Seasonality and site fidelity of the zebra shark, Stegostoma fasciatum, in southeast Queensland, Australia

Animal Behaviour 85 (2013) 471e481

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Seasonality and site fidelity of the zebra shark, Stegostoma fasciatum, in southeast Queensland, Australia Christine L. Dudgeon a, b, *, Janet M. Lanyon a, Jayson M. Semmens c a

School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia School of Veterinary Science, The University of Queensland, Gatton, Queensland, Australia c Fisheries, Aquaculture and Coasts Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia b

a r t i c l e i n f o Article history: Received 5 September 2012 Initial acceptance 23 October 2012 Final acceptance 6 December 2012 Available online 7 January 2013 MS. number: 12-00678 Keywords: acoustic telemetry elasmobranch GLMM leopard shark migration movement philopatry Stegostoma fasciatum

Site fidelity and migratory movements of vertebrate animals occur at many spatial and temporal scales. Larger migratory movements tend to occur in species that live in seasonal environments in which food supplies vary markedly, while species found in thermally stable environments are more site-attached. In the marine environment, seasonal migrations are often associated with predictable temporary aggregations that have largely been targeted for exploitation. We employed passive acoustic telemetry to investigate inter- and intraseasonal site fidelity of zebra sharks to an aggregation site in southeast Queensland, Australia, close to the southern latitudinal extent of this species’ range. We tracked 10 zebra sharks over two aggregation seasons (21 months). We applied a generalized linear mixed-effects model to investigate the presence/absence of these zebra sharks with respect to several environmental variables. We found that different environmental factors were associated with site fidelity of zebra sharks at different temporal levels and that these may be indicative of the mechanisms driving the movements. Seasonal patterns may be driven by endogenous systems, and cues such as photoperiod and water temperature are likely to be important. Intraseasonal patterns are more likely to be indicative of direct behavioural responses to changes in environmental conditions such as increased wave heights, as well as foraging bouts away from a core refuge. Understanding the relative contributions of these environmental parameters, as well as biological factors, will be important for making predictions of site fidelity and movements of migratory marine vertebrates under differing future scenarios such as increases in sea temperature. Ó 2012 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Site fidelity, also known as philopatry, can be defined as the tendency to return to or stay at a particular location (Mayr 1963). Site fidelity and the related migratory behaviour occur at many spatial and temporal scales. In general, migratory movements tend to occur in species that live in seasonal environments in which food supplies vary markedly, while species found in thermally stable environments are more site-attached (Berthold 1993). These patterns can also be found for different populations within species, where migratory patterns are more pronounced in populations that breed in cooler, more seasonal areas than in those in warmer ones (Cramp 1988). In the marine environment, recent developments in acoustic and satellite telemetry have increased our knowledge of site fidelity and migration across a broad range of taxa (Shillinger et al. 2012) including elasmobranchs: the sharks, skates and rays (Speed et al. 2011; Papastamatiou & Lowe 2012). Elasmobranchs comprise * Correspondence and present address: C. L. Dudgeon, School of Veterinary Science, The University of Queensland, Gatton, Queensland 4343, Australia. E-mail address: [email protected] (C. L. Dudgeon).

about 1200 species (White & Last 2012), are found in most marine and some freshwater environments, and show considerable diversity in niche from widely migrating, pelagic species (e.g. white sharks, Carcharodon carcharias: Jorgensen et al. 2010) to highly sedentary benthic species (e.g. angel sharks, Squatina californica: Gaida 1997). Some patterns with respect to site fidelity are emerging across elasmobranch taxa. Springer (1967) proposed that large viviparous shark species are highly migratory but show strong seasonal site fidelity to pupping and nursery areas. A novel paradigm has been proposed for smaller viviparous sharks: these may be less migratory (i.e. have smaller home ranges) and show less site fidelity to nursery areas than larger species, presumably because there is less predation on juveniles from their conspecific adults (Knip et al. 2010). Thus body size and reproductive strategy were suggested to be important with respect to natal and pupping site fidelity. Comparatively little is known for oviparous elasmobranch species. Oviparous species tend to be small and a few studies indicate strong site fidelity and restricted movements over short (<1 year) durations (Sims et al. 2001; Wearmouth & Sims 2009; Awruch et al. 2012) as well as recaptures close to initial tagging

0003-3472/$38.00 Ó 2012 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.anbehav.2012.12.013

