MARINE ECOLOGY PROGRESS SERIES Mar Ecol Prog Ser Vol. 653: 167–179, 2020 https://doi.org/10.3354/meps13488 Published October 29§ 1. INTRODUCTION Variation in the spatial distribution of wild animals is largely determined by shifts in habitat use of indi- viduals. The habitat of an animal is the natural envi- ronment in which it normally lives and is defined by numerous, co-varying abiotic and biotic factors (Par- tridge 1978). Most animals actively select appropri- ate habitat through movements in response to physi- cal (e.g. temperature and sunlight) and biological conditions (e.g. food availability and presence of con- specifics) (Nathan et al. 2008). Vagile organisms, e.g. © The authors 2020. Open Access under Creative Commons by Attribution Licence. Use, distribution and reproduction are un - restricted. Authors and original publication must be credited. Publisher: Inter-Research · www.int-res.com *Corresponding author: jlys3@cam.ac.uk Environmental conditions are poor predictors of immature white shark Carcharodon carcharias occurrences on coastal beaches of eastern Australia Julia L. Y. Spaet1,2,*, Andrea Manica1, Craig P. Brand3, Christopher Gallen4, Paul A. Butcher2,3 1Evolutionary Ecology Group, Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK 2National Marine Science Centre, Marine Ecology Research Centre, School of Environment, Science and Engineering, Southern Cross University, Coffs Harbour, New South Wales 2450, Australia 3Fisheries NSW, NSW Department of Primary Industries, National Marine Science Centre, Coffs Harbour, New South Wales 2450, Australia 4Fisheries NSW, NSW Department of Primary Industries, Port Stephens Fisheries Institute, Nelson Bay, New South Wales 2315, Australia ABSTRACT: Understanding and predicting the distribution of organisms in heterogeneous envi- ronments is a fundamental ecological question and a requirement for sound management. To implement effective conservation strategies for white shark Carcharodon carcharias populations, it is imperative to define drivers of their movement and occurrence patterns and to protect critical habitats. Here, we acoustically tagged 444 immature white sharks and monitored their presence in relation to environmental factors over a 3 yr period (2016−2019) using an array of 21 iridium satellite-linked (VR4G) receivers spread along the coast of New South Wales, Australia. Results of generalized additive models showed that all tested predictors (month, time of day, water temper- ature, tidal height, swell height, lunar phase) had a significant effect on shark occurrence. How- ever, collectively, these predictors only explained 1.8% of deviance, suggesting that statistical sig- nificance may be rooted in the large sample size rather than biological importance. On the other hand, receiver location, which captures geographic fidelity and local conditions not captured by the aforementioned environmental variables, explained a sizeable 17.3% of de viance. Sharks tracked in this study hence appear to be tolerant to episodic changes in environmental conditions, and movement patterns are likely related to currently undetermined, location-specific habitat characteristics or biological components, such as local currents, prey availability or competition. Importantly, we show that performance of VR4G receivers can be strongly af fected by local envi- ronmental conditions, and provide an example of how a lack of range test controls can lead to mis- interpretation and erroneous conclusions of acoustic detection data. KEY WORDS: Acoustic telemetry · New South Wales · Generalized additive model · GAM · Range test · Receiver performance · Seasonality · Spatial · Temporal §Corrections were made after publication. For details see www.int-res.com/abstracts/meps/v653/c_167-179 This corrected version: October 30, 2020 OPEN ACCESS Mar Ecol Prog Ser 653: 167–179, 2020 larger shark species, typically only frequent coastal waters when conditions are favourable and move away when confronted with adverse conditions (see Schla" et al. 2014 for a review). The main drivers of these movements are commonly species- and context- specific (Udyawer et al. 2013, Wintner & Kerwath 2018) and can include factors as diverse as water tempera- ture (e.g. Heupel et al. 2007, Werry et al. 2018); tidal cycle (e.g. Ackerman et al. 2000, Car lisle & Starr 2010); barometric pressure (e.g. Matich & Heithaus 2012, Udyawer et al. 2013); rainfall (Werry et al. 2018); and pH (Ortega et al. 2009). The influence of these drivers on individuals of the same species can also be greatly affected by sex, onto genetic stage, geographic location and season (Schla" et al. 2014). In light of the growing anthropogenic threats faced by marine pred- ators worldwide, such as alterations of coastal habitat, pollution and climate change, understanding how these orga nisms re spond to rapid environmental change is be coming increasingly important. Coastal habitats along the Australian east coast are regularly frequented by juvenile and sub-adult (here- after referred to as immature) white sharks Carcharo- don carcharias (Linnaeus 1758) which, except for oc- casional across-ocean excursions (Bruce et al. 2019, Spaet et al. 2020), primarily move among a relatively small number of interconnected habitats and the 120 m depth contour (Bruce et al. 2006, Werry et al. 2012). These animals belong to a single, relatively small population (ca. 2500−6750 individuals) inhabiting the waters surrounding eastern Australia and New Zea - land (Hillary et al. 2018), hereafter referred to as east- ern Australasian white sharks. Fine-scale patterns and site fidelity to foraging, aggregation and nursery areas (Robbins 2007, Bruce & Bradford 2012, Spaet et al. 2020) make the juvenile subset of this population particularly vulnerable to potential threats, such as in- cidental capture in recreational and commercial fish- eries (Bruce & Bradford 2012, Lowe et al. 2012, Oñate- González et al. 2017), capture in bather protection programmes (Lee et al. 