Skip to main content

Active acoustic telemetry reveals ontogenetic habitat-related variations in the coastal movement ecology of the white shark

Abstract

Background

Little is known about the fine-scale behavioural choices white sharks make. The assessment of movement at high spatio-temporal resolution can improve our understanding of behavioural patterns. Active acoustic telemetry was used along a coastal seascape of South Africa to investigate the movement-patterns of 19 white sharks tracked for 877 h within habitats known to host different prey types.

Results

A three-state hidden Markov model showed higher ontogenetic variability in the movements of white sharks around estuary-related coastal reef systems compared to around a pinniped colony. Our results further suggest white sharks (1) use the same searching strategy in areas where either pinnipeds or fishes are present; (2) occupy sub-tidal reef habitats possibly for either conserving energy or recovering energy spent hunting, and (3) travel directly between the other two states.

Conclusions

White sharks appear not to simply roam coastal habitats, but rather adopt specific temporally optimized behaviours associated with distinct habitat features. The related behaviours are likely the result of a balance among ontogenetic experience, trophic niche, and energetics, aimed at maximizing the use of temporally and spatially heterogeneous environments and resources. The possible implications for the future conservation of white sharks in coastal areas are discussed, with particular attention to South Africa’s present conservation and management challenges.

Background

The drivers of animal movement range from individual daily survival (e.g., regular foraging, predator avoidance, resting) to long-term breeding success and multigenerational gene flow obtained through dispersal and migration of individuals [86]. As animal movements are intrinsic to behavioural strategies, the assessment of movement states may provide a tool to better understand the underlying behavioural choices animals make, result of internal (e.g., metabolism, searching for a mate) and external (e.g., thermal optimum range, predator/prey presence) factors. For example, these factors may motivate a hungry individual to search for food over a small area or flee from a predator when threatened in a more directional path. Therefore, investigating how an animal moves can provide insights into both the internal motivations and the adaptive behaviours responsible for maximizing the utilization of temporally and spatially heterogeneous environments and resources [75].

Nevertheless, our ability to understand movement data is still exceeded by our ability to collect it [59]. Such limitations are due in part to the complexity of factors determining animal movement, including physiological, environmental, ecological, and genetic elements [40]. Behavioural studies on non-air breathing marine species are further complicated by the difficulty of keeping an animal in sight (either visually or through telemetry) continuously and for extended periods. To date, most fish research has focused on describing and characterizing movement patterns, paying limited attention to identifying the drivers behind the movement [66, 79]. Integrating statistical approaches with local knowledge of species ecology and the seascape in which it moves allows us to bridge the gap between describing where/when/how an animal moves and understanding why it moves [47].

Satellite telemetry provides important information on large-scale migrations [8, 14] and population connectivity [7, 29]. Behavioural decision-making processes often operate on a temporal and spatial scale much smaller than the resolution of satellite telemetry [34, 40]. This is where active acoustic telemetry provides an advantageous means of obtaining multi-day movement data in a coastal area with higher spatial and temporal resolution [3]. Few studies have focused on the movement-related behavioural characterization of white sharks, Carcharodon carcharias, in different areas of the world [10, 62, 94, 96, 99], however they were limited by the durations of the tracking bouts. Mossel Bay is one of the few sheltered embayments along the exposed South African coastline and provides a unique opportunity to collect extended, continuous, and repeated movement data on semi-resident white sharks. In Mossel Bay, white sharks use the inshore areas of the bay. This area encompasses a Cape fur seal (Arctocephalus pusillus pusillus) rookery, coastal reef systems, and three small estuaries [55, 58].

The hidden Markov model (HMM) allows for input of a time series with serially correlated observations of regular animal displacement data (step lengths and turning angles) and estimates the underlying, “hidden”, time series of movement-based states driving the “observed” time series [64]. Within an HMM framework [105], the Markov chain of states is a stochastic process for which the probability of a state is dependent only on the state of the previous step. These states influence the distributions of the observed step lengths and turning angles.

Recent research has shown that white sharks use different swim speeds (and thus step lengths) to optimize energy expenditure across different behaviours [99]. The extension of HMMs to include covariates [71] thus shifted the aim of this study from describing movement per se, to identifying and modelling behaviour-related movements. These movements may be driven by inter and intra-specific ecological processes such as ontogenetic patterns, diel foraging cycles, and the use of fragmented habitats. Because the area where pinnipeds predictably occur in Mossel Bay is well separated from where coastal reef fishes abound, one of the objectives of this study was to understand how the movement patterns of white sharks may change with the presence of pinnipeds or in areas where other prey resources are available. A further objective was to quantify how ontogenetic plasticity, which is especially important in a predator–prey context [44, 65], influences the spatio-temporal habitat use patterns of this species in a coastal environment.

Several hypotheses were tested: (1) the movement pattern used by white sharks to hunt pinnipeds is spatially restricted only to the area where pinnipeds are predictably found (i.e., Seal Island); (2) if foraging for fish was sporadic, no spatial or temporal pattern should be discernable over reef structures; (3) each movement pattern should differ by size classes linearly if the result of a gradual learning of skills.

The importance of understanding how white sharks use the coastal seascape has become even more pressing following the recent disappearance of white sharks from Gansbaai and False Bay, the other two main coastal aggregation sites in South Africa [39], Towner et al. in review).

Results

During the study period, 19 white sharks ranging from 1.5 to 4.2 m estimated TL, were externally tagged and manually tracked within Mossel Bay for a combined duration of 877 h (mean = 46.1 h, SD = 43.6 h: Table 1). We ran both a three-state and a two-state (to confirm our choice quantitatively) HMM over 97 bivariate independent time series of step lengths and turning angles, with the same variables and similar constraints (AIC were 143,399 and 151,832, respectively).

Table 1 Summary of the data obtained from 19 white sharks acoustically tagged and manually tracked in Mossel Bay between 2008 and 2012 (1.5–1.9 m 16%, 2–2.4 m 21%, 2.5–2.9 m 26%, 3–3.4 m 16%, 3.5–3.9 11% and 4–4.4 m TL 11%)

The selected three-state model was evaluated for possible lack of fit via Quantile–Quantile plots and the autocorrelation of the pseudo-residuals (Additional file 1). The model appears to capture the values of step length well, while there is some mismatch around the median of turning angles. Overall, the calculated pseudo-residuals appear to fit well with the theoretical quantiles and can be considered normally distributed. The autocorrelation function (ACF) shows the model captured the autocorrelation better when compared to the initial autocorrelation in the data, especially for the turning angles. However, there is significant remaining autocorrelation not captured by the model (Additional file 1).

The state with (1) the highest mean angular concentration; (2) the lowest standard deviation for step length and (3) the highest mean step length (229 m) (all indicative of directed movement) was inferred to represent traveling between focal sites (Fig. 1). The resulting mean rate of movement (ROM) for the traveling state was 0.8 m s−1 (2.9 km h−1). The remaining two states showed lower angle concentration than the more directed traveling state, with little difference in turning angle distributions between the two. These two states were inferred to represent a faster ARS (fARS) with a mean step length of 172 m (mean ROM of 0.6 m s−1 or 2.2 km h−1) and a slower ARS (sARS) with a mean step length of 92 m (mean ROM of 0.3 m s−1 or 1.1 km h−1).

Fig. 1
figure 1

Weighted state-dependent conditional densities of step lengths (left) and turning angles (right) for the tracked white sharks in Mossel Bay (sARS = slower ARS; fARS = faster ARS)

The final model, incorporating a 12-h period for time of day (TOD as a circular variable with a 24-h period), a linear relationship with size, and a threshold distance of 500 m to either the closest estuary mouth or to Seal Island, was selected by AIC (Table 2 and Fig. 2). The stationary distributions obtained from the selected model for the winter period (90 days either side of the winter solstice) were plotted by size classes to assess movement patterns for white sharks (1) within the bay (Fig. 3), (2) at 400 m from the nearest estuary mouths (Fig. 4), or (3) at 400 m from the pinniped colony (Fig. 5). Winter is the main hunting season for Cape fur seals and when white sharks use both primary habitat types [89].

