Great Basin Naturalist Great Basin Naturalist

Volume 54 Number 2 Article 11

4-29-1994

Needle biomass equations for singleleaf pinyon on the Virginia Needle biomass equations for singleleaf pinyon on the Virginia

Range, Nevada Range, Nevada

T. R. De Rocher University of Nevada, Reno

R. J. Tausch U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Reno, Nevada

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Recommended Citation Recommended Citation De Rocher, T. R. and Tausch, R. J. (1994) "Needle biomass equations for singleleaf pinyon on the Virginia Range, Nevada," Great Basin Naturalist: Vol. 54 : No. 2 , Article 11. Available at: https://scholarsarchive.byu.edu/gbn/vol54/iss2/11

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Great &sin Naturalist 54(2), ©1994, pp. 117-181

NEEDLE BIOMASS EQUATIONS FOR SINGLELEAF PINYONON THE VIRGINIA RANGE, NEVADA

T. R. De Rocher' and R. J. Tausch2

A8~'TRAL,..-Foliar biomass of singleleaf pinyon (PintL.' monophyUa Torr. & Frem.) was estimated on the VirginiaMountains, Nevada, based on the easily measured dimensions of crown volume and sapwood area. Leaf biomass estima­tion techniques used in other studies of pinyon where total leaf bioma5s was collected were supported, Both sapwoodarea (cm2) and crown volume, calculated as one-half of an e)Jipsoid (m3), were found to be significantly related to totaldry weight needle mass (g). Best predictive equations for crown volume were obtained with nonlinear regression analy­sis. A previously rep0l1ed two-part relationship based on tree size for predicting needle biomass with sapwood area wassupported. Foliar biomass of singleleaf pinyon can be accunltely estimated with a minimum of 10 sapwood cores.

Key words, singldeafpinyon, fa"age biomass, sapwood =a, bWma8s prediclUm.

In recent years land managers trying to ful­fill the goal of multiple use have needed toascertain the potential value and use of pin­yon-juniper woodlands extending over 17 mil­lion ha across the Rock)' Mountain and GreatBasin regions (Chojnacky 1986). Single1eafpinyon and Utah juniper \]uniperw; osteosper­rna Little) woodlands cover more than 6 mil­lion ha in the Great Basin alone (Tausch andTueller 1990). This forest type has historicallysupplied fuelwood, charcoal, nuts, fence posts,and poles (Fogg 1966), especially duringheightened mining activity in the late 1800s(Budy and Young 1979), Pinyon-juniper forestsalso provide essential areas for wildlife habitat(McCulloch 1969, Short and McCulloch 1977,Balda and Masters 1980). Balancing out thesebenefits is the nearly complete loss of forageand potential increased soil erosion resultingfrom the establishment of this species(Doughty 1987).

Estimates of biomass are essential to moststudies of plant community competition, suc­cession, and resource allocation includingstudies of pinyon-juniper woodlands. Totalfoliar biomass, or phytomass, can he vital toassessment of plant water-use efficiency (Longet a!. 1981, Waring 1983), nutrient cycling(Waring and Running 1978), soil moisture con­ditions (Grier and Running 1977), the hydro­logic environment (Nemani and Running1989), and competitive interactions (Tausch andTueller 1990). The amount of foliage biomass

on a tree is strongly reI.,ted to the area of con­ducting tissue transporting water and nutrientsto these tissues. This relationship has beenfound for many conifers (Grier and Waring1974, KauJinann and Troendle 1981, Marchand1984, Miller et a!. 1987), including pinyon(Tausch and Tueller 1989). Survival of trees inarid environments demands efficiency, andproduction of excess xylem and associatedsupporting tissue would waste preciousresources. Alternately, insufficient develop­ment of conducting tissue would dehydrateand starve a tree, Also, as a tree grows, its massand the spatial volume it inhahits expand in anonlinear fashion (Tausch and Tueller 1988).

Many studies have shown close relation­ships between whole-tree phytomass and easi­ly measured plant dimensions (Budy et a!.1979, Miller et aJ. 1981, Cochran 1982, Hatch.ell et al. 1985, Tausch and Tueller 1989, 1990).Measuring needle mass of entire siogleleafpinyon (Pinus motwphyUa Torr. & Frem.) treesthrough harvest can be inefficient and expen­sive (Meeuwig and Budy 1979), especially onlarge trees in remote areas. The ahility toaccurately estimate phytomass based on sim­ple allometric measurements can greatly easethe process. Gross-sectional sapwood areas forpinyon pine can be estimated with a highdegree of accuracy using increment cores andtrunk diameter measurements (Whitehead1978, Tausch 1980). Equations generated bythese procedures can apparently vary for this

lDt:p..rtm<:llt.i Range. Wildlife and Forestry, Univcmty orNevada-Heno, Ileoo, Nevada 89512.2U.S. Departmenl orAgncultun;, Furest Service, Intermounlain Foresl and R:mge E~perim~nt SIllI,,,n, ReDO, Nevada 89512.

