Different determinants of radiation use efficiency in cold and temperate forests

Manuela Balzarolo,Yongshuo H Fu,Matteo Campioli,Lennert Schepers,Nadia Valdameri,Ivan A Janssens,Andrew Kerkhoff

doi:10.1111/geb.12985

Abstract

AbstractAimTo 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.LocationForty‐eight cold and temperate forests distributed across the Northern Hemisphere.Major taxa studiedEvergreen and deciduous trees.Time period2000–2011.MethodsWe 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.ResultsMean 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 conclusionsCold 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.

Highlights

Radiation use efficiency (RUE; gC/MJ) has emerged in recent de‐ cades as a key parameter to determine photosynthetic carbon uptake by vegetation, and the carbon exchange between the at‐ mosphere and biosphere (Monteith, 1972)
RUE represents the effi‐ ciency of vegetation to transform absorbed light energy into organic compounds; it is the ratio between gross primary production (GPP; gC/m2/year) and the absorbed photosynthetically active radiation (APAR; MJ/m2/year), with APAR being the product of the incident photosynthetically active radiation (PAR; MJ/m2/year) and its frac‐ tion absorbed by vegetation (Monteith, 1972): GPP
We report results of one‐way ANOVA (p value) and post hoc Tukey’s honestly significant difference (HSD) test. cFor stepwise backwards regression analysis (SBRA), we report the variables of the final model, representing the key predictors of RUE, and the coefficient of determination (R2) of the final model; dSoil fertility was classified as H = high; M = medium; L = low. eLeaf habit was classified as N = needleleaved; B = broadleaved; BN = mixed habit. fLeaf type was classified as D = deciduous; E = evergreen. gLeaf N: variable tested only for univariate analysis as with fewer sites than other variables

Summary

| INTRODUCTION

Radiation use efficiency (RUE; gC/MJ) has emerged in recent de‐ cades as a key parameter to determine photosynthetic carbon uptake by vegetation, and the carbon exchange between the at‐ mosphere and biosphere (Monteith, 1972). Stand age has been identified both as a significant (Bracho et al, 2012; Chasmer et al, 2008) and non‐sig‐ nificant driver of RUE (Fernández‐Martínez et al, 2014), whereas leaf habitat and type were found to be non‐significant (Fernández‐ Martínez et al, 2014) It has been reported by several authors that RUE varies with vegetation type (Field, Randerson, & Malmstrom, 1995, Garbulsky et al, 2010; Prince & Goward 1995; Schwalm et al, 2006; Turner et al, 2003) because of the different ratio of respira‐ tion to photosynthesis. The focus on RUE8days, RUEann and RUEmax offers the most comprehensive insight into RUE dynamics and their possible implementation in global monitoring tools

| MATERIALS AND METHODS

| DISCUSSION

Findings

CONFLICT OF INTEREST