Global Change Biology (2008) 14, 1–15, doi: 10.1111/j.1365-2486.2008.01552.x
Climate controls on C3 vs. C4 productivity in North
American grasslands from carbon isotope composition
of soil organic matter
J O S E P H C . V O N F I S C H E R *, L A R R Y L . T I E S Z E N w and D AV I D S . S C H I M E L z
*Department of Biology, Colorado State University, Ft. Collins, CO 80523, USA, wUS Geological Survey, Center for Earth Resources
Observation and Science (EROS), Mundt Federal Facility, Sioux Falls, SD 57198, USA, zNational Center for Atmospheric Research,
Climate and Global Dynamics Division, PO Box 3000, Boulder, CO 80305, USA
Abstract
We analyzed the d13C of soil organic matter (SOM) and fine roots from 55 native
grassland sites widely distributed across the US and Canadian Great Plains to examine
the relative production of C3 vs. C4 plants (hereafter %C4) at the continental scale. Our
climate vs. %C4 results agreed well with North American field studies on %C4, but
showed bias with respect to %C4 from a US vegetation database (STATSGO) and weak
agreement with a physiologically based prediction that depends on crossover temperature. Although monthly average temperatures have been used in many studies to predict
%C4, our analysis shows that high temperatures are better predictors of %C4. In
particular, we found that July climate (average of daily high temperature and month’s
total rainfall) predicted %C4 better than other months, seasons or annual averages,
suggesting that the outcome of competition between C3 and C4 plants in North American
grasslands was particularly sensitive to climate during this narrow window of time. Root
d13C increased about 1% between the A and B horizon, suggesting that C4 roots become
relatively more common than C3 roots with depth. These differences in depth distribution likely contribute to the isotopic enrichment with depth in SOM where both C3 and
C4 grasses are present.
Keywords: carbon, climate, competition, C3, C4, isotope, photosynthesis, precipitation, soil, temperature
Received 22 August 2006; revised version received 6 July 2007 and accepted 26 July 2007
Introduction
The grass communities on the Great Plains are dominated by C3 grasses in the north, grading to C4 dominance in the south (Sage et al., 1999). In their influential
study of North American grassland ecology, Teeri &
Stowe (1976) found that most of the variability in the
fraction of local species that are either C3 or C4 (i.e.
floristic abundance) was correlated with growing season temperatures. Similarly, Paruelo & Lauenroth (1996)
found that temperature was the primary control of the
relative aboveground productivity of C3 vs. C4 plants,
while the magnitude of precipitation and the proportion of precipitation that fell in summertime explained
small but significant components of the variance. A
number of additional factors have been found to modulate the effects of temperature on C3 vs. C4 activity,
Correspondence: Joe C. von Fischer, tel. 11 970 491 2679,
fax 11 970 491 0649, e-mail: [email protected]
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including fire and grazing (Ojima et al., 1994), soil
nutrient status (Barnes et al., 1983; Wedin & Tilman,
1990), topography (Barnes et al., 1983), water (Knapp &
Medina, 1999) and soil texture (Archer, 1984; Epstein
et al., 1997). However, the importance of these factors is
consistently secondary to temperature and often local
and site specific (Sage et al., 1999).
The strength of temperature for controlling the outcome of C3 vs. C4 competition has been interpreted
primarily in light of photorespiration (Sage & Monson,
1999), a pathway of carbon loss that is sensitive to
temperature and important only in C3 plants. In photorespiration, the enzyme rubisco catalyzes the reaction of
ribulose bisphosphate with O2 instead of CO2, and the
oxidation/carboxylation ratio for this enzyme increases
with temperature (Brooks & Farquhar, 1985). Because
the physiological mechanisms that limit photorespiration in C4 grasses also impose a cost for rates of net
assimilation, C3 grasses have greater net assimilation
1
2 J. C.
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F I S C H E R et al.
(and thus a competitive advantage) only at cooler
temperatures where photorespiration losses are low,
while C4 grasses have greater net assimilation at higher
temperatures (Sage & Monson, 1999).
Physiological models of leaves at modern CO2 levels
have been used to quantify the relationship between
temperature and net carbon assimilation rates. These
models predict that the C3 vs. C4 crossover temperature
(i.e. the temperature above which C4 plants have higher
net assimilation rates than C3 plants) is approximately
22 1C (Ehleringer et al., 1997; Collatz et al., 1998). Application of these models has allowed regional and global
predictions of the spatial and interannual patterns in C3
vs. C4 productivity (Collatz et al., 1998). It is important
to understand the controls on C3 vs. C4 productivity in
North American grasslands because this balance forms
the basis of diverse ecological studies ranging from the
global carbon cycle (Still et al., 2003a; Suits et al., 2005;
Zhou et al., 2005) to isotopic studies of bird migrations
(Hobson, 2005).
Despite the sound principles and success of physiological models for predicting C3 vs. C4 productivity, our
understanding of this climate–biology relationship remains incomplete. For example, it is not clear how to
apply the crossover temperature principle given that
daily growing season temperatures in C3-dominated
areas may regularly cycle above and below 22 1C.
Similarly, the ecological significance of monthly, seasonally or annually averaged temperatures is obscured
by the differing phenologies of C3 and C4 plants (Williams, 1974; Dickinson & Dodd, 1976; Ode et al., 1980).
In addition, C4 grasses appear to be detrimentally
affected by cool temperatures during development
(Haldimann, 1999; Pittermann & Sage, 2000), likely
due to limiting rubisco content (Kubien & Sage, 2004).
We anticipate that a more detailed examination of the
relationship between climate and C3 vs. C4 production
may yield insights into the physiological and ecological
processes that influence the relative performance of C3
and C4 plants, and perhaps help constrain the effects of
future climate on the C3/C4 composition of grasslands.
In order to help clarify the regional-scale climate
controls on the percentage of production by C3 vs. C4
plants (hereafter %C4) in the North American grasslands, we have characterized the carbon isotope composition of fine roots and soil organic matter (SOM)
from native prairie relicts across the US and Canadian
Great Plains. Use of stable isotopes to determine the
relative productivity is possible because C3 and C4
grasses differ in their d13C (Cerling et al., 1997). Sage
et al. (1999) concluded that the d13C of SOM is preferred
over aboveground metrics of %C4 because SOM integrates carbon inputs over many years (Tieszen &
Archer, 1990). Thus, we expect that the d13C of SOM
in the North American Great Plains will primarily
reflect the relative productivity of C3 vs. C4 plants, with
particular sensitivity to belowground production. Despite the promise of this approach, a number of factors
could obscure the direct interpretation of carbon isotopes for %C4: the isotopic compositions of the C3 and
C4 end members may vary (e.g. Johnson et al., 1990;
Weiguo et al., 2005), C3 and C4 grasses may systematically differ in their belowground allocation of carbon
(Fargione & Tilman, 2005), decomposition of biomass or
biochemical components may be unequal between the
types, leading to selective preservation of material in
the SOM pool (Gleixner et al., 1999; Fernandez et al.,
2003; Hobbie & Werner, 2004), and/or isotopic fractionation may alter the d13C as plant material becomes
SOM (Wedin et al., 1995).
