1
Supporting Information, Figure S1. MuSICA model simulations of PLC in piñon pine using
different vulnerability curves to predict loss of xylem conductivity with decreasing xylem water
potential.
Piñon pine-MuSICA simulations
70
Less vulnerable
More vulnerable
60
PLC (%)
50
40
30
20
10
0
0
200
400
600
800
1000
Days since 1/1/2007
1200
1400
1600
1800
2
Supporting Information, Figure S2. Modeled versus measured predawn leaf water potential
via the Sperry model.
0
2
Modeled Predawn Pressure (MPa)
r 0.82piñon pine r2 0.82
juniper r2 0.97
-2
-4
-6
-8
-8
-6
-4
Measured Predawn Pressure (MPa)
-2
0
3
Supporting Information, Figure S3. Example of the prediction of soil water potential using
ED(X) for an ambient plot at 15cm soil depth.
2
Soil water potential (MPa)
0
-2
-4
-6
-8
ED(X) simulated
Observed
-10
0
100
200
Day of year 2008
300
400
4
Supporting Information, Table S1. A summary of how empirical variables were utilized or
simulated by models. The model input parameters are typically static (e.g. soil texture), model
driver parameters typically change over time (e.g. micrometeorological data), and model output
represents simulated variables. n/a is not applicable.
Variable
FINNSIM Sperry
TREES
MuSICA ED(X)
Mortality
n/a
n/a
n/a
n/a
Output
LAI
Input
Input
Input
Input
Output
Density
Input
n/a
n/a
Input
Input
Height
Input
n/a
Input
Input
Input
Cover
Input
n/a
n/a
n/a
Output
NSC
Output
n/a
Output
Output
Output
SLA
Input
n/a
Input
Input
Input
1
2
Driver/output Driver
In/Output Output
N/A
pd, md
E3
Driver
Output
In/Output2 Output
Output
1
Respiration
Driver/output n/a
Output
In/Output n/a
Vcmax
n/a
n/a
Input
Input
Input
Vulnerability Input
Input
Input
Input
Input
Driver
n/a
Output
Output
Output
soil
SWC
n/a
n/a
Output
Output
n/a
Soil depth
n/a
n/a
Input
Input
Input
Soil temp.
n/a
n/a
Input
Output
n/a
Soil texture
n/a
Input
Input
Input
Input
Allometry
Input
Input
Input
Input
Input
Air temp.
Driver
n/a
Driver
Driver
Input
VPD
n/a
n/a
Driver
Driver
Input
PAR
n/a
n/a
Driver
Driver
Input
Wind speed n/a
n/a
Driver
Driver
Input
Atm Press.
n/a
n/a
Driver
Driver
Input
Hyd Cond
Output
Output
Output
Output
Input
1
pd input, md output; maintenance R input, refilling R output.
2
pd, md, and E at saturated K input; values at other times output
3
Used for evaluation of all models
CLM(ED)
Output
Output
Output
Output
Output
Output
Input
n/a
Output
Output
Input
n/a
Output
Output
Input
Output
Input
Input
Driver
Driver
Driver
Driver
Driver
n/a
5
Supporting Information, Notes S1. Vulnerability to Cavitation
Variation in vulnerability to cavitation. To examine the importance of vulnerability curves on
PLC simulations, we compared the original MuSICA PLC simulations to those with an
alternative set of curves. The degree of modeled PLC depended on the shape of the hydraulic
vulnerability curves. Neither set of vulnerability curves resulted in prediction of 100% PLC,
thus we conclude that our original interpretation, that PLC was elevated but did not reach 100%
in the trees that died, is robust to some variation in vulnerability curves. We note that we did not
have vulnerability curves for the most vulnerable tissues, fine roots.
Vulnerability curves describing the loss of xylem hydraulic conductivity as a function of
xylem pressure were constructed using the air-injection method (Sperry & Saliendra, 1994). The
air-injection method is based on the assumption that the xylem tension required to pull air into a
conduit and cause embolism is equal in magnitude to the positive air pressure required to push
air into the conduit when the xylem water is at atmospheric pressure. Branch segments were
collected in the field, and air emboli were removed by soaking the samples in perfusion solution
under vacuum for 48 h (Domec et al., 2009). The vulnerability curves were generated by first
pressurizing the air chamber to 0.05 MPa to avoid water extrusion from needle scars when axial
flow was induced, and allowing the system to equilibrate for 3 min. Water flow through the
branch was initiated and maximum hydraulic conductivity was measured. A pressure of 0.50
MPa was then applied and held constant for 2 min. After equilibration, the air chamber pressure
was reduced to 0.05 MPa, and conductivity re-measured. This process was repeated for pressures
ranging from 0.5 to 6.00 MPa, or until the conductivity of the segment was negligible.
For piñon pine, new vulnerability curves determined on branches collected from the site
gave comparable results to the ones from Plaut et al. (2012), with a 50 percent loss of branch
xylem conductivity reached at a xylem pressure ranging from -2.9 MPa to -3.5 MPa. Curves
measured on juniper trees, however, showed that the branches were less resistant to cavitationinduced embolism, with a 50% loss of conductivity reached at -7.9 MPa versus -11.3 MPa in
Plaut et al. (2012). In both species the slopes of these new vulnerability curves (the slope is
indicative of the rate at which embolism spreads as the xylem pressure decreases) were steeper
than the slopes of the curves described in Plaut et al. (2012). In piñon pine the slope increased
from 0.2 (20%) PLC MPa-1 to 0.27 (27%) PLC MPa-1, and in juniper from 0.1 (10%) PLC MPa-1
to 0.16 (16%) PLC MPa-1.
Using equation (1), the less vulnerable curve parameters are: b = 3.992, c = 2.6. The
more vulnerable curves are: b = 3.43, c = 1.65. Both sets of curves were generated from trees
on-site but in different years (2008 for the more vulnerable curves, 2011 for the less vulnerable
curves).
Additional references provided in Notes S5.
6
Supporting Information, Notes S2a. Estimates of maintenance respiration and allometric
calculations.
We estimated annual foliar and sapwood maintenance respiration for all plots for conditions
prior to the precipitation manipulation using published estimates of tree respiration rates per unit
of biomass, biomass estimated from measured basal area, tree density, and published allometric
equations (Grier et al., 1992). Foliar maintenance respiration was estimated as 40 nmolC mol C1 -1
s at 15°C (60% of the rate for Pinus contorta foliage biomass reported in Ryan, 1995) and
sapwood maintenance respiration estimated as 0.8 nmolC mol C-1 s-1 at 15°C (rate for Pinus
contorta sapwood biomass, Ryan, 1990).
