Analysis of 15 Years of Data From the
California State Parks Prescribed Fire Effects Monitoring Program
R7 SNPLMA Project # 6A07
Prepared by:
Alison E. Stanton and Bruce M. Pavlik
BMP Ecosciences
3170 Highway 50 Suite #7
South Lake Tahoe, CA 96150
Prepared for:
California State Parks, Sierra District
P.O. Box 16
Tahoe City, CA 96145
This research was supported through a grant with the USDA Forest Service Pacific Southwest
Research Station and using funds provided by the Bureau of Land Management through the sale
of public lands as authorized by the Southern Nevada Public Land Management Act.
http://www.fs.fed.us/psw/partnerships/tahoescience/
The views in this report are those of the authors and do not necessary reflect those of the USDA
Forest Service Pacific Southwest Research Station or the USDI Bureau of Land Management.
1
Table of Contents
Key Findings ................................................................................................................................... 3
Introduction ..................................................................................................................................... 5
Methods ........................................................................................................................................... 7
Monitoring Program .................................................................................................................... 7
Site Description ........................................................................................................................... 8
Prescribed fire treatment ............................................................................................................. 8
Vegetation measurements ......................................................................................................... 10
Surface and ground fuel measurements .................................................................................... 11
Plot Selection............................................................................................................................. 11
Data Analysis ............................................................................................................................ 13
Results ........................................................................................................................................... 13
Forest Structure and Composition ............................................................................................. 13
Fuel Loading ............................................................................................................................. 18
Understory Vegetation .............................................................................................................. 23
Discussion ..................................................................................................................................... 29
Monitoring Recommendations ...................................................................................................... 31
State Parks outside of the Lake Tahoe basin ............................................................................. 32
State Parks in the Lake Tahoe basin.......................................................................................... 39
Unburned conditions in four CA State Parks ........................................................................ 43
2010 Re-sample effort ............................................................................................................... 47
References ..................................................................................................................................... 50
2
Key Findings
Forest Structure and Composition
• Prescribed fire reduced the density of live trees (>2.5 cm DBH) an average of 46% in
the year following fire. By ten years, the density was 65% lower.
• Significant tree mortality occurred only in pole-size (15-30 cm DBH) and sapling
(2.5- 15 cm DBH) sapling size classes.
• On average, 73% of tagged trees pre-burn were white fir <30cm DBH.
• Reduced tree density after fire did not shift the proportion of white fir, which still
accounted for 75% of all trees ten years post-fire.
• Average tree size significantly increased by about five cm (QMD) the year after fire.
• Snag density increased significantly in the first five years following fire, but
returned to pre-fire levels by ten years post-fire.
• Average basal area (BA) and seedling density did not change in response to fire.
Fuel Accumulation
•
•
•
•
•
The pre-treatment surface and ground fuel load was significantly reduced an
average 67% by prescribed fire.
With an average rate of accumulation of 0.542 kg/m2 for all fuel components
combined, the surface and ground fuel load would be expected to equal the pre-fire
fuel load by 2010.
Prescribed fire significantly reduced fine surface fuels (FWD) and the subsequent
rate of accumulation was nearly zero.
Prescribed fire significantly reduced the rotten component of coarse surface fuels
(CWD) but did not reduce the sound component which accumulated to nearly three
times pre-fire levels within ten years.
The duff layer comprised the largest portion of the total pre-treatment fuel load and
showed the largest response to prescribed fire with the greatest reduction in
average loads following fire and the greatest accumulate rate in subsequent years.
Understory Vegetation Response
•
•
Pre-fire understory vegetation was sparsely distributed on the landscape with an
average cover of only 16%.
Prescribed fire significantly reduced understory cover by an average of 58% the
year following fire, mainly due to a decline in shrub cover.
3
•
•
•
Understory percent cover recovered to pre-fire levels by ten years post-burn, likely
due to a significant increase in the nitrogen fixing shrub whitethorn (Ceanothus
cordulatus).
Sub-shrub percent cover appears to have been significantly reduced in all years by
fire, but forb cover did not show any response.
Species richness did not decline in the year following fire, but was significantly
greater five and ten years later, when sample plots had on average three to four
more species than before the burn.
Monitoring Recommendations
Malakoff Diggins SP
• Average surface fuel load was moderately reduced by prescribed fire, but overstory
stand characteristics and understory vegetation cover did not change, possibly due
to very low severity of the 2006 prescribed fire. A new monitoring plan is warranted
over additional post-treatment sampling of the existing FMH plots.
Plumas-Eureka SP
• No prescribed fire has been applied to the FMH plots installed in 2000, but 2010
presents an opportunity to conduct sampling using modified protocols in order to 1)
evaluate change in the overstory and fuel loads and 2) develop an effective
treatment prescription and 3) inform the scheduling of subsequent treatments.
Lake Tahoe Basin State Parks
• A limited comparison indicates that unburned forest and fuel conditions in Burton
Creek and Emerald Bay SPs may be comparable to Sugar Pine SP, especially if the
plot data for the two other parks are combined.
• It was not possible to include D.L Bliss SP in the comparison because of insufficient
data. If any treatments are planned in the future in D.L. Bliss a new monitoring plan
should be developed.
• Conduct a re-sampling effort in 2010 as follows:
o Streamline sampling protocol and limit data collection to those variables that
yield statistically robust results.
o Add collection of tree height, live crown base height, and canopy cover to
determine current crown fire potential.
o Limit re-sampling of treatment plots in 2010 to the 15 FMH plots burned in
the 1995-1996 prescribed fires in Sugar Pine Point.
o Re-sample 10 control plots in Sugar Pine and 2 control plots in Emerald Bay
in 2010.
4
Introduction
A significant portion of the Lake Tahoe basin is considered a high-risk environment for
severe wildfires (Murphy et al 2006). The elevated threat originates from human land use
practices over the last 150 years, beginning with Comstock era logging in the 1860’s and
continuing because of effective fire suppression. By the turn of the 20th century, nearly
two-thirds of the lower elevation pine forest was clear-cut (Murphy and Knopp 2000).
Recovery of the forest over the last 100 years was irreversibly altered by management
focused on fire suppression. Prior to European settlement, frequent, low intensity fires
shaped forest structure, composition, and resilience (Martin and Rice 1990). Fire return
intervals ranged from 5 to 18 years at the lowest elevations around the lake and from this it
is estimated that approximately 850 -3,237 ha (2,100 to 8,000 ac) burned each year in the
Tahoe Basin because of human and natural ignitions, compared to fewer than 200 ha (500
ac) burned per year today through prescribed fire and wildfire (Manley et al. 2000).
Modern forests that developed under fire suppression after extensive logging are overly
dense and crowded with small trees and extraordinary accumulations of fuels (Barbour et
al 2002, Taylor 2004).
A large body of scientific evidence supports the utility of prescribed fire in reducing crownfire potential or improving the resilience of forest stands to wildfire, but these studies are
largely based on informal observations (Brown 2002; Carey and Schumann 2003), post-fire
inference (Omi and Kalabokidis 1991; Pollet and Omi 2002) and modeling (Finney 2001;
Stephens 1998; Agee and Skinner 2005, Peterson et al. 2006). Controlled empirical studies
on the effectiveness of modern fuel reduction techniques are rare, but becoming more
common ((van Wagtendonk 1996; Stephens 1998; Graham et al. 2004; Stephens and
Moghaddas, 2005; Stanton and Dailey 2007;Youngblood et al. 2008).
Despite a dearth of scientific guidance, the pace of planned fuels reduction treatments in
the Lake Tahoe Basin is accelerating. Sensitive environmental resources and a multipleagency regulatory framework have made fuels projects costly and complex compared to
other geographic areas. As the amount of money spent on fuels treatment programs
increases every year, the development of strong research and monitoring programs to
track the implementation and effectiveness of treatments is urgently needed (Carey and
Schumann 2003).
The California Department of Parks and Recreation or CA State Parks (CSP) manages
approximately 6,800 acres in the basin and has had an active prescribed fire program in
place since 1984. Beginning in 1992, monitoring plots based on the guidelines and
protocols in the Fire Monitoring Handbook (FMH) handbook (USDI 1991) have been
installed in six different California State Parks within the Sierra District. As of 2006, 10 year
post-fire data was available from prescribed burn treatment plots. The long-term goals of
the prescribed fire program are to: 1) Reintroduce fire as a natural ecological process 2)
change stand composition to favor yellow-pine regeneration and 3) mimic pre-settlement
5
fire regime and stand characteristics and 4) increase biological diversity. Short-term goals
are to 1) improve forest health 2) reduce fire hazard and 3) increase white fir mortality.
The central objective of this study is to analyze the existing FMH dataset to evaluate the
effects of prescribed fire treatments on vegetation composition and structure, fuel loading,
and potential fire behavior in mixed conifer stands. A portion of the monitoring data from
three years post-fire has been previously analyzed to evaluate specific short-term effects of
prescribed fire (Madeno, 2000). The larger dataset now available provides the opportunity
to investigate longer term effects. A secondary objective is to evaluate the effectiveness of
the CSP fire monitoring program and provide recommendations for future monitoring
efforts.
The CSP prescribed fire monitoring program is unique in the Lake Tahoe basin in terms of
sampling intensity and longevity. However, the use of prescribed fire to address the
wildfire threat and implement restoration measures presents unique challenges. A
constellation of factors has severely limited the number of acres that have been treated
with prescribed fire on the California side of Lake Tahoe. First and foremost are limited
resources. Cost estimates for California Tahoe Conservancy public lots and California State
Park land for planning, environmental compliance, and final layout for approximately 10acre projects range from $1,500 to $1,800 per acre. Although funding in the basin for fuel
reduction has become more readily available in recent years through the Sierra Nevada
Public Lands Management Act (2000), over 68,000 acres are proposed for treatment within
a ten year period (2008-2018) and this funding is likely running out in 2011. Even when
funds are available, local suppression crews used as backup during CSP prescribed fire
operations are often out of the area fighting wildfire in Southern California or big fires in
other states during acceptable burn windows, so there is a lack of man power for
prescribed fire implementation.
Stringent regulations at local, state, and federal levels also severely limit the use of
prescribed fire in the Lake Tahoe basin. The number of days on the California side of the
Lake that meet air quality criteria and are within burn prescription is very limited and has
gotten smaller in recent years. In the mid 1990’s there were approximately 13-14 days per
year that qualified, but this has declined to only 3 or 4 days per year that are open for
burning (R. Adams, pers. comm.. 2010). Agencies are forced to compete with each other for
priority to burn during these rare open windows.
Another important limitation on the use of prescribed burning as a management option is
the proximity of California State Park land (and other areas targeted for restoration) to
populated areas and the importance of tourism in the region. A quagmire of smoke
management and liability issues must be addressed before a burn plan can be approved
and burning on the weekends is often not feasible due to the regular occurrence of outdoor
events where public health and safety concerns take precedence.
6
Despite all of these limitations, prescribed fire is an important tool in efforts to mimic
natural processes, reduce wildfire potential, and improve the resilience of forest stands to
wildfires that do occur in the degraded forests of the Lake Tahoe basin.
Methods
Monitoring Program
The California State Parks (CSP) Sierra District has had an active prescribed fire program in
place since 1984, and a quantitative monitoring program was established in 1992.
Monitoring plots have been installed in six different California State Parks within the Sierra
District: Burton Creek, D.L. Bliss, Emerald Bay, Sugar Pine Point, Malkoff Diggins, and
Plumas-Eureka. The monitoring effort within the Lake Tahoe basin includes 54 plots in four
state parks. The majority of the plots are in Sugar Pine Point, with 10 or fewer each in
Burton Creek, D.L. Bliss, and Emerald Bay. Malakoff Diggins and Plumas Eureka occur
outside of the basin in lower elevation forest dominated by oak. Very limited data was
available from these two parks and they were excluded from the present analysis.
