Citizen Science Data Reveals Environmental Influence on Sightings of Great White Sharks in Mossel Bay, South Africa

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Peter Klimley, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2259544/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Whilst previous studies have described the impact of various environmental conditions on behaviour and abundance of great white sharks (GWS), existing knowledge gaps must be addressed in order to turn the tide on their population declines. This study used data collected by a diving tour operator, to investigate how environmental and anthropogenic variables affected the rate of GWS sightings. Observation data were collected by trained crew and volunteers alongside tourists, and combined with externally sourced environmental data. Hurdle modelling identified that the probability of sighting at least one GWS fluctuated seasonally (peaking during winter), but was also correlated with minimum running air temperature, water visibility and the length of time the boat stayed at anchor. The rate GWSs were sighted also rose in winter, and was associated with maximum running air temperature, the arrival time, seal activity, and the wind direction and speed. These findings indicate that environmental conditions directly impacted upon the sighting frequency, but also influenced habitat selection on a fine spatial scale. This study emphasises that collaboration with ecotourism companies could represent a valuable, inexpensive alternative for scientific data collection, as long as powerful statistical methods are used, the influence of human activity is considered and results are interpreted with consideration of the data collection methodology. Biological sciences/Ecology Biological sciences/Ecology/Behavioural ecology Biological sciences/Ecology/Conservation Biological sciences/Ecology/Ecological modelling elasmobranchs conservation biodiversity Hurdle model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In today’s changing world, the general public, scientists and policy-makers alike, have become increasingly concerned regarding global biodiversity loss, and there is increasing demand for efforts to protect endangered species [ 1 ]. Arguably one of the most alarming losses, is the cataclysmic decline of sharks throughout all the world’s oceans [ 2 , 3 ]. As a result of overexploitation in fisheries, bycatch, habitat loss and degradation, mortality in shark control programs and culling, 37% of all sharks and rays are now considered to be at risk of extinction [ 2 – 4 ]. Besides these threats, researchers have also become increasingly aware that fundamental gaps in our knowledge about sharks in general, have contributed to their poor management [ 2 , 3 ]. One such threatened species is the iconic great white shark ( Carcharodon carcharias , Linnaeus, 1758, hereafter GWS). GWSs are listed as ‘vulnerable’ by the IUCN, as they are estimated to have declined by as much as 79% worldwide. Due to their K-selected life history strategies, and their broad-scale, heterogeneous distribution, these sharks are especially vulnerable to over-exploitation [ 5 , 6 , 8 – 10 ]. In response to their precipitous declines, GWSs are now protected by legislation in several countries and trade in their products is restricted by their listing on CITES appendix II [ 5 , 6 ]. GWSs have a circumglobal distribution, with several major aggregations found in ‘hotspots’ in the Western North Atlantic, the Mediterranean Sea, and the eastern North Pacific, and along the coasts of New Zealand, southern Australia and southern Africa [ 5 ]. With regional endothermy providing a wide thermal tolerance, GWSs, commonly inhabit inshore regions down to the continental slope, in temperate, anti-tropical oceans and are able to undertake large-scale migrations, characterised by directed, trans-oceanic travelling and distinct philopatry [ 9 , 12 – 13 ]. Estimated to reach a maximum size of 6.0 m total length (TL) [ 7 ], GWSs are versatile generalists, regularly incorporating a wide range of teleosts, invertebrates and other chondrichthyans into their diet. When reaching a size of approximately 3.0 m TL, GWSs undergo an ontogenic diet shift, whereby they expand their diet to also incorporate marine mammals, such as dolphins and pinnipeds [ 9 , 10 ]. Having previously targeted predominantly midwater and bottom-dwelling species, GWS shift their predation behaviours after their ontogenetic shift, in order to target their prey at the surface [ 14 ]. Several studies have sought to understand how environmental conditions affect GWS behaviour, hunting strategies and abundance. Sea surface temperatures (SST) [ 9 , 14 , 20 – 30 ], water visibility [ 14 , 20 , 21 , 26 , 31 ], cloud cover [ 20 ], ambient light levels [ 31 ], lunar phase [ 24 , 29 , 32 , 75 ], ocean depth [ 31 , 33 ], chlorophyll concentration [ 30 ], swell height [ 20 , 21 , 29 ], wind direction and speed [ 26 , 31 ], and tidal height [ 20 , 21 , 29 , 31 , 34 , 35 ] are all known to affect GWS abundance or predatory behaviour. There are several other factors, including prey availability [ 14 , 20 , 36 ], that fluctuate as a result of these changing environmental conditions, which are also known to indirectly affect GWSs. These studies are numerous because it is vital to understand how GWSs utilise their habitats, in order to design effective management plans for their conservation. Yet, performing these rigorous scientific studies in the field is logistically challenging, expensive and time-consuming. Finding alternative methodologies or data sources which allow scientists to investigate the behaviour of GWS would be extremely beneficial to further our knowledge on this threatened species. Shark diving ecotourism has become increasingly popular all over the world over the last 20 years. Ecotourism companies offering trips to watch and cage-dive with GWSs, spend a large amount of contact time with this species in various regions, including Australia (the Neptune Islands, South Australia), South Africa (at least 5 sites along the coast between False Bay and Algoa Bay), Mexico (Guadalupe Island), the USA (the Farallon Islands, California) and New Zealand (Stewart Island) [ 37 , 38 ]. Ship’s logbooks or recorded observational data from these companies could represent a significant, fishery-independent sampling effort and potentially an invaluable resource for scientific research, which is yet underexploited [ 19 , 39 – 45 ]. This study used the data obtained by the ethical ecotourism company White Shark Africa UK (WSAUK). This company offered day trips for tourists to see GWSs in the wild and also extended volunteer placements, whereby students learned to work with sharks over weeks to months. These data were used to model how environmental conditions and anthropogenic activity were related to sightings of GWSs in Mossel Bay, South Africa. Gaining a comprehensive understanding of how GWSs utilise their habitat and how the environment impacts upon these animals is critical to designing effective management strategies to secure their future. The goal of this study is to provide a better understanding of how the environment can affect GWS behaviour and to critically evaluate whether data sourced from ecotourism may have the potential to contribute to shark monitoring or scientific research in the future. Results The analysed dataset included a total of 287 sampling trips, totalling 662h 35min of observation and 1,527 individual shark sightings (Fig. 2 ). A total of 64 GWSs were identified individually. Overall, the average size of GWSS sighted was 2.5 ± 0.37 m TL. The smallest GWS sighted was 1.4 m and the largest was 4.0 m TL (six sightings of a 4.0 m GWS were all sightings of an identified female in September of 2010). No mature GWSs were sighted. Overall, the majority (80.6%) of GWSS sighted were juveniles. Juvenile females represented 69.0% of sightings, YOY GWSs made up only 0.4% of sightings (all females), sub-adults represented 19.0% of sightings, the majority of which were females (17.8% overall). The sex ratio was 1.0 : 6.8 : 4.3 (male : female : unsexed) overall. GWS count averaged eight over the whole sampling period and the highest count was 19 sharks. SSR averaged 2.4 ± 2.0 sightings/hour (range 0–11.6 sightings/hour) throughout the whole study period. Zero sharks were sighted on 49 trips, but the success rate for sighting at least one shark per trip was 83% when considering the whole study period. Data Quality Assessment Of a total of 448 excursions included in the submitted data set, 288 (64%) were considered to include adequate environmental data to be included in analysis. A total of 51 trips conducted in 2009 were included in the analysis, 137 trips from 2010 and 100 trips from 2011 (Fig. 2 ). Seal Island was the most common sampling location; for 121 of the trips (42%). Grootbrak was visited on 99 trips (34%) and Kleinbrak for 68 trips (24%, Fig. 1 ). Pre-modelling assessment indicated colinearity between several pairs of explanatory variables related to air temperature and barometric pressure. Thus, absolute minimum and maximum daily air temperatures, temperature variation, absolute barometric pressure and variation in barometric pressure were excluded before modelling. In their stead, three-day running averages for barometric pressure, and minimum and maximum air temperatures were considered (Appendix Table 1 ). Table 1 Description of the three sampling sites visited by WSAUK within Mossel Bay. Site Coordinates Description 1. 1 Seal Island 34 ◦ 09 ′ 04.4 ′ S Waters surrounding a 100 × 50 m island, 800 m from shore, hosting a colony of 4,000 cape fur seals. 2. 2 Klein Brak 34 ◦ 06 ′ 20.3 ′ S Nearshore region at the Klein Brak river delta. 3. 3 Groot Brak 22 ◦ 08 ′ 28.6 ′ E Nearshore region at the Groot Brak river delta. Hurdle Model Zero-Part Presence of GWSs (Table 2 ) correlated strongly with the spline coefficients, suggesting the probability of observing any sharks depended on the time of year. GWS presence was highest in winter, with a 100% success rate from June through August. The model also indicated wind direction weakly correlated with the probability of observing at least one GWS, with the positive coefficient for the x-direction indicating wind from the east resulted in slightly greater probability of observing any sharks. Water visibility correlated positively, and the three-day minimum air temperature and stay duration both correlated negatively with the probability of observing at least one GWS. Therefore, the probability of sighting at least one GWSs was highest when running average air temperatures were cooler, water visibility was higher and the boat stayed at anchor for a shorter period. Yet none of these effect sizes were particularly large compared to the overall seasonality. There was also a trend towards a lower probability of observing GWSs when sampling at Grootbrak, compared to the reference location at Seal Island. Table 2 Estimated coefficients for the presence of GWSs. Shown in bold are p-values less than α = 0.10. Adjusted p-values are corrected for multiple testing. The p-value of the intercept was not relevant to the conclusion, and therefore not taken into account for this correction. †: Temperature extremes shown as moving averages within a three-day window. Est. SE z-value p-value adj. p-value (Intercept) 3.12 7.22 0.43 0.665 - Spline coef. 1 74.36 21.10 3.52 0.000424 0.00226 Spline coef. 2 17.83 6.58 2.71 0.00672 0.0307 Spline coef. 3 42.22 9.78 4.32 1.59·10E − 5 0.000127 Lunar phase 0.63 0.67 0.94 0.345 0.425 Wind (x-direction) 0.51 0.31 1.65 0.0997 0.177 Wind (y-direction) 0.72 0.50 1.44 0.151 0.234 log(Wind speed) 0.31 0.20 1.60 0.109 0.183 Air temp. (min) † 0.57 0.29 1.94 0.0521 0.098 Air temp. (max) † -0.06 0.15 -0.43 0.671 0.692 Seal activity 1.36 1.07 1.27 0.206 0.286 Time of arrival -0.10 0.15 -0.69 0.492 0.583 Stay duration 0.64 0.32 2.00 0.0454 0.0969 log2 (Water depth) -2.18 1.55 -1.41 0.159 0.234 log2(Water visibility) 0.73 0.36 2.02 0.043 0.0969 Location 2 0.26 0.60 0.44 0.662 0.692 Location 3 -2.72 1.39 -1.95 0.0508 0.098 Hurdle Model Count-Part The number of GWSs sighted, if any (Table 3 ), also correlated with the spline coefficients, though not as strongly as the presence of GWSs. Interestingly, lunar phase, wind direction, wind speed, maximum air temperature, seal activity and time of arrival correlated significantly with the number of GWSs, even though they did not correlate significantly with the presence of sharks. Table 3 Estimated coefficients for the counts of sharks, if present. P -values less than 0.10 are shown in bold. The p -value of the intercept and θ are not relevant to the conclusion, and therefore not considered for this correction. †: Temperature extremes are moving averages with a 3-day window. Est. SE z -value p -value adj. p -value (Intercept) 1.85 0.77 2.40 0 . 0162 - Spline coef. 1 1.60 1.56 1.03 0.304 0.389 Spline coef. 2 1.66 0.79 2.10 0 . 0354 0 . 0969 Spline coef. 3 2.84 0.58 4.86 1 . 18 · 10 − 6 1 . 26 · 10 − 5 Lunar phase 0.18 0.09 2.07 0 . 0381 0 . 0969 Wind ( x -direction) -0.16 0.04 -4.11 3 . 91 · 10 − 5 0 . 00025 Wind ( y -direction) -0.39 0.07 -5.96 2 . 46 · 10 − 9 7 . 86 · 10 − 8 log(Wind speed) -0.06 0.03 -2.06 0 . 0397 0 . 0969 Air temp. (min) † -0.01 0.03 -0.37 0.71 0.711 Air temp. (max) † 0.04 0.02 2.02 0 . 0429 0 . 0969 Seal activity -0.20 0.08 -2.40 0 . 