Table 2 Summary of model selection for data obtained for this study
Fig. 2
figure 2

On the left panel, map of Mossel Bay, South Africa, showing sites mentioned in the text and bathymetry of the bay. Thicker black lines represent the main coastal reefs utilized by white sharks, as also identified by [55]. On the right panel, frequency distribution of the coded states from the actively tracked white sharks within Mossel Bay (sARS = slower ARS, fARS = faster ARS)

Fig. 3
figure 3

Stationary distributions (and relative 95% confidence intervals) of hypothetical white sharks of different size classes (mean ± 0.25 m TL) in winter period (winter solstice ± 90 days) within the Mossel Bay area. Rug plots are added on the x-axis as index of effort. sARS = Slower ARS, fARS = Faster ARS

Fig. 4
figure 4

Stationary distributions (and relative 95% confidence intervals) of hypothetical white sharks of different size classes (mean ± 0.25 m TL) in winter period (winter solstice ± 90 days) at 400 m from a river mouth in Mossel Bay. Rug plots are added on the x-axis as index of effort. sARS = slower ARS, fARS = faster ARS

Fig. 5
figure 5

Stationary distributions (and relative 95% confidence intervals) of hypothetical white sharks of different size classes (mean ± 0.25 m TL) in winter period (winter solstice ± 90 days) at 400 m from Seal Island in Mossel Bay. Rug plots are added on the x-axis as index of effort. sARS = slower ARS, fARS = faster ARS

The use of different movement states by different size classes is more variable within the bay or near an estuary mouth when compared to the movement choices adopted around Seal Island. Close to an estuary, the probability of being in the slower ARS state becomes higher with an increase in shark size. Near an estuary mouth larger size classes tend to be in this slower state with a higher probability before sunset and sunrise, with the smaller size classes occurring in the middle of day and night. In these areas the faster ARS state still occurs but it is used more by smaller white sharks and mainly around sunset and sunrise.

When not close to either an estuary mouth or Seal Island, the smaller white sharks have an almost equal probability of traveling or using the faster ARS state. Instead, larger white sharks are more likely to be in a traveling mode than using an active search pattern.

Lastly, even though all size classes are more likely to use the faster ARS movement pattern around the pinniped colony, the time spent in that higher energy-consuming state decreased with size (Fig. 6), ANOVA [F(2,168) = 6.9487, p = 0.0013)].

Fig. 6
figure 6

Average time spent in the faster ARS when within 500 m from Seal Island in Mossel Bay by size class: smaller than 2.5 m TL, between 2.5 and 3.5 m TL and larger than 3.5 m TL (the top of the barplots indicate the mean times and the error bars the standard deviations)

Discussion

Understanding the daily decisions that an individual makes throughout its ontogeny to maximize its survival, while modifying its Eltonian niche, requires the collection of high-resolution data, both spatially and temporally, as those are the scale at which fine-scale behavioural choices occur [80]. Movement data can be used as a behavioural proxy, especially when correlated to habitat features and a species’ phenotypic characteristics [71, 72, 88]. In this study, 19 white sharks were acoustically tracked and their positions were modelled using a hidden Markov model. Movement-based behavioural states were a function of ontogenetic development, time of day, season, and habitat features.

The ACF for step length suggests the possibility that some other variables, which were not accounted for, could explain the remaining autocorrelation. This could be the focus of future projects, for example collecting in situ data on environmental variables while manually tracking white sharks in coastal areas.

The extended use of Mossel Bay’s main coastal habitats (coastal reef systems and the pinniped colony) by all the tracked white sharks during this study was similar to previous studies which investigated white shark activity in the area since 2005 [55, 58, 89]. This suggests a consistent use of this bay by white sharks.

A potential caveat of our modelling approach lays in the difficult ecological interpretation of the states [105]. One of the original objectives was to investigate whether different size classes show certain movement types in different areas and at different times of the day and/or the year. Consequently, the states were initially loosely named “At Seal Island”, “At estuary mouths” and “Travelling”. The choice of three states was an a priori decision but was also tested, as suggested by [83] and similarly to what Towner et al. [96] did. However, the states turned out to have different movement characteristics than expected: for instance, the occurrence of both ARS states close to estuary mouths. Therefore, the behavioural interpretation of the three states and their names had to change. Hence, the constraint of non-transition between the two ARS states and/or the subjective interpretation of the results could not hold any longer. Future research using different technologies, such as multi-sensor data loggers equipped with a combination of a video camera and a high-resolution accelerometer [99], could show whether our interpretations of the states are still valid as well as, for instance, whether transitions between the two ARS states do indeed occur. Another potential limitation of our movement-based behavioural interpretation was related to the fact that, although marine animals move within a three-dimensional environment, we defined behaviours based on two-dimensional movements (as also done by others: e.g., [96]). Because the parts of the bay where white sharks moved seldom exceeded 20 m depth and lacked a thermocline for most of the year, we are confident that the horizontal dimensions of the movement patterns we assessed over 5-min intervals in Mossel Bay, can adequately approximate the three-dimensional movement of white sharks within this bay, and possibly other shallow coastal environments, as suggested when comparing our results to the mode cruising speeds for white sharks assessed using speed sensors in Harding et al. [41] (Additional file 1).

Model selection highlighted a few temporal aspects of the behaviour of white sharks in Mossel Bay. Firstly, seasonality is important, not only in terms of the general distribution of white sharks within the bay [89], but also because the use of different modelled states appear to vary at different times of the year. Secondly, white sharks behave with a 12-h cyclical periodicity in Mossel Bay, and not circadian as shown in other nearby white shark aggregations such as Gansbaai [56, 96] and False Bay [51]. In False Bay white sharks have shown a preference for smaller groups or solitary Cape fur seals traveling back to the colony, mainly around sunrise [69]. In Mossel Bay, the temporal variations in the traversing behaviour of Cape fur seals around the colony, especially in winter [73], peak the probability of encounter for white sharks around both dusk and dawn.

Examination of the state-dependent distributions of the movement parameters showed that, although the two ARS states were almost identical in the distribution of turning angles, their step length distributions were statistically different, with one mean being around double than the other. As one of the main assumptions of HMMs requires equally spaced measurements of the observed variables, a longer step length (over the same time interval) relates to a faster movement segment. Therefore, the difference between these two states has more to do with the speed of movement than the tortuosity of the search pattern. Hence, these two states were named “faster” and “slower” ARS.

In proximity to the pinniped colony in winter, the faster ARS state, interpreted here as “patrolling”, was the most likely state to be used by all white shark size classes identified in this study. This may be because higher speeds are required to increase the frequency of encounters with traversing Cape fur seals. This pattern can then be followed by momentary burst speeds by the shark, quantified at up to 6.5 m s−1 [91]. The faster ARS is still slower than the traveling bouts used to arrive in or leave an area of interest (as confirmed by [99]. A “random walker” must indeed exercise a trade-off between moving too fast (with the risk of leaving a spatially limited, resource-rich, patch) against maximizing the probability of encountering traversing prey [97]. A shark will also increase its attention to its surroundings when reducing its speed and increasing its frequency of turning [60, 99].

The larger tracked white sharks spent less time in the patrolling state near the pinniped colony compared to the smaller conspecifics. These larger size classes were also more likely to be found in this faster ARS state before sunrise and sunset. During these two optimal scotopic periods, the ambient light levels are too low for the prey to distinguish an ambushing predator below but sufficient for a white shark to identify the silhouette of its prey at the surface [69]. Intraspecific competition and/or less accrued hunting experience [70] are likely to force the smaller white sharks to forage for longer and outside these crepuscular, optimal, hunting periods. While the hunting behaviour of smaller white sharks changes spatially to suboptimal conditions in False Bay [70] and at the Farallon Islands [36] the shift towards suboptimal conditions appears to be temporal in Mossel Bay. This is possibly because of the small size of its pinniped colony: different behavioural patterns related to different size in the islands with pinniped colonies was also observed in two other white shark aggregations, such as Guadalupe and South Farallon Islands [48].

