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  • Telemetry case report
  • Open Access

Diel patterns in three-dimensional use of space by sea snakes

  • 1Email author,
  • 1 and
  • 1, 2
Animal Biotelemetry20153:29

  • Received: 20 March 2015
  • Accepted: 22 July 2015
  • Published:



The study of animal movement and use of space have traditionally focused on horizontal and vertical movements separately. However, this may limit the interpretation of results of such behaviours in a three-dimensional environment. Here we use passive acoustic telemetry to visualise and define the three-dimensional use of space by two species of sea snake [Hydrophis (Lapemis) curtus; and Hydrophis elegans] within a coastal embayment and identify changes in how they use space over a diel cycle.


Monitored snakes exhibited a clear diel pattern in their use of space, with individuals displaying restricted movements at greater depths during the day, and larger movements on the surface at night. Hydrophis curtus generally occupied space in deep water within the bay, while H. elegans were restricted to mud flats in inundated inter-tidal habitats. The overlap in space used between day and night showed that individuals used different core areas; however, the extent of areas used was similar.


This study demonstrates that by incorporating the capacity to dive in analyses of space use by sea snakes, changes over a diel cycle can be identified. Three-dimensional use of space by sea snakes can identify spatial or temporal overlaps with anthropogenic threats (e.g. trawling, dredging) and help develop targeted management policies that mitigate any adverse effects to ensure healthy populations of sea snakes.


  • Hydrophis (Lapemis) curtus
  • Hydrophis elegans
  • Kernel utilisation distribution (KUD)
  • 3D


The identification of patterns in the movements of individuals, and their relationship to ecological phenomena, have been a critical aspect in studies of terrestrial, avian, and marine organisms [1]. Traditionally, studies on the movement and use of space by animals have been represented in two dimensions (e.g. Latitude–Longitude or Easting–Northing); this, however, may not fully represent the reality of the environment that most animals occupy. Recent advances in technology and analytical techniques have allowed integration of the vertical axis into studies examining the use of space to a high degree of spatial resolution. These advances have improved our understanding of the spatial ecology of a range of terrestrial, avian, and marine organisms [25].

Since aquatic animals live in a three-dimensional environment and have the ability to move in all three dimensions, their use of space is most accurately represented in the same number of dimensions. Sea snakes are a group of marine elapid snakes that spend their entire lifecycle in the marine environment and are found in a range of habitats, including coral reefs, open oceans, and coastal embayments [6]. Past studies have used mark-recapture and genetic studies to infer broad-scale movements and population connectivity between patchy reef environments over large temporal scales [7, 8].

A few attempts to understand movement and use of space by reef-associated olive sea snakes [Aipysurus laevis9, 10] and pelagic yellow-bellied sea snakes [Hydrophis (Pelamis) platura; 11, 12] have contributed the majority of what is currently known about these taxa. Rubinoff and colleagues [11, 12] studied the short-term movements (3.8–31.2 h monitored) of fifteen H. platura tagged with depth-sensing ultrasonic tags and examined vertical and horizontal patterns of movement separately, publishing their results in two articles. The first, examined the movements of tagged snakes in the vertical axis, looking at the depths and durations of dives [11] followed by the second that focused on the horizontal movements of tagged individuals [12]. They found that despite H. platura being considered a surface-dwelling pelagic sea snake, tagged individuals spent the majority of the monitoring period (87%) underwater and dove as deep as 50 m. Burns and Heatwole [9] found that A. laevis displayed restricted movements around their home reef with small home ranges (1,500–1,800 m2) and that home ranges of all snakes (n = 11) overlapped with two or more individuals. Estimates of space used and overlap between monitored individuals, however, did not consider their use of depth. Studies of eel movements by Simpfendorfer et al. [2] revealed that failure to consider vertical movement can result in an overestimation of home range overlap if individuals are using the same two-dimensional location but different depths.