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sites over longer periods (e.g. up to 7 years for the draughtboard shark, Cephaloscyllium laticeps: Awruch et al. 2012). Studies on the repeated use of oviposition sites are currently lacking, although natal philopatry has been proposed as one of several hypotheses to explain nonrandom oviposition site choice in other oviparous taxa (insects, teleosts, amphibians, reptiles and birds, Refsnider & Janzen 2010). Other forms of site fidelity have also been proposed including ‘mating site fidelity’ and ‘feeding site fidelity’ which are the repeated use of an area to mate and feed, respectively (reviewed in Speed et al. 2010). Environmental variables associated with site fidelity and migration can be useful for predicting under what conditions these behaviours are most likely to occur as well as the possible effects of irregular events or climatic changes. While seasonal changes in sea temperature have often been associated with site fidelity and some migration patterns in elasmobranchs (Jorgensen et al. 2010; Speed et al. 2011), other environmental as well as biotic factors have also been implicated including salinity (Ubeda et al. 2009; Knip et al. 2011), tidal movements, lunar events (Jaine et al. 2012), oceanic currents (Couturier et al. 2011), time of day (Wilson et al. 2001; Speed et al. 2011), distribution of predators (Semeniuk & Dill 2005; Guttridge et al. 2012) and seasonal prey availability (Barnett & Semmens 2012). This study focuses on the site fidelity of the zebra shark, a demersal shark endemic to the Indo West-Pacific region. Zebra sharks are the largest (ca. 2.5 m total length) of the oviparous elasmobranch species (Last & Stevens 2009). They aggregate annually in southeast Queensland, Australia, over the austral summer months. The primary aggregation site appears to be at a submarine rocky reef known as The Group and the aggregation comprises mature adults with a strong female sex bias (Dudgeon et al. 2008). Markerecapture population studies estimate that an annual mean of 458 (95% confidence interval, CI ¼ 298e618) individuals visit this aggregation site (Dudgeon et al. 2008). This is the largest known aggregation and greatest density of zebra sharks globally. Zebra sharks are not fished in Australian waters, so this aggregation has commercial importance for the diving tourism industry and aquarium trade only. Outside Australian waters, zebra sharks are heavily exploited and are considered a threatened species (classified as ‘Vulnerable’ on the IUCN Red List, Pillans & Simpfendorfer 2003). Zebra sharks have a tropical to subtropical distribution and southeast Queensland is close to the latitudinal extreme of their range (Compagno et al. 2005), undergoing larger annual temperature fluctuations than occur in tropical locations. Therefore, it is likely that water temperature would be an important driver of this aggregating behaviour. Observations of resting zebra sharks during daylight hours at the primary aggregation site at The Group suggest that this site may be a daytime refuge (C. L. Dudgeon, personal observation) and time of day, sea condition, tidal phase and lunar phase may also influence the aggregating behaviour. We employed passive acoustic telemetry to quantify the site fidelity of zebra sharks to the primary aggregation site in southeast Queensland. Our specific objectives were (1) to determine the degree of site fidelity shown by zebra sharks to The Group over different temporal scales and (2) to use a modelling approach to ascertain which environmental parameters might influence the philopatric behaviour of zebra sharks.

North Stradbroke Island, southeast Queensland, Australia (Fig. 1). The area comprises a series of large submerged rocks (‘bommies’) separated by sand patches and lined by a sand gutter (see Fig. 1) in water depths of 5e18 m. Shark Capture and Tagging Zebra sharks were tagged with V16-4H (16 mm  68 mm, 34 g) coded acoustic transmitters (VEMCO, Halifax, Nova Scotia, Canada). Each acoustic tag transmitted a unique code ID contained in a string of seven pings at a frequency of 69 kHz followed by a random delay of 40e114 s. All sharks were captured by free diving (within 15 m water depth) and grasping the resting or swimming shark by the distal end of the tail. This induced a state of tonic immobility so that the shark could be towed to the surface. When the free-diver approached the surface with the shark, a second person would assist by hugging the shark around the pectoral fins. The shark would then be manoeuvred into a stretcher that was submerged next to the side of the research vessel. As zebra sharks can actively pump water over their gills (buccal pumpers), it was not necessary to ventilate the gills as the sharks were kept submerged for the duration of the capture. The present study involved the attachment of external tags to a total of 15 zebra sharks. All sharks had numerical stainless steel dart tags (SSD; Hallprint, Hindmarsh Valley, South Australia) attached by embedding the dart head into the dorsal musculature at the base of the first dorsal fin using a tagging pole. An initial pilot study was conducted on five zebra sharks (acoustic tag numbers ST301eST305), from the end of the aggregation season in April 2004 to the beginning of the next aggregation season in December 2004, to assess tag attachment method and ensure site coverage by the five VR2 receivers deployed around the primary aggregation area at The Group (Fig. 1). The acoustic tags were attached with nylon trace to a dart tag head (Hallprint) that was embedded in the dorsal musculature on the other side of the dorsal fin from the SSD. Of these five sharks tagged in April 2004, only one shark (ST304) was recorded again by the VR2 receivers in the following season (commencing in December 2004). Tag loss was confirmed for one of the sharks (ST303) that was tagged in April 2004 and was subsequently recaptured and identified using photo ID (see Dudgeon et al. 2008) in November 2004 without the acoustic tag. The other three sharks were not recorded following the pilot study nor identified using photo-ID and therefore it is not possible to distinguish between acoustic tag loss and emigration for these cases. However, zebra sharks have been observed rubbing their backs against the sand (C. L. Dudgeon, personal observation) and this type of action may have resulted in tag loss. The main study was conducted for 21 months from December 2004 to August 2006. Seven female and three male adult zebra sharks were tagged (Table 1). To ensure longer-term tag attachment to sharks, acoustic tags were first secured to cattle ear tags with glue and cable ties, which were attached through small holes (diameter 5 mm) punched in the first dorsal fin using a leather punch. All tagged individuals were photographed for identity and were sexed visually by the presence/absence of genital claspers (Dudgeon et al. 2008). Ethical Note

METHODS Study Site This study focused on a group of rocky reef outcrops, The Group (153 330 E, 27 230 S), located approximately 500 m offshore from

To the best of our knowledge, zebra sharks showed no negative response to the tagging procedures and started swimming strongly immediately after release. Zebra sharks were kept submerged and facing into the current during the procedure. With the exception of one individual, all were recorded on the array on subsequent days

C. L. Dudgeon et al. / Animal Behaviour 85 (2013) 471e481

473

(a)

500m B6

Flat Rock 27°24' S

The Group

27°25' S

Point Lookout

153° 33'E

(b)

N 50 0 50 100 m

B2

B4 B3 B1 B5

Point Lookout

Figure 1. Location of the study site, The Group, with the position of the VR2 receivers (B1eB5) shown (inset b). The detection range for each receiver is indicated by the black lines around the receivers. The rock substratum is indicated by light grey shading, sandy areas are in white and dark grey codes for exposed rock and land areas. Inset (a) also shows the location of the Flat Rock receiver.