The monitoring program is based on the guidelines and protocols in the Fire Monitoring
Handbook (FMH) handbook (USDI 1991) developed by the National Park Service (NPS) to
facilitate and standardize monitoring for units that are subject to burning by wildland or
prescribed fire. The handbook defines and establishes different levels of monitoring
activity relative to fire and resource management objectives to ensure that a park collects
at least the minimum information deemed necessary to evaluate their fire management
program. At each successive level, monitoring is more extensive and complex. Level 1
covers environmental monitoring only, while levels 2, 3, and 4 call for monitoring of fire
conditions, short-term change, and long-term change, respectively. Procedures for
monitoring levels 3 and 4 are similar, but differ in timing and emphasis. The Recommended
Standard (RS) for monitoring short-term change (level 3) is to collect detailed descriptive
information on fuel load, vegetation structure, and vegetation composition. California State
Parks adopted the standard Level 3 protocol and has implemented it over a 15 year time
period in order to monitor long-term change at Level 4.
Resource Management Objectives
The following objectives are specified in the 1996 Burn Plan for Sugar Pine State Park.
Objectives marked with an * were also specified in the 1995 Burn Plan for Sugar Pine State
Park.
1. Use prescribed fire to begin restoration of fire into the ecosystem.
2. *Establish permanent monitoring plots using the protocols outlines in the Western
Region Fire Monitoring Handbook (National Park Service 1991) in order to gauge
the short and long-term success of the prescribed fire program. Develop review
mechanism to evaluate the effects of burn prescriptions regarding fuel load
reduction, percent understory mortality, etc.
7
3. Protect and enhance habitat conditions for species dependent on mature seral stage
interior forest conditions. Reduce surface fuels and understory fuel ladders to
provide protective buffers adjacent to suitable northern goshawk and California
spotted owl nesting and roosting habitat.
4. Reduce 1 hr and 10 hr fuels by a minimum of 70% averaged over the plot, Maintain
at least 60% of sound downed logs and 30% rotten downed logs over 24 in DBH.
Treat slash generated by pre-burn thinning activities.
5. *Create mineral soil seed bed for establishment of new trees and brush species over
30% of the burn complex. Maintain a mosaic of burned and unburned patches
within plots. Monitor fire effects on soil productivity and water quality attributes to
prevent adverse effects.
6. *Reduce understory density of sapling firs and raise canopy base height to
approximately 15 feet over 60% of the plot. Increase moisture and nutrient
availability to remaining vegetation.
7. *Reduce long term stress on overstory pines, particularly those greater than 24 inch
DBH.
8. Track fire history, fire effects, and changes in wildlife habitat structure and
composition over the entire watershed. Do planning analysis at multiple levels
resulting in the development of specific objectives and implementing prescriptions
at the stand (burn unit) level.
Site Description
The four parks in the basin are on the western shore of Lake Tahoe within an elevation
range from lake level at 1,899 – 2,054 m (6,230-6,740 ft). The dominant vegetation is a
Sierran mixed conifer forest with an overstory of white fir (Abies concolor), red fir (Abies
magnifica), Jeffrey pine (Pinus jeffreyi), sugar pine (P. lambertiana), and incense cedar
(Calocedrus decurrens).
Most of the soils in the Lake Tahoe Basin are of granitic or volcanic parent material. These
soils are generally young and poorly developed. Most soils are shallow, coarse textured,
have low cohesion, and contain small amounts of organic material (Burton Creek State Park
Resource Inventory, 1990). The soil survey for the Lake Tahoe Basin (USDA 1974)
described 22 soil series and 73 separate mapping units. Erosion potential ranges from low
to high. Slopes across the area average less than 30%.
Climate is Mediterranean with a summer drought period that extends into fall. The
majority of precipitation falls as snow in winter and spring with an average snowfall of 482
cm and annual rainfall of 80 cm (Western Regional Climate Center). Average temperatures
in January range between -7 and 4° C. Summer months are mild with average August
temperatures between 6 and 25°C, with infrequent precipitation from thunderstorms.
Prescribed fire treatment
The sample plots in the present analysis were treated with prescribed fire during the
period from 1992 to 1997. Individual burn prescriptions were developed for each unit
according to topography, slope, aspect, canopy coverage, fuel type, and prevailing wind.
8
The large majority of the plots were located in two burn units in Sugar Pine Point that were
treated in 1995 or 1996. The 1995 burn encompassed 45 ha (112 ac) and the 1996 burn
was 104 ha (258 ac) (Figure 1). In 1991, those units were prepared for burning under an
early October prescription. Some ladder fuels were thinned and scattered, fire-line snags
were removed, small piles were created in some areas, and some clearing occurred around
legacy sugar pines to protect them from fire. The focus of the preparation was to prevent
any fire-line escapes and the overall effect on forest structure was negligible so park staff
does not consider the units to have been thinned before burning (R. Adams pers. comm.
2010)
Figure 1. Map of all installed FMH sample plots in Sugar Pine Point State Park showing
prescribed fire treatment units (with year burned)
All burns were conducted in September through November under the following conditions:
relative humidity 25-60%, mid-flame wind speed 0-10 mph, temperature 35-70°F. The
moisture content of 1-hr, 10-h, and 100-h fuels ranged from 8 to 16%, 1000 h fuel moisture
was between 10 to 20%. The desired rate of spread was <6 chains per hour with <4 foot
flame lengths.
9
Fire intensity was not calculated but burn severity was measured according to FMH
protocols. Burn severity is a qualitative term used to describe the relative effect of fire on
organic matter consumption and soil heating. Burn severity classification ranges from a
rating of 1 for heavily burned areas where litter and duff are consumed down to bare
mineral soil and all plant parts are consumed leaving some or no major stems/trunks to a
rating of 5 for unburned areas. Burn severity codes were recorded every 5 feet along each
50 ft fuel transect in a plot. Table 1 lists the monitoring type, plot location, burn date, and
the mean burn severity within each plot included in the analysis.
Table 1. FMH plot locations and average burn severity rating.
Monitor Type
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FPIJE1D05
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABMA1D10
FPIJE1D05
FPIJE1D09
FABCO1D10
Plot
ID
14
16
28
13
5
2
3
4
6
7
8
18
24
25
26
40
112
113
1
1
3
5
Park
Em Bay
Em Bay
Sugar
Em Bay
Bliss
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Burn
Date
1992
1992
1993
1994
1994
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1996
1996
1996
1996
1996
1996
1997
Burn
Severity
2.63
3.53
1.50
4.13
4.50
1.73
1.73
3.55
1.78
1.00
1.70
1.40
1.34
1.80
2.13
2.58
2.97
2.87
2.10
4.58
2.49
2.63
Vegetation measurements
The monitoring protocols are based on the Fire Management Handbook (USDI 1992)
developed by the National Park Service. Vegetation and fuels were measured in 50 x 20 m
rectangular plots of 0.1 ha (0.25ac). Plots were randomly placed within potential burn
units. Plot centers and all four corners were permanently marked with rebar and labeled
with aluminum tags. Tree species, diameter at breast height (DBH), crown position
(dominant, co-dominant, intermediate, suppressed), and damage (31 possible codes for
various structural defects and signs of disease) were recorded for all trees greater than
15cm DBH. Post-burn char height, scorch height, and percent scorch were also recorded.
The species, DBH, and height were recorded for all trees greater than 1.37m (4.5ft) tall and
10
less than 15cm DBH on a 25 x 10 m subplot (0.025ha). Seedlings were tallied in a 10 x 5 m
subplot (0.005ha) by species and height class.
Understory species were measured with a line intercept method on a single 50m (166 feet)
transect. Plant species and height were recorded every 0.3m (1 ft) for a total of 166 points
per transect. The number of hits for each species provides an estimate of percent cover.
Additional species present in the plot were recorded to obtain a measure of species
composition. Shrub species were tallied by age class (mature, re-sprout, immature) on one
side of the herbaceous transect within a 2.5m belt width to obtain a density estimate.
Surface and ground fuel measurements
Surface and ground fuels were sampled on four 75ft (22.9 m) random azimuth transects
using the line-intercept method (Brown 1974). One-hour (0-0.64cm) and 10-h (0.642.54cm) were sampled over 6 ft (2 m), 100-h (2.54-7.62cm) over 12 ft (5 m), and 1000-h
(>7.62m) along the entire transect. Duff and litter depth were measured every 5 ft (1.5 m)
on each transect for a total of 45 points per plot.
Plot Selection
Of the 54 plots installed in the four parks in the Lake Tahoe basin, only 27 had 10 year
post-burn data. A total of 5 plots were further excluded from the analysis for various
reasons including receiving more than one burn, inconsistent or missing data, or in one
case the plot size changed. Table 2 lists the 22 plots included in the current analysis and
the 6 untreated plots that could be used as controls. The monitoring type code includes the
following components: the letter f signifies that it is a forested plot, the next four letters
and one following number is the code of the dominant species (ABCO1 is white fir, PIJE1 is
Jeffry pine, and ABMA1 is red fir), the next letter is the phenology of the vegetation at the
time of data collection ( d= dormant), and the last one or two numbers are the fuel model
type (from Anderson1984). For example, FABCO1D10 is a forested plot with an overstory
of white fir that exhibits fuel model 10.
The previous analysis of three year post-burn data included a similar analysis pool of 28
plots and 6 controls (Mandeno 2000). That analysis drew a distinction between firdominated plots (ABCO monitoring type) and pine dominated plots (PIJE monitoring type).
The sample size was very small for the pine plots because only five were combined for
analysis. However two plots were excluded from the present analysis because they were
burned twice in the ten year period, leaving a sample size of three, which is insufficient for
statistical purposes. However, a quick evaluation of tree density and species composition
by monitoring type revealed that the distinction between ABCO and PIJE plots was not
sufficient to warrant separate analysis. Within a unit, all of the plots were in close
proximity to one another and the PIJE designated plots simply had a few very large Jeffrey
pine that contributed a significant amount to total basal area, but in terms of density the
forest was still heavily dominated by white fir.
It was not possible to evaluate the difference between fuel model types 5 and 9 because
there were only two PIJE05 plots and one PIJE09 plot. To determine if it was appropriate to
11
combine different burn years, fuel loading was compared between the 9 ABCO10 plots
burned in 1995 with the 10 ABCO10 plots that were burned in other years. No significant
differences were detected between the two groups for any of the fuel load components in
any of the sampled years (pre, years 1, 5, and 10 post-burn). Therefore, all 22 plots were
pooled for analysis regardless of monitoring type or burn date.
Table 2. FMH plot location and year of installation, prescribed burn, and ten year postburn data collection.
Plot ID
Park
Install Date
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FPIJE1D05
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FPIJE1D09
FPIJE1D05
FABMA1D10
FABCO1D10
14
16
28
13
5
25
24
26
6
18
7
8
2
3
4
40
112
113
3
1
1
5
Em Bay
Em Bay
Sugar
Em Bay
Bliss
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
1992
1992
1993
1992
1994
1993
1993
1993
1992
1992
1992
1992
1992
1992
1992
1996
1996
1996
1996
1995
1996
1992
CONTROLS
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FPIJE1D05
41
1
10
19
12
3
Sugar
Sugar
Sugar
Sugar
Em Bay
Sugar
1996
1992
1992
1993
1992
1995
Monitor Type
Burn
Date
1992
1992
1993
1994
1994
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1996
1996
1996
1996
1996
1996
1997
YR 10
2002
2002
2003
2004
2004
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2006
2006
2006
2006
2006
2006
2007
2006
2005
2005
2005
2004
2006
The 6 controls were also pooled for analysis. Data was selected from plots that were
sampled ten years following the corresponding burn in that unit and not necessarily 10
years after the plot installation date. For instance, the major burn events in Sugar Pine SP
occurred in 1995 and 1996 so the matching control plots were those that were sampled in
2005 or 2006, regardless of the year of installation.
12
Data Analysis
FMH was the state of the art monitoring protocol when CSP established its fire monitoring
program in 1992. From the beginning, the CSP prescribed fire monitoring data has been
entered into the DOS- format FMH database (version 3.10.2.1). In 2003, a new relational
database was developed for storing, managing, and analyzing NPS fire effects monitoring
data called the Fire Ecology Assessment Tool (FEAT, Sexton 2003). The FEAT system is
based on an integration of ESRI ArcView, Microsoft Access, and a statistical package.
