Convergence of terrestrial plant production across global climate gradients

A Corrigendum to this article was published on 18 May 2016

This article has been updated


Variation in terrestrial net primary production (NPP) with climate is thought to originate from a direct influence of temperature and precipitation on plant metabolism. However, variation in NPP may also result from an indirect influence of climate by means of plant age, stand biomass, growing season length and local adaptation. To identify the relative importance of direct and indirect climate effects, we extend metabolic scaling theory to link hypothesized climate influences with NPP, and assess hypothesized relationships using a global compilation of ecosystem woody plant biomass and production data. Notably, age and biomass explained most of the variation in production whereas temperature and precipitation explained almost none, suggesting that climate indirectly (not directly) influences production. Furthermore, our theory shows that variation in NPP is characterized by a common scaling relationship, suggesting that global change models can incorporate the mechanisms governing this relationship to improve predictions of future ecosystem function.

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Figure 1: Global variation in annual net primary production for 1,247 woody plant communities grouped by age class.
Figure 2: Net primary production of woody plant communities across global climate gradients.
Figure 3: Global variation in annual net primary production of woody plant communities expressed as a general scaling function of plant age a and stand biomass Mtot.
Figure 4: Partial regression plots illustrating relationships between monthly net primary production (NPP/lgs) and individual covariates from equation (4) for 1,247 woody plant communities.

Change history

  • 06 August 2014

    Figure 3 y-axis label was incorrect and has been fixed.


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S.T.M. and B.J.E. were supported by an NSF MacroSystems award (1065861) and a fellowship from the Aspen Center for Environmental Studies. D.C. was supported by the National Natural Science Foundation of China (31170374 and 31370589) and Fujian Natural Science Funds for Distinguished Young Scholar (2013J06009). A.J.K. was supported by a sabbatical supplement from Kenyon College, and by a National Science Foundation ROA supplement (1065861) to the NSF MacroSystems award (1065861) to B.J.E.

Author information




S.T.M., D.C., A.J.K. and B.J.E. compiled data, developed theory, performed analyses and wrote the paper.

Corresponding authors

Correspondence to Dongliang Cheng or Brian J. Enquist.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Partial residual plots showing linearization of NPP relationships by power and exponential transforms of precipitation and plant age.

Relationships were best linearized by power transforms of both precipitation and age, so power laws were used to characterize precipitation- and age-dependence of NPP in Supplementary Information Equation (S6). Multiple regression models used average growing season temperatures <1/kT>gs and mean growing season precipitation Pgs, but similar results were observed using mean annual estimates. Dashed line, OLS linear regression line; solid line, Loess smooth. a, b, Power transform for precipitation and age; c, d, power transform for precipitation and exponential transform for age; e, f, exponential transform for precipitation and power transform for age; g, h, exponential transform for precipitation and age.

Extended Data Figure 2 Relationship between mean annual temperature and growing season length (r2 = 0.853, P < 2.2 × 10−16).

Extended Data Figure 3 Partial regression plots showing relationships between annual net primary production (NPP) and each covariate.

Both variables in each plot are residuals. Plots show the correct relationship (slope and variance) between NPP and each covariate while controlling for the influence of all other model covariates. All relationships were significant at α = 0.001, except for growing season length (P = 0.026). However, total stand biomass and plant age explained most of the variation in NPP, while temperature, growing season length, and mean annual precipitation each explained less than 10% of the variation (Table 1). a, Relationship between NPP and average growing season temperature <1/kT>gs. b, Relationship between NPP and mean growing season precipitation Pgs. c, Relationship between NPP and growing season length lgs. d, Relationship between NPP and total stand biomass Mtot. e, Relationship between NPP and plant age a.

Extended Data Figure 4 Global variation in annual net primary production (NPP) for 1,247 forest stands expressed as a general scaling function of age a and total stand biomass Mtot.

Stands grouped according to standard biome definitions39. Grey, desert; light orange, savannah; light blue, temperate forest; black, temperate rainforest; yellow, taiga; dark blue, tropical rainforest; dark orange, tropical seasonal forest; pink, tundra; green, woodland/shrubland.

Extended Data Table 1 Bivariate regression fits of net primary production on temperature and precipitation data for 1,247 woody plant communities
Extended Data Table 2 Standardized major axis (SMA) regression fits of annual net primary production (NPP) on stand biomass for 1,247 woody plant communities
Extended Data Table 3 Multiple regression fits of metabolic scaling theory for terrestrial net primary production (equations (3) and (4)) to a global compilation of data for root (subscript R; 1,236 stands), aboveground woody (subscript AGW; 1,233 stands) and foliage (subscript F; 1,234 stands) components of net primary production

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Michaletz, S., Cheng, D., Kerkhoff, A. et al. Convergence of terrestrial plant production across global climate gradients. Nature 512, 39–43 (2014).

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