Total plot surveys of juniper and piñon basal area and tree density were used to estimate
the diameter of the tree of average basal area by species for each plot. Biomass was estimated
for the tree of average basal area using equations in Grier et al. (1992), with measured sapwood
area to basal area ratios used to estimate sapwood biomass for stem wood. Branches <2.5 cm
were assumed to be 100% sapwood; branches 2.5 - 7.6 cm were assumed to be 50% sapwood for
juniper (and assumed to have the same sapwood ratio as the sapwood to basal area ratio for
piñon), and branches > 7.6 cm were assumed to have the same sapwood ratio as the sapwood to
basal area ratio for both species. Leaf area for the plot was estimated using a locally-developed,
species-specific allometric equation with basal area as the predictor (NG McDowell, unpublished
data), and leaf mass from leaf mass per area for piñon and juniper given in Grier et al. (1992).
Annual, plot-level estimates were generated using respiration estimates for the species-specific
tree of average basal area, tree density, plot area, mean annual temperature for the site reported
for 1985-2011 for the nearby Mountainair, NM RAWS weather station
(http://www.raws.dri.edu/), and a Q10 of 2.
Additional references provided in Notes S5.
7
Supporting Information, Notes S2b. Measurement of non-structural carbohydrates (NSC)
Leaf samples were collected starting in 2007, and twig samples were added to the collection
protocol in 2009. Samples were collected from target trees of both species on all plots. Wholetree samples were collected concurrently off-plot to evaluate whole-tree carbohydrate
distribution and in attempt to scale on-plot leaf and twig data to whole-tree carbohydrate content
(LT Dickman et al., unpublished results).
Nonstructural carbohydrates are defined here as free, low molecular weight sugars
(glucose, fructose, and sucrose) plus starch. All samples were collected on dry ice and stored at 70°C. Samples were microwaved at 800 watts for 5 min. to stop enzymatic activity, then dried at
65°C for 48hrs. Leaf tissues were ball-milled to a fine powder (High Throughput Homogenizer,
VWR). Woody tissues were milled to 40 mesh prior to ball-milling (Wiley Mini Mill, Thomas
Scientific).
Samples were analyzed following the protocol described by Hoch et al. (2002), with
minor modifications. Approximately 12mg of fine ground plant material was extracted in a 2mL
deep-well plate with 1.6 mL distilled water for 60 min. in a 100°C water bath (Isotemp 105,
Fisher Scientific). After removal of a 700µL aliquot for starch analysis, the remaining extract
was centrifuged (Allegra X-15R, Beckman Coulter) for 45min at 4450x g rpm, and 20µL of
untreated supernatant was used for the determination of free sugars (glucose and fructose). The
20µL aliquot was incubated in a microplate shaker (BioShaker M.BR-022UP, TAITEC) for
45min. with phosphoglucose isomerase (from Baker’s yeast – Type III, Sigma-Aldrich), glucose
hexokinase and glucose-6-P dehydrogenase (Glucose Assay Reagent, Sigma Aldrich), to convert
fructose to glucose and glucose to gluconate-6-phosphate. The concentration of free glucose in a
sample was determined photometrically in a 96-well microplate spectrophotometer (Cary 50
UVVis), relative to glucose standards of known concentration, by the increase in optical density
at 340nm resulting from the reduction of NAD+ to NADH as glucose-6-P is oxidized.
To hydrolyse sucrose to glucose and fructose, a 100µL aliquot of centrifuged supernatant
was incubated in a microplate shaker for 40mins with 50µL invertase (Grade VII, from Baker’s
yeast, Sigma-Aldrich) buffered to pH 4.6 with 0.4 M,NaOAc (Sigma Aldrich). A 20µL aliquot of
invertase-treated sample was used for determination of total glucose as described above. Sucrose
was calculated as low molecular weight sugars minus free sugars.
To break down total NSC to glucose (Pazur & Kleppe, 1962), 700µL of extract was
transferred to a new deep-well plate, prior to centrifugation, for overnight incubation in a 48°C
water bath with amyloglucosidase (from Aspergillus niger, Sigma Aldrich) buffered to pH 4.5
with 0.1 M,NaOAc (Sigma Aldrich). Following incubation, the plate was centrifuged for 60min
at 4450x g rpm, and a 20µL aliquot of supernatant was used for determination of total glucose as
described above. Starch was calculated as total NSC minus low molecular weight sugars. All
NSC values are expressed as percent dry matter.
Additional references provided in Notes S5.
8
Supporting Information, Notes S3, Model-specific developments and application
FINNSIM: FINNish SIMulation was used to simulate whole-tree hydraulic conductance and
cavitation (based on (Hölttä et al., 2009), including feedback loops with phloem transport (based
on Hölttä et al., 2006) to allow investigation of carbon-water interdependencies. We modified
the model from Hölttä et al. (2009) to include whole tree level carbon balance calculations,
phloem transport and the resulting distribution of carbon within a tree, and a simple estimation of
the carbon cost of refilling. Whole tree-level carbon balance was calculated as a balance between
photosynthesis, temperature driven respiration rate, sugar to starch conversion dynamics
(Nikinmaa et al., 2012), and sub-models for embolism refilling and potassium-aided phloem
transport, which was required for continued photosynthesis at low xylem water potentials in
junipers. The model was used to predict tree carbohydrate concentrations (soluble sugars and
starch), xylem water potential, phloem turgor gradients, and PLC. Data sources for FINNSIM
included vulnerability curves (Plaut et al., 2012), temperature dependency of respiration, xylem
and phloem cross-sectional areas estimated by visual inspection from dead trees on the drought
plots (AJ Boutz et al., unpublished data), and the xylem and phloem conductances from relations
between flow rates and measured/estimated pressure gradients (S Sevanto et al., unpublished
data). Soil water potential, transpiration, and photosynthesis rates were used from MuSICA
output. All other required parameters and model structures (described in Hölttä et al., 2006,
2009) were taken from allometric equations or on-site measurements (Table S1).
Sperry: The model of Sperry et al. (1998) is a resistance-network model of the soilplant-atmosphere continuum. Hydraulic resistances were obtained from vulnerability-tocavitation curves for xylem (Plaut et al., 2012) and unsaturated conductivity curves for soil. The
model predicted the relationship between steady-state transpiration rate (E) and xylem water
potential () up to the maximum possible values (Ecrit and crit) at total hydraulic failure i.e.,
zero hydraulic conductance. Actual E and hydraulic conductance were predicted from measured
or estimated . For this study, the seasonal maximum tree hydraulic conductance was calibrated
to yield the best fit of modeled to measured pre-dawn , and the model filled in the missing predawns across all growing season days (Figure S2). The predawn time course was used with the
mid-day  (interpolated between measurement days) to model the daily time course of midday
E, Ecrit, and loss of hydraulic conductance. Re-calibration was necessary every growing season,
and occasionally for post-monsoonal periods, because of substantial recovery in plant hydraulic
conductance indicated by observations (Plaut et al., 2012; R Pangle et al., unpublished data). Six
trees per species were individually modeled, 3 from drought plots and 3 from ambient plots.