However, the CSP data from the west shore of Lake Tahoe was never migrated into FEAT.
In 2006, the National Interagency Fuels Coordination Group sponsored a project to replace
FEAT with a new generation tool call FFI. FFI (FEAT/FIREMON Integrated) is a monitoring
software tool that employs an SQL Express 2005 database and provides summary report
and analysis tools and GIS functionality. It was constructed through a complementary
integration of the Fire Ecology Assessment Tool (FEAT) and FIREMON (Lutes et al. 2006).
FIREMON (Fire Effects Monitoring and Inventory Protocol) has been the recent tool
employed by the US Forest Service and the integration of the two methodologies is
intended to facilitate greater exchange of data among all federal agencies and a
standardization of reporting.
Once the dataset was compiled in FFI, it was possible to organize the set of plots for
analysis. FFI computes all the basic calculations of density, basal area, and fuel loadings and
presents summary reports of the variables of interest for any selection of plots. There is an
analysis tool that conducts pairwise comparisons of pre and post-burn sampling events
using basic t-tests. However, some of the features of the tool are cumbersome and an
export function lets the user extract text files of the summary reports to Microsoft Excel
and other statistical software.
Most of the current analysis was conducted using the JMP Statistical Software (Sall et al.
2001). Although multiple prescribed fires were conducted, all plots were pooled for
analysis so there was only a single burn treatment. Significant differences in the mean
values of all variables between pre-fire and subsequent sampling events (1, 5, and 10 year
post-fire) were investigated with ANOVA (p<0.05). Student’s t test and Tukey Honestly
Significant Difference (HSD) were used to make further pairwise comparisons when the
ANOVA was significant.
Results
Forest Structure and Composition
Prescribed fire significantly reduced the average pre-burn density of live overstory trees
(>2.5cm DBH) by 46% in the year after fire (Table 3). A further decline in density in year
five was not significant but the density ten years post-burn was significantly lower and
represented a reduction of 65% from the pre-burn level. The average density of dead trees
(snags > 15cm) significantly increased in the first five years following the burn, but had
declined to pre-fire levels again by year ten. Average basal area (BA) did not change
13
significantly over the monitoring period but average tree size, measured by the quadratic
mean diameter (QMD), increased significantly by about 5 cm in the year following the burn.
Further slight increases in QMD were not significant.
Table 3. Average pre –burn and post-burn (after 1, 5, and 10 years) vegetation structure.
Mean values in a column followed by the same letter are not significantly different
(p<0.05).
Event
N Plots
Trees per ha
>2.5 cm
Snags
per ha >
15 cm
BA
(sq.m/ha)
QMD
(cm)
PRE
yr01
Yr05
yr10
22
22
22
22
1,250.0a
700.9b
485.5bc
430.9c
425.5b
725.9a
680.5a
288.6b
57.9a
54.7a
47.0a
46.2a
31.7b
36.9a
38.7a
40.9a
No significant differences were observed between the burned plots and the controls in the
pre-treatment sample event in average density of live trees or snags, basal area, or tree size
(Table 4). This was expected, given the close proximity of the analyzed FMH plots in Sugar
Pine SP. In the year immediately following the prescribed burns, no data was collected in
any of the control plots so it was not possible to make a direct short-term comparison
between burned and unburned plots. However, by the tenth year following fire, the average
number of trees per ha was significantly lower in the burned plots than the controls and
tree size (QMD) was significantly greater.
Table 4. Average vegetation structure pre –burn and 10 years post-burn. For each sample
event, mean values in a column followed by the same letter are not significantly different
(p<0.05).
Event
Type
N
Plots
Trees per ha
>2.5 cm
Snags
per ha >
15 cm
BA
(sq.m/ha)
QMD
(cm)
PRE
burn
control
22
7
1250.0a
1424.3a
425.5a
437.1a
57.9a
53.1a
31.7a
30.0a
yr10
burn
control
22
7
430.9b
1222.9a
288.6a
284.3a
46.2a
55.8a
40.9a
32.5b
On average, almost 84% of tagged trees were saplings (2.5-15cm, 1-6 in) or pole-size (15.130cm, 6-12 in) trees less than 30cm (12in) DBH. Prescribed fire signifcantly reduced these
two size classes by an average of 54% in the year following fire (Figure 2). Trees larger
than 30 cm (12 in) were so sparsely represented on the landscape that the mean densities
of small (30.1-61cm, 12-24 in), medium (61.1-91.3cm, 24-36 in), and large (>91.4cm, >36
in) trees did not change significantly in response to fire over the subsequent monitoring
period. The control plots were also heavily dominated by sapling and pole size trees, and
no significant changes were observed in the size class distribution of tagged trees over the
monitoring period (data not shown).
14
Sapling
Pole-size
Small
Medium
Large
1000
900
Mean trees per ha
800
700
600
500
400
300
200
100
0
00PRE
01yr01
01yr10
Event
Figure 2. Average pre-burn and post-burn (at 1, 5, and 10 years) tree density by diameter
class with the standard error of the mean represented by the narrow bars.
The size class distribution of each of the 6 overstory tree species represented in the mixed
conifer forest was also evaluated. Prior to burning 73% of the tagged trees across all
sample plots were white fir (ABCO) <30cm (12”) DBH (Table 5). After white fir, the next
most common species in the sapling and pole-size categories were red fir (ABMA) followed
by Jeffrey pine (PIJE) and incense cedar (CADE). Sugar pine (PILA) and lodgepole pine
(PICO) were represented by less than 3 trees per hectare (tph) across all size classes. White
fir density was significantly greater than every other species in all but the two largest size
classes. The average density of medium white fir and Jeffrey pine was not significantly
different, and there was actually slightly more large size Jeffrey pine than white fir
(p<0.10).
Table 5. Average pre-burn and post-burn (at 1, 5, and 10 years) tree density by diameter
class of six overstory species (white fir ABCO, red fir ABMA, incense cedar CADE, lodgepole
pine PICO, Jeffrey pine PIJE, and sugar pine PILA).
Event
Species
Sapling
(2.5-15cm)
Pole-size
(15.1-30cm)
Small
(30.1-60cm)
Medium
(60.1-91.3cm)
Large
(>91.4cm)
00PRE
01yr01
01yr05
01yr10
ABCO
652.7
232.0
112.4
100.0
261.4
165.5
139.0
107.7
111.4
120.0
113.3
113.2
8.6
10.0
8.1
8.2
0.9
1.0
1.0
1.4
1035.0
528.5
373.8
330.5
00PRE
01yr01
ABMA
32.7
16.0
31.8
22.0
10.9
12.0
0
0
0.5
1.0
75.9
51.0
15
Total
Event
Species
01yr05
01yr10
ABMA
00PRE
01yr01
01yr05
01yr10
Sapling
(2.5-15cm)
3.8
3.6
Pole-size
(15.1-30cm)
10.0
6.4
Small
(30.1-60cm)
8.1
7.3
Medium
(60.1-91.3cm)
0
0
Large
(>91.4cm)
1.0
0.9
Total
CADE
16.4
8.0
5.7
7.3
10.0
3.0
8.6
7.7
0.5
0.5
0.5
0.9
0
0
0
0
1.4
1.5
1.0
0.9
28.2
13.0
15.7
16.8
00PRE
01yr01
01yr05
01yr10
PICO
1.8
0
0
0
4.5
3.5
1.9
0.9
1.8
2.0
1.0
1.4
0
0
0
0
0
0
0
0
8.2
5.5
2.9
2.3
00PRE
01yr01
01yr05
01yr10
PIJE
14.5
10.0
1.9
1.8
20.5
17.5
12.4
9.1
35.0
32.0
29.0
27.3
17.3
11.5
11.9
13.2
4.5
5.0
4.3
5.5
91.8
76.0
59.5
56.8
00PRE
01yr01
01yr05
01yr10
PILA
3.6
2.0
0
0
2.7
2.5
1.9
1.8
2.7
2.5
2.9
2.7
1.4
2.0
1.9
1.4
0.5
1.0
0.5
0.5
10.9
10.0
7.1
6.4
22.9
18.2
When the two fir species were combined into a fir category and all other species (including
incense cedar) combined in to a pine category, prescribed fire significantly reduced the
density of fir only in the year following fire (Figure 3). The sharp reduction in tree density
did not shift the proportion of white fir, which still accounted for 75% of all trees in year
ten.
Individual plot estimates of tree seedling densities were wildly variable, with pre-burn
average densities ranging from zero to 16,400 seedlings per ha. In the year following fire,
seedling response across the plots varied from a decline of 12,000 per ha to an increase of
13,000 per ha. Consequently, the difference in average density between sample events was
not significant (Figure 4). Average pre-burn seedling density in the controls was
comparable with the burn plots and showed a similar, but insignificant, decline by year ten
(Figure 5). The similar decline in the control plots indicates that the reduction in seedlings
was not in response to prescribed fire but may instead have been due to self-thinning of
some kind.
16
Mean trees per ha
500
400
300
200
100
0
00PRE
01yr01
01yr5
01yr10
Event
Fir
Pine
Figure 3. Average pre-burn and post-burn (at 1, 5, and 10 years) tree density of fir (ABCO
and ABMA) and pine (PIJE, PICO, PILA, and CADE) with the standard error of the mean
represented by the narrow bars.
Figure 4. Average pre-burn and post-burn (at 1, 5, and 10 years) seedling density with the
standard error of the mean represented by the narrow bars.
17
burn
control
Figure 5. Average pre-burn seedling density of burn plots (n-=22) and control plots (n=7)
with the standard error of the mean represented by the narrow bars
Fuel Loading
Surface fuel loads are comprised of fine woody debris (FWD) of the one, ten, and one
hundred hour size classes and coarse woody debris (CWD) of 1000 hour fuels. The ground
fuel load is comprised of the duff and litter layers. Duff comprised the largest portion of the
total pre-treatment fuel load, while CWD comprised the largest fraction in all years
following fire (Figure 6). FWD comprised the smallest fraction in all years.
18
16
14
12
Duff
kg/m2
10
Litter
8
FWD
6
CWD
4
2
0
00PRE
01yr01
01yr5
01yr10
Figure 6. Average pre-burn and post-burn (at 1, 5, and 10 years) fuel loadings of duff, litter,
fine woody debris (FWD), and coarse woody debris (CWD).
The total combined pre-treatment average fuel load was significantly reduced by
prescribed fire by 67%. The dramatic reduction was due to significant declines in all
components except sound CWD (ANOVA p <0.5). Over the next four years, the average total
fuel load nearly doubled, but by the tenth year, the average total fuel load was still
significantly lower than pre-fire levels by 25%.
No significant differences were observed between the burned plots and the controls in the
pre-treatment sample event in any fuel load component (Table 6). This was expected, given
the close proximity of the analyzed FMH plots in Sugar Pine SP with the control plots. In the
year immediately following the prescribed burns, no data was collected in any of the
control plots so it was not possible to make a direct short-term comparison between
burned and unburned plots. However, by the tenth year following fire, the average load of
every fuel component but FWD was significantly lower in the burned plots than the
controls. While the declines in all components except sound CWD were significant in the
burn plots, the observed changes in the control plots were not significantly different,
although the 26% decline in litter load was mildly significant at p=0.08.
Table 6. Average fuel loading pre –burn and 10 years post-burn. For each sample event,
mean values in a column followed by the same letter are not significantly different
(p<0.05).
Event
PRE
yr10
Load (kg/m2)
Duff
Litter
Plot
type
Burn
Control
N Plots
FWD
CWD
22
6
1.33a
1.42a
3.52a
4.82a
5.49a
7.08a
Burn
Control
22
6
0.75a
1.05a
5.50b
11.15a
3.10b
6.70a
19
Depth (cm)
Duff
Litter
3.54a
3.95a
Total
Surface
13.88a
17.26a
6.23a
8.04a
8.04a
8.96a
0.89b
2.05a
10.23b
20.95a
3.52b
7.60a
2.02b
4.66a
Surface Fuel Load
Prescribed fire significantly reduced FWD by an average of 67% the first year (Figure 7).