Table 1 Deployment dates and detections for acoustically tagged zebra sharks at The Group Acoustic Tag ID

Tagging date

Sex

First detection Season 1

Last detection Season 1

ST304 ST306 ST308 ST309 ST310 ST311 ST312 ST313 ST316 ST317

14 14 14 14 14 14 14 14 14 14

F F F F F F M F M M

16 22 18 17 17 17 17 19 18 17

7 Jan 05z 28 Dec 04 23 Feb 05 18 Mar 05 9 Feb 05 15 Jan 05 28 May 05 18 Mar 05 7 Feb 05 18 Mar 05

* y z x

Apr 04 Dec 04 Dec 04 Dec 04 Dec 04 Dec 04 Dec 04 Dec 04 Dec 04 Dec 04

Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec

04 04 04 04 04 04 04 04 04 04

First detection Season 2

Last detection Season 2

4 Dec 05 21 Nov 05 15 Nov 05

22 Dec 05 1 Jan 06 25 Jan 06x

23 Nov 05

30 Jan 06

(S1/S2): Season 1/ Season 2. Total number of acoustic detections not binned into daily intervals. 18 Jan 06: shark observed with dart tag head and remains of acoustic tag trace visible. 25 Jan 06: tag removed from shark.

Total days detected (S1/S2)*

Total detectionsy

14 5 18 19 32 (18/14) 51 (16/35) 116 (53/63) 10 79 (20/59) 24

4087 131 1304 1581 13111 16656 42851 549 21074 2829

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(Fig. 2). The use of cattle ear tags was based on a previous study that had been conducted on zebra sharks at this site in December 1999 (D. Bell, unpublished data) that had successfully involved attaching cattle ear tags (without acoustic tags) to four adult zebra sharks to monitor their presence. Two of these zebra sharks were observed at the beginning of this study (January 2004, December 2004; C. L. Dudgeon, personal observation). There was evidence of minor marine fouling on the tags (algal growth) but the dorsal fins were in good condition and there was no obvious damage from the tags to the sharks. One shark was recaptured and the cattle ear tag was removed and replaced with the cattle ear tag/acoustic tag in December 2004. The second shark was observed without the cattle ear tag after a period of 22 months and it was clear that the tag had migrated out of the fin and the fin had grown back together and

healed. The total weight of the acoustic tag with cattle ear tag and dart tag is ca. 50 g. The tagged zebra sharks ranged from 198 to 230 cm total length (mean ¼ 219) and based on length to weight measurements of two captive sharks of similar length (193 cm and 230 cm; J. Semmens, unpublished data) these would correspond conservatively to 20 and 47 kg. It is not possible to extrapolate across all sharks; however, based on the weight of the smallest tagged zebra shark the combined tag weight would be at the most 0.25% of the total weight of the shark. We did observe one fintagged shark 13 months after tagging and the tag was removed, as there was considerable marine fouling on the tag (primarily algae and barnacles). To avoid the problem with marine fouling, for future studies it is recommended to insert tags surgically as has now been conducted successfully for other shark species with

ST317 ST316 ST313 ST312 ST311 ST310 ST309 ST308 ST306

6 Mar 06

4 Feb 06

5 Jan 06

6 Dec 05

6 Nov 05

7 Oct 05

7 Sep 05

8 Aug 05

9 Jul 05

9 Jun 05

10 May 05

10 Apr 05

11 Mar 05

9 Feb 05

10 Jan 05

(a) 11 Dec 04

Acoustic Tag ID

ST304

ST317

3

ST316 2.5

ST313 ST312

ST310 1.5

ST309

Hsig (m)

2

ST311

ST308 1

ST306 ST304

0.5

5 Apr 05

26 Mar 05

16 Mar 05

6 Mar 05

24 Feb 05

14 Feb 05

4 Feb 05

25 Jan 05

15 Jan 05

5 Jan 05

26 Dec 04

16 Dec 04

(b)

Date Figure 2. Time series of acoustic detections across all VR2 receivers for 10 tagged zebra sharks. (a) Both seasons, (b) season 1 only also showing daily average significant wave heights (Hsig) in metres.

C. L. Dudgeon et al. / Animal Behaviour 85 (2013) 471e481

similar size ranges (e.g. Papastamatiou et al. 2010; Barnett et al. 2011). The research was conducted with approval from the University of Queensland Ethics Committee (no. ZOO/ENT/490/05), the Queensland Department of Primary Industries and Fisheries (General Fisheries Permit no. PRM03978K) and the Queensland Parks and Wildlife Service (Moreton Bay Marine Park permit no. QS2004/CVL974). Receiver Deployment The underwater acoustic array consisted of five VEMCO VR2 receivers with omnidirectional hydrophones deployed 1 m above the substratum on submerged moorings that each consisted of a stainless steel post bracketed to a flat 60  60 cm concrete paver. Receivers were positioned so as to provide maximum coverage at The Group where zebra sharks were most commonly observed (Dudgeon et al. 2008). Because the rock structures on the reef interrupted the sound propagation of the acoustic tags, the receivers were positioned within 50e160 m of each other (Fig. 1). Four receivers (B1eB4) were deployed between 15 December 2004 and 14 August 2006 and an extra one (B5) was deployed from 21 January 2005. All five receivers at The Group were removed either once or twice during the study to download the data and were then redeployed without having been reinitialized. One receiver (B4) became buried under sand for a while (first noticed missing: 9 September 2005; unburied: 12 November 2005); however, this is unlikely to have affected data collection because none of the other receivers recorded acoustic tags during this period, suggesting that no sharks were present. An extra receiver (B6) was deployed for part of the study (19 February 2005e12 November 2005) at a nearby reef, Flat Rock (located 4 km north of The Group), where zebra sharks have been observed at much lower frequency (Dudgeon et al. 2008; Fig. 1). Range Test VR2 receiver detection range was ascertained by submerging an acoustic tag 1 m below the water surface from a drifting vessel up to 1 km from the deployed receivers and by placing a tag on the substratum next to the vessel stationary at anchor and referring GPS locations back to receiver recordings. The detection range (i.e. distance from receiver that the tag signal could be recorded) was plotted graphically using ARCView GIS (ESRI v 3.3, Esri, Redlands, CA, U.S.A.; Fig. 1). Data Analysis Performance metrics of the VR2 receivers were assessed across the entire 21-month deployment interval as described by Simpfendorfer et al. (2008). A linear manual time drift alteration (www.vemco.com.au) was applied to the data as recommended by the manufacturer (www.vemco.com.au). Payne et al. (2010) demonstrated that background reef noise can affect the detection capacity of the VR2 and result in false detection patterns, for example because of increased background noise at night from invertebrates including snapping shrimp. To quantify any such detection effects at this study site, a sentinel tag was deployed in rocks about 5 m from the VR2 mooring for 6 months after the study. To investigate concordance of acoustic presence data with visual photoidentification surveys of sharks conducted by SCUBA (data from Dudgeon et al. 2008), the average percentage of days per month on which all sharks were detected and the total number of sharks identified per month standardized by survey effort were correlated using Pearson correlations implemented in R (R Core Development Team 2012). Values for the months of December