TREES: The Terrestrial Regional Ecosystem Exchange Simulator (TREES) (Samanta et
al., 2007; Loranty et al., 2010; Mackay et al., 2012; Roberts 2012) is a dynamic model of plant
water and carbon flows. A unique methodological improvement in TREES is a full coupling of
the Sperry et al. (1998) model of plant water balance and cavitation with stomatal conductance
(GS), photosynthesis (A), and E driven by energy supply and vapor demand. Thus, TREES
explicitly incorporates A and dynamic plant hydraulic conductance into a unified numerical
solution. It also predicts PLC, NSC, and growth efficiency. The model was calibrated using predrought gas exchange, transpiration, water potentials, and vulnerability curve measurements
(Plaut et al., 2012, Limousin et al., 2013). TREES was set up to re-adjust the plant hydraulic
conductance after substantial soil recharge events to account for refilling, but the model was
manually re-calibrated to measured predawn water potentials at specific events (as done for the
Sperry model). The rooting zone was partitioned into a shallow layer (30 cm) comprised of two
9
root modules, and a deep layer with one root module extending a further 30 cm. Soil water
balance was updated in each half-hour time step for each layer using precipitation inputs,
drainage, and rhizosphere fluxes. Water potentials, hydraulic conductances, and fluxes were
calculated based on the updated soil moisture, cavitation status, and transpiration demand.
TREES was used to simulate the same trees described for the Sperry model.
MuSICA: The MuSICA model is a multilayer, multi-leaf process-based biosphereatmosphere gas exchange model that simulates the exchanges of mass (water, CO2) and energy
in the soil-vegetation-atmosphere continuum (Ogée et al., 2003). The version of the model used
in this study includes a more detailed description of root water uptake and plant water storage
dynamics, as well as soil water hydraulic redistribution and root cavitation (Domec et al., 2012)
and plant NSC storage dynamics (Ogée et al., 2009). In this study the soil was divided into seven
layers of 10cm depth each. Stand density, biomass, leaf area, and soil properties were taken from
Pangle et al. (2012) and Plaut et al. (2012). Maximum rooting depth and root distribution for
both species were determined by fitting modeled xylem water potential to measured predawn
water potential from spring 2007 only. Both species were modeled at the same time and thus
competed for the same soil water.
ED(X): The Ecosystem Demography (ED) model tracks cohorts of trees based on their
sizes (Moorcroft et al., 2001). ED(X) simulates tree mortality of cohorts based on the assumption
of carbon starvation (Fisher et al., 2010). To better present the seasonal cycles of carbon storage,
instead of using GPP directly for growth, it is first all fed into the NSC pool, which is then used
by respiration and then growth of new tissue determined by carbon sink strength. The sink rate is
simulated to be dependent on a targeted storage specified by the user. In the study, the target
carbon storage was set to be 20% of leaf biomass based on the observational data. Another new
development in this study was simulation of soil water potential and calculation of water supply
to each leaf layer based on tree hydraulic conductivity and the water potential gradient from leaf
to soil. Soil water potential was simulated as a nonlinear function of soil water saturation (Figure
S3). The plant halts photosynthesis if the minimum leaf water potential becomes lower than
simulated soil water potential and gravitational potential resulting from tree height.
CLM(ED): The CLM(ED) model is a hybrid of the CLM4.0 model (Oleson et al., 2010)
and the Ecosystem Demography model (Moorcroft et al., 2001) subject to the modifications
described in Fisher et al. (2010) and Bonan et al. (2012), which include the development of a
carbon storage model that predicts starvation as an empirical function of low carbohydrate
reserves. The model is based on the concept of ‘average’ individuals, that have similar height
and are in similar post-disturbance states. Thus, the community-scale mortality rate, which is a
function of the internal carbon status of the average individual, must be parameterized to avoid
the entire community being subject to mortality at a single mortality threshold (Fisher et al.,
2010). In contrast to the individual and plot-scale approaches described above, the CLM(ED)
model has prognostic predictions of leaf area index, plant size structure, density and canopy
cover, as well as soil moisture and surface energy balance.
10
Detailed model descriptions:
FINNSIM: A model of xylem transport and cavitation (Hölttä et al., 2009), combined with a
model a phloem transport (Hölttä et al., 2006), was used in FINNSIM simulations. Temperature
driven respiration rate, sugar to starch dynamics, and a sub-model for embolism refilling were
added to the already existing models. Whole tree level transpiration and photosynthesis rates,
soil water potential, and temperature (soil water potential was taken from the pre-dawn water
potential measurements, and the other values were taken from MUSICA output) were used as
environmental drivers for the model. The model was used to predict tree carbohydrate
concentrations (sugar+starch), water balance, and PLC. The model structural parameters (e.g.
root, xylem and phloem hydraulic conductivities, and PLC curves) were estimated based on
measurement results and literature values.
Embolism refilling-submodel: Embolism refilling was made to occur at a pre-determined rate
(the rate was varied in the simulations) when xylem water potential was larger than a threshold
value (a threshold value of -1.0 MPa was used in the simulations). The water which was taken
into the refilling conduits was taken from the xylem, so refilling decreased the water potential of
the xylem. The amount of sugar required for refilling was estimated from the total amount of
sugar required to raise the osmotic potential high enough for water to flow into the refilling
conduits from the surrounding xylem. It was further assumed, that a certain amount of these
sugars could be retrieved after the refilling process, so that only a part of the sugars used to
create the osmotic pull for refilling were irreversibly lost to metabolism of refilling (sugar pumps
etc.). The refilling rate (and the corresponding rate of sugar consumption in refilling) in the
figures refers to the fraction of conduits that could be refilled in one second provided the
conditions for refilling were otherwise met (i.e. water potential above – 1 MPa and sugars
available). For “fast”, “intermediate” and “slow” refilling, the fraction is 10-5 s-1, 10-6 s-1, 10-7 s-1,
respectively.
