Genetic Biodiversity Analysis

PCR was used to amplify the 16S gene, and then we also sent out our DNA to Rush University for them to use MiniSeq for us to compare our values to make sure we were accurate. Then we made DNA agarose, diluted soil samples, and pipetted them into the wells of the DNA agarose. These plates then underwent gel electrophoresis to confirm the results of the PCR reaction. We then used the results in Nephele to analyze our data and make our graphs for our 4d figure above. 

Figure 4 Evidence: From our Figure 4 graphs, we can conclude that there is no statistically significant difference in the average values of richness, Shannon diversity index, evenness, and phyla percentages. From figure 4A, soil richness, we can see that the averages are very similar at 155.5 for the native condition, and 138.25 for the non-native condition which is only a difference of 17.23%. The standard deviations for 4A were also very similar at 26.54 for the native condition and 12.15 for the non-native condition which is a difference of roughly 14.5. For figure 4B, Shannon diversity, the averages are very close with native being 4.62 and non-native being 4.47 which is only about a 0.15% difference between the two. It is also evident that the standard deviation in graph 4B has a small difference of about 0.028 with native being 0.139 and non-native being 0.111. For figure 4C, evenness, the averages again have a very small difference between the native and non-native conditions, with the native being 0.917 and the non-native being 0.908 which is a difference of only 0.09%. The standard deviation for our conditions in figure 4C is also very similar, being 0.0049 for our native condition and 0.0075 for our non-native condition which is a difference of 0.0026 which would be pretty insignificant. For all three of these data sets the p-values from unpaired t-tests, assuming unequal variance, were greater than the threshold of 0.05, holding at a value of 0.2997 for richness, 0.154 for Shannon diversity, and 0.095 for evenness. This means that there is no significant statistical difference in the data for the genetic biodiversity of the native and non-native plants. Similarly, when looking at the phyla graph, there are many phyla bacterial numbers that are equal in both sample sets. For example, from the native soil samples, the presence of Proteobacteria makes up between 26.657 – 29.670 %, while in the non-native soil sample it makes up between 26.995 – 28.127%. These results are similar across the board for the phyla groups. It can also be seen that in general our native soil samples have slightly more variation in the larger groups of phyla, but in the end are extremely comparable to the non-native groups. This once again proves that there is no statistically significant data from our experiments when it comes to genetic biodiversity.  

Figure 4 Conclusion: Based on the results for our graphs above we can conclude that there is no statistical significance between the native condition and the non-native condition. This can be seen by the fact that none of our graphs have any extreme or unique values, and that is because all the averages (4A, 4B, 4C) and the percentages (4D) are all relatively similar. Figure 4C has the smallest difference between the averages of the two samples at 0.09%, and 4B with a difference of 0.15%. The largest was 4A with a difference in value of 17.23%. Since all of these values being extremely low, the data is extremely similar. The difference between soil samples is not significant. Similarly, in regard to the standard deviation values, in figure 4A the native and non-native values are at 12.15 and 14.5 respectively, 4B at 0.139 for the native condition and 0.111 for the non-native condition, and 4C at 0.0049 for the native condition and 0.0075 for the non-native condition. With all these values again being extremely close together, the small change doesn’t make enough of a difference when it comes to the genetic soil biodiversity to be noteworthy. All of this makes sense with our calculated p-values all being above the threshold of 0.05, at 0.2997 for richness, 0.154 for Shannon diversity, and 0.095 for evenness, meaning there is no statistically significant difference between the native and non-native conditions. This trend continues when looking at figure 4D, there is no outlying percentages of bacteria present in any of the samples meaning that all the data is relatively similar. With all our data following the same pattern we can confidently conclude that there is no statistically significant data difference between the genetic biodiversity of our native and non-native soil samples.  

Figure 4 Explanation: All of this makes sense with what we have observed in the garden and with what we know about plants. When at the garden, there wasn’t a visible difference between the health of the plants, they both appeared to be growing healthy and producing enough produce/flowers. When we picked up the soil, we felt that the native plant had dryer and more dusty soil, while the non-native plant had moister and softer soil. We originally thought that this difference would lead to significant differences in biodiversity, but our data shows otherwise. With what we know about plants and through research, it makes sense for our native and non-native samples to give us the results that they did. From an article by B.L. Anacker et al., we learn that the amount of richness in the soil increases and decreases depending on what the plants need, but that most of the time, a high richness means the soil will be more diverse and be able to do more, like hold more water and store more nutrients (2021). For our data, this shows us that because we don’t have extremely different values in richness, our biodiversity levels should be relatively the same. In the article “Diversity Indices: Shannon’s H and E” we learn that Shannon diversity index shows us the abundance of bacteria and how many or how few different species of bacteria are in the soil you sample. (Beals et al., 2000) When comparing this to our data, there are very similar amounts of bacterial species in both of our native and non-native samples, meaning that the variance in diversity between the two samples is low. From our research we also found that higher diversity was majorly contributed by a higher evenness in the community, which for us means that because we had similar evenness values, our biodiversity will not change between our conditions (Tan, 2021). And finally, in an article called talking about soil diversity and soil composition, mentions that the biodiversity and community composition are the foundation of an ecosystem, for us meaning that because our soil samples have very similar levels of the same phyla bacteria present in the soil samples, our biodiversity will not change. (Wagg, 2014). With all of what we know about plants, what we have researched, and what we have observed, we can conclude that our data proves there will be no change in the genetics of the biodiversity between the native condition and the non-native condition. 

Beta Biodiversity and Rare Fraction Curves: Graphs that are not shown above include our Beta Biodiversity and Rare Fraction Curves. Our Beta Biodiversity graph did not provide any valid data, meaning nothing could even be concluded from that graph therefore we did not use it to determine our data results. And our Rare Fraction Curves are slightly off from what we would want to see to get confirm our data.(N=4). We have one native group as an outlier from the rest of the group being above the rest of the samples as well as longer than the rest of the samples and one outlier on the lower and shorter side being a non-native sample. All of the other samples both native and non-native end in length in about the same spot and are about the same height. The only other difference is that the Non-native 4 lines are split with two above the three native sample lines and two below the native sample lines indicating a difference between the two. The Rare fraction curve is important because it is a “statement of control” (Adler, J. 2024) but it also shows how many bacterial groups are observed (y-axis) and how long the sequences were when looking at the DNA (x-axis). Because our rare fraction curves overlap slightly, this helps us to conclude the overall biodiversity of our samples which will tell us the integrity of our conditions in our genetic experiments.