Analysis of 15 Years of Data from the California State Parks Prescribed Fire Effects Monitoring Program

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.

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

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/m 2 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.

• 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.

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 20 th 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 crown- fire 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 multiple- agency 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 10- acre 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.

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.

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.

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.

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 Plot Park Burn Burn ID Date Severity FABCO1D10 14 Em Bay 1992 2.63 FABCO1D10 16 Em Bay 1992 3.53 FABCO1D10 28 Sugar 1993 1.50 FABCO1D10 13 Em Bay 1994 4.13 FPIJE1D05 5 Bliss 1994 4.50 FABCO1D10 2 Sugar 1995 1.73 FABCO1D10 3 Sugar 1995 1.73 FABCO1D10 4 Sugar 1995 3.55 FABCO1D10 6 Sugar 1995 1.78 FABCO1D10 7 Sugar 1995 1.00 FABCO1D10 8 Sugar 1995 1.70 FABCO1D10 18 Sugar 1995 1.40 FABCO1D10 24 Sugar 1995 1.34 FABCO1D10 25 Sugar 1995 1.80 FABCO1D10 26 Sugar 1995 2.13 FABCO1D10 40 Sugar 1996 2.58 FABCO1D10 112 Sugar 1996 2.97 FABCO1D10 113 Sugar 1996 2.87 FABMA1D10 1 Sugar 1996 2.10 FPIJE1D05 1 Sugar 1996 4.58 FPIJE1D09 3 Sugar 1996 2.49 FABCO1D10 5 Sugar 1997 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 (0.64- 2.54cm) 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 fir- dominated 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 post- burn data collection. Monitor Type Plot ID Park Install Date Burn YR 10 Date FABCO1D10 14 Em Bay 1992 1992 2002 FABCO1D10 16 Em Bay 1992 1992 2002 FABCO1D10 28 Sugar 1993 1993 2003 FABCO1D10 13 Em Bay 1992 1994 2004 FPIJE1D05 5 Bliss 1994 1994 2004 FABCO1D10 25 Sugar 1993 1995 2005 FABCO1D10 24 Sugar 1993 1995 2005 FABCO1D10 26 Sugar 1993 1995 2005 FABCO1D10 6 Sugar 1992 1995 2005 FABCO1D10 18 Sugar 1992 1995 2005 FABCO1D10 7 Sugar 1992 1995 2005 FABCO1D10 8 Sugar 1992 1995 2005 FABCO1D10 2 Sugar 1992 1995 2005 FABCO1D10 3 Sugar 1992 1995 2005 FABCO1D10 4 Sugar 1992 1995 2005 FABCO1D10 40 Sugar 1996 1996 2006 FABCO1D10 112 Sugar 1996 1996 2006 FABCO1D10 113 Sugar 1996 1996 2006 FPIJE1D09 3 Sugar 1996 1996 2006 FPIJE1D05 1 Sugar 1995 1996 2006 FABMA1D10 1 Sugar 1996 1996 2006 FABCO1D10 5 Sugar 1992 1997 2007

CONTROLS FABCO1D10 41 Sugar 1996 2006 FABCO1D10 1 Sugar 1992 2005 FABCO1D10 10 Sugar 1992 2005 FABCO1D10 19 Sugar 1993 2005 FABCO1D10 12 Em Bay 1992 2004 FPIJE1D05 3 Sugar 1995 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.

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 Snags BA QMD >2.5 cm per ha > (sq.m/ha) (cm) 15 cm PRE 22 1,250.0a 425.5b 57.9a 31.7b yr01 22 700.9b 725.9a 54.7a 36.9a Yr05 22 485.5bc 680.5a 47.0a 38.7a yr10 22 430.9c 288.6b 46.2a 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 Trees per ha Snags BA QMD Plots >2.5 cm per ha > (sq.m/ha) (cm) 15 cm PRE burn 22 1250.0a 425.5a 57.9a 31.7a control 7 1424.3a 437.1a 53.1a 30.0a

yr10 burn 22 430.9b 288.6a 46.2a 40.9a control 7 1222.9a 284.3a 55.8a 32.5b

On average, almost 84% of tagged trees were saplings (2.5-15cm, 1-6 in) or pole-size (15.1- 30cm, 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).

