# Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition

Katrin Fleischer, Anja Rammig, Martin de Kauwe, Anthony Walker, Tomas Domingues, Lucia Fuchslueger, Sabrina Garcia, Daniel Goll, Adriana Grandis, Mingkai Jiang, et al.

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Katrin Fleischer, Anja Rammig, Martin de Kauwe, Anthony Walker, Tomas Domingues, et al., Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition. Nature Geoscience, 2019, 12 (9), pp.736-741. 10.1038/s41561-019-0404-9. hal-02975103

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Distributed under a Creative Commons Attribution 4.0 International License
Amazon forest response to CO₂ fertilization
dependent on plant phosphorus acquisition

Katrin Fleischer ID 1*, Anja Rammig<fnref n="1" />, Martin G. De Kauwe 2,3, Anthony P. Walker ID 4,
Tomas F. Domingues ID 5, Lucia Fuchslueger ID 6,7, Sabrina Garcia ID 6, Daniel S. Goll ID 8,9,
Adriana Grandis¹⁰, Mingkai Jiang ID 11, Vanessa Haverd 12, Florian Hofhansl ID 13, Jennifer A. Holm ID 14,
Bart Kruijt 15, Felix Leung ID 16,17, Belinda E. Medlyn ID 11, Lina M. Mercado 16,18, Richard J. Norby ID 4,
Bernard Pak ID 19, Celso von Randow ID 20, Carlos A. Quesada 6, Karst J. Schaap 6,
Oscar J. Valverde-Barrantes 21, Ying-Ping Wang ID 19, Xiaojuan Yang ID 4, Sönke Zaehle ID 22,
Qing Zhu ID 14 and David M. Lapola 23

Global terrestrial models currently predict that the Amazon rainforest will continue to act as a carbon sink in the future, pri-
marily owing to the rising atmospheric carbon dioxide (CO2) concentration. Soil phosphorus impoverishment in parts of the
Amazon basin largely controls its functioning, but the role of phosphorus availability has not been considered in global model
ensembles—for example, during the Fifth Climate Model Intercomparison Project. Here we simulate the planned free-air CO2
enrichment experiment AmazonFACE with an ensemble of 14 terrestrial ecosystem models. We show that phosphorus avail-
ability reduces the projected CO2-induced biomass carbon growth by about 50% to 79 ± 63 g C m⁻² yr⁻<fnref n="1" /> over 15 years compared
to estimates from carbon and carbon-nitrogen models. Our results suggest that the resilience of the region to climate change
may be much less than previously assumed. Variation in the biomass carbon response among the phosphorus-enabled models
is considerable, ranging from 5 to 140 g C m⁻² yr⁻<fnref n="1" />, owing to the contrasting plant phosphorus use and acquisition strategies
considered among the models. The Amazon forest response thus depends on the interactions and relative contributions of the
phosphorus acquisition and use strategies across individuals, and to what extent these processes can be upregulated under
elevated CO2.

The intact Amazon rainforest acts as a substantial carbon (C) sink that completely offsets the carbon dioxide (CO2) emissions from fossil fuel combustion and land use change in the Amazon region<fnref n="1" />,². Increasing atmospheric CO2 concentrations from anthropogenic activity may be the primary factor for the current Amazon net C sink<fnref n="1" />,³, via so-called CO2 fertilization (an increase in photosynthetic C uptake by plants under a higher CO2), which is projected to continue into the future by global models⁴⁻⁶. The CO2 fertilization effect has been observed in field experiments that were conducted predominantly in the temperate zone. In these experiments, the elevated carbon dioxide (eCO2)-induced increase in C uptake was generally low when other factors, such as soil nitrogen (N), were limiting⁷⁻⁹. So far, whole-ecosystem-scale experiments,

that is, free-air CO₂ enrichment (FACE) experiments, have not been
conducted in the tropics¹⁰,¹¹.

Over large parts of the Amazon and the tropics worldwide, phos-
phorus (P), not N, is assumed to be the key limiting nutrient, as
most P has been lost or occluded from plant uptake during millions
of years of soil pedogenesis¹²,¹³. Forests that grow on these highly
weathered old soils may nonetheless be highly productive due to
the evolution of multiple strategies for P acquisition and use, which
enable a tight cycling of P between plants, microorganisms and
soils¹⁴,¹⁵. Despite this knowledge, to quantify the control of P on
plant physiology, growth and plant-soil-microbial interactions in
global models, and hence its role in the forests' responses to eCO2,
remains a challenge¹⁶,¹⁷. This challenge is exacerbated by the scarcity

