The role of stoichiometric flexibility in modelling forest ecosystem responses to nitrogen fertilization Johannes Meyerholt1,2 and S€ onke Zaehle1 1

Biogeochemical Integration Department, Max Planck Institute for Biogeochemistry, Hans-Kn€oll-Str. 10, D-07745 Jena, Germany; 2International Max Planck Research School (IMPRS) for

Global Biogeochemical Cycles, Hans-Kn€oll-Str. 10, D-07745 Jena, Germany

Summary Author for correspondence: Johannes Meyerholt Tel: +49 3641 576262 Email: jme[email protected] Received: 8 April 2015 Accepted: 2 June 2015

New Phytologist (2015) 208: 1042–1055 doi: 10.1111/nph.13547

Key words: carbon-nitrogen cycle coupling, ecosystem modelling, fertilization, forest carbon balance, nitrogen cycle, plant stoichiometry.

 The response of the forest carbon (C) balance to changes in nitrogen (N) deposition is uncertain, partly owing to diverging representations of N cycle processes in dynamic global vegetation models (DGVMs). Here, we examined how different assumptions about the degree of flexibility of the ecosystem’s C : N ratios contribute to this uncertainty, and which of these assumptions best correspond to the available data.  We applied these assumptions within the framework of a DGVM and compared the results to responses in net primary productivity (NPP), leaf N concentration, and ecosystem N partitioning, observed at 22 forest N fertilization experiments.  Employing flexible ecosystem pool C : N ratios generally resulted in the most convincing model–data agreement with respect to production and foliar N responses. An intermediate degree of stoichiometric flexibility in vegetation, where wood C : N ratio changes were decoupled from leaf and root C : N ratio changes, led to consistent simulation of production and N cycle responses to N addition. Assuming fixed C : N ratios or scaling leaf N concentration changes to other tissues, commonly assumed by DGVMs, was not supported by reported data.  Between the tested assumptions, the simulated changes in ecosystem C storage relative to changes in C assimilation varied by up to 20%.

Introduction Nitrogen (N) availability limits plant growth (net primary production, NPP) in many terrestrial ecosystems (Vitousek & Howarth, 1991; Elser et al., 2007). N availability affects the terrestrial carbon (C) balance and its response to global change, and thereby also the C cycle climate feedback (Sokolov et al., 2008; Thornton et al., 2009; Wang & Houlton, 2009; Zaehle et al., 2010b). Human activities such as fossil fuel burning, land-use change, and fertilizer use have contributed to increased atmospheric CO2 concentrations and deposition of reactive N to ecosystems (Gruber & Galloway, 2008). The likely consequences of these changes for the functioning of the terrestrial biosphere call for a profound understanding of the underlying processes in the Earth system (Bonan, 2008). However, while the major N fluxes and processes controlling them are known, they are still poorly quantified in terms of their magnitudes and geographic distribution (Galloway et al., 2004). Process-based terrestrial biosphere models can project small-scale process understanding of the C and N cycles to larger scales and thereby help to elucidate global patterns (Schimel et al., 1996; Thomas et al., 2013). On the ecosystem level, each flux of the biological C cycle (e.g. growth, litterfall, soil organic matter (SOM) decomposition) is associated with a corresponding organic N flux, 1042 New Phytologist (2015) 208: 1042–1055

implying that changes occur simultaneously in both cycles. The plasticity of ecosystem stoichiometry, meaning here the mass ratio of C to N atoms (C : N ratio), affects the C cycle responses to perturbations in the N cycle, for example, to enhanced N deposition, and vice versa (Aber et al., 2003; Gruber & Galloway, 2008; Austin & Vitousek, 2012; Sistla & Schimel, 2012). In plants, tissue C : N ratios and N concentrations correlate with many aspects of their metabolism, such as the photosynthetic activity of leaves (Field & Mooney, 1986; Reich et al., 1998) and the net C balance of plants through the respiration cost of maintaining N-rich tissues (Reich et al., 2008). Changes in soil N availability have been linked to altered plant growth and variability in vegetation C : N ratios, although the tissue-level physiology leading to this variability is only partially explored (Sterner & Elser, 2002). For instance, N shortage has been observed to elevate leaf C : N ratios through the accumulation of sugars and starch, and the plant N status has been shown to affect the expression of genes associated with the acquisition and metabolism of both C and N (Hermans et al., 2006). The variability in N content of plant material may propagate to SOM and the microbial biomass, which attain characteristic ranges of C : N ratios (Cleveland & Liptzin, 2007). Notwithstanding this general understanding, global models make differing assumptions about the coupling of the ecosystem Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

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C and N cycles, leading to substantial uncertainty in their predictions (Zaehle & Dalmonech, 2011). Many field campaigns have assessed the effect of N addition on vegetation growth, most prominently in the form of N fertilization experiments carried out in temperate and boreal forest ecosystems (Hyv€onen et al., 2008; LeBauer & Treseder, 2008). These local estimates of forest NPP responses to N addition only offer limited insight into general N processes and pathways. Nonetheless, they provide the opportunity to evaluate dynamic global vegetation models (DGVMs), which increasingly incorporate representations of the N cycle (Thornton et al., 2007; Yang et al., 2009; Gerber et al., 2010; Zaehle & Friend, 2010) (Fig. 1). This family of models uses a wide variety of N cycle representations that differ in formulation and parameterization, illustrating an insufficient global understanding of the underlying processes (Zaehle & Dalmonech, 2011). This variety includes two common, alternative assumptions about stoichiometry. The C : N ratio of any tissue is either treated as homeostatic (timeinvariant, fixed C : N ratio), or time-variant as the outcome of the fluxes of C and N in and out of each tissue (flexible C : N ratio). The paradigm of flexible C : N ratios has been applied in ecosystem-scale biogeochemistry models for decades (Parton et al., 1987; Rastetter et al., 1991; Comins & McMurtrie, 1993).




Gaseous & leaching losses


Biological N fixation



Atmospheric deposition


Litter Roots


Mineralization Immobilization

Soil microbiota


Inorganic nitrogen

However, it is not commonly applied in comprehensive DGVMs (e.g. Thornton et al., 2007; Yang et al., 2009). Thomas et al. (2013) speculated that differences in stoichiometric flexiblity may have caused two DGVMs to predict radically different responses to N addition, yet they were unable to rule out the significance of other differences in N cycle representation. Zaehle et al. (2014) highlighted C–N stoichiometry as a cause for uncertainty in responses to elevated CO2 concentrations, but this result was again confounded by other model structural issues. Thus far, although recognized as a potentially important control on modelled C–N coupling, stoichiometric flexibility has not been thoroughly assessed in a manner that would help to make informed decisions about its proper treatment in DGVMs. In this study, to gain an insight into the particular contribution of C–N stoichiometry to model uncertainty, we tested the approaches to stoichiometric flexibility in DGVMs. We simulated the effects of the alternative assumptions on ecosystem responses to N addition, focusing on: comparison of site-level simulations to field data on NPP and leaf N concentration responses to N fertilization at 22 temperate and boreal forest sites; comparison of the modelled N partitioning between vegetation and soil pools to site-scale data from an N tracer experiment; analysis of the simulated fertilization responses with respect to the main mechanisms governing modelled vegetation C and N dynamics; and analysis of modelled controls of soil and vegetation stoichiometry on the total ecosystem and vegetation C budgets. We further conducted a complementary analysis of modelled ecosystem responses to enhanced atmospheric CO2 concentrations (eCO2) at two temperate forest free-air CO2 enrichment (FACE) sites to evaluate the effect of stoichiometric flexibility on increased C rather than N availability. As the main tool in our analysis, we employed the O-CN model (Zaehle & Friend, 2010) in four versions. These model versions only differed in their treatment of stoichiometry in various ecosystem pools by assigning them either fixed or flexible C : N ratios, meant to represent the variety of stoichiometry approaches employed in the current generation of DGVMs (Thornton et al., 2007; Xu & Prentice, 2008; Gerber et al., 2010; Zaehle & Friend, 2010), as reviewed by Zaehle & Dalmonech (2011). In this way, we aimed to characterize the uncertainty in biospheric C sink projections that arises between DGVMs only as a result of alternative representations of C–N stoichiometry, identifying the mechanisms that led to these differences and evaluating the ecological plausibility of the tested approaches.

