USDA Forest Service Research Note RMRS-RN-32. 2007 

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Research Note

RMRS-RN-32

April 2007

Whitebark Pine Diameter Growth 

Response to Removal of Competition

Robert E. Keane, Kathy L. Gray, and Laura J. Dickinson 

Keane, Robert E.; Gray, Kathy L.; Dickinson, Laura J. 2007. Whitebark 

pine diameter growth response to removal of competition. Res. Note 

RMRS-RN-32. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 9 p.

Abstract—Silvicultural cutting treatments may be needed to restore whitebark pine (Pinus albicaulis) forests, but 

little is known of the response of this species to removal of competition through prescribed burning or silvicultural 

cuttings. We analyzed stem cross-sections from 48 whitebark pine trees in Montana around which most of the 

competing vegetation was removed by timber harvest treatments. We compared tree ring growth rates before 

and after the harvest treatment using intervention analysis to determine 1) the potential of release for this littlestudied tree species and 2) whether the release is related to tree and stand characteristics. We defined release as 

a statistically significant increase in radial growth after competing trees were removed. All but one of our 48 

sampled trees increased in diameter growth after competition was removed, while 40 trees showed a statistically 

significant (p<0.05) increase in growth. Diameter release was greatest in stands that were dense prior to tree 

cutting and greatest in old trees with large diameters. Recommendations for appropriate silvicultural cutting are 

included to aid managers in designing effective restoration treatments. 

Key words: Whitebark pine, diameter release, logging, restoration treatments, intervention analysis, advance 

regeneration

Introduction ____________________

It is well known that whitebark pine (Pinus albicaulis) 

is declining rapidly across many parts of its range in the 

United States and Canada due to the combined effects of 

the exotic white pine blister rust (Cronartium ribicola), 

the native mountain pine beetle (Dendroctonus ponderosae), and the exclusion of fires (Arno 1986; Kendall 

and Keane 2000; Tomback and others 2000). Recent 

research efforts are attempting to restore whitebark pine 

forests using prescribed fire and silvicultural cuttings 

(Keane and Arno 1996, 2001). These treatments could 

be implemented in stands with significant amounts of 

advance whitebark pine regeneration in the understory 

or slow-growing saplings or pole-sized whitebark pine 

trees in the overstory. These suppressed trees can be 

quite old since the species is moderately shade tolerant having the ability to survive under partial shade 

for many decades (Minore 1979, Arno and Hoff 1989). 

Since other high elevation trees appear to respond to 

harvest cuttings (Crossley 1976; Helms and Standiford 

1985; McCaughey and Schmidt 1982), managers need to 

know if suppressed whitebark pine trees have the ability to release (increase in growth as a response to the 

elimination of competition) after silvicultural cuttings 

and eventually grow into cone-bearing trees.

Few studies have explored the ability of crowded 

whitebark pine trees to respond to a sudden decrease 

in competition after overstory removal. Eggers (1990) 

observed that “whitebark pine ... responded little to 

release,” but recognized that additional data was needed 

in this area. However, Kiper and others (1994) evaluated 

crown response in whitebark pine using a distance dependent competition index in three Montana logged stands 

and found many trees increased crown dimensions after 

release. Since the success of some restoration treatments 

depends on the ability of understory whitebark pine to 

respond to overstory removal, managers need to know 

the circumstances under which whitebark pine seedlings 

and saplings will release so that cutting prescriptions 

can be designed to maximize the success of restorative 

treatments.

In this study, we sampled 1-3 whitebark pine trees in 

21 logged stands where most of the competing vegetation was removed by recent timber harvest activities. 

We compared tree ring growth rates before and after 

the harvest treatment to determine 1) the thresholds 
USDA Forest Service Research Note RMRS-RN-32. 2007

of diameter release for this little-studied tree species 

and 2) the relationship of the magnitude of release to 

tree and stand characteristics. We defined release as a 

statistically significant increase in radial growth after 

logging. Recommendations for appropriate silvicultural 

cutting are included in this paper to aid managers in 

designing effective restoration treatments.