Originally, we expected the faster ARS to be related only to Seal Island, the area where Cape fur seals predictably occur. However, this patrolling state also occurred over coastal reefs nearby the estuary mouths, changing our initial interpretation of both ARS states. Due to the absence of pinnipeds confirmed by the tracking teams the faster ARS state in these areas may be interpreted as an intensive, meticulous, search pattern for other prey: most likely smaller demersal elasmobranchs and bony fishes, as also predicted by [48]. These groups are common in the diet of white sharks, especially when smaller than 3 m TL [19, 31, 33]. It is important to note that while movement data can be used as a proxy for behaviour, it provides no insight into prey availability. Other studies support the high frequency of occurrence for both teleost and elasmobranch species over reef systems in Mossel Bay (Ralph Watson, PhD thesis submitted) and neighbouring coastal areas [26, 77]. The patrolling pattern over reefs associated with estuary mouths may potentially define these sites as important foraging grounds, not only for many bony and cartilaginous fish species [2, 101], but also for white sharks. White sharks are known to prey on such reef-associated demersal species [23, 38] specifically in inshore areas and at other white shark aggregations in South Africa [33, 50, 51]. Future research using baited remote underwater video systems (BRUVs) concurrent with active acoustic tracking, or multi-sensor data loggers fitted with video cameras deployed on white sharks (as done by [22] around a pinniped colony), could shed light on the composition and abundance of possible prey species, as well as the behavioural choices white shark make at these coastal reef sites.

The patrolling state of white sharks over reefs mainly occurred around twilight conditions. This conforms to the activity patterns of other predators which are also able to predict and respond to periodical prey availability [37]. Around sunset, diurnal fish species seek cover, and after a “quiet period” the nocturnal species emerge (twilight hypothesis: [74]. An ambushing predator seeks those species exiting their refuges, giving it a predictable advantage [43, 82]. White sharks could exploit moments of higher competition for refuges, between diurnal or nocturnal species: a competition that may causes reef fish to become less vigilant toward predators [87].

Being an obligate ram ventilator [67] a white shark is expected to either actively search for mates and or food (patrolling) or travel between important resource-rich areas [70]. A possible explanation for the main movement pattern found over estuary-related reef structures may be related to its slower nature, possibly to the need to reduce swimming-related energy costs at times, while maintaining the higher metabolic requirements of a regional endothermic species [32, 98]. This type of behaviour was regularly recorded when the tracked sharks slowed down for hours along those reefs, and often they were even visually observed drifting in the slow current at the surface. This behaviour could be furthermore facilitated by environmental variability in specific areas, within the context of the energy landscape [92]: for example, a possible increase in oxygen content, caused by coastal wave breaking around the estuary-related reefs of Mossel Bay (as firstly suggested by [58], and confirmed by in situ measurements of dissolved oxygen by Logston [52]), could allow white sharks to slow down their swimming requirements while turning to remain in the same advantageous area. This slowest movement pattern was parameterized as an ARS (sARS) although not functionally a search for food (not an ARS sensu stricto). White sharks use this behavioural pattern over coastal reefs mainly around sunrise and sunset. This is likely in anticipation of, or following, higher energy expenditure at the pinniped colony, as witnessed a few times while tracking, when directed movement from Seal Island to one of the estuaries followed a natural predation, or vice versa. The slower ARS state over coastal reef habitats, described as “resting”, was particularly prevalent in the larger white shark size classes. As previously discussed, the larger and more experienced sharks are found at Seal Island during the optimal temporal windows for hunting pinnipeds. Thus, they are more likely to be successful during those hunting forays aimed at pinnipeds. A greater hunting success would be more often linked to higher metabolic heat [90] related to the digestion of pinniped blubber which is high in energy and lipid content [17]. When moving away from the pinniped colony (as described by [58] they would need to recover from the Specific Dynamic Action, more often than their smaller counterparts. Few of the tracked white sharks were also fed with an acoustic transmitter fitted with a temperature sensor which confirmed, together with visual observations, few predation events on Cape fur seals: in those cases a straight move toward those coastal reefs was followed by an increase in stomach temperature and a concomitant reduction in rate of movement (Gennari et al. in preparation). If this behavioural choice was confirmed also in terms of energy expenditure, it could represent an energetically adaptive advantage [12, 78] for these regional endothermic, obligated ram ventilators, with a high metabolic scope [11, 63]. Ontogenetically, while the ratio between body volume and body surface area increases so does the importance of recovering spent energy [5, 18]. ARS state close to an estuary mouth could reveal the possible importance of estuary-related reef systems for white sharks.

The high probability of this slower and tortuous movement pattern could emphasizes the importance of those coastal reefs for white sharks, especially around estuary mouths, not only for foraging but also for energy conservation, as suggested by Johnson et al. [58]. Testing this area-specific and energy-related hypothesis, linking environmental and movement variables, looking at explaining the higher presence of white sharks around estuaries, in Mossel Bay and elsewhere [15, 51, 76, 93] could be of interest to future studies using animal-borne accelerometers and speed sensors.

Larger, presumably more experienced, white sharks were more likely to be in the traveling state, particularly after sunrise and after sunset, when moving away from estuary-related reefs and the pinniped colony. This would allow them to move quicker among important areas of the bay. In most animal species, the cost of transport decreases with increasing mass, which is also valid for ram-ventilating sharks [20], and so larger white sharks can maintain a higher traveling speed for longer. Smaller conspecifics are equally likely to be in search of food or travelling, possibly related to their higher reliance on a fish-based diet [31, 33]. This reiterates the higher focus, and experience, the larger white sharks showed in this study by patrolling mainly over very restricted areas of coastal reefs, possibly over areas with higher fish abundance [103].

Another study modelled the movement of white sharks in a nearby bay [96] and identified two separate hunting strategies to prey upon pinnipeds at the surface: a slower sit-and-wait state (ARS) and a faster active searching (patrolling) state [98]. While we believe their patrolling state is similar to our faster ARS state, their slower ARS state (with similar distributions of the movement parameters to the sARS state in this study) was assigned to an ambushing, foraging-related state, because they observed five predation attempts on Cape fur seals. One could argue that a white shark constantly in a pinniped-hunting mode (patrolling or ambushing) would be extremely inefficient [98] particularly in inshore areas where pinnipeds are not predictable. Observation of a predation by an opportunistic forager might not necessarily define the movement state it was in just before the predation attempt. A white shark could be moving slowly and resting, but if a prey happened to be nearby, the shark could still take its chance. Our alternative hypothesis to Towner et al. [96] suggests that similarly to other marine predators [42] white sharks might use a single, general, search strategy (patrolling) to target different prey types over different habitats: around the pinniped colony or over coastal reef systems, especially nearby an estuary mouth. When a prey is identified a white shark will increase its activity and the final metabolic cost of its search. However, that final bout is not discernible with a method based on 5-min sampling intervals. This will require further investigation, possibly using continuous technology like accelerometers and gyroscopes.

Even though the large sample size, we did not manage to obtain sufficient data from all size classes, across all habitats and throughout the entire diel period. While this could be seen also as a result in terms of behavioural preferences, we decided not to focus our interpretation of the results on specific size classes but rather describe ontogenetic trends in movement-based behavioural choices. Through model selection, the data on the white shark movement strategy presented here suggests a linear ontogenetic trend. This is in line with the suggestion [36, 49, 55] that habitat knowledge and behavioural experience, not only concerning foraging on pinnipeds, may be gradually gained and accumulated as a white shark grows. Owing to the absence of parental care in all elasmobranchs, white sharks may take years to learn and refine their behaviours through trial and error. This would explain different size-related patterns, both spatially and temporally [13, 36, 55, 70]. The increased efficiency in the use of resources is not related to an ontogenetic variation in speed used when foraging [14]. These speeds are likely to be adaptive and related to body shape and energetics. Rather, the improvement can be linked to a decrease in the area used [55] and/or in the time used during these phases by larger individuals. In this study older white sharks covered smaller areas and appeared to spend less time on faster, and thus more energy-costly, movement patterns. Differences in spatial and temporal patterns could also relate to intra-specific competition, forcing smaller white sharks to use specific behaviours within suboptimal, wider, spatio-temporal boundaries.

Conclusions

While most of the literature on the foraging ecology of white sharks focuses on their relationship with pinnipeds, we show here how all the movement-based behavioural patterns described for white sharks in a coastal environment were possibly focused on specific aims, namely foraging, resting, or directional movement. We recommend that some of these hypothesized behaviours should be the subject of further investigations using alternative technologies, such as multi-sensor data loggers and baited remote underwater video systems.

