Seasonal associations with light pollution trends for nocturnally migrating bird populations

This project was supported by The Leon Levy Foundation, The Wolf Creek Charitable Foundation, Lyda Hill Philanthropies, Amon G. Carter Foundation, National Aeronautics and Space Administration (80NSSC21K1143), and National Science Foundation (ABI sustaining DBI-1939187, GCR-2123405). Computing support was provided by the National Science Foundation (CNS-1059284 and CCF-1522054), and the Extreme Science and Engineering Discovery Environment (XSEDE; National Science Foundation, ACI-1548562) through allocation TG-DEB200010 run on Bridges at the Pittsburgh Supercomputing Center.Artificial light at night (ALAN) is adversely affecting natural systems worldwide, including the disorienting influence of ALAN on nocturnally migrating birds. Understanding how ALAN trends are developing across species' seasonal distributions will inform mitigation efforts, such as Lights Out programs. Here, we intersect ALAN annual trend estimates (1992-2013) with weekly estimates of relative abundance for 42 nocturnally migrating passerine bird species that breed in North America using observations from the eBird community science database for the combined period 2005-2020. We use a cluster analysis to identify species with similar weekly associations with ALAN trends. Our results identified three prominent clusters. Two contained species that occurred in northeastern and western North America during the breeding season. These species were associated with moderate ALAN levels and weak negative ALAN trends during the breeding season, and low ALAN levels and strong positive ALAN trends during the nonbreeding season. The difference between the breeding and nonbreeding seasons was lower for species that occurred in northern South America and greater for species that occurred in Central America during the nonbreeding season. For species that occurred in South America during the nonbreeding season, positive ALAN trends increased in strength as species migrated through Central America, especially in the spring. The third cluster contained species whose associations with positive ALAN trends remained high across the annual cycle, peaking during migration, especially in the spring. These species occurred in southeastern North America during the breeding season where they were associated with high ALAN levels, and in northern South America during the nonbreeding season where they were associated with low ALAN levels. Our findings suggest reversing ALAN trends in Central America during migration, especially in the spring, would benefit the most individuals of the greatest number of species. Reversing ALAN trends in southeastern North America during the breeding season and Central America during the nonbreeding season would generate the greatest benefits outside of migration.Publisher PDFPeer reviewe

The role of artificial light at night and road density in predicting the seasonal occurrence of nocturnally migrating birds

The Leon Levy Foundation; The Wolf Creek Charitable Foundation; Lyda Hill Philanthropies; Amon G. Carter Foundation; National Science Foundation, Grant/Award Number: ABI sustaining DBI-1939187 and ICER-1927743. Computing support was provided by the National Science Foundation, Grant/Award Number: CNS-1059284 and CCF-1522054, and the Extreme Science and Engineering Discovery Environment (XSEDE), National Science Foundation, Grant/Award Number: ACI-1548562, through allocation TG-DEB200010 run on Bridges at the Pittsburgh Supercomputing Center.Aim: Artificial light at night (ALAN) and roads are known threats to nocturnally migrating birds. How associations with ALAN and roads are defined in combination for these species at the population level across the full annual cycle has not been explored. Location: Western Hemisphere. Methods: We estimated range‐wide exposure, predictor importance and the prevalence of positive associations with ALAN and roads at a weekly temporal resolution for 166 nocturnally migrating bird species in three orders: Passeriformes (n = 104), Anseriformes (n = 27) and Charadriiformes (n = 35). We clustered Passeriformes based on the prevalence of positive associations. Results: Positive associations with ALAN and roads were more prevalent for Passeriformes during migration when exposure and importance were highest. Positive associations with ALAN and roads were more prevalent for Anseriformes and Charadriiformes during the breeding season when exposure was lowest. Importance was uniform for Anseriformes and highest during migration for Charadriiformes. Our cluster analysis identified three groups of Passeriformes, each having similar associations with ALAN and roads. The first occurred in eastern North America during migration where exposure, prevalence, and importance were highest. The second wintered in Mexico and Central America where exposure, prevalence and importance were highest. The third occurred throughout North America where prevalence was low, and exposure and importance were uniform. The first and second were comprised of dense habitat specialists and long‐distance migrants. The third was comprised of open habitat specialists and short distance migrants. Main conclusions: Our findings suggest ALAN and roads pose the greatest risk during migration for Passeriformes and during the breeding season for Anseriformes and Charadriiformes. Our results emphasise the close relationship between ALAN and roads, the diversity of associations dictated by taxonomy, exposure, migration strategy and habitat and the need for more informed and comprehensive mitigation strategies where ALAN and roads are treated as interconnected threats.Publisher PDFPeer reviewe

A Double Machine Learning Trend Model for Citizen Science Data

1. Citizen and community-science (CS) datasets have great potential for estimating interannual patterns of population change given the large volumes of data collected globally every year. Yet, the flexible protocols that enable many CS projects to collect large volumes of data typically lack the structure necessary to keep consistent sampling across years. This leads to interannual confounding, as changes to the observation process over time are confounded with changes in species population sizes. 2. Here we describe a novel modeling approach designed to estimate species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double Machine Learning, a statistical framework that uses machine learning methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. Additionally, we develop a simulation method to identify and adjust for residual confounding missed by the propensity scores. Using this new method, we can produce spatially detailed trend estimates from citizen science data. 3. To illustrate the approach, we estimated species trends using data from the CS project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends in the face of real-world confounding. Results showed that the trend estimates distinguished between spatially constant and spatially varying trends at a 27km resolution. There were low error rates on the estimated direction of population change (increasing/decreasing) and high correlations on the estimated magnitude. 4. The ability to estimate spatially explicit trends while accounting for confounding in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species, regions, or seasons without rigorous monitoring data.Comment: 28 pages, 6 figure

ART^2 : Coupling Lyman-alpha Line and Multi-wavelength Continuum Radiative Transfer

Narrow-band Lya line and broad-band continuum have played important roles in the discovery of high-redshift galaxies in recent years. Hence, it is crucial to study the radiative transfer of both Lya and continuum photons in the context of galaxy formation and evolution in order to understand the nature of distant galaxies. Here, we present a three-dimensional Monte Carlo radiative transfer code, All-wavelength Radiative Transfer with Adaptive Refinement Tree (ART^2), which couples Lya line and multi-wavelength continuum, for the study of panchromatic properties of galaxies and interstellar medium. This code is based on the original version of Li et al., and features three essential modules: continuum emission from X-ray to radio, Lya emission from both recombination and collisional excitation, and ionization of neutral hydrogen. The coupling of these three modules, together with an adaptive refinement grid, enables a self-consistent and accurate calculation of the Lya properties. As an example, we apply ART^2 to a cosmological simulation that includes both star formation and black hole growth, and study in detail a sample of massive galaxies at redshifts z=3.1 - 10.2. We find that these galaxies are Lya emitters (LAEs), whose Lya emission traces the dense gas region, and that their Lya lines show a shape characteristic of gas inflow. Furthermore, the Lya properties, including photon escape fraction, emergent luminosity, and equivalent width, change with time and environment. Our results suggest that LAEs evolve with redshift, and that early LAEs such as the most distant one detected at z ~ 8.6 may be dwarf galaxies with a high star formation rate fueled by infall of cold gas, and a low Lya escape fraction.Comment: 20 pages, 16 figures, accepted for publication in MNRA

Human–agent collaboration for disaster response

In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked

The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

UK Biobank is a population-based cohort of half a million participants aged 40–69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world’s largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future direction