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The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent-variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season. 

Abstract Climate change models often assume similar responses to temperatures across the range of a species, but local adaptation or phenotypic plasticity can lead plants and animals to respond differently to temperature in different parts of their range. To date, there have been few tests of this assumption at the scale of continents, so it is unclear if this is a large‐scale problem. Here, we examined the assumption that insect taxa show similar responses to temperature at 96 sites in grassy habitats across North America. We sampled insects with Malaise traps during 2019–2021 (N = 1041 samples) and examined the biomass of insects in relation to temperature and time of season. Our samples mostly contained Diptera (33%), Lepidoptera (19%), Hymenoptera (18%), and Coleoptera (10%). We found strong regional differences in the phenology of insects and their response to temperature, even within the same taxonomic group, habitat type, and time of season. For example, the biomass of nematoceran flies increased across the season in the central part of the continent, but it only showed a small increase in the Northeast and a seasonal decline in the Southeast and West. At a smaller scale, insect biomass at different traps operating on the same days was correlated up to ~75 km apart. Large‐scale geographic and phenological variation in insect biomass and abundance has not been studied well, and it is a major source of controversy in previous analyses of insect declines that have aggregated studies from different locations and time periods. Our study illustrates that large‐scale predictions about changes in insect populations, and their causes, will need to incorporate regional and taxonomic differences in the response to temperature.