Understory biomass in ponderosa pine following mountain pine beetle infestation

Forest Ecology and Management, 13 (1985) 53--67

53

Elsevier Science Publishers B.V., Amsterdam -- Printed in The Netherlands

UNDERSTORY BIOMASS IN PONDEROSA PINE FOLLOWING MOUNTAIN PINE BEETLE INFESTATION

DAVID A. KOVACIC 1'4, MELVIN I. DYER 2 and ALEXANDER T. CRINGAN 3

1Natural Resource Ecology Laboratory, Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, CO 80523 (U.S.A.) ~Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830 (U.S.A.) 3Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, CO 80523 (U.S.A.) "Present address: Savannah River Ecology Laboratory, Drawer E Aiken, SC 29801 (U.S.A.) (Accepted 11 June 1985)

ABSTRACT Kovacic, D.A., Dyer, M.I. and Cringan, A.T., 1985. Understory biomass in ponderosa pine following mountain pine beetle infestation. For. Ecol. Manage., 13: 53--67. Understory herbaceous biomass was estimated in a chronosequence of ponderosa pine sites. Sites represented recovery ages ranging from 0 to 10 years after attack by the mountain pine beetle. Total understory biomass peaked on 5-year-old (5 years post-attack) sites. Five-year-old site means ranged from 102 to 201 g m -~. Biomass gradually declined through 10 years post-attack; however, understory biomass levels remained considerably greater than those of non-infested live stands. The dominant vegetation classes (forbs, grasses, and sedges) all followed a similar biomass trend with time. Shrub biomass was too heterogeneous to measure accurately. Polynomial regressions of site biomass means plotted against time revealed a relatively poor fit. Therefore, multiple linear regression models of understory biomass were developed using measurements of time since beetleattack, canopy opening, litter, duff, slope, insolation, basal area and mean tree diameter.

INTRODUCTION

A major mountain pine beetle (Dendroctonus ponderosae Hopk.) epidemic began in the ponderosa pine (Pinus ponderosa Dougl. ex Laws) ecosystem of the Colorado Front Range in the mid-1960s. Beetle populations reached a peak in 1977, infesting 1.25 million trees. The epidemic ended approximately 1980, after affecting an estimated 250 000 ha of Front Range ponderosa pine (D. Leatherman, pers. commun., 1981). During the outbreak there was wide-spread concern over apparent tree loss and damage to the ponderosa pine ecosystem. Although the mountain pine beetle may be a destabilizing force in the ponderosa pine ecosystem, it could function alternately as a cybernetic reg-

54 ulator to channel the flow of energy, nutrients and water to better adapted species (Mattson and Addy, 1975). Following the widespread destruction of the ponderosa pine forest, large openings were created, resulting in the development of successional communities. Grass, forb, shrub, and sedge species invaded these open patches. The reduction in the overstory led to an overall increase in understory productivity (McCambridge et al., 1982), which would enhance secondary production. A similar increase in understory forage production has been documented following southern pine beetle infestation (Leuschner and Maine, 1980). A negative relationship exists between overstory and understory vegetation. Removal of the canopy affects insolation, competition for water and nutrients, forest floor accumulation, and allelopathy. These are important factors controlling understory growth in ponderosa pine forests (Molt, 1966; Jameson, 1967; Pearson, 1967; Clary et al., 1968; Clary, 1969; Anderson et al., 1969; Lacey, 1971; Dodd et al., 1972; Rice and Pancholy, 1972, 1973; Metz, 1974). Many studies have reported increased understory biomass following thinning. The increased biomass is attributed to (1) scarification of soils after logging, (2) buildup of inorganic N from increased nitrification as soil temperatures increase, (3) reduced tree uptake, which resulted in a flush of available N to the understory, and (4) increased insolation (Clary and Ffolliott, 1966). The response of forests affected by beetle-attack should be similar; however, this response, differs from logged sites because soils on these sites are not scarified. The purpose of this study was to estimate understory productivity of forbs, shrubs, grasses, and sedges on sites representing a range of successional recovery stages from 0 to 10 years after tree death, and to use these estimates to develop prediction equations for understory biomass following beetle-kill. Several multiple regression modeling approaches were explored to determine appropriate biomass prediction models. Such models will enable range, wildlife, and land managers to predict understory biomass and forage dynamics following future beetle outbreaks. SITE DESCRIPTION Location The study was conducted in the lower montane ponderosa pine forest of the Colorado Front Range near Fort Collins, CO, at elevations ranging from 2073 to 2245 m. Study sites were located between 10.8 and 13.8 km west of Fort Collins in or near Charles A. Lory State Park. Study sites Twelve 0.1-ha (34 × 34 m) sites were selected and sampled for understory biomass in July 1978. In July 1979, the original 12 sites were again sampled

