FOREST FLOOR CHARACTERISTICS AND MOISTURE DYNAMICS IN JEFFREY
PINE-WHITE FIR FORESTS OF THE LAKE TAHOE BASIN, USA
by
Erin M. Banwell
A Thesis
Presented to
The Faculty of Humboldt State University
In Partial Fulfillment
Of the Requirements for the Degree
Masters of Science
In Natural Resources: Forestry
December, 2011
FOREST FLOOR CHARACTERISTICS AND MOISTURE DYNAMICS IN JEFFREY
PINE-WHITE FIR FORESTS OF THE LAKE TAHOE BASIN, USA
by
Erin M. Banwell
Approved by Master’s Thesis Committee:
J. Morgan Varner, Major Professor
Date
Eric Knapp, Committee Member
Date
Rob Van Kirk, Committee Member
Date
Coordinator, Natural Resources Graduate Program
Date
Natural Resources Graduate Program Number
Jena’ Burges, Vice Provost
Date
ii
ABSTRACT
FOREST FLOOR CHARACTERISTICS AND MOISTURE DYNAMICS IN JEFFREY
PINE-WHITE FIR FORESTS OF THE LAKE TAHOE BASIN, USA
Erin M. Banwell
In temperate coniferous forests, forest floor fuels have been linked to variation in
important effects of fire, most notably mineral soil heating and post-fire tree mortality.
To improve the understanding of fire effects, I collected forest floor fuels in longunburned Jeffrey pine (Pinus jeffreyi) - white fir (Abies concolor) forests of the Lake
Tahoe Basin, USA. Fuels from each forest floor horizon (litter, fermentation, and
humus), as well as other important woody fuels (1-hour fuel, 10-hour fuel, and cones),
were collected around the bases of large (> 50 cm diameter) Jeffrey pine and white fir.
To isolate the effects of spatial position, I quantified fuel loading, depth, and bulk density
at the base of each tree, beneath the crown drip line, and beyond the crown in open
“gaps”. Seasonal fuel moisture trends were measured during the 2009 and 2010 fire
seasons. To understand the structure of the litter horizon, both spatially and between
species, litter composition was examined, and mineral ash contents of each forest floor
horizon were measured. Little variation was detected in forest floor bulk densities,
depths, and moisture contents between the two conifers. There was also little variability
in forest floor moisture across the Lake Tahoe Basin, but isolated rainfall events
significantly altered forest floor moisture patterns. Duff (the fermentation and humus)
moisture varied spatially throughout stands, whereas other important forest floor
components did not. Results from the interannual moisture study revealed that moisture
of the forest floor horizons differed between years while woody fuel moisture did not.
iii
The variability in field results underscore the importance of measuring duff moisture
content, as well as 10-hour woody fuel moisture, prior to prescribed fire. Forest floor
bulk density and depth did not differ between Jeffrey pine and white fir. However, Jeffrey
pine forest floors were more flammable than white fir forest floors in the laboratory,
where samples of forest floor litter were burned under controlled conditions. Forest floor
fires beneath Jeffrey pines could potentially burn at greater intensities and smolder longer
than white fir forest floors due to differences in burning characteristics. In the Tahoe
Basin, a better understanding of the complexity of forest fuels will help land managers
manage and restore these fire-prone forests. More broadly, these results help inform the
understanding of fuels dynamics in other temperate coniferous forests.
iv
ACKNOWLEDGEMENTS
This study was funded by Southern Nevada Public Land Management Act
(SNPLMA) and Humboldt State Sponsored Programs Foundation. I cannot thank
Morgan Varner enough for the knowledge and confidence he has given me throughout
the years. Morgan’s passion and enthusiasm for fire ecology motivates me every day.
Data analyses would still be at a standstill without the hard work of Rob Van Kirk. Eric
Knapp traveled to Arcata on numerous occasions and visited field sites to provide insight
on the project. He also organized summer field crews, through Pacific Southwest
Research Station, who helped with data collection. Jonathan Szecsei worked relentlessly
on laboratory work, including transferring thousands of forest floor samples from plastic
bags to paper bags. Many other people aided in field collection, laboratory work, and
brainstorming ideas for this project, including Chet Madden, Brendon Banwell, Shannon
Banwell, Tristan Banwell, Erin Taylor, Aubyn Banwell, Eamon Engber, Matt Cocking,
Dean Matheson, and Al Gertiz. I am forever indebted to Chet Madden, who encouraged
and supported me throughout this process.
v
TABLE OF CONTENTS
ABSTRACT ....................................................................................................................... iii
LIST OF TABLES ........................................................................................................... viii
LIST OF FIGURES ........................................................................................................... ix
CHAPTER ONE: Forest floor characteristics in long-unburned Jeffrey pine-white fir
forests of the Lake Tahoe Basin, USA................................................................................ 1
1. INTRODUCTION .......................................................................................................... 1
2. METHODS ..................................................................................................................... 7
2.1. Study Sites ............................................................................................................... 7
2.2. Field Data Collection ............................................................................................. 10
2.3. Laboratory Data Collection.................................................................................... 10
2.4. Data Analysis ......................................................................................................... 13
3. RESULTS ..................................................................................................................... 15
4. DISCUSSION ............................................................................................................... 23
LITERATURE CITED ..................................................................................................... 29
CHAPTER TWO: Spatial and temporal variation of forest floor moisture in longunburned Jeffrey pine-white fir forests of the Lake Tahoe Basin, USA. ......................... 33
1. INTRODUCTION ........................................................................................................ 33
2. METHODS ................................................................................................................... 40
2.1. Study Sites ............................................................................................................. 40
2.2. Field Data Collection ............................................................................................. 40
2.3. Laboratory Data Collection.................................................................................... 44
2.4. Data Analysis ......................................................................................................... 46
vi
TABLE OF CONTENTS (continued)
3. RESULTS ..................................................................................................................... 49
4. DISCUSSION ............................................................................................................... 63
LITERATURE CITED ..................................................................................................... 70
vii
LIST OF TABLES
Table
Page
1
Characteristics of four study sites located in the Lake Tahoe Basin in
California and Nevada………………………………………………………..9
2
Mean bulk density (kg m-3) and 95% confidence intervals (95% C.I.) for
forest floor horizons across positions in relation to distance from trees and
across sites in long-unburned Jeffrey pine and white fir forests throughout
the Lake Tahoe Basin, USA…………………………………………………..16
3
Mean percent composition (%) values of the litter horizon sorted by
components between species and among position in relation to distance
from trees in long-unburned forests of the Lake Tahoe Basin, USA………….19
4
Average forest floor moisture (%) across sites throughout the 2010 fire
season in the Lake Tahoe Basin, USA………………………………………...54
5
Average moisture content (%) of forest floor components in 2009
(white) and 2010 (gray) at the Pioneer site in the southern region of the
Lake Tahoe Basin, USA………………………………………………………57
viii
LIST OF FIGURES
Figure
Page
1
Location of the study sites in the Lake Tahoe Basin in California and
Nevada. All sites were dominated by mature Jeffrey pine-white fir
forests…………………………………………………………………………8
2
From left to right: preparing a bulk density sample from the base of a
white fir tree using a 20 × 20 cm frame; view of a bulk density sample
before collection of each forest floor horizon (litter, fermentation, and
humus); depth measurements…………………………………………………11
3
Bulk density samples were collected 0.5 m away from the tree bole at the
“base” of each tree, 2.0 m away from the tree bole at the crown “drip
line”, and 5.0 m away from the tree bole beyond the crown in open “gaps”…12
4
Average depths (cm) of the forest floor (litter, fermentation, humus)
between Jeffrey pine and white fir in relation to distance from trees in
long-unburned forests of the Lake Tahoe Basin, USA…………………….….17
5
Average loading (g m-2) of the litter surface sorted by components
between Jeffrey pine and white fir in relation to distance from trees in
long-unburned forests of the Lake Tahoe Basin, USA…………………….….21
6
Left: Mean ash content (%) of the forest floor horizons at the base of
Jeffrey pine and white fir trees, along with 95% confidence interval
bounds. Ash content significantly differed (P < 0.001) across horizons
and species × horizon combinations. Right: Profile of forest floor
horizons in a long-unburned Jeffrey pine-white fir forest in the Lake
Tahoe Basin, USA……………………………………………………….........22
7
Factors influencing combustion (top) and simplified effects of combustion
(bottom). A “-” symbol indicates a negative response in combustion to an
increase in the given variable, a “+” symbol indicates a positive response
in combustion to an increase in the given variable, and the asterisk
signifies the more complex dependence of combustion on forest floor
composition…………………………………………………………………...27
8
Location of study sites in the Lake Tahoe Basin in California and Nevada.
Pioneer was selected for the interannual (2009, 2010) and diurnal
ix
moisture studies, and all four sites were selected for the 2010 moisture
study. All sites were dominated by mature Jeffrey pine-white fir forests……41
9
Forest floor moisture samples were collected 0.5 m away from the tree
bole at the “base” of each tree, 2.0 m away from the tree bole at the crown
“drip line”, and 5.0 m away from the tree bole beyond the crown in open
“gaps”…………………………………………………………………………42
10
Location of the diurnal moisture collection site (Pioneer) in the southern
region of the Lake Tahoe Basin, USA, dominated by mature Jeffrey pine
and white fir……………………………………………………………….......45
11
Forest floor samples used in a laboratory time lag experiment: Jeffrey pine
cones; Jeffrey pine litter; Jeffrey pine 1-hour and 10-hour woody fuelbeds;
and white fir litter collected from the Lake Tahoe Basin, USA…………….…47
12
Mean moisture content (%) of the fermentation horizon across positions in
relation to tree boles, during the 2010 fire season in Jeffrey pine-white fir
forests of the Lake Tahoe Basin, USA………………………………………...50
13
Mean moisture content (%) of different forest floor fuels (top) and
horizons (bottom) during the 2010 fire season throughout the Lake Tahoe
Basin, USA. Different scale on y-axis for fuel moisture (0 to 20%) and
forest floor horizon moisture (0 to 120%)…………………………………….52
14
Total precipitation (cm; represented by bars) and average maximum
temperature (°C; represented by lines) from February through October of
2009 and 2010 in the southern region of the Lake Tahoe Basin, USA………..55
15
Temperature (C) and relative humidity (%) patterns during a 24-hour
forest floor moisture collection in the Lake Tahoe Basin, USA on 10
August 2010…………………………………………………………………...58
16
Moisture content (%) of forest floor components in long-unburned Jeffrey
pine and white fir forests in the southern region of the Lake Tahoe Basin,
USA across a 24-hour period in August, 2010………………………………...60
17
Relative moisture content (E) of forest floor components in relation to
duration of desorption (hours) for mature Jeffrey pine and white fir
forests in the Lake Tahoe Basin, USA. Time lags (hours) for each forest
floor component, standard errors (SE), and multiple comparisons are
shown in the top right corner with superscripted lowercase letters (a, b, c,
d)…………………………………………………………………………….....62
x
18
Conceptual model representing the spatial and temporal scales measured
in this study (light gray) and previous forest floor moisture studies
(dark gray)……………………………………………………………………...64
xi
CHAPTER ONE: Forest floor characteristics in long-unburned Jeffrey pine-white fir
forests of the Lake Tahoe Basin, USA
1. INTRODUCTION
Prior to European settlement, pine-dominated forests in the Sierra Nevada burned
at frequent fire return intervals (2 to 20 years) with low to moderate-severity (Parsons and
DeBenedetti, 1978; Kilgore and Taylor, 1979; Skinner and Chang, 1996; Taylor and
Beaty, 2005). Before the turn of the 20th century, coniferous forests in the Carson Range
located near Lake Tahoe were open and dominated by Jeffrey pine (Pinus jeffreyi Grev.
and Balf.), lodgepole pine (Pinus contorta var. murrayana [Grev. and Balf.] Crichf.),
sugar pine (Pinus lambertiana Dougl.), and white fir (Abies concolor [Gord. and Glend.]