Currently, very little is known about how sea snake species that occupy coastal and inshore waters use space, with the majority of past studies in these habitats often focusing on abundance and diversity based on incidental capture in trawl fisheries [13, 14]. To provide information on the movement patterns of sea snakes in a coastal system, we tracked two species of sea snake in Cleveland Bay, Queensland Australia (Fig. 1, Additional file 1, to define three-dimensional movement patterns. The aims of this study were to: (a) use passive telemetry to understand and visualise how sea snakes use space within the water column, and (b) examine any diel patterns in the use of three-dimensional space by tagged sea snakes. Patterns in the three-dimensional use of space were also considered in the context of their potential to inform on the susceptibility of sea snakes to anthropogenic threats (e.g. trawling, dredging).
Fig. 1
Fig. 1

Study site in Cleveland Bay. Points represent locations of acoustic receivers deployed at the study site. Broken grey lines indicate bathymetry. An interactive, three-dimensional model of the study site is available in the additional files (Additional file 1)


Twenty-five individuals from two species of sea snake, spine-bellied sea snake (Hydrophis curtus previously Lapemis curtus: n = 19) and elegant sea snake (Hydrophis elegans: n = 6), were tagged within the study site and monitored between January and November 2013. The majority of monitored individuals were juvenile, which were difficult to sex using external morphological feature. Only two adult female H. curtus and a single adult male H. elegans were monitored; therefore, to avoid inaccurate conclusions related to the small sample size of reproductively mature individuals in this study, sex was excluded as a covariate in further analyses. Data from depth sensors showed that individuals from both species displayed a strong diel pattern in use of the water column (Fig. 2). Snakes were found at significantly greater depths during the day (06:00–18:00 h) and were active on the surface at night (t test, H. curtus: t = 26.37, p < 0.05, H. elegans: t = 9.51, p < 0.05). Hydrophis curtus displayed a more varied dive profile, diving to an average depth of 3.2 m (SE: ±0.03 m; max depth = 7.5 m) during the day and 2.1 m (SE: ±0.03 m; max depth = 7.4 m) at night. While, H. elegans generally used comparatively shallower water and dived to an average of 2.5 m (SE: ±0.05 m; max depth = 5.7 m) during the day and 1.8 m (SE: ±0.04 m; max depth = 6.2 m) at night. Individuals of H. curtus were generally present in deep water on the eastern side of Cleveland Bay (Fig. 3a, Additional file 2,, whereas H. elegans were restricted to shallow water directly adjacent to the southern shore (Fig. 3c, Additional file 3,
Fig. 2
Fig. 2

Patterns in diel use of different depths by tagged a Hydrophis curtus (n = 19) and b Hydrophis elegans (n = 6) over the monitoring period. Mean depths recorded by day (red) and night (blue) are represented as ticks on the y-axis

Fig. 3
Fig. 3

Three-dimensional space use by a representative Hydrophis curtus (a) within the study site and b in closer detail, and by a representative Hydrophis elegans (c, d). Screenshots showing day-time core (50%-3DKUD; dark red) and extent (95%-3DKUD; light red) 3DKUD as well as night-time core (50%-3DKUD; dark blue) and extent (95%-3DKUD; light blue) 3DKUD. Surrounding bathymetry and sea surface are also rendered to provide context. Black points in a, c represent the locations of acoustic receivers within the study site. Interactive, three-dimensional versions of these 3DKUD models are available in the additional files (Additional files 2, 3, 4 and 5)

The integration of depth data into analysis of space use showed a difference in the three-dimensional kernel utilisation distributions (3DKUD) for individuals of both species during day and night (Fig. 3b, d; Additional files 4, 5;, Sufficient data from 12 of the 19 tagged H. curtus, and 5 of the 6 H. elegans were available to calculate reliable 3DKUDs to compare diurnal and nocturnal use of space. Despite the difference in depths occupied by individuals, generalised linear mixed models (GLMM) showed that volumes of core (50%-3DKUD) and extent (95%-3DKUD) of space used by H. curtus were not significantly different between day and night (50%-3DKUD: F 1,11 = 0.44, p = 0.52; 95%-3DKUD: F 1,11 = 0.20, p = 0.66; Fig. 4a). Similarly, the volume of 50%-3DKUD used by H. elegans was not significantly different between day and night (F 1,4 = 5.58, p = 0.07); however, H. elegans displayed significantly larger 95%-3DKUD volumes at night compared to the day (F 1,4 = 18.79, p = 0.01; Fig. 4b).
Fig. 4
Fig. 4

Diel differences in the use of volumetric space by tagged sea snakes. Mean diurnal (50%-3DKUD: dark red; 95%-3DKUD: light red) and nocturnal (50%-3DKUD: dark blue; 95%-3DKUD: light blue) volumetric space used by a Hydrophis curtus (n = 12) and b H. elegans (n = 5). Overlap between c 50%-3DKUD and d 95%-3DKUD diurnal and nocturnal use of space by H. curtus (white bars) and H. elegans (grey bars)

The overlap between areas used by individuals during the day and at night showed that 50%-3DKUDs of both species had a low proportion of overlap (Fig. 4c), suggesting that despite similar volumes, there was little overlap in the core areas used. The mean proportion of overlap in 50%-3DKUDs between day and night was significantly lower than that of a random pattern for both species (t test, H. curtus: t = −2.47, p = 0.02; H. elegans: t = −3.93, p = 0.02). In contrast, 95%-3DKUDs showed a high degree of overlap between day and night (Fig. 4d). The mean proportion of overlap in 95%-3DKUD between day and night for both species was not significantly different from random (t test, H. curtus: t = 1.52, p = 0.15; H. elegans: t = 0.07, p = 0.94).


