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

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

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

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

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

Inherent limits of light-level geolocation may lead to over-interpretation

In their 2015 Current Biology paper, Streby et al. [1] reported that Golden-winged Warblers (Vermivora chrysoptera), which had just migrated to their breeding location in eastern Tennessee, performed a facultative and up to “>1,500 km roundtrip” to the Gulf of Mexico to avoid a severe tornadic storm. From light-level geolocator data, wherein geographical locations are estimated via the timing of sunrise and sunset, Streby et al. [1] concluded that the warblers had evacuated their breeding area approximately 24 hours before the storm and returned about five days later. The authors presented this finding as evidence that migratory birds avoid severe storms by temporarily moving long-distances. However, the tracking method employed by Streby et al. [1] is prone to considerable error and uncertainty. Here, we argue that this interpretation of the data oversteps the limits of the used tracking technique. By calculating the expected geographical error range for the tracked birds, we demonstrate that the hypothesized movements fell well within the geolocators’ inherent error range for this species and that such deviations in latitude occur frequently even if individuals remain stationary

Ecological Energetics of an Abundant Aerial Insectivore, the Purple Martin

We thank T. Fagin for help with estimating the seasonal range area occupied by Purple Martins. We thank an anonymous reviewer, Mark Brigham, and J Boyles for their comments on this manuscript.Conceived and designed the experiments: JFK ESB WFF PBC. Performed the experiments: JFK PBC. Analyzed the data: JFK PBC. Wrote the manuscript: JFK ESB WFF PBC. Developed the model in Matlab: JFK PBC.The atmospheric boundary layer and lower free atmosphere, or aerosphere, is increasingly important for human transportation, communication, environmental monitoring, and energy production. The impacts of anthropogenic encroachment into aerial habitats are not well understood. Insectivorous birds and bats are inherently valuable components of biodiversity and play an integral role in aerial trophic dynamics. Many of these insectivores are experiencing range-wide population declines. As a first step toward gaging the potential impacts of these declines on the aerosphere’s trophic system, estimates of the biomass and energy consumed by aerial insectivores are needed. We developed a suite of energetics models for one of the largest and most common avian aerial insectivores in North America, the Purple Martin (Progne subis). The base model estimated that Purple Martins consumed 412 (± 104) billion insects*y-1 with a biomass of 115,860 (± 29,192) metric tonnes*y-1. During the breeding season Purple Martins consume 10.3 (+ 3.0) kg of prey biomass per km3 of aerial habitat, equal to about 36,000 individual insects*km-3. Based on these calculations, the cumulative seasonal consumption of insects*km-3 is greater in North America during the breeding season than during other phases of the annual cycle, however the maximum daily insect consumption*km-3 occurs during fall migration. This analysis provides the first range-wide quantitative estimate of the magnitude of the trophic impact of this large and common aerial insectivore. Future studies could use a similar modeling approach to estimate impacts of the entire guild of aerial insectivores at a variety of temporal and spatial scales. These analyses would inform our understanding of the impact of population declines among aerial insectivores on the aerosphere’s trophic dynamics.Yeshttp://www.plosone.org/static/editorial#pee

Pediatric T- and NK-cell lymphomas: new biologic insights and treatment strategies

T- and natural killer (NK)-cell lymphomas are challenging childhood neoplasms. These cancers have varying presentations, vast molecular heterogeneity, and several are quite unusual in the West, creating diagnostic challenges. Over 20 distinct T- and NK-cell neoplasms are recognized by the 2008 World Health Organization classification, demonstrating the diversity and potential complexity of these cases. In pediatric populations, selection of optimal therapy poses an additional quandary, as most of these malignancies have not been studied in large randomized clinical trials. Despite their rarity, exciting molecular discoveries are yielding insights into these clinicopathologic entities, improving the accuracy of our diagnoses of these cancers, and expanding our ability to effectively treat them, including the use of new targeted therapies. Here, we summarize this fascinating group of lymphomas, with particular attention to the three most common subtypes: T-lymphoblastic lymphoma, anaplastic large cell lymphoma, and peripheral T-cell lymphoma-not otherwise specified. We highlight recent findings regarding their molecular etiologies, new biologic markers, and cutting-edge therapeutic strategies applied to this intriguing class of neoplasms