<footnote n="1">¹Land Surface-Atmosphere Interactions, Technical University of Munich, Munich, Germany. ²Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia. ³ARC Centre of Excellence for Climate Extremes, Sydney, New South Wales, Australia. ⁴Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA. ⁵FFCLRP, Department of Biology, University of São Paulo, Ribeirão Preto, Brazil. ⁶National Institute of Amazonian Research (INPA), Manaus, Brazil. ⁷Department of Biology, University of Antwerp, Antwerp, Belgium. ⁸Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Saint-Aubin, France. ⁹Lehrstuhl für Physische Geographie mit Schwerpunkt Klimaforschung, Universität Augsburg, Augsburg, Germany. ¹⁰Department of Botany, University of São Paulo, São Paulo, Brazil. ¹¹Hawkesbury Institute for the Environment, Western Sydney University, Sydney, New South Wales, Australia. ¹²CSIRO Oceans and Atmosphere, Canberra, Australian Capital Territory, Australia. ¹³International Institute for Applied Systems Analysis, Laxenburg, Austria. ¹⁴Lawrence Berkeley National Laboratory, Berkeley, CA, USA. ¹⁵Alterra Wageningen, Wageningen, The Netherlands. ¹⁶College of Life and Environmental Sciences, University of Exeter, Exeter, UK. ¹⁷Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, Hong Kong. ¹⁸Centre for Ecology and Hydrology, Wallingford, UK. ¹⁹CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia. ²⁰National Institute for Space Research (INPE), São José dos Campos, Brazil. ²¹International Center of Tropical Botany, Florida International University, Miami, FL, USA. ²²Max-Planck Institute for Biogeochemistry, Jena, Germany. ²³Center for Meteorological and Climatic Research Applied to Agriculture, University of Campinas, Campinas, Brazil. *e-mail: katrin.fleischer@tum.de</footnote>
of observations and distinctive plant species responses in hyperdverse tropical forests¹⁸.

## Predicted nutrient feedbacks to eCO₂ for AmazonFACE

Here we study the potential interactions between eCO₂ and nutrient (N and P) feedbacks in a mature Amazonian rainforest by simulating the planned AmazonFACE experiment (+200 ppm; https://amazonface.inpa.gov.br/) with an ensemble of ecosystem models (n=14 (the model details are given in Supplementary Table 3)), which includes three C, five carbon-nitrogen (CN) and six carbon-nitrogen-phosphorus (CNP) models¹⁹⁻²⁴. The AmazonFACE experiment is located in a well-studied, highly productive tropical forest in Central Amazonia²⁵,²⁶, which grows on a strongly weathered terra firme ferralsol. This ecosystem represents the low end of the plant-available P spectrum in the Amazon, consistent with ~32% of the Amazon rainforest's cover fraction²⁷. In situ measurements were used to parameterize the models and to evaluate simulated ambient conditions (Supplementary Tables 1 and 2). Our aim was to generate a priori model-based hypotheses to highlight the current state of knowledge and guide measurement strategies for AmazonFACE and other ecosystem manipulation experiments to gain crucial process understanding of P control on the CO₂ fertilization effect.

Simulated eCO₂ (+200 ppm) had a positive effect on plant biomass C across all the models, but was weakest in the CNP models (Fig. 1a). The eCO₂ conditions induced average biomass C gains of 163 ± 65, 145 ± 83 and 79 ± 63 g C m⁻² yr⁻¹ (mean ± s.d.) over 15 years in the C, CN and CNP models, respectively (Fig. 1a). Limitations by P thus reduced the predicted biomass C sink by 52 and 46% compared to that in the C and CN models, respectively, with considerable variation across and within model groups (Supplementary Fig. 1). Plot inventories at the AmazonFACE site during the 2000s indicate an above-ground biomass increment of 23 g C m⁻² yr⁻¹, substantially below the Amazon-wide¹ estimate of 64 g C m⁻² yr⁻¹. The model ensemble represents ambient conditions, such as productivity and leaf area index, reasonably well, but ensemble members show divergence in other ecosystem characteristics, such as the biomass C increment, which range from 5 to 114 g C m⁻² yr⁻¹. There is, however, no clear pattern in performance between the model groups, so that we judge that these differences do not have a bearing on the conclusions of our study (further discussion in Supplementary Fig. 2).

## Differing model responses to P limitation

Gross and net primary productivity (GPP and NPP, respectively) are both stimulated by eCO₂ in all models, both initially (after one year of eCO₂) and until the end of the simulation. The CNP models show the strongest decline from the initial response due to P limitation (Fig. 1b,c). The final response of NPP to eCO₂ was a 35%, 29% and 9% stimulation for the C, CN and CNP models, respectively. In general, in the CN and CNP models, nutrient limitation is defined as the nutrient demand being greater than the nutrient supply. However, the models differ in their assumptions on how nutrient limitation controls productivity and C allocation in response to eCO₂, so that divergent responses on plant C use efficiency (CUE = NPP/GPP) are simulated (Supplementary Table 3). In some CN models, CUE increases because the N limitation is hypothesized to reduce autotrophic respiration via a lower tissue N content. Some CNP models, however, assume a direct downregulation of growth and hence the plant CUE decreases (Supplementary Fig. 3). Elevated CO₂ induced higher fine root investments of NPP in some CN and CNP models to aid nutrient acquisition (Fig. 1c and Supplementary Fig. 4). The predicted changes in allocation with eCO₂ cause a general increase in biomass turnover across all but one of the models, and partially offset the positive biomass response (Supplementary Table 4). Changes in turnover play a minor role in our 15 year simulation period, but rather control the long-term future CO₂ effect on the biomass C sink²⁸,²⁹.