475

2004 and August 2006 were adjusted to account for deployment and retrieval dates of receivers. Residency of individual tagged sharks was assessed by determining both the total number of detections and the number of days on which an individual shark was present at the study site between the first detection and the last detection for each tag (not including the tagging day). Continuous hourly presence was investigated by coding detections for each shark, from data pooled across all five VR2 receivers, into ‘visits’, whereby a new code was designated if the shark had been absent for longer than 1 h. Percentage of hours present per day was averaged for each shark across all days that the shark was present at The Group. Continuous daily presence was assessed by grouping all detections of individual sharks into daily bins and counting the number of consecutive days a shark was present at the study site. The time series of the daily patterns of mean hourly detections for sharks with greater than 20 days of data were analysed using fast Fourier transformation analysis (FFT) with Hamming window smoothing. FFT is a periodic interpolation that decomposes a regular time series into the sum of its sine and cosine components. Data were truncated, to fit a series with base two, by starting from the hour of the first time the shark was detected (not including tagging day) and finishing at the last hour of the base two series up to 85.3 days (2048 h). FFT was performed on eight tagged sharks only, as two sharks (ST306 and ST313) were recorded for fewer than 20 days (512 h). Four sharks were recorded in the first season only, with analysis conducted for 512 h (ST304), 1024 h (ST308) and 2048 h (ST309 and ST317). Four sharks were recorded in both seasons and analyses were conducted for each season separately: ST310 (1024 h and 256 h for seasons 1 and 2 respectively); ST311 (512 h both seasons); ST312 (1024 and 512 h); ST316 (1024 h and 2048 h). We used a generalized linear mixed-effects modelling framework (GLMM) that incorporates both random and fixed variables, to examine the effects of time of year, time of day and several environmental parameters (lunar phase, tidal stage, sea surface temperature and wave height) on the presence of zebra sharks at The Group. Acoustic Tag ID (N ¼ 10) was incorporated as a random variable, rather than fixed factor, to account for pseudoreplication and enable model prediction to extend to the rest of the population (Venables & Dichmont 2004). For the fixed variables, time of year was categorized into calendar month (variable Month). The seasonality of the ‘Month’ variable was modelled by including the variable as the cyclical function of its sine and cosine components. Time of day was categorized into daytime and night-time (variable Day) based on sunrise and sunset data obtained from the Geoscience Australia website (http://www.ga.gov.au/geodesy/astro/sunrise). Moon phase (variable Moon) was categorized into three qualitative levels following that of Dewar et al. (2008): ‘new’ (<10% illumination), ‘half’ (10e90% illumination) and ‘full’ (>90% illumination) based on data obtained from the Naval Observatory Astronomical Applications Department (http://aa.usno.navy.mil/data/docs/MoonPhase). Tidal stage data (variable Tide) were obtained from Maritime Safety Queensland and hourly detections were binned into one of four categories: high, ebb, low and flood tide. The high and low tide categories comprised the hour with the peak tide plus 1 h before and after. Sea surface temperature (SST) and significant wave height (Hsig) data were obtained from the closest Datawell Waverider Buoy (Queensland Environmental Protection Agency) moored approximately 15 km east of The Group (153 380 S, 27 300 E). Hsig is calculated as the average of the highest third of the wave heights recorded by the wave buoy within a 26.6 min wave record. Analysis was implemented using the lmer() function in the lme4 package (Bates et al. 2011) within R version 2.5.1 (R Core