The representation of space used by sea snakes in their natural environment requires the integration of their diving capabilities. Here we show that the use of new techniques in three-dimensional spatial analyses is appropriate when considering how sea snakes use space and facilitate detection of diel changes in movement. Previous attempts at understanding sea snake movements and their use of space by Rubinoff et al. [11, 12] and Burns and Heatwole [9] utilised ultrasonic transmitters to reveal short-term movements of the pelagic yellow-bellied sea snake, Hydrophis (Pelamis) platura, and the reef-associated olive sea snake, Aipysurus laevis, respectively. These studies were very useful in defining movement; however, as the vertical and horizontal patterns of movement were examined separately [e.g. 11, 12], the interpretation of the movements and use of space of these species was limited. The present study demonstrates that with the current advances in statistical analyses, the integration of vertical and horizontal axes will allow for a more accurate interpretation of the patterns of movement and of the use of space in sea snakes.

The results in the present study indicate a clear diel change in diving patterns, use of space and habitat by both species of sea snakes within Cleveland Bay. The drivers of change are potentially numerous, but one factor that most likely has an important influence on diurnal and nocturnal behaviour is the abundance and activity of prey. Hydrophis elegans almost exclusively prey on snake eels (Family Ophichthidae) [15, 16], which are abundant within the shallow mud-flat benthos of Cleveland Bay. This may not only restrict the use of space by H. elegans to mud flats close to the mouths of creeks within Cleveland Bay, but might also drive some of their movements at night. The movements and activity patterns of eels within coastal habitats are closely related to diel patterns, with greater activity and movement at night [17, 18]. Increased activity of prey species at night may explain the greater use of space by H. elegans at night and the low proportion of overlap between core areas between day and night. On the other hand, the diet of H. curtus is varied and includes a wide range of prey species [16] suggesting H. curtus are not restricted to a particular habitat within the bay. This may explain the larger volumes and increased overlap between spaces used between day and night. The results also indicate that different species of sea snakes likely display different patterns in diel movements and, therefore, susceptibility to natural and anthropogenic threats will vary.

One aspect of the present study that needs to be considered is that the majority of individuals that were monitored were juvenile. The few adults that were monitored displayed similar patterns of diel movements to juvenile conspecifics; however, previous studies have shown that movement patterns in adult sea snakes can vary seasonally, with increased movements displayed by males during breeding season related to mate-searching behaviours [6, 19]. This is an additional factor that needs to be considered when exploring diving and movement patterns of sea snakes over multiple seasons. In the present study, low numbers of tagged adults and limitations in tag life restricted the ability to explore such seasonal changes in three-dimensional movement patterns related to breeding cycles. Additionally, ontogenic shifts in habitat use and diets in sea snakes during different life stages may also alter movement patterns. Presently, very little information is available on ontogenic changes in diet or habitat use in sea snakes. Further long-term studies are required to investigate if dietary preference and use of habitat changes with life stages in sea snakes, which can potentially influence how individuals move within the environment and use space.

Globally, the primary anthropogenic threat to marine reptiles is capture in coastal trawl fisheries [20]. Trawl fisheries target a range of commercially valuable species (e.g. Penaeus esculentus, Penaeus longistylus, Amusium ballotti) that are fished during different times of the year and at different times of the day [21]. The diel patterns observed in the present study, with increased space used in the water column at night suggest that these species of sea snakes have an elevated susceptibility to nocturnal trawling activity; however, this may vary for other species and differ among life stages. In Australia, bycatch in the trawl fishery is effectively managed by mandatory use of bycatch-reduction devices (BRD) as well as spatial and temporal restrictions to fishing. The combination of BRDs and fishing restrictions has been shown to be effective in reducing landings of sea snakes in Australia [22]. These management practices, however, may not be practical or enforceable in other parts of the world where coastal fisheries overlap with sea snake populations. Monitoring the movements of sea snakes and understanding how they use space within heavily trawled areas can help identify areas of spatial or temporal overlap that can be more effectively mitigated using targeted management policy.