Methods _______________________

Field Sampling

During the summers of 2003 and 2004, we opportunistically searched for high elevation harvest areas across 

Montana where surviving whitebark pine trees remained 

in good health. We attempted to sample across a wide 

range of tree diameters, heights, and ages. Only previously logged areas where healthy, surviving whitebark 

pine trees were in the pre-harvest understory or overstory 

were sampled. We did not sample trees with observable 

signs and symptoms of insects or diseases, especially 

those with blister rust or mountain pine beetle. We 

sampled only those trees where all competing trees were 

removed within an area surrounding the tree (defined 

by a circle with a radius greater than the surviving tree’s 

height). Smaller whitebark pine trees found within this 

surrounding area were sometimes sampled, up to three 

trees per plot, to obtain a full range of diameters in our 

sample. All the sampled trees were individuals and did 

not occur as a cluster, either before or after the cutting. 

We determined the cutting dates from stand records 

filed in National Forest District offices and we verified 

these dates from diameter release dates estimated from 

increment cores taken at each site from surviving trees 

of any species taken outside the plot.

We gathered additional site and vegetation information 

within a 0.1-acre circular plot surrounding the tree(s) 

using FIREMON sampling techniques (Lutes and others 2006, www.fire.org/firemon). The site variables 

measured were slope, aspect, elevation, geo-referenced 

location, habitat type (Pfister and others 1977), ground 

cover, and landform. We measured the tree diameter 

at breast height (DBH), height, live crown base height, 

and crown position of all living trees above 4.5 feet 

(1.37 meters) tall. To estimate pre-harvest stand densities 

and basal areas by species, we measured cut diameter 

and determined the species of all stumps >4 inches 

(10 cm) in diameter. We also recorded detailed notes 

on the 1) pre-harvest and current stand conditions, 

2) tree species compositional changes in the plot, and 

3) silvicultural treatment paying special attention to past 

disturbances and evidence of disease.

We felled each sample tree and cut a cross-sectional 

disk at three heights along the bole of the tree: 1) 4.

5 feet (1.37 meters) from the base of tree, 2) at bottom 

of crown, and 3) at 1 foot (0.3 meters) down from the top 

of crown. Each disk was about 0.5 to 1.5 inch (1-3 cm) 

in thickness. These disks were transported to the laboratory in burlap sacks where they were air-dried and 

mounted on a board using wood glue. The top of the 

disk was smoothed using progressively finer sand paper 

until all growth rings were visible with the naked eye. 

We then measured the annual ring widths along four 

radii of the pith: two radii along the widest diameter of 

the cross section and two radii that were perpendicular 

to this longitudinal diameter. We measured ring width 

using a tree ring chronometer and associated software 

specifically designed to measure within 0.01 mm tolerance. Assuming circuit uniformity across diameters, 

we calculated average annual growth for each core as 

an average across the four radii for each year since the 

tree’s origin.

Data Analysis

We used a statistical technique called “intervention 

analysis” (Sridharan and others 2003) to test for significant release or change in tree ring growth for the years 

following logging and to estimate the magnitude and 

significance of the release. Intervention analysis is a 

time series tool developed by Box and Tiao (1975) used 

to detect a change over the time that the data were collected. Time series analysis is a statistical tool that uses 

autocorrelation in data collected sequentially in time to 

predict future observations (Brockwell and Davis 2002). 

Once stationarity has been achieved (in other words, when 

the trend and drift in temporal data has been removed), 

an ARIMA (Autoregressive, Integrated, and Moving 

Average) model is fit to the time series data before the 

intervention (harvest) has occurred (pre-harvest).

We calculated the first difference of the ring width 

time series before performing the intervention analysis 

to achieve stationarity. We then used the ARIMA model 

to make forecast predictions for the post-intervention 

period. If the forecasted values were significantly different than the observed values for the post-intervention 

period, we concluded that the intervention (release of 

competition) had an effect (Box and Tiao 1976). We 

used a chi-square test statistic compared to a chi-square 

distribution with degrees of freedom equal to the number 

of forecasted values to determine the significance of 

the effect (SPLUS 2001). The intervention analysis was 

performed using the ITSM 2000 software developed by 

Brockwell and Davis (2002).
USDA Forest Service Research Note RMRS-RN-32. 2007

To quantify the magnitude of the change, a regression 

model with a time series component was fit to each 

tree ring series by modeling both the pre-harvest and 

the post-harvest periods. We analyzed the magnitude 

of ring width change because we found evidence that 

some trees appeared to have a gradual increase in growth 

rate for a few years after logging and then a gradual 

decrease. We estimated the magnitude using only the 

pre-intervention period (25 years prior to release) and the 

post-intervention period (at least 20 years after release 

or until growth rate started to decrease). The following 

regression model was fit to each tree:

Xt=bgt+Nt where t=2,3,...n

and Xt=Yt-Yt-1

where Xt is the change in growth at time t, Yt is the tree 

ring growth at time t, gt equals 1 from the time of observed 

release to the time of the observed decline in growth, 

and gt equals 0 before the release. Nt is the stationary 

sequence to which an appropriate ARIMA model was fit 

(Brockwell and Davis 2002). The above model is similar 

to a regression model with a correlated error structure. 

The slope coefficient, b, is a measure of the magnitude 

of the release for a given tree. We used ring widths in 

this analysis instead of basal area increment because the 

differences in diameter across the 45+ years of analysis 

were relatively small (1 to 8 cm) and the variable transformation would favor post-harvest response.

We used average tree ring growth across the four 

radii measured for the cross-section taken at DBH for 

our analysis because we found that the other two height 

sections either contained nearly the same information 

as the DBH core or there was not the required ring 

count (40 years) for the higher cross-sections. We did 

not perform the intervention analysis on trees that did 

not have enough pre-intervention years (<40 years) 

because the limited record did not provide statistically 

valid comparisons (p<0.05). This reduced the number 

of valid trees for some of the sites. The intervention or 

release date was determined from stand records. There 

were no stand records for two trees, so we used release 

dates determined from increment cores of subalpine-fir 

trees taken on-site but off the plot.

Three measures of release were developed from the 

results of the time series analysis. Once the ARIMA 

model was fit, the estimate of b (slope) was estimated 

to quantify the magnitude of the release (Sridharan 

and others 2003). The number of years that the release 

lasted (length) was estimated by visual inspection of 

time series graphs for individual trees. And the total 

release (product of length and slope coefficient) was 

also estimated. We then calculated Pearson’s Correlation 

coefficient between each of the three release variables 

(magnitude, length, and total release) (SPSS 1999) and 

each of the following tree and stand variables that were 

calculated from collected field data:

1. Diameter of tree (outside bark at breast height 

1.37 m; DBH) at the time of sampling (summer 

of 2003 or 2004)

2. Age of tree at breast height at time of sampling 

(summer of 2003 or 2004)

3. DBH of tree (inside bark) at time of release

4. Age of tree at breast height at time of release

5. Stand density at time of release

6. Stand density at time of sampling

7. Change in stand density from release to 

sampling

8. Stand basal area at time of release

9. Stand basal area at time of sampling

10. Change in basal area from release to sampling

11. Mean growth for the 25 years prior to 

intervention

The age of the tree at the time of release and at the time 

of sampling was estimated from the cross section taken 

at breast height with no adjustments to estimate true age. 

To determine the influence of the tree and stand variables 

on the magnitude and length of release, we performed 

extensive regression analyses using the tree and stand 

variables as independent variables and the three release 

variables as dependent variables (SPSS 1999).

Results ________________________

We sampled 59 trees from 21 stands on 10 sites across 

the northern Rocky Mountains in Montana, USA (fig. 1). 

These sites had a wide variety of pre-harvest densities 

and stand ages (table 1). It was difficult to find suitable 

sites that contained healthy, residual trees after logging 

because there are very few harvest activities in the high 

elevation whitebark pine stands due to a lack of roads 

and valuable timber. Moreover, many trees in the western portion of Montana were infected with blister rust 

or dying because of mountain pine beetle (Kendall and 

Keane 2001). The sampled trees had a wide range of 

diameters (1 to 63 cm DBH) and ages (51 to 395 years) 

(table 1). A total of 14 trees had to be removed from the 

statistical analysis because they did not contain a sufficient number of tree rings (<40 rings prior to harvest 

date) to adequately perform the time series analysis.