FWD is the primary carrier of fire and ignition source so this is an important piece of
information. By five years post-fire, FWD was still 40% lower than pre-fire levels and this
level was maintained ten years post-fire without any significant change.
a
b
b
c
Figure 7. Average pre-burn and post-burn (at 1, 5, and 10 years) fuel loadings of combined
1, 10, and 100 hour fuel size class. Narrow bars represent the standard error of the mean.
Mean values with the same letter are not significantly different (p<0.05).
Rotten fuels of decay class 4 and 5 comprised the majority of the pre-treatment CWD load
and were significantly reduced by an average of 65% by prescribed fire (Figure 8). By the
fifth year after fire, the average load of rotten CWD was no longer significantly lower than
pre-fire levels and by year 10 the average loading had returned to 85% of pre-fire levels. In
contrast, sound fuels of decay classes 1-3 were not significantly reduced by fire, instead
increasing significantly within five years to twice the average pre-fire fuel load. A further
increase over the next five years was also highly significant.
20
Sound
Rotten
Figure 8. Average pre-burn and post-burn (at 1, 5, and 10 years) fuel loadings of sound
(decay class 1-3) and rotten (decay class 4-5) 1000 hour fuel size class. Narrow bars
represent the standard error of the mean.
Ground Fuel Load
The duff layer comprised the largest portion of the total pre-treatment fuel load and
showed the largest response to prescribed fire. The average duff load was significantly
reduced from pre-fire levels by nearly five times in the year following fire (Figure 9). The
duff layer significantly increased by over three times over the subsequent five years from
an average of 0.56kg/m2 to 1.62kg/m2. Although the steady accumulation continued, the
average ten year post-fire duff load was still an average of 43% lower than pre-fire levels.
Prescribed fire also significantly decreased the litter layer, but the reduction appeared to
be more long-lasting. The ten year post-fire litter load was on average 75% lower than prefire levels.
Prescribed fire also significantly reduced the depth of the duff and litter layers from prefire levels in all post-fire sample events (Figure 10). The duff layer was reduced from an
average depth of 6.2 cm to less than one cm in the year following fire. By ten years postfire, the duff layer was still 43% lower on average than the pre-fire level. The response of
the litter layer was more complex, but the initial average reduction of 58% in the year
following fire was sustained into the tenth year.
21
Duff
Litter
Figure 9. Average pre-burn and post-burn (at 1, 5, and 10 years) fuel loadings of duff and
litter. Narrow bars represent the standard error of the mean.
A) Duff
B) Litter
a
a
b
b
bc
c
c
d
Figure 10. Average pre-burn and post-burn (at 1, 5, and 10 years) depth (cm) of A) duff
and B) litter layers. Narrow bars represent the standard error of the mean. Mean values
with the same letter are not significantly different (p<0.05).
22
Post-fire accumulation rates
The rate of post-fire ground and surface fuel accumulation was calculated by subtracting
the loading of each component in each plot in year 10 following the fire from the loading in
year 1 and then dividing by ten. The average is for 20 plots. Of the fuel components, only
the litter layer decreased over the ten year post-fire period with an average accumulation
rate of -0.059 kg/m2 (Table 7). The negative rate is derived from an insignificant change in
the depth of the litter layer after the initial reduction from the burn. However, litter
decomposes in a few years, becoming part of the duff layer, and therefore the highest
accumulation rate was observed in the duff layer, which experienced significant increases
in depth in each five year period following the fire.
Fine woody debris (FWD) had a very low average accumulation rate despite a significant
increase in loading between year one and year five (see Figure 7). Rotten CWD
accumulated at a moderate rate because the average loading was no longer significantly
lower than pre-fire levels by the fifth year after fire (see Figure 8). Sound CWD was not
significantly reduced by prescribed fire and continued to accumulate at a high rate over the
ten year period to three times the average pre-fire loading. With an average rate of
accumulation of 0.542 kg/m2 for all fuel components combined, the total fuel load would be
expected to increase by 2.71kg/m2 over the next five years. By 2010, the average fuel load
would be expected to be 12.91kg/m2, nearly equal to the pre-fire average load of 13.8
kg/m2.
Table 7. Average rate of accumulation (standard error) of the surface and ground fuel
loads.
Type
FWD
Sound
Rotten
Duff
Litter
Total Surface Load
kg/m2/year
0.027 (.01)
0.207 (.03)
0.140 (.05)
0.227 (.02)
-0.059 (.02)
0.542 (.09)
Understory Vegetation
Three methods were used to sample the understory vegetation; point-intercept, shrub belt,
and observation of plot species composition. Over 200 species were detected in the
complete sampling effort of 81 plots, but in the subset of 28 plots analyzed here, a total of
94 species were detected. The species list (Appendix A) includes 63 forbs, 15 shrubs, 4 subshrubs, and 12 grasses (4 unknown). A total of 60 species were identified and measured in
the point-intercept method and 18 shrub and sub-shrubs were measured in the shrub belt
transect. An additional 29 species that were not captured in either method were recorded
in the observation of species composition. Only three non-native forbs were detected at
very low levels in the analyzed monitoring plots: common dandelion (Taraxacum
officinale), prickly hawkweed (Hieracium horridum) and bull thistle (Cirsium vulgare).
23
Two plots in Sugar Pine SP that were burned in 1995 (plot 7 and 8) were huge outliers with
high species richness and large percent cover values and these were removed from all
understory analyses.
Species Richness
The number of species in each plot was determined by summing the number of species
recorded for all three sampling methods. Across all sample events, the number of recorded
species per plot was generally low, but ranged from 0 to 15 (sample size, N=20). Prescribed
fire did not significantly reduce species richness in the year following fire, but species
richness was significantly greater in year five and ten after the fire (Figure 11). The
increase in average richness was due to a significant increase in the number of forbs
recorded per plot in year five and a significant increase in the number of shrub species per
plot in years five and ten (Table 8). There were only four sub-shrub species recorded so
mean richness of that lifeform did not change nor did the sparsely distributed grasses.
Figure 11. Average pre-burn and post-burn (at 1, 5, and 10 years) species richness per
plot.
Table 8. Average pre-burn and post-burn (at 1, 5, and 10 years) species richness per plot
of four lifeforms. Mean values in a row with the same letter are not significantly different
(p<0.05).
Event
00PRE
01yr01
01yr5
01yr10
FORB
2.1bc
1.5c
4.1a
3.3ab
1.7
1.6
2.6
2.5
SHRUB
1.3b
1.0b
2.6a
2.6a
1.0
0.9
1.1
1.3
24
SUBSHRUB
0.95a
0.75a
0.8a
0.8a
0.6
0.5
0.7
0.7
GRASS
0.0b
0.0b
0.5a
0.6a
.
.
1.1
0.8
It was not possible to compare the species richness of the control plots in the year
following fire because only three plots were sampled. By year ten, average richness in the
controls of 4.8 species per plot was not significantly different from the pre- treatment
richness of 4.3 species per plot. The lack of change in the controls could indicate that
prescribed fire was a factor in the increase in richness observed in the treatment plots.
Shrub and herbaceous cover
Understory percent cover for each sampled species was obtained from the point-intercept
method. In addition to the outlier plots of 7 and 8, one plot had no species detected by the
point intercept method in any sample event and this plot was excluded from analysis (since
it would not help explain changes in observed cover) reducing the sample size to N=19.
Across all sample events, total cover per plot was extremely variable, ranging from 0 to
45%. The 58% decline in average total cover from 16.3 % to 6.8% the year following fire
was significant (Figure 12). By year five, the apparent reduction of 46% from pre-burn
levels was not significant, indicating that total mean cover recovered fairly quickly.
Figure 12. Average pre-burn and post-burn (at 1, 5, and 10 years) percent cover per plot of
herbaceous and shrub species (N =19). Narrow bars represent the standard error of the
mean.
Total covered was summed for four different lifeforms: forb, grass, shrub, and sub-shrub.
The grass category also included sedges and the sub-shrubs encompassed a narrow
category of low-growing shrubs that only included four species: pinemat manzanita
(Arctostaphylos nevedensis, ARNE), creeping snowberry (Symphoricarpos mollis SYMO),
roundleaf snowberry (S. rotundifolius SYRO) and mahala mat (Ceanothus prostratus CEPR).
Total mean forb cover per plot was very low (<2%) and did not change significantly during
the sample period (Table 9). It was not possible to obtain significance by removing all plots
that had no forbs recorded or by comparing only plots with greater than 10% cover, since
25
so few plots fit that criteria. Forbs were very sparsely distributed across the landscape and
no individual species was encountered frequently enough across the sample period to
enable analysis of individual species response. Dogbane (Apocynum androesmafolia) was
the most frequently encountered forb, but it was only present in three plots pre-burn and
in 5 or 6 plots in subsequent sample events, with total cover ranging from 0.6 to 6%. The
next most common forb, kellogia (Kellogia galloides), was present in five plots pre-burn
and in four plots or fewer in subsequent sample events, with total cover ranging from 1.2%
to 12%.
Table 9. Average pre-burn and post-burn (at 1, 5, and 10 years) percent cover of four life
forms. Mean values in a row with the same letter are not significantly different (p<0.05).
Lifeform
Forb
Grass
Shrub
SubShrub
Pre
2.08a
0.00
10.77a
3.42a
Yr01
1.81a
0.00
4.11a
0.85b
Yr05
1.77a
0.16
6.12a
0.76b
Yr10
1.06a
0.32
9.73a
0.82b
Only four grass species were identified in the plots-:Bromus orcuttianus, Agrostis
thurberiana, Elymus elymoides, and E. glaucus. Grasses were not recorded in any of the plots
pre-burn or the year following fire, but were present in 2 plots by year five and four plots
by year ten. In one of the outlier plots that was excluded from the analysis, grass increased
over time from 3.6 % pre-burn to 4.2, 13.9 and 16.9% in years 1, 5, and 10 respectively.
Despite this example, there was too little data to be able to say that fire stimulated growth
of grass.
Shrubs were also very sparsely distributed across the sampled landscape. Of the 19 plots in
the analysis, four did not have any shrubs at all, and ten plots had less than 10% shrub
cover in any sample event. Consequently, the variability of the data was large and the
apparent sharp decline in cover of 62% from 10.8% to only 4.1% was not significant. No
significance was obtained by eliminating plots with no shrubs. When those plots with less
than 10% cover were also excluded from analysis, a decline of 60% from 15.6 to 6.2%
became marginally significant at p=0.06 (n=9 plots). Despite this lack of significance, the
decline in shrub cover was likely responsible for the overall significant decline in total
cover of all lifeforms of 58%.
Huckleberry oak (Quercus vacciniifolia QUVA) was the most frequently encountered shrub
but it was only present in five plots pre-burn and in six plots in all subsequent years. There
was no significant change in average cover across the sample events when all plots were
included (n=19) or when only plots with it present were analyzed (n=7) (Table 10).
Whitethorn (Ceanothus cordulatus CECO) was encountered almost as frequently and
because it is a nitrogen fixer, is of special interest. However, mean percent cover per plot
was very low and there was no significant change in cover when all plots were included in
the analysis (n=19). When only the 5 plots with CECO were analyzed, the increase in cover
from pre- burn (2.6%) to ten years post-burn (14.2%) was significant. The next most
common shrubs were greenleaf manzanita (Arctostaphylos patula) and chinquapin
26
(Chrysolepis sempervirens), but these were recorded in fewer than 5 plots so the sample
size was too small to reveal any trends.
Table 10. Average pre-burn and post-burn (at 1, 5, and 10 years) percent cover of the two
most common shrub species (huckleberry oak QUVA and whitethorn CECO) and one subshrub (creeping snowberry SYMO). Different letters within a row are significantly different
at the indicated ANOVA p value and F ratio.
