Defluorination of HFCs by a magnesium reagent.

Reaction of a series of HFCs with a main group reagent containing a Mg-Mg bond results in defluorination to form the corresponding magnesium fluoride complex. In the case of 1,1,1,2-tetrafluoroethane (HFC-134a) generation of the fluoride occurs alongside selective formation of 1,1-difluoroethene. DFT calculations have been carried out to better understand the selectivity and compare the barriers for sp3 C-F bond activation with sp3 C-H bond activation in this system.

Doubling the life of Cu/ZnO methanol synthesis catalysts via use of Si as a structural promoter to inhibit sintering.

Cu/ZnO/Al2O3 catalysts used to synthesize methanol undergo extensive deactivation during use, mainly due to sintering. Here, we report on formulations wherein deactivation has been substantially reduced by the targeted use of a small quantity of a Si-based promoter, resulting in accrued activity benefits that can exceed a factor of 1.8 versus unpromoted catalysts. This enhanced stability also provides longer lifetimes, up to double that of prior generation catalysts. Detailed characterization of a library of aged catalysts has allowed the most important deactivation mechanisms to be established and the chemical state of the silicon promoter to be identified. We show that silicon is incorporated within the ZnO lattice, providing a pronounced improvement in the hydrothermal stability of this component. These findings have important implications for sustainable methanol production from H2 and CO2.

Artificial light at night is a top predictor of bird migration stopover density.

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.

The effects of resistance training to near failure on strength, hypertrophy, and motor unit adaptations in previously trained adults.

Limited research exists examining how resistance training to failure affects applied outcomes and single motor unit characteristics in previously trained individuals. Herein, resistance-trained adults (24 ± 3 years old, self-reported resistance training experience was 6 ± 4 years, 11 men and 8 women) were randomly assigned to either a low-repetitions-in-reserve (RIR; i.e., training near failure, n = 10) or high-RIR (i.e., not training near failure, n = 9) group. All participants implemented progressive overload during 5 weeks where low-RIR performed squat, bench press, and deadlift twice weekly and were instructed to end each training set with 0-1 RIR. high-RIR performed identical training except for being instructed to maintain 4-6 RIR after each set. During week 6, participants performed a reduced volume-load. The following were assessed prior to and following the intervention: (i) vastus lateralis (VL) muscle cross-sectional area (mCSA) at multiple sites; (ii) squat, bench press, and deadlift one-repetition maximums (1RMs); and (iii) maximal isometric knee extensor torque and VL motor unit firing rates during an 80% maximal voluntary contraction. Although RIR was lower in the low- versus high-RIR group during the intervention (p < 0.001), total training volume did not significantly differ between groups (p = 0.222). There were main effects of time for squat, bench press, and deadlift 1RMs (all p-values < 0.05), but no significant condition × time interactions existed for these or proximal/middle/distal VL mCSA data. There were significant interactions for the slope and y-intercept of the motor unit mean firing rate versus recruitment threshold relationship. Post hoc analyses indicated low-RIR group slope values decreased and y-intercept values increased after training suggesting low-RIR training increased lower-threshold motor unit firing rates. This study provides insight into how resistance training in proximity to failure affects strength, hypertrophy, and single motor unit characteristics, and may inform those who aim to program for resistance-trained individuals.

Room Temperature Defluorination of Poly(tetrafluoroethylene) by a Magnesium Reagent.

Perfluoroalkyl substances (PFAS) are pervasive in the environment. The largest single use material within the PFAS compound class is poly(tetrafluoroethylene) (PTFE), a robust and chemically resistant polymer. Despite their widespread use and serious concerns about their role as pollutants, methods for repurposing PFAS are rare. Here we show that a nucleophilic magnesium reagent reacts with PTFE at room temperature, generating a molecular magnesium fluoride which is easily separated from the surface-modified polymer. The fluoride in turn can be used to transfer the fluorine atoms to a small array of compounds. This proof-of-concept study demonstrates that the atomic fluorine content of PTFE can be harvested and reused in chemical synthesis.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

Quantifying long-term phenological patterns of aerial insectivores roosting in the Great Lakes region using weather surveillance radar.