Lindl.). There was four times more Jeffrey pine than white fir (Taylor and Beaty, 2005).
Large ( ̅ diameter at breast height [DBH] = 67.5 cm), old (> 250 years) trees were
common. In the late 19th century, fire frequency was reduced throughout the Sierra
Nevada due to removal of fine fuels caused by grazing, severely altered forest structure
due to logging and mining, land clearing for settlement, fire suppression, and extirpation
of Native Americans (Vankat and Major, 1978; McKelvey et al., 1996; Skinner and
Chang, 1996; Lindström, 2000; Miller and Urban, 2000; Stephens, 2001; Taylor, 2004).
These changes and a century of fire exclusion in the Sierra Nevada caused an increase in
forest density. Shade-tolerant white fir have encroached into Jeffrey pine dominated
woodlands and forests. Current Jeffrey pine-white fir forests have experienced an
increase in small diameter trees, an increase in basal area, and a decrease in structural
variability (Taylor, 2004). White fir branches act as ladder fuels and increase dead
1
2
surface fuel loads (Vankat and Major, 1978; Parsons and DeBenedetti, 1979; McKelvey
et al., 1996; Miller and Urban, 2000).
The lack of fire and change in forest structure throughout Sierra Nevada forests
led to the development of deep organic matter accumulations, with distinct horizons on
top of the mineral soil (hereafter, ‘forest floor’) (Stephens and Finney, 2002; Stephens et
al., 2004). Litter (Oi), the uppermost horizon of the forest floor, consists of freshly
fallen, non-woody organic matter (Pritchett, 1979). Fermentation and humus horizons
(Oe and Oa, respectively), known collectively as duff, are created by decomposition of
surface litter. Fermentation is made up of partially decomposed but still recognizable
organic matter, and humus is made up of dark, unrecognizable, decomposed organic
material (Pritchett, 1979; van Wagtendonk et al., 1998). Deep duff typically occurs in
long-unburned forests and accumulates around bases of large trees (Ryan and Frandsen,
1991; Swezy and Agee, 1991). Annual rates of decomposition and time since last fire are
two key factors of forest floor accumulation (Pritchett, 1979).
Jeffrey pine forests typically accumulate fuels slowly (Skinner and Chang, 1996).
As stands age, litter and duff weights and depths tend to increase (Ryan and Frandsen,
1991; van Wagtendonk et al., 1998). van Wagtendonk and Moore (2010) studied fuel
deposition rates of 11 conifers in the Sierra Nevada. Foliage deposition rates were higher
beneath medium sized (60 to 120 cm DBH) white fir than Jeffrey pine (178.3 and 141.4 g
m-2 year-1, respectively), although bark and crown fragments were similar between the
two species (74.4 and 71.7 g m-2 year-1). They also studied fuelbed characteristics for 22
species of conifers found in the Sierra Nevada. Litter depth was found to be lowest in
white fir and red fir (Abies magnifica A. Murr) forests ( ̅ = 0.2 cm). Jeffrey pine had a
3
greater mean litter depth (1.1 cm) than white fir, although duff depth was greater in white
fir forests than Jeffrey pine forests (6.5 cm and 5.4 cm, respectively) (van Wagtendonk et
al., 1998). Litter and duff depths varied from 2 to 6 cm in white fir forests and from 2 to
16 cm in ponderosa pine (Pinus ponderosa Dougl. x Laws.) forests in the Sierra Nevada
(Stephens et al., 2004).
Duff depths may vary within stands because bark slough accumulates directly
beneath trees rather than farther away (Ryan and Frandsen, 1991; Miyanishi and Johnson,
2002). Litter and duff depths decrease with distance from trees in mixed conifer forests
of Montana (Ryan and Frandsen, 1991), Oregon (Swezy and Agee, 1991) and California
(Hille and Stephens, 2005), with litter depths decreasing from 5 cm to less than 2 cm two
meters away from tree bases, and duff depths decreasing from 13 to 3 cm seven meters
from tree bases (Swezy and Agee, 1991). Understanding the spatial variability of forest
floor horizon depths within a stand is important because litter and duff depths affect the
moisture content of the forest floor, which in turn affects flaming and smoldering
combustion and consumption during both prescribed fire and wildfire. Deeper duff
depths are known to sustain smoldering at higher moisture contents (Miyanishi and
Johnson, 2002). Therefore large trees are at greater risk for basal injury caused by duff
smoldering (Ryan and Frandsen, 1991; Sweezy and Agee, 1991; Stephens and Finney,
2002; Varner et al., 2007). Variability in duff depth and duff moisture within a stand can
also influence post-fire forest floor consumption patterns, which affects rates of erosion
and runoff following fire (Morris and Moses, 1987; Cannon et al., 1998; Cannon and
Reneau, 2000; Beeson et al., 2001).
4
Within the duff horizons, bulk density increases with depth (van Wagtendonk et
al., 1998; Stephens et al., 2004). Needle morphology influences litter depth and forest
floor fuelbed arrangement (Stephens et al., 2004). Conifers with short needles have
shallower litter horizons than conifers with medium to long needles. Fuelbeds of
medium-to-long needled conifers tend to be more porous than those of short-needled
conifers. These “fluffy” fuelbeds will burn with greater intensity than denser forest floors
(van Wagtendonk et al., 1998). These authors also found that bulk densities of litter and
duff were two times greater in white fir stands than Jeffrey pine stands. A study by
Stephens et al. (2004) found bulk densities within the lowest stratum of white fir forest
floors to be four times greater than ponderosa pine forests.
Bulk density affects forest floor moisture dynamics and contributes to
consumption during and extinguishment of smoldering fires (Frandsen, 1987; Stephens et
al., 2004; Hille and Stephens, 2005). Although moisture was the most important factor
influencing forest floor consumption, inorganic content of the forest floor was also found
to be an important component of smoldering fires (Frandsen, 1987; Reardon et al., 2007,
Garlough and Keyes, 2011). Garlough and Keyes (2011) found that consumption of the
fermentation horizon was constrained by moisture content, and consumption of the
humus horizon was constrained by moisture content and mineral content in duff mounds
beneath large ponderosa pines. Inorganic matter within the forest floor acts as a flame
retardant; where fuels with high mineral contents experience decreased rates of
volatilization, increased residue, and a decrease in endothermic intensity and energy
output throughout smoldering combustion (Philpot, 1970).
5
Forest floors with large duff accumulations release a majority of energy during
smoldering combustion, not during the flaming front, and the vast majority of duff is
consumed by slow smoldering combustion (Frandsen, 1987; Frandsen, 1991). While
smoldering fires can reduce forest floor accumulation, they can also induce large tree
mortality due to basal injury (Ryan and Frandsen, 1991; Sweezy and Agee, 1991;
Stephens and Finney, 2002; Varner et al., 2007). Fuel loading, fuel moisture, and forest
floor bulk density have been found to influence the connectivity of burnable area (Miller
and Urban 2000). Spatial variations in forest floor fuels are important for understanding
and predicting post-fire consumption patterns, yet little research has focused on this
variation. Knowledge gaps in the variation of forest floor fuels – spatially in relation to
the distance from a tree, among forest floor horizons, and between important conifer
species in the Sierra Nevada – have huge management implications. Understanding
variation in forest floor fuels will help us to predict and understand forest floor
consumption patterns, which affect seedling regeneration, smoke production, largediameter tree mortality, mineral soil heating, soil-nutrient cycling, and runoff and
leaching (Neary et al., 1999), as well as restoration management and prescribed fire
objectives.
This research addressed the following questions: (1) how does forest floor bulk
density and depth differ between tree species (Jeffrey pine and white fir), among forest
floor horizons (litter, fermentation, and humus), across positions in relation to distance
from trees (tree base, crown drip line, and in open gaps), and across study sites; (2) how
does forest floor ash content differ between species, and among forest floor horizons; and
(3) how does composition and mass of the litter horizon change both spatially and
6
between species? Land managers will benefit from detailed information of forest floor
characteristics in long-unburned ecosystems, an area of increasing management and
conservation concern (Hood, 2010). Understanding the spatial variability of forest floor
bulk density, depth, ash content, and composition at a small scale (between species and
among forest floor horizons) and large scale (spatially within a stand and throughout the
Basin), will help land managers make appropriate prescribed fire and wildfire
management decisions.
2. METHODS
2.1. Study Sites
The Lake Tahoe Basin’s climate is characterized by cold, wet winters and warm,
dry summers. Average temperatures range from -0.7° C during winter to 23.7° C in
summer (Loftis, 2007). On average, the Lake Tahoe Basin receives 831.9 mm of
precipitation annually, and over 80% falls as snow, which accumulates between
November and April (Loftis, 2007). Bedrock in the Tahoe Basin consists of
metamorphic and granitic igneous rocks (Hill, 2006), giving rise to shallow soils (< 1 m)
that are excessively drained and are of medium acidity (Loftis, 2007).
The four study sites chosen on U.S. Forest Service land were identified by
managers as high priority burn units throughout the Lake Tahoe Basin Management Unit
in California and Nevada (Fig. 1). No fires were recorded at any of the sites during the
20th century. All sites varied in slope, aspect, elevation, woody fuel loading, and basal
area (Table 1). The Baldy unit is located on the north shore of Lake Tahoe in Nevada,
with soil classified as Jorge, a very cobbly, fine sandy loam that is rubbly and welldrained (Loftis, 2007). Baldy’s overstory species composition consisted of 30.1% Jeffrey
pine and 69.9% white fir. Secret (located on the east shore of Lake Tahoe in Nevada)
and Pioneer (located at the southern end of Tahoe) share similar soils, the Cassenai series,
which contains gravelly, loamy coarse sand that is very stony and somewhat excessively
drained (Loftis, 2007). The overstory species composition at Secret was 48.9% Jeffrey
pine, 25% white fir, 18.2% incense cedar (Calocedrus decurrens [Torr.] Florin.), and 8%
sugar pine. Overstory composition at Pioneer was 62.3% Jeffrey pine, 36.9% white fir,
and 0.8% lodgepole pine. Bobwhite is on the west shore in California, and the soil
7
8
Baldy
Aspect: 216
Elevation: 2293 m
Secret
Aspect: 280
Elevation: 1951 m
Lake
Tahoe
Bobwhite
Aspect: 310
Elevation: 2055 m
Nevada
Pioneer
Aspect: 289
Elevation: 2000 m
Fig. 1. Location of the study sites in the Lake Tahoe Basin in California and
Nevada. All sites were dominated by mature Jeffrey pine-white fir forests.
