The power of national acoustic tracking networks to assess the impacts of human activity on marine organisms during the COVID-19 pandemic.

COVID-19 restrictions have led to an unprecedented global hiatus in anthropogenic activities, providing a unique opportunity to assess human impact on biological systems. Here, we describe how a national network of acoustic tracking receivers can be leveraged to assess the effects of human activity on animal movement and space use during such global disruptions. We outline variation in restrictions on human activity across Australian states and describe four mechanisms affecting human interactions with the marine environment: 1) reduction in economy and trade changing shipping traffic; 2) changes in export markets affecting commercial fisheries; 3) alterations in recreational activities; and 4) decline in tourism. We develop a roadmap for the analysis of acoustic tracking data across various scales using Australia's national Integrated Marine Observing System (IMOS) Animal Tracking Facility as a case study. We illustrate the benefit of sustained observing systems and monitoring programs by assessing how a 51-day break in white shark (Carcharodon carcharias) cage-diving tourism due to COVID-19 restrictions affected the behaviour and space use of two resident species. This cessation of tourism activities represents the longest break since cage-diving vessels started day trips in this area in 2007. Long-term monitoring of the local environment reveals that the activity space of yellowtail kingfish (Seriola lalandi) was reduced when cage-diving boats were absent compared to periods following standard tourism operations. However, white shark residency and movements were not affected. Our roadmap is globally applicable and will assist researchers in designing studies to assess how anthropogenic activities can impact animal movement and distributions during regional, short-term through to major, unexpected disruptions like the COVID-19 pandemic.

Personal electric deterrents can reduce shark bites from the three species responsible for the most fatal interactions.

The frequency of unprovoked shark bites is increasing worldwide, leading to a growing pressure for mitigation measures to reduce shark-bite risk while maintaining conservation objectives. Personal shark deterrents are a promising and non-lethal strategy that can protect ocean users, but few have been independently and scientifically tested. In Australia, bull (Carcharhinus leucas), tiger (Galeocerdo cuvier), and white sharks (Carcharodon carcharias) are responsible for the highest number of bites and fatalities. We tested the effects of two electric deterrents (Ocean Guardian's Freedom+ Surf and Freedom7) on the behaviour of these three species. The surf product reduced the probability of bites by 54% across all three species. The diving product had a similar effect on tiger shark bites (69% reduction) but did not reduce the frequency of bites from white sharks (1% increase), likely because the electrodes were placed further away from the bait. Electric deterrents also increased the time for bites to occur, and frequency of reactions and passes for all species tested. Our findings reveal that both Freedom+ Surf and Freedom7 electric deterrents affect shark behaviour and can reduce shark-bite risk for water users, but neither product eliminated the risk of shark bites entirely. The increasing number of studies showing the ability of personal electric deterrents to reduce shark-bite risk highlights personal protection as an effective and important part of the toolbox of shark-bite mitigation measures.

Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish.

BACKGROUND: Tri-axial accelerometers have been used to remotely describe and identify in situ behaviours of a range of animals without requiring direct observations. Datasets collected from these accelerometers (i.e. acceleration, body position) are often large, requiring development of semi-automated analyses to classify behaviours. Marine fishes exhibit many "burst" behaviours with high amplitude accelerations that are difficult to interpret and differentiate. This has constrained the development of accurate automated techniques to identify different "burst" behaviours occurring naturally, where direct observations are not possible. METHODS: We trained a random forest machine learning algorithm based on 624 h of accelerometer data from six captive yellowtail kingfish during spawning periods. We identified five distinct behaviours (swim, feed, chafe, escape, and courtship), which were used to train the model based on 58 predictive variables. RESULTS: Overall accuracy of the model was 94%. Classification of each behavioural class was variable; F1 scores ranged from 0.48 (chafe) - 0.99 (swim). The model was subsequently applied to accelerometer data from eight free-ranging kingfish, and all behaviour classes described from captive fish were predicted by the model to occur, including 19 events of courtship behaviours ranging from 3 s to 108 min in duration. CONCLUSION: Our findings provide a novel approach of applying a supervised machine learning model on free-ranging animals, which has previously been predominantly constrained to direct observations of behaviours and not predicted from an unseen dataset. Additionally, our findings identify typically ambiguous spawning and courtship behaviours of a large pelagic fish as they naturally occur.