<figure><img src="image_2.png" /><figcaption>**Fig. 1 |** The predicted effect of eCO₂ on biomass C, productivity and biomass compartments for C, CN and CNP models. **a,** The final response of biomass growth, calculated as the mean annual response over 15 years of eCO₂. **b,** The first-year response of productivity (GPP and NPP) and CUE. **c,** The 15-year response of productivity, CUE and leaf, fine root and wood C (calculated as the mean response of the 13th to 17th years). Responses to eCO₂ are the differences between the elevated and ambient model run, shown as mean and s.d. (black lines) per model group, with individual model results as dots.</figcaption></figure>

Plant growth under eCO₂ is lowest in the CNP models as the low availability of soil labile P restricts P uptake either immediately or over time (Supplementary Fig. 5). We considered the modelled P limitation on plant growth to be realistic, as the models and observations agree that soil labile P is very low (Supplementary Fig. 2). Other site observations support the fact that P is extremely critical for plant productivity, such as high leaf N:P ratios of 37 and a high plant P resorption (before litter fall) of 78% (Supplementary Table 1). Although P limitation consistently reduces the eCO₂-induced biomass C sink, there is significant variation among CNP models due to contrasting process representations (Fig. 2 and Supplementary Table 3). P shortages downregulate growth (that is, NPP) in all the CNP models, directly or via photosynthesis. The major differences in the model assumptions relate to how they modify P supply and demand to alleviate plant P shortages, which include either (1) enhancing the plant P use efficiency (PUE = NPP/P uptake) or (2) upregulating the P acquisition mechanisms. The models assume that PUE may change if the tissue nutrient ratios are flexible, if the C allocation changes among tissues with different stoichiometry and/or if P resorption is variable (Fig. 2). Flexible stoichiometry is considered in all the CNP models except ELM-CTC, although with varying degrees of flexibility. A greater fine root C allocation with plant P stress is considered in some, and P resorption is a fixed fraction of leaf tissue P in all the models (Fig. 2).

The CNP models differ in their representation of soil P acquisition mechanisms; three of the six models (ELM-ECA, ELM-CTC and GDAY) consider the desorption of P from mineral surfaces (that is, the secondary or strongly sorbed P pool), whereas the others assume P in these pools to be unavailable to plants. All the models include the biochemical mineralization of organic P via phosphatase, but only three (ELM-ECA, ELM-CTC and ORCHIDEE) include the functionality to increase the P acquisition via this mechanism under plant P stress (Fig. 2 and Supplementary Table 3). Litter and soil stoichiometry are considered with varying degrees of
<figure><img src="image_3.png" /><figcaption>**Fig. 2** | Strength of P feedbacks in controlling the biomass C response to eCO₂ for the six CNP models. The degree to which the modelled P feedback on ecosystem processes controls the response of biomass C to eCO₂ in our simulations (none, intermediate and high). P limitation downregulates photosynthesis or growth under eCO₂ in all the models. Maintenance respiration, leaf turnover and P resorption are not controlled by the P cycle in any of the models. The leaf N:P ratio responds to eCO₂ in most of the models. The desorption of P from mineral surfaces is considered only in ELM-ECA and ELM-CTC, and biochemical P mineralization is considered in many of the models, but effectively responsive only in ELM-CTC (Supplementary Table 3). SOM, soil organic matter.</figcaption></figure>

Soil labile P limits the microbial decomposition rates of litter and soil, so that decomposition is reduced when immobilization demands for P exceed the soil labile P availability (Fig. 2 and Supplementary Table 3).

## Enhanced PUE and acquisition due to eCO₂

Diverging representations of plant P use and acquisition among the CNP models cause predictions of the eCO₂-induced biomass C sink to range from 5 g C m⁻² yr⁻¹ to 140 g C m⁻² yr⁻¹ (Fig. 3a and Supplementary Fig. 1). Greater plant PUE occurred in four of the models, for which shifts in tissue C:N and N:P ratios due to eCO₂ led to increases in biomass C:P that ranged from ~200 to 1,600 g C g P⁻¹ (Fig. 3c). A higher fine root investment with eCO₂, at the expense of less ‘P-costly’ wood, offset some increases in PUE in some models (Fig. 3b). A flexible biomass stoichiometry altered the decomposition dynamics and induced progressive P limitation in response to eCO₂, that is, the litter stoichiometry shifted towards a lower quality (less N and P in relation to C), which reduces the net P mineralization rates from microbial decomposition and so causes P to become increasingly unavailable to plants and accumulating in soil organic matter (Fig. 3d,e). This plant-soil-microbial feedback slowed the cycling of P in the ecosystem and exacerbated the initial P limitation (Vitousek³⁰ reports a similar feedback during pedogenesis).

Enhanced plant P acquisition under eCO₂ effectively alleviated the plant P limitation in two CNP models (ELM-CTC and ELM-ECA) (Fig. 3e). In both, eCO₂ increased the liberation of P from the secondary pool, as a higher plant P demand and uptake diminished the labile P pool, which in turn caused higher desorption rates. P desorption is thus only indirectly, and not mechanistically, enhanced by plants in these models. Biochemical mineralization of organic P under eCO₂ responded positively in both of the models, but added only notably to additional P acquisition in ELM-CTC (Fig. 3e). Although three of

the CNP models simulated higher fine root investments, the actual P uptake return per fine root increment was marginal or came only into effect in the long term (Supplementary Fig. 6).