9
Table 1. Characteristics of four study sites located in the Lake Tahoe Basin in
California and Nevada.
Slope (%)
Aspect (°)
Elevation (m)
Baldy
26
216
Secret
18
Pioneer
7
Bobwhite
30
Total Woody Fuel Basal Area
Loading (t ha -1 )
(m2 ha-1 )
2293
17.3
43
280
1951
20.9
40
289
2000
17.5
60
310
2055
*
28
*Woody fuel loading was not estimated for Bobwhite due to hand piling of fuel
10
classification is Meeks, a gravelly, loamy coarse sand (Loftis, 2007); overstory
composition was 24.6% Jeffrey pine, 50.8% white fir, 13.1% sugar pine, and 11.5% red
fir.
2.2. Field Data Collection
During the summers of 2009 and 2010, across all four study sites (Fig. 1), 40
Jeffrey pine ( ̅ DBH = 68.8 cm) and 40 white fir ( ̅ DBH = 66.8 cm) trees greater than 50
cm DBH were randomly selected for bulk density and depth sampling (Fig. 2). For each
forest floor horizon (litter, fermentation, and humus), depth and bulk density
measurements were collected using a 20  20 cm sampling frame (Fig. 2). All
measurements were taken at the base of each tree, at the crown drip line, and beyond the
crown in open “gaps” (Fig. 3).
A total of 589 bulk density samples were collected, and 2,356 forest floor depth
measurements were made. Collected fuels were oven-dried for 48 hours at 60°C and
weighed to the nearest 0.01 g. Forest floor horizon depths, combined with frame area
dimensions, were used to calculate the sample volumes (cm  cm2). Bulk densities were
then calculated using dry sample weights divided by sample volumes.
2.3. Laboratory Data Collection
To understand the structure of the litter horizon both spatially (tree base, crown
drip line, and open “gaps”) and between species, litter samples were sorted into
components. Each bulk density sample was sorted by hand into bark, pine leaves, fir
leaves, all other leaves, cone parts, and reproductive structures (strobili). Sub-sampling
was completed from a total of 36 transects (18 under Jeffrey pine; 18 under white fir) for
a total of 108 sorted samples and 648 strata measurements.
Fig. 2. From left to right: preparing a bulk density sample from the base of a white fir tree using a 20 × 20 cm frame; view of a bulk
density sample before collection of each forest floor horizon (litter, fermentation, and humus); depth measurements.
11
12
Tree Base - 0.5 m
Drip Line - 2.0 m
Open Gap - 5.0 m
Fig. 3. Bulk density samples were collected 0.5 m away from the tree bole at the
“base” of each tree, 2.0 m away from the tree bole at the crown “drip line”, and 5.0
m away from the tree bole beyond the crown in open “gaps”.
13
Ash content, the inorganic residue remaining after all organic matter is burned off,
was determined using forest floor bulk density samples (n = 72; 12 replicates per horizon
per species) from the base of sample trees. Each sample was submerged in water,
agitated for 1 minute to remove any bound mineral soil, and oven-dried for 48 hours at
60°C. The forest floor samples ( ̅ = 3.0 g) were then ground in a Wiley mill
(5KH33GG106F, A.H. Thomas Co., Philadelphia, PA) using a 1 mm screen and
composited for consistent sub-sampling. Ground samples were placed in a muffle
furnace (F6020, Thermolyne Corp., Dubuque, IA) for 24 hours at 600°C to allow for
complete combustion of organic matter. Combusted samples were then placed in
desiccators containing magnesium perchlorate desiccant and cooled to room temperature.
Once cooled, samples were weighed to the nearest 0.0001 g, and converted to percent ash
content on a dry basis by dividing the ash content by the original sample’s weight
(Horwitz, 1965).
2.4. Data Analysis
All field collections were completed in a split plot experimental design (Fisher,
1925), with individual sample trees nested within site. A mixed-effects linear model
(ANOVA) was used to analyze forest floor bulk density, depth, litter mass, and ash
content data. All data were log or logit transformed to meet model assumptions. There
was a slight imbalance in the bulk density data (589 rather than 720 bulk density
samples) due to lack of humus during collections, although this imbalance did not affect
the outcome, as indicated by little difference between adjusted sum of squares and
sequential sum of squares. Analyses assessed whether there were any significant
relationships and/or interactions between the continuous response variables: bulk density;
14
depth; litter mass; and percent ash content, respectively, and the four categorical predictor
variables: site; species; forest floor horizons; and position in relation to the tree base.
When analyzing ash content, only two of the predictor variables (species and forest floor
horizons) were considered. Species was considered a random effect, because
observations were not independent of one another at the site and species level. Where
main effects or interactions were found to be significant ( < 0.05), multiple comparisons
tests with the Bonferonni correction were used to investigate differences among
categorical variable levels.
3. RESULTS
Bulk density of the forest floor did not differ between Jeffrey pine and white fir
(61.3 and 57.7 kg m-3, respectively, P = 0.492). There were no species × horizon or
species × location effects. Litter was significantly less compact than fermentation and
humus horizons (P < 0.001), with bulk density of fermentation (105.7 kg m-3) and humus
(115.9 kg m-3) ca. 4 times greater than litter (23.7 kg m-3). At each collection location,
bulk density of all forest floor horizons differed from one another; litter was less compact
across all collection locations, fermentation had the highest bulk density values at tree
bases, and humus was most compact beneath crown drip lines and in open gaps (P =
0.002; Table 2).
Site variation of forest floor bulk density was low throughout the Tahoe Basin.
Bulk density of the litter horizon was statistically similar at all sites excluding Baldy and
Pioneer, where litter was two times more compact at Baldy than Pioneer. Fermentation
and humus bulk density did not differ across the four study sites (P < 0.001; Table 2).
On average, Jeffrey pine and white fir forest floor depths did not differ from one
another (P = 0.421), and there were no species × location effects (P = 0.601). Litter
horizons were deeper around Jeffrey pines than white firs (P < 0.001), although litter was
the only forest floor horizon that differed between species. Forest floor depths decreased
with increasing distance from trees. Forest floor depths decreased by ca. 30% 2 m away
(crown drip line) and by ca. 50% 5 m away (open gaps) from tree bases (P < 0.001; Fig.
4). All forest floor horizon depths differed significantly from one another (P < 0.001).
Fermentation was the deepest horizon (0.3 to 14.2 cm). Litter ranged from 0.1 to
15
Table 2. Mean bulk density (kg m-3) and 95% confidence intervals (95% C.I.) for forest floor horizons
across positions in relation to distance from trees and across sites in long-unburned Jeffrey pine and
white fir forests throughout the Lake Tahoe Basin, USA.
Position
Site
Tree Base
Crown Drip Line
Open Gap
n
214
196
179
26.7bA
21.1aA
23.7abA
Bulk Density kg m -3 (95% C.I.)
Litter
Fermentation
Humus
bC
aB
120.2
(22.2, 32.2)
(110.7, 130.5) 93.5
(74.9, 116.6)
aB
bC
99.3
125.9
(17.9, 24.9)
(88.0, 112.0)
(100.3, 157.9)
bC
98.9aB
(19.8, 28.3)
(87.3, 112.1) 168.9
(135.8, 210.2)
Pioneer
Secret
Bobwhite
Baldy
135
159
156
139
14.7a
19.8ab
24.9ab
42.6b
(12.1, 17.9)
(17.6, 22.2)
(20.9, 29.6)
(34.5, 52.6)
84.2a
95.3a
128.4a
121.1a
(75.5, 93.9)
(82.6, 109.8)
(113.9, 144.7)
(107.6, 136.3)
95.3a
133.2a
158.3a
109.0a
(72.1, 125.7)
(97.2, 182.3)
(122.5, 204.4)
(85.4, 139.0)
Superscripted lowercase letters (a, b, c) represent significant dif ferences vertically.
Superscripted uppercase letters (A, B, C) represent significant dif ferences horizontally.
16
17
10
Litter
2.7
Fermentation
1.5
Depth (cm)
8
Humus
1.4
6
2.0
6.2
4
1.7
5.7
1.0
4.7
4.1
2
1.5
3.3
0.2
0.4
8
9
1.6
0
1
3.5
2
Tree Base
0.5 m
3
0.6
0.7
4
5
Crown Drip Line
2.0 m
6
7
Open Gaps
5.0 m
Fig. 4. Average depths (cm) of the forest floor (litter, fermentation, humus) between
Jeffrey pine and white fir in relation to distance from trees in long-unburned forests of the
Lake Tahoe Basin, USA.
18
7.2 cm. Humus depths were similar to litter (0.1 to 7.3 cm). Depth of the fermentation
horizon differed across all positions and decreased by ca. 40% 5 m away from tree boles
in gaps. Litter depths were similar at tree bases and beneath crown drip lines and ca.
25% shallower in open gaps. Humus at the base of trees was 70% deeper than beneath
crown drip lines and open gaps (P < 0.001; Fig. 4).
On average, Jeffrey pine litter had a higher loading (216.0 g m-2) than white fir
litter (69.0 g m-2). Among the litter horizon components, pine needles comprised over
80% (173.3 g m-2) of Jeffrey pine litter. A combination of pine and fir needles made up
ca. 60% (41.0 g m-2) of litter beneath white fir. Combinations of cone and bark slough
were also common, composing ca. 12% (31.9 g m-2) of the litter beneath Jeffrey pines
and greater than 20% (22.6 g m-2) of the litter beneath white firs. Reproductive structures
and leaves/needles of other plant species were rare, comprising less than 10% of the litter
beneath both conifers (Table 3).
Of the six components of surface litter, only the mass of bark slough and other
leaves were similar between species. The litter beneath Jeffrey pines consisted of ca. 7
times more pine needles, ca. 2 times more cone matter, and ca. 2 times more reproductive
structures than the litter beneath white firs, although the litter beneath white firs
contained ca. 18 times more fir needles than Jeffrey pine litter (P < 0.001). Pine needles,
fir needles, and other leaves did not differ across position (tree base, crown drip line,
open gaps). Bark slough was the only component that differed across all locations, with
22 times more bark at the base of trees than 5 m away, ca. 6 times more bark at the base
of trees than under the crown drip line, and 3.5 times more bark beneath the crown drip
Table 3. Mean percent composition (%) values of the litter horizon sorted by components
between species and among position in relation to distance from trees in long-unburned
forests of the Lake Tahoe Basin, USA.