0162 0 . 0648 Time of arrival -0.09 0.02 -4.87 1 . 11 · 10 − 6 1 . 26 · 10 − 5 Stay duration -0.03 0.05 -0.65 0.517 0.591 log 2 (Water depth) -0.16 0.15 -1.07 0.283 0.378 log 2 (Water visibility) 0.08 0.05 1.40 0.161 0.234 Location 2 -0.05 0.10 -0.43 0.666 0.692 Location 3 0.44 0.21 2.13 0 . 0334 0 . 0969 log( θ ) 4.14 1.06 3.88 0 . 000102 - On average, the closer the lunar phase was to 0 (new moon), the fewer GWSs were observed and the closer to 1 (full moon), the more sharks were observed. Wind from the south and from the west correlated to the largest number of GWS sightings, indicated by the negative coefficients of both x- and y-wind directions. Wind coming from the south (y-direction) had about twice as large an effect as wind coming from the west (x-direction). Therefore, the largest number of GWSs may presumably be expected during a SSW wind, or conversely, the number of sharks observed lowest on average during a NNE wind. Wind speed negatively affected the number of GWSs observed. Higher maximum air temperature corresponded to more sharks sighted and fewer GWSs were observed when there was seal activity. Finally, the later the boat arrived, the fewer GWSs were generally observed. The number of GWSs observed at Seal Island and Kleinbrak were more or less equal, but Grootbrak had nearly double the number of sharks observed on average. Assessing the marginal effects of each of the significant covariates, whilst holding all other explanatory variables constant at representative values, the model predicted that the largest number of GWSs were observed on average, when the lunar phase was (close to) a full moon (Fig. 3 A), wind direction came from the west (Fig. 3 B) and/or from the south (Fig. 3 C), wind speed was minimal (Fig. 3 D), the three-day running average of maximum recorded air temperature was at its warmest (Fig. 3 F), there was no seal activity observed (Fig. 3 G), water visibility was maximal (Fig. 3 K), the boat arrived early in the morning (Fig. 3 H) to anchor at Grootbrak (Fig. 3 L) and the stay duration was short (Fig. 3 I). Considering the minimum air temperature (Fig. 3 E) and the water depth (Fig. 3 J), the confidence bands were too wide to make a meaningful conclusion with regards to the direction of effect. Goodness-of-Fit Several diagnostic plots used for evaluation of the hurdle model, indicated that the model was a very good fit to these data (Fig. 4 ). The QQ-plot (Fig. 4 A) to assess the goodness-of-fit for the count data, displayed only very slightly heavier tails than may be expected; indicated by the spread of the residuals below the line in the bottom left and above the line in the upper right, but no obvious violations from the distributional assumptions. Furthermore, the rootogram (Fig. 4 B) indicated that the model slightly underestimated GWS counts of 13, but overall suggested the model was a good fit to the count data. Additionally, predictions made by the separate parts of the hurdle model, as well as both parts combined, showed the predictive model produced no large overestimations of the number of GWSs sighted (Fig. 5 ). Discussion This study sought to utilise data sourced from a shark diving ecotourism company to study the behaviour of great white sharks (GWS) in Mossel Bay, South Africa and to critically evaluate the value of this alternative data source for scientific research. By removing explanatory variables from the model via backwards selection, we were able to discern that the environmental conditions and the sampling methodology were both significant (p < 0.1) for predicting GWS sightings in Mossel Bay. Yet, as several significant variables retained in the model mirrored the findings of previous studies, the authors are confident that the method of employing a robust hurdle model, allowed elucidation of genuine patterns in these data, whilst countering the potential biases associated with the data collection methodology. It is our conclusion that collaboration with ethical ecotourism companies could have great value for scientific research and offer an alternative, fisheries-independent source for scientific data in the future. The Effect of Environmental Conditions on GWS Sightings The moderate to very strong significance of the cyclical spline indicated that both shark presence and shark sighting rate (SSR) followed a cyclical seasonal pattern in Mossel Bay. Yet, the model also showed that environmental conditions did have an impact on GWS sightings. There was weak to moderate evidence that probability of sighting at least one GWS, was correlated to wind direction, minimum air temperature, water visibility and how long the boat stayed at anchor. Comparatively, the SSR was associated with lunar phase, maximum air temperature, seal activity, time of arrival, and wind direction and speed. As has been previously reported, GWS were sighted year-round in Mossel Bay, South Africa [ 14 , 49 , 50 , 68 ]. Throughout sampling, SSR averaged 2.4 ± 2.0 sightings/hour, which was higher than rates previously reported in Mossel Bay [ 49 ]. Yet the maturity stages and the sex ratio of the sharks sighted were consistent with those reported in previous studies of the region [ 49 , 50 , 68 ]. Juveniles dominated the demographic structure in these data, supporting the hypothesis that Mossel Bay is not used for GWS parturition or mating [ 14 , 49 ]. Despite their year-round presence, the strong to very strong correlation of the spline coefficients in this model, indicated that GWS sightings cycled seasonally in Mossel Bay, with shark presence more assured and SSR higher during the winter months (June - August). At many aggregation sites, seasonal peaks in GWS abundances have been attributed to prey availability [ 7 , 10 , 15 , 39 , 46 , 48 , 69 ]. Whilst they are generalist predators, after their ontogenetic diet shift, high calorie marine mammals make up an important component of the GWS’s diet [ 9 , 10 ]. As the cape fur seal pupping season occurs from November to December in Mossel Bay, it seems logical to conclude that the seasonal abundance of GWSs in the region, was caused by the immigration of sub-adult sharks into Mossel Bay, to exploit a seasonally abundant prey resource [ 17 , 35 , 51 , 69 ]. Yet, the average size of GWSs sighted in this study was only 2.5 m TL and 81.0% of sharks were smaller than the 3 m TL associated with the ontogenetic diet shift. There have been reports of GWSs smaller the 3 m TL threshold predating seals in South Africa [ 18 ], and reporting similar findings, Milankovic et al (2021) hypothesised that Mossel Bay may act as a training ground; with smaller GWSs observing the hunting behaviour of conspecifics in anticipation of their shift to predating marine mammals. It is possible that the seasonal fluctuations in GWS sightings modelled here were driven by the immigration of pre-ontogentic-shift juvenile GWSs into Mossel Bay during the winter months to take advantage of social learning [ 14 ]. Yet seal abundance alone may not explain shark presence and the fluctuations in SSRs reported in these data. Seal activity was negatively correlated with SSR and the model highlighted the significance of the sampling location both for predicting GWS presence and counts. The higher probability of observing GWSs at Seal Island compared to Kleinbrak, supports the hypothesis that GWSs immigrated into Mossel Bay to target seal prey. Yet Grootbrak boasted on-average almost double the SSR compared to Seal Island and Kleinbrak. Stomach contents analyses have identified dusky sharks (Carcharhinus obscurus), chub mackerel (Scomber japonicus) and sea bream (Sparidae spp.) make up a significant proportion of the South African GWSs’ diet [ 10 ]. Several estuaries in Mossel Bay house nursery habitats for many teleost fish species [ 49 ], and the Grootbrak river mouth particularly has been identified as a core habitat for smaller GWS targeting teleost and elasmobranch prey [ 47 ]. Therefore, it seems likely that seasonal migrations and/or seasonal breeding of teleosts and other elasmobranchs were also driving GWS immigration into Mossel Bay during the winter months, and that GWS habitat selection based on prey availability occurred on a fine spatial scale within the bay [ 17 , 23 , 35 , 48 , 50 ]. The significance of several other variables in the model implied that seasonal fluctuations in environmental conditions were also responsible for driving GWS sightings in Mossel Bay to some degree. For instance, modelling indicated a relationship between the minimum air temperature and GWS sightings. Despite their regional endothermy, environmental temperatures have been found to be a significant driver in GWS distributions, as in low temperatures, GWSs must maintain their internal body temperature at a slightly higher metabolic cost [ 11 , 70 , 71 ]. Therefore, these findings indicate that GWSs vacated the study site during cold conditions, to avoid physiological stress. On the other hand, if our conclusions that GWSs immigrate into Mossel Bay during the winter holds true, it seems doubtful that a higher number of sharks were present during periods of warmer maximum air temperatures. Rather it seems these findings indicate that warm conditions had an impact on GWS behaviour [ 30 ]. When colder, great whites experience significant physiological changes, including slower metabolic rate and cooling muscles [ 11 , 70 , 71 ]. Under these conditions, with less energy readily available for high-octane displays, GWSs would be less likely to be sighted by observers. Comparatively, in warmer conditions, excess energy would be readily available to mount surface attacks on the bait, making the sharks particularly conspicuous, and resulting in the higher SSR recorded in these data. Therefore, this finding may be highlighting a change in GWS behaviour, as opposed to a direct relationship between air temperatures and GWS abundance. The arrival time was found to be significant in the counts portion of this model, with highest SSRs reported when sampling began earlier in the morning. GWSs practice a diel pattern of behaviour and habitat use; most actively hunting and patrolling at dusk and dawn, and spending the day at rest, digesting food and conserving energy [ 7 , 29 , 47 , 51 ]. Hammerschlag et al (2006) reported that GWS predations on cape fur seals were most frequent during low light intensities and predation success rates dropped dramatically as light increased during the day. High octane hunting behaviours and high activity levels of the early morning would have made GWSs especially conspicuous to observers, leading to higher SSRs during early morning sampling trips. Therefore, the significance of arrival time when modelling shark sightings was likely caused by the diel pattern of behaviour practiced by GWSs. The model also indicated that GWS sightings fluctuated on a lunar cycle, with higher SSRs recorded when the previous night experienced a full moon. These findings conflict with those of Weltz et al (2013), in Cape Town, South Africa, who reported that GWS sightings off bather beaches increased four-fold during darker new moons. GWSs have been reported to hunt during the night [ 75 ] and Klimley et al (2001) have suggested that a full moon may create optimal hunting conditions for GWSs, as the light conditions mean seals become silhouetted against the relatively bright night sky. The increased GWS sightings on days precluded by a brighter night highlighted by this modelling, may have been caused by GWSs concentrating within Mossel Bay to exploit the optimal foraging conditions experienced during the full moon [ 75 , 18 , 72 ]. This model indicated there was a complex relationship between the wind conditions and GWS sightings in Mossel Bay. The presence portion of the model implied a weak correlation between shark presence and wind direction, yet the count part indicated a moderate correlation between wind direction and wind speed with SSR. As ambush predators, surface chop and swell generated in strong winds, would presumably make GWSs more cryptic to their prey at the surface [73, unpublished data]. Therefore, we would expect GWS presence in Mossel Bay to peak during high winds, as sharks would enter the bay to exploit favourable hunting conditions [ 18 ]. Yet, this model indicated a negative correlation between SSR and wind speed. It is possible that our model is highlighting a sampling bias here. If observers were unable to perform as rigorously during high winds, low shark sightings may have been recorded even when GWS were present in higher numbers. Yet, the significance of wind conditions in both phases of the model, suggest we should not discount the effects of wind without further investigation, as wind directions may have genuinely had an impact on GWS behaviour. The presence portion of the model suggested there was a slightly lower probability of sighting a GWS when winds were from the west. Yet highest GWS counts were reported for winds from the south and/or west. Winds coming from the south (y-direction) had approximately twice as large an effect on SSR compared to the effect of winds coming from the east (x-direction). In combination, these findings indicate that certain wind conditions were associated with GWS absence in the Bay. During winds from the west, olfactory stimulants would blow from the prey-rich Mossel Bay out into open ocean, potentially attracting GWSs from offshore into Mossel Bay and