The Aerosphere as a Network Connector of Organisms and Their Diseases

Aeroecological processes, especially powered flight of animals, can rapidly connect biological communities across the globe. This can have profound consequences for evolutionary diversification, energy and nutrient transfers, and the spread of infectious diseases. The latter is of particular consequence for human populations, since migratory birds are known to host diseases which have a history of transmission into domestic poultry or even jumping to human hosts. In this chapter, we present a scenario under which a highly pathogenic avian influenza (HPAI) strain enters North America from East Asia via postmolting waterfowl migration. We use an agent-based model (ABM) to simulate the movement and disease transmission among 106 generalized waterfowl agents originating from ten molting locations in eastern Siberia, with the HPAI seeded in only ~102 agents at one of these locations. Our ABM tracked the disease dynamics across a very large grid of sites as well as individual agents, allowing us to examine the spatiotemporal patterns of change in virulence of the HPAI infection as well as waterfowl host susceptibility to the disease. We concurrently simulated a 12-station disease monitoring network in the northwest USA and Canada in order to assess the potential efficacy of these sites to detect and confirm the arrival of HPAI. Our findings indicated that HPAI spread was initially facilitated but eventually subdued by the migration of host agents. Yet, during the 90-day simulation, selective pressures appeared to have distilled the HPAI strain to its most virulent form (i.e., through natural selection), which was counterbalanced by the host susceptibility being conversely reduced (i.e., through genetic predisposition and acquired immunity). The monitoring network demonstrated wide variation in the utility of sites; some were clearly better at providing early warnings of HPAI arrival, while sites further from the disease origin exposed the selective dynamics which slowed the spread of the disease albeit with the result of passing highly virulent strains into southern wintering locales (where human impacts are more likely). Though the ABM presented had generalized waterfowl migration and HPAI disease dynamics, this exercise demonstrates the power of such simulations to examine the extremely large and complex processes which comprise aeroecology. We offer insights into how such models could be further parameterized to represent HPAI transmission risks as well as how ABMs could be applied to other aeroecological questions pertaining to individual-based connectivity

The Aerosphere as a Network Connector of Organisms and Their Diseases

Aeroecological processes, especially powered flight of animals, can rapidly connect biological communities across the globe. This can have profound consequences for evolutionary diversification, energy and nutrient transfers, and the spread of infectious diseases. The latter is of particular consequence for human populations, since migratory birds are known to host diseases which have a history of transmission into domestic poultry or even jumping to human hosts. In this chapter, we present a scenario under which a highly pathogenic avian influenza (HPAI) strain enters North America from East Asia via postmolting waterfowl migration. We use an agent-based model (ABM) to simulate the movement and disease transmission among 106 generalized waterfowl agents originating from ten molting locations in eastern Siberia, with the HPAI seeded in only ~102 agents at one of these locations. Our ABM tracked the disease dynamics across a very large grid of sites as well as individual agents, allowing us to examine the spatiotemporal patterns of change in virulence of the HPAI infection as well as waterfowl host susceptibility to the disease. We concurrently simulated a 12-station disease monitoring network in the northwest USA and Canada in order to assess the potential efficacy of these sites to detect and confirm the arrival of HPAI. Our findings indicated that HPAI spread was initially facilitated but eventually subdued by the migration of host agents. Yet, during the 90-day simulation, selective pressures appeared to have distilled the HPAI strain to its most virulent form (i.e., through natural selection), which was counterbalanced by the host susceptibility being conversely reduced (i.e., through genetic predisposition and acquired immunity). The monitoring network demonstrated wide variation in the utility of sites; some were clearly better at providing early warnings of HPAI arrival, while sites further from the disease origin exposed the selective dynamics which slowed the spread of the disease albeit with the result of passing highly virulent strains into southern wintering locales (where human impacts are more likely). Though the ABM presented had generalized waterfowl migration and HPAI disease dynamics, this exercise demonstrates the power of such simulations to examine the extremely large and complex processes which comprise aeroecology. We offer insights into how such models could be further parameterized to represent HPAI transmission risks as well as how ABMs could be applied to other aeroecological questions pertaining to individual-based connectivity

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

Variation in reversal learning by three generalist mesocarnivores

Urbanization imposes novel challenges for wildlife, but also provides new opportunities for exploitation. Generalist species are commonly found in urban habitats, but the cognitive mechanisms facilitating their successful behavioral adaptations and exploitations are largely under-investigated. Cognitive flexibility is thought to enable generalists to be more plastic in their behavior, thereby increasing their adaptability to a variety of environments, including urban habitats. Yet direct measures of cognitive flexibility across urban wildlife are lacking. We used a classic reversal-learning paradigm to investigate the cognitive flexibility of three generalist mesocarnivores commonly found in urban habitats: striped skunks (Mephitis mephitis), raccoons (Procyon lotor), and coyotes (Canis latrans). We developed an automated device and testing protocol that allowed us to administer tests of reversal learning in captivity without extensive training or experimenter involvement. Although most subjects were able to rapidly form and reverse learned associations, we found moderate variation in performance and behavior during trials. Most notably, we observed heightened neophobia and a lack of habituation expressed by coyotes. We discuss the implications of such differences among generalists with regard to urban adaptation and we identify goals for future research. This study is an important step in investigating the relationships between cognition, generalism, and urban adaptation