55 TABLE 1 Site d e s i g n a t i o n , age, e s t a b l i s h m e n t , g e o g r a p h i c l o c a t i o n s a n d elevations o f t h e 20 s t u d y sites

Site Age a

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1978

1979

0

0 0 0 0 0 2 2 3 3 4 4 5 5 5 5 8 8 8 10 10

0 1 1 2 2 3 3 4 4 7 7

Elevation (m)

Latitude

Longitude

2037 2073 2245 1989 2073 2037 1959 2037 1959 2074 2074 2074 2103 2074 2103 2257 2097 2257 2097 2074

4 0 ° 3 4 ' 1 7 '' 34'18" 35'37" 34'17" 34'30" 34'12" 34'29" 34'15" 34'19" 34'07" 34'14" 34'16" 34'21" 34'14" 34'09" 35'42" 34'24" 35'40" 34'27" 34'16"

105°11'26" 11'59" 14'09" 11'26" 11'56" 11'12" 11'54" 11'54" 11'56" 11'51" 11'51" 12'23" 12'07" 12'26" 12'08" 14'06" 12'05" 14'06" 12'04" 11'51"

aAge refers to years post beetle-attack, 0 = sites not infested by the mountain pine beetle, 1978 and 1979 refer to the years the sites were sampled. Site established in 1978 were sampled again in 1979 and advanced 1 year in age.

(these were advanced 1 year in age class). Eight additional 0.1-ha sites were established and sampled in July 1979. The 20 sites represented a range of successional recovery stages from 0 through approximately 10 years post tree death. Site ages (years post beetle-attack) were established on the basis of aerial photographs and United States Forest Service Records. Site designations, elevations and geographic locations are listed in Table 1.

Geology The surficial geological strata of the study area are of Precambrian origin, 1.7 X 109 years old. Parent material on sites, 1, 2, 6, 8, 13, 15, 17 and 19 is knotted mica schist. Parent material on sites 4, 5, 7, 9, 10, 11, 12, 14 and 20 is classified as Boulder Creek granodiorite. Parent material on sites 3, 16 and 18 is composed of amphibolite {United States Geological Survey, 1977). Soils on the sites are fine loamy mixed or loamy skeletal typic~eutroboralfs.

56 TABLE 2 Species composition of vegetation occurring on study sites a Forbs

Achillea lanulosa Allium cernuum Anaphalis margaritacea Anemone cylindrica Antennaria neglecta A. parvifolia Aquilegia caerulea Arabis hirsuta Arnica cordifolia Artemisia frigida Artemisia ludoviciana Aster laevis A. porteri Astragalus spp. Camelina mierocarpa Campanula rotundifolia Centaurea maculosa Cerastium arvense Chenopodium hybridium C. leptophyllum Cirsium arvense Collinsia parviflora Collomia linearis Convolvulus spp. Conyza canadensis Corallorhiza maculata Cystopteris fragilis Delphinium geyeri D. nelsonii Delphinium spp. Dodecatheon pulchellu m Draba nemorosa Erigeron speciosus E. flagellaris Eriogonium umbellatum Erysimum asperum Fragaria vesca Gaillardia aristata Galium boreale Gayophytum diffusum Geranium caespitosum Harbouria trachypleura Heterotheca villosa Heuchera parvifoliai Hieracium fendleri Hieracium spp.

