# Differentiation in Aquatic Metabolism between
## 3 Results
### Exploring Relationships between Metabolic Estimates and Environmental Variables  
Correlations between the metabolic estimates, abiotic variables, and chlorophyll-a concentration showed that rates of GPP, NEP and R were strongly related with temperature (both air and water), PAR and DO. Furthermore, GPP and NEP showed significant correlations with pH. Chlorophyll-a concentration correlated significantly with the metabolic estimates, but with lower coefficients. High correlations between GPP and NEP with R were also noted (Table 4). The best linear models that were built using daily data (GPPd, Rd, NEPd in Table 5) showed as significant descriptors of GPPd (\(p<0.001\)) the water temperature, PAR, wind speed, and conductivity, followed by Chl-a (\(p=0.086\)). For Rd, PAR, water temperature and conductivity were significant model descriptors (\(p<0.001\)). PAR, wind and Chl-a were primarily the most significant model parameters \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline  & **GPP** & **R** & **NEP** & **Wnd** & **Rh** & **PAR** & **AirT.** & **Chl-a** & **EC** & **DO** & **pH** \\ \hline
**R** & \(-0.77\)** & 1 & & & & & & & & & \\
**NEP** & 0.04 & 0.61 ** & 1 & & & & & & & & \\
**Wnd** & \(-0.13\)** & 0.04 ** & \(-0.14\)** & 1 & & & & & & & \\
**Rh** & \(-0.27\)** & \(-0.09\)** & \(-0.19\)** & \(-0.17\)** & 1 & & & & & & \\
**PAR** & 0.38 ** & \(-0.11\) & \(-0.29\)** & \(-0.07\) & \(-0.77\)** & 1 & & & & & \\
**AirT.** & 0.48 ** & \(-0.25\)** & \(0.24\)** & \(0.05\) & \(-0.47\)** & \(0.67\)** & \(1\) & & & & \\
**Chl-a** & \(-0.08\)** & 0.14 ** & \(-0.12\)** & \(-0.05\) & \(0.08\)** & \(-0.09\) & \(-0.07\) & \(1\) & & & \\
**EC** & \(-0.17\)** & \(-0.13\)** & \(-0.01\) & \(0.24\)** & \(-0.15\)** & \(0.18\)** & \(-0.05\)** & \(-0.14\)** & \(1\) & & \\
**DO** & \(-0.22\)** & \(0.51\)** & \(0.52\)** & \(0.08\)** & \(0.07\) & \(-0.14\)** & \(-0.25\)** & \(0.16\)** & \(-0.01\) & \(1\) & \\
**pH** & \(0.4\)** & \(-0.14\)** & \(0.33\)** & \(0.03\) & \(-0.23\)** & \(0.39\)** & \(0.52\)** & \(0.14\)** & \(-0.25\)** & \(0.16\)** & \(-0.01\) & \(1\) \\
**Wtr.** & \(0.53\)** & \(-0.31\)** & \(0.17\)** & \(-0.01\)** & \(-0.45\)** & \(0.7\)** & \(0.91\)** & \(-0.07\) & \(-0.06\)** & \(-0.35\)** & \(0.54\)** \\ \hline \end{tabular}
\end{table} 
Table 4: Pearson correlation coefficients between daily estimates of gross (GPP) and net (NEP) ecosystem production, respiration (R), daily mean wind speed at 10 m above surface (wind), relative humidity (Rh), daily mean photosynthetically active radiation (PAR), daily mean air temperature (AirT), daily mean chlorophyll-a concentration, daily mean electrical conductivity (EC), daily mean dissolved oxygen concentration (DO), daily mean pH and daily mean water temperature (Wtr). **\({}^{**}\)** indicates significance level \(p<0.001\) and **\({}^{*}\)** significance \(p<0.05\). Figure 9: Mean values of metabolic estimates (GPP, R, and NEP) per each site. Error bars indicate standard error. explaining the NEPd variation (\(p<\) 0.001) followed by conductivity (\(p\) = 0.039). Sites S3 and S4 also had a significant effect (\(p<\) 0.001) on GPP and R. In addition, R and NEP differed significantly between S2, S3, S4, and the S1. The pseudo-R\({}^{2}\) value for the GPPd, Rd, and NEPd models were 0.544, 0.334 and 0.170 respectively (Table 5). Concerning the models that were built with monthly data there were some obvious differences compared to the output of the \"daily models\". The most significant predictors (\(p<\) 0.001) for the