Observations document ample N cycling in the system, for example, high leaf N contents, indicative δ¹⁵N values, high rates of N oxide emissions and low leaf N resorption³¹,³², and thereby suggest that plant growth is not directly affected by N availability. The CN models, however, simulate an increased N use efficiency and biomass C:N ratios in response to insufficient N uptake under eCO₂ (Supplementary Fig. 5). Plant N availability may be underestimated in the models because the plant-available mineral N supply was <7 g N m⁻² across all the models, as opposed to 17.5 g N m⁻² observed in the top 10 cm alone (Supplementary Fig. 2). These results highlight an important gap in our knowledge that relates to the dynamics of tropical N cycling and its potential interaction with P dynamics (Table 1).

## Model-based hypotheses for the AmazonFACE experiment

In summary, the model ensemble encapsulates a range of plausible hypotheses and represents a potential range of biomass C responses to eCO₂ under a low soil P availability. The assumption of a lacking ability of plants to acquire more soil P and a limited capacity for plants to use P more efficiently resulted in an effectively zero biomass C gain with eCO₂. Conversely, flexible stoichiometry, in combination with an enhanced plant P acquisition, were the key mechanistic responses that led to biomass C gain with eCO₂. Divergences in the simulated eCO₂ response lead us to the following testable hypotheses, and call for directed field measurements (Table 1):

* H1: low soil P availability will strongly constrain the future plant biomass growth response to eCO₂ either by downregulating photosynthesis or limiting plant growth directly, or a combination thereof.
<figure><img src="image_4.png" /><figcaption>**Fig. 3** | Key responses of biomass C gain, stoichiometry, allocation and P dynamics to eCO₂ for the CNP models. Blue denotes a positive response; red denotes a negative response. a, Mean annual change in standing leaf, fine root and wood C over 15 years (g C m⁻² yr⁻¹), which increases across the models from left to right (CABLE, GDAY, ELM-CTC, CABLE-POP, ELM-ECA, and ORCHIDEE). b, The mean change in C allocation for fine roots and wood (%). c, Mean change in tissue stoichiometry in absolute terms (g C g P⁻¹) and change in PUE over 15 years (g C g P⁻¹ yr⁻¹). d, The P ecosystem retention in terms of the mean change in the ecosystem P input and output (leaching) fluxes (g P m⁻² yr⁻¹) and in the final P stock in the biomass, organic soil, mineral soil and total ecosystem (g P m⁻²). e, Mean change in plant P acquisition processes, namely net P mineralization (min), biochemical (bioch) P mineralization, and P uptake (g P m⁻² yr⁻¹), and in the secondary and labile P pools (g P m⁻²). For both d and e, the P flux changes are differences in the cumulative fluxes after 15 years and the P pool changes are differences in pools after 15 years.</figcaption></figure>

* H2: despite the limited soil P supply, plasticity in vegetation stoichiometry and allocation patterns will allow for some biomass growth under eCO₂.

* H3: plants will increase investments in P acquisition to increase P supply and allow biomass growth under eCO₂ either via a greater P interception through fine root production or via a greater P liberation from P desorption or the biochemical mineralization of organic P.

These model-based hypotheses deepen a previous analysis of potential N and P limitation on terrestrial C accumulation based on mass balance principle³³. Furthermore, we add to a model intercomparison carried out in advance of the EucFACE experiment³⁴ by extending the range of plant P feedbacks considered across CNP models. This work highlights H1: two stoichiometrically constrained CNP models predicted that a strong P limitation will curtail the growth response to eCO₂ in Australia. Consistent with this hypothesis, above-ground growth has not increased with eCO₂ in that experiment over the initial years³⁵. This finding underlines that monitoring efforts need to place a strong(er) focus on below-ground C and nutrient dynamics, in addition to canopy-scale photosynthesis and above-ground growth dynamics. Additionally, the autotrophic respiration dependence on P content and plant stress from drought or nutrient limitation needs further monitoring during experiments to fully elucidate the plant C budget and address H1 (Table 1).

Nutrient fertilization experiments support H2, as plasticity in leaf stoichiometry at the individual level, along with plasticity in P resorption, was observed³⁶. Across the Amazon, community-weighted

leaf N:P ratios in the field varied from 13 to 42 g N g P⁻¹ (n=64)³², which place our site, with a mean of 37, closer to the high end. GDAY predicted the most plausible increase in the leaf N:P ratio from 34 to 38 (Supplementary Fig. 7). Two models predicted strong increases in the leaf N:P ratio with eCO₂, but began with much lower initial values. The degree to which plasticity in stoichiometry and resorption can aid plant PUE under eCO₂ in highly P-limited sites that are already at the end of the observed spectrum remains to be seen (H2). Monitoring plant-tissue stoichiometry, which includes wood with much higher N:P ratios, combined with assessments of the P resorption in CO₂ and nutrient fertilization experiments, will reduce the uncertainties (Table 1).