2018, Tate et al. 2019) habitat de struction, pollution (Suchanek 1994, Mull et al. 2013) and climate change (Chin & Kyne 2007). Globally, white sharks are listed as Vulnerable based on Inter- national Union for Conservation of Nature (IUCN) Red List criteria (Rigby et al. 2019), and have been af- forded protection under various national jurisdictions and international treaties, such as listing in Appendix II of the Convention on International Trade in Endan- gered Species of Wild Fauna and Flora (CITES), and the Convention on the Conservation of Migratory Species of Wild Animals (CMS). This has fostered wide-ranging research and conservation efforts over much of their global distribution (Huveneers et al. 2018). White sharks are listed as threatened in Aus- tralia’s Environment Protection and Biodiversity Con- servation Act of 1999, and conservation objectives at a national level have been formulated under a national recovery plan (Department of Sustainability, Environ- ment, Water, Population and Communities 2013). A key priority of research under this plan is the charac- terisation of patterns and drivers of spatial and tempo- ral variability in habitat occupancy. Elucidating the mechanisms behind white shark distribution and movements is a prerequisite to the implementation of ecologically sound conservation strategies (Southall et al. 2006, Certain et al. 2007) and has re cently also been identified as one of the top 10 re search priorities for this species globally (Huveneers et al. 2018). Evidence of the effects of abiotic factors on white shark movements and temporal residency has been reported from various locations across their range. For most abiotic factors, there are observed linkages to white shark presence and behaviour; however, these appear to be highly region- and context-specific, and hence cannot be expanded to the species as a whole. For example, in response to new moon, white shark presence increased at 2 beaches in South Africa and at a seal colony in California (Pyle et al. 1996, Weltz et al. 2013). Similarly, white shark catch rates increased during the new moon in shark control programmes along the Australian east coast (Werry et al. 2012, Lee et al. 2018). In contrast, lunar phase was not a signifi- cant predictor of white shark catch rates in a bather- protection programme along the east coast of South Africa (Wintner & Kerwath 2018). The most widely studied abiotic factor in relation to white shark presence and distribution is tempera- ture. Variations in water temperature have been linked to white shark abundance and catch rates along the east coasts of Australia and South Africa, and the Farallon Islands, California (Pyle et al. 1996, Towner et al. 2013, Weltz et al. 2013, Lee et al. 2018, Wintner & Kerwath 2018). However, whether tem- perature is directly influencing white shark presence by affecting thermoregulation or indirectly by affect- ing prey distribution and abundance remains un - clear. In addition to temperature and lunar phase, tidal height and wind speed also appear to play a role in the presence and behaviour of white sharks, al - though results across studies are inconsistent (Pyle et al. 1996, Robbins 2007, Weltz et al. 2013). Given the described importance of environmental drivers in the distribution and movements of white sharks and the susceptibility of the eastern Aus- tralasian population to habitat modification (Depart- 168 Spaet et al.: Drivers of white shark occurrence ment of Sustainability, Environment, Water, Popula- tion and Communities 2013), we explored a range of environmental and temporal variables that could in- fluence the occurrence of immature white sharks along the coast of New South Wales (NSW), eastern Aus- tralia. We used a 3 yr (2016−2019) acoustic telemetry dataset of 444 white sharks tagged in eastern Aus- tralia to: (1) determine the seasonal and diurnal vari- ability in white shark occurrence; (2) model the rela- tive influence of month, time of day, water temperature, tidal height, swell height and lunar phase on their presence; and (3) determine the impact of these variables on receiver performance by conducting range test experiments where possible. A better understanding of how these environmen- tal factors affect site fidelity and movement dynamics is critical to forecast potential shifts in these traits under rapid environ- mental change, and will ultimately en - hance our ability to predict where and when immature white sharks occur along the Australian east coast. 2. MATERIALS AND METHODS 2.1. Tagging A total of 444 white sharks were tagged with Vemco V16-6L acoustic transmitters (Innovasea Marine Systems) with trans- mission inter vals of 40−80 s and a 10 yr battery life. Transmitters were fitted to sharks be tween 26 August 2015 and 29 November 2019. Tagging operations were conducted in NSW coastal shelf waters between Byron Bay (28.76° S, 153.60° E) and Eden (37.36° S, 150.07° E) within ~0.5 km of the coast (Fig. 1). Most sharks (n = 406) were caught using Shark Man- agement Alert in Real Time (SMART) drumlines (Guyomard et al. 2019), while others were either (1) visually located from a vessel or helicopter before being pre- sented with a baited hook from a vessel (n = 14) (Harasti et al. 2017), (2) caught on surface-buoyed setlines (n = 7) (Bruce & Bradford 2012) or (3) incidentally caught in bather protection nets (n = 17) (Reid et al. 2011) (Table S1 in the Supple- ment at www. int-res. com/ articles/ suppl/ m653p167_ supp. pdf). Following capture, sharks were brought alongside the boat and secured with a belly and tail rope. A total of 329 sharks were fitted with external transmitters by embed ding nylon umbrella anchors into the dorsal musculature using applicator needles mounted on a hand-shaft. An - other 99 sharks were internally tagged with transmit- ters surgically implanted into the abdominal cavity following the general procedure of Heupel et al. (2006b). Another 16 individuals were dual-acousti- 169 Fig. 1. Spatial distribution