Forest Ecology and Management 258 (2009) 2249–2260
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Forest Ecology and Management
journal homepage: www.elsevier.com/locate/foreco
Biogeochemical cycling in forest soils of the eastern Sierra Nevada Mountains, USA
D.W. Johnson a,*, W.W. Miller a, R.B. Susfalk b, J.D. Murphy a, R.A. Dahlgren c, D.W. Glass a
a
Natural Resources and Environmental Science, University of Nevada, Reno, NV 89557, USA
Desert Research Institute, Reno, NV, USA
c
University of California, Davis, CA, USA
b
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 19 June 2008
Received in revised form 17 December 2008
Accepted 13 January 2009
We review some of the unique features of biogeochemical cycling in forests of the eastern Sierra Nevada
Mountains, USA. As is the case for most arid and semi-arid ecosystems, spatial and temporal variability in
nutrient contents and fluxes are quite high. ‘‘Islands of fertility’’ are common in these forests, a result of
spatial variations in both litterfall and decomposition rates. Dry summer conditions greatly inhibit
biological activity in the O horizon, and thus most annual litter decomposition takes place beneath the
snowpack when moisture is available. Snowmelt duration is shortened near tree boles because of local
warming, resulting in earlier drying of the O horizon, significantly lower decomposition rates, and
increased O horizon mass. Water and nutrient fluxes vary spatially because of snowdrift in winter and
surface runoff over hydrophobic soils in summer and fall. Moisture variability in the vertical as well as
the horizontal dimension has significant consequences for nutrient fluxes. Because of the very dry
summers, rooting in the O horizons is absent in these forests, and thus competition between microbes
and trees for nutrients in that horizon is non-existent. Nutrients mineralized from the O horizon and not
taken up by plants enrich runoff through the O horizons over hydrophobic mineral soils, resulting in very
high concentrations of inorganic N and P in runoff waters. Substantial temporal variations in water and
nutrient fluxes occur on a seasonal (with snowmelt being the dominant hydrologic event of the year),
annual, and decadal basis. The most significant temporal variation is due to periodic fire, which we
estimate causes annualized N losses that are two orders of magnitude greater than those associated with
leaching and runoff. We hypothesize that fire suppression during the 20th century may have contributed
to the deterioration of nearby Lake Tahoe by allowing buildups of N and P in O horizons which could
subsequently leach from the terrestrial ecosystem to the Lake in runoff. In general, we conclude that
biogeochemical cycling in these forests is characterized by greater spatial and temporal variability than
in more mesic forest ecosystems.
ß 2009 Elsevier B.V. All rights reserved.
Keywords:
Semi-arid forest
Nutrients
Decomposition
Snow
Runoff
Leaching
1. Introduction
Conceptual models for nutrient cycling in forest ecosystems are
generally oriented toward mesic systems where nutrients cycle
steadily, changes in ecosystem nutrient pools are slow, and
transport via water is the major mechanism by which nutrients are
lost from the ecosystem (Cole et al., 1968; Curlin, 1970;
Duvigneaud and Denaeyer-DeSmet, 1970; Johnson and Van Hook,
1989; Likens et al., 1977; Swank and Crossley, 1988; Switzer and
Nelson, 1972). Switzer and Nelson (1972) defined three major
components to the nutrient cycle: (i) the geochemical cycle, which
encompasses inputs and outputs from the ecosystem, most of
which are associated with hydrologic fluxes, and chemical
weathering of parent material; (ii) the biogeochemical cycle,
* Corresponding author.
E-mail address: [email protected] (D.W. Johnson).
0378-1127/$ – see front matter ß 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2009.01.018
which encompasses soil–plant relationships and is characterized
by fierce competition between microbes and plant roots for
limiting nutrients in the O horizon and soil; and (iii) the
biochemical cycle, which encompasses the internal translocation
of nutrients within the vegetation. This model has worked well for
mesic forest ecosystems, but needs modification for application to
semi-arid forests of the southwestern US where the hydrologic
cycle is dominated by snow, spatial and temporal variation in
water and nutrient fluxes are very large, and fire is common
(Johnson et al., 1997).
In this paper, we review and synthesize results from our studies
on nutrient cycling processes in forests of the eastern Sierra
Nevada Mountains of western Nevada and eastern California. Over
the past few decades, we have determined that several important
nutrient cycling processes in these ecosystems differ substantially
from those in more mesic and warmer ecosystems and we herein
explore the collective implications of these findings. Specifically,
we review previous studies on horizontal spatial variability in soil
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D.W. Johnson et al. / Forest Ecology and Management 258 (2009) 2249–2260
nutrients, water fluxes, and leaching rates; and couple this with
previously unpublished data on horizontal spatial variability in
decomposition rates. We also review studies on vertical spatial
variability in nutrient contents and fluxes and specifically how
rooting patterns affect plant-microbial competition for nutrients
and how in turn this affects nutrient concentrations in runoff.
Finally, we explore temporal variability in nutrient fluxes on scales
ranging from seasonal to inter-annual and decadal. Our synthesis
centers on a site in Little Valley, Nevada as a case study because we
have the most comprehensive data sets for that site, and also draws
on information obtained from other nearby sites where appropriate (for example, on the effects of fire).
2. Sites
Our composite studies of nutrient cycling processes in the
eastern Sierra Nevada Mountains have taken place at several sites
in and near the Lake Tahoe Basin. Fig. 1 shows the general location
of the study sites from which some data will be drawn for the
summary and synthesis presented in this paper. The reader is
referred to previous literature for full details (Johnson et al., 1998,
2001, 2005, 2008; Miller et al., 2005, 2006; Murphy et al., 2006a,b;
Glass et al., 2008).
Little Valley has been the most intensely studied site, including
studies measuring baseline nutrient cycling processes (Johnson
et al., 1998, 2001; Stark, 1973; Susfalk, 2000), the effects of N-fixing
shrubs on soils and nutrient fluxes (Johnson, 1995; Stein, 2006),
and detailed studies on soil hydrology and water quality (Burcar
et al., 1994, 1997).
Little Valley is located approximately 30 km southwest of Reno,
Nevada in the eastern Sierra Nevada Mountains between Lake
Tahoe, Nevada, California, to the west and Washoe Valley, Nevada,
to the east (Fig. 1). Elevation ranges from 2010 to 2380 m, and is
2010 m at the study site. The climate is characterized by warm, dry
summers and cold, moist winters; the major hydrologic process is
snowmelt. Mean annual air temperature near the valley floor is
5 8C and mean annual precipitation is 550 mm, over 50% of which
falls as snow. Overstory vegetation at the baseline monitoring site
is dominated by lodgepole pine (Pinus contorta Dougl.) with
occasional Jeffrey pine (Pinus jeffreyii [Grev. and Balf.])
