Effects of fire on bark beetle presence on Jeffrey pine in the Lake Tahoe Basin

Forest Ecology and Management 142 (2001) 205±214

Effects of ®re on bark beetle presence on Jeffrey pine in the Lake Tahoe Basin Tim Bradley, Paul Tueller* University of Nevada Reno, Department of Environmental and Resource Sciences, 1000 Valley Road, Reno, NV 89512-0013, USA Received 8 October 1999; accepted 26 January 2000

Abstract An investigation into the effects of low intensity, late-season prescription ®re on Jeffrey pine (Pinus jeffreyi Grev. & Balf.) and associated short-term presence of various bark beetles of the family Scolytidae was completed on forests along the north edge of Lake Tahoe, Nevada. A total of 38 permanent 0.040-ha plots were located among ®ve different prescription burn sites treated during October 1997. An additional twenty-seven 0.040 ha plots were located in adjacent unburned forest stands. All trees within-study plots were visited thrice between June and October of 1998. Results showed a highly signi®cant correlation between burning and bark beetle presence. Over 24% of trees in prescription burn plots were attacked by one or more species of bark beetle. Less than 1% of all non-burned trees were similarly attacked. Highly signi®cant multiple logistic regression models were developed for each of the two occurring species of Dendroctonus and a composite model for all observed species of Ips. The indirect burn severity measurements of crown scorch, duff consumption, and bole scorch were highly signi®cant; other tested variables were species speci®c or not signi®cant. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Prescription ®re; Jeffrey pine; Bark beetles; Dendroctonus; Logistic regression

1. Introduction Historical records indicate that ®re has exerted an in¯uence on Sierra ecosystems for thousands of years (Arno and Brown, 1989; Skinner and Chang, 1996). Due to aggressive ®re suppression policies and other management activities, the historical function that ®re has played on these landscapes has changed. In the Sierra Nevada and other lightning-prone regions, ®re exclusion has been linked to increased accumulations of volatile fuels and changes in vegetative assemblages. Inadvertently, these changes frequently lead *

Corresponding author. Tel.: ‡1-775-784-4053; fax: ‡1-775-784-4583. E-mail address: [email protected] (P. Tueller).

to an increasing incidence of severe, large-scale con¯agrations that can only be controlled when weather conditions become favorable (Arno and Brown, 1989; McKelvey et al., 1996). In response to a growing body of research, ®re policy has again shifted. Today, there is a general understanding that some ®res can be bene®cial to the landscape and that complete ®re exclusion is not a sustainable course of action. In recognition of this, land managers within the Lake Tahoe Basin have increasingly turned to the application of prescription ®re to mitigate the ecological effects resulting from decades of ®re suppression (Rowntree, 1998). Although the effects of prescription ®res can vary considerably from those of naturally occurring ®res, prescription ®res are still viewed as an essential

0378-1127/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 1 1 2 7 ( 0 0 ) 0 0 3 5 1 - 0

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component of an adaptive management strategy in ®re-prone landscapes (Brennan and Hermann, 1994; Weatherspoon and Skinner, 1996). Prescription ®res are conducted under narrow ranges for winds, humidity, temperature, fuel moisture, fuel loading, fuel continuity, season of burn, slope, and aspect. Still, convection currents in association with pockets of ladder fuels are such that most ®res that meet general managerial objectives will result in varying degrees of damage to the overstory canopy. In addition, patterns of duff mounds around trees and characteristics of these fuels can lead to varying degrees of basal injury to trees (Ryan and Frandsen, 1991). Given the overall variability and randomness of thermal effects, a broad spectrum of effects can result from ®re (Schmidt, 1996). Studies examining the effect of ®re on trees have led to the construction of binary logistic regression models for the purpose of predicting tree mortality. Independent variables used in such models include bark thickness, diameter at breast height (DBH), bole char height, and/or crown scorch percentage (Ryan and Reinhardt, 1988; Regelbrugg and Conard, 1993). Other studies have looked at tree mortality in relationship to ®ne root mortality, ground char, crown scorch height, or season of burn (Petterson, 1985; Harrington, 1987; Swezy and Agee, 1990; Harrington, 1993). Information gained from these studies has shown that trees have species speci®c responses to varying levels of ®re injury and that most western conifers could survive at least some level of ®re-induced injury. A dominant tree in the Tahoe Basin and throughout much of the Eastern Sierra Nevada is Jeffrey pine (Pinus jeffreyi Grev. and Balf). It is characterized as a ®re-tolerant species, primarily due to its protective bark, although it does establish better on open slopes typical of intense wild®res. Jeffrey pine is susceptible to a number of different pathogens, including annosus root disease (Heterobasidion annosum), dwarf mistletoe (Arceuthobiumsp.), and black-stain root disease (Leptographium wageneri) (Ferrell, 1996). These pathogens normally will not kill trees, but do cause stress that, in association with other events, can result in mortality of individual trees. Frequently, this association is with one or many species of bark beetles of the family Scolytidae. In fact, one species of bark beetle, the Jeffrey pine beetle (Dendroctonus jeffreyi Hopk.), is one of the few agents capable of killing

