Results
Mean of Net photosynthesis
The four graphs below are showing the mean net photosynthesis of each tree species in different treatment groups. The y-axis is the value of net photosynthesis. Its unit is μmol CO2 m^-2 s^-1. A, B, C, and D in the x-axis represent four different treatment groups, respectively.
The four graphs below are showing the mean net photosynthesis of each tree species in different treatment groups. The y-axis is the value of net photosynthesis. Its unit is μmol CO2 m^-2 s^-1. A, B, C, and D in the x-axis represent four different treatment groups, respectively.
A. Aerated control: 50% Hoagland solution
B. Aerated tailing water treatment: 50% Hoagland solution : tailing water (1:1, v/v)
C. Hypoxic control: 50% Hoagland solution
D. Hypoxic tailing water treatment: 50% Hoagland solution : tailing water (1:1, v/v)
B. Aerated tailing water treatment: 50% Hoagland solution : tailing water (1:1, v/v)
C. Hypoxic control: 50% Hoagland solution
D. Hypoxic tailing water treatment: 50% Hoagland solution : tailing water (1:1, v/v)
We can see that in trembling aspen and paper birch, the net photosynthesis in control groups, which are group A and C, are significantly higher than the tailing water treated groups. Besides, in tailing water treated groups, the net photosynthesis in group D is also lower than group B. This situation can be attributed to the hypoxic environment, which can aggravate the harmful effects of tailing water on reclamation plants. When it comes to Jack pine and black spruce, their data are significantly different from trembling aspen and paper birch. The data of black spruce is similar to trembling aspen and paper birch, the addition of tailing water inhibits the photosynthesis process. And the hypoxic condition still aggravate the negative effects of tailing water. However, for jack pine, the condition is slightly different. The addition of tailing water under hypoxic environment in group D significantly inhibit the photosynthesis. While the addition of tailing water under aerated environment in group B showed higher net photosynthesis than the aerated control group. This phenomena can be probability explained by the shading effect.
Morphological influence
For the shading effect in my project, these two pictures are showing us the growth condition of the plants in the aerated control group and the aerated tailing water treated group. We can see that the leaves of trembling aspen and paper birch in the aerated group (Fig 22) are relatively more dense than the aerated tailing water treated group (Fig 23). Since the height of jack pine is significantly lower than trembling aspen and paper birch, the light will be blocked by these higher plants, inhibiting the photosynthesis process of jack pine in the aerated control group.
Morphological influence
For the shading effect in my project, these two pictures are showing us the growth condition of the plants in the aerated control group and the aerated tailing water treated group. We can see that the leaves of trembling aspen and paper birch in the aerated group (Fig 22) are relatively more dense than the aerated tailing water treated group (Fig 23). Since the height of jack pine is significantly lower than trembling aspen and paper birch, the light will be blocked by these higher plants, inhibiting the photosynthesis process of jack pine in the aerated control group.
2-factor ANOVA
I performed 2-factor ANOVA on the change of net photosynthesis of four species from three treatment groups. Although the dataset contains three experimental factors (tailings, aerated, and plant species), the three-factor ANOVA will make me lose statistical power. Therefore, I choose to use the 2-factor ANOVA to save more statistical power. Besides, the objective of this project is to select the plant species with high tailings and hypoxia-resistant properties. According to the practical situation, the reclamation plants must survive on the land with oil sands tailings and a hypoxic environment. In such a case, it is meaningless to examine the performance of plants in the hypoxic alone environment. Therefore, I chose to perform two sets of 2-factor ANOVA on the results of 1. aerated control group and aerated tailings group (Fig 18); 2. aerated tailings group and hypoxic tailings group (Fig 19).
The below two figures (Fig 18 and Fig 19) are showing us the 2-factor ANOVA results. From these results, we can see that tree species (VARIETY), tailings (TREAT), and aerated condition (AIR) have statistically significant effects on the values of net photosynthesis (p-value < 0.05). At the same time, the sum of square also shows that the effect of tailings is almost four times the effect of tree species on the net photosynthesis (Fig 18), while the effect of the aerated condition is 2 times higher than the effect of tree species on the net photosynthesis (Fig 19).