Soil organic matter

Fig. 1 Schematic depiction of carbon (C; blue) and nitrogen (N; red) pathways in forest ecosystems, as commonly represented in dynamic global vegetation models (DGVMs). Atmospheric soil N inputs from biological N fixation (BNF) and wet or dry N deposition provide inorganic N for plant root uptake. Dead plant material is subject to microbial decomposition, liberating or immobilizing N in the soil. Microbial processes converting different forms of N (ammonium, nitrate, nitrite, nitric oxide, nitrous oxide, dinitrogen) are associated with gaseous losses, and soluble forms of N that are transported by the soil water below the root zone are subject to leaching. Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

Materials and Methods We first introduce a conceptual framework we apply to synthesize the detailed process responses simulated by DGVMs into a few ecosystem-level characteristics, helping with the interpretation of model results (see the Results, ‘Model analysis’ section). We then give a brief overview over the O-CN model (Zaehle & Friend, 2010) and describe the alternative stoichiometry models applied here. Finally, we describe the forest N fertilizer experiments used in this study and the modelling protocol to replicate them. New Phytologist (2015) 208: 1042–1055

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Conceptual framework All variables and units are listed in Table 1. The gross primary production of plants (GPP) can be described as a function of leaf N concentration (Nleaf), leaf biomass (BL) and photosynthetic Nuse efficiency (PNUE): GPP ¼ f ðPNUE  Nleaf  BL Þ;

Eqn 1

where PNUE describes the plants’ photosynthetic N-use efficiency as the flux of assimilated C per unit leaf N, and f is a generic function that characterizes the effect of the plant’s N status on GPP. The fraction of GPP that is avaliable for NPP is defined as the plant’s C-use efficiency (CUE): CUE ¼


Eqn 2

where CUE depends on the plant’s growth and maintenance respiration. We expect CUE to differ amongst the model versions assuming fixed or flexible vegetation C : N ratios, as maintenance respiration varies with tissue N content (Reich et al., 2008). The ratio of plant growth to nitrogen uptake (Nup) defines the plant’s N-use efficiency (NUE): NUE ¼


Eqn 3

N-use efficiency is an implicit plant property that depends on the tissue C : N ratios, as well as the relative allocation of NPP to the various plant organs. Decreasing tissue C : N ratios as a result of fertilization will reduce the NUE of plants, assuming other factors unchanged. Ultimately, Nup is constrained by the available mineral N in the soil (Na), which in turn is altered by various processes: DNa ¼ Nmin þ Nin þ F  Nloss  Nup

Table 1 List of variable names used in the conceptual framework and model analysis Variable




Gross primary production Photosynthetic N-use efficiency Leaf N concentration (g N g1 C ql 9 100) Mass fraction of leaf C per unit leaf DM Leaf biomass Net primary production C-use efficiency (NPP/GPP) Plant N uptake N-use efficiency (NPP/Nup) Net N mineralization Soil N inputs from N deposition and N fixation Soil N inputs from fertilization Gaseous and leaching N losses from the soil Plant-available soil inorganic N Total organic ecosystem C Total vegetation C Total organic ecosystem N Total vegetation N Fraction of vegetation N in ecosystem N (Nveg/Norg) Organic soil C (soil organic matter + litter) Organic soil N C in nonwoody vegetation pools (foliage + roots) N in nonwoody vegetation pools Fraction of nonwoody N in vegetation N (Nnw/Nveg) C in woody vegetation pools N in woody vegetation pools Canopy N, corresponds to Nleaf 9 BL

g C m2 yr1 g C g1 N yr1

Nleaf ql BL NPP CUE Nup NUE Nmin Nin F Nloss Na Corg Cveg Norg Nveg fveg Csoil Nsoil Cnw Nnw fnw Cw Nw Ncan

C, carbon; N, nitrogen; DM, dry mass. New Phytologist (2015) 208: 1042–1055

% g C g1 DM g DM g C m2 yr1 – g N m2 yr1 g C g1 N g N m2 yr1 g N m2 yr1 g N m2 yr1 g N m2 yr1 g N m2 g C m2 g C m2 g N m2 g N m2 – g C m2 g N m2 g C m2 g N m2 – g C m2 g N m2 g N m2

Eqn 4

where Nmin is N added to the soil through net mineralization of substrate organic N via microbial decomposition, Nin comprises natural N inputs from atmospheric N deposition and biological N fixation (BNF), and F is a term accounting for additional direct inputs of reactive N, that is, fertilization. Nloss denotes the sum of N losses occurring in gaseous form during microbial processes (nitrification, denitrification) or as leaching. To separate the effects of added N and change in stoichiometry on total ecosystem and vegetation C (Corg, Cveg), we follow Rastetter et al. (1992): Corg

   Csoil Cveg ¼ fveg  þ 1  fveg   Norg Nveg Nsoil

Eqn 5

  Cnw Cw ¼ fnw  þ ð1  fnw Þ   Nveg Nnw Nw

Eqn 6

and Cveg

where Csoil, Nsoil, Cw, Nw, Cnw, Nnw describe the C and N pools for organic soil (SOM, including litter), woody, and nonwoody vegetation, respectively. The fractions of vegetation N in ecosystem N and nonwoody N in vegetation N are described by fveg = Nveg/Norg and fnw = Nnw/Nveg. Eqns 5 and 6 attribute modelled changes in ecosystem and vegetation C caused by N addition to particular components of ecosystem C–N stoichiometry. O-CN We employed the O-CN model (Zaehle & Friend, 2010; see Supporting Information Notes S1 for description), a modified version of the ORCHIDEE DGVM (Krinner et al., 2005), featuring inter alia a fully prognostic representation of the N cycle (Fig. 1). O-CN includes a canopy photosynthesis scheme that explicitly considers the N dependence of leaf-level photosynthesis (Friend & Kiang, 2005), simulates the allocation of assimilates to Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

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plant organs depending on phenology, plant size and N status, and applies SOM C and N dynamics based on the CENTURY model (Parton et al., 1993). Soil inorganic N inputs consist of atmospheric deposition, BNF, and net mineralization. The latter is controlled by the relation of available soil N, the C : N ratio of the decomposed litter, and the C : N ratio of the SOM pools (Parton et al., 1993). Plant uptake of inorganic N depends on the plant’s N demand, root mass, and soil N availability. The acquired N is allocated to plant tissues according to the current growth rate (which depends on GPP), allocation fractions (which respect allometric relationships between foliage, root mass, and sapwood), and a prescribed relationship between the C : N ratios of leaves, roots, and wood. Root and wood tissues are assigned C : N ratios relative to the leaf C : N ratio with factors 0.86 and 0.145, respectively (Friend et al., 1997). Tissue C : N ratios are thereby a prognostic outcome of the modelled plant growth, constrained within observed bounds (Table 2). Whole-plant maintenance respiration is associated with tissue N concentrations to represent the maintenance costs of protein-rich tissue (Reich et al., 2008). Litterfall makes N sequestered in vegetation available for microbial decomposition, closing the internal N cycle. N losses occur through leaching depending on the soil inorganic N concentration, and as gaseous losses associated with nitrification and denitrification (Zaehle et al., 2011). Given the model structure of O-CN, adding N to the simulated ecosystem’s soil inorganic N pool will increase plant N uptake. This will support the growth of new biomass and therefore also increase litterfall and the turnover of organic material in general. Increased soil N availability will also lead to increased N losses. Fixed vs flexible C : N ratios The standard version of O-CN assumes flexible ecosystem C : N ratios that are variable within prescribed bounds (Table 2). The Table 2 Prescribed fixed, minimum, and maximum values of ecosystem carbon : nitrogen (C : N) ratios, as used in the fixed and flexible stoichiometry versions of the O-CN model

Fixed stoichiometry Fixed leaf C : N; Nleaf (%) Flexible stoichiometry Min. leaf C : N; max. Nleaf (%) Max. leaf C : N; min. Nleaf (%) Fixed stoichiometry Active SOM C : N; slow SOM C : N Flexible stoichiometry Min. active SOM C : N; min. slow SOM C : N Max. active SOM C : N; max. slow SOM C : N

