ArticlePublisher preview available

Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass

September 2019
Nature Climate Change 9(9)

DOI:10.1038/s41558-019-0545-2

Authors:

Cesar Terrer

Massachusetts Institute of Technology

Iain Colin Prentice

Imperial College London

Show all 32 authorsHide

Elevated CO2 (eCO2) experiments provide critical information to quantify the effects of rising CO2 on vegetation1–6. Many eCO2 experiments suggest that nutrient limitations modulate the local magnitude of the eCO2 effect on plant biomass1,3,5, but the global extent of these limitations has not been empirically quantified, complicating projections of the capacity of plants to take up CO27,8. Here, we present a data-driven global quantification of the eCO2 effect on biomass based on 138 eCO2 experiments. The strength of CO2 fertilization is primarily driven by nitrogen (N) in ~65% of global vegetation and by phosphorus (P) in ~25% of global vegetation, with N- or P-limitation modulated by mycorrhizal association. Our approach suggests that CO2 levels expected by 2100 can potentially enhance plant biomass by 12 ± 3% above current values, equivalent to 59 ± 13 PgC. The global-scale response to eCO2 we derive from experiments is similar to past changes in greenness⁹ and biomass¹⁰ with rising CO2, suggesting that CO2 will continue to stimulate plant biomass in the future despite the constraining effect of soil nutrients. Our research reconciles conflicting evidence on CO2 fertilization across scales and provides an empirical estimate of the biomass sensitivity to eCO2 that may help to constrain climate projections.

Soil C:N and soil P are key plant resources driving the CO2 fertilization effect on above-ground biomass Model selection identified the most important drivers of the effect in the dataset of CO2 experiments (n = 138), indicating responses to CO2 were modulated by mycorrhizal type. a,b, Meta-analytic scatterplots showing the relationship between the CO2 effect and soil C:N (an indicator of nitrogen availability) in AM studies (n = 86) at 0–10 cm (a), and soil available phosphorus in ECM studies (n = 52) measured by the Bray method at 0–10 cm (b). The type of fumigation technology used (FACE, growth chamber and open top chamber) significantly influenced (P < 0.001) the magnitude of the CO2 effect. Regression lines represent the response found in FACE studies, based on a mixed-effects meta-regression model (pseudo-R² = 0.94) and their 95% confidence intervals. Dot sizes are drawn proportional to the weights in the model and represent, on average, an increase in atmospheric CO2 of 250 ppm. G, growth chamber; OTC, open top chamber.

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Potential above-ground biomass enhancement in terrestrial ecosystems under elevated CO2 These global estimates were upscaled using the empirical relationships from the key drivers of the effect across experiments synthesized through meta-analysis. a–d, The effects (a,b) and uncertainties in the estimations (c,d) shown in this figure represent an increase in atmospheric CO2 of 250 ppm in relative (a,c) and absolute (b,d) terms. Uncertainties are based on the standard error of the effect adjusted for climate and nutrient sampling coverage, that is, increasing in areas with values of nutrient availability, temperature and precipitation poorly represented by CO2 experiments.

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Corrected Fig. 3a

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https://doi.org/10.1038/s41558-019-0545-2

1Department of Earth System Science, Stanford University, Stanford, CA, USA. 2Institut de Ciència i Tecnologia Ambientals, Universitat Autònoma de

Barcelona, Barcelona, Spain. 3Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria.

4Woods Institute for the Environment and Precourt Institute for Energy, Stanford University, Stanford, CA, USA. 5AXA Chair Programme in Biosphere

and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, UK. 6Department of Biological Sciences,

Macquarie University, North Ryde, New South Wales, Australia. 7Department of Earth System Science, Tsinghua University, Beijing, China. 8Department of

Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA. 9Climate and Ecosystem Sciences Division, Lawrence Berkeley National

Laboratory, Berkeley, CA, USA. 10Department of Microbiology and Ecosystem Science, Division of Terrestrial Ecosystem Research, Faculty of Life Sciences,

University of Vienna, Vienna, Austria. 11Evolution and Ecology Program, International Institute for Applied Systems Analysis, Laxenburg, Austria.

12Centre of Excellence PLECO (Plants and Ecosystems), Biology Department, University of Antwerp, Wilrijk, Belgium. 13Jet Propulsion Laboratory, California

Institute of Technology, Pasadena, CA, USA. 14Joint Institute for Regional Earth System Science and Engineering, University of California at Los Angeles,

Los Angeles, CA, USA. 15Department of Forest Resources, University of Minnesota, St. Paul, MN, USA. 16Hawkesbury Institute for the Environment,

Western Sydney University, Penrith, New South Wales, Australia. 17CREAF, Cerdanyola del Vallès, Spain. 18Center for Ecosystem Science and Society,

Northern Arizona University, Flagstaff, AZ, USA. 19Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA. 20CSIC, Global

Ecology Unit CREAF-CEAB-UAB, Bellaterra, Spain. 21Environmental Biology Department, Institute of Environmental Sciences, Leiden University, Leiden,

the Netherlands. 22College of Marine and Environmental Sciences, James Cook University, Cairns, Queensland, Australia. 23Department of Forest,

Rangeland and Fire Sciences, College of Natural Resources, University of Idaho, Moscow, ID, USA. 24Sino-French Institute for Earth System Science,

College of Urban and Environmental Sciences, Peking University, Beijing, China. 25Institute of Tibetan Plateau Research, Chinese Academy of Sciences,