476

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Development Team 2012). The analysis used a binomial error structure with a logit link function. The binomial dependent variable was coded with a value of one if a zebra shark was detected (‘present’) and zero when no zebra sharks were detected (‘absent’). ‘Presence’ was evaluated per calendar day (24 h) for each level of the qualitative variables ‘Day’ and ‘Tide’, and was modelled for the duration of the 21-month deployment of the VR2 receivers. The other qualitative variables (‘Month’, ‘Moon’) have only one value per calendar day and the continuous variables were calculated as a daily average. Therefore, each individual shark had a total of eight records for each calendar day. To account for nonlinearity in the continuous response variables, SST and Hsig were transformed into nonlinear natural cubic splines with the crossbasis() function (df ¼ 4) in the dlnm package (Gasparrini & Armstrong 2011). Akaike’s information criterion (AIC) and the Bayesian information criterion (BIC) were used to compare relative model support, where lower AIC and BIC values indicate greater support for the model (Burnham & Anderson 2002). Owing to the unequal numbers of male (N ¼ 3) and female sharks (N ¼ 7), sex was not included in the models. RESULTS Receiver Range Testing and Performance Metrics Range testing of the five VR2 receivers showed that the rocky reef substrate formed an effective barrier to sonic tag detection, and as a result receiver detection ranges varied from 50 to 400 m depending on relative positions of tags and receivers to the reef structure (Fig. 1). Receivers B3 and B4 had overlapping detection ranges while the other three receivers showed unique coverage in parts of their ranges. Owing to the complex nature of the rocky reef, at least four receivers were required to enable coverage of an approximate 0.25 km2 area of reef. Single detections on the receivers were removed from all data analyses to avoid the potential of false detections (see Semmens et al. 2010). Nine of the 10 tagged sharks were detected equally by all five receivers when the total number of detections was standardized by the total deployment time (chi-square goodnessof-fit tests: P > 0.05). One male shark (ST312) had unequal detection across the receivers with most detections by the most northern receiver B2 (c24 ¼ 40:06, P < 0.01). The three performance metrics were similar for all five of the receivers. Code detection efficiency (CDE) ranged from 0.559 to 0.678 with a mean of 0.603 indicating that more than 50% of the tag codes transmitted were detected by all receivers. Rejection coefficients (RC) were low and ranged from 0.036 to 0.051 with a mean of 0.042, indicating that the tag code was correctly identified more than 95% of the time if the entire seven-ping tag signal was detected by the receiver. Noise quotients were all positive ranging from 7026 to 23 872 (mean ¼ 14 548) indicating that there was considerable environmental noise (Simpfendorfer et al. 2008). For the sole receiver deployed at Flat Rock, the RC and noise quotient were similar to the other receivers; however, the CDE was 10-fold lower (0.03), indicating that only 3% of the codes transmitted were detected. There were 11 detections for two sharks on the Flat Rock receiver. This was much lower than for The Group where the total number of detections pooled across all five receivers for the equivalent time period was 3146. The numbers of detections at Flat Rock were too few to conduct statistical analyses so that the following analyses refer only to data collected on receivers at The Group. Visit Rates and Seasonality Acoustic data were recorded for 10 adult zebra sharks during the 21-month study. One (ST304) of the five sharks tagged during the

pilot study in April 2004 was also detected during the main data collection phase (from 16 December 2004 to 7 January 2005) and was subsequently included in the analysis (10 sharks total). Of the 10 sharks with the fin tag attachment type, only one (ST315) was undetected after tagging. The results from this point forward refer to a total of 10 zebra sharks: the remaining nine sharks tagged with the fin tag attachment type and the one shark from the pilot study (ST304). Six zebra sharks (ST304, ST306, ST308, ST309, ST313, ST317) were recorded during one austral summer period only (referred to as Season 1: November 2004eMay 2005) while four sharks (ST310. ST311, ST312, ST316) were also recorded during the second summer period (Season 2: November 2005eJanuary 2006; Fig. 2a). These 10 zebra sharks were detected on a minimum of five and a maximum of 116 days in total, with a median of 18 days in Season 1 (range 5e53) and median of 47 days in Season 2 (range 14e63). For both seasons combined, the median number of days on which males were recorded was 79 (range 24e116). This was more frequent than for females with a median of 18 days (range 5e51). Tagging in Season 1 commenced in December 2004; however, in Season 2, three sharks arrived at The Group in November 2005 and the fourth arrived in early December 2005. The last detections for Season 1 ranged from December 2004 to March 2005, with one shark (ST312) recorded on 1 day at the end of May 2005. The last detections for individual sharks in Season 2 were earlier than for Season 1 and were either in December 2005 or January 2006 (Table 1). The acoustic tag on shark ST312 was removed owing to observed fouling on 25 January 2006. The average percentage of days per month that sharks were detected by the receivers was strongly correlated with the number of visual sightings standardized by survey effort from Dudgeon et al. (2008) (t7 ¼ 4.8, P ¼ 0.002, r ¼ 0.8758). Seasonality in presence of zebra sharks at The Group was also supported by the GLMM analyses (Tables 2, 3), with the greatest presence of sharks occurring in the months of NovembereFebruary (Fig. 3a). Outside these months one shark was detected during May, with a total of 32 detections over 2 h suggesting that these were real and not false detections. The factor ‘Month’ still accounted for ca. 29% of the deviance in the model after removing the positive hits from May, showing that the pattern was not due to these anomalous records (Table 2). The GLMM model with the greatest support (i.e. lowest AIC and BIC values), accounted for 38.21% of the deviance in comparison to the null model that only included the random ID variable (Table 2). In this model, the covariates for sea surface temperature (SST),

Table 2 GLMM analyses model comparison results Model covariates

AIC

BIC

D Deviance (%)

Pres w Month þ Day þ SST þ Hsig þ (1jID) Pres w Month þ Day þ Moon þ SST þ Hsig þ (1jID) Pres w Month þ Day þ Moon þ Tide þ SST þ Hsig þ (1jID) Pres w Month þ (1jID) Pres w Hsig þ (1jID) Pres w SST þ (1jID) Pres w Day þ (1jID) Pres w Moon þ (1jID) Pres w Tide þ (1jID) Pres w (1jID)

5942 5945

6056 6075

38.21 38.22

5950

6107

38.23

6766 8718 8833 9413 9525 9583 9578

6801 8771 8885 9439 9560 9627 9595

29.41 9.07 7.87 1.74 0.60 0.01 0

The table shows the dependent variable (Pres ¼ Presence) and the covariates for each model, where (1jID) designates the random variable Acoustic Tag ID. Also shown are the Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and the % change in deviance (D Deviance) from the designated null model which includes the random variable (1jID) as a covariate.

