Net Primary Production and Biomass
Distribution in the Blue Oak Savanna 1
John J. Battles, 2 Randall D. Jackson, 3 Ayn Shlisky, 4 Barbara
Allen-Diaz,2 and James W. Bartolome2
Abstract
The complexity of savanna ecosystems with the shared dominance between woody and
herbaceous primary producers poses challenges to measuring such fundamental ecosystem
characteristics as net primary productivity (NPP). We address these challenges in the blue oak
savanna in California by constructing comprehensive estimates of NPP for three adjacent
watersheds. We directly assessed annual biomass increment of all plant components in 12
randomly stratified plots (380 m2) per watershed. Annual estimates of mean NPP for the three
experimental watersheds during 2001 to 2002 ranged from 4.35 to 5.69 Mg ha-1 yr -1 of dry
biomass. On average, belowground NPP accounted for 22 percent of total NPP. In general,
there was much greater uncertainty in the belowground estimates. Trees accounted for
approximately 50 percent of aboveground NPP in all three watersheds. Across these
watersheds, tree productivity increased in a linear fashion with canopy closure. In contrast,
herb productivity was nearly constant for relatively open sites (canopy closure < 40 percent)
and then monotonically declined as canopy closure increased. The result is that total NPP
increased gradually from the most open sites in the watersheds to a maximum around 55
percent canopy closure.
Keywords: Biomass distribution, carbon budget, Mediterranean ecosystem, net primary
production, temperate savanna.
Introduction
A fundamental characteristic of savanna ecosystems is the co-dominance of tree and
grass life forms. Savannas and synonymous designations (e.g., woodland, rangeland,
shrubland) constitute a gradient of ecosystems that fall between grasslands and
forests. Together they account for more than an eighth of the terrestrial biosphere
(Scholes and Archer 1997). They are economically and ecologically important in
tropical and temperate regions throughout the world. Despite their extent and value,
basic ecosystem functions of savannas are poorly understood relative to grasslands
and forests.
In a recent review, House and others (2003) noted that challenges to developing
a robust understanding of savanna ecosystems included a preponderance of studies
that focused on either the grass or tree component in isolation and a lack of studies
that addressed belowground productivity and biomass. Yet it is this sort of integrated
ecosystem-level information that is crucial to understanding savanna dynamics and to
1
An abbreviated version of the paper was presented at the Sixth California Oak Symposium: Today’s
Challenges, Tomorrow’s Opportunities, October 9-12, 2006, Rohnert Park, California.
2
University of California, Berkeley, Department of Environmental Science Policy and Management,
137 Mulford Hall, Berkeley, CA 94720-3114.
3
University of Wisconsin-Madison, Department of Agronomy, 1575 Linden Drive, Madison, WI 53706.
4
The Nature Conservancy, 2424 Spruce St., Boulder, CO 80302.
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GENERAL TECHNICAL REPORT PSW-GTR-217
managing them appropriately. In this paper, we summarize our efforts to confront
these challenges for the blue oak savanna in California.
A critical management objective in California oak savanna is to sustain livestock
productivity while maintaining long-term ecosystem health. Despite a wealth of
information regarding the productivity of certain components of this ecosystem
including forage yield (Holland 1980, Kay 1987, Connor and Willoughby 1997), leaf
and acorn production (Dahlgren and Singer 1994, Knops and others 1996, Koenig
and others 1996, Koenig and others 1999), fine root growth (Jackson and others
1990, Millikin and Bledsoe 1999), and tree growth (Kertis and others 1993), there
exists no estimate of total net primary productivity (NPP). Without NPP estimates,
we cannot address basic ecological questions about NPP allocation under various
biotic and abiotic scenarios–information that is useful for predicting the effects of
land-use modifications or climate changes on C cycling and plant community
distribution. Moreover, in savannas where NPP has been estimated, there exists what
House and others (2003) describe as the “NPP conundrum”–competing hypotheses
about the nature of NPP allocation between trees and herbs as tree dominance varies.