Tree ring growth rates for all trees in the study are 

shown in table 2 for 25 years prior to release and for 5-year 

time intervals after release (post-logging). Most trees 

(>80 percent) showed a statistically significant increase 
 USDA Forest Service Research Note RMRS-RN-32. 2007

Figure 1—Distribution of sample sites 

across Montana.

Table 1—General description of the study sites sampled and the 45 trees sampled at those sites. We sampled 

59 trees, but only 45 met our criteria for analysis. 

 Number Year of Pre-harvest Pre-harvest

Site sample trees harvest densitya basal areab Tree DBH(s) Tree age(s)c

	 	 	 (trees ha–1)	 (m2 ha–1)	 (cm)	 (years)

Ninemile 1 1 1970 790 42.5 19 242

Ninemile 2 1 1992 741 52.3 22 257

Bluenose 1 2 1973 444 16.1 5, 36 115, 135

Bluenose 2 2 1973 593 28.6 3, 36 61, 304

Corvalis 2 1968 395 21.5 5, 23 93, 122

Deadman 1 3 1993 543 28.1 3, 7, 25 102, 145, 225

Deadman 2 1 1963 370 64.7 26 216

Deadman 3 1 1978 593 22.5 20 214

Freezeout 1 3 1996 1,259 52.4 8, 10, 31 51, 74, 240

Freezeout 2 2 1996 1,309 66.8 3, 23 63, 251

Gazelle 1 3 1997 741 54.6 5, 8, 25 110, 190, 252

Gazelle 2 3 1992 469 22.4 1, 5, 20 49, 185, 228

Gazelle 3 3 1973 1,259 34.8 13, 16, 24 149, 213, 237

Hagen 1 2 1992 642 28.5 5, 28 61, 93

Lemhi 1 1 1963 519 38.7 43 172

Lemhi 2 2 1978 765 34.3 23, 36 95, 120

Mica 1 3 1963 1,235 43.0 3, 11, 17 70, 119, 115

Mica 2 3 1978 1,086 34.7 9, 9, 18 117, 128, 132

Mica 3 3 1992 543 22.9 5, 12, 25 99, 113, 123

Wise 1 2 1967 444 30.2 10, 19 159, 179

Wise 2 2 1953 444 34.6 9, 61 67, 395

aDensity includes all trees greater than 1 cm in diameter at stump height. bBasal area was measured at stump height.

cTree ages were estimated from a ring count at DBH.
USDA Forest Service Research Note RMRS-RN-32. 2007

Table 2—Summary of tree ring growth (mm year–1) before the release date and tree ring growth for 5-year growth periods up 

to 20 years past the release date. Numbers in parentheses indicate the standard deviation. Numbers in bold and 

italics indicate statistical significance (p<0.01). An asterisk (*) indicates a statistically significant decrease in growth 

after release.

Average annual growth (mm year–1)

 Tree Release 25 years prior 1-5 yrs post- 6-10 yrs post- 10-15 yrs post- 16-20 yrs postPlot number year (AD) to release release release release release

