Effects of Nitrogen Deposition on the Carbon Allocation and Nutrient Concentration of Southern California Vegetation.
Shelley Lawrence
[email protected]
California State University, San Marcos
Department of Biological Sciences
333 S. Twin Oaks Valley Rd.
San Marcos, CA 92096
Contents
ABSTRACT.................................................................................................................................................. 2
INTRODUCTION ........................................................................................................................................ 3
Carbon allocation ..................................................................................................................................... 3
Hypotheses ................................................................................................................................................ 8
METHODS ................................................................................................................................................... 8
Site descriptions ........................................................................................................................................ 8
Experimental design and sample analysis .............................................................................................. 10
Statistical Analysis .................................................................................................................................. 12
RESULTS ................................................................................................................................................... 14
Artemisia californica carbon allocation ................................................................................................. 14
Artemisia californica nutrient concentrations ........................................................................................ 15
Adenostoma fasciculatum carbon allocation ......................................................................................... 16
Adenostoma fasciculatum nutrient concentrations................................................................................. 17
Precipitation ........................................................................................................................................... 18
DISCUSSION ............................................................................................................................................. 19
Effects of experimental N deposition ...................................................................................................... 19
Precipitation and Nutrient Uptake.......................................................................................................... 23
ACKNOWLEDGEMENTS........................................................................................................................ 28
LITERATURE CITED ............................................................................................................................... 29
ABSTRACT
Coastal sage scrub and chaparral vegetation of Southern California have become
fragmented due to a loss of habitat over the past several decades, which has been caused by several
contributing factors, such as agriculture, urbanization, increased fire frequency and intensity.
Although nitrogen deposition has also been found to be a contributing factor to the loss of coastal
sage scrub (CSS) and chaparral habitats in previous studies, the mechanism for these effects not
been examined. Leaf tissue from existing field plots, fertilized with nitrogen since 2003, was
analyzed for carbon allocation patterns and nutrient retention on a seasonal and annual basis from
2006, 2008 and 2010. Nitrogen fertilization did not have an effect on carbon allocation to cellulose,
holocellulose or lignin fractions of leaf tissue in CSS California sagebrush (Artemisia californica)
or chaparral chamise (Adenostoma fasciculatum) shrubs. However, it was found that seasonal and
interannual variation in soluble carbon were highest in both species, but without any N treatment
interaction. It was also found that year and season did have a significant effect on carbon allocation,
and these temporal variations were correlated with precipitation rates and nutrient availability. The
lack of nitrogen effect in the soluble carbon, holocellulose and lignin fractions suggests these
avenues of carbon allocation are linked to life history traits that are specific to each species such
as drought tolerance, woodiness, and maturation.
2
INTRODUCTION
Coastal sage scrub (CSS) and chaparral vegetation of Southern California have become
fragmented due to a loss of habitat over the past several decades due to several contributing factors
such as agriculture, urbanization, increased fire frequency and intensity (Allen et al., 1998;
Minnich and Dezzani, 1998). One contributing factor may be anthropogenic nitrogen (N)
deposition, which is a by-product of urbanization and agriculture, and has the potential to affect
fire regimes and primary production (Allen et al., 1998; Bytnerowicz and Fenn, 1996; Fenn et al.,
2003; Minnich and Dezzani 1998; Vourlitis 2012). Previous studies have shown some shrublands
in highly polluted areas can receive up to 45 kg N ha-1 y-1, which primarily falls as dry deposition
during summer and early fall due to many of these same anthropogenic factors (Bytnerowicz and
Fenn, 1996; Fenn et al., 2003). Atmospheric N deposition is an important source of pollution to
plants, soil and stream water, especially in Southern California N limited ecosystems that are
critical habitat to over 200 sensitive plant species and several federally listed animal species
(Phoenix et al., 2006). Although N deposition has already been found to directly contribute to the
loss of coastal sage scrub and chaparral habitats in previous studies, the specific mechanism for
the effects of N deposition on habitat has not been examined (Chapin et al., 2000, Phoenix et al.,
2006). It is unknown if increasing N availability within these ecosystems will cause a change in
carbon allocation, but the effects of N deposition are important in terms of large scale ecosystem
changes like species diversity, nutrient retention, decomposition rates, and fire frequency and
intensity.
Carbon allocation
Following Farrar’s (1980) model that plant carbon chemistry can be divided into soluble
and structural components, this study aims to quantify how N deposition affects carbon allocation
3
to leaf soluble, semi-structural and structural compartments, such as sugars and starches. A
positive carbon balance, as well as appropriate carbon allocation, is vital to any living organism
and each species may have various demands on carbon allocation depending on nutrient
availability, season, and life history traits (Borland and Farrar 1985, Burke et al., 1991). A shift in
carbon allocation between carbon fractions in response to N fertilization could have long term
effects on N limited ecosystems in terms of carbon storage, decomposition rates, litter quality and
nutrient cycling (Farrar, 1980).
Water and ethanol soluble carbons, like glucose, fructose, sucrose and fructans, are broken
down by plants to provide for metabolic processes, and are the path in which all carbon exchanges
must pass (Borland and Farrar 1985, Farrar 1980, Graham et al., 2006). This fraction includes any
free carbon that is readily available for respiration, short-term storage and semi-structural
components. This readily available soluble carbon fraction is one of the main fractions that will be
examined in this study.
One fate of soluble carbon is the production of holocellulose, which is comprised of long
and short chains of sugars, known as cellulose and cellulose-like substances, respectively. This
fraction is resistant to hydrolysis and, therefore, must be isolated through the use of substances
stronger than water or alcohol, such as an acid. This property makes holocellulose useful for
storage as well as cell wall structure, yet it is not as rigid as the lignin fraction. Cellulose is the
main component of cell walls and its fibrous nature can be attributed to its hydrogen bonded
carbohydrate polymer composition (Graham et al., 2006; Moorhead and Reynolds, 1993).
Holocellulose is an additional carbon fraction that will be examined in this study.
Another fate of soluble carbon is the allocation to the main structural component of a plant
leaf tissue, lignin. The lignin fraction includes phenolic acids, essential oils, waxes and flavonoids,
4
which aid in allelopathy, water retention, and structural support (Austin and Ballare, 2010).
Changes in carbon allocation to lignin may affect leaf litter decomposition rates, fire patterns and
successional dynamics (Graham et al., 2006; Rundel et al., 1982). Lignin plays a key role in the
process of decomposition because it is the structural component of a plant that resides in the cell
wall, represents about 30% of total plant carbon allocation, and is only broken down by specialized
biota and abiotic factors (Boerjan et al., 2003; Melillo et al., 1982; Moorhead et al., 1993).
Nitrogen:lignin ratios are more highly correlated to rates of decomposition than any other chemical
fraction (Melillo et al., 1982; Taylor et al., 1989). Additional studies found a positive correlation
between litter decomposition rates and N abundance in detritus (Enriquez et al., 1993; Moorhead
et al., 1993). Such studies exemplify the important role of N:lignin ratios in litter decomposition
and nutrient cycling. Added N fertilization in this study may cause lower carbon allocation to
lignin and in turn, affect rates of litter decomposition at the ecosystem level. Lignin is the third
carbon fraction that will be analyzed in this study.