Organisms have been shifting their timing of life history events (phenology) in response to changes in the emergence of resources induced by climate change. Yet understanding these patterns at large scales and across long time series is often challenging. Here we used the US weather surveillance radar network to collect data on the timing of communal swallow and martin roosts and evaluate the scale of phenological shifts and its potential association with temperature. The discrete morning departures of these aggregated aerial insectivores from ground-based roosting locations are detected by radars around sunrise. For the first time, we applied a machine learning algorithm to automatically detect and track these large-scale behaviors. We used 21 years of data from 12 weather surveillance radar stations in the Great Lakes region to quantify the phenology in roosting behavior of aerial insectivores at three spatial levels: local roost cluster, radar station, and across the Great Lakes region. We show that their peak roosting activity timing has advanced by 2.26 days per decade at the regional scale. Similar signals of advancement were found at the station scale, but not at the local roost cluster scale. Air temperature trends in the Great Lakes region during the active roosting period were predictive of later stages of roosting phenology trends (75% and 90% passage dates). Our study represents one of the longest-term broad-scale phenology examinations of avian aerial insectivore species responding to environmental change and provides a stepping stone for examining potential phenological mismatches across trophic levels at broad spatial scales.

Repurposing of F-gases: challenges and opportunities in fluorine chemistry.

Fluorinated gases (F-gases) are routinely employed as refrigerants, blowing agents, and electrical insulators. These volatile compounds are potent greenhouse gases and consequently their release to the environment creates a significant contribution to global warming. This review article seeks to summarise: (i) the current applications of F-gases, (ii) the environmental issues caused by F-gases, (iii) current methods of destruction of F-gases and (iv) recent work in the field towards the chemical repurposing of F-gases. There is a great opportunity to tackle the environmental and sustainability issues created by F-gases by developing reactions that repurpose these molecules.

Drivers of fatal bird collisions in an urban center.

Millions of nocturnally migrating birds die each year from collisions with built structures, especially brightly illuminated buildings and communication towers. Reducing this source of mortality requires knowledge of important behavioral, meteorological, and anthropogenic factors, yet we lack an understanding of the interacting roles of migration, artificial lighting, and weather conditions in causing fatal bird collisions. Using two decades of collision surveys and concurrent weather and migration measures, we model numbers of collisions occurring at a large urban building in Chicago. We find that the magnitude of nocturnal bird migration, building light output, and wind conditions are the most important predictors of fatal collisions. The greatest mortality occurred when the building was brightly lit during large nocturnal migration events and when winds concentrated birds along the Chicago lakeshore. We estimate that halving lighted window area decreases collision counts by 11× in spring and 6× in fall. Bird mortality could be reduced by ∼60% at this site by decreasing lighted window area to minimum levels historically recorded. Our study provides strong support for a relationship between nocturnal migration magnitude and urban bird mortality, mediated by light pollution and local atmospheric conditions. Although our research focuses on a single site, our findings have global implications for reducing or eliminating a critically important cause of bird mortality.

Complete deconstruction of SF6 by an aluminium(I) compound.

The room-temperature activation of SF6, a potent greenhouse gas, is reported using a monovalent aluminium(i) reagent to form well-defined aluminium(iii) fluoride and aluminium(iii) sulfide products. New reactions have been developed to utilise the aluminium(iii) fluoride and aluminium(iii) sulfide as a nucleophilic source of F- and S2- for a range of electrophiles. The overall reaction sequence results in the net transfer of fluorine or sulfur atoms from an environmentally detrimental gas to useful organic products.

A weather surveillance radar view of Alaskan avian migration.