P. jeffreyi
54
Pine
Leaves
84.2
A. concolor
54
47.4
25.0
14.9
8.3
4.2
0.2
Tree Base
36
56.7
4.6
16.1
17.8
4.9
0.1
36
79.0
2.3
11.3
3.9
3.5
0.1
36
81.8
4.6
9.7
1.0
2.7
0.2
n
Species
Location Crown Drip Line
Open Gaps
Fir
Leaves
0.4
% Composition
Cone
Reproductive
Bark
Matter
Structures
10.0
2.3
3.1
Other
Leaves
0.1
19
20
line than out in open gaps. There was ca. 9 times more cone matter and ca. 3 times more
bark slough directly beneath Jeffrey pines than white firs (cone = 101.6 and 11.4 g m-2;
bark = 55.9 and 19.4 g m-2, respectively). Cone matter and reproductive structures only
differed between the tree base and in open gaps, with ca. 3 times more cone matter and
reproductive structures at the tree base opposed to open gaps (P < 0.001) (Fig. 5).
White firs had significantly higher ash content than Jeffrey pines across all forest
floor horizons, with twice the ash in the litter than Jeffrey pine (P < 0.001). Ash content
differed among all horizons (P < 0.001) with litter < fermentation < humus (Fig. 6).
21
Cone
400
Reproductive Structures
Loading (g m-2)
Bark
Other Leaves
300
Fir Leaves
Pine Leaves
200
100
0
PIJE
TB
ABCO
TB
Tree Base
0.5 m
PIJE
DL
ABCO
DL
Crown Drip Line
2.0 m
PIJE
O
ABCO
O
Open Gaps
5.0 m
Fig. 5. Average loading (g m-2) of the litter surface sorted by components between Jeffrey
pine and white fir in relation to distance from trees in long-unburned forests of the Lake
Tahoe Basin, USA.
22
30
Litter
15
Litter
Ash Content (%)
0
30
ABCO
PIJE
Fermentation
Fermentation
15
0
30
ABCO
PIJE
Humus
Humus
15
Mineral Soil
0
ABCO
PIJE
Fig. 6. Left: Mean ash content (%) of the forest floor horizons at the base of Jeffrey pine
and white fir trees, along with 95% confidence interval bounds. Ash content significantly
differed (P < 0.001) across horizons and species × horizon combinations. Right: Profile
of forest floor horizons in a long-unburned Jeffrey pine-white fir forest in the Lake Tahoe
Basin, USA.
23
4. DISCUSSION
Forest floor flammability and post-fire effects are driven by composition, loading,
and structure of litter fuels (van Wagtendonk et al., 1998; Miller and Urban, 2000; Fonda,
2001; Stephens et al., 2004). In the Tahoe Basin, litter loading and composition between
Jeffrey pine and white fir differed substantially (Table 3; Fig. 5). Beneath pines, over
80% of the litter was composed of Jeffrey pine needles and less than 1% consisted of
white fir leaves. This compared to about 50% Jeffrey pine needles and 25% white fir
needles beneath firs. Although two of the four study sites contained higher percentages
(by density and basal area) of white fir than Jeffrey pine (Baldy and Bobwhite), Jeffrey
pine needles still dominated the litter horizon at all sites. van Wagtendonk and Moore,
(2010) found that in Yosemite National Park, foliage deposition rates for medium (15 to
60 cm DBH) diameter Jeffrey pine were slightly higher than white fir (214.4 and 206.5 g
m-2, respectively). Regardless of the mechanism, the overwhelming abundance of pine
litter in this forest type strongly affects flammability of Jeffrey pine-white fir forest
floors.
Under laboratory conditions (Fonda, 2001; where litter moisture was the same for
both species), Jeffrey pine litter burned with greater maximum flame heights than white
fir litter (83.9 and 21.8 cm, respectively). A greater percentage of Jeffrey pine litter was
consumed during burns (89.0% and 39.4%, respectively) (E. Banwell and J.M. Varner,
unpublished data). Jeffrey pine litter also smoldered longer (82 sec) than white fir
needles (E. Banwell and J.M. Varner, unpublished data), which increases the chances of
igniting imbedded cones and bark slough in the forest floor horizons. There was much
more cone matter and bark slough directly beneath Jeffrey pine than white fir. Jeffrey
24
pine cones burn differently than needles, with longer flaming and smoldering times (total
cone burn time = 78 minutes, Fonda and Varner, 2004; total needle burn time = 7
minutes, E. Banwell and J.M. Varner, unpublished data). During a fire, smoldering cone
and bark fragments in the litter horizon could increase pre-heating of the underlying
forest floor horizons, amplifying the potential of smoldering duff fires surrounding the
bases of trees.
In contrast to previous studies on Sierra Nevada forest floor characteristics (van
Wagtendonk et al., 1998; Stephens et al., 2004), forest floor bulk densities did not differ
between Pinus and Abies species. Bulk density results for Jeffrey pine litter were
comparable to those reported by van Wagtendonk et al. (1998), although the bulk density
of litter beneath white fir was almost twice previous estimates (78.4 versus 33.1 kg m-3).
The bulk density values for duff (fermentation and humus combined) found by van
Wagtendonk et al. (1998) were higher for both Jeffrey pine (101.8 versus 168.5 kg m-3)
and white fir (110.0 versus 183.0 kg m-3). Comparisons of bulk density values among
these studies are problematic due to differences in tree size (2.5 to 120 cm DBH in van
Wagtendonk et al. (1998) versus 50 to 112 cm DBH in this study), collection locations,
and differences in forest floor horizon delineation (categorizing forest floor by depths
rather than horizons). Bulk density samples were also collected from pure stands of
species sampled by van Wagtendonk et al. (1998) and Stephens et al. (2004), whereas
this study focused on mixed Jeffrey pine-white fir forests.
Forest floor depths among these studies also differed, ranging from 2 to 6 cm in
white fir forests and 2 up to 16 cm in ponderosa pine forests (Stephens et al. 2004),
whereas white fir forest floor depths ranged from 1 to 20 cm and forest floor depths
25
beneath Jeffrey pines ranged from 2 to 19 cm in this study. Average litter depths were
greater in our study compared to van Wagtendonk et al. (1995). Average Jeffrey pine
litter depth was 2.1 cm compared to 1.1 cm in van Wagtendonk et al. (1995) and white fir
litter depths averaged 1.3 cm compared to 0.2 cm, respectively. Differences in average
litter depths could be due to van Wagtendonk et al. (1998) excluding cone matter from
the litter horizon.
Jeffrey pine and white fir forest floor bulk densities were similar throughout the
Basin, and fermentation had the highest bulk density values of any horizon, yet there was
little variability across sites. Total woody fuel loading was also similar among sites
(Table 1). The uniformity of forest floors throughout the Basin shows that there is a
similar need for prescribed fire to reduce fuel and forest floor accumulations across the
Lake Tahoe Basin.
Bulk densities of the forest floor litter and fermentation horizons did not differ in
relation to distance from trees, although the humus horizon was more compact at the base
of trees. Average duff depths were also greater at the base of trees (7.5 cm) compared to
open gaps (3.5 cm). Humus accumulation at the bases of mature trees can lead to
increased duff consumption during fire (Hille and den Ouden, 2005), and deep duff can
extend smoldering at higher moisture contents and sustain smoldering fire longer than
shallow duff horizons because deeper duff traps heat that would otherwise be lost to
convection at the surface (Miyanishi and Johnson, 2002).
Basal smoldering is affected by forest floor moisture, ash content, bulk density,
depth, and composition (Fig. 7). Smoldering duff mounds can cause heat-related injury
to the vascular cambia of thick-barked trees typically resistant to low-intensity fires
26
(Ryan and Frandsen, 1991; Swezy and Agee, 1991; Stephens and Finney, 2002; Hille and
Stephens, 2005; Varner et al., 2007). Higher ash content within the forest floor affects
smoldering duff fires by decreasing volatilization rates, decreasing energy output and
endothermic intensity, and by increasing rates of residue (Philpot, 1970). This study
found ash contents of Jeffrey pine litter around 2.3% and white fir litter roughly 4.7%
(pure Jeffrey pine needle ash = 2.2%, pure white fir needle ash = 5.2%; E. Banwell and
J.M. Varner, unpublished data). Other studies have found similar ash contents. Stephens
et al. (2004) found that the uppermost horizon in ponderosa pine forest floors had an
average ash content of 3.5%, and white fir forest floors had an ash content of 6.5%.
Philpot (1970) found ash content of ponderosa pine needles to be 3.9%. Minerals in the
forest floor also absorb heat and decrease the propagation of smoldering (Frandsen, 1987;
Frandsen, 1997). The data suggest that white fir forest floors may be less flammable than
Jeffrey pine forest floors. White fir forests may need drier conditions in order to ignite
and smolder in their ash-rich litter.
Lower elevations of the Tahoe Basin are dominated by Jeffrey pine and white fir
and account for over 50% of the total vegetated area, covering 15,346 and 14,927 ha,
respectively (Greenberg et al., 2006). As lower elevations throughout the Basin are
increasingly encroached by human development, it is imperative to understand fuel
characteristics of the dominant vegetation in these wildland-urban interfaces (Safford et
al., 2009). The comprehensive data on Jeffrey pine-white fir forest floors from this study
will help land managers effectively reduce forest floor accumulation, reducing the risk of
major wildfire events in the developed areas surrounding Lake Tahoe.
Lower elevations of the Tahoe Basin are dominated by Jeffrey pine and white fir
27
Bulk Density
Ash Content
Composition
Moisture
(Chapter 2)
Depth
SMOLDERING
COMBUSTION
Tree
Mortality
Soil Heating
Forest Floor
Consumption
Emissions
Post-fire
Erosion
Fig. 7. Factors influencing combustion (top) and simplified effects of combustion
(bottom). A “-” symbol indicates a negative response in combustion to an increase in the
given variable, a “+” symbol indicates a positive response in combustion to an increase in
the given variable, and the asterisk signifies the more complex dependence of combustion
on forest floor composition.
28
and account for over 50% of the total vegetated area, covering 15,346 and 14,927 ha,
respectively (Greenberg et al., 2006). As lower elevations throughout the Basin are
increasingly encroached by human development, it is imperative to understand fuel
characteristics of the dominant vegetation in these wildland-urban interfaces (Safford et
al., 2009). The comprehensive data on Jeffrey pine-white fir forest floors from this study
will help land managers effectively reduce forest floor accumulation, reducing the risk of
major wildfire events in the developed areas surrounding Lake Tahoe.
Results from this study show that Jeffrey pine forest floors may be at a greater
risk for excessive basal smoldering during fire. Forest floor fires beneath Jeffrey pines
may burn at greater intensities and smolder longer than white fir forest floors due to
differences in burning characteristics of Jeffrey pine needles versus white fir needles,
higher cone and bark loadings beneath Jeffrey pines, and lower levels of ash content in all
forest floor horizons compared to white fir forest floors. Since Jeffrey pine forest floors
beneath trees may burn at greater intensities and smolder longer, there is a greater risk of
pine mortality than fir mortality during fire. It is difficult to restore these encroached
Jeffrey pine-white fir forests using prescribed fire as a sole management tool. Manual
thinning in this fire-excluded ecosystem may be necessary to remove fire resistant trees.