Species
QUVA
All plots
QUVA only
CECO
All plots
CECO only
SYMO
All plots
SYMO only
N
Pre
Yr01
Yr05
Yr10
F ratio
p value
19
7
6.5
20.5
2.8
8.8
3.6
11.2
4.2
13.5
0.64
0.591
1.48
0.251
19
5
0.8
2.6a
0.4
0.9a
1.6
5a
4.5
14.2b
2.63
0.056
5.22
0.008*
19
9
2.9a
5.5a
0.6b
1.1b
0.4b
0.8b
0.4b
0.8b
3.88
0.013*
5.26
0.004*
Of the four sub-shrub species, creeping snowberry (Symphoricarpos mollis SYMO)
comprised the majority of cover and it was the most frequently encountered of all species
in the point- intercept method. There was a significant decline in mean cover per plot in
the year following fire from 2.9% to less than 1 % when all plots were included and the
pattern was even stronger when only the nine plots with snowberry were analyzed (Table
10). There was no recovery in years five or ten, indicating that sub-shrub percent cover
was perhaps permanently reduced from the pre-fire level.
Average percent cover of the different lifeforms pre-burn was the same in the control and
burn plots (Table 11). It was not possible to compare cover one year post-burn because
only three plots were sampled. By ten years post-burn, average forb cover was
significantly greater in the control plots compared to the burned plots.
Table 11. Average pre-burn and ten years post-burn percent cover of four life forms in
burned and unburned plots.
Event
Pre
Yr10
Plot
type
Burn
Control
N Plots
Forb
Grass
Shrub
Subshrub
20
7
1.8a
2.9a
0.0a
0.5a
7.8a
9.6a
2.9a
1.5a
Burn
Control
20
7
0.8b
4.6a
0.3a
0.3a
8.6a
11.7a
0.8a
1.7a
Shrub density
Shrub density was measured with a belt transect method. A total of 18 shrub and subshrub species were recorded across all sample years, but 11 of these were recorded in
fewer than 5 sample events (i.e. presence in one plot in any year). FFI calculates densities
for individual species in each of three age classes (immature, mature and re-sprout). When
all species and age classes were combined the average shrub density was 1,215.8, 1,028.6,
27
1,784.6, and 822.3 in pre-burn, and 1, 5, 10 years post-burn, respectively, but there was no
significant difference in density between the sample events.
It was only possible to calculate densities for the most frequently encountered species with
more than 20 sample occurrences: whitethorn (CECO), huckleberry oak (QUVA), greenleaf
Manzanita (ARPA), chinquapin (CHSE), and snowberry (SYMO). The low number of
recorded hits per sample event and missing data resulted in wildly variable densities and
made the data difficult to interpret. Looking at frequency, or the number of plots with a
recorded occurrence in each age class, was a simpler indicator of change, although it does
not allow for any statistical analysis. The frequency of QUVA, both mature and immature,
did not change over the sample period, while the frequency of CHSE may have declined
slightly (Table 12). It appears that the frequency of mature ARPA increased by year ten
along with a flush of immature seedlings in years five and ten. The most dramatic changes
occurred in the frequency of CECO and SYMO. Mature CECO was only recorded in the belt
transects of two plots pre-burn, but was present in 12 plots 10 years post-burn and the
immature age class experienced a similar increase. In contrast, the frequency of mature
SYMO declined from 14 plots pre-burn to only 5 plots 10 years post-burn.
Table 12. Average pre-burn and post-burn (at 1, 5, and 10 years) frequency of five shrub
species for two age classes (M= mature, I= immature). The frequency is the number of plots
in each sample event in which a species was recorded.
Species
ARPA6
CECO
CHSE11
QUVA
SYMO
Age
Class
M
I
M
I
M
I
M
I
M
I
Pre
Yr1
Yr5
Yr10
1
0
2
1
4
3
6
5
14
8
1
0
1
4
1
0
5
4
3
1
1
9
4
13
1
1
6
7
1
7
4
5
12
13
1
2
5
5
5
4
To determine if the change in the frequency of SYMO and CECO was significant, average
density was calculated using on those plots in which the species was detected. The average
density of mature CECO in occupied plots ( n=10) was 96, 32, 680, and 2,008 for pre-burn
and years 1, 5, and 10 years post-burn, respectively and the ANOVA was significant
(P<0.05). Likewise, the average density of mature SYMO in occupied plots ( n=12) was
2753, 420, 6.7, and 366 for pre-burn and years 1, 5, and 10 years post-burn, respectively
and the ANOVA was also significant (P<0.05). It was not possible to analyze the control
plots due to the small number of plots sampled and the variability of the sampling method.
28
Discussion
The purpose of a monitoring program like FMH is to provide data that will help a manager
evaluate whether specific actions are meeting resource management objectives. A large
part of the present analysis utilizes data from prescribed fires that were conducted in Sugar
Pine SP in 1995 and 1996. A suite of about 8 objectives were specified in the Burn Plans for
those fires that are representative of the general resource management objectives of the
CSP prescribed fire program ( see list in Methods section). Some of the objectives are
procedural, some are very subjective and not easy to measure, and others are quantitative.
The procedural objectives are to use prescribed fire to begin restoration of fire into the
ecosystem and to establish a monitoring program capable of evaluating the effects of
prescribed fire on forest health and fuel load characters of interest. The CSP monitoring
program meets these basic objectives and is unique in the Lake Tahoe basin in both
sampling intensity and longevity. Other agencies have struggled to get funds for monitoring
fuels treatment projects or have been unable to overcome conflicting management
objectives within the agency and have settled for reporting the bottom-line amount of acres
treated and the cost rather than addressing specific objectives for reducing fire risk or
improving forest health.
The CSP monitoring data is capable of addressing specific quantitative objectives,
especially those regarding fuel loads. Quantifiable fuels reduction objectives in the Sugar
Pine Burn Plan are to “ reduce 1hr and 10 hr fuels by a minimum of 70% averaged over the
plot and maintain at least 60% of sound downed logs and 30% rotten down logs”. FWD (1100hr fuels) loads, which comprised the smallest fraction of the total fuel load, were
reduced an average of 67% in the year following prescribed fire and there was still a 40%
reduction ten years later. CWD (both rotten and sound) comprised the largest fraction of
the fuel load in all years following fire. Sound logs were not decreased at all by prescribed
fire, instead the load increased to three times pre-fire levels over ten years. In contrast, the
increase in sound CWD in the control plots was not significant. Rotten logs in the burn plots
were significantly reduced in the year following fire, but returned to pre-fire levels within
five years. Rotten log CWD also increased dramatically in the control plots so this is not
necessarily a response to the prescribed burning.
Changes in fuel loads necessarily depend on the intensity of the prescribed fire. The
average burn severity rating across the analyzed plots was 2.48, indicating moderately
burned conditions where the duff and litter layers are mostly consumed, foliage and small
twigs are mostly consumed, and wood structures are charred. The data suggest that the
prescribed fires were severe enough to consume a large portion of the fuel load, including
large diameter logs that were already rotten, but lacking the severity to consume the sound
logs. However, the average burn severity rating for a plot ranged from 1.0 to 4.48,
indicating that the burns created a mosaic of burned and unburned patches across the
landscape. Achieving a mosaic pattern was a specific objective in the Burn Plan, and higher
intensity fire would have likely decreased patchiness.
29
There were fewer quantifiable objectives specified for desired forest structure. The
objective to “reduce understory density of sapling firs and raise canopy base height (CBH)
to approximately 15 feet over 60% of the plot” could only be partially addressed with the
FMH protocol that was used. All trees over 15 cm (6 in) were tagged and the DBH and
species were recorded to it was possible to calculate tree density by species and size class.
The prescribed fire caused significant mortality in saplings and pole-size trees less than
30cm (12 in), while the larger trees did not decline. The reduction in small diameter trees
did not result in a desired shift in species composition toward to a more pine –dominated
forest, due to the extreme over-representation of white fir.
It was not possible to determine if the reduction in small trees resulted in an overall
increase in CBH within the burn unit. Both tree height and a measure of live crown base
height are necessary for calculations of CBH and canopy bulk density (CBD). CBD and CBH
are measures of canopy fuels that have been found to be significantly correlated with
crown fire initiation (Van Wagner 1977, Omi and Martinson, 2002) and the potential for
active or passive crown fire spread (Scott and Reinhardt, 2001). Increasing canopy base
height (CBH) and reducing crown bulk density (CBD) has been determined to be effective
in reducing crown fire initiation and minimizing crown fire behavior (Agee 1996).
Although reducing the potential for crown fire was not an explicit objective of the Sugar
Pine SP Burn Plans, these metrics offer a concise way of determining if wildfire risk has
been reduced.
The forest health objective to “create a mineral soil seed bank for establishment of new
trees and brush over 30% of the burn complex” was not directly measured. The understory
vegetation point-intercept transect did not include a category for bare ground, which
would be a simple measure of the amount of mineral soil available for seedling
establishment. Seedlings were tallied, but the resulting densities were wildly variable,
indicating that the 10 x 5 m subplot (0.005ha) was too small. An ongoing study on
mechanical fuel reduction treatments in similar white fir dominated forest on the west
shore also found that seedlings were sparsely distributed and a circular sub-plot of 0.01 ha
(.025 ac) was too small for most of those units (Stanton and Dailey 2007). Determining the
appropriate plot size for seedlings would require a small pilot field study.
Other forest health objectives to “reduce the long-term stress on overstory pines (>24in)”
and " increase moisture and nutrient availability to remaining vegetation” would require
measurements that are generally outside the scope of a basic vegetation and fuel load
monitoring protocol. Most stress to overstory trees in the Lake Tahoe basin is derived from
insect and pathogen infestations that have proliferated under the overcrowded conditions.
While it may be appropriate to record damage codes for overstory trees while tree
measurements are taken, this data is often difficult to interpret and specialized knowledge
is needed for the accurate assessment of such infestations.
The objectives to “ protect and enhance habitat conditions for late seral species” like
northern goshawk and California spotted owl were only partially measured because
canopy closure was not included in the current protocol. The California Wildlife Habitat
30
Relationship (CWHR) type is a structural stage classification scheme that is commonly used
to summarize habitat conditions for wildlife based on the average DBH and canopy closure
in a stand. More specific and measurable objectives for wildlife habitat can be addressed
with the simple inclusion of overstory canopy data.
Finally, there were not resource management objectives specifically identified for
understory vegetation, but it is recognized as an important component in both fire
behavior and wildlife habitat conditions. The point-intercept transect method provides a
good measure of percent cover, but the high variability of the data indicate that a single
transect did not sufficiently capture the abundance or distribution of the understory
vegetation. Percent cover declined by almost 60% in the year following fire, but this did
not translate into a significant change in species richness. With very severe fire, richness
will decline, especially if hydrophobic soils forms. With moderate fire, richness often
increases due to an increase in fire-adapted species or an increase in non-natives that are
introduced during suppression efforts or while conducting the prescribed fire. Only three
non-native forbs were detected in the analyzed monitoring plots, but at very low levels.
While common dandelion and prickly hawkweed are not aggressive species that would
cause concern, bull thistle has the capacity for rapid spread and should be monitored. A
more intensive sampling of the understory with a greater number of transects would
improve the strength of the conclusions that can be drawn about the response to
prescribed fire.
Monitoring Recommendations
A total of 75 monitoring plots have been installed in six different California State Parks
within the Sierra District: Burton Creek, D.L. Bliss, Emerald Bay, Sugar Pine Point, Malakoff
Diggins, and Plumas-Eureka (Table 13). The monitoring plots have been tracked in an Excel
spreadsheet over the years and that “master plot list” actually lists 85 plots. However, no
data was present in the FMH database for 10 plots and it is not clear if data was taken and
lost or if it was never taken in those locations. Paper datasheets are archived and they were
consulted in the plot selection process for the analysis to help reconcile discrepancies.
Table 13. FMH plot locations and status in Malakoff Diggins SP. Unburned plots were only
sampled at the time of installation, while control plots have data from subsequent sample
events.