Sedum lanceolatum Senecio integerrimus Senecio spp. Silene antirrhina Solidago missouriensis S. sparsiflora Taraxacum spp. Thermopsis divaricarpa Townsendia grandiflora Tragopogon spp. Verbascum thapsus Shrubs

Ceanothus fendleri Cereocarpus montanus Juniperus communis Physocarpus monogynus Ribes cereum Rosa woodsii Ru bus deliciosus R. idaeus Sy mphoricarpos albus Grasses

Agropyron albicans Agrostis spp. Bromus tectorum Calamagrostis purpurascens Danthonia parryii D. spieata Elymus canadensis Festuca saximontana Hesperachloa kingii Koeleria cristata K. nitidai Muhlenber~a montana Poa pra tensis Sitanion hystrix Stipa comata S. occidentalis S. robusta S. viridula

57

Hymenopappus filifolius Iris missouriensis Lesquerella montana Liatriz punctata Liliacea spp. Lithospermum incisum Lupinus argen teus Mertensia lanceolata Monarda pectinata Oxytropis spp. Penstemon secundiflorus P. virens Phacelia heterophylla Polygonum convolvulus Potentilla fissa Prunus virginiana Scutellaria brittonii

Sedges

Carex eleocharis C. heliophila C. interior C. occidentalis C. petasata C. pityophila C. spp. Trees

Pinus ponderosa Pseudotsuga menziesii Acer glabrum

aSpecies names from Harrington (1964).

Climate

Annual precipitation in Fort Collins is 360 mm, of which 112 mm , or 31%, falls from June to August. The average annual temperature is 8.9°C. Actual site temperatures would be somewhat lower, while site precipitation would be somewhat higher. The growing season in the Front Range is approximately 180 days, extending from April into October (Smith, 1967).

Vegetation Ponderosa pine was the dominant species on all sites; however, Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) also occurred. Site understory plant species are listed in Table 2. METHODS

Understory productivity was determined by clipping above-ground standing biomass. Twenty 30 × 61 cm plots from each of 12 sites were randomly selected and clipped at the end of July 1978 (understory biomass peak). In 1979, 20 plots per site were clipped on 20 sites. Standing live and the current year's dead plant material were collected from each plot and combined as an estimate of the total year's production. During clipping, vegetation was separated into four classes: shrubs, grasses, forbs and sedges. Vegetation and litter were dried for 2 days at 100°C. During the summer of 1979, extensive measurements of litter depth (01 horizon), duff (02 horizon) depth, aspect, slope and canopy opening were taken at each of 400 microplots. Mean microplot litter and duff depth (to the nearest cm) were derived from four measurements per clip plot. All trees at each 0.1-ha site were counted and

58 diameter at breast height (cm) was measured. This information was used to calculate absolute stand total basal area and absolute stand mean diameter. Slope measurements were taken at each clip plot using an Abney level. Compass readings of aspects were taken and combined with the slope measurements to determine potential insolation at each plot. Potential solar radiation calculations for each plot were based on the equivalent slope theory of Lee (1963). With this theory, it is possible to equate slope and azimuth of a plot with a parallel equivalent latitude and longitude on the earth's surface that has the same angular relationship to the solar beam. Using known relationships, the potential radiation for the equivalent location can be determined and related to the specific microplot. Details of the methods used for developing potential solar radiation estimates are described by Huff et al. (1977). Spring potential solar radiation was calculated, since this potential revealed the greatest differences between slopes and azimuths (Frank and Lee, 1966) and because the effects of solar radiation were considered critical to cool-season plants in the early spring. Canopy opening was measured using a camera with a fisheye lens. Photographs were taken at 0.46 m above each of the 1979 clip plots. The top of the camera was positioned to the north to maintain consistency in the direction of all photographs. A bubble level was attached to the front of the camera body below the lens to level the camera to the horizontal. Negatives from each of the clip plots were analyzed with a Vidicon System image slicer, using it in the analog mode to determine the percent of canopy opening above each plot. This system is described by Williams (1976). Multiple and simple linear regression analyses were performed using the BMDP9R Statistical Pacl~age (Frane, 1979). This program uses all possible subsets regression to determine the best prediction equations. A first principles approach was used to determine which predictor variables to enter into the all possible subsets routine. Final selection of the best prediction equation was made following the principles of parsimony and common sense to insure that the biomass multiple regression models selected were concise and logical. RESULTS

Understory biomass Biomass estimates of understory above-ground peak standing crop are plotted in Figs. 1 and 2; along with regression equations of the site means versus age post-attack. Regression lines for each of the biomass classes are also given. Total biomass on these sites ranges from 201 g m -2 (year 5) to 2 gm -2 (year 0). Total production followed a trend which peaked at 5 years post-attack. Forb biomass (Fig. 2A) ranged from 0.2 g m -2 (year 0) to 91 g m -: (year 5). It peaked at 5 years and gradually decreased through 10 years post~attack. Grass biomass (Fig. 2B) ranged from 0 g m -2 (year 0) to

59

200

Y=-3.:37+ r == O . 6 6 ,

•1978Estirnotes

3:3.7 X - 2 . 5 1 X = p
ol979

Estimote$

N i

E 150 w 03 03 E

=E I00 O

"

m ,.J

ko t-

50

O

I TIME

AFTER B E E T L E K I L L

(yeors)

Fig. i. Total above-ground standing understory biomass, 1978 (open) and 1979 (solid). Nonoverlapping bars indicate significant differences at the P = 0.05 level. Above-ground standing biomass includes the year's live and standing dead material.

