The extent to which terrestrial ecosystems slow climate change by
sequestering carbon hinges in part on nutrient limitation. We used a coupled
carbon–climate model that accounts for the carbon cost to plants of
supporting nitrogen-acquiring microbial symbionts to explore how nitrogen
limitation affects global climate. To do this, we first calculated the
reduction in net primary production due to the carbon cost of nitrogen
acquisition. We then used a climate model to estimate the impacts of the
resulting increase in atmospheric
Mingjie Shi's and Joshua B. Fisher's copyright for this publication is transferred to the California Institute of Technology.
The magnitude of carbon (C) uptake by the terrestrial biosphere strongly depends on the availability of nutrients to support net primary production (NPP) (Wang et al., 2010; Zaehle et al., 2015; Wieder et al., 2015). Most soil nutrients exist in unavailable forms and consequently plants must expend a portion of their assimilated C on nutrient acquisition (Johnson, 2010; Mohan et al., 2014). Many plants allocate up to 20 % of their C to support symbiotic mycorrhizal fungi, which can be responsible for almost half of plant nitrogen (N) uptake in ecosystems (Hobbie, 2006; Högberg and Högberg, 2002; Parniske, 2008) or to support symbiotic N-fixing bacteria (Shi et al., 2016). Given the magnitude of these C expenditures, Earth system models (ESMs) that do not account for the costs of supporting symbiotic microbes may overestimate NPP and the ability of terrestrial ecosystems to slow climate change.
Nearly all land plants have evolved symbiotic strategies for coping with
nutrient limitation. Plant associations with mycorrhizal fungi such as
arbuscular mycorrhizae (AM) and ectomycorrhizae (ECM), or with N-fixers, are
critical for the uptake of soil nutrients and, as such, impact C and nutrient
cycling (Phillips et al., 2013; Wurzburger et al., 2017). Recent data
syntheses have shown that ECM and AM ecosystems have divergent C-nutrient
economies that respond differently to elevated
Global C–climate models represent the scientific community's integrated hypotheses on how climate responds to anthropogenic forcing. In addition to forecasting climate, ESMs can be used to perform “experiments” at spatial and temporal scales that are logistically unfeasible to identify important feedbacks and processes in the Earth's climate system (Fisher et al., 2014). Accordingly, our objective was to explore the potential feedbacks between the C cost of supporting symbiotic N acquisition with climate by performing model experiments with and without these costs in a C–climate model. To streamline the complexity of the Earth-scale computations, we used the Community Atmosphere Model (CAM) with prescribed sea surface temperatures and sea ice and a version of the Community Land Model (CLM) which predicts the impacts of symbiotic processes on coupled C and N dynamics. We are focusing on the dynamic processes between the land and atmosphere, and this C–climate model assessment represents the first effort to determine the sensitivity of the Earth's climate system to plant–microbe symbiotic interactions.
We used the Fixation and Uptake of Nitrogen (FUN) sub-model to dynamically compute the C cost and N benefit of AM fungi, ECM fungi, and N-fixers. FUN optimally allocates the C gained from NPP to N acquisition through the following pathways: uptake from soil (via AM or ECM roots, or non-mycorrhizal roots), retranslocation from senescing leaves, and symbiotic biological N fixation (Brzostek et al., 2014; Fisher et al., 2010). FUN then downregulates NPP based upon the integrated C cost across each pathway and how much N was acquired to fix C into biomass. The C cost of each pathway is calculated using functions that relate costs to drivers with soil uptake a function of soil N concentration and root biomass, retranslocation a function of leaf N, and fixation a function of temperature (Brzostek et al., 2014; Shi et al., 2016). In FUN, AM plants benefit when N is relatively abundant, ECM plants benefit when N is strongly limiting, and N-fixers thrive in high energy environments with high N demand (Brzostek et al., 2014).
We used the Community Land Model version 4 (CLM) (Lawrence et al., 2011; Oleson et al., 2010). CLM is a terrestrial biosphere model that predicts the impacts of greenhouse gases and meteorological conditions on the land surface's energy, carbon, and water budgets. Importantly, CLM includes coupled C and N cycles whereby the internal recycling, loss, and inputs of N in the soil pool are dynamically modeled to predict the availability of N to support plant biomass synthesis (Lawrence et al., 2011; Oleson et al., 2010).