Knowledge of forest floor fire behavior in Jeffrey pine-white fir ecosystems is essential
to land managers, because basal duff consumption can exacerbate large tree mortality
(Ryan and Frandsen, 1991; Sweezy and Agee, 1991; Stephens and Finney, 2002; Varner
et al., 2007). This study adds to the understanding of forest floor complexity throughout
the Tahoe Basin, and similar studies are needed in other fire-excluded forest types to aid
in the restoration of these ecosystems.
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bottom-up approach to vegetation mapping of the Lake Tahoe Basin using
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Hille, M.G., den Ouden, J., 2005. Fuel load, humus consumption and humus moisture
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Hille, M.G., Stephens, S.L., 2005. Mixed conifer forest duff consumption during
prescribed fires: Tree crown impacts. Forest Sci. 51, 417–424.
Hood, S.M., 2010. Mitigating old tree mortality in long-unburned, fire-dependent forests:
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California. Forest Ecol. and Manage. 94, 47–59.
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60, 129–42.
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conditions. In: Murphy, D.D., Knopp, C.M. (Eds.), Lake Tahoe watershed
assessment. USDA Forest Service, Pacific Southwest Research Station. Technical
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Wagtendonk, J.W., Weatherspoon, C.P., 1996. An overview of fire in the Sierra
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Colorado Front Range. Ann. Assoc. Am. Geogr. 77, 245–254.
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Neary, D.G., Klopatek, C.C., DeBano, L.F., Ffolliott, P.F., 1999. Fire effects on
belowground sustainability: a review and synthesis. Forest Ecol. and Manage.122,
51–71.
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forest. Forest Ecol. and Manage. 2, 21–33.
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Forest Sci. 16, 461–471.
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ponderosa Laws. Internat. J. Wildl. Fire 1, 107–118.
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severity in an area of wildland-urban interface, Angora Fire, Lake Tahoe Basin,
California. Forest Ecol. and Manage. 258, 773–787.
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Ecosystem Project: Final Report to Congress, Vol. 2. Assessments and Scientific
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32
Taylor, A.H., Beaty, R.M., 2005. Climatic influences on fire regimes in the northern
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Sierra Nevada conifers. Western Journal of Applied Forestry 13, 73–84.
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CHAPTER TWO: Spatial and temporal variation of forest floor moisture in longunburned Jeffrey pine-white fir forests of the Lake Tahoe Basin, USA.
1. INTRODUCTION
Prior to the turn of the 20th century, coniferous forests throughout the Lake Tahoe
Basin were open and dominated by Jeffrey pine (Pinus jeffreyi Grev. and Balf) (Taylor
and Beaty, 2005). In the late 19th century, fire frequency was reduced throughout the
Sierra Nevada (Taylor, 2004; Taylor and Beaty, 2005), causing an increase in forest
density and surface fuel accumulation (Vankat and Major, 1978; Parsons and
DeBenedetti, 1979; McKelvey et al., 1996; Miller and Urban, 2000; Taylor, 2004).
Shade tolerant and fire-sensitive white fir (Abies concolor [Gord. and Glend.] Lindl.) is
among the species that benefitted from fire exclusion. The potential for high severity
wildfires has increased (Vankat and Major, 1978; Parsons and DeBenedetti, 1979;
McKelvey et al., 1996; Miller and Urban, 2000) due to the change in forest structure and
development of forest floors with distinct, deep organic horizons (Stephens and Finney,
2002; Stephens et al., 2004).
Fire effects in long-unburned forest floors are linked to duff moisture, bulk
density, and depth (Frandsen, 1987; Miyanishi and Johnson, 2002; Varner et al., 2005;
Raaflaub and Valeo, 2008). The following aspects of fire are affected by forest floor
moisture: pre-heating and ignition, flame temperature, rate of spread, fire intensity,
emissions, residence time, consumption, and rate of energy release (Stocks, 1970;
Harrington, 1982; Nelson, 2001).
In contrast to other drivers of forest floor fire behavior (bulk density, depth, and
mineral content; see Chapter 1), moisture content can change rapidly in response to
33
34
metrological conditions over the short term on an hourly-weekly scale (Stocks, 1970;
Nelson, 2001; Raaflaub and Valeo, 2008). Meteorological conditions that influence
moisture content of forest floor fuels include air temperature, relative humidity, wind
speed and direction, precipitation (including fog and dew), solar radiation, and canopy
interception of precipitation (Nelson, 2001). Evaporation is the most important factor
influencing drying patterns of the forest floor (Stocks, 1970). Drying of a conifer needle
initially occurs through evaporation of the free water on top of and between needles. The
energy needed for evaporation reduces the needle temperature by a few degrees (Nelson
and Hiers, 2008). When there is no longer free water available for evaporation, the
drying procedure changes: water within the needle moves to the needle’s surface and into
the air, using the combined diffusion of bound water and water vapor (Van Wagner,
1979). The two main drivers of water loss from a fuelbed include the difference between
the vapor pressure of the external air and the vapor pressure within the fuelbed (Nelson
and Hiers, 2008).
Air temperature, relative humidity, and fuel moisture are interrelated
(Countryman, 1977). Air temperatures typically increase during the day and decrease at
night. Relative humidity follows the inverse pattern of air temperature. Litter moisture
patterns track relative humidity patterns closely throughout the day, although temperature
and relative humidity change more rapidly than litter (Countryman, 1977, Ferguson et al.,
2002). Litter typically has a higher moisture content than the equilibrium moisture
(moisture content at which fuel is neither gaining nor losing moisture) during the day and
a lower moisture content at night. In the early morning and late evening, when
35
temperature and relative humidity are reversing patterns, the moisture of the litter horizon
has time to “catch up” to the equilibrium moisture (Countryman, 1977).
Forest floors tend to dry from the surface downward to mineral soil. A study by
Harrington (1982) showed that when the moisture content of 1-hour woody fuel is less
than 10%, 1-hour woody fuel moisture is a good predictor of litter moisture. Needles
have higher percentages of extractives (organic compounds that can be highly
flammable) than wood. Extractives tend to reduce moisture exchange rates and decrease
water uptake (Nelson, 2001). Lower forest floor horizons, insulated from atmospheric
changes, tend to have slower drying times than the uppermost forest floor horizon
(Stocks, 1970). Weathered fuel particles without a waxy surface gain and lose moisture
much faster than un-weathered particles (Van Wagner, 1969). Fuel particles in latter
stages of decomposition have higher moisture holding capacities than un-decomposed
fuels, and the maximum moisture holding capacity of the forest floor increases with
increasing depth (Stocks, 1970). Forest floor bulk density also affects fuel moisture
dynamics. Denser forest floor profiles have slower drying rates than more porous forest
floor profiles (Stephens, 2000).
Fuel time lag, also known as fuel response time, is defined as the amount of time
(hours) required to lose 63.2% (1 – 1/e) of the initial fuel moisture content above
equilibrium (Byram, 1963). Response times are important for determining how rapidly
fuels lose and gain moisture in response to wetting and drying sequences (Anderson,
1985). Fuel time lags for conifer litter range from 1 to 21 hours (Fosberg, 1975;
Anderson et al., 1978). Response times for subalpine fir (Abies lasiocarpa [Hook.] Nutt.)
and grand fir (Abies grandis Lindl.) litter ranged from 10.6 to 11.4 hours, respectively
36
(Anderson, 1985). Ponderosa pine (Pinus ponderosa Dougl.) litter had an average
response time of 5.8 hours (Anderson, 1985). Time lag studies often convert moisture
content to relative moisture content (E). Relative moisture content is known as the
fraction of remaining water in the fuelbed available for evaporation at time t (hours)
(Fosberg, 1970; Anderson, 1985; Nelson and Hiers, 2008) and is given by:
(
E=(
)
)
(Eq. 1)
where
E = relative moisture content
= moisture content at time t (hours)
= equilibrium moisture content
= initial moisture content; “fiber saturation”.
Duff moisture, bulk density, and depth are strong determinants of the amount of
duff consumed during fire (Frandsen, 1987; Stephens et al., 2004; Hille and Stephens,
2005) and vary depending on spatial position (Frandsen, 1987; Miyanishi and Johnson,
2002; Raaflaub and Valeo, 2008). Spatial variation in duff moisture can be influenced by
precipitation throughfall, which is related to overstory canopy structure and understory
vegetation (Hille and Stephens, 2005). In a mixedwood boreal forest, Miyanishi and
Johnson (2002) found that duff had a significantly lower moisture content beneath trees
than in open gaps and found that post-fire patch sizes were more evenly distributed when
duff was dry. The moisture content of duff was also higher in gaps than beneath trees in
a mixed-conifer forest of the Sierra Nevada (Hille and Stephens, 2005), and in a jack pine
37
(Pinus banksiana Lamb.) stand located in Saskatchewan, Canada (Chrosciewicz, 1989).
Many studies have observed spatial patterns in duff consumption in coniferous forests
(Sweeney and Biswell, 1961; Robichaud and Miller, 1999; Miyanishi and Johnson, 2002;
Hille and Stephens, 2005; Knapp et al., 2005; Knapp and Keeley, 2006). Distance from a
tree’s bole strongly predicts the amount of duff remaining after prescribed burns. There
is a lower probability of duff remaining closer to trees. This is likely due to lower
moisture levels beneath tree canopies than in open gaps, and deeper forest floor
accumulation near the base of trees (Miyanishi and Johnson, 2002; Hille and den Ouden,
2005; Hille and Stephens, 2005).
Higher duff moisture contents lead to less duff consumption (Nelson, 2001;
Varner et al., 2007). Smoldering fires can ignite in dry patches of the forest floor and
spread to moist areas due to heat transfer ahead of the smoldering front (Frandsen, 1987).
Previous studies have found complete forest floor consumption to occur at moisture
contents below a 30% threshold (Sandberg, 1980; Brown et al., 1985; Stephens and
Finney, 2002; Hille and Stephens, 2005) and no smoldering combustion to take place
above 120% moisture content (Sandberg, 1980). In a long-unburned longleaf pine (Pinus
palustris Mill.) forest, Varner et al. (2007) found that prescribed fire during dry duff
conditions (55% duff moisture content) consumed six times the amount of forest floor
compared to wet duff conditions (115% duff moisture content). Understanding forest
floor moisture patterns is critical to the prediction post-fire forest floor consumption.
The amount of woody fuel consumed and duration of flaming front is also
dependent on the amount of duff consumption (Sandberg, 1980). Prescribed fires with
fuel reduction objectives have greatest efficiency during drier summer and fall conditions
38
in white fir forests and during spring, summer, or fall in western pine forests (Agee et al.,
1978). Aspect and topography can also influence post-fire forest floor mosaics (Taylor
and Skinner, 2003). Studies have found positive relationships between amounts of duff
consumed and increases in rates of runoff, erosion, hyper-concentrated flow, severe
flooding, sedimentation, and debris flow (Morris and Moses, 1987; Cannon et al., 1998;
Robichaud and Miller, 1999; Cannon and Reneau, 2000; Beeson et al., 2001). Knowledge
of forest floor moisture dynamics throughout the fire season will help land managers
predict post-fire consumption patterns and outcomes because duff moisture is the primary
predictor of duff consumption (Frandsen, 1987). Duff moisture varies depending upon
spatial position (Frandsen, 1987; Miyanishi and Johnson, 2002; Raaflaub and Valeo,
2008).