Based on previous observations⁸, a number of models assume an increased fine root investment, as well as higher biochemical P mineralization and P desorption from mineral surfaces, under an eCO₂-induced nutrient limitation (H3). The effect of an increased fine root biomass on nutrient uptake was limited in our simulations and the ambient fine root allocation fractions were highly variable among the models, ranging from 5 to 30% of NPP (Supplementary Figs. 4 and 6). Both these modelled results highlight the model deficiencies in below-ground processes³⁷ that need addressing (Table 1). There is evidence that phosphatase activity in the litter and soil and the presence of low-molecular-weight acids used to liberate P from organic matter or from mineral surfaces increase with plant P demand³⁸. This was predicted by ELM-CTC in our simulations, which also showed Amazon-wide that with “enhanced phosphatase production, productivity in the highly P-limited areas can be sustained under elevated
<table><caption>**Table 1 | Key processes and variables requiring observational estimates to reduce uncertainty in P cycle control on eCO₂ effects in ecosystem models**</caption><thead><tr><th>Process</th><th>Measurements needed</th></tr></thead><tbody><tr><td><strong>H1, Plant C budget</strong></td><td></td></tr><tr><td>Canopy scale C assimilation</td><td>Seasonal dynamics of leaf area and photosynthetic capacity<br>Photosynthetic acclimation</td></tr><tr><td>Plant tissue respiration</td><td>Control of drought stress, nutrient limitation and P content<br>Wood and root respiration</td></tr><tr><td>Biomass growth</td><td>Below-ground biomass compartments<br>Long-term growth rates</td></tr><tr><td><strong>H2, Plant P use</strong></td><td></td></tr><tr><td>Plant tissue C:P and N:P stoichiometry</td><td>Plasticity versus adaptation to environmental change<br>Functionality of tissue P<br>Wood P content and stoichiometry</td></tr><tr><td>Plant tissue P resorption</td><td>P content in live tissue and/or fresh litter (resorption efficiency and/or proficiency)<br>Plasticity versus adaptation to environmental change</td></tr><tr><td><strong>H3, Plant P acquisition</strong></td><td></td></tr><tr><td>P desorption due to plant exudation</td><td>Flux and interactions with microorganisms<br>Cost of exudation versus P return for plant</td></tr><tr><td>P acquisition due to fine root production</td><td>Surface litter fine root dynamics<br>Fine root allocation fractions<br>Fine root productivity vs. plant P uptake</td></tr><tr><td>Biochemical P mineralization (via phosphatase)</td><td>Phosphatase activity and relation to P mineralization<br>Plant production of phosphatase vs. plant-induced production by microorganisms<br>Cost of phosphatase production vs. plant P acquisition</td></tr><tr><td><strong>Other interactions</strong></td><td></td></tr><tr><td>Plant N availability</td><td>Ecosystem N budget<br>Symbiotic and free-living N fixation<br>Control of N availability on P acquisition and other N:P interactions</td></tr><tr><td>Plant water availability</td><td>Control on P mineralization and transport dynamics<br>Interacting control of water and P limitation on the eCO₂ effect</td></tr></tbody></table>

CO₂ conditions”³⁹. Plants invest in P liberation and acquisition, but whether these mechanisms can be upregulated under eCO₂ and over what time frame this may occur remain open questions. Quantification of such a response is lacking, as are estimates of the associated plant C costs to acquire P via these and other mechanisms, such as mycorrhizal symbiosis¹⁵,⁴⁰ (Table 1). The P gain and C cost for P acquisition mechanisms, as well as the associated plant-soil-microbial interactions, need to be assessed by analyses of soil, microbial and root nutrition, and via novel techniques to investigate enzyme and labile C dynamics⁴¹. Monitoring of below-ground fine root dynamics needs to include the surface litter layer, commonly explored by fine roots in P-impoverished ecosystems in the Amazon, but not yet quantified nor considered in ecosystem models (Table 1).

## Implications of P control on the CO₂ fertilization effect

Previous model projections suggest a sustained fertilization effect of eCO₂ on the Amazon C sink, but did not consider feedbacks from a

low soil P availability across much of the Amazon basin⁵,⁶. Our study demonstrates that, based on the current generation of CNP models, the omission of P feedbacks is highly likely to cause an overestimation of the Amazon rainforest's capacity to sequester atmospheric CO₂. Considering the P limitation on the CO₂ fertilization effect in future predictions may indicate that the forest is less resilient to higher temperatures and changing rainfall patterns than previously thought⁶,⁴². Periods of water deficit may contribute to the eCO₂ fertilization effect on productivity due to its water-saving effect³⁴, or due to alterations of the decomposition processes. Our study site experienced years with significantly less than average precipitation, for example, in 2000 and 2009; however, in our simulations this only marginally increased the positive response of GPP and NPP to eCO₂ (Supplementary Figs. 8 and 9). Models lack the appropriate sensitivity for plant responses to changes in water availability, and even more so when precipitation sums are high⁴³. Interactions of water and P availability and their consequences on the CO₂ fertilization effect remain uncertain⁴⁴ and so this is an area in which field measurements will allow us to better constrain model responses (Table 1).

