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Most large carnivores feed on prey infrequently and may expend large amounts of energy to locate, capture and kill their prey. This makes them probabilistically vulnerable to fluctuating rates of energy acquisition over time, especially within the increasingly human-altered landscapes that dominate their remaining range. Consequently, quantifying their hunting behaviors and success rates is critical, yet direct observation of these events is rarely feasible. We theorized that we could determine prey pursuit and capture in African wild dogs (Lycaon pictus) using a mechanistic approach by constructing Boolean algorithms applied to accelerometer data derived from collar-mounted tags. Here, we used this method and then iteratively improved algorithms by testing them on observed hunts and kills of collared packs. Using this approach on 47 days of acceleration from three wild dogs in three packs, we identified 29 hunts with 10 kills, all of which were confirmed by direct observation except for a single kill. Our results demonstrate that hunting effort and success can largely be determined from acceleration data using a mechanistic approach. This is particularly valuable when such behaviors are rarely quantified and offers a template for research on foraging in canid species, while also contributing to the expanding body of literature that employs similar methods to quantify hunting in large carnivores. [ABSTRACT FROM AUTHOR]

Policy epidemiology utilizes human subject-matter experts (SMEs) to systematically surface, analyze, and categorize legally-enforceable policies. The Analysis and Mapping of Policies for Emerging Infectious Diseases project systematically collects and assesses health-related policies from all United Nations Member States. The recent proliferation of generative artificial intelligence (GAI) tools powered by large language models have led to suggestions that such technologies be incorporated into our project and similar research efforts to decrease the human resources required. To test the accuracy and precision of GAI in identifying and interpreting health policies, we designed a study to systematically assess the responses produced by a GAI tool versus those produced by a SME. We used two validated policy datasets, on emergency and childhood vaccination policy and quarantine and isolation policy in each United Nations Member State. We found that the SME and GAI tool were concordant 78.09% and 67.01% of the time respectively. It also significantly hastened the data collection processes. However, our analysis of non-concordant results revealed systematic inaccuracies and imprecision across different World Health Organization regions. Regarding vaccination, over 50% of countries in the African, Southeast Asian, and Eastern Mediterranean regions were inaccurately represented in GAI responses. This trend was similar for quarantine and isolation, with the African and Eastern Mediterranean regions least concordant. Furthermore, GAI responses only provided laws or information missed by the SME 2.14% and 2.48% of the time for the vaccination dataset and for the quarantine and isolation dataset, respectively. Notably, the GAI was least concordant with the SME when tasked with policy interpretation. These results suggest that GAI tools require further development to accurately identify policies across diverse global regions and interpret context-specific information. However, we found that GAI is a useful tool for quality assurance and quality control processes in health policy identification. [ABSTRACT FROM AUTHOR]

The cardiac conduction system in large carnivores, such as the African lion (Panthera leo), represents a significant knowledge gap in both veterinary science and in cardiac electrophysiology. Short QT intervals have been reported from zoo‐kept, anaesthetized lions, and our goal was to record the first ECGs from wild, conscious lions roaming freely, and compare them to zoo‐kept lions under the hypothesis that short QT is unique to zoo‐kept lions. Macroscopic and histological examinations were performed on heart tissue removed from nine healthy zoo lions. ECGs were recorded from the nine anaesthetized zoo‐kept lions, and from 15 anaesthetized and conscious wild lions in Africa. Our histological and topographical description of the lion's heart matched what has previously been published. In conscious lions, the ECG recordings revealed a mean heart rate of 70 ± 4 beats/min, with faster heart rates during the night. PQ and QT intervals were heart rate dependent in the conscious lions. Interestingly, QT intervals recorded in wild lions were markedly longer than QT intervals from zoo lions (398 ± 40 vs. 297 ± 9 ms, respectively; P < 0.0001). Anaesthesia or heart rate did not account for this difference. We provide a comprehensive description of the cardiac anatomy and electrophysiology of wild and zoo‐kept lions. QT intervals were significantly shorter in zoo lions, suggesting functional disparities in cardiac electrophysiology between wild and zoo‐kept lions, potentially related to physical fitness. These findings underscore the plasticity of cardiac electrophysiology and may be of value when reintroducing endangered species into the wild and when managing lions in human care. What is the central question of this study?How do electrocardiograms from zoo lions and free‐roaming wild lions compare?What is the main finding and its importance?QT intervals in zoo lions are shorter compared to wild lions, which may be relevant for our understanding of animals in human care and may influence conservation strategies. [ABSTRACT FROM AUTHOR]