(230 stems ha 1). Understory consists primarily of bitterbrush
(Purshia tridentata D.C.) with various grasses and forbs (Johnson
Fig. 1. Map of research sites. Sagehen, CA is a baseline monitoring and prescribed
fire study site with Jeffrey pine growing on Afisols derived from andesite and lahar.
North Lake Tahoe, CA is a prescribed fire site in mixed conifer forests growing on
Alfisols derived from andesite and basalt. Little Valley is a baseline monitoring and
wildfire site in Jeffrey and lodgepole pine forests growing on Entisols and
Inceptisols derived from decomposed granite. Gondola is a wildfire study site with
mixed conifer forest growing on Entisols derived from decomposed granite.
et al., 2001). Soils at the baseline monitoring site are of the Marla
series (coarse-loamy, mixed, frigid Aquic Haploxerepts) derived
from decomposed granite.
The hillside to the east of Little Valley, which previously
consisted of a 100-year-old Jeffrey pine forest, burned in a standreplacing wildfire in 1981. The wildfire did not consume much of
the large standing woody tissues (tree boles and large branches),
and, as is often the case, the area was salvage logged for
merchantable timber (snags) the year after the fire. Since that
time, the burned area has been dominated by snowbush
(Ceanothus velutinus Dougl.), a N-fixing species that often invades
after fire (Youngberg and Wollum, 1976; Zavitovski and Newton,
1968; Binkley et al., 1982). The burned site is also occupied by
lesser amounts of green leaf manzanita (Arcostaphylos patula
[Greene]) and spotty regeneration (440 stems ha 1) of Jeffrey pine
planted in 1985. Adjacent to the wildfire is a site dominated by
130-year-old Jeffrey pine (140 stems ha 1) with a negligible
understory component (no understory was present in the sample
plots). Soils at the wildfire site are of the Corbett series (sandy,
mixed frigid Typic Xeropsamments) derived from colluvium of
decomposed granite.
Sagehen has been the subject of baseline nutrient cycling
(Johnson et al., 1998), surface runoff (Miller et al., 2005), and
prescribed fire research (Murphy et al., 2006a; Johnson et al.,
2008). The Sagehen baseline monitoring site is located in the
Sagehen Experimental Watershed, 10 km north of Truckee, CA.
Elevation ranges from 1830 to 2500 m, mean annual temperature
at the weather station is 4.8 8C, and mean annual precipitation is
870 mm, most of which falls as snow. Vegetation consists of 80–
160-year-old Jeffrey pine and occasional white fir (Abies concolor
[Gord. and Glend.] Lindl.), with an understory of Ribes spp. Mahalamat (Ceanothus prostratus Benth). Soils are of the Fugawee series
(fine-loamy, mixed, frigid Ultic Haploxeralfs) derived from
andesitic lahar. The prescribed fire site is located approximately
5 km southeast of Sagehen at an elevation of 1767 m. This site
receives an average of 940 mm annual precipitation, most of which
occurs as snow. Overstory vegetation is dominated by 95–107year-old Jeffery pine with a few scattered white fir
(365 stems ha 1). Understory vegetation consists of sagebrush
(Artemesia tridentata [Nutt.]), bitterbrush, mule’s-ears (Wyethia
mollis [A. Gray]), green leaf manzanita, and Mahala-mat. Soils are of
the Kyburz series (fine-loamy, mixed, frigid Ultic Haploxeralfs)
derived from andesitic lahar.
The Gondola wildfire site has been the subject of studies of
wildfire effects on soils (Murphy et al., 2006b; Saito et al., 2007),
water quality (Loupe et al., 2007; Miller et al., 2006; Murphy et al.,
2006b), erosion (Carroll et al., 2007), and nutrient budgets
(Johnson et al., 2007). The Gondola wildfire site is located on
the southeastern portion of the Lake Tahoe basin in Nevada just
north of the Nevada–California state line. The site ranges in
elevation from approximately 1950 to 2100 m and receives
870 mm of average annual precipitation, most of which occurs
as snow. Overstory vegetation consists of Jeffrey pine, white fir,
and a scattered distribution of sugar pine (Pinus lambertiana
[Dougl.]) and incense-cedar (Calocedrus decurrens [Endl.])
(670 stems ha 1). Understory vegetation is primarily green leaf
manzanita and snowbush. Soils are of the Cagwin Series, coarseloamy, mixed, frigid Typic Xeropsamments derived from granite.
Sixteen 400 m2 research plots were established in the fall of 2001
and baseline sampling was initiated the following spring. A
wildfire occurred in July of 2002, completely burning five plots and
partially burning four others. All of the five completely burned
plots and two of the four partially burned plots had been
previously sampled for O horizon and soil nutrients. As a result,
we were able to assess pre- and post-wildfire vegetation, soil, and
water quality conditions as well as quantifying fire-induced
D.W. Johnson et al. / Forest Ecology and Management 258 (2009) 2249–2260
erosion (Carroll et al., 2007; Johnson et al., 2007; Loupe et al., 2007;
Miller et al., 2005, 2006; Murphy et al., 2006b; Saito et al., 2007).
The North Lake Tahoe prescribed fire site is located on the north
shore of Lake Tahoe, California between Kings Beach and Tahoe
City. Elevation is 1935 m and average annual precipitation is
890 mm, most of which occurs as snow. Soils are from the Jorge
series, loamy-skeletal, isotic, frigid Andic Haploxeralfs derived
from volcanic parent material of andesite and basalt. Overstory
vegetation at the site consists of mixed conifer overstory including
Jeffrey pine, ponderosa pine (Pinus ponderosa Laws.), sugar pine,
white fir, and incense-cedar (530 stems ha 1). The shrub understory contains snowbush and whitethorn (Ceanothus cordulatus
Kellog), both of which are N-fixers. Three fire mitigation
treatments were applied to this site: (i) a mechanical thinning
plus chipping, (ii) combination of thinning and fire, and (iii) an
untreated control. Thinning occurred in the summer of 2003, with
a goal to remove ladder fuels, diseased trees, and reduce stand
basal area. All logging slash was chipped and chips spread over the
thinned area. The prescribed fire for both the burn only and thin
plus burn treatments occurred in June 2004. Treatment effects on
soil nutrients and runoff have been published (Glass, 2006; Glass
et al., 2008; Loupe, 2007; Loupe et al., 2009).
3. Methods
Instrumentation and sampling methods for the various studies
have been described in detail in earlier publications (Glass, 2006;
Johnson et al., 1997, 1998, 2001, 2005, 2007; Miller et al., 2005,
2006; Murphy et al., 2006a,b); hence only a brief summary will be
provided here.