Ninemile 1 1 1970 0.90 (0.24) 1.12	 1.46	 1.73	 1.76

Ninemile 2 1 1992 0.71 (.26) 1.98	 3.89

Bluenose 1 1 1973 1.32 (.24) 1.68	 2.37	 1.96	 2.25

2 1973 0.25 (.07) 0.41	 0.39	 0.63	 0.59

Bluenose 2 1 1973 1.57 (.38) 1.65	 1.64	 1.39	 1.54

2 1973 0.13 (.03) 0.19	 0.20	 0.19	 0.29

Corvallis 1 1968 0.66 (.18) 1.39	 2.81	 3.22	 3.05

2 1968 0.12 (.03) 0.11 0.18 0.30	 0.25

Deadman 1 1 1993 0.66 (.25) 1.08	 1.07

2 1993 0.62 (.31) 0.74	 1.72

3 1993 0.41 (.21) 0.53 0.78	

Deadman 2 1 1963 0.34 (.10) 0.19 0.33	 0.79	 0.90

Deadman 3 1 1978 0.14 (.07) 0.19 0.74	 0.83	 0.89

Freezeout 1 1 1996 0.15 (.04) 0.23

2 1996 0.31 (.11) 0.72	

3 1996 0.21 (.15) 0.86

Freezeout 2 1 1996 0.56 (.17) 2.95	

2 1996 0.16 (.07) 0.53	

Gazelle 1 1 1997 0.56 (.11) 0.47

2 1997 0.48 (.13) 1.04	

3 1997 0.08 (.03) 0.09

Gazelle 2 1 1992 0.67 (.43) 1.09	 0.66

2 1992 0.66 (.18) 0.83	 1.12

3 1992 0.011 (.05) 0.48	 0.80

Gazelle 3 1 1973 0.30 (.14) 0.99	 1.55	 1.30	 1.23

2 1973 0.76 (.37) 1.55	 2.37	 2.55	 2.12

3 1973 0.54 (.11) 0.55 0.95 1.01 0.76

Hagen 1 1 1992 1.47 (.39) 2.37	 3.72

2 1992 0.23 (.07) 0.60	 0.55

Lemhi 1 1 1963 2.56 (.30) 2.23 2.41*	 2.04*	 2.37*

Lemhi 2 1 1978 1.98 (.40) 2.04	 1.62	 2.60	 3.90

3 1978 1.27 (.22) 2.05	 1.77	 2.72	 3.19

Mica 1 1 1963 0.58 (.15) 0.65 1.61	 1.20	 0.49

2 1963 0.13 (.03) 0.25	 0.90	 0.70	 0.36

3 1963 0.06 (.03) 0.25	 0.33	 0.26	 0.13

Mica 2 1 1978 0.19 (.09) 0.93	

2 1978 0.11 (.06) 0.09

3 1978 0.68 (.21) 0.86

Mica 3 1 1992 0.61 (.37) 1.48	 1.62

2 1992 1.18 (.63) 2.49	 1.65

3 1992 0.33 (.35) 1.43	 1.33

Wise 1 1 1967 0.62 (.12) 0.72	 0.67	 0.83	 1.38

2 1967 0.29 (.07) 0.15 0.12 0.15 0.39

Wise 2 1 1953 0.73 (.25) 0.97	 1.15	 1.42	 2.24

2 1953 0.17 (.06) 0.21 0.17 0.16 0.23
USDA Forest Service Research Note RMRS-RN-32. 2007

in ring growth after the harvest treatments while only 

one tree (2 percent) showed a significant decrease in ring 

growth—a small suppressed sapling. The increase in ring 

growth for some trees was dramatic (fig. 2a, 2c; Mica 3 

Tree 3 table 2) while for others it was difficult to detect 

(fig. 2b; Gazelle 1 Tree 1 table 2). Most trees (67 percent) 

showed an increase in ring growth immediately after 

release, but some trees did not increase diameter growth 

until 10 to 15 years had elapsed (Corvallis Tree 2 in 

table 2). The delay in release may be due the shock of 

the sudden removal of competition (Crossley 1976; McCaughey and Schmidt 1982). The period of significantly 

higher post-release ring growth rates was over 20 years 

for 30 percent of the sampled trees. Some sampled trees 

had limited post-intervention data because logging had 

Figure 2—Tree ring growth rates for three trees sampled in this study to illustrate the magnitude of release. The forecasted 

ring growth (horizontal bar) after intervention is plotted along with 95% confidence intervals (green parabaloid). A) Tree 1 at 

the Wise 2 site experienced a large increase in ring growth after release, but, at the same time, B) Tree 2 at the same site 

did not experience an increase in growth for over 20 years, while C) Tree 1 at the Ninemile site experienced the greatest 

increase in ring growth after release.

occurred fairly recently. It is doubtful that the increase 

in ring growth is related to climate factors because the 

release years do not seem to correlate with average springtime temperature or precipitation, which are important 

climate variables for upper subalpine ecosystems (fig. 3) 

(Arno and Hoff 1989; Perkins and Swetnam 1996).

Results of the regression and correlation analyses 

of ring growth release with stand and tree variables 

revealed that the stand variable describing the change 

in density was significantly related to the magnitude 

of ring growth release, while the tree variables of age 

and DBH were significantly related to length of release 

and total release (table 3). DBH and mean ring growth 

were significantly related to the total release of a tree 

(table 3). The change in stand density from release to 
USDA Forest Service Research Note RMRS-RN-32. 2007

Figure 3—Long term springtime (May to June) average temperature and 

precipitation from 1895 to 2005 and each of the release dates used in this 

study (shown by the vertical bars). No apparent trends between climate and 

ring release were found.