After that, I rearranged two ANOVA tables by VARIETY in R and created corresponding average value bar plots. From Fig 20 and Fig 21, the results show that tailings have statistically significant effects on the net photosynthesis of all tree species apart from Jack Pine. As for the aerated condition, Fig 22 shows that the hypoxic environment has statistically significant effect on the net photosynthesis of all four tree species. Besides, Fig 23 also showed that hypoxia can further decrease the average value of net photosynthesis based on tailing water.
I performed 2-factor ANOVA on the change of net photosynthesis of four species from three treatment groups. Although the dataset contains three experimental factors (tailings, aerated, and plant species), the three-factor ANOVA will make me lose statistical power. Therefore, I choose to use the 2-factor ANOVA to save more statistical power. Besides, the objective of this project is to select the plant species with high tailings and hypoxia-resistant properties. According to the practical situation, the reclamation plants must survive on the land with oil sands tailings and a hypoxic environment. In such a case, it is meaningless to examine the performance of plants in the hypoxic alone environment. Therefore, I chose to perform two sets of 2-factor ANOVA on the results of 1. aerated control group and aerated tailings group (Fig 18); 2. aerated tailings group and hypoxic tailings group (Fig 19).
The below two figures (Fig 18 and Fig 19) are showing us the 2-factor ANOVA results. From these results, we can see that tree species (VARIETY), tailings (TREAT), and aerated condition (AIR) have statistically significant effects on the values of net photosynthesis (p-value < 0.05). At the same time, the sum of square also shows that the effect of tailings is almost four times the effect of tree species on the net photosynthesis (Fig 18), while the effect of the aerated condition is 2 times higher than the effect of tree species on the net photosynthesis (Fig 19).
After that, I rearranged two ANOVA tables by VARIETY in R and created corresponding average value bar plots. From Fig 20 and Fig 21, the results show that tailings have statistically significant effects on the net photosynthesis of all tree species apart from Jack Pine. As for the aerated condition, Fig 22 shows that the hypoxic environment has statistically significant effect on the net photosynthesis of all four tree species. Besides, Fig 23 also showed that hypoxia can further decrease the average value of net photosynthesis based on tailing water.
Fig 20. Aerated control treatment and aerated tailings treatment 2-factor ANOVA by variety on the change of net photosynthesis of 4 tree species from the aerated control treatment and aerated tailings treatment. Fig 22. Aerated tailings treatment and hypoxic tailings treatment 2-factor ANOVA by variety on the change of net photosynthesis of 4 tree species from the aerated tailings treatment and hypoxic tailings treatment.
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Fig 21. Bar chart of variety effects of 4 tree species from the aerated control treatment and aerated tailings treatment. Error bars represent 95% confidence intervals. Lower case letters indicate significant differences among treatments. Treatments with the same letter are not significantly different at α = 0.05.
Fig 23. Bar chart of variety effects of 4 tree species from aerated tailings treatment and hypoxic tailings treatment. Error bars and lower case letter represent the same meaning as Fig 23.
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Effect size statistics
At last, I used the effect size statistics in R to create table 2, which shows us the reduction percentage of net photosynthesis of four tree species from three different treatment groups and the probability of percent reduce. Specifically, I conducted two comparisons of the net photosynthesis of tree species: 1. aerated control group and aerated tailing water group; 2. aerated tailing water group and hypoxic tailing water group. From table 2, it is obvious that the net photosynthesis of Jack Pine and Black Spruce in the aerated tailing water treated group almost does not have any reduction compared to the areated control group. And the net photosynthesis of the trembling aspen and paper birch has a 60% reduction with 99% and 81% probability respectively in the aerated tailing water treated group. When it comes to the hypoxic tailing water treated group, we can see that the addition of the hypoxic environment to the tailing water treated group further decrease the net photosynthesis of all tree species based on tailing water. For trembling aspen and paper birch, their net photosynthesis decreases 60% with about 40% and 57% probability. For jack pine (96%) and black spruce (93%), they have a much higher probability of 60% reduction percentage than trembling aspen and paper birch.