We measured nutrient fluxes in spring snowmelt and soils
using resin snowmelt collectors and lysimeters as described by
Susfalk and Johnson (2002). Resin-based lysimeters consist of
mixed bed, cation and anion exchange resin sandwiched between
screens, which were held in place within a 2.5 cm diameter by 5 cm
high pvc coupling. Washed sand is packed on either end of the
screens to maintain continuous contact with the soil. Snowmelt
collectors are similar in design except that no sand is used and the
upper portion of the pvc is extended to a height of 14 cm. The resin
lysimeter and snowmelt results reported here are from collections
taken at six randomly assigned sampling points within the Little
Valley baseline monitoring plot for the 1997–2004 snow seasons
(sampling during the 2000–2001 season was omitted because of a
debilitating injury to the PI during that period). One set of
lysimeters was placed directly beneath the O horizon and one at
15 cm depth within the mineral soil profile. Care was taken to
install the mineral soil lysimeters in the same locations each year;
however, this was not always possible for the O horizon lysimeters
in cases where the O horizon was very thin (e.g., within the
interspaces). The resin snowmelt collectors are placed directly on
the ground (with no resin contact with the soil) and propped up
with wooden stakes. Snowmelt and lysimter units are installed in
the autumn just before snowfall and recovered in spring after
snowmelt.
Water-based snowmelt collectors consist of buried collection
bottles to which above ground open collectors are attached. The
open collectors consist of bottles which have bottoms cut-off and
are inverted. A glass wool plug is inserted at the bottom of the open
collector to filter out debris. A sample tube runs through to the
bottom collector to pump out snowmelt and a vent tube allows air
to enter as samples are withdrawn. The bottom collection bottle
and the lower half of the upper (cut-off) bottle are buried and thus
collected snowmelt seldom freezes. Water-based soil solution
collectors in our studies consist of commercially available (Soil
Moisture Inc., Santa Barbara, CA) falling head ceramic cup
lysimeters outfitted so they can be pumped out with above-
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snowpack tubes during the snowmelt season. The water-based
snowmelt results reported here include six randomly located plots
referred to above in the Little Valley baseline monitoring plot.
Runoff collectors consist of a buried bucket (>8 L) fitted with a
collection funnel, vent stack roof flashing, screen, and a highdensity polyethylene (HDPE) cover (Miller et al., 2005). The top of
the collection funnel was located approximately 5 cm below the
soil surface and the roof flashing was aligned perpendicular to the
slope either at the soil surface of bare soil (typically water repellent
in summer and early fall) for the collection of overland flow, or at
the O horizon/mineral surface interface for the collection of O
horizon interflow. The excavated hole is backfilled for insulation
and to secure the collection bucket assembly. A small V-notch
equilateral triangular opening (length = 1.0 cm; area = 0.43 cm2) is
cut into a 20 cm 30 cm piece of high-density polyethylene and
secured over the roof flashing with the V-notch opening coincident
to the opening in the flashing, which was screened to prevent the
entry of forest debris. A sufficient length of tubing was attached to
the top of each sample and vent bulkhead fittings to allow for
sampling under winter snowpack. The potential artifacts associated with runoff over the actual surface area of the collector are
minimal because of the small opening.
Decomposition rates were measured using the litterbag
technique (Harmon et al., 1999). We placed the litterbags on
top of the O horizons on four transects radiating from near tree
boles to interspaces. Senesced lodgepole pine foliage was collected
in September 1996 by brushing gently against tree branches.
Litterbags were placed on the ground with pre-weighed amounts
of foliage (approximately 10 g per bag) in September 1996 and
retrieved in May 1997 for drying and re-weighing. Along the same
transects, we placed Onset1 (Onset Corp, Plainfield, NJ) temperature recorders, to determine snowpack duration. The interval
during which temperatures were stable and near 0 8C were
interpreted as under snow whereas periods during which diurnal
variations in temperature were recorded were interpreted as being
snow-free.
Methods of estimating carbon and nutrient contents in aboveground live biomass, O horizon and soils have been described in
detail elsewhere (Johnson et al., 2005, 2008; Murphy et al.,
2006a,b). Biomass was estimated with the allometric equations
based on diameter breast height given by Gholz et al. (1979)
(checked earlier for accuracy in Little Valley trees; Johnson et al.,
2005). Nutrient contents in biomass were then estimated by
multiplying nutrient concentrations of biomass components
(foliage, branch, bole) by the mass of these components. O horizon
mass was estimated by destructive sampling and by sub-horizon
(Oi, Oe, and Oa) and category (needle and other, the latter including
small sticks, cones, etc.). O horizon nutrient content was estimated
from nutrient analyses on subsamples of each sub-horizon. The
fine earth (<2-mm) and coarse fragment (>2-mm) soil masses
(kg ha 1) were measured by horizon using a modification of the
quantitative pit method (Hamburg, 1984) and as described by
Johnson et al. (2008).
4. Results and discussion
4.1. Horizontal spatial variation in O horizon and soil: relation to
decomposition rates
Spatial variations in carbon and nutrients in O horizon and
mineral soils in forests of the eastern Sierra Nevada Mountains are
often substantial, a result of variations in tree cover, litterfall,
decomposition rates, and snowdrift among other things. A good
example of the ‘‘islands of fertility’’ (Garner and Steinberger, 1989)
effect from our data sets comes from Little Valley. Fig. 2 illustrates
the variation in O horizon and soil total C and N contents among six
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D.W. Johnson et al. / Forest Ecology and Management 258 (2009) 2249–2260
Fig. 2. O horizon and mineral soil C and N contents at various sampling points at the
Little Valley baseline monitoring site. The Oi horizon is the least decomposed, being
comprised mainly of whole leaves and needles; Oe is partially decomposed, being
comprised primarily of broken needles and leaves which are still recognizable as to
origin; and Oa is the final stage of decomposition, which is relatively homogenous
and from which the origin of the material cannot be determined. Bole indicates
position near the tree bole; Edge indicates position nearer the edge of a tree canopy;
Inter. indicates an interspace position between tree canopies with minimal canopy
cover. Average values and standard deviations (n = 6) are shown on the right hand
side.
randomly located quantitative soil pits in the Little Valley baseline
monitoring site. Total O horizon C content varies by 9-fold (from
10.3 to 90.4 Mg ha 1) and O horizon N content varies by over 7-fold
(from 180 to 1432 kg ha 1) depending on location (under tree
canopy or interspace). Mineral soil C and N contents (to a depth of
50 cm in mineral soil) vary by almost 3-fold (from 22.1 to
49.1 Mg ha 1 for C and from 632 to 1703 kg ha 1 for N). Mineral
soil C and N contents do not directly correspond to O horizon C and
N contents. For example, pit 3 has the greatest litter C and N
accumulations but rather low mineral soil C and N contents.
