A wide array of technologies are available for gaining insight into the movement of wild aquatic animals. Although acoustic telemetry may lack the fine-scale resolution of some satellite tracking technologies, the substantially longer battery life can yield important long-term data on behaviour and movement for low per-unit cost. Typically, however, receiver arrays are designed to maximise spatial coverage at the cost of positional accuracy leading to potentially longer detection gaps as individuals move out of range between monitored locations. This is particularly true when these technologies are deployed to monitor species in hard-to-access locations. We develop a novel approach to analysing acoustic telemetry data, using the timing and duration of gaps between animal detections to classify movement behaviours into ‘restricted’ or potential wider ‘out of range’ movements synonymous with longer distance dispersal. We apply this method to investigate spatial and temporal segregation of inferred movement patterns in two sympatric species of reef shark within a large, remote Marine Protected Area. Drivers of these movements were identified using generalised linear mixed models and multi-model inference. Species, diel period and season were significant predictors of ‘out of range’ movements. Silvertip sharks were overall more likely to undertake ‘out of range’ movements, compared to grey reef sharks, indicating spatial segregation, and corroborating previous stable isotope work between these two species. High individual variability in ‘out of range’ movements in both species was also identified. We present a novel gap analysis of telemetry data to help infer differential movement and space use patterns where acoustic coverage is imperfect and other tracking methods impractical. In remote locations, inference may be the best available tool and this approach shows that acoustic telemetry gap analysis can be used for comparative studies in fish ecology, or combined with other research techniques to better understand functional mechanisms driving behaviour.

Acoustic telemetry data was collected in the BIOT MPA between 2014 and 2018. Throughout the archipelago there have been situated up to 93 permanent and temporary acoustic receivers (VR2W, VR4-UWM, VR4G and VR2AR receivers, Vemco Inc, Nova Scotia, Canada), as configured in Figure 1 from Williamson et al (accepted). The BIOT MPA is characterised by numerous small islanded atolls with submerged banks and reefs, with depths of 1,000 m or more separating each atoll or reef system (Sheppard et al. 2013). Acoustic receivers in the BIOT MPA are mainly based on areas accessible to divers, such as coral reef systems, with few receivers covering the deep pelagic waters of the region.The BIOT MPA receiver array was initially started in 2013, and expanded throughout subsequent years [for more information see Carlisle et al. (2019) and Jacoby et al. (2020)], covering a perimeter of 700 km and an area of 25,500 km² within the MPA, for the detection of acoustically tagged marine fauna. Of the 93 receivers, 82 are in depths 45 m or less. All receivers were situated far enough apart to avoid overlap in their detection range, with mean distance to closest receiver being 2.15 km, with a range of 0.55 – 4.57 km. 

The raw data has been processed to keep only data from 2014-2018, and then processed using network metrics such as time difference and self loops, to be used to calucate movement data into two movement behaviour types 'recursive' and 'transitions'.

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