Sapling Pole-size Small Medium Large

1000 900 800 700 600 500 400

Mean treesMean per ha 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 Pole-size Small Medium Large Total (2.5-15cm) (15.1-30cm) (30.1-60cm) (60.1-91.3cm) (>91.4cm)

00PRE ABCO 652.7 261.4 111.4 8.6 0.9 1035.0 01yr01 232.0 165.5 120.0 10.0 1.0 528.5 01yr05 112.4 139.0 113.3 8.1 1.0 373.8 01yr10 100.0 107.7 113.2 8.2 1.4 330.5

00PRE ABMA 32.7 31.8 10.9 0 0.5 75.9 01yr01 16.0 22.0 12.0 0 1.0 51.0

Event Species Sapling Pole-size Small Medium Large Total (2.5-15cm) (15.1-30cm) (30.1-60cm) (60.1-91.3cm) (>91.4cm) 01yr05 ABMA 3.8 10.0 8.1 0 1.0 22.9 01yr10 3.6 6.4 7.3 0 0.9 18.2

00PRE CADE 16.4 10.0 0.5 0 1.4 28.2 01yr01 8.0 3.0 0.5 0 1.5 13.0 01yr05 5.7 8.6 0.5 0 1.0 15.7 01yr10 7.3 7.7 0.9 0 0.9 16.8

00PRE PICO 1.8 4.5 1.8 0 0 8.2 01yr01 0 3.5 2.0 0 0 5.5 01yr05 0 1.9 1.0 0 0 2.9 01yr10 0 0.9 1.4 0 0 2.3

00PRE PIJE 14.5 20.5 35.0 17.3 4.5 91.8 01yr01 10.0 17.5 32.0 11.5 5.0 76.0 01yr05 1.9 12.4 29.0 11.9 4.3 59.5 01yr10 1.8 9.1 27.3 13.2 5.5 56.8

00PRE PILA 3.6 2.7 2.7 1.4 0.5 10.9 01yr01 2.0 2.5 2.5 2.0 1.0 10.0 01yr05 0 1.9 2.9 1.9 0.5 7.1 01yr10 0 1.8 2.7 1.4 0.5 6.4

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.

500

400

300

200 Mean treesMean perha

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.

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.

16 14 12 10 Duff Litter 8 FWD kg/m2 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). Load (kg/m2) Depth (cm) Event Plot N Plots FWD CWD Duff Litter Total Duff Litter type Surface PRE Burn 22 1.33a 3.52a 5.49a 3.54a 13.88a 6.23a 8.04a Control 6 1.42a 4.82a 7.08a 3.95a 17.26a 8.04a 8.96a yr10 Burn 22 0.75a 5.50b 3.10b 0.89b 10.23b 3.52b 2.02b Control 6 1.05a 11.15a 6.70a 2.05a 20.95a 7.60a 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.

b b

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.

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/m 2 to 1.62kg/m 2. 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 pre- fire levels.

Prescribed fire also significantly reduced the depth of the duff and litter layers from pre- fire 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 post- fire, 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.

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).

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/m 2 (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/m 2 for all fuel components combined, the total fuel load would be expected to increase by 2.71kg/m 2 over the next five years. By 2010, the average fuel load would be expected to be 12.91kg/m 2, nearly equal to the pre-fire average load of 13.8 kg/m 2.

Table 7. Average rate of accumulation (standard error) of the surface and ground fuel loads. Type kg/m 2/year FWD 0.027 (.01) Sound 0.207 (.03) Rotten 0.140 (.05) Duff 0.227 (.02) Litter -0.059 (.02) Total Surface Load 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 sub- shrubs, 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 ).