resulting in the high SSRs recorded. Conversely, winds coming from the east or north, from open ocean, would lack such intensive attractants, potentially resulting in a lower probability of GWS sightings in the bay [ 51 ]. Water visibility was also shown to be correlated to GWS sightings in Mossel Bay. GWS presence peaked when waters were very clear, although this effect was substantially less marked compared to seasonality. These findings mirror those of Milankovic et al (2021), who reported that GWS sightings in Mossel Bay peaked with vertical water visibility between 3 and 7 m. Yet, this finding is somewhat surprising, as studies have shown a negative correlation between water clarity and GWS attack frequency and success [ 17 , 18 , 20 ]. It has been suggested that GWSs favour murkier, turbid conditions, because clear water makes them all too visible to their prey [ 21 ]. Milankovic et al (2021) hypothesised that, as they are yet to shift to predating upon marine mammals at the surface, the small GWSs observed in Mossel Bay did not attempt to remain cryptic, and so were abundant when waters were very clear. The findings reported here could be caused by the inexperience of the GWSs sighted. Yet, it also seems very plausible that this finding is an artefact of the sampling methodology; with increased visibility improving sampling conditions for the observers and therefore biasing GWS counts to be higher. Similarly, the stay duration was also found to have a significant impact on shark presence, with shark counts higher during shorter stay durations. This is confounding and is likely an artefact caused by the sampling methodology. As WSAUK assured their tourists a sighting of at least one GWS, the team stayed at anchor for longer periods when shark activity was low. Therefore, higher GWS counts and SSRs recorded during shorter stay durations are unlikely to have been driven directly by GWS behaviour, but by a bias associated with the data collection method [ 35 ]. Whilst modelling highlighted some potential biases created by the sampling methodology, it is the authors’ opinion that these data collected from by ecotourism company had great value. The relationships between significant environmental conditions and GWS behaviour highlighted in this model were repeatedly corroborated by scientific research previously conducted in the region. These data can and will be used in future studies of GWS behaviour, with applications ranging from GWS personality, to ethology, and movement ecology and philopatry. Ecotourism as a Source for Scientific Data Projects to better understand the ecology and behaviour of endangered species are absolutely critical if we are to develop effective conservation initiatives [ 1 , 45 ]. Yet, researchers face numerous logistical and financial challenges. Namely finite funding opportunities severely limiting data availability and stunting the advancement of vital knowledge. Collaboration with ecotourism companies could hugely cut the costs associated with data collection for this vital work. Citizen science projects have already been utilised throughout ornithology, ichthyology, palaeontology, and astronomy, and are spreading into include herpetology and botany [ 74 ]. Shark diving ecotourism companies are capable of generating colossal data sets, over long-term study periods, throughout the world, and they also have an enormous influence to educate tourists, and generate public support for critical shark conservation measures [ 34 , 39 – 41 , 43 , 45 ]. The nature of data sets sourced in this way, is that they are generally large (larger than could be collected by a single scientific team) and that they range in their reliability depending on the individual who collected the data (as it is likely the majority of participants will not be professional scientists). Therefore, from a statistical standpoint, the biases and inaccuracies generated by the methodology of data collection should be drowned out by statistical power of the large sample size [ 74 ]. Yet, the authors strongly recommend that researchers employ powerful statistical methods, which can manage the biases associated with the sampling methodology when conducting their research using ecotourism data. We also advise cultivating an open dialogue with the ecotourism company. During this project, close collaboration with the ecotourism team proved vital in both formatting the historical data and for interpreting the results of statistical modelling. Ecotourism professionals have a huge amount of experience and knowledge, and amass an enormous amount of contact time with their subject animals in the wild. The WSAUK shark expert spent 12 years observing GWSs in the wild; experiencing an estimated 6000 GWS sightings over approximately 3500 trips. Utilising these expertise will be very beneficial to future scientific research. WSAUK was a conscientious GWS cage diving company, which adhered to a strict policy of ethics in accordance with South African law [ 53 , 54 ]. Baits were removed and/or bait lines dropped upon approach, to ensure minimal provisioning, and to dissuade unnaturally aggressive behaviour. Chumming was also only conducted in designated areas. Hence, the data were collected in accordance with strict ethics and dedication to accuracy. If data are to be sourced from other ecotourism companies, we believe it will be imperative to screen and evaluate potential contributors, to ensure that collaborations only occur alongside companies that are as ethical as WSAUK. From an ecotourism professional’s perspective, collaboration alongside scientists could validate their company and elevate public perception of their work. Ecotourism could also be critical to the conservation initiatives that scientists are advocating for. The enormous local and national capital generated by ecotourism companies may have the power to switch economic gain from extractive industries, towards non-consumptive exploitation of sharks [ 7 , 40 , 44 , 45 ]. The inevitable increase in shark sightings that would come with a healthier population size, would benefit both ecotourism businesses and conservationists. In the future, when employing data sourced from ecotourism in a scientific setting, the authors would advise that the data collection protocol is: 1) designed with care and rigour, 2) structured to incorporate the knowledge and experience of the ecotourism crew (in order to ensure that the research goals are feasible and realistic), 3) data collection sheets are provided for consistency, 4) blank pre-formatted spreadsheets are provided to reduce data formatting times, and )5all aforementioned methods are piloted to ensure adequate training and to allow for revision of poor methodology. With these provisions, there is no reason that data sourced from ecotourism cannot be used for scientific research. If scientists would reach out and collaborate with ecotourism companies, a mutually beneficial alliance could evolve into something remarkably powerful for science, conservation and economy alike. Methods Study Site A year-round aggregation of GWSs can be found along the South African coast, from KwaZulu-Natal along to the Western Cape [ 46 – 48 ]. It has been suggested that a GWS nursery habitat may exist in the Eastern Cape and that sharks move westwards along the coastline with increasing age/size [ 10 , 49 , 50 ]. Hotspots of GWS sightings are found in waters surrounding colonies of cape fur seals ( Arctocephalus pusillus pusillus ) at Seal Island in False Bay, Dyer Island off Gansbaai and Seal Island in Mossel Bay [ 46 – 48 ]. WSA conducted daily tourist excursions to primarily three sites in Mossel Bay, South Africa (Fig. 1 , Table 1 ). Mossel Bay is a relatively calm, semi-enclosed bay, protected from prevailing weather by the peninsula to the southwest (Fig. 1 ). The bay is characterised by a relatively shallow depth, around 20 m (with the depth contour reaching up to 2,760 m offshore) and a generally sandy, flat bottom, which is interspersed with exposed reef [ 51 ]. Several rivers empty into the bay, and the estuary mouths are nursery areas for a number of fish species predated upon by GWSs [ 49 ]. Previous sampling of GWSs in Mossel Bay, has implied that the area is predominantly occupied by juveniles and that the bay is not used for parturition or mating [ 14 , 49 ]. Data Collection WSAUK left harbour based on the tides and the time of sunrise, to arrive at their anchor point between 07:00h and 10:00h for the morning trip and between 11:00h and 14:00h for the afternoon trip. The WSAUK team selected a location based on their own experience (choosing a site based on previously successful trips with many GWS sightings). After anchoring their vessel (Shark Warrior: 11 m twin-hull catamaran with twin outboard Yamaha 250 amp motors) the WSAUK crew deployed the diving cage in the lea of the boat, by lashing it to the gunwale using several separate ropes. Divers entered the cage directly from the boat and the cage lid was closed over from the top. The cage housed six people at any one time, who could hold the lid at the surface of the water and descend into the cage when a shark was sighted. SCUBA equipment was not used. During each trip the crew consisted of the boat captain, onboard team leader/shark expert, two permanent crew members trained in shark observation, several student volunteers and up to 20 tourists. The boat was licensed for a maximum of 25 people. WSAUK attracted sharks to their vessel using olfactory and visual stimulants; chumming the water with a combination of sea water, liquidated fish blood and tissues, and displaying a bait (composed of fish tissues) at the end of a steel bait line suspended via plastic floats. Only WSAUK crew members handled the bait-line, as they were trained to pull bait away when a shark made an attempt, in order to adhere to the non-provisioning stipulation in their ecotourism license [ 53 , 54 ]. If a shark inadvertently took a bait, the bait handler was trained to immediately drop the line and not to wrestle the shark. The use of attractants began immediately after anchoring and ceased in preparation for departure. Covariates Both anthropogenic and environmental variables were included in modelling in order to untangle any confounding effects which may have occurred in response to ecotourism activities (Appendix Table 1 ). For each trip, multiple variables were recorded by the WSAUK team, including air temperature, sea surface temperature (SST), water depth and visibility, and current direction and speed. To supplement the data collected by WSAUK, meteorological data (including air temperatures, barometric pressure, and wind direction and speed) were sourced from the South African Weather Service (SAWS), from their nearest recording station, at Hermanus (station 0006386A7, located 34◦25′55.2′′S, 19◦13′26.4′′E). Additionally, lunar phase at Hermanus was retrieved at the nearest available location (Cape Town, South Africa) [ 55 ] (Appendix Table 1 ). For every GWS sighted the onboard expert identified if the animal was known based on scarring and/or pigmentation identifiers, upon which its unique ID was recorded (Appendix Table 2 ). New animals were recorded as ‘unknown’ and if seen repeatedly, assigned a unique ID. For each individual (both identified and unknown), TL and sex were recorded. Sex was determined by the presence of claspers and where sex could not be determined definitively, sex was denoted as ‘unsexed’. TL was estimated as the length from the tip of snout to the tip of upper caudal fin to the nearest 0.5 m, based on known dimensions of the boat and/or diving cage. Immature GWSs were sub-categorised according to Bruce & Bradford (2012): YOY as 1.2–1.75 m TL, juvenile as TL 1.76–3.0 m TL and sub-adult as > 3.0 m TL. GWSs were considered to be mature with a TL of ≥ 3.6 m (males) / ≥ 4.8 m (females). For each variable associated with GWS, the crew and students generated estimates based on consensus, which were confirmed by the onboard shark expert, to be recorded by the assigned observer. A GWS sighting was defined as an observation of an individual GWS during one sampling trip. For each trip, a GWS count and a GWS sighting rate (henceforth, SSR) were reported. The shark count equated to the total number of GWS sightings per trip and therefore, did not increase if an individual was seen more than once. The SSR was calculated as the total number of GWSs sighted over the duration of the trip, in order to utilise a measure sensitive to sampling effort [ 21 , 49 , 50 ]. Success rate was also calculated for various portions of sampling (e.g. season/month). The success rate was calculated as the percentage of trips within a certain period where at least one GWS was sighted. Statistical Analysis Statistical analyses were conducted in R, version 4.1.2, using the RStudio IDE [ 56 , 57 ]. On many expeditions no sharks were observed, resulting in an excess of zeros in the data. Hence, a hurdle model was fitted to the number of sharks observed. In this two-stage approach, a separate conditional distribution for the probability of observing any GWSs (Bernoulli), and for the number of GWSs observed, if any (negative binomial), was fitted to the data: with r failures and probability of success p , and \(CD{F}_{NB}\left(0 \right| r, p)\) its cumulative distribution function. The advantage of this approach over a zero-inflated model was that it