Beijing, China. 26Land & Environmental Management, AgResearch, Palmerston North, New Zealand. 27School of Biological Sciences, University of Tasmania,

Hobart, Tasmania, Australia. 28Rangeland Resources & Systems Research Unit, Agricultural Research Service, United States Department of Agriculture,

Fort Collins, CO, USA. 29School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China. 30Department of

Plant Ecology, Justus Liebig University of Giessen, Giessen, Germany. 31School of Biology and Environmental Science, University College Dublin, Belfield,

Ireland. 32Smithsonian Tropical Research Institute, Balboa, Republic of Panama. 33Department of Psychiatry and Neuropsychology, Maastricht University,

Maastricht, the Netherlands. 34Department of Methodology and Statistics, Utrecht University, Utrecht, the Netherlands. 35Soil Chemistry, Wageningen

University, Wageningen, the Netherlands. 36Institute of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Japan. 37Graduate School of

Agriculture, Hokkaido University, Sapporo, Japan. 38USDA, Agricultural Research Service, Grassland, Soil and Water Research Laboratory, Temple,

TX, USA. *e-mail: cesar.terrer@me.com

Elevated CO2 (eCO2) experiments provide critical information

to quantify the effects of rising CO2 on vegetation1–6. Many

eCO2 experiments suggest that nutrient limitations modulate

the local magnitude of the eCO2 effect on plant biomass1,3,5,

but the global extent of these limitations has not been empiri-

cally quantified, complicating projections of the capacity of

plants to take up CO27,8. Here, we present a data-driven global

quantification of the eCO2 effect on biomass based on 138

eCO2 experiments. The strength of CO2 fertilization is pri-

marily driven by nitrogen (N) in ~65% of global vegetation

and by phosphorus (P) in ~25% of global vegetation, with

N- or P-limitation modulated by mycorrhizal association.

Our approach suggests that CO2 levels expected by 2100 can

potentially enhance plant biomass by 12 ± 3% above current

values, equivalent to 59 ± 13 PgC. The future effect of eCO2

we derive from experiments is geographically consistent with

past changes in greenness9, but is considerably lower than the

past effect derived from models10. If borne out, our results

suggest that the stimulatory effect of CO2 on carbon storage

could slow considerably this century. Our research provides

an empirical estimate of the biomass sensitivity to eCO2 that

may help to constrain climate projections.

Levels of eCO2 affect the functioning and structure of terrestrial

ecosystems and create a negative feedback that reduces the rate of

global warming8,9,11–14. However, this feedback remains poorly quan-

tified, introducing substantial uncertainty in climate change projec-

tions7,8. Experiments with eCO2 simulate the response of plants to

eCO2 and thereby provide important empirical and mechanistic

Nitrogen and phosphorus constrain the CO2

fertilization of global plant biomass

César Terrer 1,2,3*, Robert B. Jackson 1,4, I. Colin Prentice5,6,7, Trevor F. Keenan 8,9,

Christina Kaiser10,11, Sara Vicca 12, Joshua B. Fisher13,14, Peter B. Reich15,16, Benjamin D. Stocker 17,

Bruce A. Hungate 18,19, Josep Peñuelas 17,20, Ian McCallum3, Nadejda A. Soudzilovskaia 21,

Lucas A. Cernusak 22, Alan F. Talhelm 23, Kevin Van Sundert 12, Shilong Piao 24,25,

Paul C. D. Newton26, Mark J. Hovenden 27, Dana M. Blumenthal28, Yi Y. Liu29, Christoph Müller30,31,

Klaus Winter32, Christopher B. Field 4, Wolfgang Viechtbauer33, Caspar J. Van Lissa34,

Marcel R. Hoosbeek35, Makoto Watanabe36, Takayoshi Koike37, Victor O. Leshyk18,19, H. Wayne Polley38

and Oskar Franklin3

There are amendments to this paper

NATURE CLIMATE CHANGE | VOL 9 | SEPTEMBER 2019 | 684–689 | www.nature.com/natureclimatechange

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Climate Warming Alters Nutrient Cycling and its Constraint on CO2 Fertilization in Global Forests

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Sep 2017

Caspar J. Van Lissa

Meta-analyses in psychology often lack the power to adequately account for between-studies heterogeneity. The number of studies on any topic is typically low, because research is cost- and time-intensive. At the same time, a host of potential moderators are introduced when similar research questions are examined in different labs, sampling from different populations, using idiosyncratic methods and instrumentation. Such between-studies heterogeneity presents a substantial challenge to data aggregation in classic meta-analysis. When the causes for heterogeneity are known a-priori, they can be accounted for using meta-regression. What is currently lacking, however, is an exploratory approach, to be used when heterogeneity is suspected, but it is not known which moderators most strongly influence the observed effect size. Recently, weighted regression trees have been used to explore heterogeneity in meta-analysis. Although this provides a promising first step, single trees have many limitations, which can be overcome by using random forests: A powerful learning algorithm, which is flexible, yet relatively robust to overfitting. The present paper introduces MetaForest: An adaptation of random forests for meta-analysis. We present two simulation studies, which illustrate that, in datasets as small as 20 cases, MetaForest outperforms single trees, in terms of three metrics: 1) Predictive performance; 2) power, as evidenced by the proportion of datasets in which the algorithm achieved a positive R2cv; and 3) the ability to distinguish relevant moderators from irrelevant moderators, using variable importance measures. We discuss how MetaForest can enhance the exploration of between-studies heterogeneity when conducting meta-analyses in diverse bodies of literature.