Needle-leaved evergreen

Broadleaved deciduous

42 : 1; 1.14

25 : 1; 1.92

28 : 1; 1.71 75 : 1; 0.64

16 : 1; 3.00 45 : 1; 1.07

13.5 : 1; 19 : 1

3 : 1; 12 : 1 15 : 1; 20 : 1

Leaf C : N ratios and leaf N concentrations (Nleaf, mass %) for two plant functional types (PFTs), along with the C : N ratios assumed for the active and slow soil organic matter (SOM) pools. Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

leaf C : N ratios were extracted from the global plant trait database TRY (Kattge et al., 2011), whereas the stoichiometric variability in the SOM pools was adapted from the CENTURY model (Parton et al., 1993). For the purpose of this study, we ran simulations for four configurations (termed ‘stoichiometry models’; Table 3), representing different combinations of approaches to stoichiometric flexibility (fixed or flexible) for the vegetation and soil compartments: FIX applied fixed C : N ratios in all compartments. FVG combined flexible vegetation C : N ratios with prescribed C : N ratios for the SOM pools. WFL allowed variable stoichiometry in all ecosystem pools except the wood pool, where the prescribed C : N ratio from the FIX model was applied. FLX allowed C : N ratios to vary in all compartments. A number of model processes function differently when changing from flexible to fixed stoichiometry. N limitation In the case of variable leaf C : N ratios (FVG, WFL, FLX), there is a direct effect of changed N concentration in leaves on GPP and consequently also NPP. In the case of fixed leaf C : N ratios (FIX), this representation of N ‘limitation’ cannot operate. Rather, the plant’s N stress factor is then determined by the ratio of N available for growth (determined by the plant’s labile N reserve) to the plant’s N demand (Thornton et al., 2007), which implicitly depends on the plant’s GPP, CUE and NUE. This factor then causes PNUE to vary, leading to an acclimation of GPP and NPP in response to varying plant available N. C- and N-use efficiency For fixed C : N ratios (FIX), changes in the plant’s CUE and NUE can only occur through changes in the allocation fractions. With flexible stoichiometry (FVG, WFL, FLX), changes in the tissue C : N ratios alter both the N needed to construct new tissue and the biomass-specific maintenance respiration. In these cases, the N demand thus varies according to the plant’s N status. SOM Fixed vs flexible stoichiometry in SOM defines the ratio of N requirement to N release during litter and SOM decomposition. This ratio depends on the litter N content and the SOM pool C : N ratios. In case SOM stoichiometry is flexible (WFL, FLX), its C : N ratios depend linearly on the available soil N Table 3 Stoichiometry models employed in this study and the flexibility of carbon : nitrogen (C : N) ratios in their respective pools Model






fixed flexible flexible flexible

fixed flexible fixed flexible

fixed flexible flexible flexible

fixed fixed flexible flexible

These configurations allow for the direct comparison of entirely fixed (FIX) and flexible (FLX) stoichiometry, as well as the separate examination of the roles of stoichiometric flexibility in soil organic matter (SOM; FVG vs FLX) and the wood pool (WFL vs FLX). FVG, flexible stoichiometry in vegetation pools, fixed stoichiometry in SOM pools; WFL, flexible stoichiometry in all ecosystem pools except the wood pool (fixed). New Phytologist (2015) 208: 1042–1055

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(Parton et al., 1993), which then affects net N mineralization under N fertilization. If the SOM C : N ratios are fixed and unaffected by soil N availability (FIX, FVG), this ‘buffering’ effect is disabled. Field experiments To evaluate the accuracy of modelled NPP and leaf N concentration responses to N addition, we tested the model versions against 22 N fertilizer addition experiments in boreal or northern temperate needle- or broadleaved closed-canopy forest sites (Table S1). While many more fertilizer experiments have been published, we only included site studies that observed measures of production responses (above-ground NPP, woody biomass production, wood volume increment, n = 18) and/or changes in leaf N concentrations (mass %, n = 11) (Fig. S1), and that had N-only treatments and an unfertilized control. This site selection does not include experiments that observed effects not sufficiently represented in O-CN, such as tree mortality caused by soil acidity (pine stand in Magill et al., 2004) or nutrient imbalances involving nonN nutrients (Nilsen & Abrahamsen, 2003). Furthermore, we omitted experiments of very short duration (Finzi, 2009) or with a recent site history of slash-burn clearance (Str asan; Hyv€ onen et al., 2008). Experiment durations and mean N applications ranged from 3 to 40 yr and 0.9 to 15 g N m2 yr1. In case multiple experiments applying different dosages of N fertilizer were conducted at the same site referring to the same control, we considered them separate experiments. For the investigation of the partitioning of newly added N between soil and vegetation, we considered a tracer experiment conducted at the hardwood site at Harvard Forest (‘low N’ treatment in table 7 of Nadelhoffer et al., 2004). To analyse model behaviour under eCO2, we simulated two FACE forest experiments at Duke Forest (McCarthy et al., 2010) and Oak Ridge National Laboratory (ORNL; Norby et al., 2010). Modelling protocol Model spin-up was performed until equilibrium in terms of the ecosystem C and N pools in 1860, using preindustrial atmospheric CO2 concentrations (ice core data; Sitch et al., 2015) and estimated N deposition for 1860 (Dentener et al., 2006), as well as climate data from randomly drawn years (1901–1930) from the CRU-TS3.00 data set (Mitchell & Jones, 2005). From equilibrium, the model was forced with transient CO2 concentrations, modelled N deposition (interpolated, following Zaehle et al., 2010a), and climate data. The site-scale simulations were performed at the 0.5° 9 0.5° grid cells closest to the respective site coordinates. BNF was prescribed as 0.2 g N m2 yr1 input to the inorganic soil pool, assuming low rates of (symbiotic + asymbiotic) N fixation at the boreal and northern temperate forest sites (Cleveland et al., 1999). For fertilization, N was added directly to the inorganic soil pool at average yearly rates corresponding to the applied dosages and treatment years of the experiments. For the model–data New Phytologist (2015) 208: 1042–1055

comparison, we calculated yearly means of fertilization responses and applied N dosages from the available data (Table S1). To investigate the partitioning of newly added N between soil and vegetation pools at Harvard Forest, we added 5 g N m2 yr1 from 1988 to 1999, in compliance with the ‘low N’ treatment described by Nadelhoffer et al. (2004). As O-CN does not simulate the fate of 15N explicitly, we compared the measured recovery of 15N tracer (7 yr after its addition, and after 10 yr of N fertilization) with the modelled ecosystem partitioning of added (fertilizer) N after 10 yr of treatment. We calculated the fractions of recovery of total added N in different pools in the year 1999. From the treatment, we substracted the respective baseline N input from deposition and fixation, restricting our analysis to the recovery of experimentally added N. To simulate the FACE experiments, we followed a protocol similar to the N addition experiments, but added 200 (Duke) and 173 (ORNL) ppm CO2 to the ambient concentrations for the 10 yr of experimentation (1996–2006; 1998–2008, respectively).

Results Model–data comparison NPP The simulated NPP responses to N addition at the Harvard Forest hardwood site exceeded the magnitude of the absolute fertilization response measured in the field (Fig. 2a,b). The measured response corresponded to an increase by 30%, whereas the models predicted NPP responses of 62  8% (FIX) and 38  6% (FLX). Over time, FIX and FLX simulations attained the same amount of yearly NPP, indicating that fertilization was sufficient to lift any N constraints and factors other than N availability limited growth. When comparing the simulated and measured NPP responses for all sites (n = 18), all models (most notably the WFL model) showed significant correlation to the data (Table 4). The analysis covered a range of ecosystems with varying annual mean temperatures, and we observed a temperature dependence of the NPP response magnitude for both data and all model results (Fig. 3). In the simulations, higher average temperatures led to larger rates of N turnover and net N mineralization in the soil, causing a higher baseline production and thereby reducing the effect of N addition. Neither observations or simulations showed a clear response pattern regarding different plant functional types (PFTs) for the ensemble of sites considered here. The FIX model generally simulated higher responses, a higher root-mean-square error (RMSE), and a higher bias than the flexible models (Fig. S2, Table 4), where stoichiometric flexibility led to decreased C : N ratios and a lower growth response. The linear fits in the x–y plots had slopes smaller than 1 for all models, as a result of the model’s inability to reproduce the large observed NPP responses. The model–data fit improved when the outlier site (Norr aker, more than a doubling of NPP) was excluded (dashed lines). Two field experiments applied different amounts of N fertilization (Magill et al., 2004; H€ogberg et al., 2006; Ellipses in Fig. 3). At the needle-leaved site (Norrliden), the large standard Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

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FIX control FLX control FIX treatment FLX treatment



0.75* 0.77* 0.84* 0.77*

0.75 0.52 0.46 0.43

27 22 22 23


13 1 7 5

– 0.53 0.46 0.48



RMSE (mass %)

Bias (mass %)

0 0.53 0.49 0.48

0.27 0.17 0.19 0.17

0.21 0.04 0.05 0.02

r, Pearson’s correlation coefficient; slope, slope of the linear fit; RMSE, root-mean-square error; bias, mean difference between model and data responses across all sites. Asterisks indicate significant correlations (P < 0.001 in all cases). Bold numbers indicate the best fits for the respective measures, that is, closest to 1 for r and slope, closest to 0 for RMSE and bias. FIX, fixed stoichiometry; FVG, flexible stoichiometry in vegetation pools, fixed stoichiometry in soil organic matter (SOM) pools; WFL, flexible stoichiometry in all ecosystem pools except the wood pool (fixed); FLX, flexible stoichiometry in all ecosystem pools.