C. L. Dudgeon et al. / Animal Behaviour 85 (2013) 471e481 Table 3 GLMM for model with best fit based on AIC rankings

(a) 0.8 P

0.6

0.4

0.2

Dec

Nov

Oct

Sep

Aug

Jul

Jun

May

Apr

N ¼ 45 372. Presence of acoustically tagged shark is the dependent variable. Significant P values (<0.05) are in bold. The random effect includes the Acoustic Tag ID. The fixed effects include the time series interpolation of factor ‘Month’, nonlinear natural cubic spline transformation for sea surface temperature (df ¼ 4: SST1e4), nonlinear natural cubic spline transformation for significant wave height (df ¼ 4: Hsig1e4) and time of day ‘Day’.

Mar

0 Feb

2eL16 2eL16 2eL16 0.793 0.988 0.981 0.309 1.52eL09 2eL16 0.020 0.827 2eL16

Jan

Z

(Intercept) 7.7880.501 15.560 Sin(2p/12Month) 1.7800.109 16.354 Cos(2p/12Month) 5.2000.212 25.524 SST1 0.7212.752 0.262 SST2 0.0271.792 0.015 SST3 0.1325.560 0.024 SST4 1.1961.176 1.017 Hsig1 1.3310.220 6.043 Hsig2 7.0000.620 11.289 Hsig3 4.2141.806 2.333 Hsig4 0.7793.561 0.219 Day (night) 0.9090.075 12.236 Random effects Acoustic Tag ID estimated varianceSE¼2.0091.417

Probability of presence

bSE

477

Month (b) 6

Residency The 10 adult sharks showed varying degrees of residency at The Group. Mean duration of visit (i.e. detections pooled across all receivers separated by less than a 60 min gap) ranged from 0.4 to 5.5 h and was similar for male (2.5 h) and female sharks (2.1 h). The maximum continuous duration for a single visit, 28 h, was for a female shark (ST311). The mean average percentage of hours per day that sharks were present at The Group varied between 7% and 44%. The mean number of consecutive days (i.e. a shark is considered present on the day if it is recorded more than once by at least one receiver) ranged from 1.6 to 4.2. The maximum number of consecutive days a shark was present at the site was 23 (for a male shark ST316). Of the two other male sharks tagged in this study, one (ST316) had the second longest consecutive daily period of 19 days, while the third male (ST317) stayed for a maximum of 4 consecutive days. Female sharks were present between 1 and 13 consecutive days.

2 0 −2 −4 −6 0

1

2

3

4

5

6

7

Hsig (m) 6 (c) 4 Log odds ratio

significant wave height (Hsig) and time of day (Day) all had significant effects on the presence of zebra sharks at The Group. Conversely the effects of moon (Moon) and tide stage (Tide) were not significant and were not supported for inclusion in the final model (Table 2). Zebra sharks were more likely to be detected at The Group when Hsig was low (<1 m), with detections dropping off rapidly once Hsig reached >1.5 m (Fig. 3b). Superimposing Hsig onto daily detections for Season 1 showed consistent absences across multiple tagged sharks coinciding with peaks in Hsig (Fig. 2b). SST showed a threshold effect with no detections below 21.75  C and then consistent detections until mean SST values of ca. 24  C followed by a gradual decline in detections at the upper temperature scale (Fig. 3c). Zebra sharks showed a significant diurnal pattern and were 2.74 times more likely to be present during the day than at night as indicated by antilogit parameter transformations (Table 3). The sentinel tag showed a significant effect of hour of detection (F23,4264 ¼ 2.099, P ¼ 0.002); however, the primary difference was between 1200 and 0800 hours with significantly more detections at midnight and more detections during the early morning hours than daylight hours. Therefore, any bias from an effect of background noise would support the diurnal visit pattern from zebra sharks.

Log odds ratio

4

2 0 −2 −4 −6 19

20

21

22

23 24 25 SST (°C)

26

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29

Figure 3. Prediction curves for the GLMM most parsimonious model. (a) Scatterplot showing the predicted probability of presence of a zebra shark at The Group for each calendar month. (b, c) Log odds ratios (comparison of logistic probability for different values of the factor compared against the mean of the factor: horizontal line at 0) for (b) ‘Hsig’ (significant wave height) and (c) ‘SST’ (sea surface temperature  C). The shading represents the 95% confidence intervals.

Periodicity Within seasons, zebra sharks made excursions away from The Group for an overall median of 7.7 h (median range 4.1e20.6 h). This excluded the one anomalous visit by shark ST312 in May 2005 and another unusually long duration (ca. 70 days) between visits by

