Contribution of tree community structure to forest productivity across a thermal gradient in eastern Asia

Abstract

Despite their fundamental importance the links between forest productivity, diversity and climate remain contentious. We consider whether variation in productivity across climates reflects adjustment among tree species and individuals, or changes in tree community structure. We analysed data from 60 plots of humid old-growth forests spanning mean annual temperatures (MAT) from 2.0 to 26.6 °C. Comparing forests at equivalent aboveground biomass (160 Mg C ha^-1), tropical forests ≥24 °C MAT averaged more than double the aboveground woody productivity of forests <12 °C (3.7 ± 0.3 versus 1.6 ± 0.1 Mg C ha^-1 yr^-1). Nonetheless, species with similar standing biomass and maximum stature had similar productivity across plots regardless of temperature. We find that differences in the relative contribution of smaller- and larger-biomass species explained 86% of the observed productivity differences. Species-rich tropical forests are more productive than other forests due to the high relative productivity of many short-stature, small-biomass species.

Fig. 1. Two hypotheses explaining higher forest woody productivity in warmer forest stands in relation to inter-specific productivity-biomass relationships.

a The species response hypothesis assumes that tree species possess distinct productivity-biomass power-law relationships depending on temperature such that relative aboveground woody productivity p_i of species i tends to be higher in tropical versus temperate forests at the same species’ standing aboveground biomass B_i, leaving that frequency distribution of species biomass and per-stand species richness (SR) of each stand are the same among biomes. b The community structure hypothesis assumes that species possess similar productivity-biomass relationships regardless of temperature, i.e., species relative woody productivity p_i is not different between tropical and temperate species at the same species aboveground biomass B_i, while SR is larger and mean species biomass is smaller in tropical forest stands. Based on synthetic data generated assuming a bivariate normal distribution of ln p_i and ln B_i, with a common correlation slope (or, power-law exponent) of –0.15. Other coefficient values for generating random data are in Methods. Each of 10 stands in each forest has SR shown in right-hand panels. In the left-hand panels, the 95% prediction ellipses are shown in inter-specific p_i-B_i relationships. The right-hand panels show species-aggregated, stand-level woody productivity P = Σ_i p_i B_i and stand biomass B = Σ_i B_i, with predicted means and 95% confidence intervals of the power-law model fitting. All axes are on log scale. Source data are provided as a Source Data file.

Fig. 2. Species-level standing biomass and woody productivity across 2604 species populations (excluding rare aggreagated species populations) in 60 forest plots in eastern Asia.

Plots are grouped by mean annual temperature (MAT). a Relationships between per-plot species-i relative aboveground woody productivity p_i, against species aboveground biomass B_i, and b those between species relative productivity against species’ maximum tree mass, Wmax_i, both a and b on log–log scale. The 95% log-normal prediction ellipses are shown for each of five biomes grouped by mean annual temperature (MAT) by corresponding colours, and the common ellipse for all species populations in all plots by filled grey. Fitted power-law models were: p_i = a_plot B_i^{–0.14±0.01} with a_plot ranging [0.011, 0.058], and p_i = a´_plot Wmax_i^{–0.18±0.01} with a´_plot ranging [0.0078, 0.043]. c Decrease of forest-plot woody productivity P = Σ_i p_i B_i with stepwise reduction of species biomass B_i from the smallest to the largest, in which lines show each plot, and the circles at the right show species woody productivity P_i = p_i B_i of the largest biomass species. Mean and s.d. percentage of the productivity of small-biomass species (B_i < 3 Mg C ha^–1, dashed line) to forest productivity P are presented; x axis is on square-root scale and y axis on normal scale. Source data are provided as a Source Data file.

Fig. 3. Forest structural measures in relation to mean annual temperature (MAT) among 60 plots in eastern Asia.

a Tree species richness (i.e., species count per plot); b aboveground forest biomass B; c mean species aboveground biomass; d standard deviation of species aboveground biomass. The regression line in each panel indicate mean and 95% confidence intervals predicted by the semi-log model. The R² values of the models are shown. Forest structure measures on y axes are on a log scale. Source data are provided as a Source Data file.

Fig. 4. Estimated forest-level woody productivity in relation to standing biomass and temperature among 60 plots in eastern Asia.

a Forest-level aboveground woody productivity estimated from original data P (i.e., estimates based on constituent species productivity) against forest aboveground biomass per area. b Estimated woody productivity representing the species response hypothesis, P_specRes, against forest biomass; P_specRes is obtained assuming that all species populations across 60 plots respond to the species productivity-biomass power-law specific to each plot of interest, and that frequency distribution of species biomass of the plot is the same as the distribution of all species populations of all plots. c Estimated woody productivity representing the community structure hypothesis, P_commStr, against forest biomass, where inferred species woody productivity is resampled from a species population with similar biomass drawn from all the plots. In a–c, regression lines indicate mean with 95% confidence intervals are shown for tropical (≥24 °C, red line) versus cool-temperate/sub-boreal forests (<12 °C, blue line) predicted by power-law models. Estimates and coefficient of determination for the power-law model are shown in each panel. d Forest-level woody productivity distribution in five biomes grouped by mean annual temperature (MAT): coloured areas show the distribution of woody productivity with kernel density estimation; inside symbols with bars are mean and 95% confidence intervals of predicted productivity at the forest biomass of 160 Mg C ha^–1 (mean across 60 plots). Source data are provided as a Source Data file.