RESUMO
Fresh waters make a disproportionately large contribution to greenhouse gas (GHG) emissions, with shallow lakes being particular hot spots. Given their global prevalence, how GHG fluxes from shallow lakes are altered by climate change may have profound implications for the global carbon cycle. Empirical evidence for the temperature dependence of the processes controlling GHG production in natural systems is largely based on the correlation between seasonal temperature variation and seasonal change in GHG fluxes. However, ecosystem-level GHG fluxes could be influenced by factors, which while varying seasonally with temperature are actually either indirectly related (e.g. primary producer biomass) or largely unrelated to temperature, for instance nutrient loading. Here, we present results from the longest running shallow-lake mesocosm experiment which demonstrate that nutrient concentrations override temperature as a control of both the total and individual GHG flux. Furthermore, testing for temperature treatment effects at low and high nutrient levels separately showed only one, rather weak, positive effect of temperature (CH4 flux at high nutrients). In contrast, at low nutrients, the CO2 efflux was lower in the elevated temperature treatments, with no significant effect on CH4 or N2 O fluxes. Further analysis identified possible indirect effects of temperature treatment. For example, at low nutrient levels, increased macrophyte abundance was associated with significantly reduced fluxes of both CH4 and CO2 for both total annual flux and monthly observation data. As macrophyte abundance was positively related to temperature treatment, this suggests the possibility of indirect temperature effects, via macrophyte abundance, on CH4 and CO2 flux. These findings indicate that fluxes of GHGs from shallow lakes may be controlled more by factors indirectly related to temperature, in this case nutrient concentration and the abundance of primary producers. Thus, at ecosystem scale, response to climate change may not follow predictions based on the temperature dependence of metabolic processes.