apparently healthy Jeffrey pine (Bright and Stark, 1973; Coulson and Witter, 1984). Host-tree suitability plays a critical role in the dynamics of the beetle±tree interaction. Tree-speci®c characteristics, such as size, age, bark thickness, phloem thickness, and resin pressure, have a primary in¯uence on survival of individual trees, as does competition with surrounding vegetation. All of these factors in¯uence the physiology of trees, which relates to the predisposition of bark beetle attack, termed susceptibility. Today, bark beetles are recognized as a signi®cant cause of the browning of forests in the Lake Tahoe Basin (Mutch, 1994). Susceptibility has increased through the combined effects of past management, drought, and other pests. The accelerated use of prescribed ®re in these ecosystems leaves many questions unanswered. Of particular concern is the response of the red turpentine beetle (D. valens LeConte), to an increased incidence of ®re (Ferrell, 1996). The red turpentine beetle is attracted to scorched pines, but the beetle's response on a large scale is not yet known. Understandably, an increased comprehension of ®re± insect interaction is essential for more effective science-based management of forest ecosystems (McCullogh et al., 1998). This study was developed to take advantage of an aggressive prescription burn program initiated in the early 1990s by the Incline Village General Improvement District (IVGID). Given the potential for research coupled with the value and interest in the area as a national resource, the propitious nature of the situation is one that holds great promise to elucidate the role of ®re on bark beetle populations in Eastern Sierra ecosystems. This study was designed to test the hypotheses that ®re-related injury would predispose Jeffrey pine trees to attack by bark beetles and, if so, to determine if ®eld-based data on tree, stand, or post-®re burn characteristics could be used to accurately model the presence of attack by bark beetles. 2. Methodology 2.1. Study area All study plots are located within the community of Incline Village near the north shore of Lake Tahoe at

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elevations between 2280 and 2480 m. The composition and structure of the forests in and around Incline Village vary primarily as a function of climate, elevation, soil and topography. Forests consist of mixed conifer stands dominated by Jeffrey pine and white ®r (Abies concolorLindl. ex Hildebr.). The understory vegetation is quite variable with respect to species density and composition, forming a patchwork mosaic. Understory regeneration is dominated by white ®r. The geologic parent material of the area consists largely of granitic rock. Slopes are mostly between 15 and 35%. A generalized prescription burn plan was prepared for IVGID, identifying four broad management objectives (Walter, unpublished report): 1. to reintroduce ®re as an ecosystem process; 2. to reduce understory and surface fuel accumulations to provide protective buffer adjacent to existing structures; 3. to create mineral seedbed for establishment of target species over <40% of burned units; and 4. to reduce understory brush density and raise the height of lower tree canopy to 3.5 m (10 feet) over 65% of individual units, limiting tree mortality to <15%. 2.2. Data Circular, 0.040-ha (1/10th acre) permanent study plots were established between 10 June and 17 July 1998 on ®ve treated units (Table 1). Burn plots were located and marked in burn units randomly with use of a map grid overlay. Control study plots were similarly placed in adjacent management units slated to burn in October of 1998. Goals were set to sample 1/10th of the area of each unit and establish control plots for 2/ 3rds of the burn plot samples. Logistical dif®culties and time constraints restricted sampling to 38 prescription burn and 26 control study plots. Initial sampling procedures involved identi®cation of all trees >5 cm DBH (diameter at 1.646 m from ground). The following additional information was recorded for each tree: species, DBH, live-crown percentage (preburn live crown was estimated by summing estimates of live and scorched foliage), crown scorch percentage, bole char height (averaged for each tree), soil