Here we applied three-dimensional kernel utilisation distribution analyses to understand how sea snakes use space in their natural environment. This study also demonstrated that such analyses allow examination of changes in how individuals move and behave over a diel timescale and identify potential drivers that cause these changes. Abundance and activity patterns in preferred prey of H. curtus and H. elegans may explain the differential diurnal and nocturnal use of space by these two species. Finally, understanding how sea snakes use space within their three-dimensional environment can also help identify spatial and temporal overlaps with anthropogenic threats (e.g. trawling, dredging) and allows managers to develop targeted policy designed to mitigate any adverse effects to vulnerable populations of sea snakes. Inclusion of the vertical axis (i.e. depth, altitude, height) in the visualisation and analysis of spatial data clearly enhances our understanding of how animals occupy space and move, and may ultimately allow for more accurate assessment of their susceptibility to threats. Similar techniques can greatly benefit future studies on the spatial ecology of aquatic, aerial and arboreal animals in their natural habitat and refine their susceptibility to natural and anthropogenic threats that operate in the same three-dimensional environment.


Field methods

This study was conducted in Cleveland Bay (19.20°S, 146.92°E), on the northeastern coast of Queensland, Australia (Fig. 1). Cleveland Bay is a shallow coastal bay (<10 m deep) that covers an area of approximately 225 km2 with the western and southern margins bounded by the mainland and Cape Cleveland, respectively, and a large continental island, Magnetic Island, to the north of the bay (Fig. 1). The majority of the bay has soft sediment substrates with extensive seagrass meadows, with the exception of a fringing reef system on the lee of Magnetic Island. Multiple tidal creeks with mud-flat and mangrove habitats line the southern shore and provide the majority of freshwater input.

Spine-bellied sea snakes (Hydrophis curtus; previously Lapemis curtus) and elegant sea snakes (Hydrophis elegans) were located at night and captured from the surface of the water using dip nets. Once captured, the maturity of each individual was recorded as either juvenile or adult, with the sex of adults determined using external morphological features (by investigating hemipenal bulges or exposing the hemipenes). The determination of sex in juvenile individuals was unreliable using external morphological features; therefore, the sex of juveniles was not considered in the present study. Snout–vent length (SVL) and mass of each captured snake were recorded, and each individual was fitted with a passive integrated transponder (PIT) tag for future identification. Individuals in good condition and exceeding the minimum weight (>300 g) to carry a transmitter were surgically implanted with acoustic transmitters with depth sensors (V9P-2H, Vemco Ltd.). Transmitters were small (diameter 9 mm, length 29 mm, weight 2.9 g) and less than 1% of the body weight of the individuals tagged (mean ± SE; 0.91 ± 0.11%) to avoid any deleterious effects. The methods of implanting tags were similar to those of Pratt et al. [23]. In general, a local anaesthetic (Xylocaine®; lignocaine) was administered at the site of implantation, a small ventro-lateral incision (ca. 2 cm) made approximately 4–5 cm anterior to the cloaca and the transmitter inserted into the peritoneal cavity, after which the incision was closed using surgical sutures. Individuals were allowed 30–40 min to recover from the anaesthetic before being released at their location of capture. Transmitters were uniquely coded for each individual, transmitted measurements of depth at 69 kHz, and had a battery life of approximately 215 days. An array of 63 VR2W acoustic receivers (Vemco Ltd.) was used to passively monitor the movements of tagged sea snakes within Cleveland Bay (Fig. 1). Range tests using sentinel tags indicated receivers had a maximum detection range of 525 m (unpublished data, M. Heupel). Data from the acoustic receiver array were downloaded every 3–4 months.

Data analysis

Raw data were first standardised for temporal variation in detections through a position-averaging algorithm that computed an individual’s centre of activity at 30-min intervals [24]. The volume of space used by tagged individuals was examined by calculating three-dimensional kernel utilisation distributions (3DKUD) for both species in the R environment [25] using the ‘ks’ package [26] and rendered using the ‘rgl’ and ‘misc3d’ packages [27, 28]. Calculations of 3DKUD and estimation of volume of core space (50% contour; 50%-3DKUD) and the extent of space (95% contour; 95%-3DKUD) used by tagged individuals were conducted using code adapted from Simpfendorfer et al. [2] and Cooper et al. [4]. Interactive plots of diel patterns in 3DKUD (Fig. 3) were rendered using the ‘brainR’ package in R and code adapted from Muschelli et al. [29]. The proportion of overlap in the space used between day and night was also calculated for 50%-3DKUD and 95%-3DKUD for both species using R code from Simpfendorfer et al. [2].