allowed for separate inference of the effects on presence and on the number of GWS, if present. In other words, we postulate a “hurdle” must be crossed (whether any GWS are observed), which could be affected by a different set of coefficients than the number of GWSs observed, if any. A cyclic spline with three degrees of freedom was constructed to model the seasonality of GWS sightings. This was done using the cSplineDes function from the package mgcv [ 58 ]. By default, this function constructed a spline that summed to a constant of 1. This constant was removed by imposing a sum-to-zero constraint, then orthogonalising the original k spline basis functions with singular value decomposition: for a low number of degrees of freedom ν, to allow the spline to pick up long-term seasonal trends, while still allowing for separate variables for short-term trends, like lunar phase and daily variation in air temperature. Using 10-fold cross-validation, the optimal ν = 3 was selected from ν ∈ {1, 2, 3, 4, 5} by optimising the logarithmic score with the tscount package [ 59 , 60 ]. The lunar phase was included as a sinusoidal function of the dates of the full moon in Cape Town between 2009–2011 [ 61 ]. Wind direction from 0–360° was converted into an x- and y-direction using a cosine and sine transformation, respectively. For wind speed ( \(m\cdot {s}^{-1}\) ), water depth (m), and water visibility (m), a logarithmic transformation was applied, as for each of these it could be argued that their effect on the outcome was multiplicative rather than additive. That is to say, a difference between 1 and 2 \(m\cdot {s}^{-1}\) was of greater influence than a difference between 10 and 11 \(m\cdot {s}^{-1}\) . Finally, as on some days there were multiple expeditions, weights were supplied to the model equal to the inverse of the number of trips on that day. Hurdle models were fitted using the hurdle function in the countreg package in R [ 62 , 63 ]. Goodness-of-fit was visualised using QQ-plots of randomised quantile residuals and rootograms [ 64 , 65 ]. Multiple testing correction was applied by correcting for the false discovery rate, using the Benjamini-Hochberg procedure [ 66 ]. Multiple testing adjusted p-values less than α = 0.10 were considered to be significant. We made this decision since the p-values used in this analysis have been corrected for the false discovery rate (FDR). These FDR values offer a lot more protection at the 0.1 level than a raw p-value at 0.05 or even 0.01. They can be interpreted as a maximum of 10% chance that there is a false positive among all significant tests, whereas the raw p-values each have their own chance of a false positive. Additionally, Muff et al., (2022) suggest a more evidence based instead of binary language when addressing significance. We therefore use a more gradual notion of evidence, which better reflects the actual information provided by the data. Instead of reporting a binary true/false test outcome, we report in this study the exact p-values and interpret that there was no (p > 0.01), weak (0.1 > p > 0.05), moderate (0.05 > p > 0.01), strong (0.01 > p > 0.001) or very strong (p < 0.001) evidence for a certain finding or effect, depending on the ranges into which the actual p-value falls. No human or animal subjects were directly involved in this study. Declarations Data availability The datasets generated during and/or analysed during the current study are available in the google drive repository, https://drive.google.com/file/d/1LfkUJqB3qu0Faxbsmpb-LIizVQmmeW6G/ Acknowledgements Great thanks are offered to the South African Weather Service (SAWS) for their data sharing. Thanks also to Harald van Mil of Leiden University, and Rob Hale for their invaluable advice and support. Enormous gratitude goes to Mr. Mike Ladley and his WSAUK team or crew and volunteers, without whom, this project would have been impossible. 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Citizen science: A developing tool for expanding science knowledge and scientific literacy. BioScience. 2009; 59: 977–984. doi: 10.1525/bio.2009.59.11.9 Klimley AP, Anderson SD, Pyle P, Henderson RP. Spatiotemporal patterns of white shark (Carcharodon carcharias) predation at the South Farallon Islands, California. Copeia. 1992; 3: 680–690. Muff S, Nilsen EB, O’Hara RB, Nater CR. Rewriting results sections in the language of evidence. Trends in Ecology & Evolution. 2022; 37:3. doi: 10.1016/j.tree.2021.10.009 Additional Declarations No competing interests reported. Supplementary Files AppendixTable1.pdf Appendix Table 1. Description of explanatory variables used in hurdle modelling. AppendixTable2.pdf Appendix Table 2. Sample identification features used by WSAUK to recognise previously known GWSs sighed in Mossel Bay. 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Peter Klimley","email":"","orcid":"","institution":"University of California, Davis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"A.","middleName":"Peter","lastName":"Klimley","suffix":""},{"id":152926854,"identity":"beecd1b3-d5b9-4e10-b2f9-7bf51fc43f5b","order_by":4,"name":"Christian Tudorache","email":"data:image/png;base64,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","orcid":"","institution":"Leiden University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Tudorache","suffix":""}],"badges":[],"createdAt":"2022-11-10 13:44:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2259544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2259544/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29347744,"identity":"7e138e19-be2c-4c62-bd09-fdf5d8682ca8","added_by":"auto","created_at":"2022-11-21 18:27:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35609,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of GWS sampling sites within Mossel Bay, South Africa (generated via the Google Maps plugin for QGIS-LTR version 3.6) [52].\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2259544/v1/afacee858d0a88702075249b.jpg"},{"id":29347743,"identity":"9b96a504-3ad7-4c5d-8774-b4c43f12cb42","added_by":"auto","created_at":"2022-11-21 18:27:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21089,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of sharks observed throughout the duration of the study. Transparent red line pieces at the bottom show the frequency of expeditions over time.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2259544/v1/008545ac8ac9e750b3530e00.jpg"},{"id":29348005,"identity":"71703c4d-dd7c-4058-b671-70bf421e35df","added_by":"auto","created_at":"2022-11-21 18:35:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117577,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal effect plots of each of the covariates. The largest number of sharks were observed, on average, when: (A) The lunar phase was (close to) a full moon; (B) Wind direction came from the west; (C) Wind direction came from the south; (D) Wind speed was minimal; (F) The 3-day running average of maximum recorded air temperature was at its highest; (G) There was no seal activity observed; (H) The boat arrived early; (I) The stay duration was at its shortest; (K) Water visibility was maximal; (L) The boat travelled to location 3. For the minimum air temperature (E) and the water depth (J), the confidence bands are too wide to make a meaningful conclusion with regards to the direction of effect.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2259544/v1/9c0005ffd623f49fcab5f9f5.jpg"},{"id":29348096,"identity":"c3a46e55-5ed9-4e75-86b4-51e1adef37cf","added_by":"auto","created_at":"2022-11-21 18:43:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32770,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic plots to assess the goodness-of-fit for the hurdle model. A. QQ-plot of quantile residuals (n = 100) to evaluate the distribution of the model residuals and B. rootogram, displaying the observed frequencies of different counts (shown as grey bars) versus those predicted by the model (blue dots)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2259544/v1/ffb58ed9cdd1ea2a906d5d02.jpg"},{"id":29347746,"identity":"d0019e6e-cd51-413a-bfae-a7d437063347","added_by":"auto","created_at":"2022-11-21 18:27:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61049,"visible":true,"origin":"","legend":"\u003cp\u003ePredictions made by the separate components of the hurdle model (top, center), and predictions using both parts combined (bottom). Colored dots represent the predictions and black dots the actual outcome. For the top figure, only predictions of zero are shown, and actual observed values \u0026gt; 1 are shown in grey. For the center figure, only non-zero predictions are shown, and actual observed values \u0026lt; 1 are shown in grey.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2259544/v1/798d28e6a3647ec5f318de1b.jpg"},{"id":29427190,"identity":"726c0631-e80b-48ab-8339-08c79ccc7e63","added_by":"auto","created_at":"2022-11-23 08:29:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":532179,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2259544/v1/2918a063-630c-4679-aff4-b0f863cee9e1.pdf"},{"id":29347749,"identity":"693127c0-d2d2-48da-a5c3-ff09853528bb","added_by":"auto","created_at":"2022-11-21 18:27:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":149392,"visible":true,"origin":"","legend":"\u003cp\u003eAppendix Table 1. Description of explanatory variables used in hurdle modelling.\u003c/p\u003e","description":"","filename":"AppendixTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2259544/v1/bfe666be2bac324d13ab38a3.pdf"},{"id":29347748,"identity":"0ffd267c-28f1-4590-bed0-44d61d2dfd23","added_by":"auto","created_at":"2022-11-21 18:27:59","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":81853,"visible":true,"origin":"","legend":"\u003cp\u003eAppendix Table 2. Sample identification features used by WSAUK to recognise previously known GWSs sighed in Mossel Bay.\u003c/p\u003e","description":"","filename":"AppendixTable2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2259544/v1/1c4baed677e00097138387d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Citizen Science Data Reveals Environmental Influence on Sightings of Great White Sharks in Mossel Bay, South Africa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn today\u0026rsquo;s changing world, the general public, scientists and policy-makers alike, have become increasingly concerned regarding global biodiversity loss, and there is increasing demand for efforts to protect endangered species [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Arguably one of the most alarming losses, is the cataclysmic decline of sharks throughout all the world\u0026rsquo;s oceans [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a result of overexploitation in fisheries, bycatch, habitat loss and degradation, mortality in shark control programs and culling, 37% of all sharks and rays are now considered to be at risk of extinction [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Besides these threats, researchers have also become increasingly aware that fundamental gaps in our knowledge about sharks in general, have contributed to their poor management [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne such threatened species is the iconic great white shark (\u003cem\u003eCarcharodon carcharias\u003c/em\u003e, Linnaeus, 1758, hereafter GWS). GWSs are listed as \u0026lsquo;vulnerable\u0026rsquo; by the IUCN, as they are estimated to have declined by as much as 79% worldwide. Due to their K-selected life history strategies, and their broad-scale, heterogeneous distribution, these sharks are especially vulnerable to over-exploitation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In response to their precipitous declines, GWSs are now protected by legislation in several countries and trade in their products is restricted by their listing on CITES appendix II [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGWSs have a circumglobal distribution, with several major aggregations found in \u0026lsquo;hotspots\u0026rsquo; in the Western North Atlantic, the Mediterranean Sea, and the eastern North Pacific, and along the coasts of New Zealand, southern Australia and southern Africa [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. With regional endothermy providing a wide thermal tolerance, GWSs, commonly inhabit inshore regions down to the continental slope, in temperate, anti-tropical oceans and are able to undertake large-scale migrations, characterised by directed, trans-oceanic travelling and distinct philopatry [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Estimated to reach a maximum size of 6.0 m total length (TL) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], GWSs are versatile generalists, regularly incorporating a wide range of teleosts, invertebrates and other chondrichthyans into their diet. When reaching a size of approximately 3.0 m TL, GWSs undergo an ontogenic diet shift, whereby they expand their diet to also incorporate marine mammals, such as dolphins and pinnipeds [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Having previously targeted predominantly midwater and bottom-dwelling species, GWS shift their predation behaviours after their ontogenetic shift, in order to target their prey at the surface [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have sought to understand how environmental conditions affect GWS behaviour, hunting strategies and abundance. Sea surface temperatures (SST) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], water visibility [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], cloud cover [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], ambient light levels [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], lunar phase [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], ocean depth [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], chlorophyll concentration [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], swell height [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], wind direction and speed [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and tidal height [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] are all known to affect GWS abundance or predatory behaviour. There are several other factors, including prey availability [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], that fluctuate as a result of these changing environmental conditions, which are also known to indirectly affect GWSs.