207

Table 1 Incline Village burn site acreage and distribution of permanent 0.040 ha (1/10th acre) bark beetle study plots Burn unit name

Unit size (ha)

Jennifer Apollo Marlene Matchless Tyner Tankhouse Total

1.84 3.25 4.38 3.92 5.56 18.95

Study plots Burn

Control

6 6 11 6 9 38

4 6 8 2 6 26

burn index, a ®ve-level burn severity estimate, from no burn to extreme burn, adapted from the National Park Service (1992), and bark beetle presence or absence within the lower 2 m of all living Jeffrey pine boles. Identi®cation of an attacking bark beetle was based primarily on actual visual observation of adult beetles, however, some identi®cation was based on pitch tubes (tree resin mixed with frass and boring particles). To simplify ®eld sampling all Ips species were grouped together. Following initial establishment (early sampling session), each plot was visited on two additional occasions, once between 2 and 19 August 1998 (middle sampling session), and again between 19 September and 11 October 1998 (late sampling session). During each of these follow-up visits, observations were made of beetle presence or absence (by species) for each Jeffrey pine in burn and control plots. Beetle presence during any of the sampling sessions constituted a positive ®nal presence rating; all other trees were treated as having no beetle presence. Determination of stand density (basal area) was made by summing the basal area of all trees (at breast height) in any given plot. 2.3. Statistical analysis The data was analyzed in two steps using a logistic regression SAS macro, REGRESS (Fernandez, 1998). This analysis uses a regression model of the form where P(b)ˆ1/[1‡exp (ÿ(a‡B1X1‡. . .‡BkXk))], P(b) is the predicted probability of bark beetles, exp the base of the natural logarithm, X1, . . . ,Xk the independent variable(s), a the intercept parameter estimate, and B1, . . . ,Bk the model slope parameters

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estimated from the data. First, a simple logistic regression model was tested comparing all burn-plot trees to all control-plot trees. Next, multiple logistic regression models were tested for each of the bark beetle species. The different dependent variables tested for model ®t were DBH, plot basal area, live crown percent, crown scorch percent, bole char height, and soil burn index rating (1±5), plus quadratic terms for each of the ®rst ®ve listed variables. Signi®cance for models using w2 model p-value was set as p<0.001 and signi®cance for dependent variables using w2 value was set as p<0.10. Selection of the best model was based on diagnostic statistics, including (SAS Institute Inc., 1995): ÿ2 LOG L and Score statistic (tests of the model null hypothesis), Wald w2 statistic p-value for each parameter coef®cient, Hosmer±Lemeshow, Deviance, and Pearson goodness-of-®t statistics, and adjusted generalized coef®cient of determination (adjusted R2). Additionally, conditional odds ratios were computed to show the relative increase or decrease in odds for a change in a given dependent variable, assuming all other variables remain the same. Chosen odds ratio values for comparison in the mod-

els, where variables were signi®cant (p<0.05), were 13 and 38 cm for DBH, 5 and 50% for crown scorch, 0.1 and 1.5 m for bole char, 25 and 75% for the live crown, and presence or absence of binary soil burn indexes. 3. Results A total of 1082 trees were inventoried in control and burn plots combined, of which 610 (56%) were Jeffrey pine, 389 in burn and 221 in control plots. Average unit basal areas were higher in burn plots than in control (38.2 m2/ha vs. 24.7 m2/ha). Average number of Jeffrey pine trees per plot was higher in burn plots (10 vs. 8.5) as was average pre-burn live crown percentage (45 vs. 42%). Average DBH of Jeffrey pine trees was 32.0 cm in burn plots and 29.2 cm in control plots. A total of 101 Jeffrey pine trees were observed with evidence of bark beetle activity, 29 of these had more than one beetle species (Table 2). Burn statistics for Jeffrey pines were highly variable indicating a broad range of bole char and crown scorch injuries (Table 3). Soil burn index ratings were also

Table 2 Summary statistics of bark beetle presence in Jeffrey pine study treesa Unit name

Red turpentine beetle

Jeffrey pine beetle

Ips species

Jennifer Burn (41) Control (55)

9 0

2 2

5 0

Apollo Burn (53) Control (14)

14 0

6 0

2 0

Marlene Burn (60) Control (57)

3 0

0 0

2 0

Matchless Burn (68) Control (10)

28 0

10 0

8 0

Tyner Tankhouse Burn (167) Control (85)

35 0

1 0

19 0

Total Burn (389) Control (221)

89 0

19 2

36 0

a Total number of beetle-attacked trees is 101, 25.4% of burn trees and 0.9% of control trees. Values enclosed in parenthesis in the lefthand column indicate total number of Jeffrey pine trees inventoried in plots on indicated units.