FUN was recently coupled into CLM (CLM-FUN) with model simulations showing
that the C cost of N acquisition reduces the C sink strength of the
terrestrial biosphere (Shi et al., 2016). CLM-FUN predicts the C cost of N
acquisition from the soil by ectomycorrhizal, arbuscular mycorrhizal, and
nonmycorrhizal roots based upon root biomass (a proxy for access) and soil
nitrogen concentrations (a measure of availability of N for plants to take
up). Previously, the parameter controlling the sensitivity of the C cost of N
acquisition to root biomass was low. As such, the C cost of N acquisition
showed little to no sensitivity to variability in root biomass across grid
cells and the ECM cost of N acquisition was always lower than the AM cost of
N acquisition even in high N biomes. We have updated this parameter so that
the updated CLM-FUN is equally sensitive to both availability and access, and
can better capture latitudinal gradients in the benefit of ECM uptake or AM
uptake as N becomes more limiting. This adjustment also ensures that while
ECM plants invest more C belowground, they get a greater return on this
investment relative to AM-associated plants when the ratio of N needed to
support NPP to available soil N increases (e.g., enhanced N limitation under
elevated
To investigate the root symbiont associated C–climate feedback, we also used
Community Atmosphere Model version 4 (CAM), an atmospheric general
circulation model that includes CLM (or CLM-FUN) (Neale et al., 2010). CAM
dynamically predicts the impacts of external forcing factors such as
anthropogenic
In the first step of our model experiment, we leveraged the ability of FUN to
downregulate NPP in order to calculate the extent to which mycorrhizal fungi
impact the balance of C in the atmosphere vs. plant biomass. We estimated
this by calculating the difference in NPP between CLM runs with FUN turned on
or off using the same meteorological forcing data (Qian et al., 2006). The
surface condition and plant functional type (PFT) data are from the standard
release of CLM4.0. The surface spin-up conditions, in which the plant and
soil C pools are at a quasi-equilibrium state, are provided with CLM4.0 by
the National Center for Atmospheric Research (NCAR). As such, both models
started from the same baseline values. We ran both CLM and CLM-FUN at the
Second, we ran two simulations of the land–atmosphere model, CAM4.0-CLM 4.0:
(1) a control simulation without mycorrhizal impacts on atmospheric
In this study, we also estimated the radiative forcing variations causing the
climate impacts. We did this in order to identify which factor, ET vs. LAI
vs. enhanced atmospheric
The C expended on symbiont-mediated N acquisition altered the
spatial patterns of
Compared to the CAM runs where N was obtained at no cost, when we included
the C cost of symbiont-mediated N acquisition (i.e., CAM-FUN), C uptake by
the terrestrial biosphere was more strongly constrained by N availability.
Consequently, N limitation reduced global NPP by
2.4 g C m
Elevated
The impacts of the C cost of symbiont-mediated N acquisition led to
a net increase in global radiative forcing. The warming due to increasing
atmospheric
While the averaged global impact of the C cost of microbial symbionts on
climate was minor (i.e., 0.1
Feedbacks between symbiont-mediated N acquisition and C have a
direct impact on global climate.
Here, we demonstrate that integrating the C cost of N acquisition into the
formulation of N limitation in CAM reduced global NPP, LAI, and ET, with the
greatest percentage decreases in boreal and alpine ecosystems (Fig. 1). These
reductions led to substantial impacts on climate, particularly in boreal and
alpine ecosystems where temperature increased by 1
The C expended by plants to support symbiont-mediated N uptake reduced the amount of C available to support leaf growth and, thus, reduced LAI. This global reduction in LAI (Fig. 1b) indirectly influenced climate through energy balance (i.e., albedo and ET) feedbacks (Buermann et al., 2001). It has been suggested that changes in the atmospheric heating pattern in the tropics as a result of the variations in latent heat flux may modify the Hadley circulation, which then can change the generation of waves along the polar front (Chase et al., 1996). As such, tropical LAI shifts (Fig. 1b) can potentially affect mid- and high-latitude climates and nearby ocean conditions through atmospheric teleconnections (Feddema et al., 2005), a possible explanation for the greater climate alterations we observed at high latitudes.
We found greater spatial heterogeneity in ET shifts than NPP or LAI shifts
when we included the C cost of microbial symbionts in the model (Fig. 1).