of acoustic VR4G receivers along the coast of New South Wales, Australia. Each location name corresponds to 1 VR4G receiver deployed at that location. Inset shows a schematic drawing of a VR4G receiver unit Mar Ecol Prog Ser 653: 167–179, 2020 cally tagged (with both internal and external trans- mitters). In addition, each shark was tagged with a uniquely numbered identification tag (spaghetti tag; Hallprint), which was inserted into the musculature at the base of the first dorsal fin for future visual iden- tification. Between 07 September 2016 and 21 November 2019, 75 sharks were recaptured; of these, 7 individuals were recaptured twice and 3 individu- als 3 times. Ten of the recaptured sharks that were originally tagged internally were fitted with an addi- tional external transmitter during recapture. Prior to release, sharks were sexed, and fork length (FL) was measured to the nearest cm. 2.2. Range testing and receiver performance Tagged sharks were monitored by an array of 21 iridium satellite-linked acoustic receivers (Vemco VR4-Global [VR4G]) (Fig. 1). VR4G moorings were de ployed 500 m from shore in 6− 16 m depth, with the hydrophone 4 m below the surface. The detection range of acoustic receivers can vary spatially and temporally based on a study system’s specific proper- ties (Medwin & Clay 1997). Based on limited avail- able range testing data, the detection envelope of VR4G receivers appears to range be tween 200 and 500 m (Bradford et al. 2011, J. L. Y. Spaet & P. A. Butcher unpubl. data). Ideally, rigorous, long-term evaluations of detection range should be completed at all stages of a field study (Kessel et al. 2014). Due to logistical constraints, however, continuous range testing at all receiver locations was not feasible throughout the present study. Instead, 132 to 138 d range tests were conducted at 5 array-representative receivers toward the end of the study period. A full description of the range test experimental methods and results are presented in the Sup plement (Text S1, Tables S2−S4, Figs. S1−S3). 2.3. Data analysis 2.3.1. Model development Acoustic data were processed and analysed in the R Statistical Environment (R Core Team 2020). We used a generalized additive model (GAM) approach in the R package ‘mgcv’ (Wood 2017), with a bino- mial error structure to model presence/ absences and smooth splines for environmental predictors, as most animals respond to the environment in a non-linear way (Aarts et al. 2008). To investigate relationships between environmental conditions and shark occur- rences, we used presence− absence of each tagged shark per hour for each day of the study period as the response variable and chose 6 variables based on previously documented relationships with the move- ments of white sharks as explanatory variables (Pyle et al. 1996, Robbins 2007, Werry et al. 2012, Towner et al. 2013, Weltz et al. 2013, Lee et al. 2018, Wintner & Kerwath 2018): (1) month; (2) time of day; (3) tem- perature; (4) tidal height; (5) swell height; and (6) lunar phase (Table 1). Given that the sample unit in this study was an hourly bin, each predictor variable was selected to match this temporal scale as closely as possible. Environmental datasets were either col- lected in situ (water temperature) or obtained from external sources (e.g. swell height; Table 1). Ambient water temperature was recorded every 240 min by sentinel tags, which were attached either to the riser rope or the base of the VR4G leg, at about 1−2 m from the hydrophone and 2−4 m below the sea sur- face. Hourly mean tidal height and swell height data were obtained through Manly Hydraulics Labora- tory, NSW (https://mhl.nsw.gov.au/). Lunar phase values were calculated using the ‘moonAngle’ func- tion in the R package ‘oce’ (Kelley & Richards 2019), with 0 corresponding to new moon, 0.25 to the first quarter, 0.5 to full moon and 0.75 to the second quar- 170 Explanatory variable Source df Spline Temporal Time of day (h) AEST/AEDT 24 Cyclic-cubic-regression Month Calendar 12 Cyclic-cubic-regression Environmental Water temperature (°C) Sentinel tags 1 Cubic-regression Swell height (cm) Manly Hydraulics Laboratory, NSW, Australia 1 Cubic-regression Tidal height (cm) Manly Hydraulics Laboratory, NSW, Australia 1 Cubic-regression Lunar phase R package ‘oce’ 0.01 Cyclic-cubic-regression Table 1. Summary of explanatory variables used during preliminary model selection. Details include unit of measure, source, de- grees of freedom and spline-based techniques used for smoothing in the generalized additive model. AEST (AEDT): Australian Eastern Standard (Daylight) Time Spaet et al.: Drivers of white shark occurrence ter. Missing temperature, tidal and swell height val- ues were interpolated using spline interpolation. As tagging efforts were spread over ca. 52 mo, the duration an individual shark was tagged within the study period varied depending on the release date. If a shark was tagged before the start of the study period (i.e. 1 December 2016), the time at liberty of this shark was appointed to 1 December 2016 until 30 November 2019 (the end of the study period). If a shark was tagged after 1 December 2016, its time at liberty started on the date that it was tagged. For the 8 sharks that died during the study period, time at liberty ended on the date they died. For statistical analyses, we constructed a presence− absence matrix of 0s (no detection) and 1s (detection). Since the col- lected data were presence-only, we imputed absence data in order to use a binominal distribution. The presence/ absence of each shark’s time at liberty was apportioned into 1 h time bins (n = 24) for each re - ceiver, following Lindholm et al. (2007). Multiple detections of the same individual within the same hour by the same receiver were treated as a single detection, whereby the first detection in the database was retained and the others discarded. Each detec- tion was then assigned a ‘1’ for that hour and individ- ual, while a ‘0’ was assigned when no