The findings of this study have implications for the conservation of white sharks in South Africa. The movement-based behavioural insights gained from this study highlight the use of ARS patterns in specific areas with higher abundances of reef-associated fish species occur, possibly foraging related. This supports the dependence of white sharks on available fish prey species (inter alia [49] and [33]) across the plasticity of its ontogeny, even in coastal areas where pinnipeds are abundant. The conservation status of the only pinniped species endemic to the African continent has improved and is now considered stable [61]. We propose that a major threat to the conservation of white sharks in South Africa may reside in the inefficient management of coastal fish species [25, 46, 81] particularly those associated with estuaries [101, 102]. This is of paramount importance to the white sharks population of southern Africa which comprises mainly juveniles and subadults along its coast [1, 28, 45, 89].

Prey availability is a major threat to several terrestrial predators [9, 104] and is likely to be even more important in marine food webs (Link 2002). In the United States a series of federal, state, and other regulations designed to restore marine populations using an ecosystem-based approach has proven successful [46]. Populations of meso-predator teleosts and elasmobranchs have been increasing in Californian waters since the mid-1990s, following better regulations imposed on commercial fisheries [84]. This management approach together with the protection of marine mammals in federal waters since 1973 has led to an increasing population of white sharks along the North Eastern Pacific [16, 27, 53, 54, 68] and North Atlantic coastal areas [24].

We, therefore, recommend that conservation efforts in South Africa should avoid a predator-centric approach (single species conservation strategy) and rather adopt a holistic ecosystem-based approach that acknowledges the management needs of important prey species to sustain the carrying capacity of a top predator population [30], particularly when the population is of conservation concern, and has a high tourism value.

Methods

Study site

Mossel Bay is a large semilunar coastal embayment situated on the southern tip of Africa (34°10′ S, 022°10′ E; Fig. 2). The town of Mossel Bay is situated on the rocky headland (Cape St. Blaize) of the southern end of the bay. The bay hosts a small island (Seal Island) with a colony of approximately 5,000 Cape fur seals [61]. The coastline is characterized by long sandy beaches interspersed by a series of coastal reefs, which extend into the bay to a maximum depth of approximately 40 m, in association with the Groot Brak paleo river [21]. The mouths of three small estuaries (Hartenbos, Klein Brak and Groot Brak) are situated in the bay. Seal Island and the coastal reef systems, particularly those adjacent to the mouths of the three estuaries, make up the primary marine habitats used by white sharks in Mossel Bay [55, 58, 89]. Estuaries are considered nursery areas for many marine species and represent biomass hotspots along the coastline [100, 103]. The elevated abundance of demersal elasmobranch and teleost species over reef structures has been confirmed in Mossel Bay (Ralph Watson, PhD candidate, unpublished data) and other neighbouring areas [26, 77].

Tagging and tracking

White sharks were attracted to a research vessel using bait and chum, consisting of a mixture of sardines and water. The total length of the tagged shark was visually estimated using the width of the research vessel as a reference. Lengths were recorded to the nearest 0.1 m, after an agreement between two or more experienced researchers on board, following Johnson et al. [58], Kock et al. [50], and Towner et al. [95]. Sharks were externally tagged below the first dorsal fin with VEMCO V16TP continuous acoustic transmitters (VEMCO, InnovaSea Systems). Tags were placed using either a tagging pole or a modified speargun, according to the methods described by Gennari et al. [35].

Working in rotation (with crew changes every 6 or 12 h), tracking teams used a VEMCO VR100 acoustic receiver on board the research vessel to locate and follow a tagged white shark according to the method set out in Johnson et al. [58]. Positions were recorded when three consecutive signals were received at strengths of at least 70 dB (equivalent to circa 200 m under calm tracking conditions: [35]. Vessel speed was managed to obtain a desired GPS sampling interval of 10 min. Tracking around Seal Island had a sampling interval of 5 min, so as not to lose the tagged shark among the complex rock structures.

When a tracked shark was too close to the surf zone, a GPS position was taken directly offshore of the animal’s location. In order to get the ‘true’ position of the tracked shark, a correction function was applied to the recorded position of the tracking vessel, using detection range testing data (after [35] and the receiver’s signal strength of the recorded tag detection. The distance to the tagged shark was calculated using the following equation:

$${\text{Distance }}\,{\text{to }}\,{\text{the }}\,{\text{tagged}}\,{\text{ shark }} = \, \left( { - 0.{4145 } \times {\text{ signal strength }} + { 39}.{4181}} \right)^{{2}} .$$

The duration of all continuous tracking sessions was terminated due to either adverse sea conditions or loss of the tracked shark. To reduce uncertainty in the location of individuals when they were momentarily lost by the tracking team, the tracking segments were split into continuous sessions using a minimum threshold of 2 h between consecutive relocations. Two hours was the maximum timeframe in the field to relocate a shark before terminating the tracking session. Continuous tracking sessions were further split into 12-h segments to improve convergence during model fitting.

Modelling approach

In order to test the influence of covariates on the state transition probabilities, a Continuous-Time Correlated Random Walk (CTCRW) model with a bivariate normal error radius of nine meters (listed GPS accuracy of the VEMCO VR100 receiver) was fitted to the filtered data using the packages momentuHMM [71] and crawl [57] in R version 4.0.0 [85]. The CTCRW model was then used to predict locations at regular 5-min intervals, meeting the requirement of regular time intervals for fitting an HMM. Tracking segments that produced no estimate of variance for the CTCRW parameters, or variance estimates that were unreasonably large, were removed. Model predictions were assessed visually and removed if the predicted locations were judged unrealistically distant from the observed locations.

A three-state HMM was then fitted to the regularized step lengths and turning angles calculated from the locations predicted by the CTCRW model [71]. Preliminary model fitting and selection by Akaike Information Criterion (AIC) suggested that a Gamma and a von Mises were the most appropriate distributions for step lengths and turning angles, respectively [71].

The choice of number of movement states by model selection was supported by our observations while tracking and by previous work [55, 58] on acoustically tracked white sharks in Mossel Bay. The first two patterns were area-specific with slower, focused, tortuous movements, over coastal reefs and around the pinniped colony. The third pattern related to faster more directed movement pattern in between the two habitats. The first two movement patterns bear similarity to the Area Restricted Search (ARS) described by Benhamou [4]. ARS patterns are known to increase encounter success with prey (yet not in all species: e.g., [6]) and to occur more often at focal sites which, in this case, are two habitat types with different prey presence: Seal Island (the pinniped colony) or the coastal reef complexes adjacent to estuary mouths, where pinnipeds are absent. As the behavioural patterns of pinnipeds and coastal reef fishes are different, different movement-related hunting patterns of white sharks may be expected. In contrast, a more direct movement was often observed when sharks moved between these focal sites.

The intervals between relocations were too long to identify bursts or short changes in activity (likely indicative of feeding events) and visual observations from a vessel of those feeding events are either too sparse (in the case of pinnipeds) or extremely unlikely (in the case of fish). Therefore, we focused on identifying ontogenetic differences in movement patterns in relation to the different habitat types within the main activity areas of white sharks [55, 58]. One of the objectives was to investigate whether the ARS patterns used by white sharks in a coastal embayment are specific to sites where different prey species are known to occur: i.e., whether the movement pattern related to foraging for pinnipeds is used only around Seal Island. We hypothesized that the ARS patterns would differ over coastal reefs and around a pinniped colony, indicating that a relationship exists between movement patterns and potential prey types.

The following parameters were constrained in the model to effectively capture the two more focused (tortuous) movement patterns, versus the more direct movement state [71]: (1) the mean parameter for the Gamma distributions on step length was higher for state one (directed movement state) than for states two or three (area related states), (2) the variance parameter for the Gamma distributions was higher for states two and three than state one; (3) the concentration parameter of the von Mises distribution was higher for state one than for states two or three.

To account for any influence of distinct habitats, such as Seal Island or the main coastal reef complexes close to river mouths (Fig. 2), on the behavioural choices of the sharks, we calculated two variables for each positional fix. These variables represented the minimum distances to Seal Island and the closest estuary mouth. We incorporated them into the model to allow behavioural changes to be influenced by habitat features: the closer a white shark was to the pinniped colony or to an estuary, the likelier the switch to one of the ARS behaviours, as observed while tracking.

