Understanding the spatial and temporal distribution and movement patterns of reef sharks in the BIOT MPA is important for effective conservation management and MPA enforcement efforts. Current analyses of acoustic telemetry data often neglect the timings and periodicity of detection gaps. In the absence of more accurate movement data for large numbers of animals, which can be prohibitively expensive for satellite telemetry, we have developed a new approach, which utilises gaps in detections from acoustic telemetry to infer presence or absence from regions of interest, such as coral reef systems, at a coarse scale. Prior research on patterns of movements between coral reef-associated elasmobranch species have been limited but primarily focus on differences in space use (15, 81, 82). To date, temporal aspects of segregation are rarely considered (83, 84). As seen here, the use of time dependent factors, such as detection gaps, has the potential, albeit at coarse scales, to identify both spatial and temporal differences in movement in sympatric shark species enhancing our understanding of the organisation and reorganisation of reef predator assemblages.
Grey reef and silvertip sharks in the BIOT MPA had significant differences in inferred ‘wider ranging’ activity. Overall, silvertip sharks were more likely to undertake ‘wider ranging’ movements than grey reef sharks. These results suggest spatial segregation between these species, with grey reef sharks, probabilistically, more likely to inhabit reef-based areas while silvertip sharks were more mobile and conducted more widespread movements (Fig. 3). Our results indicate that although there is spatial segregation between the species, there is overlap between the two in the areas they reside. This supports evidence from isotope data which suggests resource partitioning between these two species in the BIOT MPA, with both species utilising both reef and pelagic areas for foraging, but with grey reef sharks obtaining 78% of their biomass from reef resources, but silvertip sharks only 60% (85). In addition, our results are consistent with work from the Great Barrier Reef suggesting grey reef sharks are true reef sharks, and silvertip sharks less reliant on reef-based resources in this region (86). Further, these results extend previous research describing variable patterns of movement and activity in both grey reef sharks and silvertip sharks globally (87–89). Locally, previous research in the BIOT MPA found that silvertip sharks had higher mobility, larger activity spaces, and lower reef residency compared to grey reef sharks, which had small activity spaces and rarely moved more than five to ten kilometres from their tagging location (6). This study has shown that not only can gaps in detections be used to help interpret and support other types of data, but, even at coarse scales, can potentially be used to provide insight into the differences in spatio-temporal movements that underpin reef predator sympatry.
Although ‘wider ranging’ movements of reef sharks in the BIOT MPA increased at night, this is perhaps biased by silvertip shark propensity to range more during the night, with grey reef sharks showing very little diel change in ‘wider ranging’ movements (Fig. 3a). Differing patterns from the literature suggest that diel movement patterns may be population dependent, possibly driven by interspecific interactions and/or local environmental conditions (38, 87, 88). For example, grey reef shark populations in Palau utilise shallower reef platforms for foraging during the night, with deeper, pelagic, waters exploited during the day (88). The same species around Palmyra Atoll in the Central Pacific Ocean, however, are detected on the forereef frequently during the day with far fewer detections at night (87), suggesting they move away from the reefs during these periods. To date only a few studies have been conducted on movement behaviour of silvertip sharks, but work by Espinoza et al. (89) on the Great Barrier Reef found that silvertip sharks undertook deeper dives and were detected less during the day, possibly due to foraging. An alternative explanation for the reduction in detections at night, could be the avoidance of transient apex predators, such as tiger sharks (Galeocerdo cuvier), which are known to feed on reef-associated mesopredators, like silvertip sharks (35, 36, 90). Further research should be conducted in order to investigate regional and population patterns of movement both within and between different reef shark species.
The diel variance in ‘wide-ranging’ movements found in the study also indicates temporal segregation of movements between the two species, with the difference in spatial segregation of grey reef and silvertip sharks increasing at night (Fig. 3a). The reasons behind the increase in spatial segregation at night are not immediately clear. At several other reef locations grey reef sharks are central place foragers (86, 87, 91), while silvertip sharks are more dispersive, and less site attached (86, 89). As foraging occurs at night, it may be that silvertip sharks move offreef to forage, therefore increasing ‘wider ranging’ movements at night, while grey reef sharks forage on-reef, which would support the differential patterns of resource use found between these species (85). Or, alternatively, it may be that silvertip sharks occupy a greater spatial range at night during foraging, as has been seen in other reef shark species, such as whitetip reef sharks, Triaenodon obesus (92) and blacktip reef sharks, Carcharhinus melanopterus (87), but grey reef shark spatial range rarely changes. However, currently little is known about the foraging ecology of these two species in this region, and further studies will be needed to investigate the apparent diel differences in ‘wider ranging’ movements between these two species in the region.