80 g m -2 (year 4). Grass production peaked between years 4 and 5 and declined through year 10. Shrub biomass (Fig. 2C) was low on 0--4 year post-attack sites while older sites generally had higher shrub biomass estimates. Biomass estimates ranged from 0 g m -2 (years 3 and 4) to 41 g m -2 (year 8) and followed a linear trend that increased through 10 years postattack. Pine regeneration accounted for a minor portion of the shrub layer in the 10 year old site; however, it was insignificant as a c o m p o n e n t of the shrub layer in all other sites. Sedge biomass (Fig. 2D) ranged from 1 g m -2 (year 0) to 66 g m -2 (year 8). Sedge biomass followed a trend which was similar to that of grass. Correlation coefficients

The univariate correlation coefficients of the site mean variables (Table 3) reveal consistent trends between vegetation classes and several of the independent variables. All vegetation classes are either non-correlated (r 0.10) or positively correlated with age post-attack, litter depth, duff depth, and canopy opening; and either non-correlated (r ~<0.10) or negatively correlated with slope and basal area. Age post-attack, slope, basal area and canopy opening were the only variables significantly (P ~<0.05) correlated with biomass estimates. Understory biornass prediction using site means and absolute site measurements

Prediction equations were first developed using site-variable means and absolute site values (Table 4). Site means of slope (S), litter depth (L), duff

FORB I00

e l 9 7 8 Estimates 01979 Estimates

"Y = -0.81 + 7.82 X - O. 45X a ra = 0.41, p <0.001

~b O

GRASS Y , - 6 . 6 1 +15.09X-I.24X z "r |= 0.48, p
80

tt-ttt

GO

4O 'E

20

0 03 (/) 6 ' x¢ :S SHRUB o m IO0 Y- 0.23 + 1 . 5 8 X r2"O. 18, p < 0.05

'

4

~

'

~

'

¢o

SEDGE Y" 2.72 + I O . 4 7 X - O . 9 6 X 2 r == 0.43, p
80 60 40

20 0

• b

'

'~

'

:~

~

'

6 ' ~ TIME AFTER B E E T L E K I L L ( y e a r s )

'

~,

'

~

'

~

'

i'o

Fig. 2. Above-ground standing understory biomass, 1978 (open) and 1979 (solid). Non-overlapping bars indicate significant differences at the P = 0.05 level. Above-ground standing biomass includes the year's live and standing dead material.

TABLE 3 Correlation coefficients between vegetation classes and independent variables based on 1979 site means (n = 2O) Vegetation class

ln(forb biomass) In(shrub biomass) Grass biomass In(sedge biomass) In(total biomass)

Independent variables Age post-attack

Slope

Basal area

Litter Duff Canopy depth depth opening

Diameter (DBH)

Potential insolation(LY)

0.68** 0.40 0.42 0.45* 0.74**

-0.01 -0.01 -0.59** -0.21 -0.17

-0.82** -0.22 -0.59** -0.79** -0.92**

0.28 0.34 0.10 0.20 0.20

0.07 0.26 -0.17 -0.26 -0.03

0.20 -0.38 -0.10 0.37 0.03

0.20 0.23 -0.05 0.15 0.13

0.76** 0.27 0.56** 0.63** 0.79**

62

TABLE 4 P r e d i c t i o n e q u a t i o n s f o r u n d e r s t o r y b i o m a s s (g m -2) a n d p e r t i n e n t s t a t i s t i c s f o r m u l t i p l e r e g r e s s i o n s b a s e d o n 1 9 7 9 site m e a n s (n = 2 0 ) A G E = y e a r s p o s t b e e t l e - a t t a c k , B A = t r e e basal area ( m 2 ha-~), C O = m e a n c a n o p y o p e n i n g (%), D I A = m e a n t r e e d i a m e t e r ( c m ) , LY = m e a n p o t e n t i a l i n s o l a t i o n ( l a n g l e y s ) a, S -- m e a n s l o p e ( d e g r e e s f r o m h o r i z o n t a l ) Equation

r2

P

F~statistic

ln(total biomass) = 5.23 - 0.048(BA)