To evaluate the fidelity of the SOM isotopic composition as a record of %C4, we compare the %C4 that we
interpret from SOM and root isotopes to the %C4
predicted by Paruelo & Lauenroth (1996), and to
vegetation productivity information in a soil database
(STATSGO) and predictions from a crossover-temperature
approach applied by Collatz et al. (1998). We also
identify isotopic patterns within the study sites and
examine mechanisms that may drive these patterns.
In addition to generating improved understanding of
climate controls on grassland ecology, we anticipate that
this, the first systematic soil isotope investigation of the
North American Great Plains, will be useful for studies
of regional and global carbon cycles, and for paleoclimate studies on the variation in atmospheric or organic
reservoirs of 13C. Although latitudinal distributions of
the d13C of A-horizon SOM have been presented in
prior publications (Tieszen et al., 1997; Nordt et al.,
2007), there has been no systematic examination of the
patterns in the data or their underlying controls.
Methods
We selected study sites that contained native prairie
systems with intact floristic composition and no records
of intensive agricultural management other than haying, burning or grazing. We assumed that these practices did not substantially alter the plant community
composition. The sites were located from south Texas in
the United States to Saskatoon, Canada and from the
eastern edge of the tallgrass prairie in Iowa and Minnesota to the western edge of the shortgrass prairie in
Colorado and New Mexico. Most sites were protected
by the nature conservancy, state or national parks, or
long-term ecological research sites. The nature of the
prairie relict dictated sampling strategy; however, in all
cases we defined relatively flat, upland sampling areas
that were free of exotics and representative of the
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C L I M AT E C O N T R O L O F C 3 V S . C 4 P R O D U C T I V I T Y
specific relict. All soils were collected in summer, between 1989 and 1994.
Four to six quadrats (1 m2) were selected as replicates
to characterize each site. Two to four cores were taken
from each quadrat with a 5 cm diameter hydraulically
driven corer where possible, or a 2.5 cm hand driven
hammer to a depth between 60 and 100 cm. Each core
was divided along horizon boundaries (3–5 depths per
core, depending on how local soil horizons had developed) immediately or within 48 h, and samples were
pooled within each quadrat by horizon. Roots were
manually picked from the pooled samples as they air
dried within 48 h after collection. Because each soil
sample contained a large number of fine roots, the fine
roots (o2 mm) had potential to record the integrated
average carbon isotope composition of the current
vegetation. We, therefore, excluded the occasional large
roots (42 mm) that we encountered, because they
would disproportionately contribute and potentially
skew the isotopic composition of the root pool for a
given sample. We did not discriminate live from dead
roots; we assume that live and dead roots do not have
significantly different isotope composition and so the
collective root pool indicates current belowground production of C3 vs. C4 plants.
Soil texture was determined on small sample sizes by
a modification of the standard hydrometric methods
(Elliot et al., 1999). The small sample method used lowvolume settling tubes and small hydrometers designed
for densiometric measurements and allowed analyses
on representative subsamples of 5–10 g, in contrast to
the standard 40 g requirement.
Soil subsamples for SOM isotope analyses and all
roots were examined for carbonates by watching for
effervescence in soil samples in 0.5 N HCl under vacuum. Carbonates were removed by mixing in HCl
until effervescence ceased, soils were centrifuged at
12 000 g, resuspended in distilled water and recentrifuged, dried at 105 1C and pulverized. This treatment
has been found to impart no measurable effect on SOM
isotopic composition (Torn et al., 2002). Samples sufficient to provide 40.02 mL CO2 were dried, loaded into
tin combustion cups, combusted in a Carlo Erba CHN
analyzer (Thermo Fisher Scientific, Waltham, MA, USA)
that included gas chromatographic measurement of
CO2 and N2 to quantify SOM C and N content. Internal
standards were run with each batch of samples and
blind replicates were included to monitor consistency.
Combustion products from the Carlo Erba were
transferred in a helium carrier, dried with magnesium
perchlorate, automatically trapped cryogenically on a
triple-trap of a SIRA 10 isotope ratio mass spectrometer
(VG Instruments, Manchester, UK), and analyzed for
isotope ratios. Laboratory standards were run with
3
every 10 samples and the reference gas was calibrated
frequently with materials from the National Bureau of
Standards and other interlaboratory standards. Precision for carbon, including independent combustion of
samples, is better than 0.2%. Isotope ratios are expressed as a d13C value with respect to the PDB standard (std) where
13
13
d C¼
C=12 C sample 13
C=12 C std
ð13 C=12 CÞstd
1000:
We report the mean soil d13C value (and standard
deviation) for each depth increment as the average of
that depth from all plots in a site. In cases where the A
and B soil horizons were subdivided, we report the
average d13C of A subhorizons and B subhorizons.
These averages were not weighted by bulk density or
carbon content.
From isotope values of SOM, we calculate the %C4 as
the percentage of carbon derived from C4 sources. This
calculation is made from a two end-member mixing
model, assuming that the d13C of C3 plant material is
26.7% and C4 material is 12.5% (Cerling et al., 1997).
In the A-horizon SOM, fractionation appears to have
caused 1% enrichment of the SOM relative to vegetation.
To calculate %C4 for this material, we assume that both
the C3 and C4 end members are enriched equally to
25.7% and 11.5%, respectively. The %C4 determined
from the d13C of A-horizon SOM and A-horizon roots are
referred to as %C4 A-SOM and %C4 A-roots, respectively. We
did not calculate %C4 from B-horizon SOM or roots.
During data analysis, we identified some sites with
evidence of recent vegetation change as indicated by
highly unusual isotope profiles, so we excluded these
sites from further analyses. We also excluded sites
where sample handling or data processing errors left
only a small number of cores (no3 pairs of cores). These
exclusions reduced the number of sites as compared
with those analyzed in Tieszen et al. (1997) to 55; we do
not present data from the excluded sites anywhere in
this paper. Owing to sample handling errors, the root
materials for some sites were lost, thus reducing the
number of root results. Finally, statistical analysis supported exclusion of the Stavely, Alberta site as an outlier; analyses presented in this paper do not include
results from that site.
