Reproductive behaviours, drivers of activity, and effects of wildlife tourism on yellowtail kingfish (Seriola lalandi) in southern Australia

Author: Tom Clarke

Clarke, Tom, 2022 Reproductive behaviours, drivers of activity, and effects of wildlife tourism on yellowtail kingfish (Seriola lalandi) in southern Australia, Flinders University, College of Science and Engineering

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Understanding the behaviours and physiology of free-ranging animals, and the ecological processes driving them is a fundamental goal of studies in ecology. However, insights into these behaviours and responses to human impacts are poorly understood for large-bodied pelagic fish, despite these species being ecologically and economically important. Yellowtail kingfish (Seriola lalandi, family Carangidae) can be used as a model species to develop techniques to identify ecologically-important behaviours and the effects of anthropogenic pressures on the movements and physiology of large pelagic fishes. This thesis aimed to examine kingfish spawning behaviours, impacts of wildlife tourism, and drivers of activity in southern Australia.

Accelerometers and a machine learning model were used to describe behavioural classes of captive kingfish based on 624 hours of accelerometer data paired with visual observations. The model was subsequently used to predict behaviours from eight free-ranging kingfish to identify naturally-occurring reproductive behaviours (Chapter 2). Paired with environmental information and geographic location, my results show that accelerometers and machine learning provide an opportunity to identify spawning aggregations, and advise the implementation of spatial management efforts of large pelagic fish, when required.

The effects of feeding during white shark cage-diving tourism on kingfish residency and space use (Chapter 3), along with activity and physiological status (Chapter 4) were assessed through acoustic tags fitted with accelerometer sensors over two years. Kingfish were residential to the cage-diving site, with individuals detected up to 79% of days. Daily time spent at the site increased by ~27% when operators were present, and individuals were 62% closer to food-based compared to acoustic-attractants. Kingfish activity and burst events also increased during operations, by 18% and 60% respectively. However, I found that physiological condition of kingfish at the tourism site (measured using bioelectrical impedance analysis) was similar to individuals from eastern Australia that were not exposed to wildlife tourism (n = 113). Therefore, behavioural changes from tourism-interactions did not reflect a decrease in physiological status, suggesting that consumption of food-based attractant may be compensating for raised energetic expenditure.

A national network of acoustic-tracking receivers was used to identify environmental drivers of activity from 65 kingfish throughout south-eastern Australia (Chapter 5). Generalised Linear Mixed Models revealed that activity increased with temperature and was inversely correlated with moon illumination, with variable responses to tide height and time of day. Additionally, I demonstrated that continental-scale acoustic networks can be used to not only identify large-scale movements of individuals, but also evaluate activity over long-term and large-scale datasets.

Overall, kingfish provided a model species to showcase the power of multiple electronic tags to reveal important behaviours and environmental drivers of large pelagic fish energetics. My results make an original contribution by identifying the reproductive behaviours and describing the movements of kingfish in

southern Australia, and by developing widely-applicable techniques suitable across large pelagic fishes globally. In a time of shifting and unstable marine climates, and human-induced pressures, identifying drivers of movements and activity of large pelagic fish is essential to detect shifts and to employ effective and adaptable management strategies.

Keywords: kingfish, bio-logging, acoustic tracking, accelerometer, spawning, movement, machine learning

Subject: Biodiversity and Conservation thesis

Thesis type: Doctor of Philosophy
Completed: 2022
School: College of Science and Engineering
Supervisor: .