The general relationship noted by House and others (2003) is for declining grass
NPP with increasing tree dominance where dominance is measured as a function of
tree density, basal area, or canopy cover. Robust tests of this relationship remain rare,
though Reich and others (2001) found that aboveground NPP decreased with
increasing tree dominance in oak savanna of the upper Midwest supporting this
relationship. Alternatively, Mitchell and others (1999) reported that in the pinewiregrass savanna of the southeastern United States, the highest grass productivity
occurred at sites with the greatest tree density.
In the blue oak savanna, several studies have concluded that the presence of
trees enhances understory productivity (Callaway and others 1991, Dahlgren and
others 1997) primarily due to processes associated with nutrient cycling. However,
results from tree removal studies conflict (Connor and Willoughby 1997). For
example, experiments conducted at the same research station in the blue oak savanna
have drawn opposing conclusions (Holland 1980, Kay 1987).
In 2001, we initiated a study to explore the patterns in productivity and the
distribution of biomass in the blue oak savanna. We will use this information to
develop a measure of ecosystem health, based on a robust and integrated estimate of
NPP for an entire management unit (i.e., the watershed). We measured NPP across
the grass-tree mosaic using techniques from landscape and community ecology to
efficiently allocate our sampling effort and to properly extrapolate our plot-level
results. Specifically, our objectives were to produce watershed-level productivity and
biomass budgets for blue oak savannas and to examine the internal relationships
between productivity and tree abundance in the blue oak savanna.
Methods
Study Site
In 2001, we identified three adjacent watersheds and instrumented them to measure
NPP. The experimental watersheds are located in the foothills of the northern Sierra
Nevada at the University of California Sierra Foothill Research and Extension Center
(SFREC) near Marysville, CA. (39º 15' N, 121º 17' W). These three adjacent
watersheds, collectively known as the Lewis watersheds, contain hilly, rolling terrain.
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Net Primary Production and Biomass Distribution in the Blue Oak Savanna—Battles
They range in size from 35 ha (WS1) to 116 ha (WS3) and encompass an elevation
gradient from 189 to 584 m.
The climate is Mediterranean with cool, wet winters and hot, dry summers.
Based on the onsite weather records for the past 15 years (California Irrigation
Management Information System, Browns Valley Station #84,
http://wwwcimis.water.ca.gov/cimis), total annual precipitation ranges from 49 to
133 cm with a mean of 77.5 cm. Most of the rain (98 percent) falls between October
and May. There is a prolonged summer drought (June-September) where relative
humidity averages less than 45 percent and mean daily air temperature is 24°C.
Annual estimates of productivity coincide with the water year, which starts on
October 1, the typical beginning of winter rains, and ends the following September
30. Thus, the estimates reported here for 2002 include all the plant production that
occurred during the 2002 growing season, even though the year began on October 1,
2001.
Soils within the watersheds formed in basic metavolcanic (greenstone) bedrock.
They are classified as fine, mixed, thermic Typic Haploxeralfs (Dahlgren and others
1997). These soils can extend to a depth of 100 to 150 cm and overlie relatively
massive bedrock.
The vegetation of the foothills consists of an overstory dominated by the winterdeciduous blue oak (Quercus douglasii). Interior live oak (Quercus wislizenii) and
foothill pine (Pinus sabiniana) are present at lower densities (Shlisky 2001). As is
typical for savanna ecosystems, the trees are patchily distributed across the
landscape. Canopy cover varies from less than 4 percent to more than 80 percent with
a watershed-level mean of 56 percent. Based on the 2002 inventory, the three most
common plants in the grass-dominated understory (42 percent cover) are the
introduced annual grasses: Bromus hordeaceus, Bromus madritensis and Cynosurus
echinatus. The next most common species (8 percent cover each) are the introduced
annual forbs, Trifolium hirdum and Torilis nodosa. Common shrubs include poison
oak (Toxicodendron diversiloba) and coffeeberry (Rhamnus californica). The
prevailing management regime in these watersheds includes fire exclusion and
grazing at moderate intensity by a cow-calf herd in the green season (December
through May). In 2002, herbivore consumption (domestic and native) on the grass
layer was approximately 0.37 Mg yr -1 (22 percent of grass productivity).