Estimations of volume of 50%-3DKUD and 95%-3DKUD were log-transformed prior to statistical analyses. The differences in the volume of space used between day and night were compared using generalised linear mixed models (GLMM) with individuals’ ID treated as a random factor within each model (~1|ID) to account for variability between individuals and the repeated measures nature of the data. Additionally, t tests (α = 0.05) were conducted to examine whether the mean proportion of overlap between areas used during the day and at night (both 50%-3DKUD and 95%-3DKUD) varied from that expected if there were no pattern in proportions of overlap (i.e. mean proportion = 0.5). The data for proportional overlap of the occupied space were arcsine-transformed prior to analysis. All statistical analyses and plotting were conducted in the R statistical environment [25].



three-dimensional kernel utilisation distribution


the core three-dimensional home range, 50% contour of the three-dimensional kernel utilisation distribution


the extent of the three-dimensional home range, 95% contour of the three-dimensional kernel utilisation distribution


generalised linear mixed models


Authors’ contributions

VU: designed the study, conducted fieldwork, analysed data, drafted the manuscript and created the figures and graphics; CAS: designed the study, provided materials and field resources, assisted in analyses and drafting of the manuscript; MRH: designed the study, provided materials and field resources, assisted in analyses and drafting of the manuscript. All authors read and approved the final manuscript.


The authors would like to thank all volunteers who assisted with the numerous tagging trips for this project. We also thank the staff and students of the Centre for Sustainable Tropical Fisheries and Aquaculture, including F. de Faria, A. Schlaff, J. White, S. Munroe, J. Smart, M. Espinoza and S. Moore for field support. This research was conducted with the approval of the Animal Ethics Committee of James Cook University (A1799) and in accordance with the Great Barrier Reef Marine Park Authority (G14/36624.1) and the Queensland Department of Environment and Heritage Protection’s Scientific Purposes Permit (WISP11923512). This project was funded by the Australian Government’s National Environmental Research Program (Tropical Ecosystems Hub) and the School of Earth and Environmental Sciences (SEES), James Cook University. We sincerely thank S. Cooke and four anonymous reviewers who made numerous constructive suggestions to improve this manuscript.

Compliance with ethical guidelines

Competing interests The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

Centre for Sustainable Tropical Fisheries and Aquaculture, College of Marine and Environmental Sciences, James Cook University, Townsville, QLD, Australia
Australian Institute of Marine Science, Townsville, QLD, Australia