Climate data
To our knowledge, there is not a consistently interpolated climate database for the US and Canadian parts of
the Great Plains that will allow climate characterization
of our study sites, many of which lie far from climate
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4 J. C.
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F I S C H E R et al.
stations and some of which lie near the US–Canadian
border. To construct the needed climate database, we
obtained climate data for the United States by directly
contacting data managers at Regional Climate Centers.
Canadian climate data were obtained from the Meteorological Service of Canada (2004). Table 1 lists and
describes the climate and other factors considered in
our analyses.
The data from 163 climate stations represented daily
values for the period 1961–1990. The daily values were
averaged into monthly mean values. Data from some
US climate centers and from Canada (13 and five sites,
respectively) were only available in monthly values and
represent mean values for periods of at least 30 years
ending no later than 1990. All monthly values were then
entered into a database along with the latitude and
longitude of each weather reporting station and each
soil-sampling site. Surfaces III, a statistical gridding and
mapping program, was used to krig and then map the
contour lines of each climatic variable. We overlaid the
positions of the soil sampling sites on the kriged map to
determine the value of the climatic variable for each
site. Comparison between observed and krig-predicted
values showed good agreement. For example, July
precipitation had 95% of the predicted values within
0.6 cm of the actual value. Similar comparisons for April
low temperature and AMJJA high temperature showed
95% of the predicted values falling within 1.2 and 1.4 1C,
respectively.
Our climate database was also cross-checked with the
VEMAP data (Kittel et al., 2004), which represent a consistent, 100-year climatology of the region based on
thousands of station records and so should in principle
better represent the time scales over which the soil
acquired its d13C. However, the VEMAP data do not cover
the Canadian Great Plains. In the comparison between
the two data sets for the critical predictor variables, no
significant biases were found and close agreement
(0.75oR2o0.85) was found for both temperature variables and precipitation. The latter is especially important because while temperature varies fairly smoothly
across the region, precipitation, and especially seasonal
or monthly precipitation averages, exhibit some sharp
spatial gradients (Kittel et al., 2004). The comparison of
the two data sets gives us confidence that our procedures produced an accurate depiction of the long-term
seasonal climate, while including a consistently developed estimate for Southern Canada.
Comparison with other studies
We obtained an independent estimate of %C4 contribution to production from the State Soil Geographic
(STATSGO) database (Soil Survey Staff, 1993). As de-
Table 1 The climate variables used in this study and their
abbreviations
Climate variable
Time period
Daily high temperature (1C)
Year
April
May
June
July
August
April–July (AMJJ)
May–July (MJJ)
April–August (AMJJA)
Daily low temperature (1C)
Year
April
May
June
July
August
April–July (AMJJ)
May–July (MJJ)
April–August (AMJJA)
Daily average temperature (1C) Year
April
May
June
July
August
April–July (AMJJ)
May–July (MJJ)
April–August (AMJJA)
Cumulative precipitation (cm) Year
April
May
June
July
August
Mean April–July (AMJJ)
Mean May–July (MJJ)
Mean April–August (AMJJA)
Growing degree days (165 F)
Year
Frost free days
Year
Soil variable
%sand
%silt
%clay
%carbon
%nitrogen
C/N ratio
Temperatures are for daily values, averaged over the time
period. Precipitation is cumulative for the time period.
scribed in Tieszen et al. (1997), these data were collected
during vegetation surveys where the proportion of
aboveground plant production was determined for
major plant species. For each of our US sites, we
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C L I M AT E C O N T R O L O F C 3 V S . C 4 P R O D U C T I V I T Y
identified the corresponding STATSGO map unit and
determined the percentage of plant production that
was attributable to C4 grasses. We refer to this as the
%C4 STATSGO. Similar data are not available for Canada,
to our knowledge.
We also calculated the predicted %C4 for each of our
sites using our climate data and the published algorithm of Paruelo & Lauenroth (1996). We refer to this as
the %C4 P&L. The algorithm, given in the legend of their
Fig. 3, is
%C4 P&L ¼ 0:9837 þ 0:000594PA þ 1:3528PS þ 0:2710
lnðTA Þ;
where PA is the mean annual precipitation (mm), PS is
the proportion of annual precipitation that falls in
summer (June, July and August) and TA is the mean
annual temperature ( 1C).
Finally, we determined categories of %C4 productivity (i.e. 100% C3, mixed C3/C4 or 100% C4) from leaf
physiology models following the approach of Collatz
et al. (1998). Their model predicts that C4 leaves have
greater net C assimilation than C3 leaves at temperatures higher than 22 1C. Thus, assuming sufficient
precipitation for growth (425 mm month1), their model predicts that C4 grasses should competitively exclude
C3 (i.e. 100% C4) where growing season temperatures
are persistently 422 1C, while C3 and C4 mixtures will
persist where growing season temperatures fall above
and below 22 1C. Regions where all average monthly
growing season temperatures are below 22 1C are predicted to be 100% C3 vegetation.
Statistical analyses
To evaluate climate and other controls on variation in
the %C4, we used linear and multiple regression techniques. In some cases, we compared the predictions
generated by these models by examining the magnitude
of the r2 values. We also compared models using
Akaike’s Information Criterion (AIC) (Burnham &
Anderson, 2002). The AIC value for each model is
calculated as
AIC ¼ n lnðMSEÞ þ 2K;
where MSE is the mean squared error from the ANCOVA
or linear regression, n is the number of observations,
and K is the number of parameters in the model
including 1 for the intercept and 1 for the error term.
Models with lower AIC values are more strongly supported.
Our climate data included monthly, seasonal and
annual averages of daily high, daily average and daily
low temperatures. To compare the power of these
temperature indices to predict variation in %C4, we
5
determined the r2 values from linear regression of the
%C4 A-SOM, %C4 A-roots and %C4 STATSGO with each temperature index. We further examined the temperature
that best-predicted variation in isotope and STATSGO
data, and calculated the magnitude of AIC improvement by adding rainfall as an additional predictor in a
multiple regression analysis. We also compared the
predictive power of the absolute magnitude of precipitation over a time interval vs. the percent of annual
precipitation that fell during that time interval.
To evaluate a broader suite of climate and soil predictors and to identify more complex combinations of
predictors, we used step-wise multiple regression analysis, drawing from all of the climate and soil data that
we had available (Table 1) to explain variability across
both indices of C3 vs. C4 productivity (i.e. %C4 A-SOM,
%C4 STATSGO). The stepwise model was built using a
mixed approach such that parameters were added if
Po0.25 and removed if P40.1. In all models, the %C4
values were not transformed because they were normally distributed and the model never predicted values
outside the data range. All statistical analyses were
performed in JMPIN v5.1 (SAS Institute Inc.) and other
calculations performed in EXCEL 2003 (Microsoft).