150 Model NPP response (%)







Needle-leaved Broadleaved

0 150




Needle-leaved Broadleaved


error of measured responses (33% and 40% of the respective response magnitudes) prevented a meaningful evaluation of the models’ capability to reflect the small difference between fertilizer applications (3.16 and 3.83 g N m2 yr1). Measurements at the broadleaved forest site at Harvard Forest were given without error estimates. Nonetheless, the recorded increase of the NPP response with fertilizer applications (12% and 30% for 5 and 15 g N m2 yr1, respectively) was better captured by the FLX model (30  5% and 38  6%) than by the FIX model (61  8% and 62  8%). Leaf N concentration The modelled annual absolute leaf N concentration response to N fertilization at the Harvard hardwood forest site (Fig. 4a) was just within the range of the data at this site (0.43  0.28 mass % observed vs 0.68  0.03 mass % simulated), notwithstanding the underestimated baseline value simulated by the FLX model (a result of flexible leaf C : N ratios under N limitation before fertilization). Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust


0 1990

Model NPP response (%)


bias (%)



Leaf N concentration

RMSE (%)


Mean annual temperature (K)









Table 4 Overview over the statistics of model–data comparisons (see the Results section ‘Model–data comparison’, subsections ‘NPP’ and ‘Leaf N concentration’) under all stoichiometry models for net primary production (NPP; n = 18) and leaf nitrogen (N) concentration (n = 11) responses NPP

Mean annual NPP (g C m−2 yr−1)




Fer t. Start

Annual NPP (g C m−2 yr−1 )

Fig. 2 Model–data comparison of net primary production (NPP) responses to nitrogen (N) addition (15 g N m2 yr1) for the FIX (fixed) and FLX (flexible in all ecosystem pools) stoichiometry models at the Harvard Forest hardwood site. (a) Annual time series of NPP simulations – ‘control’ and ‘treatment’ refer to control and fertilized experiments in the simulations, respectively; (b) 14 yr average measured production (‘Obs’; Magill et al., 2004) in comparison with the corresponding averages from the FIX and FLX simulations.


(b) 1200






Observed NPP response (%)

Fig. 3 Model–data comparison of net primary production (NPP) responses (%) to nitrogen (N) fertilization for the FIX (fixed; a) and FLX (flexible in all ecosystem pools; (b) stoichiometry models at all sites (n = 18). Squares, broadleaved deciduous forests; circles, needle-leaved evergreen forests. Error bars indicate  1 SE where provided by the field data and  1 SE in model simulations as the deviation from the mean response over the simulated time span. Colours indicate mean annual temperatures at the sites. Black ellipses indicate pairs of experiments carried out at the same site with the same control but different dosages of N fertilizer applied. Black line, one-to-one line; orange line, linear fit; dashed orange line, linear fit without the outlier (slopes of 1.12 and 0.63 for FIX and FLX). Similar plots for the other stoichiometry models can be found as Supporting Information Fig. S2.

Considering all site studies reporting foliar N (n = 11), the FLX model represented the positive response in the data with only a small bias (Table 4). However, there was considerable New Phytologist (2015) 208: 1042–1055

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Obs control Obs treatment FIX FLX control FLX treatment


2.5 2.0 1.5 1.0 1985

1990 1995 Year


Model analysis Major processes affecting the vegetation C and N budgets The simulated differences between stoichiometry models in GPP responses were negligible (Fig. 6a). They were governed inter alia by the responses in leaf (canopy) N concentrations (Fig. 6c,b) and (leaf) biomass growth (Fig. 6e) (Eqn 1), which differed significantly between the models assuming homeostatic or flexible leaf C : N ratios. The flexible stoichiometry models increased leaf N concentrations in response to fertilization (Fig. 6c). The magnitude of this effect was highest for WFL, which had the least other flexible vegetation pools (only the root pool), leaving relatively large amounts of N uptake to be allocated to foliage. The additional growth of new vegetation gave the increase in total canopy N (Fig. 6b). For this variable, the positive response in the FIX model was only caused by new growth. Given the similar GPP responses, the differences in NPP responses (Fig. 6e) are a result of variation between models in CUE (Fig. 6d) (Eqn2). In the flexible models, increasing tissue N concentrations increased respiration at approximately the same rate as it increased GPP, leading to negligible changes in CUE. Conversely, the FIX model exhibited increased CUE, as its growth response to fertilization was not counteracted by the




Mean annual temperature (K)


Fert. Start

Leaf N concentration (mass %)

The fate of added N Models and data agreed in that the largest fraction of added N became sequestered in SOM (Fig. 5). As a major discrepancy, the models tended to overestimate N recovery in wood. The fraction of N sequestered in wood was highest in the models that assumed flexible wood C : N ratios, where predicted wood C : N ratios decreased with N fertilization. In the WFL model, which assumed a homeostatic wood C : N ratio, relatively less N was sequestered in wood. Similar results were obtained assuming whole-plant homeostasis of C : N ratios. However, the total fraction of N recovered in wood was larger in FIX than in WFL, because of the larger fertilization response of NPP (Fig. 2) and thus woody biomass growth. Nitrogen retrieval in SOM was highest for the FIX model, as a result of the larger increase in NPP and consequently the higher accumulation of high C : N litter and SOM C. This entailed the

sequestration of a high fraction of N, effectively reducing ecosystem loss of the added N to a concentration below the observed losses. In models with flexible vegetation C : N ratios, higherquality litter (lower C : N ratio) required less N immobilization for decomposition, resulting in a larger fraction of the added N lost from the ecosystem. This effect was less pronounced in the models assuming also flexible soil C : N ratios. In these models, the decomposers’ C : N ratios decreased with increasing soil inorganic N availability, leading to greater incorporation of N into SOM and fewer N losses (Figs S3, S4), roughly in correspondence with the data.

Model leaf N response (mass %)

spread between over- and underestimation of the response across sites (Fig. 4b). Model and data consistently suggested the highest responses at relatively warm sites, opposing the pattern observed for the NPP responses (see the previous section). While the model also predicted the highest responses for broadleaved forest sites (higher leaf N concentrations allowing a relatively large absolute response to N addition), the highest response in the data occurred at a site of needleleaved forest. Pairs of two-level experiments showed a positive trend in responses between treatments, which was captured by the FLX model. Despite this general agreement, none of the model results was significantly correlated to the data. Statistically, assuming fixed leaf C : N ratios resulted in the least accurate simulation of leaf N concentration responses (Table 4). Slope and RMSE values varied within a similar range between the flexible models, with FVG exhibiting the highest slope of the linear fit, as well as the smallest error. The relatively higher RMSE and bias for WFL is explained by its fixed wood stoichiometry increasing the N available to enhance leaf N.

0.8 0.6 0.4 0.2 Needle-leaved Broadleaved

0.0 0.0







Observed leaf N response (mass %)

Fig. 4 Overview of leaf nitrogen (N) concentration responses to N fertilization for the FLX (flexible in all ecosystem pools) stoichiometry model, in comparison with field data. (a) Annual time series of leaf N concentration at the Harvard site. ‘Control’ and ‘treatment’ refer to control and fertilized (15 g N m2 yr1) experiments in both simulations and data. Black lines indicate annual measured data for control and treatment,  1 SE). The dashed line shows the prescribed leaf N concentration applied in the FIX (fixed stoichiometry) model. (b) Model–data comparison of leaf N responses (absolute mass % differences between control and treatment) for the FLX model at all sites (n = 11). Squares, broadleaved deciduous forests; circles, needle-leaved evergreen forests. Error bars indicate  1 SE where provided by the field data and  1 SE in model simulations as the deviation from the mean response over the simulated time span. Colours indicate mean annual temperatures at the sites. Black ellipses indicate pairs of experiments carried out at the same site with the same control but different dosages of N fertilizer applied. Black line, one-to-one line; orange line, linear fit. New Phytologist (2015) 208: 1042–1055

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(c) N leaf

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Fig. 5 Recovery of added nitrogen (N) after 10 yr of fertilization (5 g N m2 yr1) at the Harvard Forest hardwood site. The barplots show the percentage of added N retrieved in different ecosystem compartments, referring to the tracer data from Nadelhoffer et al. (2004) (‘Obs’) and simulation results employing the four stoichiometry models. We omitted the N fraction recovered in underlying mineral soil in observations, as it is not quantified as an explicit pool in the O-CN model. FIX, fixed stoichiometry; FVG, flexible stoichiometry in vegetation pools, fixed stoichiometry in soil organic matter (SOM) pools; WFL, flexible stoichiometry in all ecosystem pools except the wood pool (fixed); FLX, flexible stoichiometry in all ecosystem pools.