As this study assesses leaf tissue in terms of soluble carbon, holocellulose and lignin
fractions; however, it must be noted that assigning any of the larger biochemical constituents (ie.
sugars, carbohydrates, lipids, phenolic acids, tannins) to any of these general chemical families is
somewhat arbitrary (Sparks and Oechel, 1993). For example, there are several studies that group
constituents into various functional families of chemicals, most of which mix and match the array
of constituents (ie. sugars, carbohydrates, lipids, phenolic acids, tannins) between categories of
growth, storage, structure, etc. (Chapin III et al., 1990; Farrar et al., 1980; Sparks and Oechel,
1993). It is acknowledged that these constituents have many functions, can be intermediates and
serve complex roles within leaf tissue, yet using a categorical system has proven helpful in
previous studies, and therefore, will be used in this study as well.
5
Abiotic influences on carbon allocation
Although carbon allocation patterns differ depending on shrub morphology (ie. evergreen
versus deciduous), abiotic environmental factors can have an impact on allocation patterns
(Grimoldi et al., 2006). Nutrient availability, particularly N and phosphorus (P), can play a key
role in carbon allocation within leaf tissue. Grimoldi and others found that high soluble carbon
fractions and low structural compounds within leaf tissue are an indication of P limited soils.
Nutrients, such as P, are needed for soluble carbon to be used in the synthesis of structural
components, like lignin (Grimoldi et al., 2006).
In addition to nutrients, intrinsic plant characteristics can also dictate relative carbon
allocation to various fractions. Several studies compare decomposition of evergreen and deciduous
plant species to find patterns of seasonal and temporal variation due to increased N availability.
Evergreen species contain higher levels of structural material, such as lignin and phenolic
compounds, which limit the rate of decomposition; as well as allocate more lignin to leaves to
maintain resilience under a complexity of environmental stressors. Deciduous plants allocate less
lignin but more N to photosynthetic enzymes and maximize carbon gain (Chapin et al., 2002;
Monk, 1966; Mooney and Rundel, 1979; Schlesinger and Chabot, 1977; Small, 1972). Evergreen
litter is considered low quality because high lignin litter takes a longer amount of time to
decompose and release nutrients; whereas, high N litter, from high quality deciduous litter, takes
less time to decompose and release nutrients (Chapin et al., 2002, Swift et al., 1979). Nutrient
cycling is expedited when high N litter from deciduous plants is present, releasing readily available
nutrients back into the soil. These inherent properties of evergreen and deciduous litter may help
better understand ways in which added N will affect carbon allocation and the nutrient cycle within
CSS and chaparral ecosystems.
6
Water is also a major limiting factor in the nutrient cycling process. Water is not only a
key factor in microbiota soil abundance, which heavily dictates litter decomposition rates, but is
also the medium of plant nutrient uptake (Nielson and Ball, 2014). Although Mediterranean
ecosystems are N limited, water availability is also a limiting factor and precipitation events can
have large implications for soil nutrient availability, leaching, and microbial biomass (Nielson and
Ball, 2014; Vourlitis, 2012). Studies by Vourlitis and others attribute increased soil, and therefore
leaf, N levels in winter and spring to precipitation events which mobilize nutrients during these
seasons (Vourlitis et al., 2007).
These studies demonstrate that seasonal variability and
precipitation events may play on the ability for added soil N to effect carbon allocation and nutrient
abundance within plant leaves. Important seasonal and annual trends in precipitation and both leaf
tissue carbon allocation and nutrient abundance were evaluated in this research.
Life history patterns are directly linked to biochemical composition and N:nutrient
stoichiometry (Elser, 1996). Nutrient stoichiometry plays a key role in life history traits like growth
and mineralization rates, and its complexity is often overlooked (Sterner and Elser, 2002). Four
naturally occurring elements (C, H, O and N) make up about 99% of all living biomass and seven
(Na, K, Ca, Mg, P, S, and Cl) are essential to all living things (Sterner and Elser, 2002). Although
these elements may only make up 1% an organism, each of these elements lend specific attributes
such as structure, energy transduction, and water regulation (Sterner and Elser, 2002). Although
most studies within the literature to date focus on the interactions between C, N and P, this study
will try to capture a larger cross-section of nutrients (N, Ca, Mg, K, and P) and the N:nutrient
ratios.
7
Hypotheses
The objectives of this study were to determine a) if added N caused a shift in carbon
allocation or nutrient abundance and/or stoichiometry in leaf tissue, b) whether carbon allocation
and nutrient abundance varied over seasonal and annual time scales, and c) if there were
interactions between added N and climate variation on the carbon and nutrient chemistry of
chaparral and CSS shrubs. Based on the known effects of N availability on semi-arid, N limited
shrubs, it is hypothesized that a) the leaf tissue soluble carbon fraction will be positively correlated
with added N while holocellulose and lignin fractions will negatively correlate to added N, b) time
will have an effect on leaf carbon allocation and nutrient concentration based on seasonal
precipitation variation, and c) there will be significant interactions between N addition and
precipitation over seasonal and interannual time scales.
METHODS
Site descriptions
Archived samples of California sagebrush and chamise leaf tissue were collected from a
coastal sage scrub (CSS) community at the Santa Margarita Ecological Reserve (SMER; 33°29 N,
-117°09’ W) and a chaparral community at the Sky Oaks Field Station (SOFS; 33°21’ N, -11634’
W) (Vourlitis et al., 2007; Vourlitis and Pasquini, 2009; Table 1). Leaf tissue samples collected
from each season (winter, spring, summer, and fall) in 2006, 2008, and 2010 were used in this
study to understand the dynamic patterns of seasonal and interannual carbon allocation and nutrient
concentration. Such analysis among seasons and year are important for field studies because
environmental factors (ie. light, climate, weather, fire) are in constant flux and may account for
confounding variables and unexpected results. These years were particularly chosen because
mature stands were in existence at both sites and steady N fertilization had occurred since 2003.
8
SMER is located west of Interstate-15 between the city of Rainbow and Temecula in
southwest Riverside County, California. Although the site falls just on the north side of Riverside
county border, SMER meets the Holland Report natural community description of Diegan Coastal
Sage Scrub due to site elevation (338m) on a 9-11° S-SW facing slope and the presence of low,
soft-wood shrubs that are most active in winter and early spring (Holland, 1986; Table 1).
Additional Diegan Coastal Sage Scrub characteristics of this site include being dominated by
California sagebrush, California buckwheat (Eriogonum fasciculatum), and black sage (Salvia
mellifera) and lacking red brome (Bromus rubens). SMER receives a mean of 360 mm of
precipitation annually, with the majority of rainfall between December and April. The soil is sandy
clay loam of the Las Posas Series derived of igneous and weathered Gabbro material with bulk
density of 1.22 g/cm3 (Vourlitis and Pasquini, 2009).
California sagebrush (Artemisia californica Less.) was the target CSS species used in this
study because it is considered a dominant, indicator species for CSS (Hauser, 2006; YoungMathews, 2010). It is a summer deciduous shrub which resides on west and north slopes in full
sun near the coast from 0-800 meters (m) (Hauser, 2006). This shrub can reach 1.5-2.5 m in height
at maturity and has woody stems near the base and is herbaceous near the tips (Hauser, 2006).