Results
Patterns in soil data
Patterns in the d13C of SOM and roots were dominated
by regional-scale clines, with the most negative values
in the north and most positive in the south (Fig. 1, Table
2). Four sites in southern Canada showed isotope values
of A-horizon soils more negative than 24% while
several sites across Texas, Oklahoma and Kansas possessed A-horizon SOM with d13C more positive than
15%. In the mid-latitudes, we also observed a tendency toward longitudinal variation in d13C. For example, four sites along the 451N parallel ranged from
17% in the east to 25% in the west.
We found that the isotopic compositions between
SOM and roots were strongly correlated: regressions
of the d13C of A-horizon SOM vs. B-horizon SOM, vs.
A-horizon roots and vs. B-horizon roots yield significant correlations (Po0.0001) with r2 values of 0.86, 0.72
and 0.66, respectively. However, we found that the four
reservoirs show persistent within-site differences in
their d13C (Fig. 2). Within a site, the SOM usually
became isotopically enriched with depth such that, on
average, B-horizon SOM was 0.54% enriched with
respect to the A-horizon above it. The magnitude of
enrichment with depth was even greater in roots, which
were, on average, 0.96% more positive in the B than in
the A-horizon. A comparison of soil and root isotopic
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6 J. C.
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Fig. 1 Map of d13C of A-horizon SOM interpolated over the Great Plains ecoregion. Points mark sampling sites; kriging is by inverse
weighting with exponential decay. SOM, soil organic matter.
properties revealed that A-horizon soils were, on average, 1.0% enriched with respect to roots. B-horizon
SOM was also enriched relative to B-horizon roots, with
a mean enrichment of 0.75%. We used the observed
enrichment in d13C between roots and SOM to adjust
the two end-member mixing model for calculating %C4
from SOM (Table 3a).
Stepwise linear regression produced weak but significant multiple regression models for the isotopic
enrichment with depth in SOM, roots and for the
difference between A-horizon SOM and roots. Isotopic
enrichment with depth in SOM (i.e. the d13C of
B-horizon SOMthe d13C of A-horizon SOM) was explained by a two predictor model that included a weak
negative correlation with %clay in the A-horizon and
a positive correlation with July low temperature
(R2 5 0.16, P 5 0.027). For the enrichment of root
d13C between A and B-horizons, the model contained
only July precipitation (positive correlation, r2 5 0.13,
P 5 0.015). A similarly small portion of the variance
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Table 2
7
Study sites and isotopic properties of organic materials in each site
Site
SD
d13C
Latitude Longitude
SOM-A SOM-B Roots-A Roots-B SOM-A SOM-B Roots-A Roots-B
(N)
(W)
Anahuac Wildlife Refuge, TX
Clymer’s Prairie, TX
Lubbock, TX
Muleshoe, TX
Tridens Prairie, TX
Sevielleta, NM
Woodward, OK
Freedom, OK
Tallgrass Prairie, OK
Diamond Grove, MO
Drover’s Prairie, MO
Land Institute, KS
Fort Hays, KS
Fall Leaf Prairie, KS
Konza Prairie, KS
Squaw Creek Wildlife Refuge, MO
Indian Cave State Park, NE
CO State/LTER, CO
Nine Mile Prairie, NE
Loess Hills Wildlife Refuge, IA
Stone State Park, IA
Niobrara Nature Preserve, NE
Second Niohbrara site
Newton Hills State Park, SD
Lange-Furgeson Site, SD
Cayler Prairie, IA
Makoce Washte, SD
Lundblad, MN
Cottonwood, SD
Schaefer Prairie, MN
Antelope Prairie, SD
Custer Battlefield, MT
Ordway Prairie, SD
Staffanson, MN
Eastern ND Tallgrass Prairie, ND
Bluestem Prairie, MN
Dickinson, ND
Sheyenne Grassland, ND
Western ND Mixed Prairie, ND
Oakville, ND
Cross Ranch, ND
Teddy Roosevelt N.P., ND
Pembina Prairie, MN
Glasgow, MT
Bainville, MT
Milk River, Alberta
Tolstoi Prairie, Manitoba
Living Prairie, Manitoba
Head Smashed In, Alberta
Grosse Isle, Manitoba
Oak Hammock, Manitoba
29.67
33.32
33.41
33.50
33.64
34.35
36.42
36.45
36.88
37.03
38.53
38.73
38.86
39.00
39.09
40.08
40.26
40.84
40.87
42.05
42.52
42.77
42.77
43.26
43.33
43.40
43.55
43.94
43.96
44.72
45.51
45.54
45.72
45.82
46.42
46.84
46.89
46.50
47.00
47.20
47.25
47.45
47.69
48.12
48.14
49.08
49.08
49.88
49.50
50.07
50.20
94.40
96.20
102.10
102.40
95.70
106.90
99.30
99.40
96.50
94.30
93.30
97.60
99.30
95.20
96.60
95.40
95.60
104.70
96.80
96.10
96.50
100.00
100.00
96.60
102.60
95.20
97.00
95.70
101.90
94.30
103.30
107.40
99.10
95.80
97.50
96.50
102.80
97.50
103.50
97.30
101.00
103.20
96.40
106.40
104.20
112.10
96.80
97.30
113.80
97.50
97.20
15.0
14.4
15.5
14.2
14.4
16.7
18.6
14.1
16.3
15.6
19.3
15.3
15.6
18.3
14.4
16.8
16.0
15.9
15.5
15.7
14.0
17.8
18.4
18.3
18.3
17.7
16.3
18.7
18.1
19.8
20.4
25.0
19.0
17.6
18.2
19.5
18.9
21.1
20.1
20.5
19.7
21.9
17.9
20.3
20.5
23.4
21.0
21.4
24.1
20.6
19.1
14.4
13.5
13.6
13.5
12.9
16.4
16.6
12.6
15.1
15.3
16.3
13.5
14.1
13.9
18.2
16.8
15.4
13.6
18.2
16.3
16.7
16.2
18.2
17.8
16.9
18.1
17.1
19.0
17.9
20.1
23.6
19.2
16.3
16.5
18.2
19.6
21.1
19.3
19.2
19.4
22.2
16.7
21.6
21.7
23.4
19.2
19.9
22.8
20.6
21.5
15.3
16.0
15.2
15.4
14.0
15.1
14.0
14.3
14.0
14.7
14.7
15.0
13.6
15.7
15.0
14.1
18.2
13.7
18.6
12.5
23.0
17.1
14.5
20.7
14.2
25.2
22.3
20.1
17.3
19.0
18.5
17.9
19.6
19.6
21.2
25.7
21.4
17.2
20.0
18.1
19.7
21.5
13.9