Insight into why this condition exists is gained by considering the
presence or absence of specific O horizons. Oa horizons are not
present at all sample locations, but are typically more prevalent
under trees. Thus, pits located in the interspaces (1–3) have no Oa
horizons whereas pits 1 and 2 have Oa masses of approximately
19 Mg ha 1. Soil C and N contents, especially in the A horizons, are
greatest in pits 1, 2 and 5, suggesting a connection between the
presence of the Oa horizon and mineral soil C and N contents. This
would be expected if, during the last stages of decomposition, finetextured organic matter is incorporated into the mineral soil. On
the other hand, the large reserves of C and N in the O horizon of pit
3 are mostly in the form of Oi material (the least advanced stage of
decay) and therefore have not yet reached the stage of decom-
position where incorporation into mineral soil has become a major
factor. In pit 6, which is in an interspace position, the low soil C and
N contents simply reflect a lack of total organic matter input, as
reflected by the very low O horizon C and N contents.
The islands of fertility seen at the Little Valley site are a function
not only of litterfall patterns but also of spatial differences in
decomposition rates. Stark (1973) first measured decomposition
rates in forests at Little Valley and found that 80% of annual
decomposition takes place under the snowpack. This was
attributed to the fact that summers are too dry for any substantial
decomposition to take place. The studies of Stark (1973) also
showed that most respiration from the O horizon occurred under
the snowpack, so the increases in litter decomposition with
increased snowpack duration was not simply due to extended
leaching of soluble organic matter from the litter. Researchers in
the Colorado Rocky Mountains (Taylor and Jones, 1990) and in the
Alaskan Arctic have also found that most decomposition takes
place under snowpack, and have noted the importance of
snowpack duration on decomposition (Brooks et al., 1996; TEAML,
1997). Similarly, Hart and Firestone (1991) found that most net N
mineralization in mixed conifer forest on the western slope of the
Sierra Nevada Mountains occurred during winter, when the soils
were wet and cold.
Stark (1973) also noted the accumulation of litter near tree
boles in Little Valley, associated with the local melting of snow in
‘‘wells’’ near tree boles, reducing the snowpack duration there.
Subsequent litterbag studies in Little Valley have verified that
over-winter needle decomposition is lower near tree boles. Fig. 3
shows one of several data sets illustrating this pattern. O horizon
temperatures and litterbag weight losses were recorded during the
winter of 1998–1999 along replicate transects from near a tree
bole (20 cm) past the canopy edge to open interspaces (between
trees). The duration of snowpack is easily discerned by the stability
of O horizon temperature; when local snow cover is present, O
horizon temperature remains steady at 0 8C, and when snow cover
is absent, diurnal fluctuations are evident. The temperature data
clearly show that the duration of snow cover is shorter near the
tree bole (in the treewell) than elsewhere, and that litter weight
loss is also significantly lower near the tree bole. Stark (1973)
hypothesized that this is due to the fact that the litter near the tree
bole dries out more quickly than elsewhere; the temperature data
also indicate that litter near the tree boles experiences much colder
episodes once the snowpack is gone.
It is interesting to compare our results with those of a series of
studies at Hubbard Brook Experimental Forest (HBEF) that
specifically addressed the effects of snow cover on soil freezing
and associated responses in nutrient cycling (Fitzhugh et al., 2001;
Groffman et al., 1999, 2001a,b; Neilson et al., 2001; Tierney et al.,
2001). In the HBEF studies, the primary emphasis was on the
effects of soil freezing brought about by artificially reduced
snowpack amount and duration. These investigators found that
reduced snowpack amount and duration resulted in earlier and
deeper freezing of the soil, which in turn caused enhanced fine root
mortality (Tierney et al., 2001) and increased N and P leaching from
the O horizon (Fitzhugh et al., 2001). Soil microbial responses to
treatment were minimal, however, and the effects on N and P
leaching were attributed to fine root mortality and reduced uptake
(Groffman et al., 2001b). In our case, we believe that drought rather
than freezing is the major reason for slower decomposition with
reduced snowpack duration, and root damage by freezing is
minimal because roots do not occur in O horizons.
4.2. Horizontal spatial variation in water and nutrient fluxes
One might expect that the pronounced islands of fertility in the
Little Valley site would have consistent effects on nutrient fluxes.
D.W. Johnson et al. / Forest Ecology and Management 258 (2009) 2249–2260
Fig. 3. Average daily temperatures in a representative transect and litterbag weight
loss along transects from tree boles to interspaces over the winter of 1998–1999 in
the Little Valley baseline monitoring site. Stable temperatures near 0 8C indicate the
presence of snow cover.
However, 7 years of monitoring N and P fluxes with resin
lysimeters in O and A horizons have revealed no consistent pattern
relatable to either islands of fertility or water flux (the latter being
measured with water-based snowmelt collectors near each set of
resin lysimeters). Fig. 4 shows inorganic N and ortho-P fluxes
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measured at six locations (different from the quantitative pit
locations, as those were obviously destroyed during sampling)
over the period 1997–2002 (four water years); data also exists for
1996, 2003, and 2004, but some replicates were lost during these
years due to animal damage and thus are omitted from the current
analysis. As can be seen from Fig. 4, there are no patterns in
inorganic N or P fluxes that can be consistently related to location
within the plot, nor are the spatial patterns consistent from 1 year
to the next. For O horizon fluxes, the latter might be expected
because it was not possible to re-establish resin lysimeters in
exactly the same locations from 1 year to the next in cases where
the O horizon was very thin. However, A horizon lysimeters were
re-established in almost exactly the same locations from 1 year to
the next. How this variability on both a spatial and temporal scales
can be explained is uncertain. Resin lysimeter fluxes are affected by
both soil water flux and soil solution concentration. It is not
possible from the data at hand to ascertain which factor
contributes most to any individual resin lysimeter measurement,
but some insight can be gained by comparing inorganic N and P
fluxes. In the O horizons, inorganic N and P fluxes were both
highest in replicate 6 (in 1999), suggesting replicate 6 had high
concentrations of both N and P, high rates of soil water flux, or both
in that year. Similarly, inorganic N and P were highest in replicate 1
in the A horizons in 1999.
Perhaps the ‘‘hot spot’’ and ‘‘hot moment’’ concepts described
by McClain et al. (2003) can be used to shed light on the subject.
These authors contend that fighting the variability of nutrient
cycling processes in the field by bulking and averaging masks
important aspects of biogeochemical cycling. We hypothesize that
the patterns in the resin lysimeter data are a manifestation of
ephemeral hot spots in the O horizon and soil. It appears that hot
spots on this scale are not necessarily related to O horizon or soil
nutrient contents because, if they were, the spatial patterns would
be consistent from year to year.
