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Climate change induced phenological shifts in primary productivity result in trophic mismatches for many organisms1–4, with broad implications for ecosystem structure and function. For birds that have a synchronized timing of migration with resource availability, the likelihood that trophic mismatches may generate a phenological response in migration timing increases with climate change5. Despite the importance of a holistic understanding of such systems at large spatial and temporal scales, particularly given a rapidly changing climate, analyses are few, primarily because of limitations in the access to appropriate data. Here we use 24 years of remotely sensed data collected by weather surveillance radar to quantify the response of a nocturnal avian migration system within the contiguous United States to changes in temperature. The average peak migration timing advanced in spring and autumn, and these changes were generally more rapid at higher latitudes. During spring and autumn, warmer seasons were predictive of earlier peak migration dates. Decadal changes in surface temperatures predicted spring changes in migratory timing, with greater warming related to earlier arrivals. This study represents one of the first system-wide examinations during two seasons and comprises measures from hundreds of species that describe migratory timing across a continent. Our findings provide evidence of spatially dynamic phenological shifts that result from climate change. Climate change affects the timing of bird migration, which can lead to mismatch with resource availability. Migration occurred earlier in spring and autumn in the United States during the past 24 years; warming led to later arrival in the western Unites States and earlier arrival in the rest of the country.

Large networks of weather radars are comprehensive instruments for studying bird migration. For example, the US WSR-88D network covers the entire continental US and has archived data since the 1990s. The data can quantify both broad and fine-scale bird movements to address a range of migration ecology questions. However, the problem of automatically discriminating precipitation from biology has significantly limited the ability to conduct biological analyses with historical radar data. We develop MistNet, a deep convolutional neural network to discriminate precipitation from biology in radar scans. Unlike prior machine learning approaches, MistNet makes fine-scaled predictions and can collect biological information from radar scans that also contain precipitation. MistNet is based on neural networks for images, and includes several architecture components tailored to the unique characteristics of radar data. To avoid a massive human labelling effort, we train MistNet using abundant noisy labels obtained from dual polarization radar data. In historical and contemporary WSR-88D data, MistNet identifies at least 95.9% of all biomass with a false discovery rate of 1.3%. Dual polarization training data and our radar-specific architecture components are effective. By retaining biomass that co-occurs with precipitation in a single radar scan, MistNet retains 15% more biomass than traditional whole-scan approaches to screening. MistNet is fully automated and can be applied to datasets of millions of radar scans to produce fine-grained predictions that enable a range of applications, from continent-scale mapping to local analysis of airspace usage. Radar ornithology is advancing rapidly and leading to significant discoveries about continent-scale patterns of bird movements. General-purpose and empirically validated methods to quantify biological signals in radar data are essential to the future development of this field. MistNet can enable large-scale, long-term, and reproducible measurements of whole migration systems.

Applications of remote sensing data to monitor bird migration usher a new understanding of magnitude and extent of movements across entire flyways. Millions of birds move through the western USA, yet this region is understudied as a migratory corridor. Characterizing movements in the Pacific Flyway offers a unique opportunity to study complementary patterns to those recently highlighted in the Atlantic and Central Flyways. We use weather surveillance radar data from spring and autumn (1995–2018) to examine migrants' behaviours in relation to winds in the Pacific Flyway. Overall, spring migrants tended to drift on winds, but less so at northern latitudes and farther inland from the Pacific coastline. Relationships between winds and autumn flight behaviours were less striking, with no latitudinal or coastal dependencies. Differences in the preferred direction of movement (PDM) and wind direction predicted drift patterns during spring and autumn, with increased drift when wind direction and PDM differences were high. We also observed greater total flight activity through the Pacific Flyway during the spring when compared with the autumn. Such complex relationships among birds’ flight strategies, winds and seasonality highlight the variation within a migration system. Characterizations at these scales complement our understanding of strategies to clarify aerial animal movements.

A Bayesian framework for network detection is developed based on random walks on graphs. Networks are detected using partial observations of their activity, and the Bayesian approach is proved to be optimum in the Neyman-Pearson sense, assuming random walk propagation on a given graph and diffusion model with absorbing states. The equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven. A general diffusion model is introduced that utilizes spatio-temporal relationships between vertices, and is used for a specific space-time formulation that leads to significant performance improvements.

A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a graph, at least one observation, and a diffusion model for threat. A link to well-known spectral detection methods is provided, and the equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven. A general diffusion model is introduced that utilizes spatio-temporal relationships between vertices, and is used for a specific space-time formulation that leads to significant performance improvements on coordinated covert networks. This performance is demonstrated using a new hybrid mixed-membership blockmodel introduced to simulate random covert networks with realistic properties., IEEE Trans. Signal Process., major revision of arxiv.org/abs/1303.5613. arXiv admin note: substantial text overlap with arXiv:1303.5613

The relationship between team friendships and burnout was investigated. The participants were counselors from 16 work teams in a short-term residential facility for emotionally disturbed children and adolescents that was located in the United States. The burnout dimensions were lack of personal accomplishment, emotional exhaustion, and depersonalization (Maslach & Jackson, 1986). Reciprocal nominations between team members were used as a measure of team friendships. Friendship between members of the same team was positively related to personal accomplishment and inversely related to emotional exhaustion. The frequency of three aspects of team friendships was also examined: personal discussions, work discussions, and having fun. Having fun with a team friend was positively related to personal accomplishment, and having work discussions with a team friend was inversely related to depersonalization. The women reported more frequent contact with team friends than the men did.