Data Collection and Analysis
Productivity sampling was based on the stratified random design described in Shlisky
(2001). Using color aerial photographs, watersheds were divided into four tree-cover
classes (<15 percent, 15 to 30 percent, 30 to 60 percent, and >60 percent). The
minimum mapping unit was 2 ha. Sampling intensity in each watershed was allocated
to each cover class proportional to its abundance. Plot locations were randomly
assigned in each cover class. From this set of 64 plots, we randomly choose a subset
of 36 locations to establish NPP plots (3 per strata, 12 per watershed). Note that all
watershed-level estimates (means and variances) were weighted by the proportional
abundance of the cover class in the watershed (Cochran 1977).
NPP plots consist of an 11-m radius circular area (380 m2). Shlisky (2001)
demonstrated that plant composition and canopy structure is relatively homogeneous
at this scale. In 2001, all live trees ≥ 5 cm diameter at breast height (1.37 m, dbh)
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GENERAL TECHNICAL REPORT PSW-GTR-217
within the plot were identified, measured, and tagged. Dendrometer bands were
installed on a size-stratified subset (15 percent of total, 94 trees) of these trees in
order to measure annual stem increment.
Within each plot, three “cattle-resistant” litterfall traps were systematically
placed at 3, 5, and 7 m from the center at 120° angle from each other. These traps
were approximately 1.5 tall to avoid disturbance from cows. The tops were outfitted
with burlap sacks. The maximum opening was 0.23 m2; however, the actual opening
was often smaller because the cages were often deformed by cattle impact. Thus the
opening size was measured at each collection. Litter is defined as leaves, seeds, and
twigs ≤1 cm in diameter. The cages were maintained year-round and litter was
collected three times per year with the last collection timed soon after maximum leaf
fall.
Shrub biomass estimates for the interior 5-m radius of each NPP plot were based
on light interception methods (sensu Reich and others 2001). A calibration curve
relating shrub biomass to shrub leaf area index (LAI) was developed by harvesting all
the biomass in the shrub layer (0.5 m to 2.5 m in height) in nine plots adjacent to the
experimental watersheds. Before harvest, we measured shrub LAI using the
techniques described above. We found a strong correlation between the two
parameters (shrub mass in Mg ha-1 = 17.53 × shrub LAI, r2 = 0.95, p <0.0001).
Herbaceous layer aboveground net primary productivity (ANPP) was estimated
by harvesting and weighing all herbaceous material from 0.0625 m2 quadrats from
within three randomly located, 1 m2 livestock exclusion cages at peak standing crop
(May 15 to June 15). Exclusion cages were randomly relocated each year postharvest to avoid resampling or potential cumulative cage effects.
Fine root NPP (roots ≤ 2 mm diameter) was estimated using sequential coring
(Vogt and others 1998, Fahey and others 1999). This method consists of estimating
fine root production as the difference between live root biomass at minimum and
maximum periods of plant growth. The method is most useful in systems that
undergo distinct growth pulses such as annual grasslands (Fahey and others 1999).
Hence, we collected three randomly located cores (15 cm depth, 5 cm inside
diameter, AMS Core Sampler, American Falls, ID, USA) during the winter slow
growth phase (December) for minimum and at peak standing crop (May/June) for
maximum fine root biomass estimation. Soil cores were stored at 5ºC until being
washed over a 1-mm mesh screen, which allowed us to collect all root and organic
matter fragments trapped by the screen.
We used a variant of the stand increment approach (sensu Clark and others
2001) to estimate wood biomass and production. First, we developed allometric
equations to predict aboveground woody biomass as a function of dbh. We combined
the results from two separate studies of blue oak trees at SFREC (Millikin and others
1997, Dahlgren and Singer 1994) to obtain dbh and biomass estimates on eight trees
(dbh range: 7.6 – 48.5 cm). For coarse root biomass (roots > 2 mm in diameter), we
used the data from six trees in Millikin and others (1997). We applied the same
allometric equations to calculate interior live oak biomass. There are no specific
allometric equations for foothill pine, so we used the general equations that predict
the aboveground woody biomass of pines from dbh (Jenkins and others 2003, table 3,
species id = 100) and the equation from Omdal and others (2001) for Pinus
ponderosa to estimate large-root biomass from dbh.