Fig. 4. Inorganic N (NH4+ + NO3 ) and ortho-P fluxes through the forest floor and 15 cm in the soil at the Little Valley baseline monitoring site as measured by resin lysimeters.
Bole indicates position near the tree bole; Edge indicates position nearer the edge of a tree canopy; Inter. indicates an interspace position between tree canopies with minimal
canopy cover. Average values and standard deviations (n = 6) are shown on the right hand side.
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Snowdrift can be a significant factor in causing heterogeneity in
hydrologic fluxes in snow-dominated ecosystems, especially ones
with significant episodes of high velocity wind turbulence such as
those in the eastern Sierra Nevada. As opposed to rainfall,
precipitation hitting the forest canopy or O horizon in the form
of snow can be substantially relocated long before it melts and
actually enters the soil profile. This can happen either during a
windy snow storm itself or during subsequent windstorms, which
are quite frequent in the eastern Sierra Nevada Mountains. Thus,
snowdrift adds considerably to the fine-scale variability in
hydrologic fluxes in these ecosystems.
Water fluxes into the soil from snowmelt over the 1994–
1995 season varied by over 2-fold at the Little Valley baseline
monitoring site (Fig. 5), with much greater fluxes in replicates 2
(near bole) and 5 (interspace) than in the other replicates. Water
fluxes are not necessarily related to tree proximity. Replicate 1 is
on the windward side of a tree and therefore snow is scoured
away from it whereas replicate 2 is on the receiving, leeward
side of a tree. Thus, the spatial heterogeneity in snowmelt water
flux is not consistent with the spatial heterogeneity in the
islands of fertility because other processes (e.g., wind) are
involved. As expected, Cl fluxes followed a pattern similar to
that for water, but K+ and inorganic N fluxes differed; K+ fluxes
in replicate 2 and inorganic N fluxes in replicate 1 are greatly
elevated compared to the other replicates. This may be due to
foliar leaching effects (especially in the case of K+), the capture
of wind-blown litter and other detritus on snowbanks in the
latter stages of snowmelt when patches of bare ground are
present, or other factors as yet unobserved. As noted in a
previous paper detailing water-born nutrient fluxes (Johnson
et al., 2001), the inputs of nutrients to the O horizon via
snowmelt are controlled to a greater extent by variations in
nutrient concentration than by water flux itself. Thus, nutrient
fluxes to the O horizon are largely uncoupled from water fluxes,
even in the case of K+ where water fluxes often dominate
aboveground cycling.
4.3. Vertical spatial variation: uncoupling of roots and microbes
Schimel and Bennett (2004) built upon the hot spot and hot
moment concept described by McClain et al. (2003) and posed a
new paradigm for plant-microbial competition where trees can
effectively compete with soil microbes by invading N-rich
microsites (hot spots) that exist temporarily (hot moments) even
in relatively N-limited conditions. Roots and associated mycorrhizae, with their elongated structure and exploratory habit can
presumably tap into these hot spots and hot moments, and thereby
might effectively mine the soil for N over time.
The new paradigm posed by Schimel and Bennett (2004) for
plant-microbial competition is probably moot for the Sierran forest
ecosystems we have investigated. Stark (1973) noted that because
of the extreme summer drought, rooting is entirely absent in the O
horizons of Jeffrey pine forests in Little Valley. Thus, decomposition
and vegetation uptake processes are spatially uncoupled, and the
intense competition for N between roots and decomposers that
characterizes the more humid forest soils is absent in the O
horizons of these forests. Because of this discoupling, the N
returned in littterfall is not recycled to the trees until: (i) N supply
exceeds microbial demand; and/or (ii) N is leached to lower
horizons where roots are present. Similarly, Hart and Firestone
(1991) noted a ‘‘low abundance of fine roots’’ in the O horizon of a
mixed conifer forest on the more mesic western slope of the Sierra
Nevada Mountains in California.
The lack of root and microbial competition for nutrients in the O
horizon has significant consequences for the quality of surface
water runoff. It is textbook knowledge that surface runoff via
overland sheet flow in forest ecosystems is minimal except in very
rare circumstances (Fisher and Binkley, 2000). This is based to a
Fig. 5. Cumulative fluxes of water, Cl , K+, and inorganic N (NH4+ + NO3 ) via under-canopy snowmelt among six replicate collectors during the 1994–1995 winter season at
Little Valley baseline monitoring site. Bole indicates position near the tree bole; Edge indicates position nearer the edge of a tree canopy; Inter. indicates an interspace position
between tree canopies with minimal canopy cover. Average values and standard deviations (n = 6) are shown on the right hand side.
D.W. Johnson et al. / Forest Ecology and Management 258 (2009) 2249–2260
large extent on the absence of direct observation or remnant
physical evidence. Early research in the eastern Sierra Nevada
supported this assumption in large measure, often failing to find
evidence of such surface runoff even when subjected to artificial
irrigation (Munn and Huntington, 1976; Trott, 1982; Guerrant
et al., 1990, 1991; Naslas et al., 1994a,b; Burcar et al., 1994) except
under conditions of high intensity application. However, there was
concern that artificial irrigation could produce artifacts that
preclude the detection of runoff that might occur under natural
conditions. For example, the application of artificial rainfall
typically involves short duration (0.25–1.0 h) high intensity (8–
10 cm h 1) events. The forceful application of large amounts of
water over a short time frame would create localized areas of
surface ponding (positive pressure) that would allow O horizon
materials to preferentially fill with water increasing surface
storage capacity and more widespread wetting prior to runoff.
Although similar to occasional summer convection events
common to the Sierras, late summer and early fall events are
often of a much lower intensity and longer duration (>12–24 h).
The latter scenario could generate entirely different runoff
characteristics, particularly under conditions of localized or
widespread hydrophobicity. Indeed, naturally derived water
repellent soils have long been identified throughout the Sierra
Nevada (DeBano, 1969; Hussain, 1968; Corey and Morris, 1969;
Bashir, 1969; Meeuwig, 1971), an effect attributed to the presence
of substituted phenols common to natural resins and vegetative
matter consisting of organic compounds with amphophilic
characteristics (Bozer et al., 1969). Consequently, it is not
surprising that these same studies have indicated unburned
surface soils in this region to be strongly hydrophobic until
rewetted (Guerrant et al., 1990, 1991; Naslas et al., 1994a,b; Burcar
et al., 1994), suggesting that in addition to spring snowmelt,
surface runoff could occur during summer rainfall or early fall rains
preceding the first major snowfall event.