Monitoring avian migration within subarctic regions of the globe poses logistical challenges. Populations in these regions often encounter the most rapid effects of changing climates, and these seasonally productive areas are especially important in supporting bird populations-emphasizing the need for monitoring tools and strategies. To this end, we leverage the untapped potential of weather surveillance radar data to quantify active migration through the airspaces of Alaska. We use over 400 000 NEXRAD radar scans from seven stations across the state between 1995 and 2018 (86% of samples derived from 2013 to 2018) to measure spring and autumn migration intensity, phenology and directionality. A large bow-shaped terrestrial migratory system spanning the southern two-thirds of the state was identified, with birds generally moving along a northwest-southeast diagonal axis east of the 150th meridian, and along a northeast-southwest axis west of this meridian. Spring peak migration ranged from 3 May to 30 May and between, 18 August and 12 September during the autumn, with timing across stations predicted by longitude, rather than latitude. Across all stations, the intensity of migration was greatest during the autumn as compared to spring, highlighting the opportunity to measure seasonal indices of net breeding productivity for this important system as additional years of radar measurements are amassed.

Near-term ecological forecasting for dynamic aeroconservation of migratory birds.

Near-term ecological forecasting has the potential to mitigate negative impacts of human modifications on wildlife by directing efficient action through relevant and timely predictions. We used the U.S. avian migration system to highlight ecological forecasting applications for aeroconservation. We used millions of observations from 143 weather surveillance radars to construct and evaluate a migration forecasting system for nocturnal bird migration over the contiguous United States. We identified the number of nights of mitigation required to reduce the risk of aerial hazards to 50% of avian migrants passing a given area in spring and autumn based on dynamic forecasts of migration activity. We also investigated an alternative approach, that is, employing a fixed conservation strategy based on time windows that historically capture 50% of migratory passage. In practice, during both spring and autumn, dynamic forecasts required fewer action nights compared with fixed window selection at all locations (spring: mean of 7.3 more alert days; fall: mean of 12.8 more alert days). This pattern resulted in part from the pulsed nature of bird migration captured in the radar data, where the majority (54.3%) of birds move on 10% of a migration season's nights. Our results highlight the benefits of near-term ecological forecasting and the potential advantages of dynamic mitigation strategies over static ones, especially in the face of increasing risks to migrating birds from light pollution, wind energy infrastructure, and collisions with structures.

La estimación ecológica a corto plazo tiene el potencial para mitigar los impactos negativos de las modificaciones humanas sobre la fauna al dirigir las acciones eficientes mediante predicciones relevantes y oportunas. Usamos el sistema de migración de aves de Estados Unidos para resaltar las aplicaciones de la estimación ecológica para la aeroconservación. Usamos millones de observaciones tomadas de 143 radares de vigilancia climática para construir y evaluar un sistema de estimaciones migratorias para la migración de aves nocturnas en los Estados Unidos contiguos. Identificamos el número de noches de mitigación requeridas para reducir el riesgo de peligros aéreos para el 50% de las aves migratorias que pasan por un área específica en la primavera y en el otoño con base en las estimaciones dinámicas de la actividad migratoria. También investigamos una estrategia alternativa: el uso de una estrategia fija de conservación basada en las ventanas temporales que históricamente han capturado el 50% del pasaje migratorio. En la práctica, durante la primavera y el otoño, las estimaciones dinámicas requirieron menos noches de acción en comparación con la selección de ventana fija en todas las localidades (primavera: promedio de 7.3 más días de alerta; otoño: promedio de 12.8 más días de alerta). Este patrón resultó en parte por la naturaleza pulsada de las migraciones aviarias capturadas en los datos del radar, en los cuales la mayoría de las aves (54.3%) se mueven durante el 10% de las noches durante la temporada migratoria. Nuestros resultados resaltan los beneficios que tienen las estimaciones ecológicas a corto plazo en comparación con las estáticas, especialmente de frente a los riesgos crecientes que encaran las aves migratorias por la contaminación lumínica, la infraestructura de energía eólica y las colisiones con las estructuras.

A place to land: spatiotemporal drivers of stopover habitat use by migrating birds.