15.3
15.5
19.8
17.1
24.1
26.3
21.9
16.3
22.1
19.8
21.4
22.0
22.6
19.7
21.1
22.8
23.9
17.0
22.7
22.0
25.0
22.8
22.3
24.9
17.8
20.8
17.2
22.3
23.6
15.4
23.8
21.4
23.3
20.8
17.7
23.7
18.9
19.5
1.53
0.33
1.15
1.15
0.28
1.62
0.34
0.72
0.90
0.88
1.21
1.35
0.63
1.20
0.76
1.43
1.18
0.44
0.92
1.08
1.38
1.42
2.10
3.10
1.41
0.89
1.06
0.28
0.76
0.18
0.66
0.86
0.87
1.29
0.43
0.47
0.87
1.68
0.67
0.80
0.82
0.33
1.27
0.55
1.72
0.76
2.57
0.54
0.80
0.52
2.19
0.72
0.70
0.35
0.64
0.33
1.37
0.45
0.53
3.74
3.82
2.69
1.78
1.66
0.64
0.55
0.95
2.33
3.16
3.94
2.81
1.00
2.86
1.60
0.00
0.90
0.56
0.82
0.83
2.67
5.07
0.95
5.62
0.73
1.97
0.68
2.04
1.18
0.98
0.76
1.05
1.99
0.42
0.81
1.48
1.09
0.80
1.73
0.31
1.42
1.06
1.28
1.60
1.33
1.14
0.35
0.49
2.28
1.13
0.70
0.83
0.90
0.62
0.87
0.61
2.15
0.52
1.88
0.53
1.13
1.47
0.49
1.37
1.48
1.18
0.83
3.67
4.63
5.13
2.34
4.16
4.63
2.09
2.01
2.36
1.64
2.43
3.06
3.60
5.72
5.36
3.33
3.63
3.83
4.52
4.34
3.85
2.13
1.13
3.18
1.94
3.16
3.48
3.41
1.79
4.17
3.13
2.40
1.78
1.57
3.50
2.39
3.53
0.70
2.84
2.12
1.54
2.29
3.74
3.13
2.28
1.52
2.07
1.76
4.00
3.08
4.02
5.53
2.60
3.91
5.46
Continued
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Journal compilation r 2008 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2008.01552.x
8 J. C.
VON
F I S C H E R et al.
Table 2. (Contd.)
Site
SD
d13C
Latitude Longitude
SOM-A SOM-B Roots-A Roots-B SOM-A SOM-B Roots-A Roots-B
(N)
(W)
Stavely, Alberta
Matador, Saskatchewan
Biddulph, Saskatchewan
Kernan Prairie, Saskatchewan
50.22
50.67
50.68
51.90
113.90
109.30
107.70
106.70
25.2
24.1
22.9
25.1
24.5
23.4
22.8
24.3
25.6
26.3
25.2
26.3
25.7
25.9
23.3
25.8
0.20
0.28
1.52
0.25
0.10
1.14
1.08
0.36
0.34
0.28
1.32
0.36
0.21
0.45
4.26
0.65
Letters A and B identify the soil horizon. SD is 1 standard deviation of the d13C value.
Difference in 13C (‰)
2.5
Table 3a Isotopic values used in two end-member mixing
models to determine %C4
2
d13C (%)
1.5
1
0.5
0
A-roots
A-SOM
B-roots
Compartment
B-SOM
Fig. 2 Average within-site differences in d13C between the
A-horizon roots and other soil compartments. Error bars are
1 SE.
in enrichment of A-horizon SOM with respect to
A-horizon roots was explained by a model depending
on April and May average temperatures (R2 5 0.18,
P 5 0.016). Parameter values for these statistical relationships are presented in the Appendix A.
Compartment
Enrichment from Fig. 2
C3
C4
A-horizon roots
A-horizon SOM
1.0
26.7
25.7
12.5
11.5
Uses values from Cerling et al. (1997) for roots, and modifies
those values for the enrichment of SOM with respect to roots
identified in Fig. 2c.
SOM, soil organic matter.
Table 3b Best fit d13C of end members to other %C4
%C4
%C4
P&L
STATSGO
Compartment
C3
C4
C3
C4
A-horizon roots
A-horizon SOM
26.7
23.4
12.5
13.0
23.9
21.9
16.7
14.9
Gives end members that would be needed to make the
regression lines for %C4 vs. d13C match the 1 : 1 lines in Fig.
3a–d.
SOM, soil organic matter.
Comparison of predicted %C4
Data in Fig. 3 illustrate that %C4 from our isotope
determinations were better predicted by the algorithm
of Paruelo & Lauenroth (1996) than by the STATSGO
database or by the algorithm from Collatz et al. (1998).
The %C4 P&L prediction had a small but significant
(Po0.05) departure from the 1 : 1 line for %C4 A-SOM
(Fig. 3a), but not for %C4 A-roots. In contrast, the STATSGO
data consistently underestimated the productivity of
the rarer plant type (Fig. 3c and d). Despite the differences in fit to the 1 : 1 lines, regressions of isotope-based
%C4 with both %C4 P&L and %C4 STATSGO had similar r2
values. The physiologically based model of Collatz et al.
(1998) showed only weak agreement (Fig. 3e and f), and
it never identified any sites as being C4 dominated, even
though seven of our 55 sites had d13C values consistent
with 475% C4 contribution. It was possible to bring
%C4 P&L predictions onto the 1 : 1 line by making rela-
tively small changes to the end member d13C (Table 3b).
However, unrealistically large end-member adjustments were needed to bring the STATSGO predictions in
line with our isotopic measure of %C4.
Statistical relationships with climate controls
The average of daily high temperature better predicted
%C4 A-SOM and %C4 STATSGO than low or average temperature (Fig. 4a and b), and the same was true for the
%C4 A-roots (data not shown). The isotope and STATSGO
data showed remarkably similar responses, with both
indices positively correlated with temperature. The
average and high temperatures were only equivalent
predictors in July, August and at the annual scale. Low
temperatures were typically much poorer predictors of
r 2008 The Authors
Journal compilation r 2008 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2008.01552.x
%C from C of A-horizon roots
%C from C of A-horizon roots
% C predicted from Paruelo & Lauenroth
%C from C of A-horizon SOM
100
90
80
70
60
50
40
30
20
10
0
−10
%C predicted from
% C predicted from Paruelo & Lauenroth
100
(b) R = 0.559
90
80
70
60
50
40
30
20
10
0
−10
−10 0 10 20 30 40 50 60 70 80 90 100
100 (c)
R = 0.642
90
80
70
60
50
40
30
20
10
0
−10
−10 0 10 20 30 40 50 60 70 80 90 100
100 (d)
R = 0.507
90
80
70
60
50
40
30
20
10
0
−10
−10 0 10 20 30 40 50 60 70 80 90 100
%C predicted from
9
(e)
100% C
Mixed
100% C
%C predicted from Collatz et al.