SOFS is located approximately 11 linear kilometers east of state route 79 and 12 linear
kilometers south of state route 74 in northeast San Diego County, California. SOFS meets the
Holland Report natural community description of semi-desert chaparral due to the site elevation
of 1,418 m on a 4-10° SE-SW facing, dry, cis-montane slope (Holland, 1986; Table 1). Prior to a
lightning strike setting fire to the stand in 2003, the landscape was dominated by chamise
(Adenostoma fasciculatum Hook. & Arn.). Currently, the landscape is dominated by chamise and
sub-dominant desert ceanothus (Ceanothus greggii), with areas of open bare ground. The site
9
receives an mean of 530 mm of precipitation annually consisting of rain with occasional snow that
occurs in winter and spring. The soil is an Ultic Haploxeroll derived of micaceous schist with a
sandy loam texture and a bulk density of 1.34 g cm-3 (Vourlitis and Pasquini, 2009).
Chamise was selected as a target plant used in this study because it is the dominant
chaparral plant throughout most of California (Jow, 1980; McMurray, 1990). Chamise is a
sclerophyllous evergreen, drought-tolerant shrub that can reach upwards of 3 m. It flowers from
May to June and resides in sandy-loamy, well-drained, nutrient poor soils. It often occurs on dry
slopes and ridges below 1,800 m, from Baja California, Mexico to northern California. This shrub
is well adapted to fire and is a strong post-fire, first successional plant due to the ability to quickly
re-sprout from the basal crown (McMurray, 1990).
Experimental design and sample analysis
This study utilized leaf tissue from California sagebrush and chamise shrubs that were
exposed to added N for a total of 3 (2006), 5 (2008), and 7 years (2010) to determine the effects
of chronic N deposition on seasonal and inter-annual variations in climate.
Each research site contains eight, 10x10 m plots, where four randomly selected plots are
untreated, un-manipulated controls which receive baseline levels of N pollutants and the remaining
four plots receive 50 kg N ha-1 granular ammonium nitrate (NH4NO3), ammonium sulfate
((NH4)2SO4), or urea (CH4N2O). A total of 50 kg N ha-1 was used based on treatment levels and
results from Vourlitis (2007, 2009, and 2012), Control plots receive approximately 6-8 kg N ha-1
y-1 of baseline level of atmospheric N deposition (Tonnesen et al., 2007) and N treatment plots
receive a total of 56-58 kg N ha-1 y-1. The N treatment is distributed in a single application every
year during the dry season with a handheld spreader.
10
Between 2 and 4 apical shoots were collected from 2 to 4 randomly located points within
each plot. It is estimated that a total of 50-100 chamise and 20-50 sagebrush leaves were collected
per plot on a quarterly basis to observe any seasonal variation (winter= January- February, spring=
March-April, summer= June-July, fall=September-October) (Vourlitis et al., 2007). The upper 10
15 cm of live apical shoots was collected because this fraction represents the newest growth for
that season (Gill and Mahall, 1986). The leaf tissue samples were stored in a drying oven at 70°C
for a minimum of 1 week then were ground to pass through a 40 mesh sieve using a mechanical
mill (Thomas-Wiley Mini Mill, Thomas Scientific, Swedesborom NJ, USA). The samples were
then stored by plot, location, date, and species at room temperature in clean zippered plastic bags.
The Moorhead and Reynolds (1993) method of assessing tissue chemistry was used, and
leaf carbon chemistry was partitioned amongst three fractions: soluble, holocellulose, and lignin.
These methods were utilized to find relative abundance of each carbon fraction, represented as a
percent of the total organic carbon. A total of 0.5 g of oven dried at 70°C, finely ground, tissue
sample was added to a preweighed 50 ml polyallomer centrifuge tube. A total of 25 ml of distilled
water was added to the sample and then it was placed into a sonicating water bath for 30 min at
60°C. Next, the tube was centrifuged at 10,000 rpm for 15 min, the supernatant was poured off,
and this washing process was repeated five times with distilled water and then repeated an
additional five times with ethanol. The samples were then be dried for 24 hours at 60° carbon and
weighed. The soluble content was calculated as the difference between the original mass and the
remaining dry mass after extraction.
A total of 0.20 g of the remaining sample after extraction was placed in a 15 ml glass test
tube containing 2 mL of 72% sulfuric acid to degrade the hydrophobic, holocellulose content. The
sample was then be incubated for 1 hour at 30°C and 56 ml of distilled water was used to transfer
11
the dried sample into a 125 mL flask. Flasks containing samples were then autoclaved for 1 hour
at 120°C, suctioned onto a preweighed 10 µm Millipore filter paper, and oven dried for 24 hours
at 60°C. The extreme heat and pressure from the autoclave allowed the sulfuric acid to react with
minute particles of the sample and further facilitated the digestion. The holocellulose content of
the sample was estimated to be the difference between the pre-acid and post-acid digested dry
sample weight. The residue consisted primarily of lignin.
Total amounts of leaf N and carbon were determined on 5-10 mg samples using an element
combustion system (ECS 4010, Costech Analytical Technologies, Inc., Valenica, California,
USA). Leaf Ca, K, P, and Mg concentrations were determined on 0.5 g leaf samples obtained
during the summer season. Only summer season leaf Ca, K, P, and Mg concentrations were
determined because this season was reflected the end of the growing season (Vourlitis et al., 2009)
and there was sufficient material to permit these more detailed nutrient analyses. Digests were
analyzed for Ca, K, P, and Mg concentration following HNO3+H2O2 digestion using an inductively
coupled plasma (ICP) analyzer by personnel of the Ecology Analytical Laboratory at San Diego
State University.
Statistical Analysis
Seasonal and annual variations in leaf tissue carbon allocation and nutrient abundance were
analyzed using a repeated measures analysis of variance (RMANOVA) to assess whether N
addition and time caused significant (p<0.05) variations in response variables. The response
variables used to describe carbon allocation were levels of soluble carbon, holocellulose and lignin
in leaf tissue samples, and season and year served as within factors. Response variables used to
describe nutrient concentrations were N, Ca, K, P, Mg, and N:Ca, N:K, N:P, N:Mg, and year served
as a within factor. Direct comparisons between California sagebrush and chamise were not
12
conducted because variation in carbon allocation and/or nutrient concentration could be attributed
to site differences, as well as inherent species differences in nutrient and carbon allocation.
Annual precipitation data collected at both sites was intermittent and data collected from
the closest nearby stations was used to fill gaps in data. Data collected from the Santa Rosa Plateau
(33 31 N, -117 13’W; elevation 603m) and Oak Grove, CA (33 23’ N, -116 47, W, elevation 839
m) were used to fill gaps in the SMER and SOFS data, respectively. Linear regression of data
collected on-site versus those derived from near-by stations indicate a close correspondence in
monthly precipitation (Vourlitis, 2012). A two-way ANOVA, with season and year as factors,
was used to determine if precipitation had a significant effect on carbon allocation, nutrient
concentrations, or N: nutrient stoichiometry (p≤0.05). In addition, a correlation matrix was used
to determine if any significant correlations existed between annual accumulated precipitation
(between December of the previous year and July of the current year), leaf carbon chemistry, and
nutrient concentrations.