\u003c/p\u003e \u003cp\u003eThese studies are numerous because it is vital to understand how GWSs utilise their habitats, in order to design effective management plans for their conservation. Yet, performing these rigorous scientific studies in the field is logistically challenging, expensive and time-consuming. Finding alternative methodologies or data sources which allow scientists to investigate the behaviour of GWS would be extremely beneficial to further our knowledge on this threatened species.\u003c/p\u003e \u003cp\u003eShark diving ecotourism has become increasingly popular all over the world over the last 20 years. Ecotourism companies offering trips to watch and cage-dive with GWSs, spend a large amount of contact time with this species in various regions, including Australia (the Neptune Islands, South Australia), South Africa (at least 5 sites along the coast between False Bay and Algoa Bay), Mexico (Guadalupe Island), the USA (the Farallon Islands, California) and New Zealand (Stewart Island) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Ship\u0026rsquo;s logbooks or recorded observational data from these companies could represent a significant, fishery-independent sampling effort and potentially an invaluable resource for scientific research, which is yet underexploited [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43 CR44\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study used the data obtained by the ethical ecotourism company White Shark Africa UK (WSAUK). This company offered day trips for tourists to see GWSs in the wild and also extended volunteer placements, whereby students learned to work with sharks over weeks to months. These data were used to model how environmental conditions and anthropogenic activity were related to sightings of GWSs in Mossel Bay, South Africa. Gaining a comprehensive understanding of how GWSs utilise their habitat and how the environment impacts upon these animals is critical to designing effective management strategies to secure their future. The goal of this study is to provide a better understanding of how the environment can affect GWS behaviour and to critically evaluate whether data sourced from ecotourism may have the potential to contribute to shark monitoring or scientific research in the future.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysed dataset included a total of 287 sampling trips, totalling 662h 35min of observation and 1,527 individual shark sightings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A total of 64 GWSs were identified individually. Overall, the average size of GWSS sighted was 2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 m TL. The smallest GWS sighted was 1.4 m and the largest was 4.0 m TL (six sightings of a 4.0 m GWS were all sightings of an identified female in September of 2010). No mature GWSs were sighted. Overall, the majority (80.6%) of GWSS sighted were juveniles. Juvenile females represented 69.0% of sightings, YOY GWSs made up only 0.4% of sightings (all females), sub-adults represented 19.0% of sightings, the majority of which were females (17.8% overall). The sex ratio was 1.0 : 6.8 : 4.3 (male : female : unsexed) overall.\u003c/p\u003e \u003cp\u003eGWS count averaged eight over the whole sampling period and the highest count was 19 sharks. SSR averaged 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 sightings/hour (range 0\u0026ndash;11.6 sightings/hour) throughout the whole study period. Zero sharks were sighted on 49 trips, but the success rate for sighting at least one shark per trip was 83% when considering the whole study period.\u003c/p\u003e \u003cp\u003eData Quality Assessment\u003c/p\u003e \u003cp\u003eOf a total of 448 excursions included in the submitted data set, 288 (64%) were considered to include adequate environmental data to be included in analysis. A total of 51 trips conducted in 2009 were included in the analysis, 137 trips from 2010 and 100 trips from 2011 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Seal Island was the most common sampling location; for 121 of the trips (42%). Grootbrak was visited on 99 trips (34%) and Kleinbrak for 68 trips (24%, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePre-modelling assessment indicated colinearity between several pairs of explanatory variables related to air temperature and barometric pressure. Thus, absolute minimum and maximum daily air temperatures, temperature variation, absolute barometric pressure and variation in barometric pressure were excluded before modelling. In their stead, three-day running averages for barometric pressure, and minimum and maximum air temperatures were considered (Appendix Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the three sampling sites visited by WSAUK within Mossel Bay.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. 1 \u0026nbsp;Seal Island\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003csup\u003e◦\u003c/sup\u003e 09\u003csup\u003e\u0026prime;\u003c/sup\u003e 04.4\u003csup\u003e\u0026prime;\u003c/sup\u003e S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaters surrounding a 100 \u0026times; 50 m island, 800 m from shore, hosting a colony of 4,000 cape fur seals.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. 2 \u0026nbsp;Klein Brak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003csup\u003e◦\u003c/sup\u003e 06\u003csup\u003e\u0026prime;\u003c/sup\u003e 20.3\u003csup\u003e\u0026prime;\u003c/sup\u003e S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNearshore region at the Klein Brak river delta.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. 3 \u0026nbsp;Groot Brak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003csup\u003e◦\u003c/sup\u003e08\u003csup\u003e\u0026prime;\u003c/sup\u003e28.6\u003csup\u003e\u0026prime;\u003c/sup\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNearshore region at the Groot Brak river delta.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHurdle Model Zero-Part\u003c/p\u003e \u003cp\u003ePresence of GWSs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) correlated strongly with the spline coefficients, suggesting the probability of observing any sharks depended on the time of year. GWS presence was highest in winter, with a 100% success rate from June through August. The model also indicated wind direction weakly correlated with the probability of observing at least one GWS, with the positive coefficient for the x-direction indicating wind from the east resulted in slightly greater probability of observing any sharks. Water visibility correlated positively, and the three-day minimum air temperature and stay duration both correlated negatively with the probability of observing at least one GWS. Therefore, the probability of sighting at least one GWSs was highest when running average air temperatures were cooler, water visibility was higher and the boat stayed at anchor for a shorter period. Yet none of these effect sizes were particularly large compared to the overall seasonality. There was also a trend towards a lower probability of observing GWSs when sampling at Grootbrak, compared to the reference location at Seal Island.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated coefficients for the presence of GWSs. Shown in bold are p-values less than α\u0026thinsp;=\u0026thinsp;0.10. Adjusted p-values are corrected for multiple testing. The p-value of the intercept was not relevant to the conclusion, and therefore not taken into account for this correction. \u0026dagger;: Temperature extremes shown as moving averages within a three-day window.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEst.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eadj. p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpline coef. 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000424\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00226\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpline coef. 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.00672\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0307\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpline coef. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.59\u0026middot;10E\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.000127\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLunar phase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind (x-direction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0997\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind (y-direction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(Wind speed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir temp. (min)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0521\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.098\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir temp. (max)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeal activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of arrival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStay duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0454\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0969\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog2 (Water depth)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog2(Water visibility)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0969\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0508\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.098\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHurdle Model Count-Part\u003c/p\u003e \u003cp\u003eThe number of GWSs sighted, if any (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), also correlated with the spline coefficients, though not as strongly as the presence of GWSs. Interestingly, lunar phase, wind direction, wind speed, maximum air temperature, seal activity and time of arrival correlated significantly with the number of GWSs, even though they did not correlate significantly with the presence of sharks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated coefficients for the counts of sharks, if present. \u003cem\u003eP\u003c/em\u003e-values less than 0.10 are shown in bold. The \u003cem\u003ep\u003c/em\u003e-value of the intercept and \u003cem\u003eθ\u003c/em\u003e are not relevant to the conclusion, and therefore not considered for this correction. \u0026dagger;: Temperature extremes are moving averages with a 3-day window.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEst.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eadj. \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0162\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpline coef. 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpline coef. 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0354\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0969\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpline coef. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e18\u003c/b\u003e \u0026middot; \u003cb\u003e10\u003c/b\u003e\u003csup\u003e\u0026minus;\u0026thinsp;\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e26\u003c/b\u003e \u0026middot; \u003cb\u003e10\u003c/b\u003e\u003csup\u003e\u0026minus;\u0026thinsp;\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLunar phase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0381\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0969\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind (\u003cem\u003ex\u003c/em\u003e-direction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e.\u003cb\u003e91\u003c/b\u003e \u0026middot; \u003cb\u003e10\u003c/b\u003e\u003csup\u003e\u0026minus;\u0026thinsp;\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e00025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind (\u003cem\u003ey\u003c/em\u003e-direction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e.\u003cb\u003e46\u003c/b\u003e \u0026middot; \u003cb\u003e10\u003c/b\u003e\u003csup\u003e\u0026minus;\u0026thinsp;\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e.