Lastly, the transition probabilities were constrained, such that the two ARS states could not switch between each other without first passing through a directed state (travel). The coastal reef areas do not overlap with the one around Seal Island and so a different movement pattern must occur over those in-between areas that white sharks do not focus on [58].

As a result, for each individual, k, the time-dependent transition probability matrix Γ is given by:

$${\Gamma }^{k}\left(t\right)=\left[\begin{array}{lll}{\gamma }_{11}^{k}(t)& {\gamma }_{12}^{k}(t)& {\gamma }_{13}^{k}(t)\\ {\gamma }_{21}^{k}(t)& {\gamma }_{22}^{k}(t)& 0\\ {\gamma }_{31}^{k}(t)& 0& {\gamma }_{33}^{k}(t)\end{array}\right],$$

where \({\gamma }_{ij}^{k}(t)\) is the conditional probability of the individual k being in state j in the time interval (t, t + 1), given it is in state i during the interval (t-1, t).

Covariates on the state transition probabilities were included by the following function:

$$\mathrm{logit}\left({{\gamma }_{ij}}^{k}\left(t\right)\right)=\left\{\begin{array}{l}{\beta }_{0ij}+{\beta }_{1Aij}{x}_{1Akt}+{\beta }_{2ij}{x}_{2kt}+\dots +{\beta }_{6ij}{x}_{6kt}+{\beta }_{7ij}{x}_{2kt}{x}_{3kt}+{\beta }_{8ij}{x}_{2kt}{x}_{4kt}+{\beta }_{9ij}{x}_{2kt}{x}_{5kt}+{\beta }_{10ij}{x}_{2kt}{x}_{6kt}+\epsilon , for i=1, j=2\\ {\beta }_{0ij}+{\beta }_{1Bij}{x}_{1Bkt}+{\beta }_{2ij}{x}_{2kt}+\dots +{\beta }_{6ij}{x}_{6kt}+{\beta }_{7ij}{x}_{2kt}{x}_{3kt}+{\beta }_{8ij}{x}_{2kt}{x}_{4kt}+{\beta }_{9ij}{x}_{2kt}{x}_{5kt}+{\beta }_{10ij}{x}_{2kt}{x}_{6kt}+\epsilon , for i=1, j=3\\ {\beta }_{0ij}+{\beta }_{2ij}{x}_{2kt}+{\beta }_{5ij}{x}_{5kt}+{\beta }_{6ij}{x}_{6kt}+{\beta }_{9ij}{x}_{2kt}{x}_{5kt}+{\beta }_{10ij}{x}_{2kt}{x}_{6kt}+\epsilon , otherwise\end{array}\right\},$$

where \({x}_{1A}\) and \({x}_{1B}\) are binary variables representing the distance of an individual from Seal Island or the closest estuary mouth, respectively. \({x}_{2}\) represents the size (TL) of the individual, \({x}_{3}\) and \({x}_{4}\) are the trigonometric functions \(\mathit{sin}\frac{2\pi t}{0.5}\) and \(\mathit{cos}\frac{2\pi t}{0.5}\) with a possible 12- or 24-h period, where tc represents the fraction of the 24-h daily cycle. \({x}_{5}\) and \({x}_{6}\) are similarly the trigonometric functions \(\mathit{sin}\frac{2\pi t}{365.25}\) and \(\mathit{cos}\frac{2\pi t}{365.25}\) with a period of 1 year when t represents the Julian day of the year.

Different variations of the complete model were fitted and compared using AIC values (Table 2 ordered according to AIC values). The starting model (final Model 8) allowed no transition between ARS states. Transition probabilities from traveling to one of the ARS states were allowed (i) to increase when the distance to Seal Island or an estuary mouth was less than 1 km, and (ii) to vary based on a) the Day of The Year (DOY as a circular variable with a 364-day period) and b) the interaction between a linear variability in body size (TL) and TOD. This was because the onset of the ARS states in specific core areas was expected to vary seasonally [89] and according to the different diel use of specific areas by different size classes [55, 58]. In the starting model, the transition probabilities from one of the ARS states to traveling were allowed to vary based on the DOY and only a linear variability in body size (TL). The end of the ARS was not expected to depend on a specific TOD, but rather on how long it took to achieve the goal of either ARS state.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Andreotti S. The conservation of South African white sharks: population number, genetic distinctiveness and global connections. PhD dissertation. Stellenbosch University. 2015;154 pp.

  2. Bangley CW, Paramore L, Dedman S, Rulifson RA. Delineation and mapping of coastal shark habitat within a shallow lagoonal estuary. PLoS ONE. 2018;13: e0195221.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  3. Barnett A, Abrantes KG, Stevens JD, Bruce BD, Semmens JM. Fine-scale movements of the broadnose sevengill shark and its main prey, the gummy shark. PLoS ONE. 2010;5(12): e15464.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Benhamou S. Efficiency of area-concentrated searching behaviour in a continuous patchy environment. J Theor Biol. 1992;159:67–81.

    Article  Google Scholar 

  5. Bernal D, Dickson KA, Shadwick RE, Graham JB. Analysis of the evolutionary convergence for high performance swimming in lamnid sharks and tunas. Comp Biochem Physiol, A. 2001;129:6957–7026.

    Article  Google Scholar 

  6. Bestley S, Patterson TA, Hindell MA, Gunn JS. Predicting feeding success in a migratory predator: integrating telemetry, environment, and modeling techniques. Ecology. 2010;91(8):2373–84.

    PubMed  Article  Google Scholar 

  7. Bonfil R, Meÿer M, Scholl MC, Johnson R, O’Brien S, Oosthuizen H, Swanson S, Kotze D, Paterson M. Transoceanic migration, spatial dynamics and population linkages of white shark. Science. 2005;310(5745):100–3.

    CAS  PubMed  Article  Google Scholar 

  8. Boustany AM, Davis SF, Pyle P, Anderson SD, Le Boeuf BJ, Block BA. Satellite tagging: expanded niche for white sharks. Nature. 2002;415:35–6.

    CAS  PubMed  Article  Google Scholar 

  9. Bowyer RT, Person DK, Pierce BM. Detecting top-down versus bottom-up regulation of ungulates by large carnivores: implications for biodiversity. In: Ray JC, Redford KH, Steneck RS, Berger J, editors. Large carnivores and the conservation of biodiversity. Washington: Island Press; 2005. p. 342–61.

    Google Scholar 

  10. Bradford RW, Hobday AJ, Bruce BD. Identifying juvenile white shark behaviour from electronic tag data. In: Domeier ML, editor. Global perspectives on the biology and life history of the white shark. CRC Press; 2012. p. 255–70.

    Google Scholar 

  11. Brill RW, Bushnell PG. Metabolic and cardiac scope of high energy demand teleost, the tunas. Can J Zool. 1991;69:2002–9.

    Article  Google Scholar 

  12. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB. Toward a metabolic theory of ecology. Ecology. 2004;85(7):1771–89.

    Article  Google Scholar 

  13. Bruce BD. Preliminary observations on the biology of the white shark Carcharodon carcharias in south Australian waters. Aust J Mar Freshw Res. 1992;43:1–11.

    Article  Google Scholar 

  14. Bruce BD, Stevens JD, Malcolm H. Movements and swimming behaviour of white sharks (Carcharodon carcharias) in Australian waters. Mar Biol. 2006;150(2):161–72.

    Article  Google Scholar 

  15. Bruce BD, Bradford RW. Habitat use and spatial dynamics of juvenile white sharks, Carcharodon carcharias, in Eastern Australia. In: Domeier ML, editor. Global perspectives on the biology and life history of the white shark. CRC Press; 2012. p. 225–53.

    Google Scholar 

  16. Burgess G, Bruce BD, Cailliet GM, Goldman KJ, Grubbs RD, Lowe CG, MacNeil MA, Mollet HF, O’Sullivan JB, Weng KC. A re-evaluation of the size of the white shark (Carcharodon carcharias) population off California, USA. PLoS ONE. 2014;9(6): e98078.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. Carey FG, Kanwisher JW, Brazier O, Gabrielson G, Casey J. Pratt HLjr (1982) Temperature and activities of a white shark, Carcharodon carcharias. Copeia. 1982;2:254–60.

