Interpretations As seen in Figure 5, the null statistical hypothesis was rejected for two out of the three trees showing there was a significant change between the two years the data was taken. The statistical testing that was taken was from the ANOVA site, which produced the means of the trees’ data. The mean for tree 20050351*08 in 2015 was 0.469666, and the mean for 2016 was 0.57209. The mean for tree 20050351*07 in 2015 was 0.264319, and the mean for 2016 was 0.546949. The mean for the tree 20080316*02 in 2015 was 0.138046, and the mean for 2016 was 0.510924. These findings showed that the data collected did not support our hypothesis. As seen in Figure 6, the null statistical hypothesis was rejected for two out of the three trees showing there was a significant change between the two years the data was taken. One of the Quercus bicolor had the largest difference in the NPQt values, and this showed the difference from the year 2015 to the year 2016. The statistical testing that was taken was from the ANOVA site, which produced these means. The mean for tree 20050351*08 in 2015 was 2.5855, and the mean for 2016 was 0.453015. The mean for tree 20050351*07 in 2015 was 0.521919, and the mean for 2016 was 0.656807. The mean for the tree 20080316*02 in 2015 was 7.9274, and the mean for 2016 was 1.558432. These findings showed that the data collected didn’t support our hypothesis. As seen in Figure 7, the null statistical hypothesis was rejected for two out of the three trees which showed there was a significant change between the two years that the data was taken. The statistical testing was taken from the ANOVA site, which determined the statistical values we used within the graphs for the mean values. The mean for tree 20050351*8 in 2015 was 45.0084, and the mean for 2016 was 32.5327. The mean for tree 20050351*07 in 2015 was 25.2079, and the mean for 2016 was 35.1915. The mean for the tree 20080316*02 in 2015 was 14.5511, and the mean for 2016 was 35.4081. These findings did not support our hypothesis. As seen in Figure 8, the null statistical hypothesis was rejected for all three of the trees showing there was a significant change between the two years that data was taken. The statistical testing was taken from the ANOVA site, which in turn determined the mean value that is shown in the graph. The mean value for tree 20050351*08 in 2015 was 19.2542, and the mean for 2016 was 45.1453. The mean value for tree 20050351*07 in 2015 was 23.3526, and the mean for 2016 was 49.2406. The mean value for tree 20080316*02 in 2015 was 13.0901, and the mean for 2016 was 39.8436. These findings showed that the data collected did not support our hypothesis.
Overall, our hypothesis of this experiment was that the trees would not change statistically from the year prior. The statistical testing taken for receiving all the means was from the ANOVA site. Although there was a tree in almost every figure that supported our hypothesis, the trees tended to change statistically from year to year. The SPAD readings in Figure 8 showed the most change overall, where the LEF readings in Figure 7 showed the least change overall. However, all the graphs overall rejected the stated hypothesis at the beginning of the year.

Discussion The statistical difference between the trees from this year to last year could potentially be the result of different weather patterns. This summer was warmer and longer than last summer, with temperatures only really dropping into the 30’s in late November. This change in temperature, combined with significantly less rain this summer than last summer, could potentially have caused the disparity. According to LI, R et al. (2006), they inferred that the approach for measuring photosynthetic traits such as chlorophyll content and fluorescence might estimate influence stress on growth due to warm climate. This shows how a warmer period could affect the chlorophyll content, and thus photosynthetic efficiency of a leaf/tree. This is demonstrated in the data with the SPAD readings in Figure 8, which measure the chlorophyll content of the leaves. All three trees had a large and statistically significant increase in SPAD readings this year after the warmer summer as compared to last year. According to Gitelson, A et al. (2003), there is a direct relationship between the light reflectance and chlorophyll content. The greater chlorophyll content seen through the SPAD readings lines up with this idea, as seen in the Phi II and NPQt readings in Figures 5 and 6, respectively. These together showed a significant increase in the amount of light used by the plant for energy to perform useful process, and a decrease in the amount of that light energy being dissipated as heat. Interestingly, the data collected for the LEF readings on the trees seemed to not display a trend of increase or decrease, but rather became more consistent between the trees in the later year. The LEF readings in 2015 were much more varied between the trees, and while the readings next year demonstrated no clear change across the board in terms of a blanket increase or decrease, they found levels more close to one another. According to Miyakel, J. et al. (2005), their experiment tested that plants grew under high light intensities and it correlated to very productive activity with cyclic electron flow. And as LEF and light intensity are directly related, this likely means that in the fall of 2016 the three trees received much more consistent levels of light intensity than they did in the fall of 2015. The broader context of the research we completed was that in a course of a year there can be a huge statistical change in the data from tree to tree with multiple factors playing a part. The importance of this research was to see the difference of the two years, which was huge. This research could be a smaller piece of a larger project that is spanned out throughout several years instead of a couple of years. Science builds in this way, off of the work of previous projects. Viewed like this, the work is of pivotal importance in making further conclusions about photosynthetic efficiency in trees from year to year. For future research one could wait years after to observe a more statistical difference. Although there were observed statistical differences with the Phi 2, it is believed a bigger time gap would make a bigger difference. Some things that could be done to improve future research would be to collect more consistent data so there aren't as many faulty data points that had to be flagged on the photosynq website. Another thing that could be done would be to improve the random sampling procedure because at times it felt as if the same leaves were selected and the same data was gathered.