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Results
Site Characterization
The overstory species’ basal area in the cinnamon fern plots were primarily red maple, white oak, and northern red oak (Figure 2a). Basal area in the hardwood plots were similarly dominated by northern red oak, and white oak (Figure 2b). In the hemlock plots 32.0% was white oak, followed closely by hemlock (28.1%) (Figure 2c). Basal area in the mountain laurel plots were primarily dominated by northern red oak (Figure 2d); however, when observing dominance by stems per hectare (Figure 3) we found the plots were dominated by mountain laurel (42.9%) (Figure 3d). Based on stems per hectare the hemlock plots were dominated by hemlock stems (23.9%) (Figure 3c). Characterization by volume (m3/ha) was similar to the basal area for all the plots (Figure 4 a-d).
Understory clip plot data clearly demonstrated that fern plots were dominated by cinnamon fern with 27.4 kg/ha of vegetation of which 99% were ferns. Hardwood and mountain laurel plots both had only 1.9 kg/ha of herbaceous vegetation respectively. The hemlock plots had nearly no understory vegetation (Table 2).
Our soil C:N ratios were highest under the hemlock (23.3±0.431) vegetation plots followed by the mountain laurel (22.1±0.472), hardwood (21.0±0.700), and lastly the cinnamon fern (20.0±0.451) plots (Table 3a). The C:N ratio for the hemlock plots was found to be statistically higher than the hardwood and cinnamon fern plots based on the Tukey’s HSD Post Hoc test. Percent potassium saturation was the only other soil chemical property to significantly differ between the vegetation types based on Tukey’s HSD Post Hoc test (Table 3a, b). The hardwood plots had significantly higher percent potassium saturation than hemlock, meaning a higher relative number of CEC sites were occupied by potassium. This can give us an indication for any gross nutrient imbalance (Maguire and Heckendorn, 2010).
Soil Temperature, Soil Moisture, Vegetation Type, and Rs
Simple linear regression indicated a strong positive relationship between soil temperature and Rs (R2=0.75, P<0.0001) while a weak negative relationship between soil moisture and Rs was found (R2=0.06, P<0.0001). On average, the cinnamon fern vegetation type had statistically higher soil moisture than the other three vegetation types (Table 4). On average, for all seasons, both soil temperature and Rs were not significantly different across all four vegetation types.
General Rs Patterns
Rs was higher in the growing season and lower in the cooler, dormant months across all vegetation types. No strong patterns between vegetation types were evident. However, April, August, December, and February had Rs values that differed significantly between two or more of the vegetation types. Rs was highest in the cinnamon fern plots during the warmer months. Hemlock plots had higher rates in the cooler months (Figure 5).
Rs in our data did not follow assumptions of normality therefore a natural logarithmic transformation was used on Rs. The model showed unequal variance. We took the square root of soil temperature and the natural log of soil moisture to resolve this issue. Soil temperature alone, explained 75% of the variation observed. Rs was best explained by a model that included soil moisture, and soil temperature (R2=0.76). There was no significant interaction between soil temperature and soil moisture. Stand characterization such as basal area, volume, and trees per ha, as well as soil chemical properties did not add any significance to our model.
When comparing the parameter estimates of our vegetation-type specific models we found significant differences (Table 7). Holding certain parameters to an average for the growing season allowed us to examine how each of the vegetation type models responded to soil temperature (Figure 6) and soil moisture (Figure 7) separately. The soil moisture slope is significantly more negative for the cinnamon fern plots than the hemlock or mountain laurel plots. For every unit increase in soil moisture, the cinnamon fern plots’ Rs lowers at a faster rate than both the hemlock and mountain laurel Rs rates. The intercept in the cinnamon fern model was significantly higher than the mountain laurel model’s intercept. The soil temperature slope values did not significantly differ between the vegetation types models (Figure 6).
Remotely-sensed variables and Rs
VIs
With the exception of NDLI, none of the VIs differed between vegetation types (Table 9). NDLI was greatest for the mountain laurel vegetation type. The annual average Rs value, the average growing season Rs, the average non-growing season Rs, and all VIs were not significantly correlated. When we examined the correlation between the VIs and the average Rs by date sampled we found some statistically significant relationships (Table9). Our Rs under each of the sixteen plots for the sample date of August showed a significant correlation (P=0.0214, R2=0.324) to the NDLI. For the Rs for the sample date of September we found a correlation between several VIs including NDLI (P=0.0462, R2=0.25), NDVI (P=0.02, R2=0.329), PRI (P=0.0285, R2= 0.30), and NDNI (P=0.025, R2=0.31). For all significant correlations the first cinnamon fern plot acted as a high leverage data point. When removed the relationship between August’s Rs and NDLI was stronger (R2=0.64), however all other significant correlations drastically decreased in strength when the high leverage value was removed. We found no reason to remove the value as the number was still an accurate representation of the site. Accounting for soil temperature and soil moisture variation, adding our vegetation indices individually into our base model did not explain any additional variation of Rs.