Although climate change is predicted to have a substantial effect on the energetic requirements of organisms, the longer-term implications are often unclear. Sloths are limited by the rate at which they can acquire energy and are unable to regulate core body temperature (Tb) to the extent seen in most mammals. Therefore, the metabolic impacts of climate change on sloths are expected to be profound. Here we use indirect calorimetry to measure the oxygen consumption (VO2) and Tb of highland and lowland two-fingered sloths (Choloepus hoffmanni) when exposed to a range of different ambient temperatures (Ta) (18 °C –34 °C), and additionally record changes in Tb and posture over several days in response to natural fluctuations in Ta. We use the resultant data to predict the impact of future climate change on the metabolic rate and Tb of the different sloth populations. The metabolic responses of sloths originating from the two sites differed at high Ta's, with lowland sloths invoking metabolic depression as temperatures rose above their apparent 'thermally-active zone' (TAZ), whereas highland sloths showed increased RMR. Based on climate change estimates for the year 2100, we predict that high-altitude sloths are likely to experience a substantial increase in metabolic rate which, due to their intrinsic energy processing limitations and restricted geographical plasticity, may make their survival untenable in a warming climate. [ABSTRACT FROM AUTHOR]

Biologging has proven to be a powerful approach to investigate diverse questions related to movement ecology across a range of spatiotemporal scales and increasingly relies on multidisciplinary expertise. However, the variety of animal‐borne equipment, coupled with little consensus regarding analytical approaches to interpret large, complex data sets presents challenges and makes comparison between studies and study species difficult. Here, we present a combined hardware and analytical approach for standardizing the collection, analysis, and interpretation of multisensor biologging data. Here, we present (i) a custom‐designed integrated multisensor collar (IMSC), which was field tested on 71 free‐ranging wild boar (Sus scrofa) over 2 years; (ii) a machine learning behavioral classifier capable of identifying six behaviors in free‐roaming boar, validated across individuals equipped with differing collar designs; and (iii) laboratory and field‐based calibration and accuracy assessments of animal magnetic heading measurements derived from raw magnetometer data. The IMSC capacity and durability exceeded expectations, with a 94% collar recovery rate and a 75% cumulative data recording success rate, with a maximum logging duration of 421 days. The behavioral classifier had an overall accuracy of 85% in identifying the six behavioral classes when tested on multiple collar designs and improved to 90% when tested on data exclusively from the IMSC. Both laboratory and field tests of magnetic compass headings were in precise agreement with expectations, with overall median magnetic headings deviating from ground truth observations by 1.7° and 0°, respectively. Although multisensor equipment and sophisticated analyses are now commonplace in biologging studies, the IMSC hardware and analytical framework presented here provide a valuable tool for biologging researchers and will facilitate standardization of biologging data across studies. In addition, we highlight the potential of additional analyses available using this framework that can be adapted for use in future studies on terrestrial mammals. [ABSTRACT FROM AUTHOR]