A Tukey-Kramer’s post-hoc analysis was used to determine specific differences between
all response variables. Box’s M and Mauchly’s tests were used to test the assumptions of equality
and compound-symmetry (sphericity), respectively of the between-group covariance matrices.
Probability values were calculated using the Geisser-Greenhouse Epsilon corrections for data that
violated these assumptions (Hintze, 2004). Data were reduced using MS Excel and all statistical
analyses were conducted using R statistical package and NCSS 2007 (version 07.120; Hintze
2004).
13
RESULTS
Artemisia californica carbon allocation
RMANOVA was used to determine possible effect of N treatment on soluble carbon,
holocellulose and lignin on two different time scales, season and year. The results of this analysis
showed no significant effects of added N treatment but there was a significant effect of time
(season and year) for soluble carbon, holocellulose and lignin (p<0.05; Figure 1; Table 2). Seasonal
variation was highest in 2006 for all three fractions; whereas 2008 and 2010 had less deviation
from annual means. A significant interaction between year and season occurred in each carbon
fraction of Artemisia californica (p<0.05). Overall mean soluble carbon across years was 52.4%;
whereas carbon allocation to holocellulose and lignin nearly split the difference at 22.3% and
25.3%, respectively.
In 2006, soluble carbon showed the highest seasonal variation, whereas lignin showed the
lowest seasonal variation than any other year (Figure 1; Table 2). Soluble carbon was consistently
more abundant than any other fraction and ranged in mean percent allocation from 49.6% in 2010
to 54.0% in 2006. Soluble carbon followed similar seasonal patterns each year with percent of
total carbon low in the winter, and rising through spring, summer, and fall. The main effect of year
was driven by the significantly lower soluble carbon levels in 2010 (p<0.05). All seasons were
different from each other, with the exception of fall and spring, which caused season to have a
significant main effect on soluble carbon abundance (p<0.05). There is also a significant
interaction between season and year (p<0.05).
Holocellulose also showed seasonal patterns with peak levels occurring in the winter and
decreasing levels in the spring, summer, and fall (Figure 1; Table 2). The significant main effect
of season was driven by the significantly higher concentration of holocellulose in winter (p<0.05).
14
In 2010, holocellulose showed a steady decrease from winter to spring, which followed the same
trend found in ’06 and ’08, but then increased again in the fall. Although there was a higher level
of holocellulose in fall of 2010, this did not cause 2010 to be significantly different (p>0.05).
Instead, the significant main effect of year was driven by the high levels of holocellulose found in
winter of 2006, which presumably explains the significantly higher mean of 2006 (p<0.05).
Overall, relatively less carbon was allocated to holocellulose with mean percentages ranging from
20.2% in 2010 to 24.8% in 2006.
Minimal annual or seasonal patterns exist in carbon allocation to lignin due to extreme
variability and the significant interaction between year and season (p<0.05; Figure 1; Table 2).
The main effect of year was driven by every year being significantly different from one another
(p<0.05). Each season was also significantly different from one another, with the exception of
summer and fall (p<0.05). During 2006, lignin allocation percentages were as high as 36.0% in
spring and as low as 14.3% in summer. Similar patterns between 2008 and 2010 exist in winter,
summer and fall seasons, but spring lignin percentages peak in 2010 and 2006. Annual mean
allocation percentages gradually increased from 2006 to 2010 and mean percentages ranged from
22.31% in 2006 to 28.17% in 2010.
Artemisia californica nutrient concentrations
Statistical analysis showed no significant effects of treatment on summer nutrient
abundance or N: nutrient ratios (p>0.05). However, general trends show N, P and Mg peak
abundance in 2008 in both control and added N groups (Figure 2). Year did not have a significant
effect on summer Ca, K, Mg or P abundance (p>0.05). Nitrogen abundance was mainly effected
by year due to 2008 N abundance being significantly higher than 2006 or 2010 (p<0.05). Year had
a main effect on N:Ca, N:K, N:Mg, and N:P. In N:Ca, N:Mg, and N:P the year main effect is due
15
to every year being significantly different from one another (Figure 3). In N:K, the main effect of
year is due to 2008 having significantly higher ratios (p<0.05). A significant interaction occurred
between treatment and year in N:Mg due to the variation between control and treatment in 2006
(p<0.05). All N: nutrient levels peaked in summer 2008 and were lowest in summer 2010 (Figure
3). The correlation matrix showed a significant positive interaction between Ca and Mg, P and K,
and P and Mg (Table 3). The correlation matrix also showed a positive correlation between N:Ca
and holocellulose (Table 3).
Adenostoma fasciculatum carbon allocation
No significant effects of added N treatment were found in carbon allocation (p<0.05;
Figure 4; Table 4), but there were significant variations over seasons and between years. The
highest seasonal variability for soluble carbon, holocellulose, and lignin occurred in 2006, while
the lowest overall seasonal variability occurred in 2008. There was also a significant interaction
between year and season for all three carbon fractions (p<0.05).
The significant main effect of year on soluble carbon concentration was driven by the
significantly lower abundance in 2006 compared to 2008 and 2010 (p<0.05; Figure 4; Table 4).
The soluble carbon fraction showed interesting seasonal variation in 2006 with a notable change
in spring when treatment allocation dropped as low as 30.3%, and in summer when control group
allocation dropped to 34.8%. Although there is a significant seasonal main effect, these seasonal
differences cannot be attributed to season alone due to the significant interaction between season
and year (p<0.05). This interaction suggests that seasonal variation depends on the year and annual
variation depends on the season. Seasonal decreases in spring and summer cannot be generalized
to the entire data set, but instead are specific to 2006 and 2010 due to the interaction. The soluble
carbon fraction was more abundant than any other fraction, regardless of year or season, with the
16
exception of summer and spring 2006 (Figure 4). Soluble carbon ranged in mean percent allocation
from 44.0% in 2006 to 51.3% in 2010.
Holocellulose had the highest seasonal variation in 2006 with a peak in spring at 46% and
a steep decline in summer and as low as 22% in winter (Figure 4; Table 4). Holocellulose seasonal
variability in 2008 and 2010 was much less, accounting for the significant season x year interaction
(Table 4), with mean percent allocation just above 30%. In 2006, holocellulose levels were
significantly higher than in 2008 and 2010 (p<0.05). Allocation to holocellulose ranged in mean
percent allocation from 31.4% in 2010 to an annual average of 34% in 2006. All seasons
significantly differed from one another, except fall and summer (p<0.05; Figure 4).
Overall carbon allocation to lignin gradually increased in 2006 and 2010 and there was a
significant main effect of year driven by 2006 being significantly less than 2008 and 2010 (p<0.05;
Figure 4; Table 4). The high seasonal variation in lignin during spring and summer of 2006 seem
to mirror the soluble carbon variation but no significant main effect of season across the data was
found (p>0.05). Allocation to lignin ranged in mean percent allocation from 15.24% in 2006 to an
annual high of 19.06% in 2010.