In order to test the hypothesis that overland sheet flow runoff
could occur in these ecosystems under natural conditions, Miller
et al. (2005, 2006) installed runoff collectors at several sites in the
eastern Sierra Nevada. Expectations were that summer runoff
would be due to the effects of soil water repellency within the O
horizon itself and/or at the mineral surface interface, whereas
winter runoff could be attributed to the effects of frozen soil; albeit
2255
more recent studies (Loupe et al., 2009) have identified the
presence of low velocity surface runoff during melt cycles even in
the absence of frozen soil. These collectors (described in detail in
Miller et al., 2005 and summarized in Section 3 above) were
specifically designed to capture interflow through the root-free O
horizon and/or overland sheet flow at the interface of the
underlying or exposed mineral soil. Not only was such runoff
routinely collected, but concentrations of inorganic N and P in
these solutions were extraordinarily high, including NH4+ and
ortho-P forms, which are adsorbed to mineral soils and are
therefore found in very low concentrations in soil solution. Table 1
provides examples of the concentrations of nutrients found in
runoff solutions collected from the unburned portions of
prescribed fire site near Sagehen and unburned portions of the
wildfire site at Gondola. Although seasonal comparisons were not
considered in the initial study (Miller et al., 2005), a subsequent
study identified the typical presence of higher nutrient concentrations in runoff collected during the summer (May–November)
compared to winter (December–April) months. Notably, the
reported concentrations, from undisturbed ecosystems, are two
orders of magnitude greater than any ever observed in soil
solutions for the region (Johnson et al., 1997, 2001), with the
exception of those collected directly after wildfire (Murphy et al.,
2006b).
We believe that these high concentrations of inorganic N and P
are a result of net mineralization of N and P in the O horizon, and,
because rooting is absent in the O horizon, mineralized N and P is not
taken up as it would be in more mesic ecosystems. We also believe
that fire exclusion over much of the 20th century in these systems
has resulted in detrital buildup in the O horizons that has provided
an increasing source of nutrients for this runoff, perhaps contributing to the well-documented deterioration of water quality in nearby
Lake Tahoe (Goldman, 1988; Miller et al., 2005, 2006). We are not as
yet able to precisely quantify the area from which this interflow
runoff is generated nor can we pinpoint where it infiltrates. We
hypothesize that this interflow could be a major factor in the
creation of hot spots if it enters into the mineral soil via preferential
flow paths (Burcar et al., 1994), or alternatively could be a significant
source of inorganic N and P to streams, perhaps contributing to the
peaks in inorganic N concentrations that are sometimes seen during
cycles of snowmelt runoff (e.g., Johnson et al., 1998).
Table 1
Cumulative runoff (L) and average and maximum concentration (mg L 1) of nitrogen (N) and phosphorus (P) forms from December 2001 through July 2003 at eight plots near
Truckee, CA, and four plots near South Shore Lake Tahoe, NV (after Miller et al., 2005).
1
1
Total runoff (L)
Average concentration (mg L
December 2001–July 2003
NH4-N
NO3-N
PO4-P
NH4-N
NO3-N
PO4-P
Truckee
P1
P3
P5
P7
P8
P9
P23
P25
5.80
50.44
19.33
33.56
5.51
45.45
16.75
23.10
4.03
2.52
2.17
30.83
3.56
5.24
4.70
2.75
3.97
1.49
2.77
6.56
8.51
2.69
1.63
2.17
0.06
0.26
0.13
2.85
0.21
0.72
1.68
0.09
23.60
12.11
5.78
87.28
23.49
21.22
36.20
8.04
21.90
11.84
8.55
95.47
31.30
16.47
12.10
14.38
0.25
0.90
0.19
13.12
0.58
4.55
11.80
0.23
Average
Standard Dev.
24.99
16.86
6.98
9.70
3.72
2.53
0.75
1.01
27.22
26.20
26.50
28.77
3.95
5.46
South Lake Tahoe
P1
P2
P3
P14
25.61
76.14
42.62
29.69
0.12
0.18
0.58
5.16
0.80
0.43
2.32
2.01
0.19
0.10
0.25
0.83
0.40
0.58
2.34
61.10
3.35
3.79
9.37
22.15
0.36
0.31
0.71
4.27
Average
Standard Dev.
43.52
22.93
1.51
2.44
1.39
0.92
0.34
0.33
16.10
30.01
9.67
8.76
1.41
1.91
)
Maximum concentration (mg L
)
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D.W. Johnson et al. / Forest Ecology and Management 258 (2009) 2249–2260
4.4. Temporal variation in water and nutrient fluxes: seasonal,
annual, and decadal
Temporal variations in nutrient cycling processes in Sierran
forests are substantial on many scales. At the smallest scale,
temporal uncoupling of the N cycle can occur during snowmelt if
the release of N from snowpack, litter, and soil does not coincide
with the period of maximum tree uptake. In most snow-dominated
systems, the majority of nutrient release occurs during the early
parts of snowmelt (Berg, 1992; Bownam, 1992; Creed et al., 1996;
Fahey and Knight, 1986; Marsh and Pomeroy, 1999; Peters and
Leavesley, 1995; Stottlemeyer and Toczydlowski, 1990; Williams
and Melack, 1991; Williams et al., 1995). In our studies in Little
Valley, however, we find the opposite: that is, the majority of
nutrient release occurs during the latter stages of snowmelt
(Johnson et al., 2001). This is illustrated in Fig. 6 for NH4+ and NO3 .
Very little N released from the snowpack appears in soil solutions,
indicating that N uptake by soils and plants during snowmelt is
highly efficient (Johnson et al., 1997, 2001). However, high
concentrations of mineral N do appear in runoff solutions, as
noted previously (Miller et al., 2005, 2006), perhaps explaining
why we find pulses of NH4+ and NO3 in streamwater during low
snowpack years (Johnson et al., 1997).
Like most Mediterranean climate ecosystems, forests of the
eastern Sierra Nevada experience substantial inter-annual variation in precipitation. However, we have found that temporal
variations in precipitation amount do not correspond well with
variation in nutrient inputs via snow (Johnson et al., 2001). Fig. 7
illustrates this for the open snowmelt collectors (not under forest
canopy) in the 1994–1995 through 1998–1999 seasons. Water
Fig. 6. Snowmelt water flux and inorganic N (NH4+ + NO3 ) concentrations in open
snowmelt (not under canopy) during the 1994–1995 winter season at Little Valley
baseline monitoring site (data from Johnson et al., 2001).
fluxes varied by nearly 2-fold from year to year, with the 1994–
1995 season being the highest. Chloride fluxes varied less, and did
not follow water flux patterns particularly well (in contrast to the
spatial patterns observed for 1994–1995 in Fig. 5). Potassium and
inorganic N fluxes seem to be completely unrelated to water fluxes
on an inter-annual basis, as was the case for the spatial patterns.