T. Bradley, P. Tueller / Forest Ecology and Management 142 (2001) 205±214 Table 3 Summary statistics for Jeffrey pine trees in prescription burn study plots (nˆ389) Burn severity class

Mean

Range

S.D.

Bole char height (m) Live crown (%) Crown scorch (%)

0.79 43 33

0±3.5 15±85 0±100

0.832 15.6 35.1

variable, with two no burn, 52 light burn, 248 moderate burn, 70 high burn, and 17 extreme burn trees. Red turpentine beetles were found in 89 (22.9%) of the burned trees, while none were found on control trees. All red turpentine attacks were observed within 1 m of the ground, frequently within a few centimeters of the soil surface. In addition, all attacks were observed by the second ®eld sampling session. No Jeffrey pine beetles were observed in the early sampling session, and by the end of the mid-summer ®eld sampling dates, only ®ve of the eventual 19 Jeffrey pine bark beetle attacked burned trees were observed. The Jeffrey pine beetle was the only bark beetle species observed in control trees. Both of these control plot beetle trees were from old attacks, with con®rmation of presence based on external, dry pitch tubes only. In contrast, all of the Jeffrey pine beetle attacks observed in burned trees were recent, characterized by copious fresh resin streaming down from entrance holes. No Jeffrey pine beetle attack in a burned tree was observed without the presence of a red turpentine beetle and none of these trees revealed presence of Ips. Ips were found in 36 ®re plot trees. The simple logistic regression model with one dependent variable (burn or control) was highly signi®cant with a p-value of 0.0001. The odds ratio for burned trees was 24.81, indicating the probability

209

of bark beetle presence was 24.81 times greater in a burn plot tree than in a control plot tree. The full logistic regression model with intercept is as follows: P…b† ˆ

1 1 ‡ eÿ…a‡B1 X1 †

where: P(b) is predicted probability of bark beetles, e the base of the natural logarithm, a ˆÿ4.2859, Bˆ3.2111, and X presence (1) or absence (0) of burn. The statistics indicate a highly signi®cant relationship between burning and presence of bark beetles. Still, the model ®t was not ideal. Diagnostics of goodness-of-®t indicated the model, based on one binary dependent variable (burn or no burn), did not agree with the data very well, having highly signi®cant p-values for Deviance, Pearson, and Hosmer±Lemeshow goodness-of-®t statistics. In addition, the maximum rescaled R2 was only 0.2011 and the contingency table of predictions listed all predictions as non-occurrences, further pointing to an inadequacy of the model. The best model ®t for the red turpentine beetle included crown scorch percentage, bole char, bole char height squared, DBH, and the extreme soil-burn index rating as dependent variables (Table 4). This model was highly signi®cant (p<0.0001) and had a maximum rescaled R2 of 0.4448. Diagnostic statistics showed that the model ®t was adequate. One outlier was observed, a large DBH tree with beetle presence, but was retained as a valid data point. The model correctly predicted beetle absence in 282 out of 300 trees and presence in 46 out of 89 trees (Table 5). Conditional odds ratios showed a 13-cm tree was 4.13 times more likely to be attacked than a 38-cm tree and that probability of attack increased by 269% with a

Table 4 Regression coefficients and statistics for best-fitting red turpentine beetle-attack model Model terma

Parameter estimate

w2 p-value

Standardized estimate

Wald w2

Intercept CS BCH BCH2 DBH ESBI

ÿ2.0871 0.0232 2.0217 ÿ0.3827 ÿ0.1381 2.1999

0.0003 0.0001 0.0001 0.0177 0.0006 0.0006

± 0.44577 0.92760 ÿ0.47226 ÿ0.43006 0.28264

± 23.409 16.745 5.6236 11.634 11.695

a Model terms: CS, crown scorch (%); BCH, bole char height; BCH2, bole char height squared, DBH; ESBI, extreme soil-burn index rating.