\u003cb\u003e86\u003c/b\u003e \u0026middot; \u003cb\u003e10\u003c/b\u003e\u003csup\u003e\u0026minus;\u0026thinsp;\u003cb\u003e8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(Wind speed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0397\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0969\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir temp. (min)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir temp. (max)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0429\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0969\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeal activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0162\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0648\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of arrival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e11\u003c/b\u003e \u0026middot; \u003cb\u003e10\u003c/b\u003e\u003csup\u003e\u0026minus;\u0026thinsp;\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e26\u003c/b\u003e \u0026middot; \u003cb\u003e10\u003c/b\u003e\u003csup\u003e\u0026minus;\u0026thinsp;\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStay duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog\u003csub\u003e2\u003c/sub\u003e(Water depth)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog\u003csub\u003e2\u003c/sub\u003e(Water visibility)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0334\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e0969\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(\u003cem\u003eθ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e000102\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOn average, the closer the lunar phase was to 0 (new moon), the fewer GWSs were observed and the closer to 1 (full moon), the more sharks were observed. Wind from the south and from the west correlated to the largest number of GWS sightings, indicated by the negative coefficients of both x- and y-wind directions. Wind coming from the south (y-direction) had about twice as large an effect as wind coming from the west (x-direction). Therefore, the largest number of GWSs may presumably be expected during a SSW wind, or conversely, the number of sharks observed lowest on average during a NNE wind. Wind speed negatively affected the number of GWSs observed. Higher maximum air temperature corresponded to more sharks sighted and fewer GWSs were observed when there was seal activity. Finally, the later the boat arrived, the fewer GWSs were generally observed. The number of GWSs observed at Seal Island and Kleinbrak were more or less equal, but Grootbrak had nearly double the number of sharks observed on average.\u003c/p\u003e \u003cp\u003eAssessing the marginal effects of each of the significant covariates, whilst holding all other explanatory variables constant at representative values, the model predicted that the largest number of GWSs were observed on average, when the lunar phase was (close to) a full moon (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), wind direction came from the west (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and/or from the south (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), wind speed was minimal (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), the three-day running average of maximum recorded air temperature was at its warmest (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), there was no seal activity observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eG), water visibility was maximal (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eK), the boat arrived early in the morning (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eH) to anchor at Grootbrak (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eL) and the stay duration was short (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). Considering the minimum air temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) and the water depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ), the confidence bands were too wide to make a meaningful conclusion with regards to the direction of effect.\u003c/p\u003e \u003cp\u003eGoodness-of-Fit\u003c/p\u003e \u003cp\u003eSeveral diagnostic plots used for evaluation of the hurdle model, indicated that the model was a very good fit to these data (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The QQ-plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) to assess the goodness-of-fit for the count data, displayed only very slightly heavier tails than may be expected; indicated by the spread of the residuals below the line in the bottom left and above the line in the upper right, but no obvious violations from the distributional assumptions. Furthermore, the rootogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) indicated that the model slightly underestimated GWS counts of 13, but overall suggested the model was a good fit to the count data. Additionally, predictions made by the separate parts of the hurdle model, as well as both parts combined, showed the predictive model produced no large overestimations of the number of GWSs sighted (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study sought to utilise data sourced from a shark diving ecotourism company to study the behaviour of great white sharks (GWS) in Mossel Bay, South Africa and to critically evaluate the value of this alternative data source for scientific research. By removing explanatory variables from the model via backwards selection, we were able to discern that the environmental conditions and the sampling methodology were both significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) for predicting GWS sightings in Mossel Bay. Yet, as several significant variables retained in the model mirrored the findings of previous studies, the authors are confident that the method of employing a robust hurdle model, allowed elucidation of genuine patterns in these data, whilst countering the potential biases associated with the data collection methodology. It is our conclusion that collaboration with ethical ecotourism companies could have great value for scientific research and offer an alternative, fisheries-independent source for scientific data in the future.\u003c/p\u003e\n\u003cp\u003eThe Effect of Environmental Conditions on GWS Sightings\u003c/p\u003e\n\u003cp\u003eThe moderate to very strong significance of the cyclical spline indicated that both shark presence and shark sighting rate (SSR) followed a cyclical seasonal pattern in Mossel Bay. Yet, the model also showed that environmental conditions did have an impact on GWS sightings. There was weak to moderate evidence that probability of sighting at least one GWS, was correlated to wind direction, minimum air temperature, water visibility and how long the boat stayed at anchor. Comparatively, the SSR was associated with lunar phase, maximum air temperature, seal activity, time of arrival, and wind direction and speed.\u003c/p\u003e\n\u003cp\u003eAs has been previously reported, GWS were sighted year-round in Mossel Bay, South Africa [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. Throughout sampling, SSR averaged 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 sightings/hour, which was higher than rates previously reported in Mossel Bay [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Yet the maturity stages and the sex ratio of the sharks sighted were consistent with those reported in previous studies of the region [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. Juveniles dominated the demographic structure in these data, supporting the hypothesis that Mossel Bay is not used for GWS parturition or mating [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eDespite their year-round presence, the strong to very strong correlation of the spline coefficients in this model, indicated that GWS sightings cycled seasonally in Mossel Bay, with shark presence more assured and SSR higher during the winter months (June - August). At many aggregation sites, seasonal peaks in GWS abundances have been attributed to prey availability [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. Whilst they are generalist predators, after their ontogenetic diet shift, high calorie marine mammals make up an important component of the GWS\u0026rsquo;s diet [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. As the cape fur seal pupping season occurs from November to December in Mossel Bay, it seems logical to conclude that the seasonal abundance of GWSs in the region, was caused by the immigration of sub-adult sharks into Mossel Bay, to exploit a seasonally abundant prey resource [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eYet, the average size of GWSs sighted in this study was only 2.5 m TL and 81.0% of sharks were smaller than the 3 m TL associated with the ontogenetic diet shift. There have been reports of GWSs smaller the 3 m TL threshold predating seals in South Africa [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e], and reporting similar findings, Milankovic et al (2021) hypothesised that Mossel Bay may act as a training ground; with smaller GWSs observing the hunting behaviour of conspecifics in anticipation of their shift to predating marine mammals. It is possible that the seasonal fluctuations in GWS sightings modelled here were driven by the immigration of pre-ontogentic-shift juvenile GWSs into Mossel Bay during the winter months to take advantage of social learning [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eYet seal abundance alone may not explain shark presence and the fluctuations in SSRs reported in these data. Seal activity was negatively correlated with SSR and the model highlighted the significance of the sampling location both for predicting GWS presence and counts. The higher probability of observing GWSs at Seal Island compared to Kleinbrak, supports the hypothesis that GWSs immigrated into Mossel Bay to target seal prey. Yet Grootbrak boasted on-average almost double the SSR compared to Seal Island and Kleinbrak. Stomach contents analyses have identified dusky sharks (Carcharhinus obscurus), chub mackerel (Scomber japonicus) and sea bream (Sparidae spp.) make up a significant proportion of the South African GWSs\u0026rsquo; diet [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Several estuaries in Mossel Bay house nursery habitats for many teleost fish species [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e], and the Grootbrak river mouth particularly has been identified as a core habitat for smaller GWS targeting teleost and elasmobranch prey [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. Therefore, it seems likely that seasonal migrations and/or seasonal breeding of teleosts and other elasmobranchs were also driving GWS immigration into Mossel Bay during the winter months, and that GWS habitat selection based on prey availability occurred on a fine spatial scale within the bay [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe significance of several other variables in the model implied that seasonal fluctuations in environmental conditions were also responsible for driving GWS sightings in Mossel Bay to some degree. For instance, modelling indicated a relationship between the minimum air temperature and GWS sightings. Despite their regional endothermy, environmental temperatures have been found to be a significant driver in GWS distributions, as in low temperatures, GWSs must maintain their internal body temperature at a slightly higher metabolic cost [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. Therefore, these findings indicate that GWSs vacated the study site during cold conditions, to avoid physiological stress. On the other hand, if our conclusions that GWSs immigrate into Mossel Bay during the winter holds true, it seems doubtful that a higher number of sharks were present during periods of warmer maximum air temperatures. Rather it seems these findings indicate that warm conditions had an impact on GWS behaviour [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. When colder, great whites experience significant physiological changes, including slower metabolic rate and cooling muscles [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. Under these conditions, with less energy readily available for high-octane displays, GWSs would be less likely to be sighted by observers. Comparatively, in warmer conditions, excess energy would be readily available to mount surface attacks on the bait, making the sharks particularly conspicuous, and resulting in the higher SSR recorded in these data. Therefore, this finding may be highlighting a change in GWS behaviour, as opposed to a direct relationship between air temperatures and GWS abundance.\u003c/p\u003e\n\u003cp\u003eThe arrival time was found to be significant in the counts portion of this model, with highest SSRs reported when sampling began earlier in the morning. GWSs practice a diel pattern of behaviour and habitat use; most actively hunting and patrolling at dusk and dawn, and spending the day at rest, digesting food and conserving energy [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. Hammerschlag et al (2006) reported that GWS predations on cape fur seals were most frequent during low light intensities and predation success rates dropped dramatically as light increased during the day. High octane hunting behaviours and high activity levels of the early morning would have made GWSs especially conspicuous to observers, leading to higher SSRs during early morning sampling trips. Therefore, the significance of arrival time when modelling shark sightings was likely caused by the diel pattern of behaviour practiced by GWSs.