Park
Burton Creek
D.L. Bliss
Emerald Bay
Sugar Pine Point
Malakoff Diggins
Plumas- Eureka
Total
Plots
installed
# burned
1x
4
6
5
39
7
14
75
1
2
3
20
6
1
33
31
# burned
2x or
more
0
1
0
3
0
0
4
#
unburned
#
controls
3
3
1
9
0
12
28
0
0
1
7
1
1
10
Most of the plots were installed from 1992 through 2000, and burning occurred from 1992
to 1998. The plots in Malakoff were not installed until 2004 and 2005 and the burn was
conducted in 2007. A total of 33 plots have been burned one time, four plots have
experienced more than one fire, 28 plots have remained unburned, and only 10 plots were
established as controls. The distinction between unburned plots and controls is that
unburned plots have only been sampled at the time of installation, while the controls have
additional data from multiple sampling events. Overall, just about half of the plots (49%)
have been treated with prescribed fire.
Malakoff Diggins and Plumas-Eureka are located outside the Lake Tahoe basin in a lower
elevation forest. Although they were excluded from the 10 year post-burn analysis, the prefire monitoring data was evaluated for both parks in order to determine if future
monitoring of those FMH plots is warranted. Results are briefly presented for overstory
forest structure and composition, fuel loading, and understory species composition along
with a general monitoring recommendation.
State Parks outside of the Lake Tahoe basin
Malakoff Diggins
Malakoff Diggins SP is located in Nevada County, California, approximately 36 miles north
of Nevada City. The park elevation ranges from 2,500 to 4,000 feet and the vegetation is
typical of lower elevation mixed conifer forest. Six monitoring plots were installed in 2004,
one additional control plot was installed in 2005, and only one prescribed fire has been
conducted in the park in January 2007(Figure 13). Post burn monitoring was carried out
early in the season in March and April of 2007, with one year post-burn data collected in
June, 2008.
Of the 7 plots that were installed, 5 were classified as dominated by CA black oak (Quercus
kellogii , QUKE) while 2 were classified as predominately Ponderosa pine (Pinus ponderosa,
PIPO) (Table 14). Because the sample size of the monitoring types is so small, tables with
plot-based results are presented to show the variability. Live pre-fire tree density was high,
ranging from 520 to 1520 tph, snag density varied from only 10 to 180 snags per ha, and
average tree size was small.
The species composition of the different monitoring types is such that it would not be
appropriate to combine plots from the two vegetation types. In addition to PIPO, the two
pine-dominated plots also had a large component of either white fir or incense cedar, but
very sparse oak while the oak-dominated plots were more variable (Table 15). One of the
plots classified as oak-dominated (B:FQUKE1D09:3) actually had a greater density of
ponderosa pine than black oak (460 vs. 230 tph, respectively), but the basal areas were
almost equivalent. Still, it might be more appropriate to classify this plot as pinedominated.
32
Figure 13. Map of all installed FMH sample plots in Malakoff Diggins State Park showing
prescribed fire treatment units (with year burned)
Tabe 14. Unburned vegetation conditions in 2004-5 in FMH plots in Malakoff Diggins SP.
Macroplot
B:FPIPO1D09:1
B:FPIPO1D09:2
B:FQUKE1D09:1
B:FQUKE1D09:2
B:FQUKE1D09:3
B:FQUKE1D09:4
B:FQUKE1D09:5
Mean
STD Error
Trees
per ha
>2.5 cm
Seedlings
per ha
Snags
per ha >
15 cm
520.0
1220.0
770.0
1520.0
940.0
600.0
910.0
925.7
132.3
4999.9
33599.3
7599.8
3599.9
2999.9
3799.9
2400.0
8428.4
4244.2
140.0
80.0
40.0
110.0
180.0
100.0
10.0
94.3
21.8
33
Total
Trees
per ha
5519.9
34819.3
8369.8
5119.9
3939.9
4399.9
3310.0
9354.1
4288.6
BA
(sq.m/ha)
53.9
67.0
30.1
53.2
37.2
36.8
41.6
45.7
4.9
QMD
(cm)
42.4
35.1
22.9
26.1
30.3
29.2
29.2
30.8
2.4
Table 15. Density of six tree species in unburned FMH plot s in 2004-5 in Malakoff Diggins
SP.
Macroplot
B:FPIPO1D09:1
B:FPIPO1D09:2
B:FQUKE1D09:1
B:FQUKE1D09:2
B:FQUKE1D09:3
B:FQUKE1D09:4
C:FQUKE1D09:5
PIPO
220
250
.
20
460
60
180
QUKE
.
40
680
420
230
480
410
QUCH
50
.
90
1070
40
40
10
ABCO
200
40
.
.
.
20
.
CADE
50
860
.
.
120
.
110
PSME
.
.
.
10
90
.
200
Despite differences in species composition, for the present analysis it was necessary to
combine the monitoring types to do any statistical analysis, but the sample size is still low
(n=6). Average pre-fire live tree and snag density did not change significantly one year
after the fire (Figure 14A) nor did QMD (Figure 14B). The slight difference in pre-fire basal
area (46.4kg/m2) and 1 year post-fire (43.7 kg/m2) was not significantly different.
A)
B)
Live trees >2.5 cm)
Snags
Figure 14. Average pre-burn and one year post-burn A) live tree density and B) QMD in
Malakoff Diggins SP. The standard error of the mean is represented by the narrow bars.
The pre-fire surface fuel data had less variability. Total surface fuel loads per plot ranged
from 8.4 to 14.1 kg/m2 with an average of 10.7 kg/m2 (data not shown). When the
34
monitoring types were combined, the average duff and litter load declined significantly in
response to fire and the total surface fuel load in the year following fire was 34% lower
(Table 16). The decline was due to a significant decrease in total ground fuel depth from
15.1 to 9.8 cm.
Table 16. Average pre-burn and 1 year post-burn fuel loads of fine woody debris (FWD),
coarse woody debris (CWD), and duff and litter layer in Malakoff Diggins SP. Values in a
column followed by the same letter are not significantly different (p<0.05)
Event
Pre
yr 1
N
Plots
6
6
FWD
CWD
Duff
Litter
0.47a
0.21a
0.78a
0.77a
6.63a
4.14b
3.32a
2.24b
Total
Surface
11.19a
7.35b
The point- intercept cover data had quite a lot of variability. Forbs were not detected in any
of the QUKE plots, only in the two PIPO plots, which had total cover of 9.6 and 9.7%,
primarily of hairy brackenfern (Pteridium aquilinum var. pubescens). Shrubs were more
uniformly distributed among the plots with total shrub cover ranging from 12.7-38% per
plot with an average of 22.8% (excluding PIPO plot #2, which had no shrubs). The primary
shrubs were poison oak (Toxicodendron diversilobum) and mountain misery (Chamaebatia
foliolosa). Prefire average shrub cover declined from 19.8 to 11.9 % in the year following
fire, but the difference was not significant, possibly due to the small sample size.
This limited analysis indicates that the average surface fuel load was moderately reduced
by prescribed fire, but overstory stand characteristics did not change, nor did understory
vegetation cover. One possible reason that very little change was detected in the Malakoff
plots is that the burn severity data indicates that the prescribed fire that occurred in 2007
was very low intensity. Mean substrate fire severity per plot ranged from 3.9 to 5 (1= high
intensity, 5=not burned) with an average of 4.6, while the vegetation severity ranged from
3.3 to 4.7 (average 4.0). With such a light burn one would not expect to see significant tree
mortality or changes in overstory forest structure. The reduction in surface fuels loads is
somewhat surprising given the low intensity of the burn, but even mild fire can reduce the
depth of ground fuels.
Third year post-treatment data could be collected in the 6 burned plots in 2010 in May or
early June, but with a sample size of one, there is no reason to re-sample the control plot.
However, in the absence of further treatments several factors limit what we can learn from
adding to the current dataset. First, the 2007 prescribed fire was very low intensity and
one year post-treatment data suggests that it did not appear to change forest structure
although it may have reduced total surface fuel loading by about a third. Second, the
understory was not adequately sampled. Third, the sample size is low and only one control
plot was installed. Therefore, third year post-treatment sampling is probably not
warranted. If more treatments are planned, these plots could be incorporated into a new
sampling design and re-sampled with the modified sampling proposed in the next section.
35
Plumas- Eureka
Plumas- Eureka SP is located in Plumas County, CA near the resort town of Graeagle. The
elevation spans 4,720 to 7,450 feet. A total of 6 plots were installed in the park in 1996 and
a prescribed fire was conducted in October 1997 that only burned one of the monitoring
plots (Figure 15). This plot has been regularly re-sampled at 1,2,3,5 and 10 years post-fire.
One of the unburned plots was re-sampled in 2006 as a control. This pair of plots was
excluded from the present analysis because the mixed conifer vegetation is much different
than the higher elevation mixed conifer forest at Lake Tahoe. An additional 8 plots were
installed in 2000 and the 5 untreated plots installed in 1996 were also re-read at that time.
Figure 15. Map of all installed FMH sample plots in Malakoff Diggins State Park showing
prescribed fire treatment units (with year burned) and forest treatment units (subjected to
thinning).
Since no prescribed fire has been conducted in the area with FMH plots, preliminary results
of unburned conditions in 2000 are presented for the 13 plots. Average forest structure
was fairly similar to the plots in the basin, with a high density of small trees (924.6 vs. 1250
per ha) and a high basal area (62.7 vs. 57.9 kg/m2) (Table 17). Average tree size was
36
almost 5 cm greater in Plumas (36.4 vs. 31.7 cm) than in the basin plots and the number of
snags was about half (194.6 vs. 425.5 per ha).
Table 17. Unburned vegetation conditions in FMH plots in Plumas-Eureka SP in 2000.
Macroplot
B:FABCO1D08:3
B:FABCO1D08:7
B:FABCO1D08:4
B:FABCO1D08:11
B:FABCO1D05:1
B:FABCO1D08:13
C:FABCO1D10:6
B:FABCO1D05:10
B:FABCO1D08:12
B:FABCO1D08:14
B:FABCO1D08:9
B:FABCO1D05:8
B:FABCO1D08:2
Mean
STD error
Trees
per ha
>2.5 cm
Seedlings
per ha
Snags
per ha >
15 cm
810.0
780.0
940.0
880.0
360.0
380.0
1380.0
530.0
740.0
640.0
1350.0
800.0
2430.0
924.6
151.7
1000.0
1400.0
600.0
1200.0
0.0
11599.8
600.0
3999.9
400.0
1000.0
400.0
1400.0
3799.9
2107.6
861.0
420.0
10.0
60.0
10.0
0.0
220.0
280.0
40.0
530.0
240.0
60.0
140.0
520.0
194.6
53.6
Total
Trees
per ha
1810.0
2180.0
1540.0
2080.0
360.0
11979.8
1980.0
4529.9
1140.0
1640.0
1750.0
2200.0
6229.9
3032.3
854.8
BA
(sq.m/ha)
75.9
53.1
56.1
40.9
21.8
78.2
65.1
39.4
94.7
100.2
64.2
59.4
66.8
62.7
6.0
QMD
(cm)
38.1
34.0
36.6
29.3
31.7
51.2
34.4
33.7
42.8
48.0
30.6
37.3
25.8
36.4
2.0
As expected, the species composition of the overstory was quite different. The lower
elevation forest at Plumas is also dominated by white fir, but the next most prevalent
species is Douglas fir, followed by incense cedar (Figure 16 A and B). Red fir is absent
because it is too low in elevation and the pine component has more Ponderosa pine than
Jeffrey pine, with a very small amount of sugar pine.
37
A)
B)
Figure 16. Average A) tree density and B) basal area in unburned FMH plots in PlumasEureka SP in 2000.
Fuel loading in the Plumas-Eureka plots is very high and the standard error does not
appear to be extreme, indicating that the sample size is adequate for making statistical
comparisons of change in the future (Table 18). The loads of FWD, CWD, duff, and litter are
quite similar to the Sugar Pine plots (1.3, 3.3, 5.5, and 3.5 kg/m2).
Table 18. Fuel loads (kg/m2) in unburned FMH plots in Plumas-Eureka SP in 2000.
