Figure 4 

Figure 4D and Key

Figure 4 Legend 

Peach Tree vs Tomato Plant Soil DADA2 Shannon Values, Richness, Evenness, and Soil Microbiome Phyla Makeup 

The DADA2 data from both of our conditions showed consistent measurements between both samples. 4A) Figure 4A shows the laboratory results of the Shannon Index (S) values, including the average (mean) and standard deviations of each of our condition samples. Our Shannon values reflected very similar values with a p-value of 0.37 > 0.05. Our average Shannon value for our peach sample was 4.81 vs. 4.73 for the tomato. Our standard deviation for our peach Shannon values was 0.0723 vs. 1.60 for the tomato sample. 4B) Figure 4B shows the laboratory results of the Richness (H) values, including our average (mean) and standard deviations of each of our condition samples. Our Richness values were very similar with a p-value of 0.25 > 0.05. Our average Richness value for our peach sample was 181.5 vs. 164.5 for the tomato plant. Our standard deviation for our peach values was 14.5 vs. 21.9 for the tomato plant. 4C) Figure 4C shows the laboratory results of our Evenness (E) values, including the average (mean) and standard deviations of each of our condition samples. Our Evenness values were very similar with a p-value of 0.82 > 0.05. Our average Evenness value for our peach sample was 0.926 vs 0.927 for our tomato sample. The standard deviation of our peach evenness values was 0.00403 vs. 0.00928 for our tomato sample. 4D) Figure 4D displays the relative percentage of our phylum makeup of each of our condition samples. Each of our conditions had very similar soil microbiome phyla makeup. We did have two outstanding phyla outside of the typical 9 phyla that make up moist soil microbiome: Crenarchaeota (5th most abundant) and Myxococcota (7th most abundant). We conducted an unpaired t-test, assuming unequal variance, to compare our data.

Genetic Biodiversity Method

In this lab, we evaluated microbial diversity in two soil conditions by analyzing 16S sequencing data using Nephele, DADA2, and QIIME 2.0. We started by uploading our lab section's sequencing data (sequenced by Meredith), registering for Nephele, and downloading the required files from Brightspace. We combined paired-end reads and used DADA2 to analyze the data after completing quality control in Nephele. This allowed us to compute alpha diversity metrics like the Shannon Index and Richness. After that, we compared the diversity of the two soil conditions using statistical analysis. We conducted a taxonomic analysis using QIIME 2.0 concurrently. 

Genetic Biodiversity Results 

For Figure 4A, there is a difference of means of just .08 and a p-value of 0.3709 from an unpaired t-test assuming unequal variance. For Figure 4B, there is a difference of means of 17.0 and a p-value of 0.2492 from an unpaired t-test assuming unequal variance. For Figure 4C, there is a difference of means of just .001 and a p-value of 0.8243 from an unpaired t-test assuming unequal variance. Because all our p values are >.05, there is not a significant difference in soil biodiversity between our two conditions. Figure 4D shows similar proportions of bacteria phyla for each sample. The percent differences for Figures 4A, 4B, and 4C are 1.68%, 9.83%, and 0.11%.

Based on our data, we conclude that each condition, the peach tree soil and the tomato plant soil, are fairly equal in terms of functional biodiversity. Neither had particularly unique values for their Shannon biodiversity indexes (p-value = .3709 > 0.05), Chao1 Richness values (p-value 0.2492 > 0.05), or evenness values (p-value = 0.8243 > 0.05). Each p-value is greater than 0.05, which is the maximum cut-off for our unique values between data sets. This leads us to be 95% confident in our conclusion.

While looking at the number of different species of bacteria in the peach and tomato samples in terms of biodiversity, both have similar SHE values, but the peach values are slightly higher than the tomatoes. Overall, both plants have very similar values for each of our figures with no significant differences, as we can determine from our t-tests that 95% of any difference is due to chance. As stated in our Figure 3 explanation, our condition samples were taken from proximal zones in the same small urban garden, leading to similar bacterial biodiversity. This statement is further proven by our results shown in Figure 4. Researchers often experiment with urban gardens, including those in University areas (Urban Gardens). This means that plants may be grown in different places, and the soil makeup could eventually become incredibly similar in the entire garden. By 2030, 60% of humans are estimated to be living in cities, so urban garden growth is increasing, and interest in the gardens are piquing, meaning that they may become more common but also more compact (Community Gardens). Because they are already compact and may need to become even more compact, the biodiversity in different areas of these urban gardens is likely to become more and more similar, meaning there will often be little difference in biodiversity in gardens that are already run with effective growth techniques, including the Lincoln Sharing Garden. Lincoln does not need to adjust their growth techniques between these two areas of the garden.