increase in maintenance respiration associated with plant N content increase. To expand further on the notable difference in simulated CUE between models assuming fixed or flexible leaf C : N ratios, we considered the simulated (FLX) relationship between needleleaved plants’ leaf N concentration and their major C fluxes of GPP and whole-plant respiration (Fig. 7a). The response of GPP to leaf N concentration changes was a saturating function, resulting mostly from increasing light limitation of photosynthesis as foliar N increased. Conversely, whole-plant respiration correlated linearly with leaf N concentrations. The resulting effect on NPP (Fig. 7b) suggested a trend of diminishing returns of increased tissue N concentrations. In some cases, the plants’ leaf N concentrations were increased beyond a value that resulted in further NPP increase. This threshold was approximately passed at an Nleaf value that coincided with the mean foliar concentration in the plant trait data base TRY (Kattge et al., 2011), which served as the prescribed value used in the FIX model (Table 2). In addition to the effect of CUE on NPP, the higher increase in plant N uptake in the flexible models (Fig. 6f) was offset by a decline in NUE associated with decreased vegetation C : N ratios (Fig. 6g), preventing a pronounced effect on NPP (Eqn 3). The higher N uptake response was partly attenuated when the SOM Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust




(g) NUE

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Mean response (%)

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Fig. 6 Mean responses (%) to nitrogen (N) fertilization for the model runs at all 22 sites. Error bars illustrate between-site variability as 1 SE. Response variables include gross primary production (GPP, a), total canopy N (Ncan, b), leaf N concentration (Nleaf, c), carbon-use efficiency (CUE, d), as well as net primary production (NPP, e), plant N uptake (Nup, f), and N-use efficiency (NUE, g) (bottom row). FIX, fixed stoichiometry; FVG, flexible stoichiometry in vegetation pools, fixed stoichiometry in soil organic matter (SOM) pools; WFL, flexible stoichiometry in all ecosystem pools except the wood pool (fixed); FLX, flexible stoichiometry in all ecosystem pools.

pools were assigned flexible C : N ratios as well (WFL, FLX), which allowed for a higher N immobilization by the microbial biomass (lower net mineralization, Eqn 4, Fig. S4). Controls of ecosystem stoichiometry on C sequestration We next investigated how the model responses resulted in ecosystem C sequestration following Eqn 5 (Fig. 8). The average absolute changes in ecosystem C (131, 119, 100, 105 g C m2 yr1 for FIX, FVG, WFL, FLX; Fig. 8a) corresponded to increases by 9.9%, 8.5%, 7.5% and 7.6%. However, the simulated fractions of newly assimilated C that remained stored in the ecosystem varied on a notably larger scale (60%, 48%, 41%, 48%). Also, the differences in CUE (Fig. 6d) manifested in variable fractions of GPP increase propagating to NPP increase (86%, 61%, 50%, 61%). The relative contributions to ecosystem C sequestration of changes in total ecosystem N, changes in vegetation and soil C : N ratios, and the vegetation fraction of total ecosystem N varied (Fig. 8b): All models showed that the majority of C sequestration was caused by N addition and increased shares of N in vegetation. The potential additional C storage was counteracted by decreasing vegetation C : N ratios in the flexible models, particularly strongly in FVG and FLX, both of which assumed fully flexible vegetation stoichiometry, giving wood a large capacity to retain N. The effect of soil (SOM + litter) C–N stoichiometry was positive in FIX and FVG, where increased litterfall elevated the total soil C : N ratio. This effect was compensated for in the New Phytologist (2015) 208: 1042–1055

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Fig. 7 Effect of leaf nitrogen (N) concentrations on the whole-plant carbon (C) balance for the FLX (flexible in all ecosystem pools) stoichiometry model. (a) Positive (gross primary production; blue) and negative (maintenance + growth + excess respiration; red) C flux (g C m2 yr1) as a function of leaf N concentration (mass %). Each point represents an annual pair of values (treatment years) from one of 17 simulations at needle-leaved forest sites. (b) Annual net primary production (NPP; g C m2 yr1) values from all sites as a function of leaf N concentration. The dashed lines indicate the prescribed Nleaf value from the FIX (fixed) stoichiometry model (1.14%). Lines show linear fits for time series of individual sites.




Fig. 8 Model comparison of site-averaged (n = 22) ecosystem carbon (C) changes resulting from nitrogen (N) fertilization. (a) Absolute increases in gross primary production (GPP) and the accompanying increases in net primary production (NPP), ecosystem C, and vegetation C (g C m2 yr1). White numbers indicate the magnitudes of C changes in the respective components relative to DGPP. (b) Relative contributions to change in ecosystem C from changes in total organic ecosystem N (DNorg), vegetation and soil organic matter (SOM) C : N ratios (DC : Nveg, DC : Nsoil) and the fraction of vegetation N in total ecosystem N (Dfveg = DNveg/DNorg). (c) Relative contributions to change in vegetation C from changes in total organic vegetation N (DNveg), woody and nonwoody (leaves, roots) C : N ratios (DC : Nw, DC : Nnw) and the fraction of nonwoody N in total vegetation N (Dfnw = DNnw/DNveg).

WFL and FLX simulations by the decrease in SOM C : N ratios, resulting in overall negative effects of soil C : N ratio changes. Despite the weak N redistribution to vegetation in FIX, the relatively high vegetation and SOM C : N ratios contributed to the highest predicted absolute ecosystem C storage increase (Fig. 8a), in agreement with the largest response of NPP (Fig. 6e). The absolute changes in total vegetation C (75, 81, 71, and 72 g C m2 yr1 for FIX, FVG, WFL and FLX, respectively) corresponded to 15.0%, 14.6%, 13.3% and 13.4% increases, illustrating the variation between stoichiometry models in allocation New Phytologist (2015) 208: 1042–1055

responses to N addition (Fig. S3). The driving factor in the vegetation C increase in all models (Fig. 8c, Eqn 6) was the increase of vegetation N, which was strongest in models assuming flexible wood stoichiometry. All flexible models simulated small negative contributions from decreased leaf and root C : N ratios, as well as from an increased fraction of vegetation N sequestered in nonwoody tissues. The latter effect was strongest in the WFL model, where fixed wood stoichiometry allowed stronger N retention in nonwoody pools. Although assuming fixed wood C : N ratios did eliminate the negative effect of changes in C : N ratios on Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

New Phytologist vegetation C storage, this did not result in increased C storage, because it also reduced vegetation N uptake (Fig. 5). Model performance under eCO2 at two FACE sites All models slightly overestimated the initial NPP responses to eCO2 of c. 25% at the FACE sites at Duke Forest and ORNL and showed little signs of increasing N limitation of the CO2 response over time (Fig. S5). Stoichiometric flexibility in the simulations allowed leaf C : N ratios to increase under eCO2 within the range of observed changes, concurrent with a substantial increase in NUE, which was larger than observed for all flexible stoichiometry models. The higher N demand of the FIX model, resulting from a lesser change in NUE, led to larger N uptake from the soil. This could be sustained initially because net N mineralization did not decrease strongly, as the C : N ratio of fresh litter did not increase, which, by contrast, was simulated by the models with flexible stoichiometry. Overall, the differences between the stoichiometry models in predicted C sequestration responses were small. However, the NPP responses of the FIX model did decline at a slightly higher pace than those of the flexible models, indicating that in the long run, progressive N limitation would become more pronounced in the FIX model opposed to the models assuming flexible stoichiometry, and the differences caused by different stoichiometry approaches would become more apparent.