Mean Canopy Height
We found no significant relationship between mean canopy height calculated from LiDAR data and average growing season Rs. We also, found no significant relationship between average nongrowing season Rs and mean canopy height. Mean canopy height correlated with the September sample date (P=0.0254, R2=0.30).
Discussion
Soil temperature explained 75% of the variation in our model, alone. Many other studies have found a similar influence by soil temperature. A study by Yu et al. (2011) in a 50-year-old oriental arborvitae (Platycladus orientalis (L.) Franco) plantation in China found soil temperature to explain the variation in Rs by 82% in the overall annual cycle. It was the main determinant for Rs when soil moisture was not limiting. This is just one of many studies including Bilal et al. (2017), and Templeton et al. (2011) that found soil temperature to be the strongest driver of Rs. Inclán et al. (2007) reported that soil temperature was the main driver of Rs variation unless soil moisture reached levels below 15% which it then became the better predictor.
The annual average soil temperature did not differ significantly between vegetation types. When looking by sampling date, we found soil temperature to be statistically lower in the hemlock plots over the growing season (Table 5). The denser, darker canopy of hemlocks may shade the soil resulting in cooler soil temperatures in the summer. In terms of soil moisture, we found cinnamon fern to have a higher average soil moisture value than all other vegetation types. The difference could simply be due to the fact that cinnamon fern requires moist environments in order to reproduce. They are usually found on poorly drained low ground, in thickets, wet marshy woods, swamps, ditches, and streambanks (Walsh R.A., 1994).
We know Rs varies both spatially and temporally. Temporal variation of Rs includes diurnal, weekly, seasonal, or annual changes (Lai et al, 2012). When we examined Rs seasonally we found significant differences among the vegetation types. The cinnamon fern’s Rs was significantly higher in the month of August most likely due to its sheer abundance at that time of the year. We expected to see significantly higher Rs rates for hemlock on our cooler sampling dates than the other vegetation types which is the case for our study. As an evergreen, when there are mild winter days, hemlocks continue photosynthesizing and in some cases with approximately the same photosynthetic capacity as in the summer (Burkle and Logan, 2003). We know Rs is influenced by substrate availability and thus strongly linked to photosynthesis, litterfall, and plant metabolism and, therefore higher activity aboveground in evergreens in the cooler months can lead to higher activity belowground (Ryan and Law, 2005). Seasonality effected Rs differently across different vegetation types for a study by Lai et al (2012). Rs for winter wheat, E. angustifolia shelterbelt, T. ramosissima scrubland, and H. ammondendron + R. soongorica scrubland was highest in July and then decreased with temperature while P. communis grassland peaked in September and declined in October. Overall, E. angustifolia had the greatest seasonal variation in Rs. We found seasonal differences between Rs rates; however, unlike the study mentioned, the average annual Rs rates did not significantly differ between vegetation types. Though we found differences in our soil moisture levels with cinnamon fern having the wetter sites, that did not provide a big enough influence on Rs rates to observe any differences between sites. Soil moisture usually explains less of the variation in Rs than soil temperature. Our results were not unlike the study by Akburak and Makineci (2013). They explored the temporal changes of soil respiration under different tree species. They found monthly Rs to be statistically different between the tree types, however there was no significant difference between the mean annual Rs rates among the tree types. Rs followed their soil moisture trend. Despite no significant difference for the annual mean Rs between vegetation types, we still encountered monthly differences. We would need to explore the root respiration rates, quality and quantity of litter, and more soil characteristics to see if perhaps these monthly differences could be due to the influence of vegetation type perhaps as a secondary effect.
