Convergence of terrestrial plant production across global climate gradients (original) (raw)

Nature volume 512, pages 39–43 (2014) Cite this article

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A Corrigendum to this article was published on 18 May 2016

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Abstract

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.

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Figure 2: Net primary production of woody plant communities across global climate gradients.

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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 _M_tot.

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Figure 4: Partial regression plots illustrating relationships between monthly net primary production (NPP/_l_gs) and individual covariates from equation (4) for 1,247 woody plant communities.

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

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Acknowledgements

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

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, 85721, Arizona, USA
    Sean T. Michaletz & Brian J. Enquist
  2. Key Laboratory of Humid Subtropical Eco-geographical Process, Fujian Normal University, Ministry of Education, Fuzhou, Fujian Province 350007, China,
    Dongliang Cheng
  3. Department of Biology, Kenyon College, Gambier, 43022, Ohio, USA
    Andrew J. Kerkhoff
  4. The Santa Fe Institute, USA, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA,
    Brian J. Enquist
  5. The iPlant Collaborative, Thomas W. Keating Bioresearch Building, 1657 East Helen Street, Tucson, Arizona 85721, USA,
    Brian J. Enquist
  6. Aspen Center for Environmental Studies, 100 Puppy Smith Street, Aspen, Colorado 81611, USA,
    Brian J. Enquist

Authors

  1. Sean T. Michaletz
  2. Dongliang Cheng
  3. Andrew J. Kerkhoff
  4. Brian J. Enquist

Contributions

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 toDongliang Cheng or Brian J. Enquist.

Ethics declarations

Competing interests

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 _P_gs, 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 (_r_2 = 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 _P_gs. c, Relationship between NPP and growing season length _l_gs. d, Relationship between NPP and total stand biomass _M_tot. 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 _M_tot.

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

Full size table

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

Full size table

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). https://doi.org/10.1038/nature13470

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  1. Troy Taylor 9 August 2014, 16:24
    I would most like to know the distinct outlier in Fig 1b and 1c &#8211 which are probably the same site &#8211 that with highest NPP yet at intermediate temp and precip. Floristics, biogeography and phytosociology of this community please?
  2. Sean Michaletz 3 September 2014, 13:27
    In the article, reference 33 is incorrect. This reference should have been:
    Larjavaara, M. & Muller-Landau, H. C. Temperature explains global variation in biomass among humid old-growth forests. Global Ecol. Biogeogr. 21, 998-1006 (2012).
    The authors apologize for any confusion caused to readers.
  3. Sean Michaletz 12 September 2014, 11:36
    Hello Troy Taylor,
    All of the source data used in the analyses are available at http://www.nature.com/natur....
    Best wishes,
    Sean Michaletz

Editorial Summary

Plant productivity response to climate

Net primary production is affected by temperature and precipitation, but is this a direct effect on plant metabolism or an indirect ecological effect mediated by changes in growing season length and plant biomass? Here, Sean Michaletz et al. develop metabolic scaling theory to test the relative importance of direct and indirect climate effects. Applying their model to a global data set of plant productivity, the authors conclude that it is indirect effects that explain the influence of climate on productivity. Temperature and water availability are fundamental drivers of plant physiology and ecosystem metabolism at local scales, but at global scales climate influences net primary production indirectly via plant age and stand biomass, which is largely driven by maximum plant size.