Although P is likely to reduce the biomass C sink response to CO₂ in regions with a low plant-available P supply, our results suggest that plasticity in plant P use and plant P acquisition mechanisms may, at least partially, alleviate the P limitation under eCO₂ and enable the CO₂ fertilization of biomass growth. The model ensemble may be interpreted as representing a range of possible tropical plant functional strategies and growth responses to a low P availability under eCO₂. Responses are expected to be species-specific, as were plant growth responses to low P supplies in another tropical region¹⁸. The ecosystem-scale response to P limitation under eCO₂ thus depends on the relative contributions of the various P acquisition and P use strategies across individuals, their interactions and to what extent these processes can be upregulated under eCO₂. All these ultimately need to be described and represented in a single model framework to accurately predict the Amazon rainforest's response to future climate change.

AmazonFACE has the unique opportunity to experimentally address these key areas of uncertainty, not only by integrating the proposed measurements across the seasons and at the ecosystem scale (summary in Table 1) but also by assessing species-specific responses to eCO₂ in relation to trait expression. An Amazon-wide expression of plant functional strategies may then be inferred by applying the mechanistic interplay between trait expression and edaphic conditions. The key to predicting the future of the world's largest tropical forest under climate change and eCO₂ thus lies in obtaining experimental data on, and subsequently modelling, different plant P acquisition and use strategies and their interactions in a competing plant community.

## Online content

Any methods, additional references, Nature Research reporting summaries, source data, statements of code and data availability and associated accession codes are available at https://doi.org/10.1038/s41561-019-0404-9.

Received: 25 September 2018; Accepted: 11 June 2019; Published online: 5 August 2019

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## Acknowledgements

The AmazonFACE research program provided logistical support to conduct this study (https://amazonface.inpa.gov.br/). This study was funded by the Inter-American Development Bank through a technical cooperation agreement with the Brazilian Ministry of Science, Technology, Innovation and Communications (Grant BR-T1284), by Brazil's Coordination for the Improvement of Higher Education Personnel (CAPES) Grant 23038.007722/2014-77, by the Amazonas Research Foundation (FAPEAM) Grant 2649/2014 and the São Paulo Research Foundation (FAPESP) Grant 2015/02537-7. We thank the many scientists, field and laboratory technicians, students and other personnel involved in the development of the models, in collecting and analysing the field data and in the planning and execution of the AmazonFACE program. We thank the German Research Foundation (DFG) for financing one of the workshops that made this study possible (grant no. RA 2060/4-1). T.F.D., S.G., A.G. and C.A.Q. thank the USAID for funding via the PEER program (grant agreement AID-OAA-A-11-00012). A.P.W. and R.J.N. were supported by the FACE Model-Data Synthesis project, X.Y. and Q.Z. were supported by the Energy Exascale Earth System (E3SM) program and J.A.H. was supported by the Next Generation Ecosystem Experiments—Tropics project; all were funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research under contract numbers DE-AC02-05CH11231 and DE-AC05-00OR22725. M.G.d.K. acknowledges support from the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023) and the New South Wales Research Attraction and Acceleration Program. Y.P.W., B.P. and V.H. acknowledge support from the National Earth System Science Program of the Australian Government. D.S.G. is funded by the 'IMBALANCE-P' project of the European Research Council (ERC-2013-SyG-610028). L.M.M. acknowledges funding from the UK's Natural Environment Research Council (NERC) grant nos NE/LE007223/1 and NE/N017951/1. F.L. acknowledges funding from EU FP7 LUC4C program (GA603542). K.F. is funded by the DFG (grant no. RA 2060/5-1).

## Author contributions

D.M.L., A.R. and K.F. conceived the study. L.F., S.G., A.G., F.H., R.J.N., C.A.Q., K.J.S. and O.J.V.-B. collected field data. K.F., D.S.G., M.G.d.K., M.J., V.H., J.A.H., F.L., L.M.M., B.P., C.vR., Y.-P.W., X.Y., S.Z. and Q.Z. performed model simulations. K.F. wrote the manuscript with contributions from all the co-authors.

## Competing interests

The authors declare no competing interests.

## Additional information

Supplementary information is available for this paper at https://doi.org/10.1038/s41561-019-0404-9.

Reprints and permissions information is available at www.nature.com/reprints.

Correspondence and requests for materials should be addressed to K.F.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© The Author(s), under exclusive licence to Springer Nature Limited 2019
## Methods

**Site description.** Model simulations were conducted at the AmazonFACE experimental site in Central Amazonia (29° 35' 39" S, 60° 12' 29" W). AmazonFACE is an integrated model-experiment project that aims to assess the effects of high CO₂ concentrations on the ecology and resilience of the Amazon rainforest (https://amazonface.inpa.gov.br/). The experiment is currently being established and is situated in a terra firme forest on a plateau characterized by a highly weathered, deep, clay sediment soil (with a clay fraction of 76%), classified as a geric ferralsol¹⁵. The site and the surrounding area have been subjected to various long-term measurement activities²⁵,⁴⁶⁻⁴⁹, coordinated by the Large-Scale Biosphere-Atmosphere Program (http://lba2.inpa.gov.br/) in Amazonia, and includes the 'K34' eddy covariance flux tower²⁶, located approximately 2 km from the AmazonFACE site. The mean annual precipitation at K34 from January 2000 to December 2015 was 2,600 mm yr⁻¹, and the mean temperature was 26°C.