Sugar to starch dynamics and potassium cycling: Sugar to starch conversion was modeled so that
sugar was turned into starch at a rate dependent on sugar concentration, and starch to sugar
conversion was made to be dependent on starch concentration. Xylem water potential has to be
balanced by a phloem osmotic potential of at least equal to xylem water potential in magnitude
order for the turgor pressure to remain positive. If the high osmotic potentials were maintained
with sucrose alone, the viscosity of the solution will become very high. For example, osmotic
potentials of –5MPa, – 7MPa, and -7.5 MPa will increase the viscosity of a sucrose solution in a
highly non-linear fashion to approximately 40-fold, 600-fold and 3000-fold in relation to pure
water, respectively (see equation in Hölttä et al., , which is valid up to 7.5 MPa osmotic
concentration). Therefore the circulation of potassium, which contributes to the osmotic potential
without inducing a major increase in viscosity (Thompson & Zwieniecki, 2005), within the
phloem and xylem along with sucrose was modeled for juniper. Potassium was only used in the
juniper case, as the pine phloem osmotic concentration never reached values that phloem sap
(sucrose solution) viscosity would increase severely. Potassium was loaded to the phloem at a
given rate when phloem osmotic concentration increased above a given threshold value (3 MPa).
It was unloaded from the phloem when osmotic concentration decreased below 3 MPa.
Loading/unloading of potassium was made to cost one mole of sugar per 100 moles of
potassium.
11
Sperry et al. 1998: As described in the text, this model predicts the steady-state transpiration vs.
xylem pressure relationship for a given soil moisture and root depth profile. Rhizosphere drying
associated with root water uptake is simulated using unsaturated soil conductivity relationships
based on soil texture or moisture release curves. Cavitation is simulated based on vulnerability
curve inputs. Richard's equations for mass balance at each node in the resistance network are
solved to yield water flow and pressure. Use of the Kirchoff transform reduces the number of
nodes required to discretize the network to just those compartments with different conductivity
functions.
The model was used to predict multi-year time courses of soil-canopy hydraulic conductance
(K), flux per sapwood area (E), and safety factors from critical flux rates (E/Ecrit). Relative loss
of hydraulic conductance (PLC = 1-K/Ksat, Ksat = maximum seasonal tree conductance) was
also predicted. Six trees per species were analyzed, three from a drought plot (plot 10) and three
from the ambient control plot (plot 12). Vulnerability curve inputs for piñon were the same used
in Plaut et al. (2012), and separate curves were inputted for roots vs. shoots. For juniper, stem
curves generated by JC Domec (described in the Supporting Notes S1) from trees on site were
used in preference to the Willson et al. (2008) data used previously (Plaut et al., 2012) that were
from trees at a distant site.
In principle, the model can divide the rooting zone into multiple layers, and use measured soil
water potentials as input. In practice, however, the data indicated that both species were drawing
water from a missing soil layer that was wetter than any of the three measured layers. On many
occasions, the predawn xylem pressure was less negative than the wettest measured soil layer,
and there was significant sapflow. Significant flow means soil water uptake, and the predawn
xylem pressure means the uptake was from a wetter (deeper) soil layer than what was measured
with soil psychrometers.
Rather than using the demonstrably incomplete soil moisture profile and estimates of the root
distribution as inputs, the model was calibrated to predict the predawn xylem pressure (^PD,
superscript ^ denotes a model prediction) from the measured sapflow (E, daily maximum) and
midday xylem pressure (MD): ^PD = E/^K + MD. The ^K term is the soil-to-canopy hydraulic
conductance at the MD pressure calculated by the model for an arbitrarily small soil-to-canopy
∆P of 0.2 MPa. The calibration was run for the subset of days (between April 1 and October 31)
on which the PD was measured (7-9 days each year). The mean square error (MSE) between the
^PD and the measured PD was minimized by varying the initial K (Ksat) at the beginning of the
time series. Days where PD < MD were excluded from the calibration. In piñon during 2007, the
monsoon season corresponded with an obvious systematic deviation where ^PD became much
more positive than the measured PD, indicating that ^K was under-predicted post-monsoon. This
suggested recovery of hydraulic conductance in the trees. In these cases, the model was
calibrated separately for pre-monsoon and post-monsoon data. The model was also calibrated
separately for each year (2007 through 2010) years (April-October data only) to account for any
changes in tree K occurring in the November to March period that was not modeled.
Once the model was calibrated, it was used to generate the full time sequence of ^K's and ^PD's
(i.e., not just on the days where PD was measured). The ^PD time sequence was re-inputted as a
substitute soil water potential for the entire rooting zone (in lieu of accurate profiles for soil
12
water potential and rooting depth). From the sequence of soil water potential and interpolated
MD pressures, the model calculated ^E and ^Ecrit values. The model fit was re-evaluated by
comparing ^E to measured E. Fit was generally very good, but occasionally with distinct outliers.
These obvious outliers (<< 0.1% of the data) were eliminated from subsequent analysis. In one
tree and season (a droughted piñon, 2007 premonsoon), the PD calibration did not work well as
evidenced by a poor fit between E and ^E. For this one tree and period, the model was recalibrated to fit measured E data (by varying ksat).
The PLC (1-K/Ksat) was calculated relative to the mean maximum K (i.e., Ksat) over the
analysis period for the surviving trees (piñon: 12-1, 12-2, 12-4, mean = 2.38 mole s-1m-2MPa-1;
juniper: 10-7, 10-8, 12-6, 12-9, 12-10, mean = 0.94 mole s-1m-2MPa-1).
TREES: The Terrestrial Regional Ecosystem Exchange Simulator (TREES) (Mackay et al.,
2003; Samanta et al., 2007; Loranty et al., 2010; Mackay et al., 2012) that operates as a
physiology model at the scale of individual trees or as an ecosystem model for whole stands. At
the plant scale the model couples photosynthesis, stomatal conductance, and transpiration in a
steady state solution for sun and shade canopy at 30-minute time steps, and forced with
micrometeorological data (air temperature, wind speed, radiation, vapor pressure deficit, soil
temperature). This coupled canopy model and the plant water balance model (Sperry et al., 1998)
were combined into a single, integrated model to explicitly simulate soil-plant hydraulics and
hydraulic failure, and to provide both demand and supply limits on stomatal control of carbon
uptake and water loss (Roberts 2012), as well as carbon utilization and allocation.
At the whole plant canopy scale stomatal conductance (GS) was calculated by combining Darcy’s
Law and Fick’s law of diffusion as
GS = KL()/D (S – L)
(1)
where KL() and L are whole-plant hydraulic conductivity and leaf water potential,
respectively, D is vapor pressure deficit in the canopy, and S is soil water potential integrated
over the rooting depth of the plant. The canopy and plant water balance model components are
solved iteratively until they converge on a transpiration rate, with simultaneous solution of
photosynthesis and stomatal conductance. For this study TREES discretized each modeled tree
into three root modules, each having an absorbing and conducting element, and one canopy
module having a conducting element and a lateral element with sun and shade sub-elements for
gas exchange. The rhizosphere around each absorbing root element was discretized into five subelements for transporting water between the bulk soil and the absorbing root (see Sperry et al.,
1998 for details). The root zone soil water balance was maintained by the model and updated, in
separate layers defined by discrete root depth, using rhizosphere flux rates determined as part of
the plant water balance model solution. The model moves water at the soil-root interface either
from soil to root or from root to soil as a function of the pressure gradients. Once the plant
hydraulic solution converges the photosynthetic assimilation is accumulated and for daily
updating of NSC.