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 FORB SHRUB SUBSHRUB GRASS 00PRE 2.1bc 1.7 1.3b 1.0 0.95a 0.6 0.0b . 01yr01 1.5c 1.6 1.0b 0.9 0.75a 0.5 0.0b . 01yr5 4.1a 2.6 2.6a 1.1 0.8a 0.7 0.5a 1.1 01yr10 3.3ab 2.5 2.6a 1.3 0.8a 0.7 0.6a 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 Pre Yr01 Yr05 Yr10 Forb 2.08a 1.81a 1.77a 1.06a Grass 0.00 0.00 0.16 0.32 Shrub 10.77a 4.11a 6.12a 9.73a SubShrub 3.42a 0.85b 0.76b 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

(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 N Pre Yr01 Yr05 Yr10 F ratio p value QUVA All plots 19 6.5 2.8 3.6 4.2 0.64 0.591 QUVA only 7 20.5 8.8 11.2 13.5 1.48 0.251 CECO All plots 19 0.8 0.4 1.6 4.5 2.63 0.056 CECO only 5 2.6a 0.9a 5a 14.2b 5.22 0.008* SYMO All plots 19 2.9a 0.6b 0.4b 0.4b 3.88 0.013* SYMO only 9 5.5a 1.1b 0.8b 0.8b 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 Plot N Plots Forb Grass Shrub Subshrub type Pre Burn 20 1.8a 0.0a 7.8a 2.9a Control 7 2.9a 0.5a 9.6a 1.5a

Yr10 Burn 20 0.8b 0.3a 8.6a 0.8a Control 7 4.6a 0.3a 11.7a 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,

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 Age Pre Yr1 Yr5 Yr10 Class ARPA6 M 1 1 1 4 I 0 0 9 5 CECO M 2 1 4 12 I 1 4 13 13 CHSE11 M 4 1 1 1 I 3 0 1 2 QUVA M 6 5 6 5 I 5 4 7 5 SYMO M 14 3 1 5 I 8 1 7 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.

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 (1- 100hr 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.

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

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 Plots # burned # burned # # installed 1x 2x or unburned controls more Burton Creek 4 1 0 3 0 D.L. Bliss 6 2 1 3 0 Emerald Bay 5 3 0 1 1 Sugar Pine Point 39 20 3 9 7 Malakoff Diggins 7 6 0 0 1 Plumas- Eureka 14 1 0 12 1 Total 75 33 4 28 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 pre- fire 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 pine- dominated.

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 Trees Seedlings Snags Total BA QMD per ha per ha per ha > Trees (sq.m/ha) (cm) >2.5 cm 15 cm per ha B:FPIPO1D09:1 520.0 4999.9 140.0 5519.9 53.9 42.4 B:FPIPO1D09:2 1220.0 33599.3 80.0 34819.3 67.0 35.1 B:FQUKE1D09:1 770.0 7599.8 40.0 8369.8 30.1 22.9 B:FQUKE1D09:2 1520.0 3599.9 110.0 5119.9 53.2 26.1 B:FQUKE1D09:3 940.0 2999.9 180.0 3939.9 37.2 30.3 B:FQUKE1D09:4 600.0 3799.9 100.0 4399.9 36.8 29.2 B:FQUKE1D09:5 910.0 2400.0 10.0 3310.0 41.6 29.2 Mean 925.7 8428.4 94.3 9354.1 45.7 30.8 STD Error 132.3 4244.2 21.8 4288.6 4.9 2.4

Table 15. Density of six tree species in unburned FMH plot s in 2004-5 in Malakoff Diggins SP. Macroplot PIPO QUKE QUCH ABCO CADE PSME B:FPIPO1D09:1 220 . 50 200 50 . B:FPIPO1D09:2 250 40 . 40 860 . B:FQUKE1D09:1 . 680 90 . . . B:FQUKE1D09:2 20 420 1070 . . 10 B:FQUKE1D09:3 460 230 40 . 120 90 B:FQUKE1D09:4 60 480 40 20 . . C:FQUKE1D09:5 180 410 10 . 110 200

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/m 2) and 1 year post-fire (43.7 kg/m 2) 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/m 2 with an average of 10.7 kg/m 2 (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 N FWD CWD Duff Litter Total Plots Surface Pre 6 0.47a 0.78a 6.63a 3.32a 11.19a yr 1 6 0.21a 0.77a 4.14b 2.24b 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.