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Table 5 Contingency table of red turpentine beetle predictions

Actual absence Actual presence Total

Table 7 Contingency table of Jeffrey pine beetle predictions

Absence prediction

Presence prediction

Total

282 43 325

18 46 64

300 89 389

Actual absence Actual presence Total

jump from 5 to 50% crown scorch. Additionally, conditional odds ratios for the model showed an increase in bole char height from 0.1 to 1.5 m increases the probability of red turpentine beetle attack by 397%, while a tree with an extreme soil burn rating is 9.81 times more likely to be attacked than all other trees combined. The best model ®t for the Jeffrey pine beetle included seven dependent variables (Table 6). This model was highly signi®cant (p<0.0001), with a maximum rescaled R2 of 0.3626. Diagnostic statistics showed that the model ®t was adequate and no outliers were observed. One marginal variable, w2 of 0.1303 for the bole char height squared, was included in the model, since it greatly improved the Pearson and Hosmer±Lemeshow goodness-of-®t tests. The model correctly predicted beetle absence in 368 out of 370 trees, but only one actual beetle-attacked tree was correctly predicted (Table 7). Conditional odds ratios for this model were very similar to the red turpentine attack model for both, crown scorch (298% increase with a jump from 5 to 50%) and bole char height (359% increase from 0.1 to 1.5 m bole char height). Conditional odds ratios for the two signi®cant soil burn indexes showed a probability increase of 5.71 for

Absence prediction

Presence prediction

Total

368 18 386

2 1 3

370 19 389

an extreme rating and 3.33 for a high rating, while a change from 25 to 75% live crown ratio had an odds ratio decrease of 277%. The best model ®t for the Ips beetles included ®ve dependent variables (Table 8). This model was highly signi®cant (p<0.0001), with a maximum rescaled R2 of 0.5169. Diagnostic statistics showed that the model ®t was very good, with no observation of outliers. The Ips beetle model correctly predicted beetle absence on 345 out of 353 trees and presence on 9 out of 36 trees (Table 9). Conditional odds ratios using the Ips beetle model were very similar to the values obtained using the other two models for crown scorch (241% increase from 5 to 50%) and bole char height (312% increase from 0.1 to 1.5 m). For DBH, conditional odds ratios for a 13-cm tree were 27.3 times greater than for a 38cm tree. All of the best model ®ts include crown scorch percentage and bole char height as signi®cant variables. Live-crown percentage, DBH, the two highest severity soil-burn index ratings, and various quadratic variable forms appear in a portion of the models. Other tested variables, such as plot basal area, plot stocking, the three lowest soil burn severity ratings were not signi®cant or caused signi®cant problems with the

Table 6 Regression coefficients and statistics for best-fitting Jeffrey pine beetle-attack model Model terma

Parameter estimate

w2 p-value

Standardized estimate

Wald w2

Intercept CS BCH LC2 LC ESBI HSBI BCH2

ÿ11.057 0.0223 2.4205 ÿ0.00364 0.2708 1.6493 1.1827 ÿ0.3909

0.0001 0.0070 0.0165 0.0270 0.0391 0.0568 0.0605 0.1303

± 0.42896 1.11059 ÿ2.83946 2.33337 0.18612 0.25081 ÿ0.48236

± 7.2834 5.7461 4.8926 4.2549 3.6273 3.5225 2.2892

a Model terms: CS, crown scorch %; BCH, bole char height; BCH2, bole char height squared; LC, live crown (%); LC2, live crown (%) squared; ESBI, extreme soil-burn index; HSBI, high soil-burn index.

T. Bradley, P. Tueller / Forest Ecology and Management 142 (2001) 205±214

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Table 8 Regression coefficients and statistics for best-fitting Ips beetle-attack model Model terma

Parameter estimate

w2 p-value

Standardized estimate

Wald w2

Intercept CS BCH CS2 DBH BCH2

ÿ4.0076 0.1154 1.9581 ÿ0.0007 ÿ0.3435 ÿ0.4154

0.0117 0.0167 0.0097 0.0616 0.0001 0.0991

± 2.22230 0.89841 ÿ1.32496 ÿ1.06978 ÿ0.51254

± 5.7315 6.6856 3.4933 14.458 2.7194

a

Model terms: CS, crown scorch %; BCH, bole char height; CS2, crown scorch (%) squared; DBH; BCH2, bole char height squared.