Plant mortality due to hydraulic failure can be predicted using TREES because of cavitation.
Plant mortality due to carbon starvation is not explicitly modeled. However, changes in NSC are
13
simulated as the difference in carbon uptake and utilization. A reduction in carbon uptake occurs
when stomatal closure reduces photosynthetic assimilation of carbon. Using hydraulic
conductance as a proxy for carbon transport reduces carbon utilization. Consequently, as a
simulated tree approaches a condition that suggests that it would be susceptible to mortality due
to stomatal closure and reduced water for carbon transport, both carbon uptake and utilization
decline, which means the rate of change of NSC can be negligible. While this would not directly
predict mortality due to carbon starvation a combination of plant hydraulic conductivity,
hydraulic safety, cavitation, changes in NSC, carbon uptake, and carbon use collectively can be
used to diagnose the health status of a simulated tree.
Changes in NSC for the whole plant were calculated at daily time steps as
dCNS/dt = CA - CG – CM
(2)
where CNS is NSC, CA is photosynthetically assimilated carbon for period t (i.e. 1 day), CG is
growth and growth respiration allocated in time t, and CM is maintenance respiration over period
t. Carbon is allocated first to CM and then to CG. CM was calculated using separate temperatebased respiration rates for leaf, stem, and roots as
CM = (Rroot Croot rTroot + Rstem Cstem rTstem e0.67*log(10Cstem)/10 + Rleaf Cleaf rTleaf) fM~K
(3)
where R terms refer to root, stem, and leaf intrinsic respiration rates (fraction), C terms are
carbon pools, T terms are temperatures, r is a respiration coefficient, and fM~K is a function that
reduces the transport of NSC to sites for maintenance respiration as a function of hydraulic
conductivity and saturated hydraulic conductivity KLsat as
FM~K = KL()/KLsat
(4)
When root temperature is at least 5 deg. C then CG is calculated as a parameterized fraction (G)
of CA as
CG = GCAfG~K
(5)
where fG~K is function that reduces the transport of NSC to sites for growth as a function of
hydraulic conductivity and saturated hydraulic conductivity KLsat as
fG~K = [KL()/KLsat]2
(6)
TREES was parameterized and run on individual trees (three drought piñon, three ambient piñon,
three drought juniper, and three ambient juniper) using individual tree data to the extent possible.
The model was tuned to each tree using species-specific allometric equations and the basal area
of each respective tree, and sap flux data for each respective tree. TREES carbon pools were
initialized for each individual tree using allometric equations for the root, stem, and leaf
structural carbon pools and measured NSC (NG McDowell et al., unpublished data). TREES
was parameterized for hydraulics by species using vulnerability to cavitation curves (Plaut et al.,
2012), and by individual tree using sap flux data to obtain midday transpiration at saturated
14
hydraulic conductivity. Measured pre-dawn and mid-day water potentials at saturated hydraulic
conductivity were also used. Site-specific soil texture data was used to parameterize the soil
hydraulic properties. The photosynthesis routines were parameterized using species and
treatment specific data collected in the study. All canopy calculations were expressed on a per
unit leaf area basis, and so leaf area index by individual tree was obtained from allometry and
taking the calculated total leaf area divided by projected crown area (Loranty et al., 2010;
Mackay et al., 2010). We assumed that each tree operated independently of its neighbors, and so
there were no interactions between root uptake rate among trees. The trajectory of carbon and
water pools and fluxes for each tree was therefore independently calculated, and determined as a
function of each respective tree’s carbon pools, hydraulic properties, and effect on its local soil
water conditions.
TREES was driven using gap-filled half-hourly micrometeorological data from the site, where
gap-filling followed standard procedures (e.g., Falge et al., 2001). To simulate a reduction of
water input in the drought plots we reduced precipitation input by 50 percent starting on June 1,
2007. All 12 trees were simulated starting from January 1, 2007. For the trees that died in August
2008 the simulations ran out to the end of August 2008. For all other trees the simulations were
run to about June 1, 2011.
MUSICA: The multilayer, multi-leaf process-based biosphere-atmosphere gas exchange model
MuSICA used here has been primarily developed to simulate the exchanges of mass (water,
CO2) and energy in the soil-vegetation-atmosphere continuum and is particularly well designed
for studies on conifer trees because it deals with needle clumping of various needles cohorts
(Ogée et al., 2003). MuSICA assumes the terrain to be relatively flat and the vegetation
horizontally homogeneous. Several species can share a common soil and the mixed canopy is
partitioned into several vegetation layers (typically 10-15) where several leaf types
(sunlit/shaded, wet/dry) for each cohort and species are distinguished. Stand structure is therefore
explicitly accounted for and competition for light and water between species can be explored.
The model typically produces output at a 30-min time step and can be run over multiple years or
decades as long as the vegetation structure is given. So far, it has been tested mostly on forest
ecosystems. However, the model is general enough to be applied to other forest types and also
crops or bare soils. The version 2.0.x of MuSICA used in this study (Domec et al., 2012) has
been upgraded compared to the versions 1.x.x used in previous publications (e.g. Ogée et al.,
2003; Ogée et al., 2009; Wingate et al., 2010). In this new version, all routines are now
organized in independent modules according to the Fortran 90 standards. The radiative transfer
scheme has been modified and is now based on the radiosity method to support multiple species
in a given vegetation layer (Sinoquet et al., 2001) and can be applied to both broad-leaf and
needle-leaf species. In particular, the so-called force-restore scheme used previously to describe
the water and energy transfer in soils and litter has been replaced by a multilayer coupled heat
and water transport scheme that explicitly accounts for root water uptake for each species. The
model also now accounts for water storage in the plants with a single water storage capacity for
each species that scales with leaf area (Williams et al., 2001) and for plant loss of hydraulic
conductivity (cavitation function). Leaf-to-air energy, water and CO2 exchange are described in a
similar fashion as in the original version and consists of a photosynthesis model (Farquhar et al.,
1980), a stomatal conductance model (Leuning, 1995), a leaf boundary-layer model (Nikolov et
15
al., 1995) and a leaf energy budget equation. The only defined parameters for the leaf
photosynthesis model (Farquhar et al., 1980) are the maximum rate of carboxylation (Vmax) And
electron transport rate (Jmax), the night respiration rate (Rd) and the quantum yield. All These
parameters are allowed to vary with leaf temperature and leaf age. Therefore, these parameters
are prescribed at a given temperature (25°C) and for young and old leaves or needles. Day
respiration is computed using the night respiration rate parameterization and a light inhibition
factor. Soil water stress is primarily affecting stomatal aperture, which in turns reduces
photosynthesis. The response curve is described by a sigmoid curve of xylem pressure (xylem)
below a certain threshold where stomatal conductance is reduced by 50% (gs_50) at a constant rate
(gs_shape) such as:
gs= 1/(1 + (xylem/gs_50)^gs_shape)
(7)
This response is similar to the response of xylem hydraulic conductivity to xylem pressure
(vulnerability to cavitation curve).