Food discovery is associated with different reliance on social learning and lower cognitive flexibility across environments in a food-caching bird

Social learning is a primary mechanism for information acquisition in social species. Despite many benefits, social learning may be disadvantageous when independent learning is more efficient. For example, searching independently may be more advantageous when food sources are ephemeral and unpredictable. Individual differences in cognitive abilities can also be expected to influence social information use. Specifically, better spatial memory can make a given environment more predictable for an individual by allowing it to better track food sources. We investigated how resident food-caching chickadees discovered multiple novel food sources in both harsher, less predictable high elevation and milder, more predictable low elevation winter environments. Chickadees at high elevation were faster at discovering multiple novel food sources and discovered more food sources than birds at low elevation. While birds at both elevations used social information, the contribution of social learning to food discovery was significantly lower at high elevation. At both elevations, chickadees with better spatial cognitive flexibility were slower at discovering food sources, likely because birds with lower spatial cognitive flexibility are worse at tracking natural resources and therefore spend more time exploring. Overall, our study supported the prediction that harsh environments should favour less reliance on social learning

Automated detection of bird roosts using NEXRAD radar data and Convolutional neural networks

Although NEXRAD radars have proven to be an effective tool for detecting airborne animals, detecting biological phenomena in radar images often involves a manual, time‐ consuming data‐extraction process. This paper focuses on applying machine learning to automatically find radar data that snapshots large aggregations of birds (specifically Purple Martins and Tree Swallows) as they depart en masse from roosting sites. These aggregations are evident in radar images as rings of elevated reflectivity that appear early in the morning as birds depart from roost sites. Our goal was to develop an algorithm that could determine whether an individual radar image contained at least one Purple Martin or Tree Swallow roost. We use a dataset of known roost locations to train three machine learning algorithms that employed (1) a traditional Artificial Neural Network (ANN), (2) a sophisticated preexisting Convolutional Neural Network (CNN) called Inception‐v3, and (3) a shallow CNN built from scratch. The resulting programs were all effective at finding bird roosts, with both the shallow CNN and the Inception‐v3 network making correct determinations about 90 per cent of the time with an AUC above .9. To the best of our knowledge, this study is the first to apply neural networks in the analysis of bird roosts in radar imagery, and these analytical tools offer new avenues of research into the ecology and behavior of flying animals, with practical applications to wind farm placement, air traffic administration and wildlife conservation. The NEXRAD radar network offers a tremendous archive of continental‐scale data and has the potential to capture entire vertebrate populations. We apply existing machine learning models to a new dataset which constitutes a valuable approach to extracting information from this archive.The funding from the NSF-DGE-1545261 grant helped make this research possible. We thank Sandra Pletschet for her time spent collecting the roost data and Dr. Phillip Chilson for his advice on the project. Some of the computing for this project was performed at the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma (OU). Article processing charges for this publication funded in part by the University of Oklahoma Libraries Open Access Fund.Ye

Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution

Species distribution models can be made more accurate by use of new “Spatiotemporal Exploratory Models” (STEMs), a type of spatially explicit ensemble model (SEEM) developed at the continental scale that averages regional models pixel by pixel. Although SEEMs can generate more accurate predictions of species distributions, they are computationally expensive. We compared the accuracies of each model for 11 grassland bird species and examined whether they improve accuracy at a statewide scale for fine and coarse predictor resolutions. We used a combination of survey data and citizen science data for 11 grassland bird species in Oklahoma to test a spatially explicit ensemble model at a smaller scale for its effects on accuracy of current models. We found that only four species performed best with either a statewide model or SEEM; the most accurate model for the remaining seven species varied with data resolution and performance measure. Policy implications: Determination of nonheterogeneity may depend on the spatial resolution of the examined dataset. Managers should be cautious if any regional differences are expected when developing policy from range‐wide results that show a single model or timeframe. We recommend use of standard species distribution models or other types of nonspatially explicit ensemble models for local species prediction models. Further study is necessary to understand at what point SEEMs become necessary with varying dataset resolutions.Article processing charges funded by University of Oklahoma Libraries. This work was funded by U.S. Department of Agriculture (USDA) NIFA grant 2013‐67009‐20369 to ESB and supported by the AWS Cloud Credits for Research program. CMC was supported by National Science Foundation (NSF) grants IDBR 1014891 and ABI 1458402 to ESB and Oklahoma Department of Wildlife Conservation grant F17AF01294 (W‐194‐R‐1) to M.A. Patten. AJC was supported by NSF grants IDBR 1014891, DGE 1545261, and DEB 0946685 and by USDA grant NIFA‐AFRI‐003536. Additional support was provided by the University Strategic Organization in "Applied Aeroecology" at the University of Oklahoma.Ye