Table 9 Contingency table of Ips predictions

Actual absence Actual presence Total

Absence prediction

Presence prediction

Total

345 27 372

8 9 17

353 36 389

various goodness-of-®t statistics, thus were not included in ®nal models. 4. Discussion High variability in ®re effects to trees was observed throughout the different study plots, consistent with the range of thermal effects mentioned by Schmidt (1996). Given this range, it is not surprising that the single variable bark beetle prediction model displayed poor goodness-of-®t statistics and a relatively low maximum rescaled R2. Still, the model does show a strong relationship between ®re and probability of ®nding bark beetles on burned trees, since the model w2 and parameter coef®cient are highly signi®cant at p<0.0001. As shown by the odds ratio, a burned tree has a 24.81 times greater chance of being attacked by a bark beetle than a similar non-burn tree. The ®nding that ®re-injured trees attract or are more susceptible to bark beetles is a general observation that has been reported by various authors (Salman, 1934; Harrington, 1987; Ryan and Reinhardt, 1988; Ryan and Frandsen, 1991; Regelbrugg and Conard, 1993). Summary statistics of the 389 burn plot Jeffrey pines showed an average crown scorch of 33%, bole char height of 0.79 m, and a moderate soil-burn index rating. These statistics indicate that the ®re treatments,

even though conducted under low intensity prescription boundaries, did cause some injury to trees. In similar controlled burns in Idaho, Safay (1981) found no signi®cant increase in endemic beetle populations from prescription burning, although a high number of red turpentine-attacked trees were observed in at least one plot. Ferrell (1996) goes further in identifying the probable increase of red turpentine beetles in the Sierra following the increased use of controlled burning. Given the injury observed in the current study, a greater incidence of red turpentine beetles could be expected. Field observations made at two other recent prescription burn locations in the Tahoe Basin (Sugar Pine State Park on the west shore and a United States Forest Service unit on the east, just south of Spooner Summit) showed a similar, if not greater proportion of red turpentine beetle attacks. Normally, the red turpentine beetle is not viewed as a serious pest to Jeffrey pine (Jenkinson, 1990). Still, at high population levels the beetles can cause mortality, particularly in trees stressed by other causes (Essig, 1934). What is more, the red turpentine beetle has been identi®ed as an agent that predisposes trees to fatal attack by other beetles (Bright and Stark, 1973; Ferrell, 1996). In the case of Jeffrey pine, the Jeffrey pine beetle is the most lethal pathogen (Jenkinson, 1990). While the Jeffrey pine beetle is normally restricted to individual trees or small groups of trees in decadent forests, it can become extremely destructive (Bright and Stark, 1973). Even though the Jeffrey pine beetle was the least common beetle in the current study, its presence in burn plots was considerably higher than in control plots. All burn-plot Jeffrey pine beetle attacks were in trees that were also attacked by red turpentine beetles, and most, if not all, of these occurred before any Jeffrey pine beetle attacks occurred. This observation supports the

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theory that the red turpentine beetle functions as a predisposing agent for the Jeffrey pine beetle in ®reinjured trees. It was assumed at the start that the plots would have approximately equal basal areas, but the average burnplot basal areas were over 150% of the average control-plot basal areas. According to established ecological theory, the greater tree densities would tend to have more susceptible trees (Stark, 1982). When included in the multiple logistic regression models, plot basal area as well as stocking had negative parameter estimates, the opposite effect of what might be expected. Further, all of the bark beetle presence noted in burned trees was recent and not pre-existing. Given these occurrences, the contribution of higher stand density and stocking level to increased beetle presence is uncertain. The ®nal multiple logistic regression models all displayed a good ®t to the data and were highly signi®cant at p<0.0001. In all three of the models, two of the burn severity measures Ð crown scorch and bole char height Ð had a positive relationship with increasing probability of bark beetle attacks. From a physiological standpoint, crown scorch is known to reduce total crown nitrogen content and potential photosynthetic capacity by removing crown biomass (Landsberg et al., 1984). Bole char height is an indirect measure of cambium damage. Similarly, the extreme and high soil-burn index levels are indirect measures of ®ne root and basal cambium injury, while live crown is a measure of overall tree vigor (Keen, 1936). Theoretically, all of these sources would work in concert to in¯uence tree xylem pressure potential, which is associated with oleoresin exudation (Barbosa and Wagner, 1989), a primarily defense system against bark beetles (Swezy and Agee, 1990). The third variable common to the models was bole char height squared, which had a negative parameter estimate in all models. Other quadratic negative parameter estimates appearing in at least one of the models are crown scorch squared and live crown squared. While the relative importance of these negative parameter estimates is model speci®c, they indicate variable interaction or changing importance of the linear terms in predicting bark beetle presence through a range of values. Tree DBH showed the most variability among the three beetle speci®c models. In the red turpentine and