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Net Primary Production and Biomass Distribution in the Blue Oak Savanna—Battles
As noted above, we inventoried all the live trees (dbh ≥ 5 cm) in our plots in
2001. We censused these plots again in late September 2002 to document tree
mortality and ingrowth. We also recorded the annual (October 2001 to September
2002) diameter growth from the subset of trees with dendrometer bands.
Statistical Framework
We evaluated the importance of differences in productivity among ecosystem
components and watersheds by comparing means and their respective 95 percent
confidence intervals. We explored the correlation of plot-level NPP with a suite of
potential indicators, including tree basal area, canopy closure, herbaceous
productivity, height of the herbaceous canopy, species richness, elevation, slope, and
aspect. To visualize changes in tree, herb, and total NPP across the vegetation mosaic
in the blue oak savanna, we smoothed the plot-level responses using locally weighted
regressions (Cleveland and Devlin 1988). We used percent tree canopy closure
(determined by light interception techniques; sensu Reich and others 2001) as the
independent variable measuring proportional tree dominance.
Results
Productivity and Biomass
Annual estimates of mean NPP for the three experimental watersheds during 2001 to
2002 ranged from 4.35 to 5.69 Mg ha-1 yr-1 of dry biomass (table 1). Confidence
intervals (95 percent) around these means averaged ± 33 percent of the mean. Given
this variation, there were no significant differences in NPP between watersheds.
Belowground production accounted for a low 18 percent of the NPP in WS1 and a
high of 27 percent in WS2 (table 1). In general, there was much greater uncertainty in
the belowground estimates.
Trees accounted for approximately 50 percent of ANPP in all three watersheds. In
contrast, herb contributions were more varied. They produced 47 percent of ANPP in
WS1, 39 percent in WS2, and only 35 percent in WS3 (table 1).
Live tree biomass was greatest in WS2 at 129 Mg ha-1 (table 2) with smaller
pools in both WS1 (113 Mg ha-1) and WS2 (111 Mg ha-1). However, like NPP, these
differences were not significant. Shoot-to-root ratios in live tree mass ranged from a
low of 2.5 in WS1 to 3.3 in both WS2 and WS3 (table 2). Shrub mass was a
consistently small component (1.4 -2.0 Mg ha-1) of the live biomass pool in these
watersheds, but they fixed proportionally more biomass (9 to 13 percent of ANPP,
table 1).
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GENERAL TECHNICAL REPORT PSW-GTR-217
Table 1—Results from the Lewis Watersheds at Sierra Foothill Research and Extension
Center, Browns Valley, CA. Fluxes reported in Mg ha-1 yr-1 of oven-dried biomass. xr refers to
the mean of 1000 randomizations of the estimated watershed value using stratified random
sampling.
Component
(Mg ha-1 yr-1)
xr
WS1
95%CI
xr
WS2
95%CI
xr
WS3
95%CI
Herbs
1.91
1.59 – 2.26
1.61
1.23 – 2.07
1.20
0.80 – 1.72
Shrubs
0.39
0.24 – 0.52
0.43
0.25 – 0.59
0.45
33 – 0.57
Bole wood
0.88
0.71 – 1.04
1.15
0.81 – 1.54
0.92
76 – 1.10
Tree litter
1.41
1.03 – 1.81
0.97
0.71 – 1.25
0.89
0.66 – 1.12
3.97 – 5.28
4.13
3.51 – 4.80
3.47
2.87 – 4.06
ANPP
4.59
Fine roots
0.80
0.20 – 1.36
1.27
0.71 – 1.81
0.65
-0.01 – 1.32
Coarse roots
0.25
0.19 – 0.30
0.30
0.19 – 0.41
0.20
0.16 – 0.23
BNPP
1.05
0.37 – 1.63
1.55
0.96 – 2.10
0.88
0.25 – 1.56
NPP
5.63
4.73 – 6.52
5.69
4.69 – 6.67
4.35
3.74 – 5.09
Table 2—Results from the Lewis Watersheds at Sierra Foothill Research and Extension
Center, Browns Valley, CA. Pools reported in Mg ha-1 of oven-dried biomass. xr refers to the
mean of 1,000 randomizations of the estimated watershed value using stratified random
sampling. CWD = coarse woody debris.