0.85

<0.001

101.61

ln(forb biomass) = - 4.41 - 0 . 0 0 3 9 ( B A ) + 0.17(DIA) + 0.0053(LY) + 0.072(CO)

0.83

<0.001

18.72

ln(shrub biomass) = 6.44 - 0 . 0 1 ( L Y ) + 0.08(CO)

0.56

0.01

6.98

Grass = 6 7 . 5 6 + 4 . 1 6 ( A G E ) -

0.59

<0.001

12.10

0.62

<0.001

29.89

2.24(S)

ln(sedge biomass) = - 3.68 - 0 . 0 0 3 9 ( B A ) a l L a n g l e y = 4 1 . 8 6 8 k J / m 2.

depth (D), canopy opening (CO) and solar potential (LY) as well as absolute stand mean diameter breast height (DIA) absolute stand basal area (BA) and AGE were used as possible independent variables. Site means of forb, shrub, grass, sedge, and total biomass were used as the dependent variables. Forb, shrub, and total biomass were best predicted by a natural log transformation of the d e p e n d e n t variables. Grasses were best predicted using nontransformed d e p e n d e n t variables..ln(total biomass) and ln(sedge biomass) were best predicted by the variable BA (r: = 0.85 and 0.62 respectively). The variables BA, DIA, LY and CO were the best predictors of ln(forb biomass) (r: = 0.83). The variables LY and CO were the best predictors of ln(shrub biomass) (r ~ = 0.56). Grass biomass was best predicted by the variables age and slope (r ~ = 0.59).

Understory biomass prediction using 1979 biomass data from 400 individual microplots Residual analysis revealed that a transformation of t b : dependent biomass variables (forb, grass, sedge and total) to natural logs resulted in reduced heteroscedasticity. Therefore, the natural log transformation was used for these variables. Shrub biomass was the only variable in which a log transformation did n o t improve the residual plots; therefore, non-transformed shrub values were used. The value 1.0 was added as convention to the values from each of the clip plots where natural log transformations were used to ensure that the natural log of zero was n o t taken. This modeling approach incorporated microsite variability, resulting in a much larger error comp o n e n t than did the former mean and absolute variable model. Biomass was regressed against the following variables: solar potential in langleys (LY) and first, second, and third orders of age post-attack (AGE),

63 TABLE 5 Understory biomass prediction equations (gm -~) and pertinent statistics for multiple regressions based on 1979 clip plot data (n = 400). Age = years post beetle-attack, CO = % canopy opening, D = duff depth in cm, L = litter depth in cm, LY = potential insolation in langleys Equation

r2

P

F-statistics

In(total biomass + 1.0) = 1.04 + 0.70(AGE)- 0.048(AGE 2) + 0.03(CO)- 0.063(D)

0.50

<0.001

89.30

ln(forb biomass + 1.0) = 0.31 + 0.39(AGE)0.24(AGE 2) + 0.027(CO)- 0.067(D)

0.28

<0.001

38.33

Shrub biomass = - 0.13 + 1.33(AGE) + 31.48(L)- 10.55(L 2) + 1.03(L 3) - 0.04(LY)

0.14

<0.001

13.10

ln(grass biomass + 1.0) -- 2.00 + 0.60(AGE) - 0.02(AGE 2) - 0.067(CO) + 0.0016(CO 2) - 0.0022(LY)

0.34

<0.001

39.57

0.22

<0.001

22.25

ln(sedge biomass + 1.0) = - 1.38 +

- 0.02(AGE 2) + 0.027(CO) + 0.0034(LY)- 0.10(D)

