Oceanographic conditions associated with white shark (Carcharodon carcharias) habitat use along eastern Australia

Abstract

Management of species with wide-ranging migrations is a complex issue, made more challenging when the species is both protected and poses a risk to humans. Understanding the oceanic conditions associated with shark habitat use can help develop mitigation strategies or warning systems that meet both conservation and human safety objectives. Using satellite tracks from 77 juvenile and sub-adult white sharks tagged over 10 yr, we modelled individual movement patterns using hidden Markov models and applied generalised additive (mixed) models to explore correlations between movement patterns (presence-absence, habitat selection and behavioural state) and oceanographic and bathymetric variables. White sharks used the whole of the continental shelf, down to depths of 350 m on the continental slope. Sharks were present over a wide range of sea surface temperatures (SSTs; 10-27°C), with the highest probability of occurring at ~20°C. However, the number of average daily tag positions was greatest when SST was between 14 and 18°C, and sharks were more likely to exhibit area-restricted movement when SST was between ~19 and 23°C. Sharks were more likely to be present and selected habitats in productive areas with moderate to high surface chl a concentrations as well as thermal and productivity fronts. Although mesoscale eddies did not influence the likelihood of individuals being present in an area, there was a higher density of sharks in cold-core eddies compared to warm-core eddies. This study indicates that white shark presence and dispersal may be linked, perhaps via prey distribution, to oceanic conditions, potentially assisting development of suitable shark bite mitigation strategies.

Integrating Biologging and Behavioral State Modeling to Identify Cryptic Behaviors and Post-capture Recovery Processes: New Insights From a Threatened Marine Apex Predator

“…HMMs are thus advantageous relative to other more conventional techniques (e.g., linear or non-linear modeling) because they provide an objective, data-driven approach to automatically classify behavioral states using remotely collected sensor data and, importantly, can predict how each state shifts through time in response to intrinsic (e.g., biological) or extrinsic (e.g., environmental) factors (Leos-Barajas et al, 2017). Behavioral applications of HMMs have mostly focused on determining animals' foraging patterns and movements, revealing how these are shaped by biological (e.g., age, sex; Grecian et al, 2018;Carter et al, 2020), environmental (e.g., oceanographic features; Byrnes et al, 2021;Lee et al, 2021) and anthropogenic factors (e.g., fishing activity, tourism operations; Towner et al, 2016;Mul et al, 2020). Similarly, combining biologging and HMMs offers a promising framework for better characterizing recovery processes following capture, studying natural behavior and, importantly, distinguishing between the two.…”

“…In New South Wales, Australia, white sharks are caught, relocated ∼1 km offshore, and released through a non-lethal mitigation approach using Shark-Management-Alert-in-Real-Time (SMART) drumlines (Tate et al, 2021a), yet their behavioral responses to capture, and hence the implications of this strategy for both sharks and people, are unknown. Although white sharks' broad scale movements have been the subject of several studies (Jorgensen et al, 2010;Skomal et al, 2017;Spaet et al, 2020a,b;Lee et al, 2021), knowledge of their fine scale behavior remains restricted to a few specific contexts (e.g., foraging near seal colonies; Jewell et al, 2019;Semmens et al, 2019;Watanabe et al, 2019a,b). A more detailed understanding of both their post-capture responses and natural behavior across ecological contexts is thus critical to their management and conservation.…”

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“…This may be due to shorter track durations for other regions (Weng et al, 2007b) or physical and/or biological differences between regions, such as prey availability, predation pressures, or bathymetry (Spaet et al, 2020). In the WNA there is also a consistent shift in increased proportion of time spent in offshore waters (longitudinal expansion) with increasing body size, similar to that observed for the white shark in Australia (Bruce et al, 2019;Lee et al, 2021) and the NEP (Jorgensen et al, 2010;Domeier, 2012;Domeier and Nasby-Lucas, 2012;Hoyos-Padilla et al, 2016). These longitudinal range expansions may reflect a reduction in predation risk after threshold sizes are reached (Skov et al, 2011;Hussey et al, 2017;Stump et al, 2017); improved access to biological or physical features to meet changing physiological demands (food sources, temperature profiles) (Ford, 1983;Breau et al, 2011;Braun et al, 2019); and/or in the case of off-shelf movements, to use alternate migratory pathways that reduce travel time to/from residency areas, or to select or avoid particular features such as the Gulf Stream current or associated habitats (Block et al, 2011;Dodson et al, 2013;Chambault et al, 2017;Gaube et al, 2018).…”

“…Contemporary research using multiple tag technologies to obtain longer-term datasets has yielded key insights into how aquatic animals interact with their environment (e.g., Vaudo et al, 2017;Braun et al, 2019;Cochran et al, 2019;Hoffmayer et al, 2021), potential drivers of vertical and horizontal movement (e.g., Coffey et al, 2017;Gaube et al, 2018;Lee et al, 2021), and how space use changes through ontogeny (e.g., Skomal et al, 2017;Ajemian et al, 2020), while also providing critical information for the implementation of effective management and conservation strategies (e.g., Acuña-Marrero et al, 2017;White et al, 2017;Bangley et al, 2020). In recent years, studies using pop-up satellite-linked archival tags (PSATs) on sharks have revealed long-term movement and migration routes as well as habitat use patterns (e.g., Weng et al, 2007a,b;Pade et al, 2009;Comfort and Weng, 2015).…”

“…Studies from various regions around the world show that most white sharks exhibit migratory and residency behaviors (Bruce et al, 2006;Duffy et al, 2012;Skomal et al, 2017;Lee et al, 2021) and in many instances these movement phases are predictable (Weng et al, 2007a;Jorgensen et al, 2010;Duffy et al, 2012). Drivers of these movements have been suggested to be abiotic factors including temperature or currents as well as biotic factors such as mating, pupping, prey availability, or predation risk (Duffy et al, 2012;Domeier and Nasby-Lucas, 2013;Hoyos-Padilla et al, 2016;Skomal et al, 2017;Jorgensen et al, 2019).…”