts for the continental United States that explained up to 65% of national variation. Our study demonstrates an approach for increasing the resolution of migration forecasts, which could facilitate the integration of radar with other data sources and inform dynamic conservation efforts at a local scale that is more relevant to threats, such as anthropogenic light at night. 

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. 

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. 

More than two billion birds migrate through the Gulf of Mexico each spring en route to breeding grounds in the USA and Canada. This region has a long history of complex natural and anthropogenic environments as the northern Gulf coast provides the first possible stopover habitats for migrants making nonstop trans‐Gulf crossings during spring migration. However, intense anthropogenic activity in the region, which is expanding rapidly at present, makes migrants vulnerable to a multitude of obstacles and increasingly fragments and alters these habitats. Understanding the timing of migrants' overwater arrivals has biological value for expanding our understanding of migration ecology relative to decision‐making for nonstop flights, and is imperative for advancing conservation of this critical region through the identification of key times in which to direct conservation actions (e.g. temporary halting of wind turbines, reduction of light pollution). We explored 10 years of weather surveillance radar data from five sites along the northern Gulf of Mexico coast to quantify the daily timing and intensity of arriving trans‐Gulf migrants. On a daily scale, we found that migrant intensity peaked an average of nine hours after local sunrise, occurring earliest at easternmost sites. On a seasonal level, the greatest number of arrivals occurred between late April and early May, with peak intensity occurring latest at westernmost sites. Overall intensity of migration across all 10 years of data was greatest at the westernmost sites and decreased moving farther to the east. These findings emphasize the differential spatial and temporal patterns of use of the Gulf of Mexico region by migrating birds, information that is essential for improving our understanding of the ecology of trans‐Gulf migration and for supporting data‐driven approaches to conservation actions for the migratory birds passing through this critical region. 

Abstract The timing of avian migration has evolved to exploit critical seasonal resources, yet plasticity within phenological responses may allow adjustments to interannual resource phenology. The diversity of migratory species and changes in underlying resources in response to climate change make it challenging to generalize these relationships.We use bird banding records during spring and fall migration from across North America to examine macroscale phenological responses to interannual fluctuations in temperature and long‐term annual trends in phenology.In total, we examine 19 species of North American wood warblers (family Parulidae), summarizing migration timing from 2,826,588 banded birds from 1961 to 2018 across 46 sites during spring and 124 sites during fall.During spring, warmer spring temperatures at banding locations translated to earlier median passage dates for 16 of 19 species, with an average 0.65‐day advancement in median passage for every 1°C increase in temperature, ranging from 0.25 to 1.26 days °C⁻¹. During the fall, relationships were considerably weaker, with only 3 of 19 species showing a relationship with temperature. In those three cases, later departure dates were associated with warmer fall periods. Projecting these trends forward under climate scenarios of temperature change, we forecast continued spring advancements under shared socioeconomic pathways from 2041 to 2060 and 2081 to 2100 and more muted and variable shifts for fall.These results demonstrate the capacity of long‐distance migrants to respond to interannual fluctuations in temperatures, at least during the spring, and showcase the potential of North American bird banding data understanding phenological trends across a wide diversity of avian species. 

Abstract The timing of avian migration has evolved to exploit critical seasonal resources, yet plasticity within phenological responses may allow adjustments to interannual resource phenology. The diversity of migratory species and changes in underlying resources in response to climate change make it challenging to generalize these relationships.We use bird banding records during spring and fall migration from across North America to examine macroscale phenological responses to interannual fluctuations in temperature and long‐term annual trends in phenology.In total, we examine 19 species of North American wood warblers (family Parulidae), summarizing migration timing from 2,826,588 banded birds from 1961 to 2018 across 46 sites during spring and 124 sites during fall.During spring, warmer spring temperatures at banding locations translated to earlier median passage dates for 16 of 19 species, with an average 0.65‐day advancement in median passage for every 1°C increase in temperature, ranging from 0.25 to 1.26 days °C⁻¹. During the fall, relationships were considerably weaker, with only 3 of 19 species showing a relationship with temperature. In those three cases, later departure dates were associated with warmer fall periods. Projecting these trends forward under climate scenarios of temperature change, we forecast continued spring advancements under shared socioeconomic pathways from 2041 to 2060 and 2081 to 2100 and more muted and variable shifts for fall.These results demonstrate the capacity of long‐distance migrants to respond to interannual fluctuations in temperatures, at least during the spring, and showcase the potential of North American bird banding data understanding phenological trends across a wide diversity of avian species. 

Abstract As billions of nocturnal avian migrants traverse North America, twice a year they must contend with landscape changes driven by natural and anthropogenic forces, including the rapid growth of the artificial glow of the night sky. While airspaces facilitate migrant passage, terrestrial landscapes serve as essential areas to restore energy reserves and often act as refugia—making it critical to holistically identify stopover locations and understand drivers of use. Here, we leverage over 10 million remote sensing observations to develop seasonal contiguous United States layers of bird migrant stopover density. In over 70% of our models, we identify skyglow as a highly influential and consistently positive predictor of bird migration stopover density across the United States. This finding points to the potential of an expanding threat to avian migrants: peri-urban illuminated areas may act as ecological traps at macroscales that increase the mortality of birds during migration.