Research Article | Open Access
Against Common Assumptions, the World’s Shark Bite Rates Are Decreasing
The trends of the world’s top ten countries relating to shark bite rates, defined as the ratio of the annual number of shark bites of a country and its resident human population, were analyzed for the period 2000-2016. A nonparametric permutation-based methodology was used to determine whether the slope of the regression line of a country remained constant over time or whether so-called joinpoints, a core feature of the statistical software Joinpoint, occurred, at which the slope changes and a better fit could be obtained by applying a straight-line model. More than 90% of all shark bite incidents occurred along the US, Australia, South Africa, and New Zealand coasts. Since three of these coasts showed a negative trend when transformed into bite rates, the overall global trend is decreasing. Potential reasons for this decrease in shark bite rates—besides an increase in the world’s human population, resulting in more beach going people, and a decrease of sharks due to overfishing—are discussed.
Sharks are at the top of most people’s minds when entering the sea, for seemingly good reasons, considering the still prevalent shark hype stemming from news outlets around the world [1–3]. But the media is not the only source of erroneous shark bite lore . Some of the experts, too, got carried away in the past when elaborating on this phenomenon [5–8]. The overall consensus is that shark bites are on the rise despite that the yearly bite counts range between eighty to a hundred incidents [9–12]. Considering that sharks still represent the most abundant top predators weighing over 50 kg on our planet and that millions of people swim in the seas each day, this yearly bite count remains extremely low when compared to other predators commonly involved in human incidents [13–16]. However, an even more surprising fact about bite count predictions is that the beach visiting populations directly affecting the bite numbers are regularly excluded from predicting long-term tendencies . Such an approach is fallacious because the number of people bitten by sharks directly corresponds to the number of people entering the sea. Hence, trends and predictions must include the respective human population. To that extent, we introduced bite rates [13–16]: the ratio of annually reported shark bites for a given region to the annual estimated beach attendance for that region. The assumption is made that an equal number of these people, at some point, will enter the sea. Since beach attendance populations are not always determined, any population number that can be recognized as akin to the number of people attending a beach can be used as a valid substitute. Therefore, the regional, state, or country populace can be utilized as such a proxy .
By creating regression models—using the software package Joinpoint—to analyze the bite rate trends for the top ten countries from 2000 to 2016, we can determine if statistically significant changes in these trends have occurred. A model for the global bite rates was also created to determine the accuracy of the chosen method by predicting the bite rates for 2018 and comparing this number with the actual incident number of that year.
The number of bites for 2000-2016 for the world’s top ten countries was drawn from the “Global Shark Attack File” incident dataset of the Shark Research Institute , which lists every encounter that ends in a human injury or damages a surfboard, boat, and so on. Due to the vast variety of what is labeled an “attack,” some of these incidents reflect biased occurrences, thus falsifying the trends, and so they were excluded from this study. Such incidents entailed spearfishing, surf and shark fishing, or shark feeding. Furthermore, all bumps into persons that did not render a wound or lacked any physical evidence of teeth marks on surfboards, kayaks, or boats were also excluded. Additionally, throughout the period, there were also a series of questionable deaths that were likely drowning incidents with later scavenging by sharks [18–20].
2.1. Replacing Bite Counts with Bite Rates
To determine the bite trends for the top ten countries, we used bite rates instead of bite counts [13–15]. A bite rate is defined as the ratio of the annual bite count for a specified region to the corresponding beach going population or any related proxy [13–15]. Here, we used the population numbers of the respective countries to determine the rates.
2.2. Incident Modeling through Time
We decided to employ the software Joinpoint 184.108.40.206 because it uses a modern nonparametric permutation-based methodology to test whether the slope of a regression line remains constant over time or whether there are so-called joinpoints at which the slope changes and a straight-line model then obtains a better fit. Such a regression test is used by the National Cancer Institute (NCI) to monitor cancer rates over time. We are using this modern epidemiology-based approach to benefit from the latest developments in this field.
