fishing & overfishing¶

As the human population grows, the rate that we harvest fish has also grown. In addition, the consumption of seafood per capita since the 1960's has more than doubled. This poses some benefits for human health and the environment, but a new problem comes into the picture: overfishing. Overfishing happens when fish are harvested faster than they naturally reproduce. At a certain degree of overfishing, fish species can become too underpopulated to come back to their normal population, and go extinct. Data science can provide helpful insights into predicting species and areas that are being overfished.

Articles w/ Relavent Information:

  • https://www.nationalgeographic.com/environment/article/critical-issues-overfishing

  • https://news.stanford.edu/2021/09/15/different-seafood-2050/#:~:text=While%20consumption%20per%20capita%20of,poultry%20has%20increased%20five%2Dfold.

In [1]:
import pandas as pd

df = pd.read_csv("fish_catches.csv")

#for i in range(10, 18):
#    df.drop(df.columns[[i]], axis = 1, inplace = True)
df.iloc[:, :10]
Out[1]:
Species Area Units Country 2014 2013 2012 2011 2010 2009
0 ANF 27 TLW BE 993.0 1633.0 1716.0 1279.0 1031.0 853.0
1 ANF 27.4 TLW BE 217.0 137.0 133.0 116.0 131.0 140.0
2 ANF 27.4.A TLW BE 0.0 0.0 0.0 0.0 0.0 0.0
3 ANF 27.4.B TLW BE 213.0 135.0 131.0 111.0 124.0 134.0
4 ANF 27.4.C TLW BE 4.0 2.0 2.0 6.0 7.0 6.0
... ... ... ... ... ... ... ... ... ... ...
49105 WHG 27.7.E TLW JE 1.0 0.0 0.0 0.0 3.0 0.0
49106 WRA 27 TLW JE 14.0 0.0 0.0 0.0 0.0 0.0
49107 WRA 27.7 TLW JE 14.0 0.0 0.0 0.0 0.0 0.0
49108 WRA 27.7.E TLW JE 14.0 0.0 0.0 0.0 0.0 0.0
49109 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

49110 rows × 10 columns

In [2]:
data_dict = {"Variable": ["Species", "Area", "Units", "Country", "2014", "2013", "2012", "2011", "2010", "2009"], "Data Type": ["str", "float", "str", "str", "int", "int", "int", "int", "int", "int"], "Description": ["fish species name", "Food and Agriculture Organization subarea number", "Tonnes Live Weight", "country name", "year", "year", "year", "year", "year", "year"]}




df_data_dict = pd.DataFrame(data_dict)

df_data_dict
Out[2]:
Variable Data Type Description
0 Species str fish species name
1 Area float Food and Agriculture Organization subarea number
2 Units str Tonnes Live Weight
3 Country str country name
4 2014 int year
5 2013 int year
6 2012 int year
7 2011 int year
8 2010 int year
9 2009 int year

I will look at species of fish that have the highest positive change in rate of being fished. Doing so allows me to discover which species of fish are being overfished compared to others. I can also discover which subareas of fish are being overfished.