0.28(AGE)

c a n o p y o p e n i n g (CO), litter d e p t h (L), d u f f d e p t h (D), and slope (S). O f these i n d e p e n d e n t variables, o n l y five ( A G E , D , L, CO, LY, and f u n c t i o n s o f t h o s e i n d e p e n d e n t variables) were selected as p r e d i c t o r s using the all-possible-subsets regression r o u t i n e . Results o f t h e all-possible-subsets regression analyses are listed in T a b l e 5. T h e l n ( t o t a l biomass) p r e d i c t i o n e q u a t i o n has t h e highest r 2 value, while t h e s h r u b biomass regression shows the l o w e s t overall r 2 value. l n ( t o t a l biomass) and l n ( f o r b biomass) were best p r e d i c t e d b y the i n d e p e n d e n t variables A G E , A G E 2, CO, a n d D (r: = 0.50 and 0.28, respectively). ln(grass biomass) was best p r e d i c t e d b y A G E 2, CO, CO:, and L Y (r: = 0.28). T h e best p r e d i c t o r s o f s h r u b biomass were A G E , L, L : , L 3 and LY (r: = 0.14). A m a x i m u m n u m b e r o f variables is r e a c h e d in the in(sedge biomass) p r e d i c t i o n e q u a t i o n , where t h e variables i n c o r p o r a t e d are A G E , AGE 2, CO, LY, a n d D (r: = 0.22). DISCUSSION In this s t u d y w e f o u n d t h a t t o t a l , grass, f o r b a n d sedge biomass p e a k e d at 5 years (5 years p o s t - a t t a c k ) . T h e s e results agree closely with the results o f a r e c e n t U.S. F o r e s t Service s t u d y c o m p l e t e d in L o r y State Park, w h e r e f o u r plots were s t u d i e d t h r o u g h t i m e f r o m 0 t o 5 years p o s t - a t t a c k (McCambridge et al., 1 9 8 2 ) . T h e e x t e n s i o n o f biomass estimates t o 10 years in 1 9 7 9 sug-

64 gests a plateau or decreasing trend in total understory biomass following the observed peak. The understory biomass time trends suggest that herbaceous vegetation remains high at least through 8--10 years. The data follow a sigmoidal pattern through year 5, after which biomass gradually declines. Polynomial models regressing site biomass means against age post-attack predicted peak biomass several years b e y o n d the observed peaks. Coefficients of determination were quite low for the polynomial models. All biomass means were positively correlated with age post beetle-attack. The positive correlation probably occurred because age integrated several factors. As trees fall with increased age of decomposition, more light and moisture reach the forest understory (Cheo, 1946). Soil temperatures increase resulting in increased decomposition, mineralization, and nitrification. These factors should enhance growth. The positive correlation with litter and duff depths may have reflected the reduction in evaporation of water from underlying layers and also may have reflected a more productive site with increased water and nutrients. The positive correlation with canopy opening reflects an increase in understory light, temperature and moisture. Negative correlations of understory biomass with slope may have been caused by increased runoff, more variable insolation and p o o r soil formation on steeper slopes. The negative correlations with basal area reflect a reduction in understory light, temperature and moisture as well as an increase in resource competition with increased basal area. Correlations between diameter and potential insolation; and understory biomass classes exhibit inconsistencies. Grass biomass and ln(sedge biomass) are negatively correlated with diameter while ln(shrub biomass) is positively correlated with diameter. The negative correlation may be a result of increased resource competition with trees. Shrubs in contrast are positively correlated with tree diameter perhaps reflecting an ability to c o m p e t e with trees for nutrients and a longer period of shrub establishment. Forbs and sedges positively correlated with potential insolation, reflecting greater tolerance to increased temperatures and to xericity. Shrubs on the other hand are negatively correlated with potential insolation perhaps reflecting a preference for cooler, wetter habitats. Regression models developed from site means rather than plot data proved to be much better estimators of understory herbaceous biomass. This was an aggregate approach in which the inherent natural heterogeneity of the system was removed by expressing biomass, slope, canopy opening, and solar potential as mean values for each site. This apprach was taken since absolute site measurements such as basal area and mean diameter were also included as independent variables in the regression equation. Although natural heterogeneity is removed, such a model m a y be more practical for purposes of forest management and biomass prediction over a large area. The dependent variables used were forb, shrub, grass, sedge, and total biomass. Mean and absolute values were much better biomass predictors than were the single clip plot variables, and r ~ values were increased approximately t w o times in

65 grasses and total vegetation, three times in forbs and sedges, and five times in shrubs.

