# Differentiation in Aquatic Metabolism between
## 3 Results
### Exploring Relationships between Metabolic Estimates and Environmental Variables
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dependent Variable** & **Parameter** & **Pseudo–R\({}^{2}\)** & **Coefficient** & \(p\) \\ \hline \multicolumn{4}{c}{**Daily time scale**} \\ \hline \multirow{9}{*}{**GPPd**} & (_intercept_) & 0.544 & \(-\)0.304 & \(p<\) 0.001 \\  & Wr. & & 0.407 & \(p<\) 0.001 \\  & PAR & & 0.211 & \(p<\) 0.001 \\  & Word & & 0.019 & \(p<\) 0.001 \\  & EC & & 0.021 & \(p<\) 0.001 \\  & Chl-a & & \(-\)0.035 & \(p=\) 0.086 \\  & S2 & & \(-\)0.072 & \(p=\) 0.148 \\  & S3 & & 0.839 & \(p<\) 0.001 \\  & S4 & & 0.548 & \(p<\) 0.001 \\ \hline \multirow{9}{*}{**NLPd**} & (_intercept_) & 0.334 & 0.252 & \(p<\) 0.001 \\  & PAR & & 0.111 & \(p<\) 0.001 \\  & Wr. & & \(-\)0.292 & \(p<\) 0.001 \\  & EC & & 0.240 & \(p<\) 0.001 \\  & S2 & & 0.303 & \(p<\) 0.001 \\  & S3 & & \(-\)0.846 & \(p<\) 0.001 \\  & S4 & & \(-\)0.623 & \(p<\) 0.001 \\ \hline \multirow{9}{*}{**NLPd**} & (_intercept_) & 0.170 & 0.003 & \(p=\) 0.946 \\  & PAR & & 0.342 & \(p<\) 0.001 \\  & Word & & \(-\)0.085 & \(p<\) 0.001 \\  & Chl-a & & 0.095 & \(p<\) 0.001 \\  & EC & & 0.055 & \(p=\) 0.039 \\  & S2 & & 0.291 & \(p<\) 0.001 \\  & S3 & & \(-\)0.201 & \(p=\) 0.006 \\  & S4 & & \(-\)0.189 & \(p=\) 0.010 \\ \hline \multicolumn{4}{c}{**Monthly time scale**} \\ \hline \multirow{9}{*}{**GPPm**} & (_intercept_) & 0.778 & \(-\)0.391 & \(p=\) 0.001 \\  & Wrd & & \(-\)0.180 & \(p<\) 0.001 \\  & Wr. & & 0.698 & \(p<\) 0.001 \\  & Chl-a & & 0.101 & \(p=\) 0.112 \\  & S2 & & \(-\)0.003 & \(p=\) 0.984 \\  & S3 & & 1.067 & \(p<\) 0.001 \\  & S4 & & 0.444 & \(p=\) 0.005 \\ \hline \multirow{9}{*}{**Rm**} & (_intercept_) & 0.606 & 0.211 & \(p=\) 0.052 \\  & Wr. & & \(-\)0.988 & \(p<\) 0.001 \\  & PAR & & 0.679 & \(p<\) 0.001 \\  & EC & & 0.147 & \(p=\) 0.052 \\  & S2 & & 0.384 & \(p=\) 0.050 \\  & S3 & & \(-\)0.943 & \(p<\) 0.001 \\  & S4 & & \(-\)0.452 & \(p=\) 0.021 \\ \hline \multirow{9}{*}{**NLPm**} & (_intercept_) & 0.401 & \(-\)0.007 & \(p=\) 0.965 \\  & PAR & & 1.134 & \(p<\) 0.001 \\  & Wr. & & \(-\)0.774 & \(p<\) 0.001 \\  & Wnd & & \(-\)0.206 & \(p=\) 0.069 \\  & S2 & & 0.569 & \(p=\) 0.013 \\  & S3 & & \(-\)0.314 & \(p=\) 0.165 \\  & S4 & & \(-\)0.206 & \(p=\) 0.361 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Linear models of GPP, R, and NEP as function of wind speed at 10 m above surface (Wnd), photosynthetically active radiation (PAR), chlorophyll-a concentration (Chl-a), electrical conductivity (EC), water temperature (Wtr.) and site. (* The suffix **d** refers to model built with daily data and **m** refers to model built with monthly means). GPP model were the water temperature and wind speed (\(p<0.001\)) followed by Chl-a (\(p=0.112\)). Sites S3 and S4 differed significantly from the base level (site S1) as they were for the GPPd too. However, in contrast with the GPPd, PAR and conductivity were dropped during model selection process. On the contrary, the best Rm model showed the same predictors with the Rd (PAR, Wtr and EC) as the most important. The spatial effect was also very similar with the only difference the lower significance levels between S2, S4, and S1. The best NEPm was quite different from NEPd as only PAR and water temperature where the top predictors and wind speed was not significant (\(p=0.069\)). An interesting result, compared to the NEPd model, is that the site did not had such an important effect on explaining the variation of monthly NEP as only S2 was found to differ significantly with S1. Finally, the pseudo-R\({}^{2}\) values were 0.778, 0.606 and 0.401 for the GPPm, Rm, and NEPm respectively, suggesting that the \"monthly\" models explain larger portion of the total variance in the metabolic estimates.