\u003c/p\u003e\n\u003cp\u003eThe model also indicated that GWS sightings fluctuated on a lunar cycle, with higher SSRs recorded when the previous night experienced a full moon. These findings conflict with those of Weltz et al (2013), in Cape Town, South Africa, who reported that GWS sightings off bather beaches increased four-fold during darker new moons. GWSs have been reported to hunt during the night [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e] and Klimley et al (2001) have suggested that a full moon may create optimal hunting conditions for GWSs, as the light conditions mean seals become silhouetted against the relatively bright night sky. The increased GWS sightings on days precluded by a brighter night highlighted by this modelling, may have been caused by GWSs concentrating within Mossel Bay to exploit the optimal foraging conditions experienced during the full moon [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThis model indicated there was a complex relationship between the wind conditions and GWS sightings in Mossel Bay. The presence portion of the model implied a weak correlation between shark presence and wind direction, yet the count part indicated a moderate correlation between wind direction and wind speed with SSR. As ambush predators, surface chop and swell generated in strong winds, would presumably make GWSs more cryptic to their prey at the surface [73, unpublished data]. Therefore, we would expect GWS presence in Mossel Bay to peak during high winds, as sharks would enter the bay to exploit favourable hunting conditions [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Yet, this model indicated a negative correlation between SSR and wind speed. It is possible that our model is highlighting a sampling bias here. If observers were unable to perform as rigorously during high winds, low shark sightings may have been recorded even when GWS were present in higher numbers. Yet, the significance of wind conditions in both phases of the model, suggest we should not discount the effects of wind without further investigation, as wind directions may have genuinely had an impact on GWS behaviour. The presence portion of the model suggested there was a slightly lower probability of sighting a GWS when winds were from the west. Yet highest GWS counts were reported for winds from the south and/or west. Winds coming from the south (y-direction) had approximately twice as large an effect on SSR compared to the effect of winds coming from the east (x-direction). In combination, these findings indicate that certain wind conditions were associated with GWS absence in the Bay. During winds from the west, olfactory stimulants would blow from the prey-rich Mossel Bay out into open ocean, potentially attracting GWSs from offshore into Mossel Bay and resulting in the high SSRs recorded. Conversely, winds coming from the east or north, from open ocean, would lack such intensive attractants, potentially resulting in a lower probability of GWS sightings in the bay [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWater visibility was also shown to be correlated to GWS sightings in Mossel Bay. GWS presence peaked when waters were very clear, although this effect was substantially less marked compared to seasonality. These findings mirror those of Milankovic et al (2021), who reported that GWS sightings in Mossel Bay peaked with vertical water visibility between 3 and 7 m. Yet, this finding is somewhat surprising, as studies have shown a negative correlation between water clarity and GWS attack frequency and success [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. It has been suggested that GWSs favour murkier, turbid conditions, because clear water makes them all too visible to their prey [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Milankovic et al (2021) hypothesised that, as they are yet to shift to predating upon marine mammals at the surface, the small GWSs observed in Mossel Bay did not attempt to remain cryptic, and so were abundant when waters were very clear. The findings reported here could be caused by the inexperience of the GWSs sighted. Yet, it also seems very plausible that this finding is an artefact of the sampling methodology; with increased visibility improving sampling conditions for the observers and therefore biasing GWS counts to be higher.\u003c/p\u003e\n\u003cp\u003eSimilarly, the stay duration was also found to have a significant impact on shark presence, with shark counts higher during shorter stay durations. This is confounding and is likely an artefact caused by the sampling methodology. As WSAUK assured their tourists a sighting of at least one GWS, the team stayed at anchor for longer periods when shark activity was low. Therefore, higher GWS counts and SSRs recorded during shorter stay durations are unlikely to have been driven directly by GWS behaviour, but by a bias associated with the data collection method [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWhilst modelling highlighted some potential biases created by the sampling methodology, it is the authors\u0026rsquo; opinion that these data collected from by ecotourism company had great value. The relationships between significant environmental conditions and GWS behaviour highlighted in this model were repeatedly corroborated by scientific research previously conducted in the region. These data can and will be used in future studies of GWS behaviour, with applications ranging from GWS personality, to ethology, and movement ecology and philopatry.\u003c/p\u003e\n\u003cp\u003eEcotourism as a Source for Scientific Data\u003c/p\u003e\n\u003cp\u003eProjects to better understand the ecology and behaviour of endangered species are absolutely critical if we are to develop effective conservation initiatives [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. Yet, researchers face numerous logistical and financial challenges. Namely finite funding opportunities severely limiting data availability and stunting the advancement of vital knowledge. Collaboration with ecotourism companies could hugely cut the costs associated with data collection for this vital work. Citizen science projects have already been utilised throughout ornithology, ichthyology, palaeontology, and astronomy, and are spreading into include herpetology and botany [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. Shark diving ecotourism companies are capable of generating colossal data sets, over long-term study periods, throughout the world, and they also have an enormous influence to educate tourists, and generate public support for critical shark conservation measures [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe nature of data sets sourced in this way, is that they are generally large (larger than could be collected by a single scientific team) and that they range in their reliability depending on the individual who collected the data (as it is likely the majority of participants will not be professional scientists). Therefore, from a statistical standpoint, the biases and inaccuracies generated by the methodology of data collection should be drowned out by statistical power of the large sample size [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. Yet, the authors strongly recommend that researchers employ powerful statistical methods, which can manage the biases associated with the sampling methodology when conducting their research using ecotourism data. We also advise cultivating an open dialogue with the ecotourism company. During this project, close collaboration with the ecotourism team proved vital in both formatting the historical data and for interpreting the results of statistical modelling.\u003c/p\u003e\n\u003cp\u003eEcotourism professionals have a huge amount of experience and knowledge, and amass an enormous amount of contact time with their subject animals in the wild. The WSAUK shark expert spent 12 years observing GWSs in the wild; experiencing an estimated 6000 GWS sightings over approximately 3500 trips. Utilising these expertise will be very beneficial to future scientific research.\u003c/p\u003e\n\u003cp\u003eWSAUK was a conscientious GWS cage diving company, which adhered to a strict policy of ethics in accordance with South African law [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. Baits were removed and/or bait lines dropped upon approach, to ensure minimal provisioning, and to dissuade unnaturally aggressive behaviour. Chumming was also only conducted in designated areas. Hence, the data were collected in accordance with strict ethics and dedication to accuracy. If data are to be sourced from other ecotourism companies, we believe it will be imperative to screen and evaluate potential contributors, to ensure that collaborations only occur alongside companies that are as ethical as WSAUK. From an ecotourism professional\u0026rsquo;s perspective, collaboration alongside scientists could validate their company and elevate public perception of their work.\u003c/p\u003e\n\u003cp\u003eEcotourism could also be critical to the conservation initiatives that scientists are advocating for. The enormous local and national capital generated by ecotourism companies may have the power to switch economic gain from extractive industries, towards non-consumptive exploitation of sharks [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. The inevitable increase in shark sightings that would come with a healthier population size, would benefit both ecotourism businesses and conservationists.\u003c/p\u003e\n\u003cp\u003eIn the future, when employing data sourced from ecotourism in a scientific setting, the authors would advise that the data collection protocol is: 1) designed with care and rigour, 2) structured to incorporate the knowledge and experience of the ecotourism crew (in order to ensure that the research goals are feasible and realistic), 3) data collection sheets are provided for consistency, 4) blank pre-formatted spreadsheets are provided to reduce data formatting times, and )5all aforementioned methods are piloted to ensure adequate training and to allow for revision of poor methodology. With these provisions, there is no reason that data sourced from ecotourism cannot be used for scientific research. If scientists would reach out and collaborate with ecotourism companies, a mutually beneficial alliance could evolve into something remarkably powerful for science, conservation and economy alike.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Site\u003c/p\u003e\n\u003cp\u003eA year-round aggregation of GWSs can be found along the South African coast, from KwaZulu-Natal along to the Western Cape [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. It has been suggested that a GWS nursery habitat may exist in the Eastern Cape and that sharks move westwards along the coastline with increasing age/size [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]. Hotspots of GWS sightings are found in waters surrounding colonies of cape fur seals (\u003cem\u003eArctocephalus pusillus pusillus\u003c/em\u003e) at Seal Island in False Bay, Dyer Island off Gansbaai and Seal Island in Mossel Bay [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWSA conducted daily tourist excursions to primarily three sites in Mossel Bay, South Africa (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Mossel Bay is a relatively calm, semi-enclosed bay, protected from prevailing weather by the peninsula to the southwest (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The bay is characterised by a relatively shallow depth, around 20 m (with the depth contour reaching up to 2,760 m offshore) and a generally sandy, flat bottom, which is interspersed with exposed reef [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. Several rivers empty into the bay, and the estuary mouths are nursery areas for a number of fish species predated upon by GWSs [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Previous sampling of GWSs in Mossel Bay, has implied that the area is predominantly occupied by juveniles and that the bay is not used for parturition or mating [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eData Collection\u003c/p\u003e\n\u003cp\u003eWSAUK left harbour based on the tides and the time of sunrise, to arrive at their anchor point between 07:00h and 10:00h for the morning trip and between 11:00h and 14:00h for the afternoon trip. The WSAUK team selected a location based on their own experience (choosing a site based on previously successful trips with many GWS sightings). After anchoring their vessel (Shark Warrior: 11 m twin-hull catamaran with twin outboard Yamaha 250 amp motors) the WSAUK crew deployed the diving cage in the lea of the boat, by lashing it to the gunwale using several separate ropes. Divers entered the cage directly from the boat and the cage lid was closed over from the top. The cage housed six people at any one time, who could hold the lid at the surface of the water and descend into the cage when a shark was sighted. SCUBA equipment was not used. During each trip the crew consisted of the boat captain, onboard team leader/shark expert, two permanent crew members trained in shark observation, several student volunteers and up to 20 tourists. The boat was licensed for a maximum of 25 people. WSAUK attracted sharks to their vessel using olfactory and visual stimulants; chumming the water with a combination of sea water, liquidated fish blood and tissues, and displaying a bait (composed of fish tissues) at the end of a steel bait line suspended via plastic floats. Only WSAUK crew members handled the bait-line, as they were trained to pull bait away when a shark made an attempt, in order to adhere to the non-provisioning stipulation in their ecotourism license [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. If a shark inadvertently took a bait, the bait handler was trained to immediately drop the line and not to wrestle the shark. The use of attractants began immediately after anchoring and ceased in preparation for departure.