LunAero: Automated “smart” hardware for recording video of nocturnal migration

Moon watching is a method of quantifying nocturnal bird migration by focusing a telescope on the moon and recording observations of flying birds silhouetted against the lunar surface. Although simple and well-established, researchers use moon watching infrequently due in part to the hours of late night observation it requires. To reduce the labor entailed in moon watching, we designed a low-cost system called LunAero that can track and record video of the moon at night. Here we present a proof-of-concept prototype that can serve as a platform for citizen scientists interested in observing nocturnal bird migration. We tested the video recording on clear nights from February 2018 to May 2019 when the moon was full or nearly full. Manual analysis of a 1.5 h sample of video revealed a total of 450 birds, which is a much higher detection rate than previous moon watching efforts have yielded. The hardware described here is part of a larger effort involving software development (currently underway) to automate recorded video analysis. We argue that LunAero can reduce the labor involved in moon watching, offer improved data quality over traditional moon watching, and provide insights into social behavior and wind-drift compensation in migrating birds.The authors would like to acknowledge funding from the University of Oklahoma’s Strategic Organization in Applied Aeroecology and donors from the LunAero crowdfunding campaign. Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye

Examination of Clock and Adcyap1 gene variation in a neotropical migratory passerine

Complex behavioral traits, such as those making up a migratory phenotype, are regulated by multiple environmental factors and multiple genes. We investigated possible relationships between microsatellite variation at two candidate genes implicated in the control of migratory behavior, Clock and Adcyap1, and several aspects of migratory life-history and evolutionary divergence in the Painted Bunting (Passerina ciris), a species that shows wide variation in migratory and molting strategies across a disjunct distribution. We focused on Clock and Adcyap1 microsatellite variation across three Painted Bunting populations in Oklahoma, Louisiana, and North Carolina, and for the Oklahoma breeding population we used published migration tracking data on adult males to explore phenotypic variation in individual migratory behavior. We found no correlation between microsatellite allele size within either Clock and Adcyap1 relative to the initiation or duration of fall migration in adult males breeding in Oklahoma. We also show the lack of significant correlations with aspects of the migratory phenotype for the Louisiana population. Our research highlights the limitations of studying microsatellite allelic mutations that are of undetermined functional influence relative to complex behavioral phenotypes.This research was funded by the National Science Foundation (grants nos. IDBR 1014891 and DEB 0946685) and by the United States Department of Agriculture (grant no. NIFA-AFRI-003536). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.YesEach submission to PLOS ONE passes through a rigorous quality control and peer-review evaluation process before receiving a decision. The initial in-house quality control check deals with issues such as competing interests; ethical requirements for studies involving human participants or animals; financial disclosures; full compliance with PLOS’ data availability policy, etc. Submissions may be returned to authors for queries, and will not be seen by our Editorial Board or peer reviewers until they pass this quality control check. Once each manuscript has passed quality control, it is assigned to a member of the Editorial Board, who takes responsibility as the Academic Editor for the submission. The Academic Editor is responsible for conducting the peer-review process and for making a decision to accept, invite revision of, or reject the article

Perceptions of the crowded sky as assessed through response to aerial infrastructure

Ever increasing numbers of wind turbines, communication towers, power lines, and aerial vehicles are clear evidence of our growing reliance on infrastructure in the lower aerosphere. As this infrastructure expands, it is important to understand public perceptions of an increasingly crowded sky. To gauge tolerance for aerial crowding, 251 participants from across the US completed a survey where they rated tolerance for a series of aerial infrastructure images (i.e., towers, turbines, and airborne vehicles) in four landscapes with varying degrees of pre-existing ground-level infrastructure that approximated rural, suburban, and urban settings. We predicted lower tolerance for aerial infrastructure 1) in more natural scenes and 2) among rural residents. In general, participants preferred an open aesthetic with relatively little aerial infrastructure across all landscape types. No clear association was found between infrastructure tolerance and natural scenes nor rural residency, with participants slightly less tolerant of infrastructure in the suburban scene. Tolerance scores were generally similar across age, income levels, and political affiliations. Women indicated less crowding tolerance than men, with this effect driven by a disproportionate number of women with zero tolerance for aerial infrastructure. African Americans and Asians had higher tolerance scores than other racial/ethnic groups, but these trends may have been affected by low sample sizes of non-white participants. Our survey revealed fewer differences in crowding tolerance across demographic groups than might be expected given widely reported political and geographic polarization in the U.S. Attitudes toward aerial infrastructure were varied with few associations with demographic parameters suggesting that public opinion has not yet solidified with regard to this issue, making possible opportunities for consensus building with regard to responsible development of aerial infrastructure