In order to decide whether it is better to use a fixed slope linear model, we used the permutation method in which we randomly permuted residuals from the straight-line model, meaning we shuffled around the distances between the regression line and each observation . We then calculated the test statistic T for the permutation dataset and measured how much evidence the data provides against the null hypothesis by estimating the proportion of the permutation datasets. The corresponding T values are at least as extreme as the one we observed with the original dataset. If the tests were significant, then at least one joinpoint existed at which the true slope has changed. Joinpoints can range from none to several within a single regression.
2.3. Regression Models Used
Since the above-mentioned analysis did not reveal any joinpoints for any country, a fixed slope linear model for the different countries was used where stands for the bite counts in year , stands for the year , a stands for the regression intercept, and b stands for the slope of the regression line.
Although this simple linear regression model was sufficient for the individual countries, it was not adequate to measure the global trend, including predicting the number of bites for 2018. While individual countries show some regularity when it comes to bites, global bites rather fluctuate due to the occasional freak incidents in countries where bites are normally rare or even previously nonexistent. This variation made the simple linear regression model insufficient, and a better fit was needed. The best outcome was reached by transforming the bite counts to their natural log, and dividing these values by the respective population counts. Thus, a new response variable was proposed, following the new model:where ln stands for the natural log function and a and b stand for the (new) intercept and slope, respectively.
In order to predict the bite count for 2018, we obtained the predicted value of the natural log of the bite counts, divided by the matching population count, and retransformed the said value to the original unit for bite counts. For the prediction to obtain the intercept and slope, we used the regression procedure proc reg from the Statistical Analysis System (SAS).
During the remainder of the paper, we will refer to the ten countries with the most shark bites as merely the “top ten countries.”
The project aimed to determine if the shark bite numbers for the top ten countries were on the increase or not, and if the individual tendencies were linear or if joinpoints existed. In addition, a global model was also created and its accuracy tested by predicting the number of bites for 2018 and comparing that prediction with the actual number for that year.
Between 2000 and 2016, more than 80% of all shark bites occurred along the US and Australian shorelines, whereas the US, including Hawaii, had nearly three times as many bites as Australia for this period (Table 1). When adding South Africa and New Zealand to the top two countries, these top four countries record more than 90% of the world’s shark bites (Table 1).
The top four countries lacked joinpoints, as did the remaining six countries; thus, linear regressions models were considered. However, as already mentioned, using simple bite counts to determine trends would be incorrect. Hence, the bite counts were transformed into bite rates and then converted into the natural log. Regression models for three of the four top countries showed a negative “b” value, hence a negative slope (Table 1).
Creating a global model for the bite rates between 2000 and 2016 revealed a negative trend (Figure 1). Using this model to predict the number of incidents for 2018 revealed 88.3 incidents with a 95% interval ranging from 76.2 to 102.9 incidents. The number of verified cases for 2018 was 82.
The global shark bite rates are decreasing. This trend is most likely caused by annually more people entering the water while the density of the incident-prone shark species decreases at the same time. Still, the number of people entering the water could be influenced by several factors, while the same is true for the sharks. In the following, several of these influencing factors are considered and discussed.
4.1. Factors Determining the Presented Trend Models
The very low number of shark bites each year makes it easily understood why there is a fluctuation of bites over time. This indicates that the annual bite rates likely depend more on national circumstances than global effects, besides the persistent overfishing of sharks. For example, a country may experience an increase in bites due to (a) more favorable meteorological circumstances throughout the year bringing more people to the beaches; (b) increased buying power allowing for more beach vacations; (c) the political stability of a country; or other factors. Of course, the reverse may also be true for another country throughout the same period; thus, the overall global bite rate trend is a result of all these influences for all the countries that report incidents with sharks.