Table 3—Results of the correlation analyses where tree and stand variables were correlated to the slope coefficient from the time series analysis (magnitude), the 

number of years that the release lasted (length), and the total release (product 

of slope coefficient and length) (total release). Numbers in bold indicate that 

the variable was significant (p-value<.05) in regression using tree and stand 

variables as predictor variables.

Tree or stand variable Magnitude Length Total release

(mm year –1)	 (years)	 (mm)

DBH at time of sampling (cm) 0.121 0.535 0.555

Age at time of sampling (years) –0.067 0.584 0.304

DBH at time of release (cm) 0.172 0.391 0.494

Age at time of release (years) 0.013 0.481 0.317

Density at release (t ha–1) 0.639 –0.377 0.198

Density at sampling (t ha–1) –0.252 0.199 –0.309

Density change (%) –0.401 0.406 –0.230

Basal area at release (m2 ha–1) 0.287 –0.079 0.142

Basal area at sampling (m2 ha–1) –0.118 –0.017 –0.176

Basal area change (%) –0.245 0.130 –0.209

Mean ring growth before release (mm) 0.193 0.150 0.399

sampling had the highest correlation to the magnitude 

and length of release (table 3) and the tree DBH at 

the time of sampling had the highest correlation to 

total release (table 3). Stand density and basal area 

at time of release were some of the best predictors of 

the magnitude of release (r=–0.28). Most of the stand 

variables measured at the time of release had the 

greatest potential to predict ring growth release but 

the same variables measured at time of sampling had 

little predictive ability (r<0.20; table 3).

Discussion _____________________

The large magnitude of release and long length of 

release for most sampled trees were somewhat surprising for a high elevation, shade intolerant tree species 

(table 2) considering Eggers (1990) observations. Some 

trees experienced four- or five-fold short-term increases 

in ring growth after logging (fig. 2), while other trees 

sustained high ring growth rates for several decades 

(Wise 2 Tree 1 growth increase lasted 52 years). Young 

trees and smaller trees did not release as well as the 
USDA Forest Service Research Note RMRS-RN-32. 2007

older and larger trees, probably because the younger 

trees grew in lower light conditions and did not have the 

morphology to take advantage of the increase in light 

and resources directly after release.

Many factors can contribute to the ability of whitebark pine trees to increase in growth after a thinning 

treatment. As measured in this study, it appears that 

the basal area and density at the time of release may be 

important for predicting the magnitude and length of 

release (in other words, greater ring growth will occur 

after harvest in denser stands; table 3). It also seems 

that the increases in ring growths for trees in dense, 

pre-harvest stands usually were short-lived (table 3). 

The stand and tree variables measured at the time of 

harvest seem to be more predictive of the potential for 

release. An interesting result was that the larger diameter 

whitebark pine trees tend to have a higher total release 

and older trees seem to maintain the release for longer 

time periods (table 3). This may mean that the release 

of older individuals might result in healthier trees that 

have the ability to produce more frequent and abundant 

cone crops. The low number of observations in the correlation analysis (n=48) may have limited the results in 

this study.

The low number of sample trees (48) reflects the difficulty of finding suitable trees to sample across the large 

study area. There are few harvest operations occurring in 

high elevation whitebark pine stands because the timber 

is of low quality and harvestable areas are remote and 

often roadless. Finding un-cut whitebark pine trees is also 

challenging because the species’ timber value is higher 

than most of its competitors in upper subalpine settings. 

The most limiting factor, however, is the extensive blister-rust infection and beetle mortality in the northern 

and western parts of whitebark pine’s range in Montana 

(Keane and Arno 1993). Most cutover stands visited in 

western Montana did not have any surviving whitebark 

pine trees that met our stringent sampling criteria of 

no rust and insect damage. The opportunistic sampling 

approach used in this study to find sample trees was 

obviously inadequate and more conventional, but more 

costly, controlled experiments might be needed to create more robust data sets. These controlled experiments 

might selectively remove all trees in small sample stands 

or plots and leave the healthy mature whitebark pine.