Rain interception, leaf wetness duration and evaporation are computed for each species and
vegetation layer. The MuSICA model allows the computation of scalar vertical profiles (e.g., air
temperature and CO2) and the different component fluxes of the carbon, water, and energy
budget. Notably, it gives separate estimates of not only tree water use, gross primary
productivity, plant respiration, soil respiration (autotrophic plus heterotrophic) and net ecosystem
exchange (NEE), but also soil moisture profile and root water uptake for each modeled soil layer.
So far, MuSICA does not incorporate a full carbon cycle model and so respiratory terms are
simply scaled using living biomass, basal respiration rates and Q10 values. Although MuSICA
does not compute changes in whole plant hydraulic conductance as soil dries, for this study we
generated some percent loss of root and branch hydraulic conductivity (PLC) from the modeled
water potentials and from the vulnerability curves. Stem respiration rates are assumed to depend
on air temperature and are scaled between crown and non-crown areas assuming all the branch
biomass is inside the crown. Leaf respiration is calculated on a leaf area basis. Soil and litter
respiration rates are a function of soil temperature and soil moisture. Tree water loss is controlled
by energy inputs, evaporative demand, and photosynthetic need for CO2. As in TREES, water
extraction is determined by the soil hydraulic conductivity, tree hydraulic conductance, water
storage capacity, soil texture, and xylem vulnerability to cavitation. Water uptake can continue as
long as hydraulic continuity is maintained from the soil through the xylem. Hydraulic failure can
occur in the model as a result of xylem cavitation if xylem pressure falls below the structurally
defined limits, or if the hydraulic conductance at the root–soil interface falls to zero due to high
rates of plant water extraction or desiccation. Values of total xylem conductance were deduced
from the ratio between transpiration rates and soil to leaf xylem pressure drops.
The soil hydraulic conductivity depended on the volumetric soil moisture content according to
the model of either Van Genutchen (1980). For each plot, the rooting zone was partitioned into
seven soil layers of 10 cm each. Maximum rooting depth and root distribution were determined
by fitting modeled root xylem pressure to measured predawn xylem water potentials when soil
was close to saturation (early 2007). For each plot, most MuSICA parameters such as sitespecific soil physical parameters (profiles of soil porosity, soil matric potential and soil hydraulic
conductivity at saturation), rooting profiles, leaf area index, hydraulic and photosynthetic
16
parameters are described in Table S1. Branch vulnerability to cavitation curves were determined
using the air injection technique (Sperry & Saliendra, 1994) on five samples collected in
December 2011 at the site from the control plot. Root vulnerability to cavitation curves were
taken from (Plaut et al., 2012).
Living tissue respiration was also parameterized using basal respiration rates and Q10 values
determined at the site (Mike Ryan, described in Supporting Notes S2a). Soil and litter respiration
rates were a function of soil temperature and soil moisture and were parameterized from soil
respiration parameters determined at the control plot in 2006 and 2007 (White, 2008). Because of
the lack of data for each needle class, MuSICA was parameterized with one set of stomatal
conductance and photosynthetic parameters (maximum rates of carboxylation, rate of
photosynthetic electron transport and mesophyll conductance) measured in September 2011, at
the peak of summer soil moisture. We forced the MuSICA model with meteorological values
(radiation, wind speed, temperature, humidity, precipitation) collected at the site and ran
MuSICA for the control plot and the droughted plot where both tree species were mixed. The
model was tuned to each tree using species-specific allometric equations and the basal area of
each respective tree, and sap flux data for each respective tree.
As described in the Sperry and TREES models’ section, we also ran MuSICA on individual trees
(3 drought PIED, 3 ambient PIED, 3 drought JUMO, and 3 ambient JUMO) using individual tree
data. Both species were modeled at the same time and so there were both competing for the same
soil water. Simulations were run for 1550 days beginning January 1, 2007. All output variables
(transpiration, GPP, NEE, Respiration) were expressed on a per ground area basis and then for
model comparisons on a leaf area basis, and so as in TREES, leaf area index by individual tree
was obtained from allometry and taking the calculated total leaf area divided by projected crown
area.
In order to estimate daily fluctuation in nonstructural carbohydrate (NSC), MuSICA was coupled
with a single-substrate tree pool model (Ogée et al., 2009). We assumed that nonstructural
carbon in the tree was represented by a single and well-mixed pool of water-soluble sugars,
which is assumed to be always large enough to supply the metabolic demand during the growing
season. This pool of sugars comprised leaf, wood and fine root sugars and was filled by leaf net
photosynthesis (Fleaf) and used as substrate for maintenance and growth woody respiration
(Rwood) and whole-tree biomass production (Pbiomass). In turn, Pbiomass was assumed to be carbon
limited and defined as Pbiomass = kNSCNSC, where kNSC represented the pool turnover rate
determined from mean whole tree growth rates (Dewar et al., 1998). The carbon budget of the
pool was then written as:
dNSC/dt = Fleaf – Rwood – kNSCNSC, or
dNSC/dt + knscCNSC = Fleaf – Rwood
(8)
(9)
Carbon pools were initialized at t=0 for each individual tree using allometric equations for the
root, stem, and leaf structural carbon pools and measured NSC at the site (NG McDowell et al.,
unpublished data).
17
ED(X): We used the Ecosystem Demography (ED) model (Moorcroft et al., 2001) with
modifications described by Fisher et al. (2010) and in this paper. The mortality in this modified
version of ED results mainly from the assumption of carbon starvation, which links tree
mortality to the carbon deficit experienced by trees. The carbon deficit is defined based on the
ratio of current carbon storage concentration in leaf [LSCcur] to user-specified critical leaf
storage carbon concentration [LSCLcrit], below which mortality occurs . Mortality due to carbon
deficit (Mstarvation , fraction of trees dead per day) is simulated as follows (Fisher et al 2010),
Mstarvation = max(0.0, Smort (1.0 - LSCcur /LSCcrit)).