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/m 2) (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 Trees Seedlings Snags Total BA QMD per ha per ha per ha > Trees (sq.m/ha) (cm) >2.5 cm 15 cm per ha B:FABCO1D08:3 810.0 1000.0 420.0 1810.0 75.9 38.1 B:FABCO1D08:7 780.0 1400.0 10.0 2180.0 53.1 34.0 B:FABCO1D08:4 940.0 600.0 60.0 1540.0 56.1 36.6 B:FABCO1D08:11 880.0 1200.0 10.0 2080.0 40.9 29.3 B:FABCO1D05:1 360.0 0.0 0.0 360.0 21.8 31.7 B:FABCO1D08:13 380.0 11599.8 220.0 11979.8 78.2 51.2 C:FABCO1D10:6 1380.0 600.0 280.0 1980.0 65.1 34.4 B:FABCO1D05:10 530.0 3999.9 40.0 4529.9 39.4 33.7 B:FABCO1D08:12 740.0 400.0 530.0 1140.0 94.7 42.8 B:FABCO1D08:14 640.0 1000.0 240.0 1640.0 100.2 48.0 B:FABCO1D08:9 1350.0 400.0 60.0 1750.0 64.2 30.6 B:FABCO1D05:8 800.0 1400.0 140.0 2200.0 59.4 37.3 B:FABCO1D08:2 2430.0 3799.9 520.0 6229.9 66.8 25.8 Mean 924.6 2107.6 194.6 3032.3 62.7 36.4 STD error 151.7 861.0 53.6 854.8 6.0 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.

A) B)

Figure 16. Average A) tree density and B) basal area in unburned FMH plots in Plumas- Eureka 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/m 2).

Table 18. Fuel loads (kg/m 2) in unburned FMH plots in Plumas-Eureka SP in 2000. Macroplot 1Hr 10Hr 100Hr FWD Sound Rotten CWD Duff Litter Total Surface FABCO1D08:3 0.09 0.48 1.22 1.79 0.27 0.86 1.13 5.21 2.12 10.26 FABCO1D08:7 0.1 0.17 0 0.27 0 0 0 4.21 1.38 5.87 FABCO1D08:4 0.03 0.27 0.68 0.98 1.41 0.16 1.57 4.88 2.14 9.57 FABCO1D08:11 0.05 0.46 0.61 1.12 0 0.05 0.05 5.24 2.31 8.73 FABCO1D05:1 0.05 0.34 0.41 0.8 0.03 0.14 0.17 3.88 1.57 6.42 FABCO1D08:13 0.14 0.56 0.81 1.52 0.89 5.08 5.98 10.92 2.82 21.24 FABCO1D10:6 0.2 0.8 1.09 2.09 0.11 9.31 9.41 7.82 3.32 22.63 FABCO1D05:10 0.04 0.8 1.77 2.62 0.2 0.21 0.41 4.28 1.8 9.1 FABCO1D08:12 0.16 0.55 0.68 1.38 0.41 2.6 3.01 7.51 1.28 13.18 FABCO1D08:14 0.13 0.38 0.95 1.46 0.57 10.24 10.82 8.26 2.18 22.71 FABCO1D08:9 0.11 0.32 0.14 0.57 1.04 0 1.04 5.49 1.17 8.28 FABCO1D05:8 0.06 0.44 0.61 1.12 0 0.06 0.06 4.62 1.01 6.81 FABCO1D08:2 0.29 0.72 0.14 1.14 0.04 1.84 1.89 6.89 1.98 11.9 MEAN 0.11 0.48 0.7 1.3 0.38 2.35 2.73 6.09 1.93 12.05 STD Error 0.02 0.05 0.14 0.17 0.13 1.00 1.02 0.57 0.18 1.70

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 Plumas- Eureka 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.

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).

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 post- burn 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).