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shark ST313. The next longest duration between visits was for a male shark (ST317) and was approximately 22 days. The median durations between visits for male and female sharks were similar (male ¼ 6.4 h, females ¼ 10.35 h). No clear biorhythms were detected by the fast Fourier analysis (FFT). Four of the 10 sharks showed some signal that appeared to be associated with tidal periodicity. Three sharks showed a signal peak corresponding to 12-hourly visit patterns within one season. However, two of these sharks were also detected in a second season and these 12-hourly signal peaks were absent. One other shark had a signal peak corresponding to 6-hourly visit patterns. Discussion Adult zebra sharks demonstrated clear inter- and intra-annual patterns of site fidelity to the aggregation site in southeast Queensland. First, there was a seasonal pattern where almost all tagged sharks were recorded in this area during the warmer months from November to March with peak visits during the summer months of December and January. Outside this period, zebra sharks have been seen in April (Dudgeon et al. 2008). One of the acoustically tagged sharks in this study was detected in May, but sightings in the winter months are relatively rare. The returning behaviour of four sharks during the second summer of the study suggests this behaviour is consistent with seasonal migratory movements (Milner-Gulland et al. 2011). The second site fidelity pattern was demonstrated within the summer aggregation period, where zebra sharks displayed frequent, short (hours to days) excursions away from the study site that are typically associated with foraging movements (Dingle & Drake 2007). Interannual Site Fidelity Seasonal migrations are characteristic of animals that live in temperate environments, in which resources undergo large changes in a predictable manner. The key survival strategy proposed to drive these movements is ‘pre-emption’ (Dingle & Drake 2007). Preemption requires that the migration commences before conditions deteriorate too much and therefore does not rely on proximate cues such as temperature and food shortages. Rather, indirect cues that forecast change in conditions such as photoperiod, increasing population density and hormonal changes are more likely to be selected upon (Dingle & Drake 2007). Photoperiod (daily daylight duration) is involved in the migratory timing of many species (Milner-Gulland et al. 2011) and is likely to account for a large component of the variation associated with the time factor ‘Month’ for zebra sharks. This factor accounted for the largest amount of deviance of all factors for the GLMM presented here. Although the sample size for this study was small, there was evidence of interindividual variation in the migratory timing of arrival and departure from the study site, which may be caused by environmental stochasticity. That is, random variation in the environment may affect parts of the population in similar ways (Lande et al. 2003) and therefore, over time, these downstream cues may vary between cohorts or individuals exposed to different feedback loops. Sea surface temperature (SST) was also found to be a significant factor affecting the site fidelity of zebra sharks to southeast Queensland and accounted for about a quarter of the deviance explained by ‘Month’. Southeast Queensland is subtropical to temperate, undergoes a substantial annual fluctuation in SST of ca. 12  C, and is close to the southernmost part of the distribution of zebra sharks (Compagno et al. 2005). Presence of zebra sharks did not peak at the highest SST values, but rather, there appeared to be a minimum temperature threshold effect. Specifically, once SST

approached 22  C in November, the sharks started to arrive, but there was no clear end to the aggregation period with sharks starting to depart the site from January onwards while water temperatures were still above the initial threshold. Where the sharks migrate after leaving The Group is unknown; however, two zebra sharks that had dart tags attached during this study were observed 140 km further south at Julian Rocks, New South Wales (NSW), 2 months later in the summer (S. Pierce, personal observation). In general, zebra sharks have been observed at Julian Rocks in the later summer months (JanuaryeApril; B. Womersley, unpublished data). It is possible that these sharks comprise the same population that visit The Group, and that at least some of this population continue to migrate further south as the water temperature increases above the 22  C threshold. This has yet to be investigated. Adaptation to different water temperature regimes in elasmobranchs is well demonstrated through their biogeographical distributions (Compagno et al. 2005). However, the direct consequence of water temperature on the physiology or other aspects of the biology of these species, or on secondary factors such as food sources, is unknown. Minimum temperature thresholds have been suggested for several marine species as a cue for movement to avoid thermal stress, including commencement of long-distance migration from foraging areas in sea turtles (Mansfield et al. 2009), offshore migration of dugongs, Dugong dugon, for overwintering in Shark Bay, Australia (Anderson 1986) and emigration from Tomales Bay in western U.S.A. for overwintering in three species of elasmobranchs (Hopkins & Cech 2003). As southeast Queensland is towards the southern edge of the distribution for zebra sharks, the temperature threshold for migration may indicate a physiological stress limit. Controlled experiments, such as comparing oxygen consumption and excess postexercise oxygen consumption (Gaesser & Brooks 1984) under different temperature regimes, would be beneficial to determining the thermal sensitivity of physiological performance (e.g. Miklos et al. 2003). Optimal temperature ranges have been associated with reproductive behaviour in elasmobranch taxa and migration into warmer waters to assist embryonic growth during gestation has been proposed for several viviparous species (e.g. Economakis & Lobel 1998; Di Santo & Bennett 2011; Jirik & Lowe 2012). Zebra sharks are the largest of the oviparous species, and little is known about how oviparous species utilize their environment since they show both seasonal and nonseasonal periodicity in reproduction (Heupel et al. 1999; Sulikowski et al. 2005). There are no studies of the reproductive biology of wild zebra sharks, although there are anecdotal observations of eggs attached to inshore channel markers and reefs in tropical northern Queensland at various times of the year (L. Squire, Jr, personal communication 6 November 2010), as well as neonatal sharks in shallow inshore seagrass areas during autumn (A. Chin, personal communication 20 May 2009). Zebra sharks kept in captivity under constant temperature regimes have demonstrated both seasonal (Kunze & Simmons 2004; Robinson et al. 2011) and year-round egg-laying patterns (M. Horton, Sea World Gold Coast, personal communication 13 August 2012). There is some evidence that warmer water temperatures increase hatching and survival rates of zebra sharks incubated artificially, while temperatures of 23  C result in zero survival of eggs (Kunze & Simmons 2004). Owing to longer incubation times and the low survival rate of zebra shark embryos at lower temperatures, it is unlikely that zebra sharks are laying eggs at The Group or in nearby locations where they would be exposed to prolonged temperatures of less than 23  C and down to 15e16  C. It is more likely that zebra sharks migrate to more tropical sites to deposit eggs. Throughout their range, zebra sharks tend to be observed singly or in low numbers on reefs, and it is possible that larger

