Different determinants of radiation use efficiency in cold and temperate forests

Abstract

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.

Global Patterns and Climate Controls of Terrestrial Ecosystem Light Use Efficiency

“…We found that higher temperature was associated with higher LUE mostly in northern middle to high latitudes (Figures S3a and S3b), which was consistent with previous reports using site‐scale observations over this region (Balzarolo et al, 2019; Kergoat et al, 2008; Schwalm et al, 2006). In the tropics, higher temperature either had little or negative influence on LUE (Figures S3a and S3b).…”

“…As a crucial parameter for terrestrial carbon cycle, identifying the response of LUE to climate variability remains a critical challenge in understanding responses of vegetation photosynthesis to climate change and predicting future carbon cycle-climate interactions. It was clear that higher precipitation was correlated with higher LUE globally (Figures 4, S3c, and S3d), which was broadly consistent with recent studies based on a few site-scale observations (Balzarolo et al, 2019;Garbulsky et al, 2010). The influence of precipitation on LUE might come from direct pathways such as affecting the water supply to regulate plant water uptake (e.g., Chaitanya et al, 2003;Stocker et al, 2018;Zhang et al, 2015), or through indirect pathways such as altering the atmospheric water demand that might regulate the stomata (e.g., Akmal & Janssens, 2004;Waring et al, 2016).…”

“…The impacts of temperature were assessed most in previous studies, but the results were so different that some found it to be significant (e.g., Kergoat et al, 2008; Schwalm et al, 2006), while some found it not (e.g., Jenkins et al, 2007; Turner et al, 2003). These contrasting reports were more or less related to the different sites/regions studied with varied vegetation and climate characteristics (Balzarolo et al, 2019). Yet the global picture of LUE and its covariations with climate remained elusive.…”

Improved Global Maps of the Optimum Growth Temperature, Maximum Light Use Efficiency, and Gross Primary Production for Vegetation

“…On the other hand, global evapotranspiration and soil moisture data sets should be improved. Second, while the lack of high-quality global maps of C4 vegetation fraction and soil nutrition content limits the efficiency of ε max mapping (Waring et al, 2010), it has been recognized that the maximum LUE will vary annually, e.g., due to the rising atmospheric CO 2 (CO 2 fertilization effect), nitrogen deposition (Balzarolo et al, 2019;De Kauwe et al, 2016;He et al, 2017), land use changes and forest age increases (King et al, 2011;Landsberg & Waring, 1997), and may even fluctuate within a year due to the seasonal variations in vegetation cover and canopy structure (Kanako et al, 2014;Li et al, 2012;Lin et al, 2017). Accordingly, both the spatial and temporal variations in ε max need further explorations.…”

Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence

“…Indeed, GPP saturates at high APAR, while SIF keeps increasing as APAR increases, leading to a hyperbolic relationship between SIF and GPP at the instantaneous timescale (Damm et al, 2015;Gu et al, 2019). This hyperbolic-shaped relationship is less apparent over longer timescales, when variability in SIF and GPP is dominated seasonal variations in canopy structure, as estimated with the Leaf Area Index (LAI), whose effects are present in both SIF and GPP signals (Lu et al, 2018;Balzarolo et al, 2019;Dechant et al, 2020). Thus, the GPP:SIF correlation becomes more linear and relatively less sensitive to faster variations in the environmental drivers and plant physiological stress.…”