This study addressed the following questions: (1) how does forest floor moisture
differ between species (Jeffrey pine and white fir), among forest floor horizons (litter,
fermentation, and humus) and other important woody fuels (cones, 1-hour woody fuels,
10-hour woody fuels), across positions in relation to the base of a tree (tree base, canopy
drip line, and open gaps), throughout the fire season (snowmelt to snowfall), and across
the Tahoe Basin (at four study sites); (2) how does forest floor moisture differ
interannually (2009 and 2010) at one site; (3) how does forest floor moisture vary over a
24-hour period between species, across positions in relation to the base of a tree, and
between important woody fuels (cones and 1-hour woody fuels); and lastly (4) how do
fuel moisture time lags vary among Jeffrey pine litter, white fir litter, Jeffrey pine 1-hour
woody fuel, Jeffrey pine 10-hour woody fuel, and Jeffrey pine cones? This study will
develop a better understanding of forest floor moisture dynamics between Jeffrey pine-
39
white fir forests, among forest floor horizons and woody fuels, at an individual tree scale,
across sites throughout Lake Tahoe Basin, and between the 2009 and 2010 fire seasons.
In-depth knowledge of forest floor moisture dynamics is important for the
development of management and conservation plans throughout Jeffrey pine-white fir
forests of the Sierra Nevada. Studying the patterns of forest floor moisture will aid in
management objectives intending to reduce forest floor accumulation in long-unburned
ecosystems, as well as develop an understanding for post-fire consumption patterns. The
ability to predict and control forest floor consumption patterns during prescribed fires
will help to achieve fuel reduction objectives while reducing large tree mortality and
limiting erosion into Lake Tahoe.
2. METHODS
2.1. Study Sites
Details of the study sites are discussed in Chapter 1, but briefly: four study sites
were chosen on U.S. Forest Service land throughout the Lake Tahoe Basin in California
and Nevada (Fig. 8). The Baldy unit is on the north shore of Lake Tahoe in Nevada at an
elevation of 2293 m. The Secret unit is on the east shore of Lake Tahoe in Nevada at
1951 m. Bobwhite is on the west shore site in California, at an elevation of 2055 m, and
Pioneer is located at the southern end of Tahoe, at 2000 m. Pioneer was the location of
the interannual (2009, 2010) and diurnal moisture studies, and all four sites were part of
the 2010 moisture study (Fig. 8).
2.2. Field Data Collection
During the summer of 2009 and 2010, large Jeffrey pine ( ̅ DBH = 68.4 cm) and
white fir ( ̅ DBH = 66.4 cm) trees greater than 50 cm DBH were randomly selected for
longitudinal moisture sampling (Fig. 9) using a random azimuth and distance (3 to 110
m; using the spin of a compass, where 360 ft = 110 m) between sample trees. The
preliminary starting point was always the same at each site, and the actual starting point
was selected after traveling the first random distance and random azimuth. The azimuth
and distance between sample trees were re-randomized from each selected tree. Samples
(> 10 g) of each forest floor horizon (litter, fermentation, and humus) and other important
woody forest floor fuels (cones, 1-hour fuels, 10-hour fuels) were collected. Cones were
included because they are “vectors” of ignition for the underlying forest floor due to their
long smoldering times (Jeffrey pine ̅ = 74 minutes; Fonda and Varner, 2004). Fine
40
41
Baldy (2010)
Aspect: 216
Elevation: 2293 m
Secret (2010)
Aspect: 280
Elevation: 1951 m
Lake
Tahoe
Bobwhite (2010)
Aspect: 310
Elevation: 2055 m
Nevada
Pioneer
Diurnal Moisture Study
Inter-annual (2009, 2010)
Aspect: 289
Elevation: 2000 m
Fig. 8. Location of study sites in the Lake Tahoe Basin in California and Nevada.
Pioneer was selected for the interannual (2009, 2010) and diurnal moisture studies, and
all four sites were selected for the 2010 moisture study. All sites were dominated by
mature Jeffrey pine-white fir forests.
42
Tree Base - 0.5 m
Drip Line - 2.0 m
Open Gap - 5.0 m
Fig. 9. Forest floor moisture samples were collected 0.5 m away from the tree
bole at the “base” of each tree, 2.0 m away from the tree bole at the crown
“drip line”, and 5.0 m away from the tree bole beyond the crown in open
“gaps”.
43
woody fuels (1-hour and 10-hour) can carry surface fires, and aid in the ignition of
underlying forest floor.
Fuel samples were collected ca. 20 days apart throughout the fire seasons of 2009
(21 July to 17 October) and 2010 (10 June to 01 October) from 6 trees (3 Jeffrey pine; 3
white fir) at each study site during each collection date (108 samples/site/collection date).
Fuels were collected at the base of each tree, at the crown drip line, and beyond the
crown in open “gaps” (Fig. 9) along transects placed at randomly selected azimuths.
All fuels were collected between 1200 to 1600 hours. Samples were sealed in
polyethylene bags and weighed within 3 hours following collection to obtain wet
weights. Collected fuels were then oven-dried for 48 hours at 60 °C to obtain dry
weights. Moisture content (% oven-dry basis) was calculated gravimetrically by:
[
(
)
]
(Eq. 2)
where
= moisture content (%)
= wet sample weight (g)
= oven-dry sample weight (g).
A total of 540 moisture samples were collected on 5 dates from July through
October 2009 from the southernmost unit, Pioneer. During the summer of 2010, three
additional sites across the Lake Tahoe Basin were selected (Fig. 8), and the methods
above were repeated. A total of 3,024 moisture samples were collected on 7 dates across
the four study sites from June through October in 2010.
44
To investigate moisture patterns across a 24-hour period at the Pioneer site, eight
Jeffrey pine ( ̅ DBH = 64.3 cm) and eight white fir ( ̅ DBH = 70.6 cm) trees greater than
50 cm DBH were randomly selected as sample trees (Fig. 10).
Three transects on the north side and three transects on the south side of each tree
were established. For each species, four transects (two north transects and two south
transects) were established every two hours over a 24-hour period, with azimuths selected
using a random number generator. At each transect, samples (> 10 g) of litter were
collected from the base of each tree, at the crown drip line, and beyond the crown in open
“gaps” (Fig. 9). A 1-hour (< 0.6 cm) woody fuel sample (> 20 g) and a cone sample (>
40 g) were also collected along each transect. A total of 480 moisture samples were
collected on 9 August to 10 August 2010, from 2300 hours to 2100 hours. Weather data,
including temperature, wind direction, wind speed, relative humidity, and dew point were
recorded during each collection period. All collected fuels were sealed in polyethylene
bags and weighed within 30 minutes following collection to obtain wet weights.
Collected fuels were then oven-dried for 48 hours at 60 °C to calculate fuel moisture
content (Eq. 2).
2.3. Laboratory Data Collection
To compare desorption rates across different forest floor fuel types, a controlled
time lag experiment was completed. Time lag, also known as response time, is defined as
the amount of time (hours) it takes for fuels to lose 63.2% of the initial moisture content
above equilibrium (Byram, 1963). Forest floor samples (5 samples each: Jeffrey pine
litterbeds; white fir litterbeds; Jeffrey pine 1-hour woody fuelbeds; Jeffrey pine 10-hour
woody fuelbeds; and Jeffrey pine cones) were oven dried at 60°C for 48 hours to obtain
45
Fig. 10. Location of the diurnal moisture collection site (Pioneer) in the southern region
of the Lake Tahoe Basin, USA, dominated by mature Jeffrey pine and white fir.
46
dry weights. Samples were then submerged in a water bath for 24 hours, drained and
weighed again prior to the time lag study. Fuels were placed in 20 × 20 cm tins with 36
evenly spaced drainage holes 2 mm in diameter and placed on top of wooden slats (Fig.
11).
The desorption experiment took place in a temperature- and humidity-controlled
room. Samples were weighed and temperature and relative humidity data ( ̅ = 21.8°C
and 42.5%, respectively) were recorded periodically throughout the 196 hours (8 days).
Moisture content was converted to relative moisture content (E; Eq. 1) (Fosberg, 1970);
the remaining fraction of water available to evaporate from the fuelbed at time t (hours),
in order to estimate response times.
2.4. Data Analysis
All field collections were completed in a split plot experimental design (Fisher,
1925). A mixed-effects linear model (ANOVA) was used to analyze forest floor
moisture (both interannually and across sites) and diurnal moisture data. All field
moisture data were log transformed to meet model assumptions. Humus was removed
from the 2010 data set, and humus and cones were removed from the interannual dataset
due to an insufficient number of samples at the collection locations. Although there was
still a slight imbalance in the forest floor moisture dataset due to missing samples, this
imbalance did not affect data analysis, as indicated by little difference between adjusted
sum of squares and sequential sum of squares. Analyses determined whether there were
any significant relationships and (or) interactions between the continuous response
variable moisture content (%); and the five categorical predictor variables site, species,
forest floor horizons, position in relation to the tree, and date of collection.
47
Fig. 11. Forest floor samples used in a laboratory time lag experiment: Jeffrey pine
cones; Jeffrey pine litter; Jeffrey pine 1-hour and 10-hour woody fuelbeds; and white fir
litter collected from the Lake Tahoe Basin, USA.
48
When analyzing interannual moisture and diurnal moisture datasets, site was not included
as a predictor variable. When analyzing moisture data across the Lake Tahoe Basin,
measurements from individual sample trees were nested within site and species as a
random effect, because observations were not independent of one another at the site and
species level. Due to lack of independence between certain variables for the interannual
moisture data, measurements from individual sample trees were nested within year of
collection and species, and collection dates were nested within year of collection. When
analyzing the diurnal moisture study, transects were nested within species and time of
collection. Where main effects or interactions were found to be significant ( < 0.05),
multiple comparisons tests with the Bonferonni correction were used to investigate
differences among categorical variable levels.
Piecewise polynomial (in this case, piecewise linear) curve-fitting was used to
analyze the time lag data (Seber and Wild, 1989). Relative moisture content (Eq. 2) was
log-transformed, and the negative inverse of the slope (-1/b1) was used to determine the
response time for each fuel type. Once time lags were obtained for each forest floor
sample, these values were compared using a multiple comparisons test, with the
Bonferonni correction, to investigate differences among categorical variable levels ( <
0.05).
3. RESULTS
Forest floor components beneath Jeffrey pines were drier than white fir forest
floors across all sites and collection locations (P = 0.007) during the 2010 fire season.
Forest floor samples collected at the tree base (8.0% moisture content) were significantly
wetter than beneath the crown drip line (6.8% moisture content) and open gaps (6.0%
moisture content), whereas moisture samples collected from the crown drip line and open
gaps did not differ from one another (P < 0.001). Among the forest floor samples
collected throughout the 2010 fire season, the litter horizon was the driest (4.8%),
fermentation had the highest moisture content (14.2%; 3 times more moist than litter),
and 1-hour and 10-hour woody fuel moisture did not differ (5.5 and 6.0%, respectively)
(P < 0.001).
