Overview of global status of plastic presence in marine vertebrates.

The presence of plastic in the environment is generating impacts on all habitats and has become a major global problem in marine megafauna. Macroplastics can cause entanglement, ingestion and loss of suitable habitats. In addition to entanglement problems, there is evidence that plastics are entering the food web through ingestion by marine organisms, which could ultimately be affecting humans. Much of the available information on the impact of plastic in biota is scattered and disconnected due to the use of different methodologies. Here, we review the variety of approaches and protocols followed to assess macro- and microplastic ingestion in marine vertebrates such as sea turtles, cetaceans and fishes in order to offer a global overview of their current status. The analysis of 112 studies indicates the highest plastic ingestion in organisms collected in the Mediterranean and Northeast Indian Ocean with significant differences among plastic types ingested by different groups of animals, including differences in colour and the type of prevalent polymers. In sea turtles, the most prevalent types of plastics are white plastics (66.60%), fibres (54.54%) and LDPE polymer (39.09%); in cetaceans, white macro- and microplastics (38.31%), fibres (79.95%) and PA polymer (49.60%); and in fishes, transparent plastics (45.97%), fibres (66.71%) and polyester polymer (36.20%). Overall, clear fibre microplastics are likely the most predominant types ingested by marine megafauna around the globe.

Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers.

Movement ecology has traditionally focused on the movements of animals over large time scales, but, with advancements in sensor technology, the focus can become increasingly fine scale. Accelerometers are commonly applied to quantify animal behaviours and can elucidate fine-scale (<2 s) behaviours. Machine learning methods are commonly applied to animal accelerometry data; however, they require the trial of multiple methods to find an ideal solution. We used tri-axial accelerometers (10 Hz) to quantify four behaviours in Port Jackson sharks (Heterodontus portusjacksoni): two fine-scale behaviours (<2 s)-(1) vertical swimming and (2) chewing as proxy for foraging, and two broad-scale behaviours (>2 s-mins)-(3) resting and (4) swimming. We used validated data to calculate 66 summary statistics from tri-axial accelerometry and assessed the most important features that allowed for differentiation between the behaviours. One and two second epoch testing sets were created consisting of 10 and 20 samples from each behaviour event, respectively. We developed eight machine learning models to assess their overall accuracy and behaviour-specific accuracy (one classification tree, five ensemble learners and two neural networks). The support vector machine model classified the four behaviours better when using the longer 2 s time epoch (F-measure 89%; macro-averaged F-measure: 90%). Here, we show that this support vector machine (SVM) model can reliably classify both fine- and broad-scale behaviours in Port Jackson sharks.

Acoustic accelerometry reveals diel activity patterns in premigratory Port Jackson sharks.

Distinguishing the factors that influence activity within a species advances understanding of their behavior and ecology. Continuous observation in the marine environment is not feasible but biotelemetry devices provide an opportunity for detailed analysis of movements and activity patterns. This study investigated the detail that calibration of accelerometers measuring root mean square (RMS) acceleration with video footage can add to understanding the activity patterns of male and female Port Jackson sharks (Heterodontus portusjacksoni) in a captive environment. Linear regression was used to relate RMS acceleration output to time-matched behavior captured on video to quantify diel activity patterns. To validate captive data, diel patterns from captive sharks were compared with diel movement data from free-ranging sharks using passive acoustic tracking. The RMS acceleration data showed captive sharks exhibited nocturnal diel patterns peaking during the late evening before midnight and decreasing before sunrise. Correlation analysis revealed that captive animals displayed similar activity patterns to free-ranging sharks. The timing of wild shark departures for migration in the late breeding season corresponded with elevated diel activity at night within the captive individuals, suggesting a form of migratory restlessness in captivity. By directly relating RMS acceleration output to activity level, we show that sex, time of day, and sex-specific seasonal behavior all influenced activity levels. This study contributes to a growing body of evidence that RMS acceleration data are a promising method to determine activity patterns of cryptic marine animals and can provide more detailed information when validated in captivity.