177

178 GREAT BASIN NATURALIST [Volume 54

species depending on location in the GreatBasin (Tausch and Tueller 1988). Develop­ment and refinement of techniques to acquirereliable mensuralional data will only enhancethe further study, understanding, and propermanagement of pinyon-juniper woodlands.The objective of this study was to applyevolved methods of estimating whole-treeneedle biomass to singleleaf pinyon on a thirdGreat Basin site.

METHODS

Study Area

Research was conducted on USDI Bureauof Land Management pinyon-juniper wood­lands 32 km SE of Reno, Nevada (39" 17'30"N,119"42'30"W). The study site lies at 1963 melevation in a near level east-west saddleformed between a basalt plug and tbe west­facing slope of the Virginia Mountains. Soilsare 0-10 em deep and poorly developed fromdecomposed Cretaceous granodiorite andPliocene-Pleistocene volcanics. This areareceives approximately 336 mm of precipita­tion annually (Desert Research Institute1991), mostly as snow. Some Utah juniperoccurs in the area, but singleleaf pinyon is thedominant tree species.

Field Techniques

Tree selection procedures were based onthe five maturity classes described in Black­burn and Tueller (1970). Ten trees from eachmaturity class were randomly selected forpotential measurement within relationships ofaccessibility associated with placing potome­ters for tree water-use studies (De Rocher1992). Five trees from each maturity classwere randomly selected for a total of 25 treesto be measured and harvested. Total treeheight (em), canopy height (em), maximumcanopy diameter and canopy diameter per­pendicular to the maximum (em), and trunkcircumference (em) at approximately 15 emabove the root crown were determined for alltrees.

Past studies have shown that including esti­mates of canopy density improves predictabil­ity of needle biomass (Miller et al. 1981), butvisual estimates used were not easily compa­rable between studies. Canopy needle bio­mass density decreases as voids develop with-

in the canopy with increasing age. Error canbe introduced in needle biomass estimationbased on crown volume calculated from sim­ple canopy dimensions without an estimate ofthe voided space. This study incorporated agrid method of determining average canopydensity similar to the method proposed byBelanger and Anderson (1989). The procedureinvolved viewing each sampled tree canopythrough a 6 X 50-em Plexiglas sheet that hada 3-cm2 grid transposed onto it. A perspectivewas chosen that approximated an average ofcanopy fullness that was sufficiently distant tovisually contain the entire tree height withinthe vertically held grid when viewed at arn:lslength. First, the left upright edge of the gridwas then aligned with the trunk and thecanopy heigbt centered within the grid. Thisplaced the grid over the rigbt side of the tree.Within the grid a smooth canopy border wasimagined as if a ribbon were stretched fromthe top along the outside edge of the canopyto the base. Next, grid squares more than half­way within this perimeter line were countedfor the maximum area covered by the canopy.Last, squares within the perimeter covered by>50% foliage (versus open space, trunk, orbranches) were counted. Dividing the numberof foliage-covered squares by the total numberof squares within the canopy perimeter deter­mined a ratio of relative canopy density. Thisprocedure was then repeated by placing thegrid over the left side of the tree canopy.

Following crown measurements and esti­mation of canopy density, all green foliage washarvested by cutling off branches and placingthem in feed bags. After being air-dried, theneedles were dried at 80' C for 24 h to achieveconsistency and then weighed. A trnnk crosssection was also cut approximately 15 cmabove the root crown. These cross sectionswere measured for sapwood area (cm2) using apaper trace, which was cut into small piecesand run through a model LI-3100 leaf areameter (LI-COR, Inc., Lincoln, Nebraska).

Analysis Techniques

Longest crown diameter, diameter peI-pen­dicular to it, and crown height for each treewere used to compute the crown volume (m3)based on the formula for one-half of an ellip­soid (Tausch 1980, Beyer 1984). Crown vol­ume was also adjusted for canopy density by

1994J SINGLELEAF PINYON BIOMASS EQUATIONS 179

TABLE 1. Singleleafpi.nyon fOllar mass prediction models using sapwood area and crown volume approximated as one­halfofan ellipse.