(10)
A storage carbon pool (𝐵𝑠𝑡𝑜𝑟𝑒) is simulated based on the balance of GPP input and the output
through respiration (R) and carbon sink (CS; or growth of new tissues). Specifically,
𝐵𝑠𝑡𝑜𝑟𝑒(𝑡 + 1) = 𝐵𝑠𝑡𝑜𝑟𝑒(𝑡) + GPP − R − Cs
(11)
where the sink rate Cs is dependant on the ratio of current leaf storage carbon concentration to a
user-specified target leaf storage concentration (LSCtar). Specifically,
4
𝐿𝑆𝐶
Cs = Cs0 { 1.0 − exp[− ( 𝐿𝑆𝐶𝑐𝑢𝑟 ) ] }
𝑡𝑎𝑟
(12)
where Cs0 ] is a user specified maximum carbon sink rate. We set Cs0 to be 10% of total plant
leaf biomass in carbon. The current leaf storage carbon concentration is calculated from the
common carbon pool, 𝐵𝑠𝑡𝑜𝑟𝑒, based on the equilibrium coefficients between leaf, root and
sapwood. Based on the NSC measurement in the ambient plots, storage carbon concentration
ratios of leaf to root and leaf to sapwood are set to be 1.0 and 0.33, respectively.
The ED(X) model simulates the control and treatment plot with two tree cohorts: a juniper cohort
and a piñon cohort. The carbon storage pools of cohorts are calculated among the carbon uptake
through photosynthesis and carbon drawn by growth, respiration and tissue turnover.
Photosynthesis in the modified ED(X) is simulated by the Farquhar photosynthesis model
(Farquhar et al 1980) for each individual leaf layers for each tree cohort. A key parameter of
Farquhar photosynthesis model is the Rubisco-limited maximum photosynthesis rate, Vc,max
(umol CO2/m2/s). The carbon allocation is based on the allometry data from our study site.
Specifically, the leaf biomass is calculated based on the diameter at breast height (DBH) as
follows,
Bl  xb DHB yb
(13)
where xb and y are parameters fitted to data. The stem biomass (total of sapwood and dead wood)
is calculated based on the allometry from original ED (Moorcroft et al., 2001) as follows,
b
Bs  0.136 DHB1.94 h0.572  0.931
(14)
18
where  is the wood density and is set to 0.5 g/cm3 in this study and
of the cohort with
h
is the mean height (meter)
h  xh DHB yh
(15)
where xh and yh are parameters fitted to data. The amount of live stem biomass (sapwood, Ba ) is
calculated based on the pipe model as follows,
Ba 
 h  SLA
10
(16)
where  is the ratio of leaf area to sapwood area (m2/cm2). The root biomass is empirically set to
be the same as leaf biomass.
Respiration is divided into maintenance respiration and growth respiration. The growth
respiration consumes 25% of the carbon used for growth (Williams et al., 1987) and the leaf
maintenance respiration (Rm) is set to be proportional to Vc,max (Arain et al., 2002).
Specifically,
Rm  0.0089 Vc, max .
(17)
The root and sapwood respiration is set to be 80% and 5% of leaf respiration (umol CO2/g
biomass), respectively (Wertin&Teskey, 2008). Hydrology in this version of ED is based on the
water supply function as determined by soil water potential, xylem conductivity and minimum
leaf water potential and the water demand function as determined by stomatal conductance and
vapor pressure deficit. This ED version is based on a single soil layer. The soil water potential
(SWP) is simulated based on an empirical equation as follows(Oleson et al., 2010),
SWP  SWP0 SAT  
(18)
where SWP0 is the reference soil water potential for saturated soil. SAT is the volumetric
saturation of water in soil pores.  is the exponent determined by soil texture as follows
  2.91  0.159Pclay
where
Pclay
(19)
is the percent of clay in the soil and is set to be 5 in this study. The water supply is
calculated based on the maximum water potential gradient between leaf and soil ( Pmax , MPa)
using the cohesion theory as follows,
Wsupp =-
Pmax
R
(20)
19
where R is the resistance of water transport from root to leaf. Specifically, it is calculated as the
sum of resistance of root, sapwood and leaf. See Hickler et al. (2006) for details. Pmax is
calculated with respect to minimum leaf water potential (LWPmin) and height of the cohort,
Pmax  LWPmin  SWP 
9.8h
.
1000
(21)
The root conductivity may reduce due to xylem cavitation (Sperry et al., 1998). For this study,
the loss of conductivity is calculated based on the calculated soil water potential using the
Weibull equation as follows (Neufeld et al., 1992),
s
Rr  Rr 0 e(  SWP /50 )
(22)
where Rr 0 is the reference root resistance with no loss of conductivity.  is the critical soil water
potential that cause 36% loss of conductivity and  is the shape parameter for conductivity loss.
The water demand of each leaf layer for a cohort is calculated based on the stomata conductance
and relative humidity. Specifically,
50
s
Wdem 
18.0 (es - ea )
rb + rs RT
(23)
where rb and rs are the boundary layer and stomata resistance of water (s/m). R is the gas
constant (8, 314 J/K/kmole) and T is the air temperature (K). es and ea are the vapor pressure
inside leaf and of the canopy air (Pa). rs is calculated based on the empirical ball-berry model
(Ball et al., 1987). Specifically,
rs 
1
A ea
(m
Patm  2000)
Cf
Ca es
(24)
where Ca is the CO2 partial pressure in the canopy air and Patm is the atmospheric pressure (Pa).
2
C f is the conversion factor from s/m to s m / umol,
Cf 
Patm 9
10 .
RT
(25)
During the drought, filling of tissue turn-over ceases in the model when photosynthesis become
zero. The only consumption of carbon storage is from maintenance respiration. Different from
the initial carbon starvation model proposed by (Fisher et al., 2010), we do not allow negative
carbon storage. Instead, we down-regulate the Vc,max by 50% when the carbon storage is below
20
the critical carbon storage (S Sevanto et al., unpublished). See table below for values of the key
parameters.