Some of this spatial variability may reflect the high sensitivity of ET to
increases in atmospheric
Our results suggest that models that do not account for plant–microbe
symbiotic interactions and the C cost of N acquisition may underestimate both
N limitation to NPP and rates of climate change. Nutrient limitation remains
a key area of uncertainty for ESMs, with the CMIP5 comparison highlighting
the limited representations of N limitation as a primary reason for mismatch
between the models and the observed C sink (Anav et al., 2013). Additionally,
CAM-FUN identifies an important underestimation of nutrient limitation and
climate shifts in boreal and alpine ecosystems that has the potential to
enhance other climate feedbacks. Boreal forests, which dominate high-latitude
regions, are characterized by low rates of soil decomposition and low N
availability (Read et al., 2004). This leads to CAM-FUN predicting that
boreal forests will expend nearly 18 % of NPP to gain N through
symbionts, a result that is supported by a recent empirical synthesis which
found that boreal forests have a 13-fold greater C cost of soil resource
acquisition than tropical forests (Gill and Finzi, 2016). However, to the
extent that the greater C cost to ECM plants (relative to AM plants) provides
a greater return on investment of N under elevated
While CAM-FUN identifies an important interaction between the C cost of N limitation and climate, there still remain key uncertainties in the model on the extent to which other processes that govern the C cost of acquiring soil resources impacts C–climate feedbacks. First, not all ecosystems are predominantly N limited (Wang et al., 2010). Nearly 30 % of terrestrial ecosystems are limited by phosphorus (P) or water (Elser et al., 2007; Fisher et al., 2012; Wieder et al., 2015). These are two key limitations that are currently absent from the model that may alter the climate trajectories shown here, particularly for strongly P-limited ecosystems like tropical forests or water-limited ecosystems like Mediterranean forests. However, FUN utilizes a modular structure based on optimal allocation theory that could incorporate the C costs of P or water acquisition on NPP and hence climate. As such, the optimal allocation parameterization in FUN could be modified to include other resource costs and thus provides a framework for ESMs to assess how multiple resource limitation impacts climate.
Second, the climate impacts we identify are sensitive to factors that alter N
availability. Across many ecosystems, increasing soil temperatures that
enhance decomposition (Melillo et al., 2011) or rising rates of N deposition
in developing countries (Liu et al., 2013) could increase N availability and
lower the C cost of N acquisition. Moreover, as currently formulated, the
model omits important feedbacks between C allocation to mycorrhizal symbionts
and their ability to upregulate soil enzyme production and prime soil organic
matter decomposition and increase N availability (Brzostek et al., 2015;
Cheng et al., 2014; Finzi et al., 2015). A recent effort to couple FUN to a
microbial soil enzyme model at the ecosystem scale has shown that the ability
of ECM fungi to prime soil organic matter allowed them to mine N at the
expense of soil C stocks to a greater extent under elevated
Finally, we acknowledge that the simplification of land–atmosphere
interactions in our model experiment may have precluded our ability to
examine fully coupled feedbacks that may have stimulated the land or ocean C
sink. This simplification was needed owing to the complexity and
computational resources needed to run the fully coupled model. As such, our
estimates of the sensitivity of climate to the C cost of N acquisition likely
represents an upper bound. This is due to three reasons. First, we assumed that all of the carbon not sequestered
as NPP was released into the atmosphere as
To fully integrate the C cost of multiple soil resource acquisition into
ESMs, there are key empirical gaps that still need to be addressed, including
advancing observational datasets of the distribution of nutrient acquisition
strategies at the global scale and expanding the
The data for all three figures as well as the model
code are available at
The supplement related to this article is available online at:
MS and ERB designed the research; MS conducted the model simulations and performed the analyses; ERB and JBF contributed essential ideas of analyzing the results; ERB and MS wrote the manuscript with contributions from JBF and RPP.
The authors declare that they have no conflict of interest.
Funding was provided by the US Department of Energy (Office of Biological and Environmental Research, Terrestrial Ecosystem Science Program) and the US National Science Foundation (Division of Environmental Biology, Ecosystem Studies Program). The computations were performed at the Jet Propulsion Laboratory and at the National Aeronautics and Space Administration (NASA) Ames Research Center. Junjie Liu assisted with the computational resources. Mingjie Shi and Joshua B. Fisher carried out the research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, and at the Joint Institute for Regional Earth System Science and Engineering, University of California at Los Angeles. Government sponsorship acknowledged. Edited by: Anja Rammig Reviewed by: two anonymous referees