# Carbon Dynamics of _Pinus palustris_ Ecosystems Following Drought
## Materials and Methods
### Data Analyses  
Statistical models were formulated to test hypotheses about relationships between recovery from drought and CO\({}_{2}\) exchange, as well as the environmental drivers of those relationships. To evaluate the differences in physiological activity following before, during and after drought, we examined changes in the parameterization of NEE light response and temperature response curves over the time period. We also examined half-hourly physiological activity for each year to determine potential hysteresis. In order to quantify the underlying frequencies in NEE and R\({}_{\text{eco}}\), we utilized orthonormal wavelet transformation (OWT), which is appropriate for time series such as these, since they do not require stationarity and allow for missing observations. We also employed OWT to analyze the frequency distributions of synchronously-measured NEE and R\({}_{\text{eco}}\) with micrometeorological drivers. Potential hysteresis was graphically examined by averaging NEE at each half-hour at each site during each year of the study, from early morning to afternoon. Physiological activity was measured as the area within the polygon via the polygon function in ImageJ [52]. Using a set value of pixels per cm and identical image sizes, the resulting measurements can be directly compared as relative values of light response hysteresis at the two sites for the years prior, during and post drought. Response curves (Equations (3) and (4)) were fit over the entire time period of the study utilizing a moving window of 30 days to characterize changes in the response curves before, during, and after drought. The five parameters \(\alpha\), \(P_{max}\), \(R_{d}\), \(R_{0}\), and \(b\) were estimated via the SAS procedure PROC NLIN (SAS software, Version 9.2; SAS Institute Inc., Cary, NC, USA), exclusively with non-gap-filled observations. Estimated parameters were then used as response variables in five separate general linear models (GLMs) via the SAS procedure PROC MIXED. In order to isolate changes in the shape and asymptotic behavior of the curve due to drought response, model effects included PDSI for Southwest Georgia and the site. A covariate was also included to indicate if the 30-day moving window included prescribed fire within 30 days. This fire recovery period was employed, as previous results have shown the effects of fire are minimal on carbon dynamics after this point [20]. In addition, a covariate for average temperature during the 30-day period was included as a proxy for seasonal effects. To meet the assumption of normality and homoscedasticity, each response variable was log-transformed prior to analysis. Due to the high temporal autocorrelation of the response variables, the assumption of independent observations was clearly violated, and therefore this analysis is presented in a descriptive context only. Marginal mean estimates of the curve parameters were used to graphically demonstrate the effect of site and drought on the NEE light and temperature response. We employed OWT to NEE, GEE and R\({}_{\text{eco}}\) measurements following Reference [53]. The authors of [53; 54] present a conceptual description of the wavelet technique and application in flux research. (A detailed discussion of wavelet analysis and application in flux research can also be found in References [53; 55; 56]). OWT has been shown to be appropriate for spectral analyses of flux data, and is efficient in decomposing flux signals into both frequency and time-scale [53; 56; 57]. Here, we use OWT to analyze the spectra of NEE, GEE and R\({}_{\text{eco}}\) during each of the 7 years of our study (2 pre-drought, 3 drought and 2 post-drought years). OWT decomposes the given time series into two coefficient components (wavelet and scale) based on the finite basis function used [57]. For our analysis, we computed orthonormal wavelet coefficients using the square-shaped Haar basis, due to its locality in the temporal domain and minimum effects of gaps or discontinuities [54].