    Article  Google Scholar 

  18. Carey FG, Casey JG, Pratt HL, Urquhart D, McCosker JE. Temperature, heat production, and heat exchange in lamnid sharks. Mem South Calif Acad Sci. 1985;9:92–108.

    Google Scholar 

  19. Carlisle AB, Kim SL, Semmens BX, Madigan DJ, Jorgensen SJ, Perle CR, Anderson SD, Chapple TK, Kanive PE, Block BA. Using stable isotope analysis to understand the migration and trophic ecology of northeastern Pacific white sharks (Carcharodon carcharias). PLoS ONE. 2012;7(2): e30492.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Carlson JK, Goldman KJ, Lowe CG (2004) Metabolism, energetic demand, and endothermy. In: Biology of sharks and their relatives. p. 203–224

  21. Cawthra HC, Compton JS, Fisher EC, MacHutchon MR, Marean CW. Submerged shorelines and landscape features offshore of Mossel Bay, South Africa. Geol Soc Lond Spec Publ. 2016;411(1):219–33.

    Article  Google Scholar 

  22. Chapple TK, Tickler D, Roche RC, Bayley DTI, Gleiss AC, Kanive PE, Jewell OJD, Jorgensen SJ, Schallert R, Carlisle AB, Pilly JS. Ancillary data from animal-borne cameras as an ecological survey tool for marine communities. Mar Biol. 2021;168(7):1–13.

    Article  Google Scholar 

  23. Cliff G, Dudley SF, Davis B. Sharks caught in the protective gill nets off Natal, South Africa. 2. The great white shark Carcharodon carcharias (Linnaeus). S Afr J Mar Sci. 1989;8(1):131–44.

    Article  Google Scholar 

  24. Curtis TH, McCandless CT, Carlson JK, Skomal GB, Kohler NE, Natanson LJ, Burgess GH, Hoey JJ, Pratt HLJr,. Seasonal distribution and historic trends in abundance of white sharks, Carcharodon carcharias, in the western North Atlantic Ocean. PLoS ONE. 2014;9(6): e99240.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. da Silva C, Winker CH, Parker D, Wilke CG, Lamberth SJ, Kerwath SE. Update and review of the NPOA for Sharks South Africa. In: IOTC—14th Working Party on Ecosystems and Bycatch. IOTC-2018-WPEB14-11_Rev1, Cape Town, South Africa. 2018.

  26. Dando JW. A baited remote underwater video survey of the Goukamma Marine Protected Area’s ichthyofauna and a subsequent community structure comparison with the Betty’s Bay, Stilbaai, and Tsitsikamma Marine Protected Areas. Master’s thesis, University of Cape Town, South Africa. 2020.

  27. Dewar H, Eguchi T, Hyde J, Kinzey DH, Kohin S, Moore J, Taylor BL, Vetter R. Status review of the northeastern Pacific population of white sharks (Carcharodon carcharias) under the Endangered Species Act. 2013.

  28. Dicken ML and Smale MJ. White shark (Carcharodon carcharias)-inflicted bite wounds observed on Cape fur seals (Arctocephalus pusillus pusillus) at Black Rocks, Algoa Bay, South Africa. Afr Zool. 2013;48(2):418–26.

    Article  Google Scholar 

  29. Duffy CAJ, Francis MP, Manning MJ, Bonfil R. Regional population connectivity, oceanic habitat, and return migration revealed by satellite tagging of white sharks, Carcharodon carcharias, at New Zealand aggregation sites. In: Domeier ML, editor. Global perspectives on the biology and life history of the white shark. CRC Press; 2012. p. 301–18.

    Google Scholar 

  30. Dunn RE, Bradley D, Heithaus MR, Caselle JE, Papastamatiou YP. Conservation implications of forage base requirements of a marine predator population at carrying capacity. Iscience. 2021;16:103646.

    Google Scholar 

  31. Estrada JA, Rice AN, Natanson LJ, Skomal GB. Use of isotopic analysis of vertebrae in reconstructing ontogenetic feeding ecology in white sharks. Ecology. 2006;87(4):829–34.

    PubMed  Article  Google Scholar 

  32. Ezcurra JM, Lowe CG, Mollet HF, Ferry LA, O’Sullivan JB. Oxygen consumption rate of young-of-the-year white sharks, Carcharodon carcharias, during transport to the Monterey Bay Aquarium. In: Domeier ML, editor. Global perspectives on the biology and life history of the white shark. CRC Press; 2012. p. 17–26.

    Chapter  Google Scholar 

  33. French GC, Rizzuto S, Stürup M, Inger R, Barker S, van Wyk JH, Towner AV, Hughes WO. Sex, size and isotopes: cryptic trophic ecology of an apex predator, the white shark Carcharodon carcharias. Mar Bio. 2018;165(6):102.

    CAS  Article  Google Scholar 

  34. Fritz H, Said S, Wimerskirch H. Scale-dependent hierarchical adjustments of movement patterns in a long-range foraging seabird. Proc R Soc Lond B. 2003;270:1143–8.

    Article  Google Scholar 

  35. Gennari E, Johnson RL, Cowley PD. Performance and reliability of active acoustic biotelemetry to best track marine pelagic species in temperate coastal waters. Mar Biol. 2018;165:128.

    Article  Google Scholar 

  36. Goldman KJ, Anderson SD. Space utilisation and swimming depth of white sharks, Carcharodon carcharias, at the South Farallon Islands, central California. Environ Biol Fish. 1999;56:351–64.

    Article  Google Scholar 

  37. Graham RT, Roberts CM, Smart JCR. Diving behaviour of whale sharks in relation to a predictable food pulse. J R Soc Interf. 2006;3:109–16.

    Article  Google Scholar 

  38. Grainger R, Peddemors VM, Raubenheimer D, Machovsky-Capuska GE. Diet composition and nutritional niche breadth variability in juvenile white sharks (Carcharodon carcharias). Front Mar Sci. 2020;7:422.

    Article  Google Scholar 

  39. Hammerschlag N, Williams L, Fallows M, Fallows C. Disappearance of white sharks leads to the novel emergence of an allopatric apex predator, the sevengill shark. Sci Rep. 2019;9(1):1–6.

    CAS  Article  Google Scholar 

  40. Hansson LA, Akesson S. Animal movement across scales. Oxford University Press; 2014. p. 304.

    Book  Google Scholar 

  41. Harding L, Jackson A, Barnett A, Donohue I, Halsey L, Huveneers C, Meyer C, Papastamatiou Y, Semmens JM, Spencer C, Watanabe Y, Payne N. Endothermy makes fishes faster but does not expand their thermal niche. Fun Ecol. 2021;00:1–9.

    Google Scholar 

  42. Heithaus MR, Wirsing AJ, Burkholder D, Thomson J, Dill LM. Towards a predictive framework for predator risk effects: the interaction of landscape features and prey escape tactics. J Anim Ecol. 2009;78:556–62.

    PubMed  Article  Google Scholar 

  43. Helfman GS. Fish behaviour by day, night and twilight. In: Pitcher TJ, editor. The behaviour of teleost fishes. London: Croom Helm; 1986. p. 366–87.

    Chapter  Google Scholar 

  44. Herczeg G, Välimäki K. Intraspecific variation in behaviour: effects of evolutionary history, ontogenetic experience and sex. J Evol Biol. 2011;24(11):2434–44.

    CAS  PubMed  Article  Google Scholar 

  45. Hewitt AM, Kock AA, Booth AJ, Griffiths CL. Trends in sightings and population structure of white sharks, Carcharodon carcharias, at Seal Island, False Bay, South Africa, and the emigration of subadult female sharks approaching maturity. Environ Biol Fish. 2018;101(1):39–54.

    Article  Google Scholar 

  46. Hilborn R, Amoroso RO, Anderson CM, Baum JK, Branch TA, Costello C, de Moor CL, Faraj A, Hively D, Jensen OP, Kurota H, Little LR, Mace P, McClanahan T, Melnychuk MC, Minto C, Osio GC, Parma AM, Pons M, Segurado S, Szuwalski CS, Wilson JR, Ye Y. Effective fisheries management instrumental in improving fish stock status. Proc Natl Acad Sci. 2020;177(4):2218–24.