There were significant interactions between location, relative to the tree bole, of
the collected samples and the forest floor components (P < 0.001). Moisture contents of
the litter horizon, along with all woody fuels (1-hour, 10-hour, and cones), did not differ
in relation to distance from tree boles. Moisture content of the fermentation horizon
differed across all locations, with moisture content lowest in open gaps and highest at tree
bases (Fig. 12). The fermentation horizon dipped below the critical moisture threshold of
30% (Sandberg, 1980; Brown et al., 1985; Stephens and Finney, 2002; Hille and
Stephens, 2005) by mid-June beneath the crown drip line and in open gaps. Moisture
contents remained below 30% throughout the fire season. The fermentation horizon at
the base of trees dropped below 30% moisture content in early July. Between the first
collection at the beginning of June and the second collection in mid-June (11 days apart),
49
100
Moisture Content (%)
75
50
25
0
10 June
21 June
05 July
21 July
05 August
30 August
01 October
Collection Date
Fig. 12. Mean moisture content (%) of the fermentation horizon across positions in relation to
tree boles, during the 2010 fire season in Jeffrey pine-white fir forests of the Lake Tahoe Basin,
USA.
50
51
moisture content of the fermentation horizon at the tree base dropped by 60% (85.7 to
34.7%, respectively). Beneath the crown drip line moisture content decreased by ca. 70%
(67.2 to 21.0%, respectively). Open gaps lost over 75% of its moisture content (55.9 to
13.2%, respectively; Fig. 12). Moisture patterns among the collected forest floor
components were similar across all locations: litter and 1-hour fuel were the driest
components and did not differ from one another in moisture content; 1-hour and 10-hour
fuel did not differ from one another in moisture content. Cones and the fermentation
horizon were significantly more moist than all other components.
There were significant interactions between collection dates and location of the
collected samples (P = 0.029). During the first and last two moisture collections in 2010
(10 June, 30 August, and 01 October), moisture across locations in relation to distance
from tree boles (Fig. 9) did not differ, and forest floor fuels had the highest moisture
contents during these dates. At the end of June, forest floor moisture at the base of trees
(9.1%) was higher than beneath crown drip lines (6.9%) and in open gaps (6.1%). In
early July through early August, forest floor components in open gaps (3.7%) were
significantly drier than at the base of trees (5.3%), although forest floor moisture beneath
crown drip lines did not differ from open gaps or tree bases. Forest floor horizons and
woody fuel were driest at the end of July through early August.
Collection dates and forest floor components also had significant interactions (P <
0.001). Moisture contents of 1-hour and 10-hour fuels were similar across all collection
dates. Cones, 1-hour, and 10-hour woody fuel moisture contents were similar from the
end of June through the culmination of the 2010 fire season (Fig. 13). Litter and 1-hour
fuel moisture contents were statistically similar across all collection dates.
20
15
10
Moisture Content (%)
5
0
120
6/10/10
6/21/10
Low
Consumption
7/5/10
7/21/10
8/5/10
Moderate
Consumption
8/30/10
10/1/10
Heavy
Consumption
90
60
30
Litter
0
10 June
21 June
05 July
21 July
05 August
30 August
01 October
Collection Date
Fig. 13. Mean moisture content (%) of different forest floor fuels (top) and horizons (bottom) during the
2010 fire season throughout the Lake Tahoe Basin, USA. Different scale on y-axis for fuel moisture (0 to
20%) and forest floor horizon moisture (0 to 120%).
52
53
Moisture contents of litter, 1-hour, and 10-hour fuels were similar across all dates except
for the first collection period (10 June). Throughout the fire season, 1-hour and 10-hour
fuels remained below 15% moisture content (Fig. 13). The fermentation horizon had
significantly higher moisture content than any other forest floor component (except
humus) from the beginning of June through early July (Fig. 13). Although humus could
not be statistically analyzed due to lack of samples, moisture content in humus did not
drop below 30% until late August. Humus maintained the highest moisture content
throughout the entire 2010 fire season (Fig. 13).
Rain events at Pioneer in late August and at Bobwhite in late September did not
allow for a true comparison of moisture patterns across the Lake Tahoe Basin. After
removing the last two collection dates from the analyses (30 August and 1 October,
2010), moisture contents across sites still differed (P = 0.027), and there were significant
interactions between collection dates and study sites (P < 0.001) located throughout the
Lake Tahoe Basin (Table 4).
Forest floor fuels were collected at the southernmost site, Pioneer, during both the
2009 and 2010 fire seasons. June through October temperatures were similar between
years, although 2009 was warmer than 2010 in April (12.6 and 10.3C, respectively) and
May (20.1 and 14.3C, respectively) (Fig. 14). Throughout the months of February
through October, 2010 was 9.2 cm wetter than 2009 (Fig. 14). During the months of
February through May, both years received 34.0 cm of precipitation, although 2009 had
6.7 cm more precipitation in May than 2010 (Fig. 14). The Palmer Drought Severity
Index (PDSI; Palmer, 1965) indicates that 2009 was a “mild drought” year ( ̅ PDSI = 1.54). 2010 was classified as a “near normal” year ( ̅ PDSI = -0.02) in Region 3 of
54
Table 4. Average forest floor moisture (%) across sites throughout the 2010 fire season
in the Lake Tahoe Basin, USA.
Moisture Content (%)
Pioneer
Bobwhite
Secret
Baldy
10 June
12.7 a
12.8 a
13.9 a
22.0 b
21 June
7.5 bc
6.9 ab
6.1 a
8.7 c
05 July
6.1 a
5.2 a
6.1 a
5.4 a
21 July
2.3 a
3.3 b
4.3 c
3.9 bc
05 August
4.4 b
5.0 b
3.2 a
4.1 b
30 August
36.2 d*
9.9 c
5.6 b
2.9 a
01 October
7.4 b
80.4 c*
7.0 ab
6.0 a
All superscripted lowercase letters (a, b, c, d) represent significant differences (vertically) between collection sites at each
collection date. Rainfall events are represented by *.
30
30
20
Precipitation (cm)
20
15
15
10
10
5
5
0
0
Average Maximum Temperature ( C)
25
25
Month
Fig. 14. Total precipitation (cm; represented by bars) and average maximum temperature (°C; represented by
lines) from February through October of 2009 and 2010 in the southern region of the Lake Tahoe Basin, USA.
55
56
California, with June to October PDSI values ranging from -0.69 to 0.71 ( ̅ PDSI = 0.11)
in 2009, and from 0.11 to 1.55 ( ̅ PDSI = 0.71) in 2010 (Enloe, 2011).
Despite similarities in annual climate between 2009 and 2010, forest floor fuels
were significantly drier in 2010 than 2009 (P = 0.003). There were no differences in
moisture content between species (P = 0.988), or across location in relation to tree bases
(P = 0.418). Moisture contents of all forest floor fuels differed significantly from one
another (P < 0.001). The fermentation horizon had greater moisture and litter had lower
moisture content than all other collected fuels. Year and forest floor components had
significant interactions (P = 0.001); whereas 1-hour and 10-hour fuel moisture did not
differ between years, litter and fermentation horizons were drier in 2010 than 2009 (Table
5). A rain event prior to the late October collection in 2009 (17 October) and the late
August collection in 2010 (30 August) caused large differences in moisture contents
(Table 5). After removal of these dates, 2010 was still significantly drier than 2009 (P =
0.001) and all fuels differed from one another between years (P = 0.003).
Throughout the 24-hour moisture collection period (9 August to 10 August 2010),
the average temperature ranged from 10.3 to 23.1C. Relative humidity ranged from 29.3
to 61.2% (Fig. 15). Dew point ranged from 2.8 to 5.5C. Wind speed was mild (0.0 to
4.2 km hr-1). Haines index (HI = 4) indicated a low potential for rapid wildfire growth.
Lightning activity level (LAL = 1) signified that no thunderstorms were expected, and
there was a 0% chance of wetting rain on 10 August 2010.
Forest floor fuels beneath Jeffrey pines were drier, on average, than beneath white
firs across the entire 24-hour sampling period (P = 0.006). Moisture content of the litter
horizon beneath crown drip lines and in open gaps did not differ (P < 0.001). In contrast
57
Table 5. Average moisture content (%) of forest floor components
in 2009 (white) and 2010 (gray) at the Pioneer site in the southern
region of the Lake Tahoe Basin, USA.
Moisture (%)
1-hour
10-hour
Litter
Fermentation
July 21
2.0
2.2
1.5
3.1
July 31
6.0
7.1
5.4
12.1
August 5
3.4
4.2
3.3
6.5
August 14
4.7
5.1
3.7
7.5
August 30
34.9
36.9
16.4
60.5
September 5
4.9
5.4
5.0
9.4
September 26
2.7
3.1
4.4
9.2
October 1
6.2
7.7
6.2
9.4
October 17
27.4
51.9
28.5
79.5
25
60
Temperature (°C)
20
50
15
40
Temperature
30
10
20
5
10
0
Relative Humidity (%)
70
0
Time of Day (hrs)
Fig. 15. Temperature (C) and relative humidity (%) patterns during a 24-hour forest floor moisture
collection in the Lake Tahoe Basin, USA on 10 August 2010.
58
59
to the 2010 moisture data across the Tahoe Basin, where litter moisture was not affected
by distance from trees, the litter horizon directly beneath the base of trees was the driest
forest floor component (2.6 to 8.7%). Jeffrey pine cones were the wettest (3.9 to 11.0%).
The species × location interaction was significant: moisture content of Jeffrey pine cones
and litter at the bases of both conifers did not differ; whereas moisture contents of 1-hour
fuel and the litter beneath crown drip lines and in open gaps were drier surrounding
Jeffrey pines (P = 0.010) (Fig. 16). The interaction between species and time of
collection was also significant (P = 0.009). White fir forest floors were drier only at
nighttime (0300 and 0500 hours) (Fig. 16; C and D). At 1500 hours, all forest floor
components dropped below 5% moisture content (Fig. 16). They then increased above
5% by 1900 hours. Throughout the 24-hour collection period, all forest floor components
underwent at least a 6% change in moisture. The moisture content of 1-hour fuel ranged
from 4.0 to 11.9%, Jeffrey pine cones ranged from 4.0 to 12.5% and of litter directly
beneath tree boles ranged from 2.1 to 10.2% (Fig. 16).
Although direction (north vs. south side of tree) of the moisture collection
transects alone was not a significant predictor of moisture (P = 0.702), time and direction
had significant interactions (P = 0.032). Jeffrey pine and white fir forest floors were drier
on the north side of trees during nighttime (0100, 0300, and 0500 hours) and on the south
side of trees during the afternoon (1300 and 1500 hours). Direction of the collection
transects also affected the moisture of the litter directly beneath tree boles (P < 0.001),
with the south face of tree bases drier than the north face, on average (5.9 and 6.9%,
respectively).