Component
WS1
WS2
WS3
xr
95%CI
xr
95%CI
xr
95%CI
Bole mass
82
70 – 96
99
76 – 122
85
69 – 102
Root mass
31
25 – 37
30
24 - 36
26
21 – 31
Shrub mass
1.4
0.72 – 2.0
1.7
1.0 – 2.4
2.0
1.4 – 2.6
CWD
3.5
0.78 – 6.3
2.8
0.99 – 4.4
1.9
0.66 – 3.2
Dead trees
14
-12 – 38
0.60
-19 – 20
9.2
-14 – 35
-1
(Mg ha )
Live mass
Dead mass
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Net Primary Production and Biomass Distribution in the Blue Oak Savanna—Battles
There were important differences in the tree composition among the watersheds
(table 3). The mean tree basal area was 14.7 Mg ha-1 (95 percent CI: 12.8 to 16.7),
but tree basal area was notably but not significantly lower on WS3 (11.7 m2 ha-1)
compared to the other two watersheds (table 3). The pine fraction was much higher in
WS2 (20 percent of basal area), whereas interior live oak was more abundant in WS3
(28 percent of basal area). Paralleling the differences in the tree stratum, understory
plant composition varied among the watersheds (Multiple Response Permutation
Procedure, p < 0.001, McCune and Grace 2002). For example, Rhamnus californica
(coffeeberry) was more abundant and frequent in WS3, while Bromus madritensis
(red brome), an introduced non-native annual grass, was a major component of the
understory flora in WS1.
Table 3—Results based on 2001 inventory from the Lewis Watersheds at Sierra
Foothill Research and Extension Center, Browns Valley, CA. Watershed-level means
(standard error of the mean) of tree basal area and density calculated from a stratified
random sample of 12 plots per watershed.
Species
WS1
WS2
WS3
density
(%)
basal
area
(%)
density
(%)
basal
area
(%)
density
(%)
basal
area
(%)
Blue oak
95.2
89.9
56.7
52.4
60.1
58.6
Foothill pine
2.8
8.1
20.5
20.2
6.0
7.8
Interior live
oak
1.9
1.9
19.2
19.2
33.0
28.1
Black oak1
0.2
0.1
3.6
8.1
--
--
Ponderosa
pine1
--
--
--
--
1.0
5.5
stems ha-1
m2 ha-1
550 (241)
16.6
(2.3)
Totals
1
stems ha1
m2 ha-1
438 (75)
15.7
(1.9)
stems ha1
m2 ha-1
317 (36)
11.7
(0.6)
Black oak (Quercus kelloggii) and ponderosa pine (Pinus ponderosa).
Patterns in Productivity
From the suite of potential indicators, tree basal area was the most strongly correlated
with plot-level NPP (r = 0.63, p < 0.001. fig.1A). The next best was a positive
relationship with herb productivity (r = 0.49, p = 0.003, fig. 1B). No other
correlations with NPP had a coefficient better than ± 0.30.
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GENERAL TECHNICAL REPORT PSW-GTR-217
Figure 1—Correlation between plot-level estimates of total NPP and tree basal area
(A) and herb productivity (B) in a Californian blue oak savanna.
Tree productivity increased in a linear fashion with canopy closure (fig. 2). In
contrast, herb productivity was nearly constant for relatively open sites (canopy
closure < 40 percent) and then monotonically declined as canopy closure increased.
