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Wildlife tourism can benefit conservation of target species, however, it can have detrimental effects on animal behaviour and physiology. Whale shark Rhincodon typus tourism has seen recent rapid growth globally, but methods and regulations vary widely. Ningaloo Reef, Australia is considered "gold standard" whale shark tourism management due to legal regulation, strict enforcement, and high compliance. Rather than relying on observational data, we used biotelemetry to collect high-resolution data (20 Hz) on whale sharks' movement behaviour in the presence or absence of tourists. Tourism encounters lasted an average of 62 min and swimming with tourists increased the activity levels of larger (> 7 m) but not smaller sharks. Given that activity levels positively correlate with energetic costs, it is likely the 18% increase seen in activity of large sharks would have incurred additional energetic costs. However, when considered as a proportion of daily energy requirements, these additional costs were only incurred for an average of 4% of a whale shark's day. The tourism-induced impacts we found on the endangered whale sharks at this highly regulated tourism site would not have been apparent from purely observational studies, highlighting the utility of biotelemetry to quantify tourism-related impacts on wildlife. [ABSTRACT FROM AUTHOR]

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]