Biomass prediction Modeling or predicting vegetative changes is a difficult task, since light is not the only factor, nor is it the major factor, controlling herbaceous understory growth in coniferous forests (Krueger, 1981; Anderson et al., 1969). Moisture is probably the limiting factor (Krueger, 1981; Anderson et al., 1969), but litter and duff depth, tree basal area, competition for nutrients, temperature, and nitrification are also important factors controlling plant growth. It is critical in a study of this type to determine the appropriate hierarchical level upon which to focus data acquisition. Although it is possible to predict biotic change on a macrosite level, it is often impossible to predict microsite changes where variables too numerous, and (or) too impractical to measure influence biomass production. An understory model which can account for 50% of the variability in total biomass with a 30 × 61 cm microsite resolution may be useful in modeling forest gap succession. However, from the forest manager's aspect, the costs of implementing such a model may exceed its benefits. The "mean"-based model, which reduced the error c o m p o n e n t and predicts biomass on a macrosite level is the most practical for the forest or range manager's use.

Implications of understory growth to wildlife Total plant biomass of southwest ponderosa pine forests is between 57 000 and 8 8 0 0 0 k g h a -1 (Clary, 1978). Of this, only 117--185 k g h a - ' is herbaceous biomass; the rest is tree biomass, little of which is available to larger herbivores as a food source. In forest openings (no trees), herbaceous biomass is 645 k g h a -1 (Clary, 1978). Although forest openings have much less total biomass, t h e y support three to five times more herbivore biomass per hectare than do pine forests (Clary, 1978). The enormous difference in live standing tree biomass between infested and noninfested sites is quite apparent to anyone who has.seen a severe mountain pine beetle infestation. The losses, however, may be balanced by the increased forage and by the natural thinning effects, which may improve not only forage quality but also the timber and site quality in these stands. The data presented here show that a peak in understory biomass was reached by 5 years post-attack. The results are similar to those of Reynolds (1962), who found a peak in understory biomass production at 6 years postlogging and a return to prelogging conditions by 11 years postlogging. It is difficult to determine just h o w long the effects of beetle-kill will last. If we assume that a gradual yearly reduction in biomass will follow the 5-year peak in total vegetation, it is possible that levels of wildlife forage will remain elevated above pre-infestation levels for 10 to 15 years following beetle infestation.

66 The biomass models developed here may prove beneficial in determining potential forage availability to wildlife populations in present and future mountain pine beetle infestations occurring in the R o c k y Mountain region. These models would be most beneficial if incorporated into larger stand models, thus enabling a more accurate estimation of understory herbaceous biomass in F r o n t Range ponderosa pine. ACKNOWLEDGEMENTS

Thanks go to Peggy Kovacic for help in data preparation and to David Swift, Ken McLeod and Justin Congdon who gave of their valuable time to review this manuscript. Drs. James Detling, Jerrold Dodd and Jesse Logan were instrumental in the development of this research. William McCambridge helped select and date the study areas. This research was funded by USDA Forest Service McIntire-Stennis grant No. 5334. Manuscript preparation was conducted under contract DE-AC09-76SR00819 between the U.S. Department of Energy and the University of Georgia.

REFERENCES Anderson, R.C., Loucks, O.L. and Swain, A.M., 1969. Herbaceous response to canopy cover, light intensity, and throughfall precipitation in coniferous forest. Ecology, 50: 255--263. Cheo, K.G., 1946. Ecological changes due to thinning red pine. J. For., 44: 369--372. Clary, W.P., 1969. Increasing sample precision for some herbage variables through knowledge of the timber overstory. J. Range Manage., 22: 200--201. Clary, W.P., 1978. Producer--consumer biomass in Arizona ponderosa pine. U S D A For. Serv. Gen. Tech. Rep. RM-56, Rocky Mountain For. Range Exp. Stn., Fort Collins, CO, 4 pp. Clary, W.P. and Ffolliott, P.F., 1966. Differences in herbage--timber relationships between thinned and unthinned ponderosa pine stands. U S D A For. Serv. Res. Note RM-57, Rocky Mountain For. Range Exp. Stn., Fort Collins, CO, 4 pp. Clary, W.P., Ffolliott, P.F. and Jameson, D.A., 1968. Relationship of different forest floor layers to herbage production. U S D A For. Serv. Res. Note RM-98, Rocky Mountain For. Range Exp. Stn., Fort Collins, CO, 4 pp. Dodd, C.J.H., McClean, A. and Brink, V.C., 1972. Grazing values as related to tree crown covers. Can. J. For. Res., 2: 185--189. Frane, J., 1979. All possible subsets regression. In: W.J. Dixon and M.B. Brown (Editors), B M D P Biomedical Computer Program P Series. Univ. California Press, Berkeley, CA,