Background We characterized the global epidemiology and seasonality of human coronaviruses (HCoVs) OC43, NL63, 229E, and HKU1. Methods In this systematic review, we searched MEDLINE, EMBASE, Web of Science, SCOPUS, CINAHL, and backward citations for studies published until 1 September 2023. We included studies with ≥12 months of consecutive data and tested for ≥1 HCoV species. Case reports, review articles, animal studies, studies focusing on SARS-CoV-1, SARS-CoV-2, and/or Middle East respiratory syndrome, and those including <100 cases were excluded. Study quality and risk of bias were assessed using Joanna Briggs Institute Critical Appraisal Checklist tools. We reported the prevalence of all HCoVs and individual species. Seasonality was reported for studies that included ≥100 HCoVs annually. This study is registered with PROSPERO, CRD42022330902. Results A total of 201 studies (1 819 320 samples) from 68 countries were included. A high proportion were from China (19.4%; n = 39), whereas the Southern Hemisphere was underrepresented. Most were case series (77.1%, n = 155) with samples from secondary care (74.1%, n = 149). Seventeen (8.5%) studies included asymptomatic controls, whereas 76 (37.8%) reported results for all 4 HCoV species. Overall, OC43 was the most prevalent HCoV. Median test positivity of OC43 and NL63 was higher in children, and 229E and HKU1 in adults. Among 18 studies that described seasonality (17 from the Northern Hemisphere), circulation of all HCoVs mostly peaked during cold months. Conclusions In our comprehensive review, few studies reported the prevalence of individual HCoVs or seasonality. Further research on the burden and circulation of HCoVs is needed, particularly from Africa, South Asia, and Central/South America. [ABSTRACT FROM AUTHOR]

Observing animals in the wild often poses extreme challenges, but animal‐borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi‐animal, long‐term studies; however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (Felis catus) as a model organism for terrestrial mammalian behaviours. Nine indoor domestic cats were equipped with collar‐mounted tri‐axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with (i) additional descriptive variables, (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 s) and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models that were validated against calibrated cat behaviours before identifying the behaviours of five free‐ranging tag‐equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free‐ranging animal behaviours. RF models accurately predicted the behaviours of indoor domestic cats (F‐measure up to 0.96) with discernible improvements observed with post‐data‐collection processing. Additional variables, standardised durations of behaviours and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high‐frequency models excelled in identifying fast‐paced behaviours (e.g. locomotion), whereas lower‐frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free‐ranging cat behaviours. While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free‐ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare and management of wild animals. [ABSTRACT FROM AUTHOR]

Abstract Behavior is an important mechanism of evolution and it is paid for through energy expenditure. Nevertheless, field biologists can rarely observe animals for more than a fraction of their daily activities and attempts to quantify behavior for modeling ecological processes often exclude cryptic yet important behavioral events. Over the past few years, an explosion of research on remote monitoring of animal behavior using acceleration sensors has smashed the decades-old limits of observational studies. Animal-attached accelerometers measure the change in velocity of the body over time and can quantify fine-scale movements and body postures unlimited by visibility, observer bias, or the scale of space use. Pioneered more than a decade ago, application of accelerometers as a remote monitoring tool has recently surged thanks to the development of more accessible hardware and software. It has been applied to more than 120 species of animals to date. Accelerometer measurements are typically collected in three dimensions of movement at very high resolution (>10 Hz), and have so far been applied towards two main objectives. First, the patterns of accelerometer waveforms can be used to deduce specific behaviors through animal movement and body posture. Second, the variation in accelerometer waveform measurements has been shown to correlate with energy expenditure, opening up a suite of scientific questions in species notoriously difficult to observe in the wild. To date, studies of wild aquatic species outnumber wild terrestrial species and analyses of social behaviors are particularly few in number. Researchers of domestic and captive species also tend to report methodology more thoroughly than those studying species in the wild. There are substantial challenges to getting the most out of accelerometers, including validation, calibration, and the management and analysis of large quantities of data. In this review, we illustrate how accelerometers work, provide an overview of the ecological questions that have employed accelerometry, and highlight the emerging best practices for data acquisition and analysis. This tool offers a level of detail in behavioral studies of free-ranging wild animals that has previously been impossible to achieve and, across scientific disciplines, it improves understanding of the role of behavioral mechanisms in ecological and evolutionary processes.