**Model descriptions.** Fourteen ecosystem models with contrasting representations of ecosystem functioning and nutrient cycling were applied to the experiment (Supplementary Table 3). C cycle dynamics without nutrient cycle feedbacks are represented in the C-only models (InLand, ED2 and ELM-FATES)⁵⁰⁻⁵², C and N dynamics are represented in the CN models (LPJ-GUESS, O-CN, JULES, CABLE-POP(CN) and GDAY(CN))⁵³⁻⁵⁵ and C, N and P dynamics are represented in the CNP models (ELM-ECA, ELM-CTC, CABLE, CABLE-POP, ORCHIDEE and GDAY)¹⁹⁻²⁴. Two models were included with a respective CN and CNP version (GDAY and CABLE-POP) to assess directly the effect of considering P dynamics. The other models were treated as a non-random sample from the possible C, CN and CNP modelling assumptions. Four of the models are dynamic vegetation models: CABLE-POP considers the dynamic establishment and mortality with a fixed plant functional type composition, and LPJ-GUESS, ED2 and ELM-FATES also consider dynamic plant functional type composition. Photosynthesis is based on formulations by Farquhar et al.⁵⁶ or derivations thereof in all the models⁷⁷,⁵⁸ (Supplementary Table 3).

Prognostic C allocation fractions are based on functional relationships among the tissues, for example, fixed ratios between sapwood and leaf area in CABLE-POP, LPJ-GUESS, ED2, GDAY, ORCHIDEE, O-CN, JULES and ELM-FATES, and on resource dependence, for example, higher root allocation under water or nutrient stress in LPJ-GUESS, ELM-ECA, GDAY, O-CN, ORCHIDEE, ED2 and ELM-FATES. C allocation fractions are fixed in InLand and CABLE.

Nutrient limitation is determined by the difference between demand and supply (via root uptake and resorption) of N or P, with the most limiting nutrient determining the degree of limitation. The photosynthetic parameters $V_{cmax}$ and/or $J_{max}$ are controlled by the leaf N in all the CN and CNP models except JULES, and leaf P additionally downregulates GPP in all the CNP models except ORCHIDEE. N controls the NPP in some of the models, namely, O-CN, JULES, ORCHIDEE, CABLE and CABLE-POP, and additionally downregulates growth efficiency (GPP/leaf area index) in CABLE and CABLE-POP.

Maintenance respiration is dependent on the temperature in all the models and is additionally controlled by the tissue N content in all the models that consider the N cycle with the exception of GDAY, in which autotrophic respiration is a fixed fraction of GPP. Plant tissue stoichiometry in the CN and CNP models is either fixed (ELM-CTC and JULES) or varies within or without bounds (all the other models). The nutrient resorption rates in the CN and CNP models are always fixed fractions of the nutrient content in leaves and roots. Competition for nutrients between plant uptake and decomposition processes is handled differently. Nutritional demands for the decomposition process (which represents microbial demands) are first met entirely in some models (CABLE, O-CN, ORCHIDEE and GDAY), are based on relative demands between decomposition and plant uptake (ELM-CTC) or are determined via a multiple consumer approach, which includes adsorption to mineral surfaces (ELM-ECA). Nutrient uptake is a function of plant demand and nutrient availability in all the models and is further controlled by a measure of the root mass in LPJ-GUESS, GDAY, ORCHIDEE and O-CN.

Soil organic matter (SOM) decomposition is limited by soil mineral N availability in most CN and CNP models (except O-CN and ORCHIDEE) and additionally by labile P availability in most CNP models (except GDAY and ORCHIDEE). P in SOM can also be mineralized via phosphatase, decoupling the P cycle from the C and N cycle, termed biochemical P mineralization in the P models. Biochemical P mineralization is a function of the slow SOM pool turnover in CABLE, CABLE-POP and GDAY, as well as substrate availability in ORCHIDEE, ELM-ECA and ELM-CTC. Biochemical P mineralization is upregulated with higher plant P stress, representing higher phosphate production (not specified if by plants or microbes), in ELM-ECA, ELM-CTC and ORCHIDEE.

N inputs originate from N deposition (prescribed by model protocol) and N fixation (prescribed individually). N fixation is either fixed, calculated empirically as a fraction of NPP or evapotranspiration (GDAY, JULES, ORCHIDEE, ELM-CTC, LPJ-GUESS, CABLE, and CABLE-POP), or based on an optimization scheme (ELM-ECA and O-CN). P inputs originate from weathering (prescribed individually) and deposition (prescribed by model protocol). Release of P from rock weathering is a fixed, soil type-specific rate in CABLE and CABLE-POP, a function of the parent P pool in ELM-ECA, ELM-CTC, and GDAY or described as a function of lithology, runoff and air temperature in ORCHIDEE. N and P

losses occur from leaching, modelled as a function of the size of the labile P and mineral N pool, respectively, and additionally controlled by runoff in ELM-ECA and ORCHIDEE.