There was also seasonal variation in the ‘wider ranging’ movements of both species (Fig. 3b). Knowledge of seasonal patterns of elasmobranch movement in the region is important to aid enforcement efforts to prevent illegal or unregulated fishing in the BIOT MPA (93). Like diel period, seasonal patterns of movement are common in multiple elasmobranch species. Such seasonality may be driven by mating in adults, ontogenetic expansion of home range, or foraging opportunities (63, 94, 95). Additionally, environmental conditions across the BIOT MPA are subject to considerable changes associated with the Indian Ocean monsoon current. In many species however, seasonal changes in movement may not be clear, with movements either unpredictable or subject to individual behavioural variability (95). Indeed, our model results suggest that inter-individual variability also plays an important role in explaining the probability of ‘wider ranging’ movements. Interindividual variability in movement behaviour is seen across shark species (96–98), and may be driven by differences in age class, sex, morphology, health and even personalities (99, 100) but currently is not fully understood (101). Model results also indicated an increase in ‘wider ranging’ movements during the wet season. However, the reasons for this increase are unclear. As the wet season (October – March) coincides with the peak historic pelagic fishing season in the BIOT MPA (102), this increase in movement may possibly be linked to a seasonal food resource, as previously hypothesised by Curnick et al. (85). Although food may be a primary driver of temporal changes in movement, it may also be driven by factors such as thermoregulation or predator avoidance, or by environmental variables such as sea surface temperature, salinity or current (89, 103). Despite recent developments to our understanding of how environmental variables effect elasmobranch movement (103, 104), they are still relatively poorly understood and investigations should be undertaken to further explore their impact on both spatial and temporal aspects of elasmobranch movement in this region.
Because acoustic telemetry only measures presence, array design and detection ranges can significantly impact results obtained using this technology (6). An inability to detect an animal could be due to the animal leaving the study area, or because it moved out of detection range (5), and consequently might result in some mis-designated movements in this study. Detection ranges for the region vary between 300 and 500 m (12, 38–40), and distances between receivers ranged, between 0.55–4.57 km with mean distance to closest receiver 2.15 km, with minimal overlap between receivers (Additional file 1; Figure S1-4). We acknowledge that this could lead to significant ‘blind spots’, where a shark could be swimming and not detected rather than engaging in ‘wide-ranging’ movements. We consider the impact of these, however, to be minimal for the following reasons; classification thresholds of movement were very conservative, giving considerable leeway for an animal to move around a reef area, detections to be missed, and still the movement be classed as ‘restricted’; furthermore, neither of these species are able to rest motionless on the bottom, as they are required to ram ventilate (105), increasing the chances of them being detected on the same or additional receivers even when frequenting ‘blind spots’ within the array.
In addition, animals using the lagoons of these atolls would also not necessarily be detected on the array, and movements across the lagoons could also be mis-designated. However, lagoon use in these species tends to be minimal (41, 106, 107) and the isotope signatures obtained from these two species indicate they are not using lagoons for foraging (85). As such, in addition to our findings complementing the work of Curnick et al. (85), we believe these potential issues should not have impacted our results significantly. However, we stress that this is an inference method being used in lieu of more accurate measurements for large numbers of free-ranging individuals and may not be suitable for regions with large gaps in receiver detection ranges, particularly when the study species may exhibit bouts of sedentary behaviour.
Although the consistent nature of our results with those obtained from stable isotope work (85) help to validate our methodological approach, further validation of our classifications could be carried out using more accurate positional data. Although there were several silvertip sharks double tagged with both acoustic and PSAT tags, unfortunately the positional data from these satellite tags was not of a high enough resolution to fully confirm our claims. At low latitudes, the error associated with light-level geolocation estimates from PSAT tags can be very high (4). For example the geolocation error from a silvertip shark tagged with a PSAT in the BIOT MPA was estimated at 0.25° or 27.83 km at the equator (6). In addition, geolocation algorithms used to reconstruct positions from PSAT data only produce a single position per day, which limits their ability to investigate diel differences in location. Although not feasible in this study, the accuracy of this technique should be validated in future studies with more accurate positional data, such as those derived from Fastloc GPS tags (e.g. Smart Positioning and Temperature transmitting tags).
There are no single ‘silver bullet’ research techniques for investigating elasmobranch ecology at an appropriate and meaningful spatial and temporal scale; each methodology has its limitations. However, by combining data from multiple approaches the limitations of any individual methodology can be overcome, and a more holistic understanding of elasmobranch ecology can be attained. Here we show that, in the absence of finer scale movement data beyond the boundaries of our acoustic detection ranges, gaps in detections can be used to support and extend conclusions about how variable behavioural strategies can influence interspecific species organisation on coral reef systems.