The result is that total NPP increased gradually from the most open sites in the
watersheds to a maximum near 55 percent canopy closure. At higher canopy cover
there was little change in total NPP (fig. 2) though the ratio of tree biomass to herb
biomass continued to increase.
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Net Primary Production and Biomass Distribution in the Blue Oak Savanna—Battles
7.25
Total
6.25
NPP (Mg ha-1 yr-1)
5.25
4.25
Tree
3.25
Herb
2.25
1.25
0.25
0
20
40
60
80
Proportional Tree Dominance (% canopy closure)
Figure 2—Regression models of productivity versus tree dominance in a Californian
blue oak savanna. The final regressions were based on local linear models with the
smoothing parameter set to 0.75 (Cleveland and Devlin 1988). Error bars for total
NPP are the standard errors of the fit. The coefficient of variation (CV = standard
error of the fit over the fit) for NPP = 10 percent; for tree productivity CV = 16 percent;
for herb productivity, CV = 15 percent.
Discussion
Our watershed-level estimates of biomass and NPP for the blue oak savanna (table 1,
table 2) fell within the wide range of values reported for this diverse biome. While
the live biomass pool was near the global mean for tropical savannas, productivity
was closer to the low end of the range (Chen and others 2003, House and Hall 2001).
As a consequence, the biomass-to-NPP ratio for the blue oak savanna (23 years) was
much higher than the global mean for savannas (3.4 years, Chen and others 2003).
Clearly carbon in the blue oak savanna is cycled much more slowly than expected for
a savanna ecosystem.
In general, the low productivity of the blue oak savanna is typical of more arid
tropical sites that support tree-grass ecosystems. For temperate savannas, our ANPP
results (table 1, watershed mean = 4.06 Mg ha-1 yr -1) closely matched two recent
findings. Reich and others (2001) measured biomass and productivity in 20 oak
savanna stands in eastern Minnesota. Extrapolating from the reported regression
relationship, ANPP in comparable stands in Minnesota (i.e., stands with a canopy
openness around 44 percent) was around 3.75 Mg ha-1 yr-1. Mitchell and others
(1999) documented an ANPP gradient from a low of 3.91 Mg ha-1 yr-1 in xeric sites
to a high of 7.43 Mg ha-1 yr-1 in mesic sites for pine-wiregrass ecosystems in
Georgia.
The productivity of our research watersheds is broadly representative of the blue
oak savanna in California. Long-term forage (i.e., herb productivity) records indicate
that Sierra Foothill Research and Extension Center (SFREC) has yields comparable
to other oak savannas in California (George and others 2001). In regard to temporal
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GENERAL TECHNICAL REPORT PSW-GTR-217
patterns, 2002 was an average year in terms of herb productivity. From monitoring
sites across SFREC between 1980 and 1999, annual average productivity ranged
from a minimum of 1.2 Mg ha-1 yr-1 to a maximum of 5.3 Mg ha-1 yr-1 (George and
others 2001). However just as we have found (fig. 2), Connor and Willoughby (1997)
showed that herb productivity in the blue oak savanna varies relative to canopy
cover. The long-term average forage yield at SFREC for sites with 50 percent canopy
cover (a canopy level close to the 56 percent mean canopy measured in our research
watersheds) was 1.58 Mg ha-1 yr-1, an estimate commensurate with the mean of our
three watersheds (1.57 Mg ha-1 yr-1).
Acorn production is a spatially and temporally variable element of the
productivity budgets of blue oak savannas. For California oaks, annual acorn output
varies greatly both interannually and geographically (Koenig and others 1996,
Koenig and others 1999). At SFREC, Dahlgren and Singer (1994) reported an
average acorn production under blue oak canopies of 2.69 Mg ha-1 yr-1 but during the
three years of observation (1990 to 1992) yields varied from 0.3 to 5.8 Mg ha-1 yr-1.
In 2002, acorns made a negligible contribution to the NPP of our research watersheds
but clearly acorn production is a major component of NPP. It will be interesting to
examine the relationship between tree growth and reproductive output as our longterm record develops.