Adenostoma fasciculatum nutrient concentrations
No significant interaction occurred between treatment and time for any of the leaf tissue
nutrient concentrations or N: nutrient ratios (p>0.05; Figure 5; Figure 6). Neither treatment nor
year had a significant main effect on summer leaf Ca or Mg concentrations (p>0.05; Figure 5).
Added N treatment caused a significant increase in leaf N concentration, but no significant effect
on any other leaf nutrient concentrations (p<0.05; Figure 5). Year also has a significant main effect
on leaf N concentration due to the significantly lower N abundance in 2006 (p<0.05). Chamise
leaf N concentration was lowest during 2006, the driest year, in both control and N treatment plots.
17
Year is a significant main effect of K concentrations due to 2008 being significantly lower than in
2010 (p<0.05). Year had a main effect on summer leaf P concentrations due to 2010 having
significantly higher levels than 2006 and 2008 (p<0.05). Both N:Mg and N:Ca ratios varied by
year due to the significantly lower levels in 2006 compared to 2008 and 2010 (Figure 6). Year has
significant main effects on leaf tissue N:K and N:P ratios in summer (p<0.05; Figure 6). The main
effect of year can be attributed to 2006 having significantly lower N:K ratios than 2008, and
significantly higher N:P ratios in 2008 compared to 2006 and 2010. The correlation matrix showed
a significant negative correlation between Ca and K, and a positive correlation between K and P,
and holocellulose and P (Table 5). In addition, there was a negative correlation between percent
lignin and N:Ca, N:K, and N:Mg (Table 5).
Precipitation
Precipitation levels at both SMER and SOFS showed very similar annual and seasonal
trends (Figure 7). General trends show annual precipitation means practically doubled from 2006
to 2008, and from 2008 to 2010 at SOFS. Peak precipitation over the course of the study occurred
in winter 2010 with a mean of 162.6 mm at SMER and 128.7 at SOFS. Precipitation fell as low as
0.08 mm during summer 2008 at SMER and 0.8 mm at SOFS during summer 2006. Total annual
precipitation at SMER was 273.05mm in 2006, 473.27mm in 2008, and 674.13mm in 2010. Total
annual precipitation at SOFS was 201.85 mm in 2006, 285.70 mm in 2008, and 543.97 mm in
2010.
Neither site had a significant annual precipitation differences but both had a significant
main effect of season, driven by a significantly higher precipitation during winter months (p<0.05;
Figure 7; Table 6). In fact, the data set shows a significant interaction between season and year
(p<0.05; Table 2) and the correlation matrices showed that variations in rainfall were key to
18
variations in carbon chemistry at SMER (Table 3). Summer sagebrush data showed soluble carbon
and holocellulose are significantly negatively correlated, and lignin was positively correlated to
rainfall accumulation (Table 3). In chamise, there was a positive correlation between accumulated
rainfall and leaf N, K, P, and holocellulose; however, leaf N concentration was positively
correlated with holocellulose content and negatively correlated with lignin content (Table 5).
DISCUSSION
Effects of experimental N deposition
The results of this study did not support the hypothesis that added N causes a shift in
carbon allocation or nutrient abundance (with the exception of N in chamise) within these two
CSS and chaparral species. Although dry season N treatments did not cause an affect on carbon
allocation in this study, other studies have found that N treatment does affect overall plant
properties, such as biomass and leaf area index, yet fails to change overall N storage and
ecosystem productivity (Vourlitis and Pasquini, 2009). In more recent studies it was found the
effect of N exposure was positively correlated with annual rainfall and N input did cause an
increase in net primary production (NPP) (Vourlitis, 2012). Although these past studies
exemplify that N does affect a variety of leaf and shrub properties, there appeared to be no N
effect in the current study. Results from this study suggest responses in carbon allocation and
overall N ecosystem storage may not be as easily manipulated through environmental factors
within 7 years of N treatment at SMER and SOFS. Instead, these responses are possibly life
history patterns and physiological traits found in CSS and chaparral shrubs.
As noted above, the added N treatment did have a significant effect on only one factor, N
concentration in chamise leaf tissue during summer months. Chamise may have been affected
because of basic life history patterns of evergreen shrubs. Although summer month precipitation
19
reached as low as 0.8 mm at SOFS, leaf tissue N abundance still managed to show a significant
increase. Previous studies by Vourlitis and others found that soil N was highest in summer and
fall at SOFS and similar results, in which N treatment effects cause an increase in tissue N
concentration across all seasons (Vourlitis and Pasquini, 2009). This finding suggests that,
although soils are dry during the summer months, there is still a demand for N within chamise
leaf tissue and ability for uptake during summer months. Although soil N is also highest in
summer and fall at SMER, the sagebrush shrub did not have a N treatment effect on leaf N
concentration (Vourlitis and Pasquini, 2009). Interestingly, SOFS maintains somewhat elevated
precipitation levels during summer months, in comparison to the minimal precipitation at SMER.
Aside from intrinsic life history traits of each shrub, the lack in treatment effect in sagebrush
could be due to the variability in precipitation and soil moisture between sites during summer
months (Figure 7). These abiotic site factors may help drive divergent life history patterns of
shrubs, such as summer growth and allocation versus storage for the next growth cycle. The
results of this study reaffirm this concept, as sagebrush did not have an increase in leaf tissue N
and chamise did have an increase in leaf tissue N concentration during summer months.
Annual means in both species showed soluble carbon to be the most abundant fraction at
the expense of holocellulose and lignin (Figure 1; Figure 4). When photosynthetically produced
soluble carbon is available for new growth there is also an abundance of above ground biomass
and productivity. However, when growth and metabolism levels become equal with respiration
rates, shrubs experience senescence, which is associated with high levels of dead above ground
biomass and low production (Sparks and Oechel, 1993). The soluble carbon fraction is the
plant’s carbon currency and must be readily available for turnover to holocellulose or lignin
fractions (Farrar, 1978). It is likely abundant soluble carbon fractions are inherent attributes of
20
both California sagebrush and chamise that have developed over time in response to
environmental stressors, such as water and nutrient availability. Being the plant’s carbon
currency, a large soluble carbon fraction allows for a buffer for other fractions and must be
readily available for quick metabolism to holocellulose or lignin (Farrar, 1980). This is directly
exemplified by the negative correlation found between soluble carbon and lignin in both species
(Table 3; Table 5). The trade-off between soluble carbon and lignin is apparent and having
plasticity within the soluble fraction allows for stable allocation to other fractions (Farrar, 1980).
