Supporting information for “Using drones equipped with thermal cameras to locate and count quail individuals and coveys: A case study using Northern Bobwhite Colinus virginianus in Mississippi, USA”

Drone flights were conducted over bobwhite individuals and coveys and information was collected on the date, time of flight, number of individuals estimated in the covey, and number of individuals flushed from the covey (i.e., actual number of individuals). We also report season of flight and the thermal sensor used for each flight. NA denotes that flush or capture was not attempted

Correlates of Bird Collisions With Buildings Across Three North American Countries

Collisions with buildings cause up to 1 billion bird fatalities annually in North America. Bird-building collisions have recently received increased conservation, research, and policy attention. However, efforts to reduce collisions would benefit from studies conducted at large spatial scales across multiple study sites, with standardized methods, and with consideration of species- and life history-related variation and correlates of collisions. We addressed these research needs with a coordinated data collection effort at 40 sites across North America. We estimated collision vulnerability for 40 bird species by accounting for their North American population abundance, distribution overlap with study sites, and sampling effort. Of 10 species we identified as most vulnerable to collisions, some have been identified in past studies (e.g., Black-throated Blue Warbler [Setophaga caerulescens]) while others emerged for the first time (e.g., White-breasted Nuthatch [Sitta carolinensis]), possibly because we used a more standardized sampling approach than past studies. Analyses of species-specific collision correlates revealed that building size and glass area were positively associated with numbers of collisions for 5 of 8 species with enough observations to analyze independently. Vegetation around buildings influenced collisions for only 1 of those 8 species (Swainson\u27s Thrush [Catharus fuscescens]). We also found that life history predicted collisions; numbers of collisions were greatest for migratory, insectivorous, and woodland-inhabiting species. This coordinated, continent-wide study provides new insight into the species most vulnerable to building collisions, making them potentially in greatest need of conservation attention to reduce collisions. This study also lends insight into species- and life history-related variation and correlates of building collisions, information that can help refine collision management efforts

Improving animal monitoring using small unmanned aircraft systems (sUAS) and deep learning networks

In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and whitetailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals

Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys

Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach. Supplemental files attached below

Dataset for Controllable factors affecting accuracy and precision of human identification of animals from drone imagery

Dataset from the results of an experiment to determine how three controllable factors, flight altitude, camera angle, and time of day, affect human identification and counts of animals from drone images to inform best practices to survey animal communities with drones. We used a drone (unoccupied aircraft system, or UAS) to survey known numbers of eight animal decoy species, representing a range of body sizes and colors, at four GSD (ground sampling distance) values (0.35, 0.70, 1.06, 1.41 cm/pixel) representing equivalent flight altitudes (15.2, 30.5, 45.7, 61.0 m) at two camera angles (45° and 90°) and across a range of times of day (morning to late afternoon). Expert human observers identified and counted animals in drone images to determine how the three controllable factors affected accuracy and precision. Observer precision was high and unaffected by tested factors. However, results for observer accuracy revealed an interaction among all three controllable factors. Increasing flight altitude resulted in decreased accuracy in animal counts overall; however, accuracy was best at midday compared to morning and afternoon hours, when decoy and structure shadows were present or more pronounced. Surprisingly, the 45° camera enhanced accuracy compared to 90°, but only when animals were most difficult to identify and count, such as at higher flight altitudes or during the early morning and late afternoon. We provide recommendations based on our results to design future surveys to improve human accuracy in identifying and counting animals from drone images for monitoring animal populations and communities

Identification of Novel High-Frequency DNA Methylation Changes in Breast Cancer

Recent data have revealed that epigenetic alterations, including DNA methylation and chromatin structure changes, are among the earliest molecular abnormalities to occur during tumorigenesis. The inherent thermodynamic stability of cytosine methylation and the apparent high specificity of the alterations for disease may accelerate the development of powerful molecular diagnostics for cancer. We report a genome-wide analysis of DNA methylation alterations in breast cancer. The approach efficiently identified a large collection of novel differentially DNA methylated loci (∼200), a subset of which was independently validated across a panel of over 230 clinical samples. The differential cytosine methylation events were independent of patient age, tumor stage, estrogen receptor status or family history of breast cancer. The power of the global approach for discovery is underscored by the identification of a single differentially methylated locus, associated with the GHSR gene, capable of distinguishing infiltrating ductal breast carcinoma from normal and benign breast tissues with a sensitivity and specificity of 90% and 96%, respectively. Notably, the frequency of these molecular abnormalities in breast tumors substantially exceeds the frequency of any other single genetic or epigenetic change reported to date. The discovery of over 50 novel DNA methylation-based biomarkers of breast cancer may provide new routes for development of DNA methylation-based diagnostics and prognostics, as well as reveal epigenetically regulated mechanism involved in breast tumorigenesis

