Sustaining biodiversity in fish populations is extremely
important to the quality of our aquatic environment. High genetic variation in
fish populations has the potential to protect them from environmental pressures
like climate change, pollutants and mass mortalities due to the spread of
diseases. According to research, the loss of biodiversity could also lead to
the decrease in the ocean’s ability to sustain water quality and resources (Worm
et al., 2006). This is why areas with high levels of biodiversity decrease are
being protected (MPA’s). MPA’s are Marine Protected Areas were human activities
like fishing is greatly reduced for conservation purposes.
Many professionals would come to a similar conclusion that
the protection of marine areas have a positive effect on fish populations and
biodiversity (Gubbay, 1995). This report studies the relationship between
MPA’s, number of threatened fish species and fish species richness using
quantitative data. This relationship is studied in order to find out if the
protection of marine areas has any significant effect on fish populations. Two hypotheses
are proposed, the first one suggests that the larger the MPA’s, the lower the
number of threatened fish species seen. The second one suggests that the larger
the MPA’s, the more species richness seen in the area.
Firstly, the data is uploaded into a programme called R, R
is a software environment most commonly used for statistical data analysis.
Once the programme is uploaded, the areas lacking MPA’s are filtered out
because we are only studying marine protected environments. Normal Q-Q plots are then made for the MPA’s,
number of threatened species and species richness, this is done to view the
normality of the data on a graph. A Sharpio-Wilk test for normality is then
used to determine if the data is modelled by a normal distribution. This test
is repeated with logs of data in order to improve the normality. The
Sharpio-Wilk test null hypothesis suggests that the data set is normally
distributed. The normality or abnormality of the data set used here determines
what tests should be taken out next. The data is plotted on scatter graphs to
show the relationship between MPA’s and the number of threatened fish species
and MPA’s and species richness. Linear models are used to show trend lines on
the graphs. The results generated allows the visual study of the effects of
MPA’s on fish populations globally, therefore, allowing the acceptance or
rejection of the hypothesis proposed.
Figure 1 shows the normal Q-Q plot for the Marine Protected
Areas. The results of the Sharpio-Wilk test suggests that the data does not
follow a normal distribution (p = 2.2e-16) this allows us to reject the null
hypothesis the sharpio test suggests. Figure 2 and 3 also shows similar trends
with p-values <0.05 (p = 5.387e-12 <0.05, 1.83e-13 <0.05), meaning they do not follow a normal distribution, allowing the rejection of the null hypothesis. Figure 4 shows that the normal Q-Q plot for the Marine Protected Areas data remained abnormal even after being logged (p = 0.002906). On the other hand, the dataset for both the number of threatened fish species and the species richness became normal after being logged (p = 0.6289, p = 0.37). This can be seen in figure 5 and 6. Figure 7 shows the relationship between Marine Protected Areas and number of threatened fish species on a scatter graph. The trend line shows a significant positive correlation (F-statistics (1,120): 9.91, p-value = 0.002074). Studying figure 8, similar trends can be seen. The relationship between Marine Protected Areas and fish species richness also has a significant positive correlation F-statistics (1,120): 23.62, p-value: 3.582e-06). These results suggests that MPA's have a positive effect on the fish species richness.