Migrating birds require en route habitats to rest and refuel. Yet, habitat use has never been integrated with passage to understand the factors that determine where and when birds stopover during spring and autumn migration. Here, we introduce the stopover-to-passage ratio (SPR), the percentage of passage migrants that stop in an area, and use 8 years of data from 12 weather surveillance radars to estimate over 50% SPR during spring and autumn through the Gulf of Mexico and Atlantic coasts of the south-eastern US, the most prominent corridor for North America's migratory birds. During stopovers, birds concentrated close to the coast during spring and inland in forested landscapes during autumn, suggesting seasonal differences in habitat function and highlighting the vital role of stopover habitats in sustaining migratory communities. Beyond advancing understanding of migration ecology, SPR will facilitate conservation through identification of sites that are disproportionally selected for stopover by migrating birds.

Defluorosilylation of trifluoromethane: upgrading an environmentally damaging fluorocarbon.

The rapid, room-temperature defluorosilylation of trifluoromethane, a highly potent greenhouse gas, has been achieved using a simple silyl lithium reagent. An extensive computational mechanistic analysis provides a viable reaction pathway and demonstrates the unexpected electrophilic nature of LiCF3. The reaction generates a bench stable fluorinated building block that shows promise as an easy-to-use difluoromethylating agent. The difluoromethyl group is an increasingly important bioisostere in active pharmaceutical ingredients, and therefore our methodology creates value from waste. The potential scalability of the process has been demonstrated by achieving the reaction on a gram-scale.

REAL-TIME MECHANISTIC BAYESIAN FORECASTS OF COVID-19 MORTALITY.

The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the US reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real-time represent a non-stationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model (MechBayes) that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes non-parametric modeling of varying transmission rates, non-parametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the US Centers for Disease Control, through the COVID-19 Forecast Hub. We examine the performance relative to a baseline model as well as alternate models submitted to the Forecast Hub. Additionally, we include an ablation test of our extensions to the classic SEIR model. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes when compared to a baseline model and show that MechBayes ranks as one of the top 2 models out of 10 submitted to the COVID-19 Forecast Hub. Finally, we demonstrate that MechBayes performs significantly better than the classical SEIR model.

Migratory flight on the Pacific Flyway: strategies and tendencies of wind drift compensation.

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.

Navigating north: how body mass and winds shape avian flight behaviours across a North American migratory flyway.

The migratory patterns of birds have been the focus of ecologists for millennia. What behavioural traits underlie these remarkably consistent movements? Addressing this question is central to advancing our understanding of migratory flight strategies and requires the integration of information across levels of biological organisation, e.g. species to communities. Here, we combine species-specific observations from the eBird citizen-science database with observations aggregated from weather surveillance radars during spring migration in central North America. Our results confirm a core prediction of migration theory at an unprecedented national scale: body mass predicts variation in flight strategies across latitudes, with larger-bodied species flying faster and compensating more for wind drift. We also find evidence that migrants travelling northward earlier in the spring increasingly compensate for wind drift at higher latitudes. This integration of information across biological scales provides new insight into patterns and determinants of broad-scale flight strategies of migratory birds.

Innovative Visualizations Shed Light on Avian Nocturnal Migration.

Globally, billions of flying animals undergo seasonal migrations, many of which occur at night. The temporal and spatial scales at which migrations occur and our inability to directly observe these nocturnal movements makes monitoring and characterizing this critical period in migratory animals' life cycles difficult. Remote sensing, therefore, has played an important role in our understanding of large-scale nocturnal bird migrations. Weather surveillance radar networks in Europe and North America have great potential for long-term low-cost monitoring of bird migration at scales that have previously been impossible to achieve. Such long-term monitoring, however, poses a number of challenges for the ornithological and ecological communities: how does one take advantage of this vast data resource, integrate information across multiple sensors and large spatial and temporal scales, and visually represent the data for interpretation and dissemination, considering the dynamic nature of migration? We assembled an interdisciplinary team of ecologists, meteorologists, computer scientists, and graphic designers to develop two different flow visualizations, which are interactive and open source, in order to create novel representations of broad-front nocturnal bird migration to address a primary impediment to long-term, large-scale nocturnal migration monitoring. We have applied these visualization techniques to mass bird migration events recorded by two different weather surveillance radar networks covering regions in Europe and North America. These applications show the flexibility and portability of such an approach. The visualizations provide an intuitive representation of the scale and dynamics of these complex systems, are easily accessible for a broad interest group, and are biologically insightful. Additionally, they facilitate fundamental ecological research, conservation, mitigation of human-wildlife conflicts, improvement of meteorological products, and public outreach, education, and engagement.