\u003c/p\u003e\n\u003cp\u003eCovariates\u003c/p\u003e\n\u003cp\u003eBoth anthropogenic and environmental variables were included in modelling in order to untangle any confounding effects which may have occurred in response to ecotourism activities (Appendix Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For each trip, multiple variables were recorded by the WSAUK team, including air temperature, sea surface temperature (SST), water depth and visibility, and current direction and speed. To supplement the data collected by WSAUK, meteorological data (including air temperatures, barometric pressure, and wind direction and speed) were sourced from the South African Weather Service (SAWS), from their nearest recording station, at Hermanus (station 0006386A7, located 34◦25\u0026prime;55.2\u0026prime;\u0026prime;S, 19◦13\u0026prime;26.4\u0026prime;\u0026prime;E). Additionally, lunar phase at Hermanus was retrieved at the nearest available location (Cape Town, South Africa) [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e] (Appendix Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFor every GWS sighted the onboard expert identified if the animal was known based on scarring and/or pigmentation identifiers, upon which its unique ID was recorded (Appendix Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). New animals were recorded as \u0026lsquo;unknown\u0026rsquo; and if seen repeatedly, assigned a unique ID. For each individual (both identified and unknown), TL and sex were recorded. Sex was determined by the presence of claspers and where sex could not be determined definitively, sex was denoted as \u0026lsquo;unsexed\u0026rsquo;. TL was estimated as the length from the tip of snout to the tip of upper caudal fin to the nearest 0.5 m, based on known dimensions of the boat and/or diving cage. Immature GWSs were sub-categorised according to Bruce \u0026amp; Bradford (2012): YOY as 1.2\u0026ndash;1.75 m TL, juvenile as TL 1.76\u0026ndash;3.0 m TL and sub-adult as \u0026gt;\u0026thinsp;3.0 m TL. GWSs were considered to be mature with a TL of \u0026ge;\u0026thinsp;3.6 m (males) / \u0026ge; 4.8 m (females). For each variable associated with GWS, the crew and students generated estimates based on consensus, which were confirmed by the onboard shark expert, to be recorded by the assigned observer.\u003c/p\u003e\n\u003cp\u003eA GWS sighting was defined as an observation of an individual GWS during one sampling trip. For each trip, a GWS count and a GWS sighting rate (henceforth, SSR) were reported. The shark count equated to the total number of GWS sightings per trip and therefore, did not increase if an individual was seen more than once. The SSR was calculated as the total number of GWSs sighted over the duration of the trip, in order to utilise a measure sensitive to sampling effort [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]. Success rate was also calculated for various portions of sampling (e.g. season/month). The success rate was calculated as the percentage of trips within a certain period where at least one GWS was sighted.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003cp\u003eStatistical Analysis\u003c/p\u003e\n \u003cp\u003eStatistical analyses were conducted in R, version 4.1.2, using the RStudio IDE [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. On many expeditions no sharks were observed, resulting in an excess of zeros in the data. Hence, a hurdle model was fitted to the number of sharks observed. In this two-stage approach, a separate conditional distribution for the probability of observing any GWSs (Bernoulli), and for the number of GWSs observed, if any (negative binomial), was fitted to the data:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"451\" height=\"88\"\u003e\u003c/p\u003e\n \u003cp\u003ewith \u003cem\u003er\u003c/em\u003e failures and probability of success \u003cem\u003ep\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(CD{F}_{NB}\\left(0 \\right| r, p)\\)\u003c/span\u003e\u003c/span\u003e its cumulative distribution function.\u003c/p\u003e\n \u003cp\u003eThe advantage of this approach over a zero-inflated model was that it allowed for separate inference of the effects on presence and on the number of GWS, if present. In other words, we postulate a \u0026ldquo;hurdle\u0026rdquo; must be crossed (whether any GWS are observed), which could be affected by a different set of coefficients than the number of GWSs observed, if any.\u003c/p\u003e\n \u003cp\u003eA cyclic spline with three degrees of freedom was constructed to model the seasonality of GWS sightings. This was done using the cSplineDes function from the package mgcv [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. By default, this function constructed a spline that summed to a constant of 1. This constant was removed by imposing a sum-to-zero constraint, then orthogonalising the original \u003cem\u003ek\u003c/em\u003e spline basis functions with singular value decomposition:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"296\" height=\"31\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003efor a low number of degrees of freedom \u0026nu;, to allow the spline to pick up long-term seasonal trends, while still allowing for separate variables for short-term trends, like lunar phase and daily variation in air temperature. Using 10-fold cross-validation, the optimal \u0026nu;\u0026thinsp;=\u0026thinsp;3 was selected from \u0026nu; \u0026isin; {1, 2, 3, 4, 5} by optimising the logarithmic score with the tscount package [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe lunar phase was included as a sinusoidal function of the dates of the full moon in Cape Town between 2009\u0026ndash;2011 [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]. Wind direction from 0\u0026ndash;360\u0026deg; was converted into an x- and y-direction using a cosine and sine transformation, respectively. For wind speed (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(m\\cdot {s}^{-1}\\)\u003c/span\u003e\u003c/span\u003e), water depth (m), and water visibility (m), a logarithmic transformation was applied, as for each of these it could be argued that their effect on the outcome was multiplicative rather than additive. That is to say, a difference between 1 and 2 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(m\\cdot {s}^{-1}\\)\u003c/span\u003e\u003c/span\u003e was of greater influence than a difference between 10 and 11 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(m\\cdot {s}^{-1}\\)\u003c/span\u003e\u003c/span\u003e. Finally, as on some days there were multiple expeditions, weights were supplied to the model equal to the inverse of the number of trips on that day.\u003c/p\u003e\n \u003cp\u003eHurdle models were fitted using the hurdle function in the countreg package in R [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]. Goodness-of-fit was visualised using QQ-plots of randomised quantile residuals and rootograms [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]. Multiple testing correction was applied by correcting for the false discovery rate, using the Benjamini-Hochberg procedure [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]. Multiple testing adjusted p-values less than \u0026alpha;\u0026thinsp;=\u0026thinsp;0.10 were considered to be significant. We made this decision since the p-values used in this analysis have been corrected for the false discovery rate (FDR). These FDR values offer a lot more protection at the 0.1 level than a raw p-value at 0.05 or even 0.01. They can be interpreted as a maximum of 10% chance that there is a false positive among all significant tests, whereas the raw p-values each have their own chance of a false positive. Additionally, Muff et al., (2022) suggest a more evidence based instead of binary language when addressing significance. We therefore use a more gradual notion of evidence, which better reflects the actual information provided by the data. Instead of reporting a binary true/false test outcome, we report in this study the exact p-values and interpret that there was no (p\u0026thinsp;\u0026gt;\u0026thinsp;0.01), weak (0.1\u0026thinsp;\u0026gt;\u0026thinsp;p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), moderate (0.05\u0026thinsp;\u0026gt;\u0026thinsp;p\u0026thinsp;\u0026gt;\u0026thinsp;0.01), strong (0.01\u0026thinsp;\u0026gt;\u0026thinsp;p\u0026thinsp;\u0026gt;\u0026thinsp;0.001) or very strong (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) evidence for a certain finding or effect, depending on the ranges into which the actual p-value falls.\u003c/p\u003e\n \u003cp\u003eNo human or animal subjects were directly involved in this study.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available in the google drive repository, https://drive.google.com/file/d/1LfkUJqB3qu0Faxbsmpb-LIizVQmmeW6G/\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGreat thanks are offered to the South African Weather Service (SAWS) for their data sharing. Thanks also to Harald van Mil of Leiden University, and Rob Hale for their invaluable advice and support. Enormous gratitude goes to Mr. Mike Ladley and his WSAUK team or crew and volunteers, without whom, this project would have been impossible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics Statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was non-invasive and data collection was conducted during tourist excursions, which were regulated by the Government of South Africa. WSAUK held a commercial cage diving permit issued by the Department of Environmental Affairs Oceans and Coasts. GWS cage diving permit stipulations were strictly adhered to, with no shark being intentionally fed and no animal being physically harmed during observation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSM wrote the manuscript and collected and analysed data. FR analysed data. ML collected data. PK wrote the manuscript. 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Trends in Ecology \u0026amp; Evolution. 2022; 37:3. doi: 10.1016/j.tree.2021.10.009\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"elasmobranchs, conservation, biodiversity, Hurdle model ","lastPublishedDoi":"10.21203/rs.3.rs-2259544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2259544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhilst previous studies have described the impact of various environmental conditions on behaviour and abundance of great white sharks (GWS), existing knowledge gaps must be addressed in order to turn the tide on their population declines. This study used data collected by a diving tour operator, to investigate how environmental and anthropogenic variables affected the rate of GWS sightings. Observation data were collected by trained crew and volunteers alongside tourists, and combined with externally sourced environmental data. Hurdle modelling identified that the probability of sighting at least one GWS fluctuated seasonally (peaking during winter), but was also correlated with minimum running air temperature, water visibility and the length of time the boat stayed at anchor. The rate GWSs were sighted also rose in winter, and was associated with maximum running air temperature, the arrival time, seal activity, and the wind direction and speed. These findings indicate that environmental conditions directly impacted upon the sighting frequency, but also influenced habitat selection on a fine spatial scale. This study emphasises that collaboration with ecotourism companies could represent a valuable, inexpensive alternative for scientific data collection, as long as powerful statistical methods are used, the influence of human activity is considered and results are interpreted with consideration of the data collection methodology.\u003c/p\u003e","manuscriptTitle":"Citizen Science Data Reveals Environmental Influence on Sightings of Great White Sharks in Mossel Bay, South Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-21 18:27:53","doi":"10.21203/rs.3.rs-2259544/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28414f96-1695-46c7-bfee-ef2cacc95d5b","owner":[],"postedDate":"November 21st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":17044214,"name":"Biological sciences/Ecology"},{"id":17044215,"name":"Biological sciences/Ecology/Behavioural ecology"},{"id":17044216,"name":"Biological sciences/Ecology/Conservation"},{"id":17044217,"name":"Biological sciences/Ecology/Ecological modelling"}],"tags":[],"updatedAt":"2022-11-23T08:29:33+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-21 18:27:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2259544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2259544","identity":"rs-2259544","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}