Discussion Our analysis has shown that the choice of stoichiometry approach in DGVMs is likely to have considerable impact on their predictions in many variables, and we have provided a basis that enables a more informed treatment of stoichiometry in the future. We illustrated differences both when fixed stoichiometry was compared with flexible stoichiometry, and when different flexible approaches were compared with each other. For example, the predicted fraction of GPP increase that was stored as new ecosystem C varied by up to 20% (Fig. 8a). As a result of the various approaches to stoichiometric flexibility, our site-scale simulations showed notable variation in additional C sequestered per added N. Averaged across experiments, the C : N ratios of new SOM were greater in the models assuming fixed stoichiometry (11 : 1 and 9 : 1 for FIX and FVG, respectively) than flexible stoichiometry (5 : 1 for both WFL and FLX). The C : N ratios of new vegetation were 150 : 1, 104 : 1, 135 : 1 and 102 : 1 (FIX, FVG, WFL, and FLX, respectively), emphasizing the large difference in C sequestration caused by the introduction of C : N flexiblity in wood. The dominant roles of wood and SOM consequently reflected in the C : N ratios of new organic material in the total ecosystem, which were 50 : 1, 46 : 1, 34 : 1 and 33 : 1 (FIX, FVG, WFL and FLX, respectively). As N input rates and the model formulation of N losses were unchanged between stoichiometry models, the differences in the C : N ratios of new material also illustrate how the choice of stoichiometry approach affected the modelled N loss rates (Fig. S4). Nonetheless, all models fell into the range of C : N ratios of new Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

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material between 24.5 : 1 and 75 : 1 summarized by Liu & Greaver (2009). The scarcity of data reporting stoichiometric changes with perturbation prevents a meaningful judgment on the most appropriate model implementation of C : N stoichiometric flexibility here. Still, reported patterns of sustained SOM C : N ratio decreases with N addition (Cusack et al., 2010), as well as the leaf N concentration increases described in our data sources, suggest that DGVMs generally need to include the paradigm of plastic C : N ratios if they aim to realistically represent the coupling of the C and N cycles. We found that the model based on homeostatic tissue-level stoichiometry generated the highest NPP response, which compared with data from site-scale experiments led to an overestimation of the N fertilization effect on growth. The models that employed flexible C : N ratios showed NPP responses closer to the observations and were, in addition, able to simulate the observed, notable magnitude of foliar N concentration changes. This diverging pattern of overestimation of NPP responses by the fixed C–N model and more moderate responses in flexible C–N models were in agreement with the findings of Thomas et al. (2013), who pointed out a similar pattern between the CLM-CN (fixed C : N ratios) and O-CN (flexible C : N ratios) models. Notwithstanding other differences between the models, using a model testing design focusing on stoichiometric flexibility, we demonstrated here that the alternative assumptions about stoichiometry can explain a large fraction of the differences reported by Thomas et al. (2013). When investigating the effect of increasing C availability rather than N availability, we only saw small differences between the stoichiometry models in the magnitudes of NPP responses within 10 yr of perturbation. This was partly the result of a negative feedback loop in models with flexible stoichiometry, by which increased NUE of the plants increased the litter C : N ratios, and therefore reduced N mineralization and N uptake. Our result implies, however, that the importance of stoichiometric flexibility under modelled eCO2 will progressively increase as N limitation becomes more severe and variable NUE becomes crucial to explain the varied responses observed in the field. Nonetheless, our results also indicate that the choice of stoichiometry approach does not explain the weak initial NPP response to eCO2 observed for the CLM model (Zaehle et al., 2014), suggesting that other model choices, such as the instantaneous down-regulation of GPP under N stress (Thornton et al., 2007), contributed to this phenomenon – a feature not shared by the FIX version of O-CN. Overall, our model–data comparison suggested that neither entirely fixed nor entirely flexible approaches to C–N stoichiometry delivered the best fit to the N fertilization data. Assuming fixed wood C : N ratios in combination with flexible foliage and root C : N ratios led to the most accurate model prediction of the fraction of added N allocated to wood tissue as observed with 15N tracers (Fig. 5). Flexible wood C : N ratios led to excessive N accumulation in wood pools (the result of C : N-regulated N allocation and wood-specific N turnover time). O-CN follows the common practice in flexible vegetation C–N models (Friend et al., 1997) of only simulating leaf C : N New Phytologist (2015) 208: 1042–1055

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ratio changes explicitly, and obtaining C : N ratios for other vegetation pools by scaling from the leaf using constant factors (Eqn S9). By decoupling the wood C : N ratio from the leaf C : N ratio, the WFL model approximately halved the N accumulation effect, giving the closest fit to the data. This strong influence of stoichiometric flexibility in wood was pointed out before for the modelling of forest responses to eCO2 (Kirschbaum et al., 1994), and we found the equivalent effect for elevated N. The hypothesis of linear scaling of stoichiometric changes between leaves and wood, employed by many models (Zaehle & Dalmonech, 2011), is based on observed patterns across species and ecosystems (e.g. Elser et al., 2010). However, our results indicated that this hypothesis leads to unrealistic estimates of N accumulation in trees under elevated N inputs. A likely explanation for this discrepancy is that C : N ratios scale linearly in approximately steady conditions, but not necessarily in response to a perturbation. Measurements of wood C : N ratios at the Harvard Forest hardwood stand during the N tracer experiment suggested little variability in relative wood N concentration under N addition (Nadelhoffer et al., 2004). Likewise, a previous study that included the same site only recovered 3% of added N in wood (Nadelhoffer et al., 1999). Results from two FACE experiments suggested only small increases in wood C : N ratios caused by eCO2 at the plot level. However, there was large interannual and between-plot variability in the measurements, preventing a statistically meaningful interpretation of these trends (Finzi et al., 2007; Norby et al., 2010; Zaehle et al., 2014). More data studying the evolution of C : N ratios particularly in wood under increased C and/or N availability is needed to corroborate our result, but we propose that flexible wood stoichiometry needs to be modelled separately from nonwoody tissues in DGVMs. We showed that the simulated diminishing return of leaf N increase in terms of growth responses (Fig. 6) was partly caused by the increase in whole-plant maintenance respiration associated with increased tissue N concentrations (Fig. 7). While observing ‘luxury uptake’ and declining N-use efficiency (Fig. 6g) is not uncommon in ecology (Hommels et al., 1989; Lipson et al., 1996), it is unlikely for plants to actively attain an unfavourable C exchange rate (Hikosaka & Terashima, 1995). We encountered this model effect when the foliar N concentrations exceeded the observed median concentration recorded in the TRY database (Fig. 7; Table 1). This may imply that plants actively regulate their N content to maximize their growth rate. Accounting for such patterns would require an alternative approach to the current purely mass-balancing approach to determine changes in stoichiometry given changes in either C or N availability (or ignoring any changes in stoichiometry). Recent advances have suggested plausible alternative hypotheses on how trees can adjust C and N allocation under perturbation (Wright et al., 2003; Franklin, 2007; McMurtrie et al., 2012; Dybzinski et al., 2013, 2015; McMurtrie & Dewar, 2013). They consider the competitiveness of tree individuals under resource limitation based on their respective costs and benefits and thereby derive tradeoffs in resource investments and evolutionary stable strategies. These may, for example, value investment of assimilates into wood (tree New Phytologist (2015) 208: 1042–1055

New Phytologist height) for light competition or fine roots for N foraging over the increase of foliar N that often takes precedence in DGVMs that allow leaf C : N ratios to vary. Optimality strategies are often obtained using simplified model frameworks in highly idealized scenarios not directly applicable in DGVMs, but offer the advantage of not requiring information about the actual biochemical and genetic controls of tissue stoichiometry (Feng et al., 2015), which are beyond the scope of general vegetation models. However, our results suggest that generalizing these optimality-based models for DGVMs should be a high priority to better constrain changes in tissue and plant stoichiometry in response to environmental change. For the sake of traceability, our results were obtained under a restrictive protocol. We assumed mature, even-aged forest stands, largely corresponding to our data selection, but possibly disregarding the effects of forest succession on N availability and C–N interactions ( Agren & Weih, 2012; Sistla & Schimel, 2012). Owing to their higher N demand, young forests would probably have stronger responses than older forests in observations and models. However, it is unlikely that this effect would have affected the relative performance of the different stoichiometry models employed here. The responses to N addition in the field experiments were also probably influenced by their respective soil histories, which we did not take into account. Furthermore, we kept all model components other than C–N stoichiometry fixed to the O-CN standard, ignoring effects of plausible alternative representations of other N-related processes (Zaehle & Dalmonech, 2011). For example, O-CN includes distinct approaches to determine plant growth, mortality, and allocation (Zaehle & Friend, 2010; Notes S1). While the resulting differences from other DGVM approaches are probably small, O-CN does not represent the DGVM family universally. We believe, however, that while other models might observe different magnitudes of particular effects, the overall results would be qualitatively similar to ours. The paradigms of fixed or flexible C : N ratios, in particular, are essentially applied similarly across DGVMs (Zaehle & Dalmonech, 2011). One important factor contributing to the uncertainty in our approach had to do with the different initial conditions in NPP between the model versions (Fig. S6a). At many sites, the FIX model simulations started from the lowest baseline NPP, resulting partly from the assumed difference in NUE. This possibly contributed to the greater responses when fertilization was sufficient to lift N limitation. However, we found that the rates of NPP at the end of the fertilization simulations did not generally saturate at similar magnitudes (Fig. S6b), meaning that the differences in the initial conditions were not the main cause of the differences in NPP responses. Furthermore, the considered field experiments were conducted across a range of experiment durations (3–40 yr) and N applications (0.9–15 g N m2 yr1). While this was recreated in our simulations, we did not explicitly investigate how far the model was capable of representing response trends along these gradients. Following up on this issue might give further insight into the accuracy of current global models in representing C–N dynamics and the influence of stoichiometric flexibility here. Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