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aggregations such as those found at The Group may facilitate mating. Mating site fidelity (Speed et al. 2010) has been proposed to drive seasonal movements for several viviparous shark species (Pratt & Carrier 2001; Bansemer & Bennett 2009). Lines of evidence that might indicate that this aggregation of zebra sharks may be related to mating behaviour include that (1) the aggregation observed in this study comprised only mature adults, (2) several observations were made of males closely following female sharks around the site (C. L. Dudgeon, personal observation) and (3) on one such follow, a male shark flared his claspers to the side. These behavioural observations are consistent with descriptions of precopulatory behaviour observed in other elasmobranchs (Johnson & Nelson 1978; Gordon 1993; McCauley et al. 2010). Seasonal patterns and changes in SST may also be affected by periodic changes in oceanic current flow. In particular, the prominent oceanic current in southeast Queensland is the East Australian Current (EAC), which experiences predictable seasonal fluctuations, with inshore southward currents more than twice as strong during the summer months than in winter (Ridgway & Godfrey 1997). The EAC pushes warm tropical water as far south as the Solitary Islands off Coffs Harbour in NSW (ca. 30 S) and is responsible for transporting tropical fish and coral larvae to this region (Malcolm et al. 2010). Couturier et al. (2011) proposed that the movements of manta rays between southeast Queensland, northern NSW and the southern GBR may coincide with temporal and spatial changes in the EAC, and that mantas may minimize their energy expenditure during migration by utilizing the EAC. Zebra sharks have single-lobed tails typical of benthic species, and may also benefit from exploiting these predictable currents. It was not possible to obtain localized ocean current data for the period of this study; however, as these types of data become available, for instance through the Australian Government’s Integrated Marine Observing System (IMOS) Ocean Portal (IMOS; http://imos.aodn.org.au/webportal/), these could be included in future investigations. Intra-annual Site Fidelity Within an aggregation season, zebra sharks showed regular site fidelity to The Group, with repeated daily visits (up to 23 consecutive days) and relatively short excursions away (median 8 h) from the site. These shorter forays are characteristic of the foraging bouts described by Dingle & Drake (2007). When present, zebra sharks tended to be sedentary on the substrate, facing into the prevailing southerly current. It is possible that during the summer aggregation period, zebra sharks use this site primarily as a resting site or refuge. Daytime refuging behaviour, where groups of conspecifics gather in a small core area during daylight hours then disperse over larger areas for foraging at dusk and night, is common in elasmobranchs (Papastamatiou & Lowe 2012). The GLMMs indicated that sea surface height exerted a strong effect on visit patterns, with presence of zebra sharks more likely when sea height was lowest, that is, in calmer waters. Furthermore, tagged sharks tended to be absent when wave heights were high. Thus it appears that within an aggregation season, zebra sharks leave The Group during rough water conditions but are probably still in the region as they tend to return to the site soon after conditions become calm. The particular features that make this site an attractive refuge are unknown but may include reef structure that channels water flow to facilitate buccal pumping (e.g. Hughes & Umezawa 1968) or affords antipredator protection (e.g. Semeniuk & Dill 2005) as well being a possible cleaning station for removal of ectoparasites (e.g. Oliver et al. 2011). Finally, detections of zebra sharks at The Group were more than twice as likely during the day than at night, which also supports the

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likelihood that this site is a daytime refuge. There is no current information on diet or movement of zebra sharks away from The Group during the aggregation period so it is unknown whether zebra sharks display nocturnal foraging activity as has been documented for other shark species (e.g. white tip reef sharks, Triaenodon obesus: Fitzpatrick et al. 2011; sevengill sharks, Notorynchus cepedianus: Barnett & Semmens 2012; silky sharks, Carcharhinus falciformis: Clarke et al. 2011). Conclusion Environmental variables and habitat strongly influence the marine biota residing in an area (Connell & Kingsford 1998). Understanding the relative contributions of different variables to site fidelity can be informative for predicting the importance of a site to a population as well as the migration patterns of species, and hence also assist in management decisions for fisheries and conservation. We found that different environmental factors were associated with site fidelity by zebra sharks at different temporal levels and that these may be indicative of the mechanisms driving the movements. Seasonal patterns may be driven by endogenous systems, and cues such as photoperiod and water temperature are likely to be important. Intraseasonal patterns are more likely to be direct behavioural responses to changes in environmental conditions such as increased wave height and possible foraging bouts away from core refugia. Information on captive zebra shark reproduction coupled with the temperature effects on site fidelity found here suggests that these zebra sharks are likely to overwinter and lay eggs in more northern, tropical areas. Future research is required to identify the migration pathways and potential oviposition sites of this population of zebra sharks, as well as dietary requirements, to elucidate the ultimate drivers behind this seasonal aggregation in southeast Queensland. Furthermore, as this study was conducted at a site close to the latitudinal limit for this species, it is likely that, as has been found for some bird and teleost species (Baker et al. 1980; Cramp 1988), populations in more tropical localities may show different seasonal patterns with more constant year-round site fidelity. However, similar patterns may exist for intra-annual site fidelity. To date, no studies have investigated site fidelity in a single elasmobranch species from populations found in different temperature regimes. Comparative studies would be useful for predicting under what conditions different site fidelity behaviours are likely to occur, as well as the possible effects of irregular or long-term climate change. Increasing sea surface temperatures may alter the seasonal aggregation and migratory behaviours of this cool-water population of zebra sharks. However, as illustrated from this study, several environmental parameters contribute to these behaviours at varying scales and, therefore, understanding the relative contributions of these, as well as biological parameters, will be important for predictions under differing future scenarios. Acknowledgments Many thanks go to the field volunteers: M. Noad, R. Slade, J. Smith, J. Kreuger, D. Broderick, J. White, M. Kospartov, R. Dunlop, D. Paton, S. Waller, A. Byatt, T. Suddendorf. We thank S. Blomberg and A.G. Barnett for assistance with data analysis, and R. Harcourt, A. Barnett and two anonymous referees for comments on the manuscript. This research was generously funded by The Winifred Violet Scott Foundation, The Australia Pacific Science Foundation, Australian Geographic, a University of Queensland scholarship to C.L.D., and in-kind support from Tasmanian Aquaculture and Fisheries Institute, Moreton Bay Research Station, Manta Lodge and SCUBA Centre and B. Yarrow.

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