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 pre- treatment data from 1992-94 were included in the analysis. In Burton Creek SP, pre-

43 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 Seedlings Snags Total BA QMD per ha per ha per ha > Trees (sq.m/ha) (cm) >2.5 cm 15 cm per ha Burton 4 1140.0a 900.0a 495.0a 2040.0a 81.9a 34.6a EMB 5 1124.0a 2320.0aa 642.0a 3444.0a 38.9b 26.3a Sugar 12 1276.7a 5183.2a 471.7a 6459.9a 61.6a 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 Seedlings Snags Total BA QMD per ha per ha per ha > Trees (sq.m/ha) cm >2.5 cm 15 cm per ha Other 10 1158.0a 1720.0a 525.0a 2878.0a 57.6a 30.2a Sugar 12 1276.7a 5183.2a 471.7a 6459.9a 61.6a 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.

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 Trees per ha >2.5 cm Sugar Other ABCO 1071.7a 921a ABMA 55.8a 3b CADE 29.2a 32a PICO 10.8a 0b PIJE 100.8a 189b PILA 8.3a 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 Mean density Sapling Pole - Small Medium Large (2.5- size (30.1- (60.1- (>91.4cm) 15cm) (15.1- 60cm) 91.3cm) 30cm) Sugar Pine 12 721.7a 330.9a 162.3a 27.3a 7.8a Other 10 510.9a 374.3a 189a 25.2a 6.7a

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/m 2) 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 FWD CWD Duff Litter Total Duff Litter Total Surface cm cm cm Burton 4 0.83a 0.47a 7.29a 2.27a 10.86a 8.27a 5.16a 13.43a EMB 5 1.32a 2.01a 6.57a 4.22a 14.12a 7.46a 9.58a 17.04a Sugar 12 1.31a 3.68a 6.10a 3.57a 14.66a 6.93a 8.09a 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/m 2) 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 FWD CWD Duff Litter Total Duff cm Litter Total Surface cm cm Other 10 1.09a 1.30a 6.42a 3.18a 12.00a 7.29a 7.23a 14.52a Sugar 12 1.31a 3.68b 6.10a 3.57a 14.66a 6.93a 8.09a 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 Sugar Other Forb 2.08a 1.95a Grass 0.00a 0a Shrub 10.77a 11.19a SubShrub 3.42a 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.

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 re- sampling 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 15 th 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 Plot Park Install Burn YR 10 ID Date Date FABCO1D10 25 Sugar 1993 1995 2005 FABCO1D10 24 Sugar 1993 1995 2005 FABCO1D10 26 Sugar 1993 1995 2005 FABCO1D10 6 Sugar 1992 1995 2005 FABCO1D10 18 Sugar 1992 1995 2005 FABCO1D10 7 Sugar 1992 1995 2005 FABCO1D10 8 Sugar 1992 1995 2005 FABCO1D10 2 Sugar 1992 1995 2005 FABCO1D10 3 Sugar 1992 1995 2005 FABCO1D10 4 Sugar 1992 1995 2005 FABCO1D10 40 Sugar 1996 1996 2006 FABCO1D10 112 Sugar 1996 1996 2006 FABCO1D10 113 Sugar 1996 1996 2006 FABMA1D10 1 Sugar 1996 1996 2006 FPIJE1D09 3 Sugar 1996 1996 2006

unburned plots ABCO 10Control 1 Sugar 1992 2005 ABCO 10Control 10 Sugar 1992 2005 ABCO 10Control 12 Em Bay 1992 2004 ABCO 10Control 15 Em Bay 1992 no ABCO 10Control 19 Sugar 1993 2005 ABCO 10Control 20 Sugar 1993 2002 ABCO 10Control 21 Sugar 1993 no ABCO 10Control 23 Sugar 1993 no ABCO 10Control 42 Sugar 1993 no PIJE1D05 Control 3 Sugar 1995 no ABCO 10Control 22 Sugar 1996 no ABCO 10Control 41 Sugar 1996 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. Standards for Tree Size Standards For Canopy Closure WHR WHR Size Class dbh WHR Closure Class Canopy Closure 1 Seedling Tree <1" S Sparse Cover 10 to 24% 2 Sapling Tree 1" to 6" P Open Cover 25 to 39% 3 Pole Tree 6" to 11" M Moderate Cover 40 to 59% 4 Small Tree 11" to 24" D Dense Cover 60 to 100% 5 Med/Large Tree >24" 6 Multi-Layered Tree Size class5 trees over a distinct layer of class3 or 4 trees, total canopy >60%

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.

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