New Phytologist Given the small number of suitable experiments for our model–data comparison, the averaged results are also prone to the influence of outliers, stressing the need for further perturbation studies that ideally coordinate with key variables commonly considered in models. Long-term N addition treatments with measurements of production responses, sampling of C : N ratios of soil and vegetation pools, as well as insights into the partitioning of assimilates between different plant pools would be most suitable to constrain the fertilizer response of DGVMs. We have isolated the uncertainty in predicting C fluxes under elevated N input associated with alternative hypotheses on C–N stoichiometry with a DGVM. We showed that model predictions best matched available data when the C : N ratios in all ecosystem pools except the wood pool were treated as flexible. We therefore advise that the application of fixed ecosystem C : N ratios, still commonly used in DGVMs, be reconsidered. From the model theory perspective, we found that the uncertainty in treating stoichiometry caused differences in predicted new ecosystem C sequestration relative to GPP increase of up to 20%. In the future, such a result needs to be put into context with the uncertainties in other N cycle processes.

Acknowledgements This work was supported by Microsoft Research through its PhD Scholarship Programme.

References Aber JD, Goodale CL, Ollinger SV, Smith ML, Magill AH, Martin ME, Hallett RA, Stoddard JL. 2003. Is nitrogen deposition altering the nitrogen status of northeastern forests? BioScience 53: 375–389.  Agren GI, Weih M. 2012. Plant stoichiometry at different scales: element concentration patterns reflect environment more than genotype. New Phytologist 194: 944–952. Austin AT, Vitousek PM. 2012. Introduction to a Virtual Special Issue on ecological stoichiometry and global change. New Phytologist 196: 649–651. Bonan GB. 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320: 1444–1449. Cleveland CC, Liptzin D. 2007. C:N:P stoichiometry in soil: is there a Redfield ratio for the microbial biomass? Biogeochemistry 85: 235–252. Cleveland CC, Townsend AR, Schimel DS, Fisher H, Howarth RW, Hedin LO, Perakis SS, Latty EF, Von Fisher JC, Elseroad A et al. 1999. Global patterns of terrestrial biological nitrogen (N2) fixation in natural ecosystems. Global Biogeochemical Cycles 13: 623–645. Comins HN, McMurtrie RE. 1993. Long-term response of nutrient-limited forests to CO2 enrichment: equilibrium behavior of plant-soil models. Ecological Applications 3: 666–681. Cusack DF, Silver WL, Torn MS, McDowell WH. 2010. Effects of nitrogen additions on above- and belowground carbon dynamics in two tropical forests. Biogeochemistry 104: 203–225. Dentener FJ, Drever J, Lamarque JF, Bey I, Eickhout B, Fiore AM, Hauglustaine D, Horowitz LW, Krol M, Kulshrestha UC et al. 2006. Nitrogen and sulfur deposition on regional and global scales: a multimodel evaluation. Global Biogeochemical Cycles 20: GB4003. Dybzinski R, Farrior CE, Ollinger S, Pacala SW. 2013. Interspecific vs intraspecific patterns in leaf nitrogen of forest trees across nitrogen availability gradients. New Phytologist 200: 112–121. Dybzinski R, Farrior CE, Pacala SW. 2015. Increased forest carbon storage with increased atmospheric CO2 despite nitrogen limitation: game-theoretic Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

Research 1053 allocation model for trees in competition for nitrogen and light. Global Change Biology 21: 1182–1196. Elser JJ, Bracken ME, Cleland EE, Gruner DS, Harpole WS, Hillebrand H, Ngai JT, Seabloom EW, Shurin JB, Smith JE. 2007. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecology Letters 10: 1135–1142. Elser JJ, Fagan WF, Kerkhoff AJ, Swenson NG, Enquist BJ. 2010. Biological stoichiometry of plant production: metabolism, scaling and ecological response to global change. New Phytologist 186: 593–608. Feng Z, Rutting T, Pleijel H, Wallin G, Reich PB, Kammann CI, Newton PC, Kobayashi K, Luo Y, Uddling J. 2015. Constraints to nitrogen acquisition of terrestrial plants under elevated CO2. Global Change Biology. doi:10.1111/ gcb.12938. Field CH, Mooney HA. 1986. The photosynthesis–nitrogen relationship of wild plants. In: Givnish T, ed. On the economy of plant form and function. Cambridge, UK: Cambridge University Press, 25–55. Finzi AC. 2009. Decades of atmospheric deposition have not resulted in widespread phosphorus limitation or saturation of tree demand for nitrogen in southern New England. Biogeochemistry 92: 217–229. Finzi AC, Norby RJ, Calfapietra C, Gallet-Budynek A, Gielen B, Holmes WE, Hoosbeek MR, Iversen CM, Jackson RB, Kubiske ME et al. 2007. Increases in nitrogen uptake rather than nitrogen-use efficiency support higher rates of temperate forest productivity under elevated CO2. Proceedings of the National Academy of Sciences, USA 104: 14014–14019. Franklin O. 2007. Optimal nitrogen allocation controls tree responses to elevated CO2. New Phytologist 174: 811–822. Friend AD, Kiang NY. 2005. Land surface model development for the GISS GCM: effects of improved canopy physiology on simulated climate. Journal of Climate 18: 2883–2902. Friend AD, Stevens AK, Knox RG, Cannell MGR. 1997. A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0). Ecological Modelling 95: 249–287. Galloway JN, Dentener FJ, Capone DG, Boyer EW, Howarth RW, Seitzinger SP, Asner GP, Cleveland CC, Green PA, Holland EA et al. 2004. Nitrogen cycles: past, present, and future. Biogeochemistry 70: 153–226. Gerber S, Hedin LO, Oppenheimer M, Pacala SW, Shevliakova E. 2010. Nitrogen cycling and feedbacks in a global dynamic land model. Global Biogeochemical Cycles 24: GB1001. Gruber N, Galloway JN. 2008. An Earth-system perspective of the global nitrogen cycle. Nature 451: 293–296. Hermans C, Hammond JP, White PJ, Verbruggen N. 2006. How do plants respond to nutrient shortage by biomass allocation? Trends in Plant Science 11: 610–617. Hikosaka K, Terashima I. 1995. A model of the acclimation of photosynthesis in the leaves of C3 plants to sun and shade with respect to nitrogen use. Plant, Cell & Environment 18: 605–618. H€ogberg P, Fan H, Quist M, Binkley DAN, Tamm CO. 2006. Tree growth and soil acidification in response to 30 years of experimental nitrogen loading on boreal forest. Global Change Biology 12: 489–499. Hommels CH, Kuiper PJC, Tanczos OG. 1989. Luxury consumption and specific utilization rates of three macroelements in two Taraxacum microspecies of contrasting mineral ecology. Physiologia Plantarum 77: 569–578. Hyv€onen R, Persson T, Andersson S, Olsson B,  Agren GI, Linder S. 2008. Impact of long-term nitrogen addition on carbon stocks in trees and soils in northern Europe. Biogeochemistry 89: 121–137. Kattge J, Dıaz S, Lavorel S, Prentice IC, Leadley P, B€onisch G, Garnier E, Westoby M, Reich PB, Wright IJ et al. 2011. TRY - a global database of plant traits. Global Change Biology 17: 2905–2935. Kirschbaum MU, King DA, Comins HN, McMurtrie RE, Medlyn BE, Pongracic S, Murty D, Keith H, Raison RJ, Khanna PK et al. 1994. Modelling forest response to increasing CO2 concentration under nutrientlimited conditions. Plant, Cell & Environment 17: 1081–1099. Krinner G, Viovy N, deNoblet-Ducoudre N, Ogee J, Polcher J, Friedlingstein P, Ciais P, Sitch S, Prentice IC. 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles 19: GB1015. New Phytologist (2015) 208: 1042–1055