Ips models, DBH had a signi®cant negative parameter estimate, indicating a decreasing probability of bark beetles with increasing tree size. This ®nding parallels studies that have shown tree resistance to the thermal effects of ®re increases with tree size (Regelbrugg and Conard, 1993; Ryan and Reinhardt, 1988). However, the lack of any size preference in the Jeffrey pine model suggests the in¯uence of tree size preferences as an additional factor of importance. One limitation of the reported logistic regression models is in their ability to predict beetle presence in individual trees. The contingency table data show that the models generally under-predict the total number of bark beetle-attacked trees, from a high of 72% for the red turpentine model to 16% for the Jeffrey pine beetle model. The accuracy of beetle presence predictions is also less than perfect, with ranges from 72% for the red turpentine beetle to 33% for the Jeffrey pine beetle. Given these relatively low numbers, the use of these models for identi®cation of actual beetle-attacked trees is not practical. Still, with an understanding of the limitations of these models, a hazard-rating system could be developed for identi®cation of susceptible trees or stands of trees. This type of information could be very useful in considering preventative and cultural treatments to mitigate beetle damage and tree vulnerability (Barbosa and Wagner, 1989). 5. Conclusion The logistic regression models show a positive relationship between burn severity measures and bark beetle presence in ®re-injured Jeffrey pine. In all of the beetle speci®c models, the burn severity measures of bole char height and crown scorch percentage were important predictor variables. Another burn severity measure, soil-burn index, was important in two of the models. A small tree size preference was noted for all, but the Jeffrey pine beetle. Most of the other tested predictor variables were not signi®cant in predicting beetle presence. Future studies are recommended to explore the relationship between stand basal area and beetle presence, and more importantly, to address questions on tree mortality from ®re and bark beetles that were not a focus of this study. Ideally these models could be applied to trees in conjunction with some quantitative

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measurements of physiological stress to expand on ®re-stress questions, such as mortality threshold and bark beetle resistance. While this study shows an undesirable beetle response in Jeffrey pine trees due to prescription burning, only a long-term study can provide more detailed ecological insight. Fire has been a functional force in Sierra forests for thousands of years. Since natural ®res are no longer a viable option in populated forest regions, the current use of prescription ®re is still, as stated by Weatherspoon and Skinner (1996), the best attempt at managing for sustainable ecosystems given the knowledge we have today. Acknowledgements Thanks are due to Steven Seybold for sharing his ideas and technical information. This study was funded as a combined project of the Nevada Cooperative Extension and the Nevada Agricultural Experiment Station. References Arno, S.F., Brown, J.K., 1989. Managing fire in our forests Ð time for a new initiative. J. Forest. 87, 44±46. Barbosa, P., Wagner, M.R., 1989. Introduction to Forest and Shade Tree Insects. Academic Press, San Diego. Brennan, L.A., Hermann, S.M., 1994. Prescribed fire and forest pests: solutions for today and tomorrow. J. Forest. 92, 34±37. Bright, D.E., Stark, R.W., 1973. The Bark and Ambrosia beetles of California Ð Coleoptera: Scolytidae and Platypodidae. In: Belkin et al. (Eds.), Bulletin of the California Insect Survey. University of California Press, Berkeley. Coulson, R.N., Witter, R.W., 1984. Forest Entomology, Ecology and Management. Wiley, New York. Essig, E.O., 1934. Insects of Western North America, New York. The MacMillan Company. Fernandez, G.C.J., 1998. REGRESS, a SAS macro for linear regression analysis with exploratory graphs and assumption checks. In: Quick Results from Statistical Analysis III. Department of Applied Economics and Statistics, MS 204, UNR Reno, NV. 89557. Ferrell, G.T., 1996. The influence of insect pests and pathogens on Sierra forests. In: Sierra Nevada Ecosystem Project, Final Report to Congress, vol II, Assessments and Scientific Basis for Management Options. Centers for Water and Wildland Resources, University of California, Davis, 1996. Harrington, M.G., 1987. Ponderosa pine mortality from spring, summer, and fall crown scorching. W. J. Appl. Forest. 2, 14±16.

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