## 4 Discussion  
### Soil Temperature, Moisture and \(\mathrm{R_{s}}\)  
As is well-established in the literature, soil temperature is the main driver of \(\mathrm{R_{s}}\) and alone, and explained 75% of the variation in our model. A study by [PERSON] et al. in a 50-year-old oriental arborvite (_Platycladus orientalis_ ([PERSON]) plantation in China found soil temperature to explain 82% of the variation in \(\mathrm{R_{s}}\) in the overall annual cycle. It was the main determinant for \(\mathrm{R_{s}}\) when soil moisture was not limiting [8]. This is just one of many studies that found soil temperature to be the strongest driver of \(\mathrm{R_{s}}\). Another example is [PERSON] et al., who reported that soil temperature was the main driver of \(\mathrm{R_{s}}\) variation unless soil moisture reached levels below 15%, in which case it then became the better predictor [22]. We know \(\mathrm{R_{s}}\) varies both spatially and temporally. Temporal variation of \(\mathrm{R_{s}}\) includes diurnal, weekly, seasonal, or annual changes [23]. When we examined \(\mathrm{R_{s}}\) seasonally, we found significant differences among the vegetation types. The cinnamon fern's \(\mathrm{R_{s}}\) 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 \(\mathrm{R_{s}}\) rates for hemlock on our cooler sampling dates than the other vegetation types, which was 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 [24]. We know \(\mathrm{R_{s}}\) 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 [25]. Though we found differences in our soil moisture levels, with cinnamon fern occupying the wetter sites, this did not provide a large enough influence on \(\mathrm{R_{s}}\) rates to observe any differences between sites. Soil moisture usually explains less of the variation in \(\mathrm{R_{s}}\) than soil temperature. Our results were not unlike those of the study by [PERSON] and [PERSON] [9]. They explored the temporal changes of soil respiration under different tree species. They found monthly \(\mathrm{R_{s}}\) to be statistically different between the tree types; however, there was no significant difference between the mean annual \(\mathrm{R_{s}}\) rates among the tree types. \(\mathrm{R_{s}}\) followed their soil moisture trend. Despite no significant difference for the annual mean \(\mathrm{R_{s}}\) 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 \(\mathrm{R_{s}}\) to soil moisture (Table 2). Cinnamon fern's model \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling Date** & **Vegetation Index** & \(p\)**-Value** & \(\mathbf{R^{2}}\) \\ \hline August & NDLI & 0.0214 & 0.32 \\ September & PRI & 0.0285 & 0.3 \\ September & NDVI & 0.02 & 0.33 \\ September & NDLI & 0.0462 & 0.25 \\ September & NDNI & 0.025 & 0.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(p\)-values and \(\mathrm{R^{2}}\) values for the \(\mathrm{VIs}\) and \(\mathrm{R_{s}}\) sampling dates that were found to have significant correlations. for predicting R\({}_{\rm s}\) was the most sensitive to soil moisture changes (Figure 3). The response of R\({}_{\rm s}\) to soil temperature between all of the vegetation types did not differ (Figure 2).