When comparing our vegetation-type specific models, there were statistically significant differences in the response of Rs to soil moisture (Table 8). Cinnamon fern’s model for predicting Rs was the most sensitive to soil moisture changes (Figure 7). The response of Rs to soil temperature between all of the vegetation types did not differ (Figure 6). We found few differences in the soil chemical properties between the vegetation types. Perhaps the soil environment was similar enough across the vegetation types that the vegetation’s effect on annual mean Rs was minimal. It’s important to note that the spatial variation in Rs is usually driven by soil characteristics as well as biological processes while temporal variation is usually driven by climatic variables (Qi and Xu, 2001). For cinnamon fern, perhaps its high Rs rates in August and its lower Rs rates in the dormant months even out, resulting in an average annual Rs rate similar to that of the hemlock that can continually photosynthesize throughout the year. A study by Martin and Bolstad (2005), examined Rs and its influences such as moisture and site characteristics under five different forest types. They noted an apparent lack of effect of dominant vegetation type on Rs. Similar to our study, the site conditions were relatively homogenous. Our topography did not significantly differ, nor did site characteristics, or soil chemical properties. In our case, vegetation type may have minimal utility in predicting Rs on a spatial scale. A continuation of Martin and Bolstad’s (2009) previous study examined more forest types including clear-cuts, ash elm, aspen, northern hardwood, wetland edges, and red pine forests. Variability in Rs was observed both within forest type and between forest type. This study included more site variability including differences in topography. They noted an interrelatedness of topographically induced hydrologic patterns and soil chemistry unlike in their previous studies where topography was the same across all forest types (Martin et al., 2009). Bilal e al. (2017) compared Rs across cover types in a southern Appalachian hardwood forest. Their model included soil temperature and soil moisture as the two drivers of variation. When comparing Rs rates between our study and theirs, we found that our Rs rates were almost double that of Bilal et al (2017) at the same soil temperature. At about a soil temperature of 10˚C, their Rs rates were about 2 µmol CO2 m-2 s-1 whereas our Rs rates at that temperature were 4 µmol CO2 m-2 s-1. Their sites were located on either foot or shoulder slopes and at lower elevation than ours. They found that the differences in site quality correlated with the differences they saw in Rs between the cover types. Perhaps similar to the study by Martin et al. (2009), they experienced topographically induced soil moisture regimes which may have limited Rs compared to our sites. In fact, our sites were wetter, overall. The average soil moisture values at our sites were higher than their highest soil moisture value. However, after using our growing season average soil moisture value (33%) in Bilal’s prediction model we found only a slight increase in Rs rates. The is a good indicator that differences between our sites are not due to soil moisture differences as it only explains very little variation in Rs.
There has been controversy in the past with some studies saying the influence of vegetation on soil microclimate is sufficient enough to explain differences in Rs among vegetation types (Raich and Tufekcioglu, 2000). Others say the correlation between climate characteristics, net primary productivity, and Rs has caused scientists to speculate which factors are truly driving the differences in Rs between different vegetation types (Raich and Schlesinger, 1992). In a study by Reichstein et al (2003), they corrected the Rs data for soil temperature and moisture influences resulting in site-specific, standardized respiration rates. These standardized rates were correlated with leaf area index and leaf production indicating that both climate and vegetation type played important roles in explaining the spatial variability in Rs. On the contrary, another study found a correlation between net primary productivity and Rs when comparing various ecosystem types. They believed the correlation was mainly caused by a background correlation of both factors with climate variables (Raich and Schlesinger, 1992). In the future, we should focus on developing models that try to isolate vegetation effects in order to get a better picture of species influence on Rs.
The use of remotely-sensed VIs to predict Rs
We found some patterns between our remotely-sensed indices and Rs. When examining the relationship between the indices and Rs by sampling date, we found significance. Both August and September had significant relationships with the NDLI. NDLI was negatively correlated with Rs for these months. The negative correlation may be related to the fact that litter with higher lignin content is more difficult to break down by heterotrophic organisms, lowering Rs (Chapin et al., 2017). The reason we may have found correlations with the Rs rates for September and August may simply be because that time of the year has high Rs as seen in Figure 5. Overall, most of the correlations we did find between Rs and the six different VIs were relatively weak (Figure 9). Seasonally, it may be difficult to correlate Rs with greenness VIs due to soil temperature or moisture limitation which may limit the influence of substrate supply from photosynthesis to Rs (Irvine and Law, 2002).
Remotely-sensed VIs, in the past, have been more closely correlated with gross primary production (GPP) than with respiration components (Ling-Hao et al., 2002). For example, Wylie et al (2003) looked at estimating daytime and nighttime carbon flux using an integrated NDVI (iNDVI). Linear relationships between the iNDVI and daytime carbon flux were strong with R2 values of 0.72 to 0.92 for the three years observed. The iNDVI even correlated better with their nighttime carbon flux (R2=0.34) than NDVI correlated with our values of Rs (R2=0.07) in our study. It is important to note that NDVI tends to saturate at high vegetation densities and can be sensitive to background reflectance (Huete et al, 2002). Our sites, therefore, could have been too highly vegetated for the use of NDVI or there was little change in GPP across our vegetation types.
Table of Contents
Abstract
Abstract (public)
Acknowledgements
Table of Contents
List of Tables
List of Figures
Introduction
General Methods
Statistical Analyses
Results
Discussion
Conclusion
Literature Cited
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Soil Respiration and Related Abiotic and Remotely Sensed Variables in Different Overstories and Understories in a High Elevation Southern Appalachian Forest.