New Phytologist

1054 Research LeBauer DS, Treseder KK. 2008. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89: 371–379. Lipson DA, Bowman WD, Monson RK. 1996. Luxury uptake and storage of nitrogen in the rhizomatous alpine herb, Bistorta Bistortoides. Ecology 77: 1277–1285. Liu L, Greaver TL. 2009. A review of nitrogen enrichment effects on three biogenic GHGs: the CO2 sink may be largely offset by stimulated N2O and CH4 emission. Ecology Letters 12: 1103–1117. Magill AH, Aber JD, Currie WS, Nadelhoffer KJ, Martin ME, McDowell WH, Melillo JM, Steudler P. 2004. Ecosystem response to 15 years of chronic nitrogen additions at the Harvard Forest LTER, Massachusetts, USA. Forest Ecology and Management 196: 7–28. McCarthy HR, Oren R, Johnsen KH, Gallet-Budynek A, Pritchard SG, Cook CW, Ladeau SL, Jackson RB, Finzi AC. 2010. Re-assessment of plant carbon dynamics at the Duke free-air CO2 enrichment site: interactions of atmospheric [CO2] with nitrogen and water availability over stand development. New Phytologist 185: 514–528. McMurtrie RE, Dewar RC. 2013. New insights into carbon allocation by trees from the hypothesis that annual wood production is maximized. New Phytologist 199: 981–990. McMurtrie RE, Iversen CM, Dewar RC, Medlyn BE, Nasholm T, Pepper DA, Norby RJ. 2012. Plant root distributions and nitrogen uptake predicted by a hypothesis of optimal root foraging. Ecology and Evolution 6: 1235–1250. Mitchell TD, Jones PD. 2005. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology 25: 693–712. Nadelhoffer KJ, Colman BP, Currie WS, Magill A, Aber JD. 2004. Decadalscale fates of tracers added to oak and pine stands under ambient and elevated N inputs at the Harvard Forest (USA). Forest Ecology and Management 196: 89–107. Nadelhoffer KJ, Emmett BA, Gundersen P, Kjonaas OJ, Koopmans CJ, Schleppi P, Tietema A, Wright RF. 1999. Nitrogen deposition makes a minor contribution to carbon sequestration in temperate forests. Nature 398: 145– 148. Nilsen P, Abrahamsen G. 2003. Scots pine and Norway spruce stands responses to annual N, P and Mg fertilization. Forest Ecology and Management 174: 221– 232. Norby RJ, Warren JM, Iversen CM, Medlyn BE, McMurtrie RE. 2010. CO2 enrichment of forest productivity constrained by limited nitrogen availability. Proceedings of the National Academy of Sciences, USA 197: 19368–19373. Parton WJ, Schimel DS, Cole CV, Ojima DS. 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Science of America Journal 51: 1173–1179. Parton WJ, Scurlock JMO, Ojima DS, Gilmanov TG, Scholes RJ, Schimel DS, Kirchner T, Menaut JC, Seastedt T, Garcia Moya E et al. 1993. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochemical Cycles 7: 785–809. Rastetter EB, McKane RB, Shaver GR, Melillo JM. 1992. Changes in C storage by terrestrial ecosystems: how C-N interactions restrict responses to CO2 and temperature. Water, Air, & Soil Pollution 64: 327–344. Rastetter EB, Ryan MG, Shaver GR, Melillo JM, Nadelhoffer KJ, Hobbie JE, Aber JD. 1991. A general biogeochemical model describing the responses of the C and N cycles in terrestrial ecosystems to changes in CO2, climate, and N deposition. Tree Physiology 9: 101–126. Reich PB, Tjoelker MG, Pregitzer KS, Wright IJ, Oleksyn J, Machado JL. 2008. Scaling of respiration to nitrogen in leaves, stems and roots of higher land plants. Ecology Letters 11: 793–801. Reich PB, Walters MB, Tjoelker MG, Vanderklein D, Buschena C. 1998. Photosynthesis and respiration rates depend on leaf and root morphology and nitrogen concentration in nine boreal tree species differing in relative growth rate. Functional Ecology 12: 395–405. Schimel DS, Braswell BH, McKeown R, Ojima DS, Parton WJ, Pulliam W. 1996. Climate and nitrogen controls on the geography and timescales of terrestrial biogeochemical cycling. Global Biogeochemical Cycles 10: 677–692. New Phytologist (2015) 208: 1042–1055

Sistla SA, Schimel JP. 2012. Stoichiometric flexibility as a regulator of carbon and nutrient cycling in terrestrial ecosystems under change. New Phytologist 196: 68–78. Sitch S, Friedlingstein P, Gruber N, Jones SD, Murray-Tortarolo G, Ahlstr€om A, Doney SC, Graven H, Heinze C, Huntingford C et al. 2015. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12: 653–679. Sokolov AP, Kicklighter DW, Melillo JM, Felzer BS, Schlosser CA, Cronin TW. 2008. Consequences of considering carbon-nitrogen interactions on the feedbacks between climate and the terrestrial carbon cycle. Journal of Climate 21: 3776–3796. Sterner RW, Elser JJ. 2002. Ecological stoichiometry: the biology of elements from molecules to the biosphere. Princeton, NJ, USA: Princeton University Press. Thomas RQ, Zaehle S, Templer PH, Goodale CL. 2013. Global patterns of nitrogen limitation: confronting two global biogeochemical models with observations. Global Change Biology 19: 2986–2998. Thornton PE, Doney SC, Lindsay K, Moore JK, Mahowald N, Randerson JT, Fung I, Lamarque JF, Feddema JJ. 2009. Carbon–nitrogen interactions regulate climate–carbon cycle feedbacks: results from an atmosphere–ocean general circulation model. Biogeosciences 6: 2099–2120. Thornton PE, Lamarque JF, Rosenbloom NA, Mahowald NM. 2007. Influence of carbon-nitrogen cycle coupling on land model response to CO2 fertilization and climate variability. Global Biogeochemical Cycles 21: GB4018. Vitousek PM, Howarth RW. 1991. Nitrogen limitation on land and in the sea: how can it occur? Biogeochemistry 13: 87–115. Wang YP, Houlton BZ. 2009. Nitrogen constraints on terrestrial carbon uptake: implications for the global carbon-climate feedback. Geophysical Research Letters 36: L24403. Wright IJ, Reich PB, Westoby M. 2003. Input of mixtures of water and nitrogen for photosynthesis. American Naturalist 161: 98–111. Xu RI, Prentice IC. 2008. Terrestrial nitrogen cycle simulation with a dynamic global vegetation model. Global Change Biology 14: 1745–1764. Yang X, Wittig V, Jain AK, Post W. 2009. Integration of nitrogen cycle dynamics into the Integrated Science Assessment Model for the study of terrestrial ecosystem responses to global change. Global Biogeochemical Cycles 23: GB4029. Zaehle S, Ciais P, Friend AD, Prieur V. 2011. Carbon benefits of anthropogenic reactive nitrogen offset by nitrous oxide emissions. Nature Geoscience 4: 601– 605. Zaehle S, Dalmonech D. 2011. Carbon-nitrogen interactions on land at global scales: current understanding in modelling climate biosphere feedbacks. Current Opinion in Environmental Sustainability 3: 311–320. Zaehle S, Friend AD. 2010. Carbon and nitrogen cycle dynamics in the O-CN land surface model: 1. Model description, site-scale evaluation, and sensitivity to parameter estimates. Global Biogeochemical Cycles 24: GB1005. Zaehle S, Friedlingstein P, Friend AD. 2010a. Terrestrial nitrogen feedbacks may accelerate future climate change. Geophysical Research Letters 37: L01401. Zaehle S, Friend AD, Friedlingstein P, Dentener F, Peylin P, Schulz M. 2010b. Carbon and nitrogen cycle dynamics in the O-CN land surface model: 2. Role of the nitrogen cycle in the historical terrestrial carbon balance. Global Biogeochemical Cycles 24: GB1006. Zaehle S, Medlyn BE, De Kauwe MG, Walker AP, Dietze MC, Hickler T, Luo Y, Wang YP, El-Masri B, Thornton P et al. 2014. Evaluation of 11 terrestrial carbon-nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies. New Phytologist 202: 803–822.

Supporting Information Additional supporting information may be found in the online version of this article. Fig. S1 Geographical locations of the field experiments considered in this study. Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust

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Fig. S2 Model–data comparison of NPP responses to N fertilization for all stoichiometry models.

Fig. S6 Model–data comparison of average yearly NPP from control plots and fertilized NPP after experimental treatments.

Fig. S3 Simulated evolution of the ecosystem compartments’ C and N stock sizes under N fertilization treatment at Harvard Forest.

Table S1 List of field experiments

Fig. S4 Modelled responses in additional variables.

Please note: Wiley Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

Fig. S5 Responses in key ecosystem variables from the simulated FACE experiments.

Notes S1 O-CN description.

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The role of stoichiometric flexibility in modelling forest ecosystem responses to nitrogen fertilization.

The response of the forest carbon (C) balance to changes in nitrogen (N) deposition is uncertain, partly owing to diverging representations of N cycle...
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