%C from C of A-horizon roots
(a)
100
R = 0.653
90
80
70
60
50
40
30
20
10
0
−10
−10 0 10 20 30 40 50 60 70 80 90 100
%C from C of A-horizon SOM
%C from C of A-horizon SOM
C L I M AT E C O N T R O L O F C 3 V S . C 4 P R O D U C T I V I T Y
100
90
80
70
60
50
40
30
20
10
0
−10
(f)
100% C
Mixed
100% C
%C predicted from Collatz et al.
Fig. 3 Comparison of %C4 determined from soil and root d13C with %C4 predicted by Paruelo & Lauenroth (1996) the STATSGO
vegetation database, and Collatz et al. (1998). Solid lines are regression lines (a–d) or means of observed data (e–f) and dashed lines
are 1 : 1 lines (a–d) or expected values (e–f).
%C4 than average temperatures. Parameter values for
the statistical relationships between %C4 and temperature are presented in the Appendix A.
In an analogous comparison, we found that the
absolute magnitude of precipitation falling during a
time interval had significantly more explanatory power
than the percent of mean annual precipitation that fell
during that same interval. The r2 values for regression
of %C4 vs. absolute precipitation were two to five times
larger than the r2 values of %C4 vs. percent of annual
precipitation.
Among the time intervals under consideration, we
found that July climate (average daily high temperature
and monthly rainfall) best explained variation in
%C4 A-SOM and %C4 STATSGO (Fig. 4b). From an AIC
perspective, the July models were significantly better
than the next best predictors (Fig. 4c), which had AIC
values 4–5 units larger. Inclusion of rainfall improved
the AIC value of the models in 16 of the 18 comparisons,
but some time intervals remained weaker predictors.
For example, April and May climate indices yielded
uniformly weaker models than did those of June and
August. Interestingly, the addition of precipitation as a
predictor improved the July climate data from among
the worst to among the best predictors (predictive
equation in Appendix A). The %C4 A-roots was similarly
better predicted by models that included temperature,
but there was comparably little change in the AIC
values among the different time periods (data not
shown).
Although our post hoc use of stepwise regression
generated models for %C4 A-SOM and %C4 STATSGO with
better AIC values than did the a priori models identified
in Fig. 4b, all post hoc models still depended on July
precipitation and one or more of the high temperatures.
The best model for %C4 A-SOM used four predictors:
April high temperature, May low temperature, July
precipitation and the AMJJA high temperature (predictive equation in Appendix A). The R2 of this model,
0.78, explained 15% more variance in soil isotopes than
did July high temperature and rainfall. The stepwise
model for %C4 STATSGO was simpler, using only July
precipitation and August high temperature to generate
an R2 of 0.82. However, this combination was only a 6%
improvement over the July high temperature and July
precipitation model. A stepwise model for %C4 A-roots
attained an R2 of 0.71 by considering annual low
temperature, July precipitation and AMJJA high temperature (predictive equation in Appendix A). In the
stepwise regressions for %C4 A-SOM and %C4 STATSGO,
soil information (i.e. soil texture, %carbon, %nitrogen
and C/N ratio) was available, but it was never included
r 2008 The Authors
Journal compilation r 2008 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2008.01552.x
F I S C H E R et al.
35
30
25
20
15
10
high temp.
5
mean temp.
low temp.
ar
Ye
A
JJ
JJ
M
JJ
AM
AM
(a)
Ju
ly
Au
gu
st
ay
Ju
Ap
r with C of A-horizon SOM
0.75
ne
0
ril
in the models. In the model for %C4 A-roots, the addition
of soil information led to the replacement of annual low
temperature with soil %carbon, but the R2 value increased by o2% (predictive equation in Appendix A).
In the field, plants experience a range of temperatures
over daily and seasonal scales, thus obscuring which
metric of field temperature is most physiologically and
ecologically relevant. Our empirically determined
crossover temperature coincided with the physiologically predicted crossover temperature of 22 1C for five
temperature indices (Fig. 5). May high temperature,
M
VON
Crossover temperature (°C)
10 J . C .
Time period of temperature data
0.70
Fig. 5 Crossover temperatures calculated from regressions of
%C4 from the d13C of A-horizon SOM vs. the various temperature metrics. The dashed line marks 22 1C, the crossover temperature predicted by physiological models of Collatz et al.
(1998). By definition, %C4 is >50% at temperatures above the
crossover temperature. SOM, soil organic matter.
0.65
0.60
0.55
0.50
0.45
0.40
0.35
July and August average temperature, and the high
temperatures of AMJJ and AMJJA all predicted a crossover temperature within 2 of 22 1C.
ar
A
JJ
Ye
JJ
AM
AM
M
JJ
st
ly
Au
gu
ne
Ju
ay
Ju
M
Ap
ril
0.30
Time period of temperature data
0.75
(b)
%C
0.70
Discussion
0.65
0.60
Controls of %C4
r with
0.55
Both isotopic and STATSGO measures identified strong
control of %C4 by mid-summer climate in the hottest
part of the day, when photon flux rates are greatest and
thus potential for growth is also highest. These findings
closely parallel the observations of Hattersley (1983) in
Australia who found summer (January) temperatures to
have highest correlations with %C4. Convergence of
isotope and STATSGO results with those of Hattersley
(1983) illustrates the general response of %C4 to
mid-summer climate, and it refutes an alternative
0.50
0.45
0.40
0.35
ar
Ye
JJ
A
JJ
AM
AM
M
JJ
t
us
ly
Au
g
e
Ju
ay
Ju
n
M
Ap
ril
0.30
Time period of temperature data
(c)
310
45
305
40
300
Ye
AM
JJ
M
gu
M
Ju
Au
Time period of climate data
ar
315
50
A
320
55
JJ
60
AM
JJ
325
st
330
65
Ju
ly
335
70
ne
340
75
ay
345
80
AIC value
350
85
Ap
ril
SOM AIC value
90
Fig. 4 Comparison of climate indices for predicting %C4 from
d13C of A-horizon SOM and %C4 from STATSGO. (a) and (b) are
correlations with daily high, average and low temperatures averaged over months, parts of the growing season, or annually. (c) A
comparison of the predictive power of high temperature alone or
high temperature and precipitation (ppt.) together. Values on the
y-axis in (c) are Aikake Information Criteria (AIC), an index that
reflects the explanatory power of a model, penalized by the
number of predictors. Lower AIC values indicate models that
are more strongly supported. SOM, soil organic matter.
r 2008 The Authors
Journal compilation r 2008 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2008.01552.x
C L I M AT E C O N T R O L O F C 3 V S . C 4 P R O D U C T I V I T Y
interpretation of our data that climate is somehow a
proxy for geography or another nonclimatic control.
