Alligators & Immortality

28 foot crocodile caught in Australia back in 1957

Negligible Senescence

Alligators and crocodiles are some of the few species that exhibit negligible senescence — a term used to describe species that do not biologically age. Normal species show signs of decreased functionality with age, such as slowed reproduction, rising death rates, and decreased fitness.

Slowed Reproduction Rates in Chickens

The above graph shows the decline of a chicken’s laying cycle. After 10 years of laying eggs, a chicken is only producing 20% of their original amount. Meanwhile, there have been reports of female Yawkey crocodiles laying large quantities of fertile eggs at the age of 70 years old.

Rather than dying from old age, species with negligible senescence will die from other causes, such as illness or predators. Alligators and crocodiles will continue to grow for their entire lives, and after a certain point can no longer feed themselves to keep up with their growing metabolisms. Thus, their bodies are no longer energy efficient and they pass away due to starvation.

By combining data from multiple CSV files, I was able to create a histogram of all alligator sizes from the years 2000 to 2018. The histogram follows a normal distribution, which concludes that most alligators reach around 8 feet before their bodies are less likely to become sustainable at greater sizes.

Pearson’s Coefficient of Skewness

While the alligator length graph follows a normal distribution, it is difficult to tell at a glance whether the data is skewed to the left or the right, and how skewed it is. Finding the skewness would tell us whether there are more alligators deviating from the mean or mode on the right side (meaning alligators are longer on average), or the left side (meaning alligators are shorter on average).

Pearson’s Coefficient of Skewness is calculated by subtracting the mode from the mean, and dividing by the standard deviation. There are two implications of the value we obtain: the direction and the size of the skew.

If the mode is greater than the mean, the skewness will be negative, resulting in a negative skew. If the mode is less than the mean, the skewness will be positive, resulting in a positive skew.

We also divide by the standard deviation of the data set. If the standard deviation is large, meaning the values are more spread out from the mean, the skewness will be smaller. If the standard deviation is small, the skewness will be larger.

Stat Data

mean = 99.353896
mode = 96
standard deviation = 21.343883

Using this formula, I got 0.15713616870932087. We can conclude there is a slight right skew. Due to the fact that my values were already spread widely apart, there was not a huge skew to my data.