Key parameter values used in the ED(X) model
Parameter
Description
LWPmin
Minimum leaf
water potential
Slope of
conductivity to
photosynthesis rate
critical soil water
potential that cause
50% loss of
conductivity
shape parameter for
conductivity loss
ratio of leaf area to
sapwood area
(m2/cm2)
Allometric
parameter 1 for leaf
biomass calculation
Allometric
parameter 2 for leaf
biomass calculation
Allometric
parameter 1 for
height calculation
Allometric
parameter 2 for
height calculation
m
50
s

xb
yb
xh
yh
Value for PIED
-2.1
Value for
JUMO
-4.1
Sources
2
2
Data
-3.57
-8.45
Data
4.07
2.2
Data
0.15
0.2
Data
0.0222
0.0386
Data
1.9172
1.686
Data
0.65
0.41
Data
0.64
0.64
Data
Data
CLM(ED): The CLM(ED) model is a development of the Community Land Model (Oleson et
al., 2010) which is coupled to a version of the Ecosystem Demography (ED) model (Moorcroft
et al., 2001) subject to the modifications described by Fisher et al. (2010). CLM(ED) as used
here contains three mortality sources: 1) background mortality (fixed at 1% per year), 2)
mortality due to carbon starvation, and 3) mortality due to low soil moisture potentials (as a
proxy for hydraulic failure).
Mortality due to carbon starvation (Mstarvation , fraction of trees dead per day) is simulated as
Mstarvation = max(0.0, Smort (1.0 - bstore/bleaf,max))
(26)
21
where bstore is the stored non-structural carbohydrate, and bleaf,max is the ideal leaf biomass (both
in KgC individual-1) for a tree of a given DBH (see Moorcroft et al., 2001 and Fisher et al., 2010
for allometry). For the purposes of this study, we augmented the carbon storage model of Fisher
et al. (2010). In that study, a fixed fraction of live biomass was replaced each day of the
simulation. Any deficit between NPP and the turnover demand was matched by removal of
carbon from the storage pool. For this study, we introduce the concept that plants may respond
to drought (and low NPP) either by 1) utilizing stored carbon or 2) by ceasing the replacement of
lost tissues. Thus, each day, we determine the carbon balance (Cbalance KgC individual day-1), as
Cbalance = NPP - dturnover
(27)
where NPP is the balance of photosynthesis and respiration (gC individual day^_1) and dturnover is
the sum of the replacement rates of leaf, fine root and sapwood tissues (aleaf, aroot and asw)
dturnover = bleaf aleaf + broot aroot + bstem asw
(28)
To partition any negative carbon balance into loss of tissue and loss of stored carbon, we define a
new parameter, falloc. This is the minimum amount of turnover demand that is met.
Aturnover,min = falloc dturnover
(29)
The change in the storage pool is
Dstore = (NPP - Aturnover,min) fstore
(30)
fstore represents the demand for carbon from the storage pool. If NPP - Aturnover,min
is negative, then carbon is removed from the storage pool.
fstore = e-1.Tf^4
fstore = 1.0
for (NPP - Aturnover,min) >0
for (NPP - Aturnover,min) < 0
(31)
(32)
Thus, if there is carbon available to the store, we adjust the flux into the store for Tf, which is the
fraction of the target store at present.
Tf = bstore / (bleaf,maxTstorage)
(33)
The flux to live tissue maintenance is therefore the minimum value plus however much carbon is
not allocated to the store, while the flux out of live tissues is dturnover.
Dleaf = Aturnover,min + (NPP - Aturnover,min) (1-fstore) - dturnover
(34)
Thus, the higher the allocation parameter (falloc) the higher the allocation to live tissue
maintenance, and the greater the removal of carbon from the store during stress. A high target
storage (Tstorage) means a higher allocation to storage, rather than tissue maintenance and growth,
but allows low NPP to be tolerated longer before mortality is induced. These two parameters are
plant life-history traits related to the trade off between growth and survival, and as such, are
22
expected to differ between species.
Mortality due to hydraulic failure is not simulated directly, but instead the reference stress
mortality rate (Smort, fraction of individuals dying per day) is imposed for each day that the
effective root-fraction soil water potential reaches a threshold value t which is variable with
plant type, reflecting different tolerances of water stress (xylem function, root:shoot ratio,
stomatal control) between species. Thus
Mhydraulic = Smort
Mhydraulic = 0.0
for plant < t
for plant > t
(35)
(36)
This simplified function allows for instantaneous mortality during intense droughts, in addition
to the carbon starvation driven mortality that will typically occur as a result of chronic low
assimilation rates (in the simulations). The total mortality (fraction individuals per day) is
therefore
Mtotal = Mbackground + Mhydraulic + Mstarvat
Additional references provided in Notes S5.
(37)
23
Supporting Information, Notes S4: On growth efficiency as a predictor of mortality.
First generation DGVM’s often calculate mortality as a function of some metric of growth
efficiency, which is often defined as NPP/LAI (McDowell et al., 2011), making this potentially
one of the most practical indexes of mortality because of the ease of use by existing DGVMs.
Annual growth efficiency has been shown to be an accurate predictor of vegetation mortality in
some northern hemisphere conifers (Waring & Pitman, 1985; Christiansen et al., 1987) and is
correlated with resin production (McDowell et al., 2007). Although growth efficiency is not as
directly related to theoretical mortality mechanisms as PLC and NSC, it does represent an
outcome of water transport and carbon allocation, and thus should contain some signature of the
net function of plants (Waring, 1987).
We compared empirical measurements of growth efficiency for trees that died and
survived to the model estimates. We estimated annual wood growth and leaf area for piñnon
pine using diameter growth for piñon pine estimated from annual ring widths measured on
extracted cores to the nearest 0.0001 mm, and tree diameter at the core extraction point (~1.4 m
height). Growth was the difference in wood biomass estimated using the allometric equations
given in Grier et al. (1992) for stem wood plus live branches using diameter at ‘year’ minus the
diameter at ‘year−1’ (from the measured ring widths with 100% of the diameter growth assumed
to be from wood). Leaf area was estimated using the allometric equation given in Grier et al.
(1992) for the diameter at ‘year−1’. Growth efficiency per tree (g wood/m2 leaf area, Waring,
1983) was calculated by annual growth ⁄ leaf area at ‘year−1’.
Modeled growth efficiency was always higher than observed; however, the relative
ranking of dying and surviving trees’ growth efficiency was captured correctly by the models.
We note that the observed growth efficiency used measured ring widths and allometry, but
assumed constant leaf area, thus the observations should not be considered accurate in absolute
terms and hence we consider this analysis inconclusive but encouraging. Using growth
efficiency as a general predictor of mortality is appealing because it can be calculated from most
DGVMs (McDowell et al., 2011). However, in our system at least, it is clear that determining
the proper threshold growth efficiency below which plants die requires more work from both
empirical and modeling perspectives. Given the uncertainty in absolute values of observed or
modeled growth efficiency, we propose that relative values (normalized within pixel) may be the
most accurate way to employ growth efficiency in current mortality simulations.
Additional references provided in Notes S5.
24
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