Tanks were defined as being stable (ES = 1) or unstable (ES =0) depending on the degree of change in macrophyte abundance between sampling periods. If a tank underwent a sharp change in the macrophyte abundance then it was classified as unstable. A sharp change was defined as a greater than 100 % reduction in PVI, having started at greater than 20% and accompanied by a reduction in pH of at least 1 unit between sampling occasions. Statistical methods The statistical modelling approach had two stages: first we tested for a temperature treatment effect on each GHG flux using linear mixed model with sampling occasion as a random effect. The performance of the random effect was assessed by model comparison based on likelihood ratio (LR), the effect of temperature treatment was assessed by comparison with a null model using LR (supplementary methods). The second stage involved determining the optimal model explaining the flux of each GHG. Here, we used LR to compare models, starting with a full model, including quadratic terms and interactions, sequentially dropping the least significant variable until all remaining covariables were significant(31). Model fit was estimated as pseudo-R2, details of programmes and packages used can be found in supplementary methods. For the calculation of total annual flux, values for October and December were derived by linear interpolation of the adjacent months. Acknowledgements This work was supported by CLEAR (a Villum Kann Rasmussen Centre of Excellence on lake restoration); the Danish Council for Strategic Research (Centre for Regional Change in the Earth System - CRES—contract no: DSF-EnMi and MARS (Managing Aquatic ecosystems and water Resources under multiple Stress), EU 7th Framework Programme, Contract No.: 603378. TD was supported by CIRCE funded by the AU ideas programme and EU fellowship (IEF - 255180 - PRECISE). JCS was supported by the European Research Council We are very grateful for the highly efficient technical assistance of Dorte Nedegaard who
Tanks were defined as being stable (ES = 1) or unstable (ES =0) depending on the degree of change in macrophyte abundance between sampling periods. If a tank underwent a sharp change in the macrophyte abundance then it was classified as unstable. A sharp change was defined as a greater than 100 % reduction in PVI, having started at greater than 20% and accompanied by a reduction in pH of at least 1 unit between sampling occasions. Statistical methods The statistical modelling approach had two stages: first we tested for a temperature treatment effect on each GHG flux using linear mixed model with sampling occasion as a random effect. The performance of the random effect was assessed by model comparison based on likelihood ratio (LR), the effect of temperature treatment was assessed by comparison with a null model using LR (supplementary methods). The second stage involved determining the optimal model explaining the flux of each GHG. Here, we used LR to compare models, starting with a full model, including quadratic terms and interactions, sequentially dropping the least significant variable until all remaining covariables were significant(31). Model fit was estimated as pseudo-R2, details of programmes and packages used can be found in supplementary methods. For the calculation of total annual flux, values for October and December were derived by linear interpolation of the adjacent months. Acknowledgements This work was supported by CLEAR (a Villum Kann Rasmussen Centre of Excellence on lake restoration); the Danish Council for Strategic Research (Centre for Regional Change in the Earth System - CRES—contract no: DSF-EnMi and MARS (Managing Aquatic ecosystems and water Resources under multiple Stress), EU 7th Framework Programme, Contract No.: 603378. TD was supported by CIRCE funded by the AU ideas programme and EU fellowship (IEF - 255180 - PRECISE). JCS was supported by the European Research Council We are very grateful for the highly efficient technical assistance of Dorte Nedegaard who
davidson_GCB_2015.pdf.docx