Genetic Biodiversity

Figure 4: Genetic Biodiversity 

Figure 4 Legend:

The figures above describe Genetic Biodiversity using the differences in the Shannon index, evenness, and richness (Chao1) values in soil with and without pesticides. The data was obtained by genetic sequencing of DNA samples found in soil with pesticide and without pesticides obtained by using PCR, and using the Nephlene software to determine the values for each sample of data. The averages for both the pesticide and no pesticide samples are shown for the Shannon Index, Evenness, and Chao 1, as well as the Rarefaction curves and the relative frequencies of the phylums found in each sample. The data used to calculate these graphs each have a sample size of 8. An unpaired t-test assuming unequal variance was conducted on each of the Shannon index, evenness, and richness. However, all of our data is statistically insignificant due to a biased rarefaction curve. 

Figure 4 Method: 

The PCR method was done to each soil sample and isolated the 16s gene. By isolating and amplifying the 16s Gene we utilized gel electrophoresis and Nephele to analyze the results. This gene was analyzed because the 16s gene is a polymorphic gene within species of bacteria that indicates unique species. 

Figure 4 Evidence:

Regarding evenness, the average for the pesticide condition was 0.92, whereas the average for the no pesticide was 0.91. There was a visible difference in this data. The standard deviation for the evenness for the pesticide condition was 0.010, whereas for the no pesticide it was insignificant. Regarding richness, the average for the pesticide condition was 167.50, whereas for the no pesticide condition the average was 130.50. There was a large difference in average between the two. The standard deviation for the richness for the pesticide condition was 14.60, whereas for the no pesticide condition it was 11.40. In regards to the Shannon index, the average for the pesticide condition was 4.75, whereas for the no pesticide condition it was 4.45. There was essentially no difference in the data. The standard deviation in regards to the Shannon Index was 0.09 for the pesticide condition, and 0.08 for the no pesticide condition. The T-tests for the richness, evenness, and Shannon Index all produced results under the critical value threshold of 0.05, therefore they were all made out to be statistically significant. Despite this, because the rarefaction curves did not have any overlap, we can make the conclusion that the two data sets could not have been statistically independent  from one another. The Shannon index and richness values for the  pesticide sample were higher as the bacterial DNA was more sought after through testing. 

Figure 4 Conclusion: 

The data collected for the evenness, richness, and Shannon diversity values in soil with and without pesticides all could not be used to support the claim that pesticides have a significant impact on moisture content. The values in the pesticide and no pesticides groups were not unique and there is no statistically significant difference in evenness, richness, or Shannon between the two treatments. This is not shown as the error bars in each graph are not overlapping, yet we know from external data analysis that due to a biased data sample the two treatments do not have a statistically significant difference. The lack of statistically significant differences is shown by the “nd” marking at the top of each graph.  The final figure of relative utilization efficiency shows the composition of both samples is relatively similar.

Figure 4 Explanation:

Since there is no statistical significance between the richness, evenness, and Shannon Index between soil with or without pesticide, it reveals that the pesticide does not have a significant impact on all of these values. A possible mechanism for this observation could be the resilience or adaptability of soil microbiomes. According to recent studies, certain soil microbes may be able to tolerate or adapt to low pesticide levels, maintaining overall biodiversity despite external chemical influences (Geisen et al). This adaptability may explain the similar biodiversity indexes between pesticide and non-pesticide samples, as microbial communities can persist without drastic shifts in richness or diversity under moderate stress (Zhalnina et al). Additionally, the lack of significant variation in Evenness and Shannon diversity proposes that soil microbial populations retain similar functional compositions across both conditions. Pesticides, while potentially harmful, may not disrupt all soil functions at low exposure levels (Chen et al). However, the observed variance in Richness values, although not statistically significant, could indicate potential impacts on specific microbial subgroups, though we would have to complete further investigation with a larger sample size and improved rarefaction curve to confirm this. In each graph for averages the data looks to be statistically significant. Despite this an “nd” was placed above each graph due to our acknowledgment of the overall bias in the data. We know the data is biased because of the rarefaction curves, since the curves for each treatment group are not overlapping with one another While the data does not support a significant impact of pesticides on soil biodiversity as measured by CHAO1, Evenness, and Shannon Index values, the consistent Richness values imply that microbial community resilience may buffer against pesticide effects at this concentration. This could lead us to exploring other ways pesticide could affect the soil as our overarching goal is to figure out if there is a difference in the microbe diversity of soil with or without pesticide.