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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

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 [

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 [

To determine the bite trends for the top ten countries, we used bite rates instead of bite counts [

We decided to employ the software

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 [

Since the above-mentioned analysis did not reveal any joinpoints for any country, a fixed slope linear model

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:

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

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

Annual average shark bites and slope of the corresponding bite rate regression model for the top ten countries between 2000 and 2016.

SD | % | b | ||
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USA | 47.5 | 9.7 | 60.4 | -0.00125 |

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Australia | 15.9 | 5.4 | 20.2 | 0.02050 |

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South Africa | 5.9 | 2.7 | 7.5 | -0.00274 |

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Venezuela | 2 | 2.3 | 2.5 | -0.00824 |

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New Zealand | 2 | 1.4 | 2.5 | -0.02043 |

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Bahamas | 1.7 | 1.1 | 2.2 | 0.31183 |

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Réunion | 1.4 | 1.9 | 1.8 | 0.15955 |

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New Caledonia | 1 | 1.3 | 1.3 | 0.05125 |

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Egypt | 0.7 | 1.3 | 0.9 | 0.00024 |

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Mexico | 0.6 | 1.2 | 0.7 | 0.00025 |

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

Creating a global model for the bite rates between 2000 and 2016 revealed a negative trend (Figure

Global model for bite rates between 2000 and 2016.

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.

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 [

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 [

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.

When it comes to incidents between humans and predatory animals, sharks rank lowest with an average of less than a hundred bites per year [

The generally accepted assumption is that shark bites are increasing [

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 [

All datasets are ready to be sent, upon request.

The authors declare that they have no conflicts of interest.

We thank “ProWin pro nature” for financial contribution to the writing of this paper.