Macroplot
1Hr
10Hr
100Hr
FWD
Sound Rotten
FABCO1D08:3
FABCO1D08:7
FABCO1D08:4
FABCO1D08:11
FABCO1D05:1
FABCO1D08:13
FABCO1D10:6
FABCO1D05:10
FABCO1D08:12
FABCO1D08:14
FABCO1D08:9
FABCO1D05:8
FABCO1D08:2
MEAN
STD Error
0.09
0.1
0.03
0.05
0.05
0.14
0.2
0.04
0.16
0.13
0.11
0.06
0.29
0.11
0.48
0.17
0.27
0.46
0.34
0.56
0.8
0.8
0.55
0.38
0.32
0.44
0.72
0.48
1.22
0
0.68
0.61
0.41
0.81
1.09
1.77
0.68
0.95
0.14
0.61
0.14
0.7
1.79
0.27
0.98
1.12
0.8
1.52
2.09
2.62
1.38
1.46
0.57
1.12
1.14
1.3
0.27
0
1.41
0
0.03
0.89
0.11
0.2
0.41
0.57
1.04
0
0.04
0.38
0.02
0.05
0.14
0.17
0.13
38
CWD
Duff
Litter
0.86
0
0.16
0.05
0.14
5.08
9.31
0.21
2.6
10.24
0
0.06
1.84
2.35
1.13 5.21
0
4.21
1.57 4.88
0.05 5.24
0.17 3.88
5.98 10.92
9.41 7.82
0.41 4.28
3.01 7.51
10.82 8.26
1.04 5.49
0.06 4.62
1.89 6.89
2.73 6.09
2.12
1.38
2.14
2.31
1.57
2.82
3.32
1.8
1.28
2.18
1.17
1.01
1.98
1.93
Total
Surface
10.26
5.87
9.57
8.73
6.42
21.24
22.63
9.1
13.18
22.71
8.28
6.81
11.9
12.05
1.00
1.02
0.18
1.70
0.57
The understory vegetation in the Plumas-Eureka is of course, quite different from the Lake
Tahoe basin. A total of 28 species were detected in the cover point intercept method but
the shrub density belts and the additional species compositions methods were not
conducted. The average number of species per plot was 4.9 species. Average percent cover
for shrubs, sub-shrubs, forbs, and grass was 15.9, 7.3, 1.8, and <1%, respectively. Of the 13
plots, 3 were classified as ABCO fuel model 5, which has a strong brush understory, while
the rest were fuel model 8, which represents a closed timber type with a strong litter
component. The three fuel model 5 plots did have very high shrub cover of 42.8, 59.0, and
62.2 %. In comparison, only 3 of the fuel model 8 plots had shrub cover between 12 to 13%,
while the rest had less than 3%. The most prevalent shrubs were huckleberry oak (Quercus
vacciniifolia ) and green-leaf manzanita (Arctostaphylos patula) with significant amounts of
the sub-shrubs creeping snowberry (Symphoricarpos mollis ) and mahala mat (Ceanothus
prostratus).
Although overstory structure and total surface fuel loads are comparable between PlumasEureka SP and plots installed in the Lake Tahoe basin, the species composition is very
different and there would not be any reason to include the Plumas plots in any analysis of
the plots in the basin. The comparisons made here is simply meant to illustrate that the
lower elevation mixed conifer in Plumas-Eureka exhibits a similar degree of departure
from historic conditions in the form of high densities of small trees and high fuel loads as is
observed in the Lake Tahoe basin.
With a sample size of 13 FMH plots in Plumas-Eureka SP, the overstory and fuel loading
data appears to capture a reasonable amount of natural variation and it is likely that
statistically robust change could be detected if future monitoring were conducted. This
year (2010) presents an opportunity to conduct sampling and determine how the
overstory and fuel loads have changed in the last 10 years. Of the 13 plots, 6 have received
no treatment and 7 were thinned at some point and it is not known if these reduced sample
sizes would provide sufficient power for detecting change in response to treatment or not.
However, it is probably worthwhile to re-sample because the data would be useful in
developing an effective treatment prescription, and would especially inform the scheduling
of subsequent treatments. The modified sampling protocols suggested in the next section
should be used.
State Parks in the Lake Tahoe basin
The total monitoring effort within the Lake Tahoe basin to date has included the
installation of 54 plots in four state parks. The majority of the plots are in Sugar Pine Point
SP, with six or fewer each in Burton Creek, D.L. Bliss, and Emerald Bay SP. (see Table 13).
The available plot data for each of the parks is discussed briefly and then the pre-fire forest
structure, fuel loads, and understory data from each park is compared with that in Sugar
Pine to determine if the forest and fuel conditions in Sugar Pine SP are similar enough to
the other parks that the Sugar Pine FMH monitoring program serves as an adequate
surrogate for evaluating response to prescribed fire planned in other parks.
39
Burton Creek SP
In Burton Creek SP, four plots (30-33) classified as white fir fuel model 10 (ABCO10) were
installed in 1993 (Figure 18). Plot 30 was burned in October 1995 and post burn sampling
has been conducted through year ten. However, it was excluded from the present analysis
because several of the interim sample events were incorrectly assigned to Sugar Pine and
so uncertainties remain over the location and identity of the plot. Plots 31 and 32 were
removed in 2001 because they had received some form of thinning treatment and plot 33
has not been re-sampled. The master plot list indicates that four more plots were installed
in 2000 and the locations appear in the GIS map layer in Figure 18, but this data, if it exists,
was not in the FMH database and as such was not migrated to FFI. The current sample size
of one burned plot and one control is insufficient for detecting response to treatment in the
park, but the pre-treatment data from 1993 from all four plots is presented in the
comparison to Sugar Pine SP. However, the sample size (n=4) is not really adequate for
statistical purposes so the comparison is coarse and not robust.
Figure 18. Map of all installed FMH sample plots in Burton Creek State Park showing
prescribed fire treatment units (with year burned).
40
D.L. Bliss SP
In D.L. Bliss, two plots were installed in August of 1994 just prior to a fall burn in October
(Figure 19). Plot 1 was classified as a Sugar pine fuel model 9 (PILA09) but it was excluded
from the 10-year post-treatment analysis because the initial plot size was larger than 50 X
20, but the exact dimension of the plot is unknown. Also the ten year post-burn data is
missing and there is no reason to collect any future data in this plot. Plot 5 was classified as
a Jeffrey pine fuel model 9 (PIJE09). It was burned in 1994, post burn monitoring has been
conducted through 2004, and it was included in the ten year post-burn analysis. No
controls were established.
Figure 19. Map of all installed FMH sample plots in Burton Creek State Park showing
prescribed fire treatment units (with year burned).
An additional 3 PIJE09 plots (9-11) were installed in the park in 2001, but these have not
been burned nor have they been re-sampled. The master plot list indicates that two other
plots (ABCO10 43 and 44) were also installed in 2001, but there is no data in the FMH
database. Plot 45, classified as ABCO10, was installed in 2003 and burned in 2006 and one
year post burn data was collected in 2007. The current sample size of five plots (one
41
burned in 1994, one burned in 2006 and three unburned) is insufficient for determining
effects of prescribed fire. In addition, there is insufficient pre-treatment data available to
compare with the Sugar Pine SP plots so a new monitoring plan for this park is warranted.
Emerald Bay SP
Emerald Bay SP was included in the first monitoring plot installation in August 1992 when
five plots (12-16) classified as ABCO10 were installed (Figure 20). Plots 14 and 16 were
burned in November that year and the ten year post-burn analysis includes the data for
both plots. Plot 13 was burned in October 1994, and it is also included in the ten year postburn dataset. Plot 12 was installed in 1992 as a control and was included in the ten year
control dataset. Plot 15 was also installed in 1992, but it has never been re-sampled.
Because Plots 12 and 15 were installed in 1992, they fit the criteria for inclusion in the 15
year unburned control analysis and these two plots are recommended for inclusion in a
2010 sampling effort. However, only plots burned in 1995 and 1996 will be re-sampled in
that effort so plots 13, 14, and 16 will not be re-sampled (see 2010 re-sample effort).
Figure 20. Map of all installed FMH sample plots in Burton Creek State Park showing
prescribed fire treatment units (with year burned).
42
Pre-treatment data from 1992 from all five plots is presented in the comparison to Sugar
Pine Sp. However, the sample size (n=5) is barely adequate for statistical purposes so the
comparison is not robust.
Sugar Pine Point SP
A total of 39 plots have been installed at Sugar Pine Point since 1992 (see Figure 1). A total
of 20 plots have been burned one time in 1993, 1995, or 1996, three plots have
experienced more than one fire, seven plots were established as controls, and nine plots
were only sampled at the time of installation and have remained unburned. A total of 18
plots that were burned one time were included in the ten year post-burn analysis, and the
six control plots from the appropriate time period that had ten year data available
constituted the bulk of the control dataset. All plots in the analysis were classified as
ABCO10 monitoring type, except for one. Plot 1 was classified as a Jeffrey pine fuel model 5
(PIJE05). In that plot, the basal area of PIJE was 292 compared to only 41 for ABCO, but
there were only two large Jeffrey pines with a corresponding density of only 97trees per
acre ( tpa) compared to 174 tpa for ABCO. Because of the similarity in densities with the
ABCO10 plots and similar fuel loadings, it was included in the dataset for the post-burn
analysis.
Of the 15 plots that were not included in the 10-year post burn analysis, 9 of them were
only sampled at the time of installation, three were burned more than one time, and the
other three lacked appropriate data. The sample size of 18 burned plots was certainly
sufficient for the ten year post-burn analysis but the number of controls (n=6) was only
marginally acceptable. In 2010, it will be possible to re-sample 15 burned plots and 10
unburned plots in the park in order to strengthen the sample size of the control dataset and
determine how the forest and fuels have responded to prescribed fire after 15 years. This
re-sample effort will be valuable for developing new treatment prescriptions.
Unburned conditions in four CA State Parks
The monitoring effort in Sugar Pine Point SP has been relatively robust and when
prescribed burn projects have been implemented in Burton Creek, D.L. Bliss, and Emerald
Bay SP it has been assumed that the Sugar Pine SP data serves the monitoring needs of the
general prescribed fire program. The new FFI database provides an opportunity to test
that assumption. In this section we expand the scope of the project to include plots not
analyzed for the ten year post burn analysis so that we may evaluate differences in pre-fire
forest structure, composition, fuel loading, and understory among all four State Parks in the
basin. The purpose is to determine if the forest and fuel conditions in Sugar Pine SP are
similar enough to the other parks that the FMH monitoring program in Sugar Pine serves as
an adequate surrogate for evaluating response to prescribed fire planned in other parks.
Plots were selected from the entire pool of 54 plots located in the basin. Plots were
installed from 1992 through 2001 and it was determined that the largest sample size
within a reasonably narrow time window would be obtained if only plots with pretreatment data from 1992-94 were included in the analysis. In Burton Creek SP, pre43
treatment data from 1993 was available for four plots (although two were removed in
2001) and from Emerald Bay SP, data from 1992 was available for five plots. In D.L. Bliss SP
pre-treatment data was only available from 1 plot (installed in 1994) for a combined
sample size of ten plots. In Sugar Pine SP, 12 plots had pre-treatment data during the
selected time period.
Although the sample size for comparison among the parks is not very robust, none of the
variables of live overstory tree density, seedling density, snag density, or mean tree size
(QMD) were significantly different between Burton, Emerald Bay, and Sugar Pine Point SP
(Table 19). However, Emerald Bay SP had significantly lower average basal area than
either park.
Table 19. Average unburned conditions in 1992-94 in FMH plots in three CA State Parks.
Values in a column followed by the same letter are not significantly different (p=0.05).
Park
N
Trees
per ha
>2.5 cm
Seedlings
per ha
Snags
per ha >
15 cm
Burton
EMB
Sugar
4
5
12
1140.0a
1124.0a
1276.7a
900.0a
2320.0aa
5183.2a
495.0a
642.0a
471.7a
Total
Trees
per ha
2040.0a
3444.0a
6459.9a
BA
(sq.m/ha)
81.9a
38.9b
61.6a
QMD
(cm)
34.6a
26.3a
32.1a
When the nine plots from Burton Creek and Emerald Bay were combined with the one plot
from D.L. Bliss to get a more equal sample size, the average forest structure was even more
similar to Sugar Pine (Table 20).