detections were recorded in a given hour. Given that model selection and inference in large datasets is computationally demanding, we used a random absence-selection procedure to reduce the high number of absences in the dataset (>124 700 000 absences vs. <7600 presences). Prevalence (i.e. the ratio between the number of presences and absences in the dataset) is believed to influence model per- formance when modelling the probability of occur- rence of a species. Yet the effect of prevalence is sig- nificant only for datasets with extremely unbalanced samples (<0.01 and >0.99) (Jiménez-Valverde et al. 2009) and in particular does not affect model per- formance of GAMs (Barbet-Massin et al. 2012). Thus, for each shark, we included all presences, but sub- sampled the total available absences to use in the model by randomly selecting only 50 absences per presence. To test whether the random sample of absence records had an effect on model results, we first repeated the resampling exercise 5 times, which resulted in 5 datasets with the same presences but different absences. We then re-ran the final model for each of these datasets and compared the resulting model coefficients. Since the receiver at Ballina Lighthouse (Fig. 1) was not deployed until 9 July 2017, this receiver was excluded from the modelling framework. To achieve a more even distribution of VR4G stations and to pre- vent the same individuals being detected by differ- ent receivers within the same time bin, the receiver at Lennox Head (Fig. 1) was also excluded, leaving 19 stations in the model. For each model, we in - cluded shark ID and receiver location as additive fixed effects to correct for pseudo-replication and ac - count for unknown differences inherent to each loca- tion that are otherwise unaccounted for in our analy- sis. Whilst shark ID could have been treated as a random effect, mixed GAMs tend to be computation- ally expensive, and model selection would have been prohibitive (an analogous approach was taken by e.g. Clay et al. 2016 and Frankish et al. 2020). We ran all possible com binations of explanatory variables alongside the null model and calculated values of Akaike’s information criterion corrected for small sample size (AICc) using the ‘dredge’ function in the R package ‘MuMIn’ (Bartoń 2019). To reduce over- fitting during model construction, we initially set the maximum number of knots to four, and increased this number only if the model response curves did not match the raw data. Candidate models were ranked according to AICc and weight. We then individually assessed the importance of variables based on the proportion of deviance explained. For each variable, we calculated the predictive deviance uniquely ex - plained by that variable by subtracting the deviance of the model excluding that variable from the full model deviance. Variables explaining <0.1% deviance were retained in the final model, but deemed to have little biological significance. A total of 87% of the dataset in this study was com- posed of juveniles. We hence assumed that integrat- ing size into the modelling framework would have very limited power and could potentially lead to mis- leading results. To validate our assumption, we ad - ded FL as an explanatory variable to the final model and compared performances of both models based on change in deviance. To investigate whether dif- ferent life stages of immature white sharks were impacted differently by the tested variables, we grouped sharks into young-of-the-year (total: n = 24; detected n = 10), sub-adults (total: n = 33; detected: n = 26) and juveniles (total: n = 387; detected: n = 303). To determine if differences in sample size influ- ence model results, we also created a subset of 15 randomly sampled indi viduals of the juvenile group. We then ran all possible combinations of explanatory variables alongside the null model, calculated AICc values using the ‘dredge’ function for each of the 4 groups and ranked can di date models according to AICc and weight. 171 Mar Ecol Prog Ser 653: 167–179, 2020 To test whether the observed response of white shark occurrence to time of day, water temperature, tidal height, swell height and lunar phase was driven by receiver performance, we fitted candidate models for the range test dataset and 4 subsets of the white shark occurrence dataset: (1) white shark presences during the range test period at range test receiver locations only; (2) white shark presences during the range test period at non-range test receiver locations only; (3) white shark presences during the range test period across all receiver locations; and (4) white shark presences across the 3 yr study period across all 19 receiver locations. We then visually compared their overlaid graphical outputs. Overlapping confi- dence intervals between range test and detection response curves represent the part of the gradient of each variable in which white shark occurrences were likely driven by receiver performance. 2.3.2. Model performance evaluation To measure the predictive accuracy of the models, we used the area under the receiver operator charac- teristic curve (AUC) to evaluate performance of mod- els in the ‘PresenceAbsence’ package in R (Freeman & Moisen 2008). AUC values designate the probabil- ity that positive and negative instances are correctly classified. The AUC ranges from 0.5 (equivalent to the prediction from a random model) to 1 (perfect predic- tions). Values of 0.5−0.69, 0.7−0.9 and >0.9 represent poor, reasonable and very good model performance, respectively. To ensure that model performance was not driven by a small number of indi- viduals, we re-ran the entire model se- lection process, ex cluding 10 individu- als that showed a disproportionately high number of detections (33% of to - tal detections) (Fig. S4). 