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Evidence on the effectiveness of small unmanned aircraft systems (sUAS) as a survey tool for North American terrestrial, vertebrate animals: a systematic map protocol

Background: Small unmanned aircraft systems (sUAS) are replacing or supplementing manned aircraft and groundbased surveys in many animal monitoring situations due to better coverage at finer spatial and temporal resolutions, access, cost, bias, impacts, safety, efficiency, and logistical benefits. Various sUAS models and sensors are available with varying features and usefulness depending on survey goals. However, justification for selection of sUAS and sensors are not typically offered in published literature and existing reviews do not adequately cover past and current sUAS applications for animal monitoring nor their associated sUAS model and sensor technologies, taxonomic and geographic scope, flight conditions and considerations, spatial distributions of sUAS applications, and reported technical difficulties. We outline a systematic map protocol to collect and consolidate evidence pertaining to sUAS monitoring of animals. Our systematic map will provide a useful synthesis of current applications of sUAS-animal related studies and identify major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that may influence future research directions and sUAS applications. Methods: Our systematic map will investigate the current state of knowledge using an accurate, comprehensive, and repeatable search. We will find relevant peer-reviewed and grey literature as well as dissertations and theses using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We will use a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted data concerning sUAS, sensors, animals, methodology, and results will be recorded in Microsoft Access. We will query and catalogue evidence in the final database to produce tables, figures, and geographic maps to accompany a full narrative review that answers our primary and secondary questions

Controllable factors affecting accuracy and precision of human identification of animals from drone imagery

Surveying animal populations using drones (unoccupied aircraft systems [UAS]) provides numerous advantages; however, few best practices exist to survey animal communities with drones. Among myriad factors that can affect human identification and counts of animals from drone images, we focused on three factors typically controlled in the study design or by the drone pilot: flight altitude, camera angle, and time of day. Identifying interactions and patterns among these three variables represents an important first step to determining best survey practices. We used a drone to survey known numbers of eight animal decoy species, representing a range of body sizes and colors, at four ground sampling distance (GSD) values (0.35, 0.70, 1.06, and 1.41 cm/pixel) representing equivalent flight altitudes (15.2, 30.5, 45.7, and 61.0 m) at two camera angles (45° and 90°) and across a range of times of day (morning to late afternoon). Expert human observers identified and counted animals from drone images to determine how the three controllable factors affected accuracy and precision. Observer precision was high and unaffected by tested factors. However, results for observer accuracy revealed an interaction among all three controllable factors. Increasing flight altitude resulted in decreased accuracy in animal counts overall; however, accuracy was best at midday than during the morning and afternoon, when decoy and structure shadows were present or more pronounced. Surprisingly, the 45° camera enhanced accuracy more than the 90° camera, but only when animals were most difficult to identify and count, such as at higher flight altitudes or during the early morning and late afternoon. We provide recommendations for improving human accuracy in identifying and counting animals from drone images used in monitoring animal populations and communities. These results should be incorporated into drone survey design in addition to considering funding, logistical, and animal behavior constraints

Correlates of bird collisions with buildings across three North American countries

© 2020 Society for Conservation Biology Collisions with buildings cause up to 1 billion bird fatalities annually in the United States and Canada. However, efforts to reduce collisions would benefit from studies conducted at large spatial scales across multiple study sites with standardized methods and consideration of species- and life-history-related variation and correlates of collisions. We addressed these research needs through coordinated collection of data on bird collisions with buildings at sites in the United States (35), Canada (3), and Mexico (2). We collected all carcasses and identified species. After removing records for unidentified carcasses, species lacking distribution-wide population estimates, and species with distributions overlapping fewer than 10 sites, we retained 269 carcasses of 64 species for analysis. We estimated collision vulnerability for 40 bird species with ≥2 fatalities based on their North American population abundance, distribution overlap in study sites, and sampling effort. Of 10 species we identified as most vulnerable to collisions, some have been identified previously (e.g., Black-throated Blue Warbler [Setophaga caerulescens]), whereas others emerged for the first time (e.g., White-breasted Nuthatch [Sitta carolinensis]), possibly because we used a more standardized sampling approach than past studies. Building size and glass area were positively associated with number of collisions for 5 of 8 species with enough observations to analyze independently. Vegetation around buildings influenced collisions for only 1 of those 8 species (Swainson\u27s Thrush [Catharus ustulatus]). Life history predicted collisions; numbers of collisions were greatest for migratory, insectivorous, and woodland-inhabiting species. Our results provide new insight into the species most vulnerable to building collisions, making them potentially in greatest need of conservation attention to reduce collisions and into species- and life-history-related variation and correlates of building collisions, information that can help refine collision management