    Article  CAS  Google Scholar 

  47. Hooten MB, Johnson DS, McClintock BT, Morales JM. Animal movement: statistical models for telemetry data. CRC Press; 2017.

    Book  Google Scholar 

  48. Hoyos-Padilla EM, Klimley AP, Galván-Magana F, Antoniou A. Contrasts in the movements and habitat use of juvenile and adult white sharks (Carcharodon carcharias) at Guadalupe Island, Mexico. Anim Biotelemetry. 2016;4:14.

    Article  Google Scholar 

  49. Hussey NE, McCann HM, Cliff G, Dudley SF, Wintner SP, Fisk AT. Size-based analysis of diet and trophic position of the white shark (Carcharodon carcharias) in South African waters. In: Domeier ML, editor. Global perspectives on the biology and life history of the white shark. CRC Press; 2012. p. 27–49.

    Chapter  Google Scholar 

  50. Kock A, O’Riain MJ, Mauff K, Meÿer M, Kotze D, Griffiths C. Residency, habitat use and sexual segregation of white sharks, Carcharodon carcharias in False Bay, South Africa. PLoS ONE. 2013;8(1): e55048.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Kock AA, Photopoulou T, Durbach I, Mauff K, Meÿer M, Kotze D, Griffiths CL, O’Riain MJ. Summer at the beach: spatio-temporal patterns of white shark occurrence along the inshore areas of False Bay, South Africa. Mov ecol. 2018;6(1):7.

    PubMed  PubMed Central  Article  Google Scholar 

  52. Logston. The influence of oceanographic variables on the distribution of white sharks (Carcharodon carcharias) in Mossel Bay, South Africa Master’s thesis, University of Cape Town, South Africa. 2014.

  53. Lowe CG, Blasius ME, Jarvis ET, Mason TJ, Goodmanlowe GD, O’Sullivan JB. Historic fishery interactions with white sharks in the Southern California Bight. In: Domeier ML, editor. Global perspectives on the biology and life history of the white shark. CRC Press; 2012. p. 169–98.

    Google Scholar 

  54. Lyons K, Jarvis ET, Jorgensen S, Weng K, O’Sullivan J, Winkler C, Lowe CG. The degree and result of gillnet fishery interactions with juvenile white sharks in Southern California assessed by fishery-independent and -dependent methods. Fish Res. 2013;147:370–80.

    Article  Google Scholar 

  55. Jewell OJD, Johnson RL, Gennari E, Bester MN. Fine scale movements and activity areas of white sharks (Carcharodon carcharias) in Mossel Bay, South Africa. Environ Biol Fish. 2013;96:881–94.

    Article  Google Scholar 

  56. Jewell OJ, Wcisel MA, Towner AV, Chivell W, Van der Merwe L, Bester MN. Core habitat use of an apex predator in a complex marine landscape. MEPS. 2014;506:231–42.

    Article  Google Scholar 

  57. Johnson DS, London JM, Lea MA, Durban JW. Continuous-time correlated random walk model for animal telemetry data. Ecology. 2008;89:1208–15.

    PubMed  Article  Google Scholar 

  58. Johnson R, Bester MN, Dudley SFJ, Oosthuizen HW, Meÿer M, Hancke L, Gennari E. Coastal swimming patterns of white sharks (Carcharodon carcharias) at Mossel Bay, South Africa. Environ Biol Fish. 2009;85:189–200.

    Article  Google Scholar 

  59. Jonsen ID, Flemming JM, Myers RA. Robust state-space modeling of animal movement data. Ecology. 2003;86(11):2874–80.

    Article  Google Scholar 

  60. Kareiva P, Odell G. Swarms of predators exhibit “preytaxis” if individual predators use area-restricted search. Am Nat. 1987;130:233–70.

    Article  Google Scholar 

  61. Kirkman SP, Oosthuizen WH, Meyër MA, Kotze PGH, Roux J-P, Underhill LG. Making sense of censuses and dealing with missing data: trends in pup counts of Cape fur seal Arctocephalus pusillus pusillus for the period 1972–2004. Afr J Mar Sci. 2007;29(2):161–76.

    Article  Google Scholar 

  62. Klimley AP, Beavers SC, Curtis TH, Jorgensen SJ. Movements and swimming behaviour of three species of sharks in La Jolla Canyon, California. Environ Biol Fish. 2002;63:117–35.

    Article  Google Scholar 

  63. Korsmeyer KE, Dewar H, Lai NC, Graham JB. The aerobic capacity of tunas: adaptation for multiple metabolic demands. Comp Biochem Physiol, A. 1996;113:17–24.

    Article  Google Scholar 

  64. Langrock R, King R, Matthiopoulos J, Thomas L, Fortin D, Morales JM. Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology. 2012;93(11):2336–42.

    PubMed  Article  Google Scholar 

  65. Laroche RK, Kock AA, Dill LM, Oosthuizen WH. Running the gauntlet: a predator–prey game between sharks and two age classes of seals. Anim Behav. 2008;76:1901–17.

    Article  Google Scholar 

  66. Liedvogel M, Chapman BB, Muheim R, Åkesson S. The behavioural ecology of animal movement: reflections upon potential synergies. Anim Migr. 2013;1:39–46.

    Article  Google Scholar 

  67. Lowe CG, Goldman KJ. Thermal and bioenergetics of elasmobranchs: bridging the gap. Environ Biol Fish. 2001;60:251–66.

    Article  Google Scholar 

  68. Lowe CG, Blasius ME, Jarvis ET, Mason TJ, Goodmanlowe GD, O’Sullivan JB. Historic fishery interactions with white sharks in the Southern California Bight. In: Domeier M, editor. Global perspectives on the biology and life history of the Great white shark. Boca Raton: Taylor & Francis; 2012. p. 169–98.

    Google Scholar 

  69. Martin RA, Hammerschlag N, Collier RS, Fallows C. Predatory behaviour of white sharks (Carcharodon carcharias) at Seal Island, South Africa. JMBA-UK. 2005;85(5):1121–36.

    Google Scholar 

  70. Martin RA, Rossmo DK, Hammerschlag N. Hunting patterns and geographic profiling of white shark predation. J Zool. 2009;279:111–8.

    Article  Google Scholar 

  71. McClintock BT, Michelot T. momentuHMM: R package for generalized hidden Markov models of animal movement. Methods Ecol Evol. 2018;9(6):1518–30.

    Article  Google Scholar 

  72. McConnell B, Fedak M, Burton HR, Engelhard GH, Reijnders PJH. Movements and foraging areas of naive, recently weaned southern elephant seal pups. J Anim Ecol. 2002;71:65–78.

    Article  Google Scholar 

  73. Morse P, Mole MA, Bester MN, Johnson R, Scacco U, Gennari E. Cape fur seals (Arctocephalus pusillus pusillus) adjust traversing behaviour with lunar conditions in the high white shark (Carcharodon carcharias) density waters of Mossel Bay, South Africa. Mar Ecol Prog Ser. 2019;622:219–30.

    Article  Google Scholar 

  74. Munz FW, McFarland WN. The significance of spectral position in the rhodopsins of tropical marine fishes. Vis Res. 1973;13(10):1829–74.

    CAS  PubMed  Article  Google Scholar 

  75. Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, Smouse PE. A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci USA. 2008;105:19052–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. Oelofse G, Kock A, Johnson R, Haskins C. Is there a relationship between white shark presence and the management of city estuaries and river mouths? In: Nel DC, Peschak TP. Finding a balance—white shark conservation and recreational safety in the inshore waters of Cape Town, South Africa. WWF South Africa Report Series–2006/Marine/001. 2006; p.71–82.

  77. Osgood GJ, McCord ME, Baum JK. Using baited remote underwater videos (BRUVs) to characterize chondrichthyan communities in a global biodiversity hotspot. PLoS ONE. 2019;14(12):e0225859.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. Owen SF. Meeting energy budgets by modulation of behaviour and physiology in the eel (Anguilla anguilla L.). Comp Biochem Physiol A Mol Integr Physiol. 2001;128(3):629–42.

    Article  Google Scholar 

  79. Papastamatiou Y, Lowe C. An analytical and hypothesis-driven approach to elasmobranch movement studies. J Fish Biol. 2012;80:1342–60.