3. RESULTS The 444 tagged sharks ranged in FL from 130−373 cm, with a mean ± SD of 228 ± 40 cm. Of those sharks, 60% were female, 87% were juveniles (155− 280 cm FL; 227 female, 160 male), 7% were sub-adults (281−350 cm FL; 29 fe- male, 4 male), and 5% were young-of- the-year sharks (130− 155 cm FL; 10 male, 14 female) at the time of tagging. Eight sharks died between April 2017 and November 2019. Three of these were euthanized following capture as part of the Queensland Shark Control Program, 3 died in shark nets in the Sydney area as part of the NSW Shark Meshing Program, 1 washed up at a beach in Evans Head 5 d after tag- ging, and 1 was caught in the gummy shark fishery managed by the Australian Fisheries Management Authority in Victoria. Within the study period, all re- ceivers operated continuously, yet due to technical issues, receivers at Kingscliff, Evans Head, Yamba, Port Macquarie, Kiama and Merimbula (Fig. 1) were not operative for different time periods ranging from 7 to 23 d be tween July and December 2018. All non- operative periods were excluded from the modelling framework. 3.1. Patterns of occurrence Of the 444 tagged sharks, 339 individuals (76%) were detected by the VR4G receiver array a total of 42 509 times after removing double detection counts, including receivers at Ballina Lighthouse and Len - nox Head (7818 after hourly binning) between 01 December 2016 and 30 November 2019 (Fig. 2). The largest number of sharks (n = 150) and detections (n = 2661) were recorded in Forster (Fig. 2). Overall, occupancy was highest between South West Rocks and Hawks Nest on the mid-NSW coast. The number of sharks tagged at a certain location did not directly correspond to the number of sharks detected by re - ceivers adjacent to that location; e.g. no sharks were tagged adjacent to the South West Rocks receiver, 172 Fig. 2. Summary of total detections and tagged vs. detected immature white sharks by location, including numbers of (1) total detections (n = 7818, after hourly binning); (2) tagged immature white sharks (n = 444); and (3) individual sharks (n = 339) detected by receiver location between 01 December 2016 and 30 November 2019 Spaet et al.: Drivers of white shark occurrence yet this location showed the second highest number of detected animals (Fig. 2). Likewise, the total num- ber of detections did not always directly correspond to the number of sharks detected; e.g. Hawks Nest recorded the second highest number of detections yet was only fifth in number of individual sharks detected (Fig. 2). This is due to extended de tection periods of a very small number of sharks (Fig. S4). For example, 5 sharks accounted for 64% of the total detections by the Hawks Nest receiver. Similarly, 1 shark accounted for 21% of the total detections at the Forster receiver and 7% of the total number of detec- tions across all locations. Pooled hourly binned de - tection data (across all receiver locations over the entire study period) indicated higher numbers of individual sharks detected during the day than at night-time (Fig. S5A). Detection data pooled over the 3 yr study period (01 December 2016 to 30 November 2019) and all receiver locations indicated a clear sea- sonality, with occurrences peaking in the austral spring (September−November) (Fig. S5B,C). 3.2. Drivers of occurrence The final GAM chosen through the model selection process considered 386 224 observations of presence and absence over a 3 yr period and retained all can- didate predictor variables (Table S5). The proportion of the variation in shark occurrence explained by the final model was 21%. Of these, 17.3% were at - tributed to the effect of receiver location (Table S6). Unique de viance explained ranged from 0.27−0.57% for month, time of day and swell height, and was <0.1% for water temperature, tidal height and lunar phase, indicating limited to negligible effects in the model (Table S6). Graphical output indicated a sea- sonal pattern of white shark occurrences, with a peak in September followed by a decline in individuals from October to April (Fig. 3). Sensitivity tests demonstrated that our approach of randomly sam- pling absences was robust. Changes in deviance among models based on the 5 resampled datasets were marginal (Table S7), and graphical outputs were virtually identical. Model results also indicated higher shark numbers during the daytime, peaking at 11:00 h (Fig. 3). The relationship between water temperature and the presence of sharks highlighted a peak in shark oc - cur rences for temperatures between 18 and 24°C. There was a negative linear relationship between the number of occurrences and swell height, with a de - crease in occurrences with increasing swell height above 2 m. Occurrences were lower at low and high tide and peaked at full moon (Fig. 3). The ability of the final model to predict shark presence was consid- ered ‘reasonable’ based on an AUC value of 0.87 (Table S5). Visual comparison between graphical 173 Fig. 3. Response curves of the 6 variables included in the most supported model predicting immature white shark occurrence along the coast of New South Wales, Australia. S(x): GAM smoother estimated for variable (x). Grey shading indicates 95% confidence limits. Positive values on the vertical axes indicate an increased probability of occurrence, while negative values indicate an increased probability of absence. Lunar phase values correspond to new moon (0), first quarter (0.25), full moon (0.5) and second quarter (0.75) Mar Ecol Prog Ser 653: 167–179, 2020 outputs of the full data set and the data set excluding the 10 most detected sharks did not indicate a signif- icant difference in model performance (Fig. S6). Size-based differences in occurrence patterns of the sharks tagged in this study could not be identi- fied. Integration of the variable FL into the final model had no significant effect on shark occurrences and resulted in a decrease of deviance explained by the full model. GAMs separated by life stage indi- cated differences in the factors driving the occur- rence of young-of-the-year, sub-adult and juvenile sharks. Young-of-the year sharks were driven by all variables except for tidal height, while only