Unlike the soluble fraction, the lignin fraction does not have plasticity and slow
adaptation must be a driving force in any long term variation (Pratt et al., 2014). Previous studies
have found that abiotic and biotic environmental stressors, such as temperature and water
availability, can put lignin in high demand but not necessarily as a structural component (Farrar,
1980; Pratt et al., 2014; Yani et al., 1993). Aside from structure, the lignin fraction consists of
aromatic, waxy terpenes which make up the leaf cuticle. Many plant leaves that are drought
tolerant, such as those of chamise, metabolize carbon into terpenes to increase allelopathy, deter
herbivores and pathogens, and prevent leaching of water and nutrients (Pratt et al., 2014). High
levels of terpenes are associated with environmental stressors, such as increased temperatures or
aridity, but can still be metabolized by drought tolerant plants during periods of high water stress
(Yani et al., 1993). Contrary to expected results, the current study showed a positive correlation
between rainfall and lignin in sagebrush and a negative correlation between leaf N concentration
and lignin in chamise. It is not likely that any of these correlations translate to causation and, as
Pratt and others found, any change in abiotic or biotic factors will result in a slow adaptation in
carbon allocation to less plastic fractions, such as lignin (Pratt et al., 2014). The correlations
found between carbon allocation and abiotic factors tested for the contribution of precipitation in
21
particular and it cannot be concluded that other abiotic or biotic factors don’t also select for
variation within the data.
Although direct comparisons regarding allocation pattern between these two species
cannot be made, general life history patterns of deciduous versus evergreen can be described. It
was found over the entire course of the study that total soluble carbon abundance in the
deciduous sagebrush was higher than in evergreen chamise (Figure 1; Figure 4). Such differences
in allocation patterns to soluble carbon can be expected between deciduous and evergreen
species (Schlesinger and Hasey, 1981). It is logical that the sagebrush deciduous leaves, which
must reach full photosynthetic potential within a single grow season, would have a higher
soluble carbon pool available for highly elastic allocation. Chamise leaves have an average
longevity of two grow seasons and may require more allocation of carbon to holocellulose or
lignin fractions for maintenance and structure over the relatively longer leaf life (Jow et al.,
1980). Therefore, this carbon allocation trade-off between soluble carbon and structural fractions
to sustain the leaf over a specific period of time is an inherent property unique to deciduous and
evergreen shrubs.
It is recognized that leaf age may play an important role in carbon allocation, as well as
nutrient abundance, due to reabsorption of nutrients prior to abscission. This phenomenon is why
the leaf sampling method of this study was sure to only sample the apical shoots (i.e., current
year growth) for analysis as to negate any variation due to leaf age. Previous studies found that
reabsorption of nutrients from leaf to stem vary based on species but could also be a source of
variation (Schlesinger and Hasey, 1981). Although reabsorbtion could be a source of variation
over time, samples were dried in the drying oven within hours of collection in order to minimize
variation due to reabsorption of nutrients from the leaf to the stem.
22
Precipitation and Nutrient Uptake
Although there was no significant effect of added N, there was a main effect of season
and year on carbon allocation in leaf tissue. Seasonal and annual precipitation patterns have
shown more of an effect on carbon allocation than the added N treatment. Although no
statistically significant annual differences in precipitation were found between intra-annual
variation in rainfall, both sites had the lowest precipitation levels and both species showed the
highest variability in carbon allocation in 2006 (Figure 7; Table 6). The negative correlation
between rainfall and soluble carbon and holocellulose, and the positive correlation between
rainfall and lignin in sagebrush during summer months, suggests carbon allocation is modified
with added rainfall (Table 3). Perhaps, an increase in lignin is not necessarily for structure, but
instead to deter herbivory or protect against pathogens.
The N:nutrient graphs all show similar annual trends in sagebrush that would,
presumably, be linked to interannual variation in rainfall. However, the correlation matrix shows
no significant correlation between any N:nutrient ratio and precipitation. Perhaps, these annual
N:nutrient patterns are driven by other environmental factors, such as microbial activity or
litterfall biomass, that were not included in this study. Although there was a significant effect of
time, the interaction between added N and time in sagebrush N:Ca and N:Mg suggests that these
nutrient ratios may be rigid and lack plasticity over time.
In chamise, the positive correlation between N:Ca and holocellulose, as well as P and
holocellulose, suggests these attributes are coupled in metabolic processes. According to Helpler,
Ca plays a key role in cross-linking acid pectin residues in the holocellulose structures, such as
the cell wall, and low levels of Ca allow for permeability of cellular membranes (Helpler, 2005).
The positive correlation between P and holocellulose highlights the important role P plays in
23
basic cell growth and maintenance as the main facilitating nutrient to photosynthesis and energy
transport (Graham et al., 2006). The negative correlation between lignin and N:Ca, N:K, and
N:Mg in chamise suggests these attributes are not necessarily linked and are likely decoupled in
metabolic processes. The lack of specific N:nutrient analysis, aside from N and P, in the
literature does hinders further investigation as to why these N: nutrient ratios exist or how they
are driven.
General sagebrush nutrient uptake was not affected by the added N treatment, but leaf
tissue N concentration was affected. The effect of water availability on nutrient uptake, specifically
nitrogen, and the measurement of water stress on photosynthesis are analogous and a decrease in
nitrogen uptake is associated with dry soils in similar Artemisia species (Matzner and Richards,
1996). Although this study took place in semi-arid, N limited ecosystem, the result that added N
treatment caused an increase in tissue N during the driest months of the year further affirms the
drought tolerant attributes of sagebrush and highlights how well adapted it is to the environment
(Matzner and Richards, 1996). As long as N fertilization continues to increase leaf tissue N
concentrations, variations in carbon chemistry may also begin to occur over a longer time scale.
As proposed by Hooper and Johnson (1999), plants in semi-arid and arid ecosystems may
be co-limited by water and N. Chamise showed a significant interaction between added N and year
in soluble carbon abundance. In fact, such interaction between rainfall and N, where both plant N
demand and N availability are co-limited by water availability, was only recently acknowledged
in the literature (Vourlitis et al., 2012). Additional rainfall and nutrient interactions found in
previous studies suggest evergreen leaves are a sink for nutrient storage during uptake of
nongrowth periods in the winter when soils are moist, but nutrients are not stored for new growth
until spring (Mooney and Rundel, 1979). One explanation for lack of N treatment effecting tissue
24
N concentrations could be that by summer, shrub N concentrations are depleted from spring growth
and is experiencing a low nutrient intermediate before uptake occurs in winter. Low precipitation
levels during winter 2006 at SOFS in 2006 may have hindered nutrient availability and uptake
throughout the remainder of the year, especially with highly mobile nutrients like N and P.
In addition to N, P concentration can also be an important indicator to carbon allocation
and physical leaf tissue properties, such as leaf mass area (Grimoldi et al., 2005). In Grimoldi’s
study it was found that soluble carbon concentration decreased dramatically from low to high
supply of P. Specifically, P deficiencies were associated with an abundance of photosynthates,
which were unable to be allocated to growth and structural compounds (Grimoldi et al., 2005).
Although significant differences occur in soluble carbon and P from 2006 to 2010 in both species,
the results of the current study do not indicate the inverse relationship between soluble carbon and
P, as found in Grimoldi’s study. The positive correlation between accumulated rainfall and leaf N,
P, K, and holocellulose in chamise suggests an increase in rainfall may lead to increased variation
in leaf carbon chemistry (Table 5).