15
A. 1-hour fuels
15
B. P. jeffreyi cones
A. concolor
Moisture Content (%)
P. jeffreyi
10
10
5
5
0
0
15
C. Litter – Tree Base
15
10
10
5
5
0
0
D. Litter – Open Gaps
Time of Day (hrs)
Fig. 16. Moisture content (%) of forest floor components in long-unburned Jeffrey pine and white fir forests in the southern
region of the Lake Tahoe Basin, USA across a 24-hour period in August, 2010.
60
61
In the laboratory desorption experiment, Jeffrey pine and white fir litterbeds were
similar. Both species’ litterbeds had the lowest relative moisture contents, whereas
relative moisture content was highest among Jeffrey pine cones (Fig. 17). Jeffrey pine
cones also had the longest average response time of 63.9 hours. The average response
time for 1-hour fuelbeds was 21.3 hours, and 10-hour fuelbeds was 33.5 hours. Jeffrey
pine and white fir litterbeds had the fastest response times (7.4 and 10.6 hours), and
litterbeds did not differ from one another (Fig. 17).
1
0.9
0.8
Relative Moisture Content (E)
0.7
Component
Time Lag (h)
P. jeffreyi litter
A. concolor litter
P. jeffreyi 1-hour fuelbed
P. jeffreyi 10-hour fuelbed
P. jeffreyi cone
7.4d
10.6d
21.3c
33.5b
63.9a
SE n
0.4
0.9
2.0
2.0
2.8
5
5
5
5
5
R2 adj = 0.96, P < 0.001
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
0
15
30
45
60
75
90
105
120
Duration of Desorption (h)
135
150
165
180
195
Fig. 17. Relative moisture content (E) of forest floor components in relation to duration of desorption (hours)
for mature Jeffrey pine and white fir forests in the Lake Tahoe Basin, USA. Time lags (hours) for each forest
floor component, standard errors (SE), and multiple comparisons are shown in the top right corner with
superscripted lowercase letters (a, b, c, d).
62
4. DISCUSSION
This study examined a broad range of spatial and temporal forest floor moisture
patterns in the Lake Tahoe Basin, USA (Fig. 18). Rather than making inferences based
on one fire season, this study was able to compare forest floor moisture during two fire
seasons in the southern region of the Tahoe Basin. Results from the interannual moisture
analysis revealed that 1-hour and 10-hour woody fuel moisture were remarkably similar
between years, although litter and fermentation horizons were significantly drier in 2010
than in 2009. This data illustrates that forest floor moisture can differ even if woody fuel
moisture remains the same. Temperatures and precipitation from June through
September were similar in both years. This explains the lack of moisture variation
between woody fuels. May received more precipitation in 2009 than in 2010. These
precipitation differences could account for increased moisture of the moisture retaining
fermentation horizon during the summer of 2009. Seasonal lows in forest floor moisture
could be more dependent on time since the last major precipitation event, rather than total
precipitation throughout a year. In order to understand spatial moisture patterns, it is
clear that multiple duff moisture samples collected throughout a stand should be made,
rather than estimating forest floor moisture based solely on 10-hour fuel moisture sticks.
Moisture contents of litter and all collected woody fuels (1-hour, 10-hour, and
cones) did not differ in relation to distance from trees throughout the entire 2010 fire
season. Among forest floor horizons, only moisture content of the fermentation horizon
varied spatially. In northern Arizona, Faiella and Bailey (2007) found that woody fuel
moisture (1-hour, 10-hour, and 100-hour) did not fluctuate across contrasting overstory
63
64
1
103
0.9
Interannual 0.8
0.7
Time (d)
Year
0.6
102
0.5
0.4
Month
Timelag
0.3
12
100.2
0.1
Diurnal
0
1002
Forest
floor
10-1
Forest floor
Stand
103
Stand
Tahoe10Basin
9
Landscape
Space (m2 )
Fig.18. Conceptual model representing the spatial and temporal scales measured in
this study (light gray) and previous forest floor moisture studies (dark gray).
65
treatments. Results from this study, as well as Faiella and Bailey’s (2007) findings,
indicate that moisture content of duff varies throughout the stand, whereas other
important forest floor components do not.
Knowledge of diurnal forest floor moisture fluctuations can help predict standscale fire behavior and forest floor consumption patterns. Few studies have quantified
diurnal changes in live fuel moisture (but see Philpot, 1965; Blackmarr and Flanner,
1968), and even fewer studies have focused on diurnal changes in forest floor moisture
(Ferguson et al., 2002). In a longleaf pine forest in Florida, Ferguson et al. (2002) found
that litter wets and dries more quickly than other forest floor components. In addition,
there was large diurnal variation in the litter moisture (ca. ± 4%) due to increased air
circulation. Ferguson et al. (2002) also found large diurnal variations in forest floor
moisture, although moisture fluctuations in the litter horizon (± 6.5%) were similar to 1hour woody fuel (± 6.0%) and Jeffrey pine cone (± 7.1%) moisture fluctuations in the
Tahoe Basin. Ferguson et al. (2002) also found that litter closely tracked relative
humidity changes throughout the 24-hour period. Factors that influence drying of forest
floor fuels include fuel thickness and density, air temperature, relative humidity, wind
speed, and the initial moisture content of the fuel (Nelson, 1969). Additional diurnal
forest floor moisture research in long-unburned ecosystems will expand information on
prescribed fire windows, giving land managers thorough knowledge to help plan
successful prescribed fires.
Forest floor fuel desorption and adsorption rates in response to wetting or drying
cycles (response time) are important factors influencing ignitability and rate of spread
(Nelson, 1969; Nelson, 2001). Anderson (1985) compared desorption rates for 16
66
different fuel types, revealing that recently cast ponderosa pine needles had an average
response time of 5.8 hours, grand fir needles averaged 11.4 hours, and subalpine fir
needles took 10.6 hours to reach 63.2% of the initial fuel moisture content above
equilibrium. These results are comparable to the response times found in Tahoe Basin
fuels, where Jeffrey pine needles averaged 7.4 hours and white fir needles averaged ca.
40% longer (10.6 hours). 1-hour and 10-hour woody fuel had response times ranging
from 40 to 87 hours in fuelbeds consisting of masticated common manzanita
(Arctostaphylos manzanita) and snowbrush (Ceanothus velutinus) (Kreye et al., 2011),
while this study estimated response times of Jeffrey pine 1-hour and 10-hour woody
fuelbeds to be 21 and 34 hours, respectively. Using 10-hour fuel moisture sticks
(suspended above the forest floor) to estimate fuel moisture may lead to underestimations
of fuel moisture in the field (Kreye, 2008). Recognizing fuelbed loading and
arrangement patterns is important when estimating fuel moisture because slight
differences in fuelbed characteristics can alter desorption rates (Nelson and Hiers, 2008;
Kreye et al., 2011) and hence resulting fire behavior.
Throughout the entire collection period, highly flammable litter was consistently
the driest forest floor horizon. Litter moisture did not differ from 1-hour or 10-hour
woody fuel moisture during the fire season (excluding 10 June). Moisture contents for
litter and all woody fuels were below 15% throughout the collection period until early
October. The litter horizon, Jeffrey pine cones, and all woody fuels dried faster and
average moisture contents were lower than the fermentation and humus horizons.
Conifer litter burns with short durations and high intensities (Fonda et al., 1998; Fonda,
2001), whereas woody fuels smolder for long durations (Costa and Sandberg, 2004).
67
Jeffrey pine cones have an average burning time of 78 minutes (Fonda and Varner, 2004).
Smoldering temperatures during woody fuel consumption increases with increasing
woody fuel diameter and decreases with increasing moisture content (Costa and
Sandberg, 2004). During fire, smoldering woody fuels and cones aid in the pre-heating
and ignition of underlying forest floor duff (Fonda and Varner, 2004). Due to the low
moisture content (< 15%) of litter and woody fuels during the majority of the fire season,
the underlying forest floor (fermentation and humus) horizons could easily ignite.
Forest floor moisture is a strong determinant influencing flaming and smoldering
combustion rates (Frandsen, 1987). Moisture variation within forest floor horizons
affects forest floor consumption patterns and post-fire effects (Miyanishi and Johnson,
2002; Hille and Stephens, 2005; Knapp et al., 2005; Knapp and Keeley, 2006). The
fermentation horizon had the highest moisture content (other than humus, which was not
included in the analysis), the deepest depths, and the highest bulk density values (Chapter
1) of any forest floor horizon. Between early and mid-June, there was a rapid decrease in
moisture of the fermentation horizon. By early July, the fermentation moisture fell below
30% across all spatial positions and remained below this threshold for the remainder of
the collection period (through 01 October). When duff moisture drops below 30%,
complete consumption often results (Sandberg, 1980; Brown et al., 1985; Stephens and
Finney, 2002; Hille and Stephens, 2005). Complete consumption increases the risk for
large tree mortality due to basal injury caused by duff smoldering (Ryan and Frandsen,
1991; Sweezy and Agee, 1991; Stephens and Finney, 2002; Varner et al., 2007). When
duff is consumed and large patches of bare mineral soil are exposed, increased rates of
runoff, erosion, hyper-concentrated flow, severe flooding, sedimentation, and debris flow
68
can result (Morris and Moses, 1987; Cannon et al., 1998; Robichaud and Miller, 1999;
Cannon and Reneau, 2000; Beeson et al., 2001).
The Lake Tahoe Basin has a short window for prescribed burning. After the
winter snowpack melts, forest floor horizons in Jeffrey pine-white fir forests dry rapidly
but moderate rainfall events can increase duff moisture above ignition thresholds (120%;
Sandberg, 1980). This can make it difficult to reduce forest floor accumulation with
prescribed fire. Moisture results for a “near normal” PDSI year (Palmer, 1965) suggest
that late spring and early summer prescribed fires would achieve patchy forest floor
consumption in the Lake Tahoe Basin (Fig. 13), helping to reduce the risk of erosion into
Lake Tahoe. Fall-season prescribed fires would result in heavy fuel consumption (Fig.
13).
Throughout the 2010 fire season, Jeffrey pine forest floors were drier than white
fir forest floors, although differences were slight. In the southern Basin at the Pioneer
study site, Jeffrey pine and white fir forest floor moisture showed no interannual
differences. This relationship also held true for forest floor bulk density and depth
between the two species (Chapter 1). During the diurnal forest floor moisture study, the
moisture content of the litter horizon beneath tree boles of Jeffrey pine and white fir were
also similar. In the laboratory fuel time lag study, Jeffrey pine and white fir litterbeds
had comparable desorption rates (Fig. 17), and this similarity could account for the lack
of moisture differences between the two species. Similarities between Jeffrey pine and
white fir forest floors throughout the Tahoe Basin may make it difficult to prepare
burning prescriptions that induce fir mortality while preserving Jeffrey pine, a primary
restoration goal in Jeffrey pine-dominated stands. Future research should focus on
69
linking spatial patterns of forest floor moisture and bulk density to post-fire forest floor
consumption patterns to allow for greater predictability and understanding of post-fire
effects in long-unburned ecosystems.
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