Of more than 500 species of sharks, only about a dozen species are commonly involved in incidents. So even a commercial fishing loss of at least 70 million sharks each year [22–24] may not affect the annual bite rates should the incident-species not being targeted. However, most of them do indeed show up in fishery statistics [25, 26]. This situation then raises the question of why the annual bite counts still range in the same bracket throughout the examined period . One possibility is that not all of these species are harvested with the same intensity since some of them live closer to shore, areas which are commonly excluded from commercial shark fisheries. Beyond the well-known tiger shark, Galeocerdo cuvier, and white shark, Carcharodon carcharias, it is the members of the genus Carcharhinus that predominantly cause incidents, such as bull sharks, C. leucas, blacktips, C. limbatus, spinners, C. brevipinna, or silky sharks, C. falciformis. Except for oceanic whitetips, C. longimanus, and silkies, many of those Carcharhinus species hardly ever venture into deeper waters, thus limiting their exposure to commercial fishing. Together with their shore-oriented distribution, their nursery grounds are also located along coastlines, and, should anthropological destruction remain low, they make shores their prime habitats. However, destruction of shorelines due to eutrophication [27, 28], dredging [29, 30], or even algal blooms [31, 32] is likely to reduce food fish for resident sharks, forcing those sharks to move into deeper water, away from potential areas where humans would be encountered. Deeper waters could also have a reverse effect, as observed, for example, for the island state of Réunion. There, the topographical features limit the involved sharks to being part of resident populations, so largely eliminating transient sharks along those shorelines, making the resident sharks more prominent year-round [33, 34]. This situation around Réunion indicates that the two main species—the tiger shark and the bull shark—are not just part of resident populations; they also seem to be more shore-oriented, again, due to the deeper waters surrounding Réunion likely providing less food for these sharks. Although shark bycatch around Réunion exists , neither the most often caught blue sharks, Prionace glauca, nor oceanic whitetips cause incidents around the island.
Réunion represents a prime area for studying incident rates in more detail, even more so when bite rates are factored against the length of this country’s shoreline . Therefore, even though 90% of all yearly bites occur in the US, Australia, South Africa, and New Zealand, thereby influencing the global trend the most, a ratio between bite rates and length of the shoreline may put Réunion in a completely different position against those other top countries.
Although the drop in global shark bite rates seems to be a simple issue between an increase of world population against the overfishing of sharks, all the factors mentioned above contribute to the outcome of the global bite rates. Thus, it is imperative to examine each of these influences in more detail to determine which one may have the most effect on the presented outcome.
4.2. Do Shark Incident Trends Matter?
When it comes to incidents between humans and predatory animals, sharks rank lowest with an average of less than a hundred bites per year . Considering that sharks are abundantly present around the millions of people entering the sea every day, these bite counts are extremely low [13–15]. Still, these relatively few incidents are trumpeted by the press far more than those by any other animal. Such prejudice against sharks greatly exaggerates their actual threat to humans [3, 37, 38]. It is paramount for the sake of sharks that the general public finally understands that these animals do not present any grave statistical danger . As a result of this unjustified fear, sharks are denied the protection they so desperately need [39–41]. One can only hope that the global decrease in bite rates will put people’s minds at ease and ameliorate this unfair prejudice against sharks [42–44]. Changing the public’s perception of sharks is crucial for both them and for the wellbeing of our oceans. Sharks still represent the most abundant top predators over 50 kg in the marine realm; as such, they serve an essential function in the ecosystem [45–47]. It is no exaggeration to contend that any further reduction in their populations could trigger an irreversible imbalance of the oceans’ food chains, leading to a catastrophic collapse of the entire marine realm itself [48, 49].
4.3. Prediction of the Bite Counts for 2018
The generally accepted assumption is that shark bites are increasing [10–12]. The foundation for the said assumption is based on the simple comparison of yearly bite counts. Such an approach is erroneous for two reasons: (1) not every incident between a shark and a human being counts, and (2) bitten persons are always part of a pool of people that offers the common denominator for different areas [13–16]. Ignoring these two focal points, predictions cannot be made. The application of including proxies of human populations in relation to actual bites, as well as eliminating incidents that have been provoked by, for example, shark fishing or shark feeding, transforms these bites into the more meaningful bite rates. These rates are decreasing on a global level. As mentioned above, the simplest scenario that could lead to such a decrease is the continuous global overfishing of sharks, combined with the increase in world population. Since neither of the two will stop, the posited prediction remains: bite rates will keep dropping in the future.