the vari- ables month and time of day were retained for sub- adults (Table S8, Fig. S7). The final model chosen for juveniles retained all variables and had a graphical output that was identical to that of the full model including all life stages. The final GAM chosen for a subset of juveniles, however, retained only the vari- ables month, time of day and swell height, indicating that model selection was influenced by sample size. GAM response curves of receiver detection effi- ciency and white shark presence showed similar trends, with partially overlapping confidence inter- vals for the variables swell height and lunar phase across all white shark presence data subsets (Fig. S8). This suggests that observed occurrence patterns of white sharks were likely negatively biased by sub- stantially reduced receiver performance with in - creasing swell height and lunar phase (from new to full moon). Shark detection response curves for the variables time of day, tidal height and water temper- ature showed only little overlap with receiver detec- tion efficiency curves across all data subsets (Fig. S8). Within the temperature range ob served during the range test period, shark detections decreased lin- early from 16−23°C, whereas receiver performance showed an optimum between 17 and 20°C, followed by a drastic decrease in detection efficiency (Fig. S9). This indicates that shark detections above 20°C were strongly negatively influenced by receiver perform- ance and are likely much higher than indicated by the model response curves (Fig. 3). 4. DISCUSSION Rising impacts of anthropogenic stressors on mar- ine predator populations have heightened the need to better understand the drivers of shark movements and occurrence patterns. We acoustically tagged an estimated 8−20% of the immature Australasian white shark population and demonstrate that envi- ronmental factors had little effect on the occurrence of these sharks along the NSW coast of Australia. The bulk of the total variation in detection data (~79%) remained unexplained by our model. Collectively, the variables month, time of day, water temperature, tidal height, swell height and lunar phase explained ~5% of deviance, while 17% were attributable to dif- ferences in receiver location. This variation probably relates to physical or biological characteristics of the adjacent or immediate receiver environment. Highly variable receiver performance somewhat complicated our occurrence analyses, but we were able to appro - priately quantify variable detection efficiency through range tests at selected receivers. Receiver perform- ance was likely influenced by both environmental conditions and biological noise, providing an exam- ple of how a lack of controls can lead to misinterpre- tation of shark occurrence patterns. 4.1. Environmental and temporal influences While there is substantial evidence that abiotic fac- tors can drive movements in sharks (see Schla" et al. 2014 for a review), our results suggest that most vari- ables assessed in this study had a limited effect on the presence of tracked sharks along the NSW coast of Australia. Month accounted for the largest amount of deviance of all factors for the GAM presented here, predicting strong seasonal variation, with most sharks occurring along the NSW coast between July and De- cember. The predicted seasonality is consistent with previous work demonstrating highest abundances duringtheaustralwinterandspring(June− November) (Bruce et al. 2019, Spaet et al. 2020) and peak catch rates from September to November (Reid et al. 2011). The overall seasonal signal in movements suggests a re sponse to an environmental cue, and several studies have linked the distribution of white sharks with water temperature (Dewar et al. 2004, Weng et al. 2007, Bruce & Bradford 2012, Weltz et al. 2013, Lee et al. 2018, Wintner & Kerwath 2018). Results of previous work modelling the effect of temperature on immature white shark occurrence on limited sample sizes, are inconsistent, identifying temperature as a predominant predictor of shifts in juvenile white shark distribution in the Southern California Bight (White et al. 2019) and as a poor predictor in the Port Stephens estuary in NSW (Harasti et al. 2017). We found that the de viance explained by temperature was only 17% of the deviance explained by the tem- poral factor ‘month’, suggesting that other environ- mental factors, not accounted for in this study, are 174 Spaet et al.: Drivers of white shark occurrence driving seasonal variation. For example, photoperiod (day length) strongly influences the migratory activity of many species (Milner-Gulland et al. 2011), includ- ing sharks (e.g. Grubbs et al. 2007, Dudgeon et al. 2013) and could be responsible for a large proportion of the variation associated with month. The limited effect of temperature encountered here is not surpris- ing given that white sharks are endotherms and their behaviours and distributions are less likely to be in- fluenced by thermal cues (Carey et al. 1982, Goldman 1997). Although temperature can also have indirect effects on white shark distribution by affecting prey distribution and abundance, based on the limited ef- fect this factor had on the 444 sharks tracked in this study, temperature does not appear to be a robust predictor of immature white shark occurrences across regions. While white sharks are known to undergo ontoge- netic shifts in habitat, clear life-stage-based variation in occurrence patterns of the sharks tagged in this study could not be identified. Although GAM results differed between life-stage groups, this variation was likely influenced by sample size, as indicated by the differences in model results within the juvenile life- stage group, when the sample size was significantly reduced. Total detections of young-of-the-year and sub-adult sharks equalled <6%, while the re maining 94% comprised detections of juveniles. Given the ob - served differences in model results be tween the full juvenile dataset and a subset thereof, we believe that the available data on young-of-the-year and sub- adult sharks are insufficient to yield robust model predictions. The effect of tidal height, which largely depends on the bottom topography of coastal areas, was neg- ligible. Receiver sites in this study all have a gradu- ally declining bathymetry and lack any sudden drop- off of the coastal shelf. Furthermore, the mean tidal range across all receiver locations was a modest 1.82 m. While some shark species have been ob served to move closer inshore with incoming tides to exploit previously unattainable resources (Ackerman et al. 2000, Carlisle & Starr 2009), in our study, the amount of available habitat which increases or de creases with incoming or outgoing tide, respectively, is likely not substantial enough to affect the movement of tracked sharks. 