Different determinants of radiation use efficiency in cold and temperate forests

Aim To verify which vegetation and environmental factors are the most important in determining the spatial and temporal variability of average and maximum values of radiation use efficiency (RUEann and RUEmax, respectively) of cold and temperate forests. Location Forty-eight cold and temperate forests distributed across the Northern Hemisphere. Major taxa studied Evergreen and deciduous trees. Time period 2000-2011. Methods We analysed the impact of 17 factors as potential determinants of mean RUE (at 8 days interval, annual and interannual level) and RUEmax (at annual and interannual level) in cold and temperate forests by using linear regression and random forests models. Results Mean annual RUE (RUEann, c. 1.1 gC/MJ) and RUEmax (c. 0.8 gC/MJ) did not differ between cold and temperate forests. However, for cold forests, RUEann was affected by temperature-related variables, while for temperate forests RUEann was affected by drought-related variables. Leaf area index (LAI) was important for both forest types, while N deposition only for cold forests and cloud cover only for temperate forest. RUEmax of cold forests was mainly driven by N deposition and LAI, whereas for temperate forests only a weak relationship between RUEmax and CO2 concentration was found. Short-term variability of RUE was strongly related to the meteorological variables and varied during the season and was stronger in summer than spring or autumn. Interannual variability of RUEann and RUEmax was only weakly related to the interannual variability of the environmental drivers. Main conclusions Cold and temperate forests show different relationships with the environment and vegetation properties. Among the RUE drivers observed, the least anticipated was N deposition. RUE is strongly related to short-term and seasonal changes in meteorological variables among seasons and among sites. Our results should be considered in the formulation of climate zone-specific tools for remote sensing and global models.