Relation

Needle vs. sapwoodMass (g) Area (em')(All maturity classes)

Needle vs. sapwoodMass (g) Area (em')(Trees <4Ocm2 sapwood)

Needle ¥s. sapwoodMass (g) Area (em')(Trees >4Dcm' sapwood)

Equation

linear regression

Y ~ 265.3 + I22.IX

Y ~ -142.5 + 106.4X

Y ~ 3234.7 + U7.4X

R'

.m

.947

.971

Standard. elTOr

ofestimate

4059

227

6822

Log transformed Jinear regression

Needle vs. sapwood lnY ~ 35.31 + I.24lnX .782 16,886Mass (g) Area (cm2)(.All maturity classes)

Needle vs. sapwood Iny = 33.9 + I.371nX .973 194Mass (g) Area (em!)(Trees <4Ocm2 sapwood)

Needle vs. sapwood InY = 186.5 + O.94lnX .970 6453Mass (g) Area (em!)(Trees >4Ocm2 sapwood)

Nonlinear regression

Needle \IS. sapwood Y = 181.4Xo.94 .989 3847Mass(g) Area (em!)(All maturity classes)

Needle vs. sapwood Y = 20.1X1.497 .995 53Mass (g) Area (em!)(frees <4Ocm2sapwood)

Needle vs. sapwood Y~ 194.4x<>·929 .970 6444Mass (g) Area (em')(Trees > 4Ocm2sapwood)

Needle vs. ellipsoid Y ~ 2.:J4XU.58 .fTl6 5643Mass (g) Volume (m3)

(All maturity classes)

Needle vs. ellipsoid Y = 1.36Xo.62 .977 5459Mass (g) Volume (m3)

(Adjusted for canopy density)(All maturity classes)

multiplying the calculated volume by thecanopy density ratio. Two types of regressionanalyses were used to evaluate relationshipsbetween oven-dry foliage weight and sapwoodarea and crown volume. Prediction equationsutilizing any fonn of data transformation 10linearize the data for least-squares analysiswere repeated using nonlinear regression with

the original untransfonned data (Tausch andTueller 1988), and the best fit results arereported. Comparisons among regressionresults were made using highest coefficientsof determination (R2) and lowest standarderror of the estimate values, to a level of sig­nificance of p < .01 for the least-squaresanalyses results.

180 GREAT BASIN NATURALIST [Volume 54

RESULTS AND DISCUSSION

The amount of foliage supported per unitarea of conducting tissue ranged from 37.4 gcm-2 for the smallest tree to 157.2 g cm-2 forone of the largest, and averaged 88.8 g cm-2

for all sampled trees. U sing a nonlinearregression technique that iteratively approxi­mates optimal fit without data transformationprovided as tight a fit to the full data set as lin­ear regression utilizing log-transformed datain relating dried needle mass and sapwoodarea. Reapplication of untransformed data tolinear models decreased the relationship(Table 1). For trees with <40 cm2 sapwoodarea (maturity classes 1-3), this relationshipwas definitely nonlinear, as was observed byTausch and Tueller (1989). The slope of thelinear regression line for these western Neva­da data is nearly identical to the slope forpinyon from southwestern Utah (Tausch1980), suggesting a potential singular relation­ship across the Great Basin. For both thesedata and the Tausch 1980 southwestern Utahdata, the entire foliage was harvested fromeach tree. A similar relationship was not foundbetween data from Tausch 1980 and Tauschand Tueller 1989. Tausch and Tueller (1989)utilized a foliage subsampling technique toestimate total needle biomass, which mayhave underestimated the needle biomass tosapwood area ratio.

Correlation between needle biomass andthe calculated elliptical crown volume wasalso significant by nonlinear regression (Table1). When adjusted for percent canopy density,the linear elliptical volume relationship wasimproved (R2 = .98), and the standard error ofthe estimate simultaneously was reduced from5643.1 to 5458.9.

CONCLUSIONS

Prediction of foliar mass using sapwoodarea of singleleaf pinyon on the VirginiaMountains, Nevada, was equivalent in preci­sion to previously reported results for thisspecies conducted in southwestern Utah(Tausch 1980). Elliptical crown volume calcu­lated from canopy widths and crown heightagain proved to be a significant predictor ofdry weight phytomass. Adding an estimate forvariations in canopy density further improvedthe relationships. As previously reported

(Tausch and Tueller 1989), application of non­linear regression analysis produced the best fitbetween phytomass and all tree dimensions,as reflected by increased coefHcient of deter­mination values and reduced standard errorsof the estimate over other regression methods,

Estimates of singleleaf pinyon phytomassfor hydrological and ecological studies of pin­yon-juniper woodlands can be most accuratelyobtained from a minimum of 10 sapwood areameasurements, Canopy dimensions and anassessment of foliage density can also be usedto reliably estimate whole-tree phytomass.This study, conducted on the western edge ofthe Great Basin, achieved needle biomassregressions based on sapwood area that werenearly identical to those from work pelformedin southwestern Utah. Future research shouldconcentrate on comparing numerous isolatedstudies across the Great Basin of whole-treefoliar mass harvest so that a regional biomassequation may be developed.

ACKNOWLEDGMENTS

This research was supported in part by fundsprovided by the Intermountain ResearchStation, Forest Service, U.S. Department ofAgriculture.

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Received 24 March 1993Accepted 4 November 1993