Management Recommendations ___

Results from this study indicate that release cuttings can 

increase whitebark pine diameter growth across a wide 

variety of diameters and ages. This increase in growth 

could also suggest larger and more frequent cone crops. 

Seeds from these cones can be widely dispersed by the 

Clark’s nutcracker and thereby improve whitebark pine 

regeneration success. Cutting guidelines for whitebark 

pine restoration efforts should consider the following:

•	 Slow growing, old sapling (<10 cm DBH) whitebark 

pine trees may not release.

•	 Any whitebark pine tree larger than a sapling will 

probably release after thinning.

•	 The thinning of dense stands with mature whitebark pine trees will probably result in the greatest 

increases in tree ring growth rate.

Author Profiles__________________

Robert E. Keane is a Research Ecologist with the USDA 

Forest Service, Rocky Mountain Research Station at the 

Missoula Fire Sciences Laboratory, Missoula, MT. Since 

1985, Keane has developed various ecological computer 

models for the Fire Ecology and Fuels Research Project 

for research and management applications. His most recent research includes the synthesis of a First Order Fire 

Effects Model; construction of mechanistic ecosystem 

process models that integrate fire behavior and fire effects 

into succession simulation; restoration of whitebark pine 

in the Northern Rocky Mountains; spatial simulation of 

successional communities on the landscape using GIS 

and satellite imagery; and the mapping of fuels and fire 

regimes for fire behavior prediction and hazard analysis. 

He received his B.S. degree in forest engineering in 1978 

from the University of Maine, Orono; his M.S. degree in 

forest ecology from the University of Montana, Missoula, 

in 1985; and his Ph.D. degree in forest ecology from the 

University of Idaho, Moscow, in 1994.

Kathy Gray is currently working for Systems for 

Environmental Management as a statistician. She also 

teaches statistics at the University of Montana and will 

be completing her Ph.D. degree in Mathematics in May 

of 2007. Her research interests are in statistical ecology 

and she has worked for the Missou;a Fire Science Lab 

and the Aldo Leopold Wilderness Research Institute as 

a statistician.

Laura J. Dickinson has been a Biological Science 

Technician since 2002 with the USDA Forest Service 

Rocky Mountain Research Station at the Missoula Fire 

Sciences Laboratory, Missoula, MT. She has contributed 

to several projects within the Fire Ecology and Fuels 

Research Project including data collection and analysis 

for studies on whitebark pine, relict ponderosa pine 

mortality in the Bob Marshall Wilderness Area, fuels, 

and fuel sampling techniques. She received her B.S. 

degree in Aquatic Wildlife Biology in 2003 from the 

University of Montana, Missoula.
USDA Forest Service Research Note RMRS-RN-32. 2007

Acknowledgments_______________

We thank Myron Holland and Wayne Lyngholm of 

the USDA Forest Service Rocky Mountain Research 

Station for their extensive fieldwork on this study. We 

also thank Ward McCaughey, Dennis Simmerman, and 

Emily Heyerdahl, Rocky Mountain Research Station and 

Carl Fiedler, University of Montana College of Forestry, 

for technical reviews.

References _____________________

Arno, S. F. 1986. Whitebark pine cone crops: a diminishing source 

of wildlife food. Western Journal of Applied Forestry 9:92-94.

Arno, S. F.; Hoff, R. 1989. Silvics of whitebark pine (Pinus 

albicaulis). Whitebark pine. Gen. Tech. Rep. INT-253. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Intermountain 

Research Station. 41 p.

Box, G. E. P.; Tiao, G. C. 1975. Comparison of forecast and actuality. Applied Statistics 25:195-200.

Box, G. E. P.; Tiao, G. C. 1976. Intervention analysis with applications to economic and environmental problems. Journal of the 

American Statistical Association 70:70-79.

Brockwell, P. J.; Davis, R. A. 2002. Introduction to time series. 

New York: Springer-Verlag. 343 p.

Crossley, D. I. 1976. Growth response of spruce and fir to release 

from suppression. The Forestry Chronicle 52:189-195.

Eggers, D. E. 1990. Silvicultural management alternatives for 

whitebark pine. In: Schmidt, W. C.; MacDonald, K. M. (comps.). 

Proceedings—Symposium on whitebark pine ecosystems: 

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