Philopatry and regional connectivity of the great hammerhead shark, Sphyrna mokarran in the U.S. and Bahamas

A thorough understanding of movement patterns of a species is critical for designing effective conservation and management initiatives. However, generating such information for large marine vertebrates is challenging, as they typically move over long distances, live in concealing environments, are logistically difficult to capture and, as upper-trophic predators, are naturally low in abundance. Large-bodied, broadly distributed tropical shark typically restricted to coastal and shelf habitats, the great hammerhead shark Sphyrna mokarran epitomizes such challenges. Highly valued for its fins (in target and incidental fisheries), it suffers high bycatch mortality coupled with fecundity conservative life history, and as a result, is vulnerable to over-exploitation and population depletion. Although there are very little species-specific data available, the absence of recent catch records give cause to suspect substantial declines across its range. Here, we used biotelemetry techniques (acoustic and satellite), conventional tagging, laser-photogrammetry, and photo-identification to investigate the level of site fidelity/residency for great hammerheads to coastal areas in the Bahamas and U.S., and the extent of movements and connectivity of great hammerheads between the U.S. and Bahamas. Results revealed large-scale return migrations (3030 km), seasonal residency to local areas (some for 5 months), site fidelity (annual return to Bimini and Jupiter for many individuals) and numerous international movements. These findings enhance the understanding of movement ecology in great hammerhead sharks and have potential to contribute to improved cons

Assessing the effects of sampling frequency on behavioural classification of accelerometer data

Understanding the behaviours of free-ranging animals over biologically meaningful time scales (e.g., diel, tidal, lunar, seasonal, annual) gives unique insight into their ecology. Bio-logging tools such as accelerometers allow the remote study of elusive or inaccessible animals by recording high resolution movement data. Machine learning (ML) is becoming a common tool for automatic classification of behaviours from these types of large data sets. These classifiers often perform best using high sampling frequencies; however, these frequencies also limit archival device recording duration through elevated battery and memory use. In this study we assess the effect of sampling frequency on a ML algorithm's ability to correctly classify behaviours from accelerometer data and present a framework for programming bio-logging devices that maintains classifier performance while optimizing data collection duration. Accelerometer data (30 Hz) were collected from juvenile lemon sharks (Negaprion brevirostris) during semi-captive trials at Bimini, Bahamas, and were ground-truthed to a discrete catalogue of behaviours through direct observation of sharks during trials. The ground-truthed data were re-sampled to a range of sampling frequencies (30, 15, 10, 5, 3 and 1 Hz) and behaviours (swim, rest, burst, chafe, headshake) were classified using a random forest ML algorithm. We demonstrate that as sampling frequency decreases, classifier performance decreases. Best overall classification was achieved at 30 Hz (F-score > 0.790), although 5 Hz was appropriate for classification of swim and rest (F-score > 0.964). For fine-scale behaviours characterised by faster kinematics (headshake, burst and chafe), classification performance was lower across the entire range of sampling frequencies (0.535–0.846, 1–30 Hz), though did not decrease significantly until sampling frequency was 5 Hz are required. However, when seeking to maximise the available device memory and battery capacity and therefore extend deployment duration, 5 Hz is an appropriate sampling frequency for classifying behaviours in similar-sized animals

Thermal performance responses in free-ranging elasmobranchs depend on habitat use and body size

Temperature is one of the most influential drivers of physiological performance and behaviour in ectotherms, determining how these animals relate to their ecosystems and their ability to succeed in particular habitats. Here, we analysed the largest set of acceleration data compiled to date for elasmobranchs to examine the relationship between volitional activity and temperature in 252 individuals from 8 species. We calculated activation energies for the thermal performance response in each species and estimated optimum temperatures using an Arrhenius breakpoint analysis, subsequently fitting thermal performance curves to the activity data. Juveniles living in confined nursery habitats not only spent substantially more time above their optimum temperature and at the upper limits of their performance breadths compared to larger, less site-restricted animals, but also showed lower activation energies and broader performance curves. Species or life stages occupying confined habitats featured more generalist behavioural responses to temperature change, whereas wider ranging elasmobranchs were characterised by more specialist behavioural responses. The relationships between the estimated performance regimes and environmental temperature limits suggest that animals in confined habitats, including many juvenile elasmobranchs within nursery habitats, are likely to experience a reduction of performance under a warming climate, although their flatter thermal response will likely dampen this impact. The effect of warming on less site-restricted species is difficult to forecast since three of four species studied here did not reach their optimum temperature in the wild, although their specialist performance characteristics may indicate a more rapid decline should optimum temperatures be exceeded

Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data

Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accelerometer-equipped juvenile lemon sharks (Negaprion brevirostris) at Bimini, Bahamas (25°44′N, 79°16′W). The sharks were observed to exhibit chafing, burst swimming, headshaking, resting and swimming in a semi-captive environment and these observations were used to ground-truth data for ML training and testing. ML methods included logistic regression, an artificial neural network, two random forest models, a gradient boosting model and a voting ensemble (VE) model, which combined the predictions of all other (base) models to improve classifier performance. The macro-averaged F-measure, an indicator of classifier performance, showed that the VE model improved overall classification (F-measure 0.88) above the strongest base learner model, gradient boosting (0.86). To test whether the VE model provided biologically meaningful results when applied to accelerometer data obtained from wild sharks, we investigated headshaking behaviour, as a proxy for prey capture, in relation to the variables: time of day, tidal phase and season. All variables were significant in predicting prey capture, with predations most likely to occur during early evening and less frequently during the dry season and high tides. These findings support previous hypotheses from sporadic visual observations

Global spatial risk assessment of sharks under the footprint of fisheries

Brucellosis is a highly contagious zoonosis affecting humans and a wide range of domesticated and wild animal species. An important element for effective disease containment is to improve knowledge, attitudes and practices (KAP) of afflicted communities. This study aimed to assess the KAP related to brucellosis at the human–animal interface in an endemic area of Egypt and to identify the risk factors for human infection. A matched case–control study was conducted at the central fever hospitals located in six governorates in northern Egypt. Face‐to‐face interviews with cases and controls were conducted using a structured questionnaire. In total, 40.7% of the participants owned farm animals in their households. The overall mean practice score regarding animal husbandry, processing and consumption of milk and dairy products were significantly lower among cases compared with controls (−12.7 ± 18.1 vs. 0.68 ± 14.2, respectively; p < .001). Perceived barriers for notification of animal infection/abortion were predominate among cases and positively correlated with participants’ education. The predictors of having brucellosis infection were consumption of unpasteurized milk or raw dairy products and practicing animal husbandry. Applying protective measures against infection significantly reduced its risk. A model predicting risk factors for brucellosis among those who own animal showed that frequent abortions per animal increased the chance for brucellosis infection among human cases by 50‐fold (95% CI: 8.8–276.9), whereas the use of protective measures in animal care reduced the odds (OR = 0.11 [95% CI: 0.03–0.45]). In conclusion, consumption of unprocessed dairy products was equally important as contact with infected/aborted animals as major risk factors for Brucella spp. infection among humans in Egypt. There is poor knowledge, negative attitudes and risky behaviours among villagers which can perpetuate the risk of brucellosis transmission at the human–animal interface. This supports the need for integrating health education into the national brucellosis control programme