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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 Anthropogenic change is predicted to result in widespread declines in insect abundance, but assessing long‐term trends is challenging due to the scarcity of systematically collected time series measurements across large spatial scales. We develop a novel continental‐scale dataset using a nationwide network of radars in the United States to generate a 10‐year time series of daily aerial insect density and assess temporal trends. We do not find evidence of a continental‐scale net decline in insect density over the 10‐year period included in this study; instead we find a mosaic of increasing and declining trends at the landscape scale. This spatial variation in density trends is associated with climatic drivers, where areas with warmer winters experience greater declines in insect density and areas with cooling winter trends see increases in density. Winter warming has a stronger negative effect on density at higher latitudes. After assessing temporal trends, we also use the 10‐year dataset and atmospheric variables to model insect aerial abundance, finding that on a typical summer day approximately a hundred trillion (10¹⁴) flying insects are present in the airspace, representing millions of tons of aerial biomass. Our results provide the first continental‐scale quantification of insect density and its response to anthropogenic warming and demonstrate the utility of weather surveillance radar to provide large‐scale monitoring of insect abundance. 

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 Long‐term monitoring of bird populations across scales is important in evaluating conservation targets and creating effective conservation strategies. For nearly six decades, the Breeding Bird Survey (BBS) has served as the primary broad‐scaled source of relative abundance trends of swallows and martins in North America. Recently, however, it has become possible to obtain breeding population trends using semi‐structured eBird community science data. Moreover, weather surveillance radar data of swallow and martin roosting populations yield a third complementary source of trend information.Using results from these three approaches, we propose a novel method of spatially combining estimates of percent change per year into a probability of directional agreement and/or disagreement that describes (1) the direction of the trend within a given region, (2) the amount of evidence associated with the estimate and (3) how much uncertainty surrounds it. We focus our efforts on an area of high Hirundinidae concentration in the North American Great Lakes region and predict trends from 2012 to 2022.We found a high probability of agreement between all three sources about observed declines in swallow and martin trends in the region surrounding Lake Ontario and to the west of Lake Michigan. Focusing future research on these regions could improve our understanding of these declines and help build more targeted conservation initiatives.Synthesis and applications. Our data integration methodology allows managers to identify regions that accumulate evidence of concerning trends across multiple wildlife monitoring schemes. These regions can thus be prioritized in conservation and management efforts. This approach can be generalized to other sources of long‐term monitoring data of different species, at different stages of their annual cycle, in any geographic location. 

Abstract Earth's lower atmosphere is a vital ecological habitat, home to trillions of organisms that live, forage, and migrate through this medium. Despite its importance, this space is seldom considered a primary habitat for ecological or conservation prioritization, making it one of the least studied environments. However, it plays a crucial role as a global conduit for the transfer of biomass, weather, and inorganic materials. Fundamental research is essential to address core ecological questions related to the ecological consequences of this habitat's intricate spatial and temporal structure. To advance our understanding of airspace use by migratory animals, we analyzed over 108 million 5‐min radar observations from 143 NEXRAD sites, focusing on 24‐h diel cycles across the contiguous United States. This extensive dataset, spanning from 1995 to 2022, allowed us to quantify aerial space use by systematically identifying peak activity times, the portion of the airspace that contained the majority of migration activity, and the percentage of migrants passing across diurnal and nocturnal diel cycles. We found that airspace is used predominantly during nocturnal periods in both spring and autumn (88%), while summer exhibited a more balanced distribution (54% nocturnal). Additionally, the percentage of nocturnal activity increased with latitude in spring and autumn but decreased in summer. Peak aerial activity typically occurred about 4 h after local sunset in both spring and autumn, with variations based on latitude and longitude. During these peak times, on average, half of the aerial movement was confined within a vertical band of 516 meters, starting around 355 m above ground level. Our research underscores the need to view the lower atmosphere as a structured habitat with significant ecological importance. 

Avian migration has fascinated humans for centuries. Insights into the lives of migrant birds are often elusive; however, recent, standalone technological innovations have revolutionized our understanding of this complex biological phenomenon. A future challenge for following these highly mobile animals is the necessity of bringing multiple technologies together to capture a more complete understanding of their movements. Here, we designed a proof-of-concept multi-sensor array consisting of two weather surveillance radars (WSRs), one local and one regional, an autonomous moon-watching sensor capable of detecting birds flying in front of the moon, and an autonomous recording unit (ARU) capable of recording avian nocturnal flight calls. We deployed this array at a field site in central Oklahoma on select nights in March, April, and May of 2021 and integrated data from this array with wind data corresponding to this site to examine the influence of wind on the movements of spring migrants aloft across these spring nights. We found that regional avian migration intensity is statistically significantly negatively correlated with wind velocity, in line with previous research. Furthermore, we found evidence suggesting that when faced with strong, southerly winds, migrants take advantage of these conditions by adjusting their flight direction by drifting. Importantly, we found that most of the migration intensities detected by the sensors were intercorrelated, except when this correlation could not be ascertained because we lacked the sample size to do so. This study demonstrates the potential for multi-sensor arrays to reveal the detailed ways in which avian migrants move in response to changing atmospheric conditions while in flight. 

Light pollution is a global threat to biodiversity, especially migratory organisms, some of which traverse hemispheric scales. Research on light pollution has grown significantly over the past decades, but our review of migratory organisms demonstrates gaps in our understanding, particularly beyond migratory birds. Research across spatial scales reveals the multifaceted effects of artificial light on migratory species, ranging from local and regional to macroscale impacts. These threats extend beyond species that are active at night – broadening the scope of this threat. Emerging tools for measuring light pollution and its impacts, as well as ecological forecasting techniques, present new pathways for conservation, including transdisciplinary approaches. 

Aeroecology is an emerging discipline founded by Tom Kunz and colleagues in the early 2000s to address the challenges of studying animal flight in the lower atmosphere [...]