Our regression model predicted 88.3 incidents for 2018 with a 95% confidence interval ranging from 76.2 to 102.9 incidents. This confidence interval puts the actual number of 82 right within the predicted range. As long as the same proxies for the human population are used for all involved countries and what qualifies for a legitimate incident, our prediction model offers a robust outlook for the years to come.
Using proper media channels, combined with mitigation programs [9, 50] to reduce the already low numbers of shark bites, may be the beginning of finally reversing the erroneous misconception that sharks pose a high-risk danger to humans.
All datasets are ready to be sent, upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
We thank “ProWin pro nature” for financial contribution to the writing of this paper.
- E. Ritter, K. Lutz, and M. Levine, “When humans and sharks meet,” in New Developments in the Psychology of Motivation, F. Olsson, Ed., pp. 45–53, Nova Biomedical Books, New York, NY, USA, 2008.
- J. J. Meeuwig and L. C. Ferreira, “Moving beyond lethal programs for shark hazard mitigation,” Animal Conservation, vol. 17, no. 4, pp. 297-298, 2014.
- B. A. Muter, M. L. Gore, K. S. Gledhill, C. Lamont, and C. Huveneers, “Australian and US news media portrayal of sharks and their conservation,” Conservation Biology, vol. 27, no. 1, pp. 187–196, 2013.
- C. McCagh, J. Sneddon, and D. Blache, “Killing sharks: the media’s role in public and political response to fatal human–shark interactions,” Marine Policy, vol. 62, pp. 271–278, 2015.
- D. H. Davies, “The shark problem,” South African Journal of Science, vol. 58, pp. 253–258, 1962.
- H. D. Baldridge, Shark Attack, Berkley Medallion Books, New York, NY, USA, 1975.
- P. W. Gilbert, “Sharks in perspective,” in Sharks, Skates and Rays, P. W. Gilbert, Ed., pp. 1–10, Mote Marine Laboratory, Sarasota, FL, USA, 1978.
- D. G. E. Caldicott, R. Mahajani, and M. Kuhn, “The anatomy of a shark attack: a case report and review of the literature,” Injury, vol. 32, no. 6, pp. 445–453, 2001.
- F. H. V. Hazin and A. S. Afonso, “A green strategy for shark attack mitigation off Recife, Brazil,” Animal Conservation, vol. 17, no. 4, pp. 287–296, 2014.
- J. G. West, “Changing patterns of shark attacks in Australian waters,” Marine & Freshwater Research, vol. 62, no. 6, pp. 744–754, 2011.
- R. Crossley, C. M. Collins, S. G. Sutton, and C. Huveneers, “Public perception and understanding of shark attack mitigation measures in Australia,” Human Dimensions of Wildlife, vol. 19, no. 2, pp. 154–165, 2014.
- J. A. Ricci, C. R. Vargas, D. Singhal, and B. T. Lee, “Shark attack-related injuries: epidemiology and implications for plastic surgeons,” Journal of Plastic, Reconstructive & Aesthetic Surgery, vol. 69, no. 1, pp. 108–114, 2016.
- R. Amin, E. Ritter, and P. Kennedy, “A geospatial analysis of shark attack rates for the east coast of Florida: 1994-2009,” Marine and Freshwater Behaviour and Physiology, vol. 45, no. 3, pp. 185–198, 2012.
- R. Amin, E. K. Ritter, and L. Cossette, “A geospatial analysis of shark attack rates for the coast of California: 1994–2010,” Journal of Environment and Ecology, vol. 3, no. 1, pp. 246–255, 2011.
- R. Amin, E. Ritter, and A. Wetzel, “An estimation of shark-attack risk for the North and South Carolina coastline,” Journal of Coastal Research, vol. 31, no. 5, pp. 1253–1259, 2015.
- R. W. Amin, E. K. Ritter, and B. A. Bonell, “Shark bite rates along the US Gulf coast: a first investigation,” Environmental Sciences Europe, vol. 6, no. 1, pp. 1–12, 2018.
- GSAF, “Global shark attack file/shark research institute, incident log,” http://www.sharkattackfile.net, 2018.