Table 20. Average unburned conditions in 1992-94 in FMH plots in CA State Parks (Other
= Burton Creek, D.L. Bliss, and Emerald Bay SPs). Values in a column followed by the same
letter are not significantly different (p=0.05).
Park
N
Trees
per ha
>2.5 cm
Seedlings
per ha
Snags
per ha >
15 cm
Other
Sugar
10
12
1158.0a
1276.7a
1720.0a
5183.2a
525.0a
471.7a
Total
Trees
per ha
2878.0a
6459.9a
BA
(sq.m/ha)
QMD
cm
57.6a
61.6a
30.2a
32.1a
All tree species were represented in Sugar Pine SP, but red fir (ABMA) and lodgepole pine
(PICO) were essentially absent in the other parks (Table 21). This is likely a factor of the
greater area sampled in Sugar Pine SP, where the large number of plots installed
encompassed a wider elevation range that captured red fir at higher elevations and also
some wetter meadow-like areas that supported lodgepole.
Tree size class distribution was also very similar between the two datasets (Table 22).
Saplings and pole-sized trees accounted for the majority of tree density in all parks, while
large trees were very sparsely represented.
44
Table 21. Average tree density of six tree species in CA State Parks (Other = Burton Creek,
D.L. Bliss, and Emerald Bay SPs). Values in a row followed by the same letter are not
significantly different (p=0.05).
Species
ABCO
ABMA
CADE
PICO
PIJE
PILA
Trees per ha
>2.5 cm
Sugar
1071.7a
55.8a
29.2a
10.8a
100.8a
8.3a
Other
921a
3b
32a
0b
189b
13a
Table 22. Average tree density of five size classes in CA State Parks (Other = Burton Creek,
D.L. Bliss, and Emerald Bay SPs). Values in a column followed by the same letter are not
significantly different (p=0.05).
Park
N
Sugar Pine
Other
Mean density
Sapling
(2.515cm)
Polesize
(15.130cm)
Small
(30.160cm)
Medium
(60.191.3cm)
Large
(>91.4cm)
721.7a
510.9a
330.9a
374.3a
162.3a
189a
27.3a
25.2a
7.8a
6.7a
12
10
Despite some apparent differences (i.e. lower CWD in Burton Creek), average surface fuel
loads were not significantly different among the parks (Table 23).
Table 23. Average fuel loads (kg/m2) and ground fuel depth (cm) in three CA State Parks.
Values in a column followed by the same letter are not significantly different (p=0.05).
Park
N
Burton
EMB
Sugar
4
5
12
FWD
0.83a
1.32a
1.31a
CWD
0.47a
2.01a
3.68a
Duff
7.29a
6.57a
6.10a
Litter
2.27a
4.22a
3.57a
Total
Surface
10.86a
14.12a
14.66a
Duff
cm
8.27a
7.46a
6.93a
Litter
cm
5.16a
9.58a
8.09a
Total
cm
13.43a
17.04a
15.02a
When the data for the other three parks were combined, the average fuel loadings were
even more similar to Sugar Pine (Table 24).
The ten year post-burn analysis revealed that only using a single point intercept transect
under-sampled the understory vegetation and did not provide significantly robust results.
In this comparison, only nine species were detected in the point intercept transects in the
parks other than Sugar Pine SP and the shrub density belts captured an additional 4 shrub
species. Average richness per plot was 2.2 species using the point method and 3.2 species
45
per plot when the shrub belts were included. The species composition method was not
used in most of the plots. In comparison, average species richness in Sugar Pine ( n=20)
was 4.2 species for all three methods.
Table 24. Average fuel loads (kg/m2) and ground fuel depth (cm) in CA State Parks (Other
= Burton Creek, D.L. Bliss, and Emerald Bay SPs). Values in a column followed by the same
letter are not significantly different (p=0.05).
Park
N
Other
Sugar
10
12
FWD
1.09a
1.31a
CWD
1.30a
3.68b
Duff
6.42a
6.10a
Litter
3.18a
3.57a
Total
Surface
12.00a
14.66a
Duff cm
7.29a
6.93a
Litter
cm
7.23a
8.09a
Total
cm
14.52a
15.02a
The most frequently detected shrubs in the other parks were huckleberry oak (Quercus
vacciniifolia ), green-leaf manzanita (Arctostaphylos patula)) and mahala mat (Ceanothus
prostratus). Whitethorn (C. cordulatus) was only detected in the shrub belt. In contrast, it
was the second most frequently encountered shrub in Sugar Pine SP. Despite the apparent
under-sampling, the average cover of the four understory vegetation lifeforms was similar
in the other parks when compared to Sugar Pine SP (Table 25).
Table 25. Average percent cover of four understory lifeforms in CA State Parks (Other =
Burton Creek, D.L. Bliss, and Emerald Bay SPs). Values in a column followed by the same
letter are not significantly different (p=0.05).
Lifeform
Forb
Grass
Shrub
SubShrub
Sugar
2.08a
0.00a
10.77a
3.42a
Other
1.95a
0a
11.19a
5.27a
In summary, this limited comparison indicates that unburned forest and fuel conditions in
Burton Creek and Emerald Bay SP may be comparable to Sugar Pine SP, especially if the
plot data for the two other parks are combined. The plot sample size for both parks was
small, so the comparison was not robust, but the proximity of the parks also adds strength
to the conclusion of that they support similar forests. It was not possible to include D.L
Bliss in the comparison (other than adding in the one plot to the combined dataset)
because of insufficient data. If any treatments are planned in the future in D.L. Bliss a new
monitoring plan should be developed.
Although the FMH plot data from Sugar Pine SP may provide a reasonable representation of
forest and fuel conditions at lower elevations on the west shore of Lake Tahoe, the question
of whether the monitoring program is adequate for addressing management objectives is a
separate matter and was addressed in the discussion.
46
2010 Re-sample effort
The main objective of a 2010 re-sampling effort is to gather 15 year post-burn data from a
subset of the sample plots in order to validate and/or strengthen the conclusions of this
report. Some of the conclusions presented here are limited by omissions or inadequacies in
the data collection protocols and by the extreme variability present in some of the data.
Therefore the 2010 re-sampling should focus only on those monitoring protocols that
yielded useful information and it should strive to reduce variability in the data by
increasing the sample size where possible and limiting the variability of the treatments(i.e.
burn years) under investigation. Simply stated, the objectives of the 2010 re-sample are to:
• Limit the variability of the data from burned plots
• Increase the sample size of the control dataset
• Streamline data collection protocols
The majority of the plots analyzed ten years post- treatment were burned in 1995 (n=10)
and the 2010 field season falls 15 years after those burns. The 2010 re-sample should focus
on those ten plots in Sugar Pine SP and it seems reasonable to include the six plots burned
in 1996 in that park in order to dramatically increase the sample size. Only 2 plots were
burned in 1994, one in Bliss and one in Emerald Bay, so the added topographic diversity
would not likely improve the variability of the data and it is recommended that the resampling of treatment plots is limited to the 15 FMH plots burned in the 1995-1996
prescribed fires in Sugar Pine Point (Table 26 ).
The main limitation for the control dataset was a small sample size of only six or seven
plots, depending on the data. Only three of the unburned plots had ten year data collected
in 2005, and only one unburned plot was installed in 2005, so it is necessary to expand the
number of sample years to increase the sample size of the control dataset. However, the
main criterion for inclusion in a control dataset is that the plots match the criteria of the
burn plots in some way. Pre-treatment data for the 15 burned plots was collected in either
1992, 93, or 96 and pre-treatment data is available from 11 unburned plots in those years.
The one control plot installed in 1995 was added to further increase the sample size
because 2010 is the 15th year for monitoring in that plot. So even though the actual number
of years elapsed in the controls plots since pre-treatment data was collected varies from 14
to 18 years the pre-treatment data is comparable to the treatment plots.
Streamlining the data collections protocols and maximizing the efficiency of the data
collection effort is another main objective of the 2010 sample effort. All but two plots
recommended for re-sampling in 2010 are located in Sugar Pine SP so that will necessarily
increase the efficiency of the sampling effort. Reducing the number of sampled variables to
those that yield statistically robust results will also increase efficiency. There are some
inadequacies in the existing sample protocol that cannot be addressed without sacrificing
the comparability of the data with previous sampling events, but some new measurements
47
can be taken that will inform future management actions. The following modifications to
the FMH protocol are recommended for the 2010 sampling effort.
Table 26. FMH plot location and status of plots recommending for sampling in 2010.
Monitor
Type
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABCO1D10
FABMA1D10
FPIJE1D09
Plot
ID
25
24
26
6
18
7
8
2
3
4
40
112
113
1
3
Park
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Install
Date
1993
1993
1993
1992
1992
1992
1992
1992
1992
1992
1996
1996
1996
1996
1996
1
10
12
15
19
20
21
23
42
3
22
41
Sugar
Sugar
Em Bay
Em Bay
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
Sugar
1992
1992
1992
1992
1993
1993
1993
1993
1993
1995
1996
1996
Burn
Date
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1996
1996
1996
1996
1996
YR 10
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2006
2006
2006
2006
2006
unburned plots
ABCO 10Control
ABCO 10Control
ABCO 10Control
ABCO 10Control
ABCO 10Control
ABCO 10Control
ABCO 10Control
ABCO 10Control
ABCO 10Control
PIJE1D05 Control
ABCO 10Control
ABCO 10Control
2005
2005
2004
no
2005
2002
no
no
no
no
no
2006
Overstory
In addition to taking the DBH and status of all previously tagged trees, tree height and live
crown base height should be collected to enable calculations of canopy bulk density (CBD)
and canopy base height (CBH). While comparable pre-treatment data will not be available,
CBD and CBH are measures of canopy fuels that offer concise metrics for determining the
current potential for active or passive crown fire. These measures would be very valuable
in identifying the need for additional fuels reduction and restoration treatments.
It is also necessary to get an estimate of canopy cover in order to address current wildlife
habitat conditions. The California Wildlife Habitat Relationship (CWHR) type is a structural
stage classification scheme that is commonly used to summarize habitat conditions for
48
wildlife species. Each type is expressed as a number code based on the average DBH and a
letter code based on the average canopy closure (Table 27). As an example: the average
condition of a forest stand typed 4M supports trees between 11 to 24 inches and 40-59%
canopy closure. The recommended method for accurate canopy cover is to use a site-tube
densitometer on a 25 sample point grid in the plot.
Table 27. California Wildlife Habitat Relationship (CWHR) classification standards.
WHR
1
2
3
4
5
6
Standards for Tree Size
WHR Size Class
dbh
Seedling Tree
<1"
Sapling Tree
1" to 6"
Pole Tree
6" to 11"
Small Tree
11" to 24"
Med/Large Tree
>24"
Multi-Layered Tree
Size class5 trees
over a distinct layer
of class3 or 4 trees,
total canopy >60%
WHR
S
P
M
D
Standards For Canopy Closure
Closure Class
Canopy Closure
Sparse Cover
10 to 24%
Open Cover
25 to 39%
Moderate Cover
40 to 59%
Dense Cover
60 to 100%
Understory
Although a single point-intercept transect did not appear to be sufficient for capturing the
abundance and distribution of the understory vegetation, adding more transects in the 15
year re-read is not necessary because the data would not be comparable to the pre-burn
data. The following modifications are recommended: conduct the sampling as before, but
list all other species observed in the plot that are not captured on the transect with zero
hits so that the entire species richness of the plot is on one datasheet. Omit the shrub
density belt method.
Surface and ground fuels
Sampling four transects for surface and ground fuels appeared adequate for detecting
change in the plots in response to prescribed fire. However, the number of duff and litter
depth measurements appeared to be excessive. The protocol to measure depth every 5 feet
for 45 feet results in 40 sample points per plot. Other studies have produced robust results
with 8 to 16 depth measurements per plot (Stephen and Moghaddas 2005, Stanton and
Dailey 2007, Youngblood et. al 2008) so we recommend sampling only 3 depths per
transect at 10, 25, and 40 feet.
49
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