pp. 418--436. Frank, E.C. and Lee, R., 1966. Potential solar beam irradiation on slopes. USDA For. Serv. Res. Pap. RM-18, Rocky Mountain For. Range Exp. Stn., Fort Collins, CO, 116 Pp. Harrington, H.D., 1964. Manual of The Plants of Colorado. The Swallow Press, Chicago, IL, 666 pp. Huff, D.D., Luxmore, R.J., Mankin, J.B. and Begovich, C.L., 1977. TEHM: A terrestrial ecosystem hydrology model. EDFB/IBP-76'78, Oak Ridge National Laboratory, Oak Ridge, TN, 160 pp. Jameson, D.A., 1967. The relationship of tree overstory and herbaceous understory vegetation. J. Range Manage., 20: 247--249.

67

Krueger, W.C., 1981. H o w a forest affects a forage crop. Rangelands, 3:71--72. Lacey, J.R., 1971. Estimating forage production under ponderosa pine canopy with the heterodyne vegetation meter. M.S. Thesis, Univ. Arizona, Tucson, AZ, 83 pp. Lee, R., 1963. Theory of the "equivalent slope theory". Mon. Weather Rev., 90: 165-166. Leuschner, W.A. and Maine, J.D., 1980. Estimating the southern pine beetle's grazing impact. Bull. Entomol. Soc. Am., 26: 117--120. Mattson, W.J. and Addy, N.D., 1975. Phytophagus insects as regulators of forest primary production. Science, 190: 515--522. McCambridge, W.F., Morris, M.J. and Edminster, C.B., 1982. Herbage production under ponderosa pine killed by the mountain pine beetle in Colorado. U S D A For. Serv. Res. Note RM-416. Rocky Mountain For. Range Exp. Stn., Fort Collins, CO, 3 pp. Metz, H.E., 1974. Relationship between ponderosa pine and understory herbage production. M.S. Thesis, Colorado State Univ., Fort Collins,CO, 90 pp. Moir, W.H., 1966. Influence of ponderosa pine on herbaceous vegetation. Ecology, 47: 1045--1048. Pase, C.P. and Hurd, R.M., 1957a. Understory vegetation as related to basal area, crown cover and litter produced by immature ponderosa pine stands in the Black Hills. Proc. Soc. Am. For., 1957: 156--158. Pace, C.P. and Hurd, R.M., 1957b. Herbage production and composition under immature ponderosa pine stands in the Black Hills. J. Range Manage., 11 : 238--243. Pearson, H.A., 1967. Phenology of Arizona fescue and mountain muhly in the northern Arizona ponderosa pine type. USDA For. Serv. Res. Note RM-99. Rocky Mountain For. Range Exp. Stn., F o r t Collins, CO, 4 pp. Reynolds, H.G., 1962. Effect of logging on understory vegetation and deer use in ponderosa pine of Arizona. USDA For. Serv. Res. Note 80. Rocky Mountain For. Range Exp. Stn., F o r t Collins, CO, 7 pp. Rice, E.L. and Pancholy, S.K., 1972. Inhibition of nitrification by climax ecosystems. Am. J. Bot., 59: 1033--1040. Rice, E.L. and Pancholy, S.K., 1973. Inhibition of nitrification by climax ecosystems. II. Additional evidence and possible role of tannins. Am. J. Bot., 60: 691--702. Smith, D.R., 1967. Effects of cattle grazing on a ponderosa pine--bunchgrass range in Colorado. USDA Tech. Bull. No. 1371, 60 pp. Szaro, R.C. and Balda, R.P., 1979. Effects of harvesting ponderosa pine on nongame bird populations. USDA For. Serv. Res. Pap. RM-212. Rocky Mountain For. Range Exp. Stn., F o r t Collins, CO, 8 pp. United States Geological Survey, 1977. Miscellaneous investigations series Boulder--Fort Collins--Greeley Area, Colorado. Map 1-855-G. Williams, R.S., 1976. Soil water change in lodgepole pine. M.S. Thesis, Colorado State Univ., F o r t Collins, CO, 89 pp.