- Y. Ihama, K. Ninomiya, M. Noguchi, C. Fuke, and T. Miyazaki, “Characteristic features of injuries due to shark attacks: a review of 12 cases,” Legal Medicine, vol. 11, no. 5, pp. 219–225, 2009.
- T. Hayashi, E. Higo, H. Orito, K. Ago, and M. Ogata, “Postmortem wounds caused by cookie-cutter sharks (Isistius species): an autopsy case of a drowning victim,” Forensic Science, Medicine and Pathology, vol. 11, no. 1, pp. 119–121, 2015.
- M. K. Stock, A. P. Winburn, and G. H. Burgess, “Skeletal indicators of shark feeding on human remains: evidence from florida forensic anthropology cases,” Journal of Forensic Sciences, vol. 62, no. 6, pp. 1647–1654, 2017.
- H.-J. Kim, M. P. Fay, E. J. Feuer, and D. N. Midthune, “Permutation tests for joinpoint regression with applications to cancer rates,” Statistics in Medicine, vol. 19, no. 3, pp. 335–351, 2000.
- K. N. Topelko and P. Dearden, “The shark watching industry and its potential contribution to shark conservation,” Journal of Ecotourism, vol. 4, no. 2, pp. 108–128, 2005.
- J. Dobson, “Sharks, wildlife tourism, and state regulation,” Tourism in Marine Environments, vol. 3, no. 1, pp. 15–23, 2006.
- B. Worm, B. Davis, L. Kettemer et al., “Global catches, exploitation rates, and rebuilding options for sharks,” Marine Policy, vol. 40, no. 1, pp. 194–204, 2013.
- J. K. Baum, R. A. Myers, D. G. Kehler, B. Worm, S. J. Harley, and P. A. Doherty, “Collapse and conservation of shark populations in the Northwest Atlantic,” Science, vol. 299, no. 5605, pp. 389–392, 2003.
- J. K. Baum and R. A. Myers, “Shifting baselines and the decline of pelagic sharks in the Gulf of Mexico,” Ecology Letters, vol. 7, no. 2, pp. 135–145, 2004.
- S. W. Nixon, “Coastal marine eutrophication: a definition, social causes, and future concerns,” Ophelia, vol. 41, no. 1, pp. 199–219, 2012.
- M. R. Heithaus, B. K. Delius, A. J. Wirsing, and M. M. Dunphy-Daly, “Physical factors influencing the distribution of a top predator in a subtropical oligotrophic estuary,” Limnology and Oceanography, vol. 54, no. 2, pp. 472–482, 2009.
- R. F. Van Dolah, D. R. Calder, and D. M. Knott, “Effects of dredging and open-water disposal on benthic macroinvertebrates in a South Carolina estuary,” Estuaries and Coasts, vol. 7, no. 1, pp. 28–37, 1984.
- D. E. Jennings, S. H. Gruber, B. R. Franks, S. T. Kessel, and A. L. Robertson, “Effects of large-scale anthropogenic development on juvenile lemon shark (Negaprion brevirostris) populations of Bimini, Bahamas,” Environmental Biology of Fishes, vol. 83, no. 4, pp. 369–377, 2008.
- D. M. Anderson, P. M. Glibert, and J. M. Burkholder, “Harmful algal blooms and eutrophication: nutrient sources, composition, and consequences,” Estuaries and Coasts, vol. 25, no. 4, pp. 704–726, 2002.
- P. M. Yates, M. R. Heupel, A. J. Tobin, and C. A. Simpfendorfer, “Diversity in young shark habitats provides the potential for portfolio effects,” Marine Ecology Progress Series, vol. 458, pp. 269–281, 2012.
- A. Blaison, S. Jaquemet, D. Guyomard et al., “Seasonal variability of bull and tiger shark presence on the west coast of Reunion Island, western Indian Ocean,” African Journal of Marine Science, vol. 37, no. 2, pp. 199–208, 2015.
- F. Taglioni, S. Guiltat, M. Teurlai, M. Delsaut, and D. Payet, “A spatial and environmental analysis of shark attacks on Reunion Island (1980–2017),” Marine Policy, vol. 101, pp. 51–62, 2019.