At last, I used the effect size statistics in R to create table 2, which shows us the reduction percentage of net photosynthesis of four tree species from three different treatment groups and the probability of percent reduce. Specifically, I conducted two comparisons of the net photosynthesis of tree species: 1. aerated control group and aerated tailing water group; 2. aerated tailing water group and hypoxic tailing water group. From table 2, it is obvious that the net photosynthesis of Jack Pine and Black Spruce in the aerated tailing water treated group almost does not have any reduction compared to the areated control group. And the net photosynthesis of the trembling aspen and paper birch has a 60% reduction with 99% and 81% probability respectively in the aerated tailing water treated group. When it comes to the hypoxic tailing water treated group, we can see that the addition of the hypoxic environment to the tailing water treated group further decrease the net photosynthesis of all tree species based on tailing water. For trembling aspen and paper birch, their net photosynthesis decreases 60% with about 40% and 57% probability. For jack pine (96%) and black spruce (93%), they have a much higher probability of 60% reduction percentage than trembling aspen and paper birch.
Table 2. The reduction percentage of net photosynthesis of four tree species between aerated control, aerated tailings treatment, and hypoxic tailings treatment
Discussion
According to the average value of net photosynthesis barplot (Fig 12-15) and ANOVA tables (Fig 18, 19), it is certain that tailing water has significant negative effects on the physiological parameters of reclamation plants. Furthermore, the negative effect also demonstrates the morphology of tailing water treated reclamation plants, which have yellow and withered leaves. Besides, since the mean net photosynthesis of paper birch and trembling aspen further decrease (Fig 12, 13) on the basis of tailing water treatment, we can confirm that hypoxia can not only inhibit the photosynthetic process of plants but also aggravate the negative effects of tailing water on some reclamation plants.
On the other side, jack pine and black spruce show relatively higher resistance to tailing water than the other two tree species. From table 2, we can see that the addition of tailing water has almost no effects on the net photosynthesis of jack pine and black spruce. However, we should also notice that the hypoxic environment makes jack pine and black spruce have significantly higher net photosynthesis reduction probability than paper birch and trembling aspen. For example, when the reduction percentage is 50% and 60%, the probabilities of these results in both jack pine and black spruce are higher than 90%. At the same time, the same reduction percentage of trembling aspen and paper birch has about 50% probability. In summary, the results in table 2 clearly show us that jack pine and black spruce are more tolerant to tailing water than trembling aspen and paper birch. But jack pine and black spruce are also more sensitive to hypoxia than trembling aspen and paper birch. These factors should be considered when we use these tree species in practical applications.
Although the above data analysis demonstrates the effects of tailing water on net photosynthesis of reclamation plants and the role of hypoxia in this process, it is still insufficient for us to determine the best plant species for oil sands land reclamation. The physiology of different plant species is complex. And many physiological factors are affecting each other. Therefore, it is unreasonable to determine the resistant properties of plants by a single factor. From my perspective, the future study should examine more physiological factors, such as transpiration rate, intercellular CO2 concentration, and water use efficiency, to observe their performances under tailing water treated and hypoxic environments. These data can provide us with a more comprehensive understanding of the true performances of different plant species during the land reclamation process. In such a case, we will create more reliable and efficient reclamation strategies.
On the other side, jack pine and black spruce show relatively higher resistance to tailing water than the other two tree species. From table 2, we can see that the addition of tailing water has almost no effects on the net photosynthesis of jack pine and black spruce. However, we should also notice that the hypoxic environment makes jack pine and black spruce have significantly higher net photosynthesis reduction probability than paper birch and trembling aspen. For example, when the reduction percentage is 50% and 60%, the probabilities of these results in both jack pine and black spruce are higher than 90%. At the same time, the same reduction percentage of trembling aspen and paper birch has about 50% probability. In summary, the results in table 2 clearly show us that jack pine and black spruce are more tolerant to tailing water than trembling aspen and paper birch. But jack pine and black spruce are also more sensitive to hypoxia than trembling aspen and paper birch. These factors should be considered when we use these tree species in practical applications.
Although the above data analysis demonstrates the effects of tailing water on net photosynthesis of reclamation plants and the role of hypoxia in this process, it is still insufficient for us to determine the best plant species for oil sands land reclamation. The physiology of different plant species is complex. And many physiological factors are affecting each other. Therefore, it is unreasonable to determine the resistant properties of plants by a single factor. From my perspective, the future study should examine more physiological factors, such as transpiration rate, intercellular CO2 concentration, and water use efficiency, to observe their performances under tailing water treated and hypoxic environments. These data can provide us with a more comprehensive understanding of the true performances of different plant species during the land reclamation process. In such a case, we will create more reliable and efficient reclamation strategies.