Our results reinforce the ecological importance of
photorespiration, by indicating that low temperatures
in spring have little direct impact on %C4, despite the
detrimental effects of low spring temperature on C4
grasses through reduced pigment production (Haldimann, 1999) and reduced rubisco capacity (Pittermann
& Sage, 2000; Kubien & Sage, 2004). Although April low
temperature was the best predictor of all low-temperature intervals, high temperatures during the early growing season (April, May and June) were generally better
predictors of %C4 than were average or low temperatures (Fig. 4).
Although we find a strong correspondence between
our isotopic determination of %C4 and those predicted
by the algorithm of Paruelo & Lauenroth (1996), our
estimates of the %C4 showed bias with respect to the
STATSGO database and substantial departure from the
%C4 predicted by the Collatz et al. (1998) algorithm. It is
unlikely that the difference in %C4 is due to error in the
end-members because the ‘best fit’ end members in
Table 3b were far outside the typical range of C3 and
C4 plants (Cerling et al., 1997). Instead, the bias between
isotopic metrics of %C4 and the %C4 STATSGO more likely
reflects differences in the study sites sampled. While
our sampling and the work of Paruelo & Lauenroth
(1996) were confined to pristine, native prairie sites, the
sampling that gave rise to the STATSGO vegetation database was targeted for livestock production and was not
limited to native prairies. Thus, the bias between
%C4 STATSGO and %C4 A-SOM likely resulted from management of the STATSGO sites, which often had C3 forages
planted in the north and C4 in the south to improve
grazing.
In contrast to the empirically based %C4 from
STATSGO, Collatz et al. (1998) predict the %C4 productivity from principles of leaf physiology. Our results
(Fig. 3e and f) and direct comparison of the predictions
of %C4 P&L with %C4 Collatz revealed weak agreement
with the Collatz et al. (1998) algorithm despite their
successful, global-scale delineation of where C4 is dominant, mixed with C3 or absent. Perhaps because finerscale prediction is not the goal of their work, we observe
distinct differences when applying this metric at regional scales. Indeed, the North American Great Plains
grasslands are a special case at the global scale because
they are dominated by C3/C4 mixtures. Most other
grasslands worldwide are pure C3 or C4, and these
grasslands ‘anchor’ the regression between temperature
and %C4. On the global scale, temperatures and rainfall
patterns vary much more widely than at the scale of
North American grasslands and so the coarser approach of Collatz et al. (1998) yields reasonable global
11
patterns. When the range of temperatures is narrowed
to those observed in central North America, subtler
differences become more important for making accurate
determinations of %C4. In any case, the poor fit of the
Collatz et al. (1998) prediction to North American grasslands illustrates a weakness of this approach for discriminating variation in %C4 in regions of mixed C3 and
C4 grasses.
Our results reveal that not all climate indices are
equally strong predictors of %C4. In particular, the
results presented in Figs 4 and 5 indicate that %C4 in
the North American Great Plains grasslands are especially sensitive to the climate in July, suggesting that the
outcome of competition between C3 and C4 plants in
was particularly sensitive to climate during this narrow
window of time. Mixed C3 and C4 systems persist in
Great Plains grasslands where July average temperature
is 21.5 3 1C; systems are C3 dominated (o33% C4)
below this range and C4 dominated (466% C4) above it.
Despite the importance of temperature for determining variation in %C4, rainfall persisted as a significant,
although weak, predictor. It was somewhat surprising
that the absolute magnitude of precipitation was a
much better predictor of %C4 than the relative amount.
Although rainfall amount is the primary control of total
productivity across the North American grasslands
(Sala et al., 1988), several studies suggest that the
percent of total precipitation in June, July and August
should be important for determining %C4 (Paruelo &
Lauenroth, 1996; Winslow et al., 2003). Our results are
consistent with experiments of Skinner et al. (2002), who
found that summer irrigation treatments to a Wyoming
grassland increased %C4. Other experimental work in
the tallgrass Konza prairie altered the timing of precipitation and revealed that greater intervals between
summer rainfall events can reduce aboveground net
primary production by C4 grasses (Knapp et al., 2002;
Fay et al., 2003). Collectively, our results and these
experimental findings indicate that either the %C4 is
driven by the magnitude of precipitation itself or by a
reduced interval between rainfall events that arises
where summer precipitation is greater.
Isotopic properties of soils
Ultimately our use of d13C to determine %C4 depends
on the fidelity of the isotopic composition of soil and
root material. Isotopic fractionation and selective preservation of plant parts during decomposition have the
potential to scramble the relationship between the isotopic composition of plants and SOM across the North
American Great Plains grasslands, limiting the power of
SOM d13C to determine local %C4. However, our data
support the conclusion of Sage et al. (1999) that the d13C
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Journal compilation r 2008 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2008.01552.x
12 J . C .
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F I S C H E R et al.
of SOM and roots reflect %C4. Comparison of our
results with the predictions of Paruelo & Lauenroth
(1996) independently confirm that the d13C of the C3
and C4 end members are not significantly scrambled by
diagenetic or pedogenic processes. We found that the
d13C of these end members, when modified for the
systematic fractionations observed across all sites, are
within the range described by Cerling et al. (1997). Any
systematic bias would have caused the isotopic determinations of %C4 to fall away from the 1 : 1 line in Fig.
3a and b, but we find no evidence that such effects were
important. Although the long residence times of SOM
have the potential to integrate plant inputs over time
scales that exceed the range of our climate data, we find
that the long-term average %C4 is very similar to the
modern %C4, as shown by the strong correlation between %C4 A-SOM and %C4 A-roots and the strikingly
similar responses of %C4 A-SOM and %C4 STATSGO to
climate (Fig. 4a vs. b).
Within-site variance in d13C was relatively small and
generally systematic (Table 1), dominated by persistent
differences in the isotopic composition among soil
carbon pools (Fig. 2). Although the trends in the isotopic enrichment of SOM with depth have been found
by many others, we here document variation in this
pattern across more sites than any other single study.