  1. Cooke SJ, Hinch SG, Wikelski M, Andrews RD, Kuchel LJ, Wolcott TG et al (2004) Biotelemetry: a mechanistic approach to ecology. Trends Ecol Evol 19(6):334–343View ArticlePubMedGoogle Scholar
  2. Simpfendorfer CA, Olsen EM, Heupel MR, Moland E (2012) Three-dimensional kernel utilization distributions improve estimates of space use in aquatic animals. Can J Fish Aquat Sci 69(3):565–572View ArticleGoogle Scholar
  3. Tracey JA, Sheppard J, Zhu J, Wei F, Swaisgood RR, Fisher RN (2014) Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation. PLoS One 9(7):e101205PubMed CentralView ArticlePubMedGoogle Scholar
  4. Cooper NW, Sherry TW, Marra PP (2014) Modeling three-dimensional space use and overlap in birds. Auk 131(4):681–693. doi:10.1642/AUK-14-17.1 View ArticleGoogle Scholar
  5. Shepard ELC, Wilson RP, Quintana F, Laich AG, Liebsch N, Albareda DA et al (2008) Identification of animal movement patterns using tri-axial accelerometry. Endanger Spec Res 10(47–60):2.1Google Scholar
  6. Heatwole H (1999) Sea snakes., Australian Natural History SeriesUniversity of New South Wales Press, SydneyGoogle Scholar
  7. Lukoschek V, Heatwole H, Grech A, Burns G, Marsh H (2007) Distribution of two species of sea snakes, Aipysurus laevis and Emydocephalus annulatus, in the southern Great Barrier Reef: metapopulation dynamics, marine protected areas and conservation. Coral Reefs 26:291–307View ArticleGoogle Scholar
  8. Lukoschek V, Shine R (2012) Sea snakes rarely venture far from home. Ecol Evol 2(6):1113–1121PubMed CentralView ArticlePubMedGoogle Scholar
  9. Burns G, Heatwole H (1998) Home range and habitat use of the olive sea snake, Aipysurus laevis, on the Great Barrier Reef, Australia. J Herpetol 32(3):350–358View ArticleGoogle Scholar
  10. Shetty S, Shine R (2002) Philopatry and homing behavior of sea snakes (Laticauda colubrina) from two adjacent islands in Fiji. Conserv Biol 16(5):1422–1426View ArticleGoogle Scholar
  11. Rubinoff I, Graham JB, Motta J (1986) Diving of the sea snake Pelamis platurus in the Gulf of Panamá: I. Dive depth and duration. Mar Biol 91(2):181–191View ArticleGoogle Scholar
  12. Rubinoff I, Graham JB, Motta J (1988) Diving of the sea snake Pelamis platurus in the Gulf of Panamá: II. Horizontal movement patterns. Mar Biol 97:157–163View ArticleGoogle Scholar
  13. Wassenberg T, Salini J, Heatwole H, Kerr J (1994) Incidental capture of sea-snakes (Hydrophiidae) by prawn trawlers in the Gulf of Carpentaria, Australia. Mar Freshw Res 45(3):429–443View ArticleGoogle Scholar
  14. Dunson W (1975) Sea snakes of tropical Queensland between 18° and 20° south latitude. In: Dunson WA (ed) The biology of sea snakes. University Park Press, Baltimore, pp 151–162Google Scholar
  15. Kerford M (2005) The ecology of the bar bellied sea snake (Hydrophis elegans) in Shark Bay, Western Australia. M.Sc. thesis, Simon Fraser University, AustraliaGoogle Scholar
  16. Voris HK, Voris HH (1983) Feeding strategies in marine snakes: an analysis of evolutionary, morphological, behavioral and ecological relationships. Am Zool 23(2):411–425Google Scholar
  17. Dutil JD, Giroux A, Kemp A, Lavoie G, Dallaire JP (1988) Tidal influence on movements and on daily cycle of activity of American eels. Trans Am Fish Soc 117(5):488–494View ArticleGoogle Scholar
  18. Helfman G (1986) Fish behaviour by day, night and twilight. In: Pitcher T (ed) The behaviour of teleost fishes. Springer, USA, pp 366–387View ArticleGoogle Scholar
  19. Lynch TP (2000) The behavioural ecology of the olive sea snake, Aipysurus laevis. Ph.D. thesis, James Cook University of North Queensland, TownsvilleGoogle Scholar
  20. Elfes CT, Livingstone SR, Lane A, Lukoschek V, Sanders K, Courtney AJ et al (2013) Fascinating and forgotten: the conservation status of marine elapid snakes. Herpetol Conserv Biol 8(1):37–52Google Scholar
  21. Courtney AJ, Schemel BL, Wallace R, Campbell MJ, Mayer DG, Young B (2010) Reducing the impact of Queensland’s trawl fisheries on protected sea snakes: report to the Fisheries Research and Development Corporation, Project No. 2005/053.2010Google Scholar
  22. Milton DA, Fry GC, Dell Q (2009) Reducing impacts of trawling on protected sea snakes: by-catch reduction devices improve escapement and survival. Mar Freshw Res 60:824–832View ArticleGoogle Scholar
  23. Pratt KL, Campbell HA, Watts ME, Franklin CE (2010) Environmental and ecological factors influencing dive behaviour in the freshwater snake Acrochordus arafurae: a field-based telemetric study. Mar Freshw Res 61:560–567View ArticleGoogle Scholar
  24. Simpfendorfer CA, Heupel MR, Hueter RE (2002) Estimation of short-term centers of activity from an array of omnidirectional hydrophones and its use in studying animal movements. Can J Fish Aquat Sci 59(1):23–32. doi:10.1139/f01-191 View ArticleGoogle Scholar
  25. R Development Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  26. Duong T (2007) ks: Kernel density estimation and kernel discriminant analysis for multivariate data in R. J Stat Softw 21(7):1–16Google Scholar
  27. Adler D, Murdoch D (2014) rgl: 3D visualization device system (OpenGL), R package version 0.93.1098. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  28. Feng D, Tierney L (2008) Computing and displaying isosurfaces in R. J Stat Softw 28(1):1–24Google Scholar
  29. Muschelli J, Sweeney E, Crainiceanu C (2013) brainR: interactive 3 and 4D images of high resolution neuroimage data. R J 6(1):41–48Google Scholar


© Udyawer et al. 2015