4.2. Influence of receiver performance The final GAM in our study suggested a thermal preference of immature white sharks in the eastern Australasian population of between 18 and 23°C. Yet, considering the dramatic negative effect of tem- perature on receiver performance above 20°C (see Text S1, Table S4 and Fig. S9), predicted presences above this threshold are likely substantially higher than indicated by our modelling framework. The lim- ited range test data available for this study restrict our ability to statistically correct for variability of the environmental variables affecting receiver perform- ance. However, based on a comparison of GAM re - sponse curves between range test and white shark de tection data (Figs. S8 & S9), we estimated the up - per limit of the predicted thermal preference to be 3−4°C above the limit indicated by the final GAM, so probably ranging from 18−27°C. This is consistent with the temperature preference reported in other studies for juvenile white sharks in the northeast Pacific, ranging from 17.5−25°C (Weng et al. 2007, Domeier & Nasby-Lucas 2008) and 19−26°C (White et al. 2019). An acoustic telemetry study of 20 juve- nile white sharks in Port Stephens, a NSW estuary (adjacent to the VR4G receiver at Hawks Nest of this study), revealed a drastic decrease in detections of immature white shark at temperatures above 20°C (Harasti et al. 2017). The authors concluded that water temperatures were correlated with the pres- ence of immature white sharks in the estuary, sug- gesting a thermal preference of 15−19°C (Harasti et al. 2017). However, potential effects of environmen- tal variables on receiver performance were not asses - sed in that study. While it cannot be ruled out that the same detection patterns appear in animal and control tag detection data, the similarities be tween this pre- vious work and the receiver performance results pre- sented here (see Text S1, Table S4 and Figs. S8 & S9) might suggest that the temperature-related detection patterns observed by Harasti et al. (2017) were strong - ly influenced by receiver performance. This example highlights the critical im portance of understanding receiver performance across variable environmental conditions (Kessel et al. 2014) and the need to distin- guish between environmental interference and ani- mal behaviour (Mathies et al. 2014). Based on the large number of sharks tracked in our study and the receiver performance results accompanying this manuscript (see Text S1), we suggest that the previ- ously proposed propensity of immature Australasian white sharks to temperatures between 18 and 20°C (Bruce & Bradford 2012) likely extends up to 27°C in nearshore areas of the Australian east coast. Receiver performance also strongly influenced oc - currence patterns related to the variables swell height and lunar phase. GAM response curves for 175 Mar Ecol Prog Ser 653: 167–179, 2020 both variables showed the largest overlap between re ceiver performance and white shark detection data. This suggests that the observed correlations be tween these variables and shark occurrences are a result of environmental interference, and do not re - flect actual white shark behaviour (see Text S1 for a discussion on potential causes of reduced receiver performance during increased swell height and lunar phase). While diel patterns of shark presence appear to be less influenced by receiver perform- ance, the general trend of increasing detection effi- ciency with time of day (from night to day) was sim- ilar for the shark de tection and range test datasets (see Text S1 for a discussion on potential causes of reduced re ceiver per formance during night-time). Satellite tracking studies investigating the vertical diving be haviour of immature white sharks in the east Pacific have re ported strong diurnal dive pat- terns, with significant ly deeper mean positions dur- ing daytime (Dewar et al. 2004, Weng et al. 2007, Domeier & Nasby-Lucas 2008). Diel-depth patterns of Austral asian sharks ap pear to be weaker, ranging from strong to negligible, but with a general trend of occupying deeper habitats during the day (Bruce & Bradford 2012, Francis et al. 2012). If the sharks tracked in this study displayed diel dive-patterns similar to the ones previously described, we would expect a re duced likelihood of detection during the day, given that all re ceivers were deployed in shallow nearshore areas. This might indicate that diel patterns of shark occurrences are more strongly biased than indicated by our results. Hence, until further infor- mation of the acoustic properties of the water body at the time of detection is available, caution should be exercised in drawing conclusions about the observed diel patterns. Our results should also be interpreted in relation to the design of the acoustic array. We deployed 21 re ceivers along a substantial stretch of coastline (~1000 km), resulting in limited coverage in propor- tion to the total study area (Fig. 1). Sampling design in acoustic surveys typically entails a trade-off be - tween optimal coverage and the substantial costs in - volved with an increasing density of receivers (Cle - ments et al. 2005, Heupel et al. 2006a). Here, the as sess ment of broad-scale environmental factors necessitated the large study scale. While electronic tag options (e.g. pop-up satellite archival tags) might have yielded a higher spatial resolution and more de - tailed patterns at this geographic scale, the deploy- ment of several hundred electronic tags would not have been financially feasible. Our approach hence represents a compromise between geographic scale, sample size and spatial resolution. Overall, we be - lieve that our design allows for general conclusions about space use in a vagile species, such as white sharks. 