Ehleringer et al. (2000) concluded that SOM isotopic
enrichment with depth is most likely driven by the
anthropogenic changes in d13C of atmospheric CO2
and the mixing of new organic material with SOM that
is old and isotopically fractionated (e.g. Wedin et al.,
1995). The subsequent findings of Torn et al. (2002),
however, weaken support for the CO2 mechanism by
showing identical patterns of enrichment with depth in
100-year-old archived soils and modern samples from
the same location. Work by Bird et al. (2003) suggests
that soil texture may drive some variability in the
degree of enrichment with depth, and we find some
evidence that clay content is associated with differences
between A- and B-horizon SOM d13C. However, in
contrast to the positive relationship observed by Bird
et al. (2003), we find a negative correlation between clay
content and enrichment with depth. The mechanism
underlying this clay effect remains unknown.
Our results show that enrichment in root 13C with
depth may contribute to the SOM enrichment with
depth. On average, B-horizon roots are enriched compared with A-horizon roots about as much as B-horizon
SOM is enriched compared with A-horizon SOM. Because decomposing roots are a key source for SOM
formation in grasslands (Gill et al., 1999), it is possible
that some of the isotopic enrichment in deeper SOM is
driven by decomposition of deeper roots that are isotopically enriched.
Few studies have documented enrichment in 13C of
fine roots (o2 mm) with depth (but see also Still et al.,
2003b), which may be driven by three mechanisms.
First, the biochemical and transport processes associated with root growth may cause isotopic enrichment
in deeper roots. Second, C4 roots may be more resistant
to decomposition and remain in the soil longer after
death. And third, C4 grasses may, on average, have
greater rooting depth than C3 grasses. Although the
tissue-specific studies of Badeck et al. (2005) and
Klumpp et al. (2005) suggest that root isotopes could
acquire systematic differences with depth, we find no
support for the first hypothesis; our data show no
significant change in root d13C with depth in any
C3-dominated stands, where d13C of A-horizon SOM
is o21%. The second hypothesis, which is neither
supported nor refuted by our data, is consistent with
the idea that C3 grass tissues are more labile (Caswell
et al., 1973) and it is supported by field measures that
show greater longevity of C4 roots as compared to C3
(Gill et al., 1999). The third hypothesis is supported by
Fargione & Tilman (2005) who found that niche partitioning between a single C4 grass species and multiple
C3 competitors was facilitated by differences in rooting
depth. In addition, our statistical analysis of the isotopic
enrichment in roots with depth shows that the enrichment with depth is positively correlated with July
precipitation, which favors C4 grasses. Further evaluation of the latter two hypotheses will depend on more
detailed examination of the C3 vs. C4 affinity of individual roots with depth and discrimination of live from
dead roots.
We anticipate that our characterization of the climateisotope relationship could provide novel insights into
paleoclimate. For example, we have already used the
July temperature approach and this dataset to interpret
paleotemperatures from the d13C of SOM in paleosols
recovered from the North American Great Plains
(Nordt et al., 2007). Given the importance of summer
temperatures for structuring %C4, we expect that past
changes in SOM d13C will reflect summertime climate,
primarily temperature, with only a weak effect of precipitation on variability in d13C of SOM.
We expect the future C3/C4 composition of North
American grasslands to respond to climate change, but
in a manner that is not yet predictable. Although
regional-scale climate predictions are somewhat tenuous, summer temperatures in central North America are
expected to increase 1–2.5 1C by 2050 (Liang et al., 2006),
and 4 1C by 2100 (Christensen et al., 2007). Given that
climate explains 70% of existing variability in %C4,
this warming alone could drastically alter the C3/C4
balance, much as a similar amount of warming did over
the past 10 000 years (Nordt et al., 2007). However,
r 2008 The Authors
Journal compilation r 2008 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2008.01552.x
C L I M AT E C O N T R O L O F C 3 V S . C 4 P R O D U C T I V I T Y
atmospheric CO2 will increase to at least 600 ppm over
this time. This latter change will both strongly favor C3
plants and thrust C4 plants into an environment that has
not existed in the 410 million years that they have been
on the earth (Cerling et al., 1997). Under such swift and
drastic environmental changes, ecological and evolutionary surprises are almost sure to happen.
Acknowledgements
We thank Norman Bliss for help with the STATSGO database,
Donovan Dejong for his assistance with climate data, and
Michael Chapman for his efforts in the field and laboratory.
Alan Knapp, Bill Lauenroth and Lee Nordt provided thoughtful
discussions and comments on this manuscript. We also thank the
many land managers who facilitated our sampling efforts and
Randy Boone for generating the color figure. This work was
funded by NSF DEB 9510065 and the Geographic Analysis and
Monitoring and Earth Surfaces Dynamics programs of the USGS.
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C L I M AT E C O N T R O L O F C 3 V S . C 4 P R O D U C T I V I T Y
15
Appendix A
Table A1
Results of linear regression with temperature as a predictor of %C4 from A-horizon SOM (%C4
High temperature
Year
April
May
June
July
August
AMJJ
MJJ
AMJJA
Table A2
SOM
enrichment
with depth
Root
enrichment
with depth
Enrichment
A-SOM vs.
A-roots
%C4 A-SOM
%C4 A-roots
%C4 A-roots
Average temperature
r2
Intercept
Slope
r2
Intercept
Slope
r2
Intercept
Slope
0.562
0.513
0.541
0.594
0.524
0.591
0.565
0.578
0.618
1.93
4.52
31.87
63.63
79.95
75.95
41.95
62.32
53.61
3.34
3.01
3.89
4.385
4.41
4.37
4.02
4.41
4.31
0.577
0.456
0.323
0.379
0.39
0.376
0.469
0.367
0.463
43.8
45.24
26.41
4.62
12.96
7.36
15.51
3.91
11.03
3.35
3.19
3.15
3.58
3.98
3.86
3.73
3.88
3.76
0.562
0.45
0.444
0.53
0.531
0.596
0.49
0.498
0.547
20.39
24.91
3.14
33.94
63.6
59.39
13.97
31.04
23.6
3.39
3.04
3.74
4.39
5.01
5.02
3.98
4.33
4.26
Results of multiple linear regressions described in the text
Y-variable
%C4 A-SOM
Low temperature
A-SOM)
Parameter
estimates
Intercept
0.6702
%clay 0.0240
Intercept
2.379
July precipitation
0.4842
Intercept
11.72
April mean temperature
0.8996
May mean temperature
1.279
Intercept
100.16
Intercept
83.30
July high temperature
4.025
April High temperature
2.484
July precipitation 4.413
May low temperature
3.853
July precipitation
4.916
Intercept
161.21
Intercept
106.83
Annual low temperature
2.398
%C 1.904
July precipitation
4.747
July precipitation
2.706
AMJJA high
temperature 7.505
AMJJA high
temperature 5.409
July low temperature
0.1290
SOM, soil organic matter.
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AMJJA high
temperature
3.735