Consecutive wet or dry years work in a compounded ways that may cause a buildup of
excess N (in dryer years) or greater plant biomass (in wetter years) that may cause a positive or
negative time lag (Hooper and Johnson, 1999). This theory could account for the lack of N
treatment effect at an annual timescale in 2008 and 2010 when annual precipitation levels were
not necessarily dry. Precipitation in 2006 could have been the end to consecutive low streak of
precipitation levels and 2008 and 2010 may have been just the start to the upswing to average
precipitation levels. This phenomenon would have left soils dry and with an accumulation of
excess N availability after multiple years of compounding dry ecosystem traits. Hooper and
Johnson suggest annual, and even seasonal, time lags in ecosystem recovery may account for lack
25
of N treatment, even when annual precipitation levels are not necessarily “dry” (Hooper and
Johnson, 1999).
In addition to an annual time scale, this theory can also be applied to a seasonal time scale.
The significant main effect of season was driven by increased precipitation during winter of 2008
and 2010 at both sites. Winter months in this study (December, January and February) captured
the first part of each year, accounted for the majority of precipitation, and set the tone for soil
moisture throughout the remainder of the year. Results show that California sagebrush had
significantly higher soluble carbon abundance than lignin in winter and spring. However, in
summer and fall there was no significant difference between soluble carbon and lignin abundance.
From these results, it can be concluded that California sagebrush shrubs may be allocating more
soluble carbons to photosynthates in winter and spring, but balances soluble carbon and lignin
abundance in summer and fall when less water is available (Vourlitis, 2012). Future studies should
take into account historical annual and seasonal peaks and troughs to see if any residual ecosystem
fluxes are a source of variation for which a control or a baseline can be established.
CONCLUSION
The results of this study suggest that carbon allocation and nutrient uptake within these
coastal sage scrub and chaparral shrubs are insensitive to chronic dry-season N input. Additionally,
shrub roots must first be exposed to water, the medium in which nutrients are transported, prior to
any nutrient absorption or carbon allocation within leaf tissue. Although added N in this study
showed no effect on carbon allocation patterns in leaf tissue, important seasonal and annual trends
have been found in both coastal sage scrub and chaparral dominant shrubs. The lack of seasonal
and annual fluctuations in carbon allocation patterns in both species demonstrated that carbon
allocation patterns are not affected by the presence of available soil N. The existing and persistent
26
balance of carbon allocation within leaf tissue has no affect of added N but was effected by annual
and seasonal environmental factors, presumably rainfall. With limited quantities of soluble carbon
available to shrubs it is imperative for proper carbon allocation to holocellulose and lignin
fractions. This study demonstrated ways in which carbon allocation priorities are more effected by
season and water availability, than available soil N.
Future studies could compare evergreen and deciduous species within one study site to see
if any species specific, intra-site variation is present would also add important findings to the
literature. For example, if a shrub has soluble carbon as the most abundant fraction and another
shrub has higher allocation to lignin, it would be interesting if correlations existed between
response variables, like those used in this study. If important correlations between the carbon
fractions and nutrient concentrations were found, nutrient concentrations could serve as important
pre-indicators to carbon allocation patterns. Additional factors associated with time could also be
investigated. In this study, time was associated with mean seasonal and annual precipitation but
other environmental factors, such as temperature or soil moisture, could also be addressed. Lags
in ecosystem attributes, due to compounded effects of drought versus consecutive wet years, would
also be an interesting topic to apply to carbon allocation patterns. In addition, the carbon fractions
could be stratified into more stringent categories. For example, the larger fraction of lignin could
be subdivided and studied at a smaller scale to focus on a more specific concentration, such as
terpenes. This would help to focus the study, minimize experimental variation, and better quantify
the effects of the added N treatment.
27
ACKNOWLEDGEMENTS
This work would not have been possible without SDSU Field Station Programs allowing access
to SOFS and SMER research sites or the past and present undergraduate and graduate students
that helped to collect samples, run experiments, and collect data in the lab. The comradery within
our lab was so encouraging and I feel lucky to have been a part of that team.
I would like to thank my committee members, Dr. Kristan and Dr. Sheath for taking the time to
contribute to my thesis. Dr. Kristan has always been a great professor in the classroom, but the
support given to my thesis work was exceptional and truly appreciated.
I would also like to express my deepest appreciation to my committee chair, Dr. George
Vourlitis, who has inspired throughout my undergraduate and now graduate career. His passion
for ecology, magnificent grant-writing ability, and guidance has allowed me so many challenging
and beautiful experiences. Without his financial and academic support this work would not have
been possible.
Most importantly, I would like to thank my husband Jeff for nearly 10 years of love and support.
28
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32
FIGURES AND TABLES
Table 1. Location and selected characteristics for the Santa Margarita Ecological Reserve
(SMER) and Sky Oaks Field Station (SOFS) study sites (Adapted from Vourlitis et al.,
2007). Soil data are mean (n=4) for the upper 0–10 cm soil layer sampled in September
2002. Rainfall data are from the SMER web site (http://fs.sdsu.edu), N deposition estimates
for other sites are from a high-resolution (4 km) model (Tonnesen al. 2007).
Table 1; Location and characteristics for the SMER and SOFS study sites. Soil data are mean (n=4) for the upper 0–10 cm soil
layer sampled in September 2002. Rainfall data are from the SMER web site, N deposition estimates for other sites are from
Tonnesen.
Characteristic
SMER
SOFS
33-29:117-09
33-21:116-34
Vegetation
CSS
Chaparral
Elevation (m)
338
1,418
Time since last fire (years as of 2006)
35
3
Annual precipitation (mm)
360
530
4
4
Sandy clay loam
Sandy loam
Soil N (mgN/g)
0.86±0.10
0.71±0.05
Soil C (mgC/g)
12.3±1.5
17.0±1.1
Soil C:N
14.2±0.3
24.1±0.7
pH
6.6±0.1
6.3±0.1
Latitude:longitude
N-deposition (kgN/ha)
Soil texture
Figure 1: Mean (n=4) leaf tissue percent composition of soluble carbon, holocellulose, and lignin shown by year and season in
Artemisia californica. Error bars depict confidence intervals shown at each season based on Tukey-Kramer’s post-hoc
comparisons. Overlapping confidence intervals represent a lack of significant difference between means. W = winter (December
January), Sp = spring (March April), S = summer (June July), F = (September October).
Table 2: Results (F-statistic and degrees of freedom) of a repeated measures ANOVA for
differences between season (S), N treatment (N), year (Y), the S x N interaction and S x Y
interaction in Artemisia californica. (p<0.05).
Table 2; Results (F-statistic and degrees of freedom) of a repeated measures ANOVA for differences between season (S), N
treatment (N), year (Y), the S x N interaction and S x Y interaction in Artemisia californica. (p<0.05).
Soluble C
Holocellulose
Source
Mean
Mean
(df)
Square
F-Ratio (p-value) Square
S3,24
1360.6
107.9 (<0.001)
N1,24
7.6
S x T3,24
Lignin
F-Ratio (p-
Mean
F-Ratio (p
value)
Square
value)
775.0
78.5 (<0.001)
505.0
43.6 (<0.001)
0.6 (0.44)
0.07
0.1 (0.93)
9.4
0.8 (0.38)
6.5
0.5 (0.67)
12.3
1.3 (0.31)
4.0
0.3 (0.79)
Y2,48
82.6
6.6 (0.003)
72.5
8.0 (0.002)
274.9
26.0 (<0.001)
S x Y4,48
205.0
16.2 (<0.001)
143.0
15.9 (<0.001)
198.1
18.7 (<0.001)
Figure 2: Mean (+se, n=4) summer leaf nutrient abundance for Artemisia californica in 2006,
2008, and 2010.