By far the most significant factor in decadal scale variations in
nutrient fluxes in Sierran forests is fire. The incidence of
catastrophic wildfire in Sierra Nevada ecosystems has increased
dramatically during the last few decades as a result of past fire
suppression and consequent fuel buildups (Neary et al., 1999;
Newland and DeLuca, 2000). Furthermore, recent analyses suggest
that climate change may be causing increases in wildfire incidence
and extent. Westerling et al. (2006) found that wildfire activity in
the United States has increased markedly since the mid-1980s,
with greater frequency, longer wildfire seasons, and longer
individual wildfire durations. These changes are associated with
increased spring and summer temperatures and have taken place
even in areas of the U.S. that have not been strongly affected by fuel
buildups. A substantial portion of the Lake Tahoe basin has been
categorized as a high-risk environment for catastrophic wildfire
(Smith and Adams, 1991).
We have, through several studies, assessed the effects of fire
(both prescribed and wildfire) on nutrient budgets in these
systems (Caldwell et al., 2002; Johnson et al., 1998, 2005, 2007,
2008; Miller et al., 2006; Murphy et al., 2006a,b). At the Little
Valley fire site, we have also estimated post-fire nitrogen inputs via
biological N fixation (Johnson, 1995; Johnson et al., 2005). Fire can
have very substantial, long-term effects on ecosystem C and N by
causing changes in vegetation, often through the facilitation of
occupancy of the burned site by N-fixing vegetation. Indeed, the
presence of N-fixing vegetation after wildfire can cause long-term
increases in ecosystem C capital in N-limited ecosystems—as long
as sufficient time elapses prior to the next fire (Choromanska and
DeLuca, 2002; Gessel et al., 1973; Johnson and Curtis, 2001;
Johnson et al., 2004, 2005).
The invasion of N-fixing vegetation on burned sites is a doubleedged sword. Whereas the benefits of replenishing N gasified and
lost from the ecosystem during fire are well known (Binkley et al.,
1982; Johnson, 1995; Youngberg and Wollum, 1976; Zavitovski
and Newton, 1968), the presence of this vegetation often presents a
significant problem for tree reestablishment. Snowbush (C.
velutinus Dougl.) is a pioneer species that invades after site
disturbances such as fire in eastern Sierran forests. Snowbush is
especially adapted to fire; heat treatment followed by cold
stratification is required for seed germination (Zavitovski and
Newton, 1968; Youngberg and Wollum, 1976). Snowbush seeds
lying dormant in forest litter for many years are activated by fire
and cold winter temperatures, resulting in prolific germination in
wildfire, clearcut, and slash burned sites. Snowbush is shade
intolerant and therefore disappears after overstory canopy closure.
Snowbush presents serious competition for tree regeneration after
fire when it is not controlled by either herbicide or mechanical
means as it may persist for decades.
Measurements of the effects of wildfire and post-fire vegetation
on C and nutrient budgets is usually problematic because of the
lack of control, or unburned sites and the time scales involved in
monitoring post-fire N fixation impacts. The Gondola fire burned
previously sampled plots, allowing measurement of nutrient
changes with good precision. This provides a comparison of post
hoc calculations and estimations made for the Little Valley fire, for
which no pre-fire measurements were available. For the Little
Valley fire, we estimated C and N losses by assuming that the
foliage and O horizon were completely consumed in the fire while
woody biomass was left standing and there were no effects on
mineral soil C or N losses. Pre-fire foliage was estimated from
D.W. Johnson et al. / Forest Ecology and Management 258 (2009) 2249–2260
2257
Fig. 7. Cumulative fluxes of water, Cl , K+, and inorganic N (NH4+ + NO3 ) via open snowmelt during the 1994–1995 through 1998–1999 winter seasons at Little Valley
baseline monitoring site (data from Johnson et al., 2001).
regressions based on stump diameters and dbh of unburned tree
boles in the former fire, and pre-fire O horizon content was
assumed to equal that in the adjacent forest.
Table 2 shows a comparison of reconstructed N budgets with
the Little Valley fire with estimates made under the same
assumptions for the Gondola fire and with estimates made from
actual pre- and post-fire measurements for the Gondola fire. Had
we used post hoc estimates for N losses in the Gondola fire, we
would have overestimated N losses from foliage by a factor of two
and overestimated N losses from the O horizon by 8%. Nitrogen
losses from combustion of the understory ( 8 kg ha 1) were
insignificant in the Gondola fire, but N losses from the combustion
of woody biomass were quite substantial ( 119 kg ha 1) and in
fact constituted the largest single loss category in the system.
Estimated N losses from the mineral soil ( 92 kg ha 1) were also
large, but were probably due at least in part to a post-fire erosion
event (Carroll et al., 2007). Post-fire leaching was elevated by a
factor of 10 (compared to both pre-fire values and values obtained
concurrently from the unburned site) for 2 years following the
Table 2
Estimated nitrogen (N) losses from the Little Valley fire (reconstructed from post
hoc measurements), calculations of N losses from the Gondola fire based on the
same assumptions as used for Little Valley, and estimates of N losses from the
Gondola fire based on actual pre- and post-fire measurements (data from Johnson
et al., 2005, 2007). Standard errors are shown where available.
Component
Little Valley
(kg ha 1)
Gondola, calculated
(kg ha 1)
Gondola, measured
(kg ha 1)
Foliage
Live
Dead
Total
110
0
110
106
0
106
95 22
+50 13
45 26
Woody biomass
Understory
Forest floor
Soil
Total ecosystem
0
0
239
0
349
0
0
155
0
261
191 72
81
142 48
92 126
491 80
Gondola fire, but this resulted in the loss of only an additional 7%
(14 kg ha 1) of N. In total, we would have underestimated N losses
from the Gondola fire by 51% (244 kg ha 1) without pre-fire
measurements, primarily because of underestimation of the losses
from woody biomass combustion. Thus, N budget construction for
forests after wildfires remains an uncertain venture unless one is
lucky enough to have pre-fire data.
Wildfire typically causes a greater amount of N gasification
losses than prescribed fire in a given year. However, the cumulative
effects of repeated prescribed fire can be very substantial and
exceed wildfire losses in the long run. We used a simple
spreadsheet model to illustrate this aspect in a previous paper
(Johnson et al., 1998). In the model, litterfall mass and N content
were kept constant over a 100-year period, and litter was allowed
to decay at a constant rate (k value) taken from field litterbag
studies (Stark, 1973). Fig. 8 depicts an example of calculated N
losses with prescribed fire at 5-, 10-, 20-, and 30-year intervals,
assuming that half of the O horizon is consumed in each burn.