- F. Poisson, “Catch, bycatch of sharks, and incidental catch of sea turtles in the reunion-based longline swordfish fishery (Southwest Indian Ocean) between 1997 and 2000,” in Global Change: Mankind-Marine Environment Interactions, H.-J. Ceccaldi, I. Dekeyser, M. Girault, and G. Stora, Eds., pp. 163–165, Springer, Dordrecht, Netherlands, 2010.
- A. Lemahieu, A. Blaison, E. Crochelet, G. Bertrand, G. Pennober, and M. Soria, “Human-shark interactions: the case study of reunion island in the south-west indian ocean,” Ocean & Coastal Management, vol. 136, pp. 73–82, 2017.
- J. Wiley Driscoll, “Attitudes toward animals: species ratings,” Society and Animals, vol. 3, no. 2, pp. 139–150, 1995.
- T. L. Thompson and J. J. Mintzes, “Cognitive structure and the affective domain: on knowing and feeling in biology,” International Journal of Science Education, vol. 24, no. 6, pp. 645–660, 2002.
- R. Philpott, “Why sharks may have nothing to fear more than fear itself: an analysis of the effect of human attitudes on the conservation of the great white shark,” Colorado Journal of International Environmental Law and Policy, vol. 13, p. 445, 2002.
- E. G. Ritchie and C. N. Johnson, “Predator interactions, mesopredator release and biodiversity conservation,” Ecology Letters, vol. 12, no. 9, pp. 982–998, 2009.
- C. A. Simpfendorfer, M. R. Heupel, W. T. White, and N. K. Dulvy, “The importance of research and public opinion to conservation management of sharks and rays: a synthesis,” Marine & Freshwater Research, vol. 62, no. 6, pp. 518–527, 2011.
- M.-F. Boissonneault, “Predator or scapegoat? the australian grey nurse shark through the public lens,” Australian Zoologist, vol. 35, no. 3, pp. 534–543, 2011.
- J. R. O’Bryhim and E. C. M. Parsons, “Increased knowledge about sharks increases public concern about their conservation,” Marine Policy, vol. 56, pp. 43–47, 2015.
- E. Ritter and R. Amin, “The importance of academic research in the field of shark-human interactions: a three-pronged approach to a better understanding of shark encounters,” in Chondrichthyes, L. F. da Silva Rodrigues Filho and J. B. de Luna Sales, Eds., pp. 63–80, InTech, Rijeka, Croatia, 2018.
- J. A. Estrada, A. N. Rice, M. E. Lutcavage, and G. B. Skomal, “Predicting trophic position in sharks of the north-west Atlantic Ocean using stable isotope analysis,” Journal of the Marine Biological Association of the United Kingdom, vol. 83, no. 6, pp. 1347–1350, 2003.
- F. Ferretti, B. Worm, G. L. Britten, M. R. Heithaus, and H. K. Lotze, “Patterns and ecosystem consequences of shark declines in the ocean,” Ecology Letters, vol. 13, no. 8, pp. 1055–1071, 2010.
- N. E. Hussey, S. F. J. Dudley, I. D. McCarthy, G. Cliff, and A. T. Fisk, “Stable isotope profiles of large marine predators: viable indicators of trophic position, diet, and movement in sharks?” Canadian Journal of Fisheries and Aquatic Sciences, vol. 68, no. 12, pp. 2029–2045, 2011.
- R. A. Myers, J. K. Baum, T. D. Shepherd, S. P. Powers, and C. H. Peterson, “Cascading effects of the loss of apex predatory sharks from a coastal ocean,” Science, vol. 315, no. 5820, pp. 1846–1850, 2007.
- J. K. Baum and B. Worm, “Cascading top-down effects of changing oceanic predator abundances,” Journal of Animal Ecology, vol. 78, no. 4, pp. 699–714, 2009.
- G. Cliff and S. F. J. Dudley, “Reducing the environmental impact of shark-control programs: a case study from KwaZulu-Natal, South Africa,” Marine & Freshwater Research, vol. 62, no. 6, pp. 700–709, 2011.
Copyright © 2019 Erich Ritter et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.