A characterization of autumn nocturnal migration detected by weather surveillance radars in the northeastern USA.

Billions of birds migrate at night over North America each year. However, few studies have described the phenology of these movements, such as magnitudes, directions, and speeds, for more than one migration season and at regional scales. In this study, we characterize density, direction, and speed of nocturnally migrating birds using data from 13 weather surveillance radars in the autumns of 2010 and 2011 in the northeastern USA. After screening radar data to remove precipitation, we applied a recently developed algorithm for characterizing velocity profiles with previously developed methods to document bird migration. Many hourly radar scans contained windborne "contamination," and these scans also exhibited generally low overall reflectivities. Hourly scans dominated by birds showed nightly and seasonal patterns that differed markedly from those of low reflectivity scans. Bird migration occurred during many nights, but a smaller number of nights with large movements of birds defined regional nocturnal migration. Densities varied by date, time, and location but peaked in the second and third deciles of night during the autumn period when the most birds were migrating. Migration track (the direction to which birds moved) shifted within nights from south-southwesterly to southwesterly during the seasonal migration peaks; this shift was not consistent with a similar shift in wind direction. Migration speeds varied within nights, although not closely with wind speed. Airspeeds increased during the night; groundspeeds were highest between the second and third deciles of night, when the greatest density of birds was migrating. Airspeeds and groundspeeds increased during the fall season, although groundspeeds fluctuated considerably with prevailing winds. Significant positive correlations characterized relationships among bird densities at southern coastal radar stations and northern inland radar stations. The quantitative descriptions of broadscale nocturnal migration patterns presented here will be essential for biological and conservation applications. These descriptions help to define migration phenology in time and space, fill knowledge gaps in avian annual cycles, and are useful for monitoring long-term population trends of migrants. Furthermore, these descriptions will aid in assessing potential risks to migrants, particularly from structures with which birds collide and artificial lighting that disorients migrants.

A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity.

BACKGROUND: Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post hoc with reference to the geographical and seasonal variations in the timing and speeds of sunrise and sunset. The increased use of geolocators has created a need for analytical tools to produce accurate and objective estimates of migration routes that are explicit in their uncertainty about the position estimates. RESULTS: We developed a hidden Markov chain model for the analysis of geolocator data. This model estimates tracks for animals with complex migratory behaviour by combining: (1) a shading-insensitive, template-fit physical model, (2) an uncorrelated random walk movement model that includes migratory and sedentary behavioural states, and (3) spatially explicit behavioural masks. The model is implemented in a specially developed open source R package FLightR. We used the particle filter (PF) algorithm to provide relatively fast model posterior computation. We illustrate our modelling approach with analysis of simulated data for stationary tags and of real tracks of both a tree swallow Tachycineta bicolor migrating along the east and a golden-crowned sparrow Zonotrichia atricapilla migrating along the west coast of North America. CONCLUSIONS: We provide a model that increases accuracy in analyses of noisy data and movements of animals with complicated migration behaviour. It provides posterior distributions for the positions of animals, their behavioural states (e.g., migrating or sedentary), and distance and direction of movement. Our approach allows biologists to estimate locations of animals with complex migratory behaviour based on raw light data. This model advances the current methods for estimating migration tracks from solar geolocation, and will benefit a fast-growing number of tracking studies with this technology.