    CAS  PubMed  Article  Google Scholar 

  80. Patterson TA, Basson M, Bravington MV, Gunn JS. Classifying movement behaviour in relation to environmental conditions using hidden Markov models. J Anim Ecol. 2009;78(6):1113–23.

    PubMed  Article  Google Scholar 

  81. Pfaff MC, Logston RC, Raemaekers SJ, Hermes JC, Blamey LK, Cawthra HC, Colenbrander DR, Crawford RJ, Krug MJ, Van Niekerk L. A synthesis of three decades of socio-ecological change in False Bay, South Africa: setting the scene for multidisciplinary research and management. Elementa Sci Anthropocene. 2019;7:32.

    Article  Google Scholar 

  82. Pitcher TJ, Turner JR. Danger at dawn: experimental support for the twilight hypothesis in shoaling minnows. J Fish Biol. 1986;29(A):59–70.

    Article  Google Scholar 

  83. Pohle J, Langrock R, van Beest FM, Schmidt NM. Selecting the number of states in hidden Markov models: pragmatic solutions illustrated using animal movement. J Agri Biol Environ Stats. 2017;22(3):270–93.

    Article  Google Scholar 

  84. Pondella DJ, Allen LG. The decline and recovery of four predatory fishes from the Southern California Bight. Mar Biol. 2008;154(2):307–13.

    Article  Google Scholar 

  85. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL. 2017. http://www.R-project.org/.

  86. Rasmussen JE, Belk MC. Individual movement of stream fishes: linking ecological drivers with evolutionary processes. Rev Fish Sci Aquac. 2017;25(1):70–83.

    Article  Google Scholar 

  87. Robertson DR, Sheldon JM. Competitive interactions and the availability of sleeping sites for a diurnal coral reef fish. J Exp Mar Biol Ecol. 1979;40:285–198.

    Article  Google Scholar 

  88. Robinson PW, Tremblay Y, Crocker DE, Kappes MA, Kuhn CE, Shaffer SA, Simmons SE, Costa DP. A comparison of indirect measures of feeding behaviour based on ARGOS tracking data. Deep Sea Res Pt II Top Stud Oceanogr. 2007;54(3–4):356–68.

    Article  Google Scholar 

  89. Ryklief R, Pistorius PA, Johnson R. Spatial and seasonal patterns in sighting rate and life history composition of the white shark Carcharodon carcharias at Mossel Bay, South Africa. Afr J Mar Sci. 2014;36(4):449–53.

    Article  Google Scholar 

  90. Secor SM. Specific dynamic action: a review of the postprandial metabolic response. J Comp Physiol B. 2009;179(1):1–56.

    PubMed  Article  Google Scholar 

  91. Semmens JM, Kock AA, Watanabe YY, Shepard CM, Berkenpas E, Stehfest KM, Barnett A, Payne NL. Preparing to launch: biologging reveals the dynamics of white shark breaching behaviour. Mar Biol. 2019;166(7):1–9.

    Article  Google Scholar 

  92. Shepard ELC, Wilson RP, Rees WG, Grundy E, Lambertucci SA, Vosper SB. Energy landscapes shape animal movement ecology. Am Nat. 2013;182(3):298–312.

    PubMed  Article  Google Scholar 

  93. Spaet JLY, Manica A, Brand CP, Gallen C, Butcher PA. Environmental conditions are poor predictors of immature white shark Carcharodon carcharias occurrences on coastal beaches of eastern Australia. Mar Ecol Prog Ser. 2020;653:167–79.

    Article  Google Scholar 

  94. Strong WR, Murphy RC, Bruce BD, Nelson DR. Movements and associated observation of bait-attracted white sharks, Carcharodon carcharias: a preliminary report. Aust J Mar Freshw Res. 1992;43:13–20.

    Article  Google Scholar 

  95. Towner AV, Underhill LG, Jewell OJ, Smale MJ. Environmental influences on the abundance and sexual composition of white sharks Carcharodon carcharias in Gansbaai, South Africa. PLoS ONE. 2013;8(8): e71197.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. Towner AV, Leos-Barajas V, Langrock R, Schick RS, Smale MJ, Kaschke T, Jewell OJD, Papastamatiou YP. Sex-specific and individual preferences for hunting strategies in white sharks. Funct Ecol. 2016;30(8):1397–407.

    Article  Google Scholar 

  97. Walsh PD. Area-restricted search and the scale dependence of patch quality discrimination. J Theor Biol. 1996;183:351–61.

    Article  Google Scholar 

  98. Watanabe YY, Goldman KJ, Caselle JE, Chapman DD, Papastamatiou YP. Comparative analyses of animal-tracking data reveal ecological significance of endothermy in fishes. Proc Natl Acad Sci. 2015;112(19):6104–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. Watanabe YY, Payne NL, Semmens JM, Fox A, Huveneers C. Swimming strategies and energetics of endothermic white sharks during foraging. J Exp Biol. 2019;222(4):jeb185603.

    PubMed  Article  Google Scholar 

  100. Whitfield AK. Ichthyofaunal assemblages in estuaries: a South African case study. Rev Fish Biol Fish. 1999;9:151–86.

    Article  Google Scholar 

  101. Whitfield AK, Cowley PD. The status of fish conservation in South African estuaries. J Fish Biol. 2010;76(9):2067–89.

    CAS  PubMed  Article  Google Scholar 

  102. Whitfield A, Lamberth S, Cowley P, Mann B. Fisheries in South African estuaries–are we on the right road: estuaries-cover story. Water Wheel. 2019;18(6):12–5.

    Google Scholar 

  103. Whitfield AK, Attwood CG, Cowley PD, Lamberth SJ, Mann BQ. No-take estuarine-protected areas: the missing armour for the conservation of fishes. Koedoe. 2020;62(1):1–7.

    Article  Google Scholar 

  104. Wolf C, Ripple WJ. Prey depletion as a threat to the world’s large carnivores. R Soc Open Sci. 2016;3(8): 160252.

    PubMed  PubMed Central  Article  Google Scholar 

  105. Zucchini W, MacDonald IL, Langrock R. Hidden Markov Models for time series: an introduction using R. 2nd ed. Chapman and Hall/CRC; 2017.

    Book  Google Scholar 

Download references

Acknowledgements

We would like to thank Ryan Johnson, Rob Lewis and all the research interns, part of the Oceans Research Institute’s field research course, who helped with the field work.

Funding

This work was supported by Oceans Research Institute of Mossel Bay; the PADI Project AWARE; Avnic-Cameogroup-Garmin; Evolushark; GIMS (Pty) Ltd.; the White Shark Trust. This research was also supported by the National Research Foundation of South Africa and the National Research Foundation—South African Institute for Aquatic Biodiversity (NRF-SAIAB): all opinions, findings and conclusions/recommendations expressed in this publication are those of the authors and the NRF accepts no liability whatsoever in this regard. Oceans Research provided logistic and field support for the research. The South African Institute for Aquatic Biodiversity and PADI Aware provided funds for the transmitters. The White Shark Trust helped in financing the tracking support vessel.

Author information

Authors and Affiliations

Authors

Contributions

EG and PDC designed the study. DTI and EG analysed and interpreted the data. All authors read and approved the final manuscript.

Corresponding author

Correspondence to E. Gennari.

Ethics declarations

Ethics approval and consent to participate

All tagging and tracking work was conducted under research permits issued by the South African Department of Environmental Affairs (including RES2009-11, RES2010-04, RES2011-27, RES2012-18).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

Visual assessment of pseudo-residuals obtained using the three-state HMM on the step lengths (top row) and turning angles (bottom row) data, obtained by manually tracking 19 white sharks in Mossel Bay.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gennari, E., Irion, D.T. & Cowley, P.D. Active acoustic telemetry reveals ontogenetic habitat-related variations in the coastal movement ecology of the white shark. Anim Biotelemetry 10, 25 (2022). https://doi.org/10.1186/s40317-022-00295-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40317-022-00295-x

Keywords

  • White shark
  • Acoustic telemetry
  • Conservation
  • Tracking
  • Movement ecology
  • Ontogeny
  • Carcharodon carcharias
  • Habitat use