Figure 3: Mean (+se, n=4) summer leaf nutrient N:nutrient ratios for Artemisia californica in 2006, 2008, and 2010.
Table 3. Linear correlation coefficients for accumulated precipitation (ppt), leaf N, Ca, K, Mg, and P concentrations, N:nutrient, and percent
soluble C, holocellulose, and lignin for Artemisia californica leaves collected in the summer of 2006, 2008, and 2010. Accumulated
precipitation corresponds to the total precipitation measured between the winter-summer sampling periods for a given year. Bold values are
statistically significant (p<0.05).
Table 3; Linear correlation coefficients for accumulated precipitation (ppt), leaf N, Ca, K, Mg, and P concentrations, N:nutrient, and percent soluble C, holocellulose, and lignin for Artemisia
californica leaves collected in the summer of 2006, 2008, and 2010.
Accumulated
ppt
Accumulated
ppt
N
Ca
K
Mg
P
N:Ca
N:K
N:Mg
N:P
Soluble
Holocellulose
Lignin
N
Ca
K
Mg
P
N:Ca
N:K
N:Mg
N:P
1
0.08
-0.27
0.51
-0.12
0.84
0.82
0.75
0.77
-0.08
0.27
-0.1
1
0.19
0.62
0.51
-0.44
-0.1
-0.41
-0.25
-0.3
-0.31
0.38
1
0.18
0.73
-0.41
-0.71
-0.51
-0.66
0.27
-0.09
-0.13
1
0.48
0.11
0.25
-0.51
0.07
-0.15
0.13
0.02
1
-0.43
-0.58
-0.56
-0.70
0.05
-0.33
0.15
1
0.82
0.92
0.87
0.07
0.45
-0.31
1
0.79
0.95
-0.13
0.31
-0.09
1
0.89
0.1
0.37
-0.28
-0.06
0.41
-0.19
Soluble Holocellulose Lignin
1
0.08
0.3
-0.18
0.16
0.07
0.08
0.26
0.02
0.14
-0.71
-0.52
0.77
1
1
0.29
-0.84
1
-0.77
1
Figure 4: Mean (n=4) leaf tissue percent composition of soluble carbon, holocellulose, and lignin shown by year and season in
Adenostoma fasciculatum. Error bars depict confidence intervals shown at each season based on Tukey-Kramer’s post-hoc
comparisons. Overlapping confidence intervals represent a lack of significant difference between means. W = winter (December
January), Sp = spring (March April), S = summer (June July), F = (September October).
Table 4: Results (F-statistic and degrees of freedom) of a repeated measures ANOVA for
differences between season (S), N treatment (N), year (Y), the S x N interaction and S x Y
interaction in Adenostoma fasciculatum. (p<0.05).
Table 4;Results (F-statistic and degrees of freedom) of a repeated measures ANOVA for differences between season (S), N
treatment (N), year (Y), the S x N interaction and S x Y interaction in Adenostoma fasciculatum. (p<0.05).
Soluble C
Holocellulose
Lignin
Mean
F-Ratio
Mean
F-Ratio
Mean
F-Ratio
Source
Square
(p-value)
Square
(p-value)
Square
(p-value)
S3,24
382.2
10.5 (<0.001)
597.4
41.1 (<0.001)
10.6
0.1 (0.94)
N1,24
91.3
2.5 (0.13)
0.1
0.1 (0.95)
42.6
0.5 (0.49)
SxN3,24
112.2
3.1 (0.047)
0.9
0.1 (0.98)
117.3
1.4 (0.27)
Y2,48
436.0
15.6 (<0.001)
1051.2
215.0 (<0.001)
1051.2
17.8 (<0.001)
SxY4,48
419.8
15.1 (<0.001)
262.1
45.0 (<0.001)
262.1
4.4 (0.001)
Figure 5: Mean (+se, n=4) summer leaf nutrient abundance for Adenostoma fasciculatum in
2006, 2008, and 2010.
Figure 6: Mean (+se, n=4) summer leaf nutrient N:nutrient ratios for Adenostoma fasciculatum in 2006, 2008, and 2010.
Table 5. Linear correlation coefficients for accumulated precipitation (ppt), leaf N, Ca, K, Mg, and P concentrations, N:nutrient, and percent
soluble C, holocellulose, and lignin for Adenostoma fasciculatum leaves collected in the summer of 2006, 2008, and 2010. Accumulated
precipitation corresponds to the total precipitation measured between the winter-summer sampling periods for a given year. Bold values are
statistically significant (p<0.05).
Table 5； Linear correlation coefficients for accumulated precipitation (ppt), leaf N, Ca, K, Mg, and P concentrations, N:nutrient,
and percent soluble C,
holocellulose, and lignin for Adenostoma fasciculatum leaves collected in the summer of 2006, 2008，2010。
Accumulated
ppt
Accumulated
ppt
N
Ca
K
Mg
N
Ca
K
Mg
P
N:Ca
N:K
N:Mg
N:P
Soluble Holocellulose Lignin
1
-0.18
1
0.68
0.11
1
0.46
-0.12
0.4
1
-0.52
0.36
-0.19
0.37
1
0.32
-0.29
0.31
0.88
0.49
1
-0.57
-0.22
0.14
0.03
-0.74
-0.21
0.12
0.31
0.71
0.49
0.22
0.39
0.47
1
0.29
0.36
0.61
0.42
0.15
0.26
-0.22
-0.26
1
0.03
-0.25 -0.18
-0.55
-0.57
-0.54
-0.31
-0.8
-0.37
1
0.54
-0.06
0.43
0.08
1
-0.17
0.23
0.2
P
0.8
0.37
N:Ca
0.51
0.84
N:K
0.12
0.69
N:Mg
0.5
0.87
N:P
Soluble
-0.45
0.07
0.3
0.38
Holocellulose
0.54
0.47
Lignin
-0.38
-0.66
1
-0.5
0.49
0.16
0.65
0.22
0.41
0.11
0
0.04
0.02
1
1
Figure 7: Mean accumulated precipitation per season (mm) at SMER and SOFS in 2006, 2008,
and 2010. Numbers in the figure captions correspond to the total annual precipitation.
Table 6: Results (F-statistic and degrees of freedom) of a two-way ANOVA for precipitation
differences between season (S) and year (Y), and S x Y interaction at SMER and SOFS.
(p<0.05).
Table 6； Results (F-statistic and degrees of freedom) of a two-way ANOVA for precipitation differences
between season (S) and year (Y), and S x Y interaction at SMER and SOFS. (p<0.05).
SMER
SOFS
Mean
F-Ratio
Mean
F-Ratio
Square
(p-value)
Square
(p-value)
S3,24
20,459.7
15.3 (<0.001)
9,823.1
9.0 (<0.001)
Y2,24
3,351.4
2.5 (0.103)
2,649.3
2.4 (0.109)
SxY6,24
3,685.9
2.8 (0.035)
2,094.7
1.9 (0.118)
Source