4.3. Potential location-specific factors In this study, the largest proportion of variation in shark occurrence was explained by differences in re - ceiver locations. Overall occurrences were highest be tween South West Rocks (30.88° S, 153.04° E) and Hawks Nest (32.40° S, 152.11° E) on the mid-coast of NSW. Using a combination of satellite and acoustic tracking data, recent research has suggested an onto - genetic range extension of the previously de scribed ‘Port Stephens nursery area’ north- and southward, from Forster (32.18° S, 152.51° W) to south of Terrigal (33.44° S, 151.44° W) (Spaet et al. 2020). Based on abundance patterns in this study, we hypothesize a further northward expansion of the nursery area from Forster to South West Rocks. The ~300 km stretch of coastline between Terrigal and South West Rocks ap- pears to represent a large ‘nursery area’, composed of a set of interconnected estuaries, bays and beach areas. Changes in temperature (Grubbs et al. 2007, Heupel et al. 2007, Yates et al. 2015), tidal conditions (Rechisky & Wetherbee 2003, Harasti et al. 2017) and lunar phase (Harasti et al. 2017) have previously been identified as likely drivers of shark abundance within nursery areas. The weak effects of these variables in our study indicate that other habitat-specific factors characteristic to the proposed, enlarged nursery area are the main drivers of immature white shark occur- rences. The Port Stephens region, for example, har- bours seasonal ag gre gations of various finfish species during periods of seasonal upwelling (Bruce & Brad- ford 2012). Additionally, chlorophyll a concentrations along the mid-NSW coast peak during October− November (Hallegrae" & Je"rey 1993), the period during which most sharks were detected across all receiver locations (Fig. S5B). The predicted seasonal cycle of oc cu pancy of this region (as opposed to anti- quated theories of resident sharks at specific beaches) might hence suggest that the use of these habitats is associated with seasonal foraging opportunities pro- vided by the local abundance of potential prey across the region. Information on biotic components and ad- ditional environmental data, such as prey availability, foraging success, stomach content data, local cur- rents, physical structures, benthic cover, movement patterns of individuals and competition between indi- viduals, were not considered in this study, but if re - 176 Spaet et al.: Drivers of white shark occurrence corded in the respective habitats, will likely in crease the explanatory power of future analyses (Heit haus 2001, Heithaus et al. 2002, Torres et al. 2006). The in- corporation of such data will also facilitate a better understanding of changes in spatial oc currence pat- terns associated with shifting environmental factors and/or prey resources (Navarro et al. 2016). More- over, experimental approaches investigating relevant intrinsic (e.g. growth rates and mortality) and ex - trinsic (e.g. habitat quality) factors will help to eluci- date the underlying mechanisms of habitat prefer- ences and spatial distributions (Valavanis et al. 2008). 4.4. Implications for conservation Immature white sharks are particularly susceptible to fishing activities, due to their relatively small size and their affinity to nearshore areas (Bruce & Brad - ford 2012, Lowe et al. 2012, Oñate-González et al. 2017). The mid-NSW coast is a populated region and an important tourist destination, rendering sharks vulnerable to interactions with recreational and com- mercial fisheries (Malcolm et al. 2001). While the unique functions provided by the proposed NSW nursery habitat remain to be elucidated, the results of this study suggest that future threat identification and mitigation for immature Australasian white sharks should focus on the area between South West Rocks and Hawks Nest. A better understanding of the factors driving habitat use patterns within this area will foster improved management practices, and facil- itate the prediction of potential shifts in distribution associated with anthropogenic threats, such as coastal development or a changing climate. Acknowledgements. Primary project funding and support was provided by the New South Wales Department of Pri- mary Industries (NSW DPI) through the Shark Management Strategy. J.L.Y.S. received financial support through the Ger- man Academic Exchange Service (DAAD), NSW DPI and the Marine Ecology Research Centre, SCU. NSW DPI provided Scientific (Ref. P01/0059[A]), Marine Parks (Ref. P16/ 0145- 1.1) and Animal Care and Ethics (ACEC Ref. 07/ 08) permits. Thank you to Barry Bruce, Russ Bradford (CSIRO) and David Harasti (NSW DPI) for providing catch details for 13 sharks tagged at Hawks Nest through a Marine Biodiversity Hub project, which is a collaborative partnership supported through funding from the Australian Government’s National Environmental Science Program (NESP). We are grateful for infrastructure support provided by NSW DPI divers, various contracted divers and compliance officers for receiver main- tenance. We also thank E. Fernando Cagua, Christopher Knox and Caitlin Frankish for advice on data organisation and analysis. LITERATURE CITED Aarts G, MacKenzie M, McConnell B, Fedak M, Matthio - poulos J (2008) Estimating space-use and habitat prefer- ence from wildlife telemetry data. 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