## 2 Materials and Methods
\(DO_{sat}\) was represented as the DO percent saturation (DO%), calculated as a function of \(T_{W}\)[42]:  
\[DO_{sat}\ =\ 14.652\ -\ 0.41022\cdot T_{W}^{1}\ +\ 0.0079910\cdot T_{W}^{2}\ - 0.000077774\cdot T_{W}^{3}. \tag{2}\]  
PAR was calculated as 45% of global radiation, which was obtained from a national meteorological station 40 km from the experimental site. Net ecosystem production (NEP) was estimated as NEP = GPP - ER. Bacterioplankton production was measured using the approach of [PERSON] and [PERSON] [43], with minor changes. Triplicate samples (subsample of depth-integrated samples from the various mesocosms) were incubated in Jena flasks in the different mesocosms with 12.5 nM \({}^{3}\)H-methylthymidine for 15-60 min, depending on the lake water temperature. The reaction was stopped by adding formalin. Less than 2 h after sampling, 7-10 mL of each sample was filtered on a 25-mm cellulose acetate filter (0.2 \(\upmu\)m) and rinsed 8 times with 1 mL of ice-cold 5% trichloroacetic acid (TCA) solution. The radioactivity retained on the filters was counted within 48 h after sampling. Bacterial production was calculated from \({}^{3}\)H-methylthymidine incorporation using the following conversion factors: 2 \(\times\) 10\({}^{9}\) cells (nmol thymidine)\({}^{-1}\)[44]. Linear mixed effect models were used to explore linkages between metabolism variables and treatments and/or environmental variables. We conducted analyses on the full data set and tested for significant treatment effects (temperature, nutrient and HW treatments). Treatment effects were included in the model as fixed effects and mesocosms as a random effect [45]. When needed, a variance structure was added (varFixed or varPower) to deal with the variance heterogeneity [46]. To elucidate the effect of the HW, we followed the method used in a study of HW effects on GHG in the same HW experiment [47]. In the models, HW had three levels: HW0, the pre- and post-HW period (i.e., June and August in A0, A2 and A2+); HW1, the HW period (July in A2 and A2+); HW2, the \"Ambient summer\" period (July in the A0),the latter because A0 was not heated the extra 5 \({}^{\circ}\)C during the HW period but A2 and A2+ were. Starting with a full model including treatments and environmental variables, model selection was made in a backward stepwise fashion using the likelihood ratio involving removal of the term of least significance until all remaining covariables were significant [45]. When HW was found to be significant in the mixed models, the variations among the different levels of the HW treatments were tested using [PERSON]'s post-hoc test. We also analyzed the data from HN and LN mesocosms separately to explore treatment effects that potentially are masked by nutrient effects, including only temperature and HW treatment effects. All mixed models were checked for normality and homogeneity of variance by visual inspection of plots of residuals against fitted values. The significance of the models was assessed by comparison with a null model using the likelihood ratio. To reduce variance heterogeneity in the data and to meet the assumptions of linear mixed effect models, the metabolism variables were log-transformed before the analysis. These statistical analysis were performed using the R package \"nlme\" and the function \"lme\" [48] in the R software (version 3.1.1) [49]. We also conducted linear mixed regression models on the full dataset using Chl-a, TP, TN and PVI as explanatory variables (fixed factors) and mesocosms as random factors. Furthermore, repeated measurements on mesocosms were considered in the covariance structure used (applying the procedure MIXED in SAS).