Cumulative N losses are plotted in these burn scenarios and range
from 738 to 1434 kg ha 1 over a 100-year period. These values
exceeded those calculated if the cumulative O horizon mass was
left unburned until complete combustion in a wildfire at 100 years
(492 kg ha 1). Furthermore, prescribed fire at intervals of 10 years
or less will prevent the reestablishment of N-fixing vegetation for
sufficiently long intervals to allow for N fixation to commence in
significant amounts. McNabb and Cromack (1983) indicate that
fixation does not go ‘‘into the black’’ (that is, fix more N than is
taken up from the soil) for at least 10 years (primarily C. velutinus in
the area in which these fires burn). Thus, the long-term effects of
prescribed fire at short intervals can, in theory, cause substantial
amounts of N loss from the ecosystem and may result in growth
declines, as has been observed in some studies in eastern Oregon
(Monleon et al., 1997).
Synthesizing the results of various studies on baseline fluxes
and fire, we can draw some comparisons of the effects of slow,
steady nutrient fluxes into and out of the ecosystem via water with
the episodic effects of fire (both wildfire and prescribed fire). Fig. 9
2258
D.W. Johnson et al. / Forest Ecology and Management 258 (2009) 2249–2260
contents that were approximately 300–400 kg ha 1 lower than in
nearby unmanaged forests. We speculate that low O horizon N
pools were a factor in maintaining the pristine historic clarity of
Lake Tahoe under pre-European conditions, and subsequent forest
floor N increases during the period of fire suppression may have
contributed to the decline in lake clarity because of increased
nutrient concentrations in the runoff.
5. Summary and conclusions
There are several unique features of biogeochemical cycling in
semi-arid forests of the eastern Sierra Nevada Mountains that are
illustrated by the studies reviewed here:
Fig. 8. Estimated forest floor N contents and losses via volatilization with various
intervals of prescribed fire using a spreadsheet model. The model assumes fire
return intervals of 5, 10, 20, and 30 years and that half the forest floor mass and N
content are consumed in each burn. Forest floor mass is calculated using a litterfall
rate of 2000 kg ha 1, litter N concentration of 10 mg N g 1 and a k value of 0.04 yr 1
(adapted from Johnson et al., 1998).
Fig. 9. Schematic diagram showing estimated N fluxes via precipitation, litterfall,
soil leaching, runoff, wildfire, prescribed fire and post-wildfire N fixation for the
Little Valley site. Numbers for fire losses are estimates from several nearby studies
(modified from Johnson et al., 1998).
presents such a synthesis, using the Little Valley site as a template.
This analysis clearly shows that wildfire at a 100-year interval is
the dominant factor in long-term N losses, exceeding leaching
losses by more than two orders of magnitude. If the wildfire is
followed by the invasion of N-fixing snowbrush, however, the lost
N is rather quickly replaced at a rate much greater than inputs of N
via atmospheric deposition (<1 kg N ha 1 yr 1). As discussed
above, prescribed fire can have an even greater annualized effect
on N losses than wildfire since prescribed fire at short intervals will
preclude (and is in fact intended to preclude) the re-establishment
of N-fixing shrubs, such as snowbrush. Thus, the numbers suggest
that reoccurring prescribed fire will lead to long-term N deficiency.
On the basis of these calculations, we hypothesize that the regular
ground fires of the past in this region (fire return frequency 5–
15 yr; Taylor, 2004) resulted in severe N deficiency in forests prior
to European settlement. By way of comparison, Hart et al. (2005)
measured many N cycling components of both restored and
unmanaged ponderosa pine (P. ponderosa Laws) stands in Arizona.
The restored stand, which was thinned and burned so as to
resemble the attributes of pre-European forests, had O horizon
1. Because of the very dry summers, rooting in the O horizons is
largely absent in most upland forests, and thus competition
between microbes and tree roots for nutrients is very limited
and uncoupled in the vertical dimension.
2. Because of the spatial uncoupling of roots and microbes, as well
as the development of extreme hydrophobicity of the mineral
soil surface horizon during summer and fall, surface runoff
through the O horizon and at the mineral soil interface occurs
regularly and is highly enriched in ions such as NH4+ and orthophosphate. We hypothesize that at least some portion of this
nutrient-enriched runoff enters the soil via preferential flow
paths creating hot spots and hot moments, as indicated by
several years of soil solution leaching measurements beneath
the O horizons.
3. Very dry summer conditions limit decomposition, and most
annual litter decomposition takes place beneath the snowpack
when moisture is available, even at 0 8C. As a consequence,
snowpack duration has a strong effect on decomposition rate, as
evidenced by the reduced decomposition and O horizon buildup
near tree boles where snowmelt duration is shortened because
of local warming.
4. Reduced decomposition near tree boles and greater litter inputs
beneath forest canopies contribute to the ‘‘islands of fertility’’
beneath the discontinuous forest canopy.
5. Snowdrift causes high spatial variability in water flux in these
snowmelt-dominated ecosystems.
6. Temporal variability in water and nutrient fluxes is substantial
on seasonal and inter-annual scales in these snow-dominated
ecosystems.
7. On a decadal scale, fire and post-fire biological N fixation
dominate nitrogen cycles, greatly exceeding fluxes via atmospheric deposition and leaching. We also hypothesize that
decades of fire exclusion has allowed buildups of O horizons in
these forests, providing a source for nutrients in runoff waters
and perhaps contributing to the deterioration of water quality in
nearby Lake Tahoe.
Many of the long-standing paradigms for biogeochemical
cycling in more mesic forest soils need significant modification
and adjustment for semi-arid Sierran ecosystems. As noted by Hart
and Firestone (1991), the Mediterranean climate has a significant
effect on N cycling in forests of the western Sierra Nevada
Mountains by imposing a high degree of seasonality on N cycling
processes and by restricting rooting in O horizons as compared to
more mesic forest ecosystems. We find that forests of the drier
eastern Sierra Nevada Mountains are even more strongly affected
by the Mediterranean climate: rooting is absent in O horizons in
most cases, allowing nutrients released by decomposition to enrich
surface runoff, which we believe has been exacerbated by O
horizon buildups during the 20th century era of fire suppression.
Further research is needed to accurately quantify the contributions
of surface runoff to nutrient fluxes, surface water quality, and
D.W. Johnson et al. / Forest Ecology and Management 258 (2009) 2249–2260
spatial heterogeneity in soil nutrient resources. Finally, we
conclude that nitrogen cycling in forests of the eastern Sierra
Nevada is controlled largely by fire (both wildfire and prescribed
fire) and post-fire N fixation rather than by atmospheric deposition
and leaching.
Acknowledgments
This research was supported by the National Science Foundation, the U.S. Forest Service, and the Nevada Agricultural
Experiment Station, University of Nevada, Reno. We greatly
appreciate technical assistance by Matt Donaldson and Damien
Domini.
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