In 2002, watershed-level means of leaf litter production varied from 1.41 Mg hayr in WS1 to 0.89 Mg ha-1 yr-1 in WS3 (table 1). These values fall toward the low
end of the range reported for blue oak savanna (1.4 to 2.1 Mg ha-1 yr-1, Dahlgren and
Singer 1994, Knops and others 1996).
1
-1
Root productivity is a notoriously difficult parameter to measure (Vogt and
others 1998) and we acknowledge the limitations of our min-max approach by
reporting the large uncertainties associated with the means (table 1). Our watershedlevel estimate of 0.9 Mg ha-1 yr-1 of fine root production (fig.1) is half the production
reported by Cheng and Bledsoe (2002). They calculated their estimate from
sequential harvesting of ingrowth cores in a nearby blue oak watershed at SFREC.
However, despite their physical proximity, direct comparisons between the studies
are difficult. Ingrowth cores tend to overestimate root biomass (Steingrobe and others
2000). Moreover, the studies were done in different years and conducted at different
spatial scales (topographic sites versus whole watersheds). Nevertheless, the range of
our values from individual plots overlapped with the range of value reported in
Cheng and Bledsoe 2002.
Living woody biomass dominated the carbon pool in these watersheds (table 2).
Dead biomass (dead trees and coarse woody debris) accounted for a characteristically
small portion of the budget. As Tietje and others (2002) documented, dead biomass
does not accumulate in the blue oak savanna. Given the compositional dominance of
the annual plants in the herb layer, it is reasonable to infer that dead herbaceous
tissue made an even smaller contribution to the biomass pool. We did not measure
soil organic matter in the research watersheds, but Dahlgren and others (2003)
reported that soil organic matter (surface 15 cm) averages approximately 110 Mg ha-1
under oak canopies and 50 Mg ha-1 in open grasslands for comparable sites at
SFREC.
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Net Primary Production and Biomass Distribution in the Blue Oak Savanna—Battles
Tree-Grass NPP Ratio
The empirical pattern we described for the relationship between NPP and the treegrass ratio in the blue oak savanna (fig. 2) does not precisely match any of the
conceptual models described in House and others (2003). Across these research
watersheds, total NPP increased linearly with increasing canopy cover until it
saturated at approximately 50 percent cover. Herb productivity did not decline until
this saturation point was reached. In contrast, tree NPP increased monotonically with
canopy cover across the observed range. Thus, it appears that the interaction between
the trees and grasses is contingent upon where they are on the landscape. In the more
open areas, tree and grass productivity do not seem to be related, as predicted by
McClaran and Bartolome (1989). However, as the proportional dominance of trees
increased beyond ~50 percent canopy cover, grass productivity was negatively
impacted. These results generally correspond to the findings of Connor and
Willoughby (1997) that canopy cover levels of 40 percent to 60 percent do not
suppress forage production in the blue oak savanna.
The factors that determine the abundance of trees and grasses in savannas
continue to be debated (Sankaran and others 2004). Resolution of this debate will
require a comprehensive approach to savanna ecology. Our results concerning the
productivity of this Mediterranean savanna explicitly incorporated the spatial
complexity of these ecosystems. We contend that the uncertainty associated with
these NPP estimates provide a crucial and underappreciated dimension to our
understanding of this ecosystem. Future work will address the temporal variability in
tree-grass productivity relationships and the impact of land-use changes. Given the
documented sensitivity of savannas to changes in land use (House and others 2003),
we need methods to evaluate the impact of these changes on ecosystem function at
relevant spatial scales. The biometric approach described here provides an initial
estimate of the resolution of any changes in NPP that we can expect to detect in a
small watershed.
Acknowledgments
This work was supported by the Integrated Hardwood Range Management Program
(Project # 00-1) and the California Agricultural Research Station. This project is part
of the research program of the Sierra Foothill Research and Extension Center. We
appreciate the hard work of our many research technicians, especially Jennifer York,
Angela Kong, and Rebecca Wenk.
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