The number of inorganic P pools and their precise definition varies among the models. We consider two inorganic P pools relevant for our analysis: the labile P pool and the secondary P pool. The labile P pool encompasses plant-available inorganic P, represented in most CNP models by two separate pools connected by sorption dynamics and effectively in equilibrium (described by Langmuir dynamics in most models and a linear approach in ORCHIDEE). The labile P pools follow different nomenclature in the models but are comparable in functionality: the P in soil solution (called labile or solution P) is readily available to plants in the model time step, whereas the non-dissolved P (referred to as sorbed or sorbed labile P pool) can become available to plants on yearly-to-decadal timescales due to desorption. The secondary P pool represents P strongly sorbed by minerals, which is largely unavailable but may enter the labile P pool on centennial timescales and, depending on the model assumptions, may be driven by plant P stress.

**Model simulations.** The models were forced with 16 years of observed local meteorology (2000 to 2015) from the K34 flux tower²⁶. Meteorological data from July 1999 to December 2015 of near-surface air temperature, rainfall, downward shortwave radiation, downward longwave radiation, vapour pressure deficit, surface pressure, relative humidity and wind speed were available for the model input. Specific humidity was calculated based on the observed relative humidity and surface pressure. All the data time series were subject to quality control (that is, removal of outliers) and gap filling using the variables' climatological mean. Precipitation data gaps were filled from a nearby weather station of the Tropical Rainfall Measuring Mission network.

Simulations were initialized with a spin-up routine that resulted in equilibrium conditions of C stocks (and N and P, if applicable) that represented the year 1850. The 16-year meteorological time series were continuously repeated throughout the whole spin-up, during the transient phase (1851–1998) and during our model-experiment phase (1999–2013), representative of a 15-year long AmazonFACE experiment. Global data sets were used as the inputs for atmospheric CO₂ (refs ⁵⁹,⁶⁰), N deposition⁶¹,⁶² and P deposition⁶³. Atmospheric CO₂, N and P deposition levels were set to 284.7 ppm, 1.43 kg N ha⁻¹ yr⁻¹ and 0.144 kg P ha⁻¹ yr⁻¹, respectively, in 1850 and followed historical changes during the transient and model experiment phase.

Other site parameters used for the parameterization of the models were derived from in situ measurements and include rooting and soil depth (set to rooting depth), soil hydraulic parameters, specific leaf area and soil texture (Supplementary Table 2). Soil hydraulic parameters were derived from pedotransfer functions⁶⁴ and site-specific measurements of the soil properties⁶⁵. Soil hydraulic parameters were included in models that accounted for this functionality to allow for a better representation of the soil water dynamics in tropical soils (Supplementary Table 2).

Two model experiments were performed over the 15-year long experiment phase by each model to assess the effect of elevated CO₂: (1) the ambient run and (2) the elevated CO₂ run. In the ambient run, the atmospheric CO₂ was set to ambient levels and employed for the model evaluation against in situ measurements, which included C fluxes from the K34 flux tower. The elevated CO₂ run represents the planned AmazonFACE experiment with a step change increase of 200 ppm at the start of the model experiment and continuous tracking of the CO₂ levels in the ambient run plus 200 ppm thereafter. Model outputs were analysed in biological years of seasonality (July to June) and the differences between the elevated CO₂ runs and the control runs were used to infer the model-based CO₂ effect.

**Model output analysis.** The analysis of the modelled output includes an evaluation of the modelled ambient conditions relative to in situ observations and hypothesised-based analyses of the modelled CO₂ responses. We employed a structural analysis of the model simulations⁷,⁶⁶⁻⁶⁸ by splitting the model outcomes into the underlying processes to identify crucial model assumptions that determine the diverging predictions for the FACE experiment. We focus here on the simulated increase in biomass C due to eCO₂ and the underlying nutrient control thereon.

Biomass C dynamics are a result of primary productivity, C allocation and turnover. We first analysed the effect of eCO₂ on GPP, NPP, autotrophic respiration and the resulting plant CUE. We then assessed changes in the NPP allocation fractions to the biomass compartments of wood, fine roots and leaves, and the resulting effect on biomass C turnover in response to eCO₂. Specific tissue turnover rates were fixed in all the models, but the overall biomass C turnover changes as a result of changing the C allocation to tissue compartments. The turnover rates of biomass C pools were calculated as the fraction of the total litter fall per total biomass pool size (Supplementary Table 4).

Plant nutrient cycle feedbacks to eCO₂ were assessed by splitting the responses into plant N uptake and plant N use efficiency and similarly into plant P uptake and PUE. The responses of N use efficiency and PUE to eCO₂ were further split into changes in tissue C allocation (differing in C:N and N:P ratios) and changes in tissue stoichiometry (flexible C:N and N:P ratios). The soil nutrient cycle feedbacks to eCO₂ were determined by separating eCO₂ responses in the N and P
mineralization rates (N and P mineralization from the microbial decomposition of SOM and the biochemical P mineralization of organic P via phosphatase) and the balance of the ecosystem N and P inputs (N fixation, N and P deposition, and P weathering) and losses (N and P leaching).

## Data availability

Model output data used for the analyses and figures are archived in a GitHub repository (https://github.com/Kaaze7/AmzFACE-model-ensemble-2019).

## Code availability

Code used for the analyses and figures are archived in a GitHub repository (https://github.com/Kaaze7/AmzFACE-model-ensemble-2019).

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