High-Elevation Forest Soils of the Southern Appalachians: I. Distributionof Parent Materials and Soil-Landscape Relationships

S. B. Feldman,* L. W. Zelazny, and J. C. Baker

ABSTRACTBecause of geomorphic instability imparted both by present-day

slope processes and past periglacial activity, soils in the high-elevation spruce-fir (Picea rubens Ssag.-Abies fraseri [Pursh.] Poir)zone of the southern Appalachians have similar morphological fea-tures that make it difficult to assess their variability on the land-scape, despite widespread differences in parent material and localgeology. We used multiple discriminant analysis to (i) determinewhether soils from three extensive areas of southern Appalachianspruce-fir forests could be separated by physical, morphological,chemical, and mineralogical properties, (ii) determine the relativeeffectiveness of these properties as differentiae, and (iii) assess thecontribution of mineral weathering in offsetting potential base-cationdepletion induced by strong-acid anion loading associated with at-mospheric deposition. A total of 35 pedons at elevations >1450 mwere characterized on the high mountain peaks of eastern Tennessee,western North Carolina, and southwestern Virginia. Physical andmorphological properties used to separate and field classify thesesoils were not significantly different between study areas. Soil chem-ical and mineralogical properties inherited from different parent ma-terials, however, were sufficiently different between study areas toresult in the clear separation of soils into distinct groups with >95%classification accuracy. Soils formed from siliceous metavolcanic

Dep. of Crop and Soil Environmental Sciences, Virginia PolytechnicInst, and State Univ., Blacksburg, VA 24061. Received 12 Nov.1990. Corresponding author.Published in Soil Sci. Soc. Am. J. 55:1629-1637 (1991).

parent materials were dominated by K-feldspar and quartz in boththe sand and silt fractions. Soils with parent materials derived frommore high-grade metamorphic rocks were less quartzitic, had pla-gioclase as the dominant feldspar, and had high contents of expan-sible 2:1 phyllosilicates. Slope processes involved in the transportof solutes and sediments resulted in sorting of surficial materials,higher pH, and greater amounts of silt-size mica and kaolinite indepositional areas.

SUBALPINE SPRUCE-FIR FORESTS of the southern Ap-palachians are biologically unique, geographically

restricted remnants of a boreal forest community thatwas widespread throughout much of eastern NorthAmerica during the end of Pleistocene glaciation (Del-court and Delcourt, 1984,1986). Today, spruce-fir for-ests in the southern Appalachians are isolated fromrelated northern vegetation and are confined to ele-vations >1450 m in the higher mountains of easternTennessee, western North Carolina, and southwesternVirginia.

Based on field morphology, the majority of soils inthese areas are classified as loamy-skeletal, mixed, frig-Abbreviations: CEC, cation-exchange capacity; ECEC, effectiveCEC; DCB, dithjonite-citrate-bicarbonate extractant; XRD, x-raydiffraction; DSC, differential scanning calorimetry; ANOVA, analy-sis of variance; MR, Mt. Rogers study area; BM, Black Mountainsstudy area; GSM, Great Smoky Mountains study area.

1630 SOIL SCI. SOC. AM. J., VOL. 55, NOVEMBER-DECEMBER

id Typic or Pachic Haplumbrepts (Table 1) on south-or north-facing slopes, respectively (Feldman, 1989).Fixed morphology alone, however, is insufficienteither to accurately classify these soils, many of whichhave weakly expressed spodic horizons, or to predictdifferences in soil chemistry or mineralogy that maybe important to soil management in these sensitiveecosystems.

Rocks from areas with diverse bedrock lithologiesthroughout the region have converged during pedo-genesis to form similar soils that exist in a uniformstage of morphological development and apparentchemical alteration. Most soils have black (10YR 2/1) or very dark brown (10YR 2/2) loamy, umbric epi-pedons overlying brown (7. SYR 4/6) or dark yellowishbrown (10YR 4/4-4/6) gravelly, cambic horizons(Feldman, 1989). Soils with distinct E horizons arevery rare and are limited to areas with coarse-grainedfeldspathic metaquartzite parent material (Lietzke andMcGuire, 1987; Feldman et al., 1991). Sapric horizonsof up to 10 cm commonly overlie the mineral soil anda layer of dense subsurface gravel limits solum thick-ness to 50 to 90 cm in most pedons.

Uniform soil morphology has resulted from thedominance of climate in pedogenesis through its effectin promoting widespread geomorphic instability atthese high elevations (Smith, 1949; Clark, 1968; Rich-ter, 1973; Connors, 1986; Clark and Ciolkosz, 1988).Present-day processes of frost churning, soil creep, andwindthrow and late Pleistocene-early Holocene peri-glacial processes of gelifluction and frost creep havedramatically increased the rate of slope movement andsediment transport, particularly in areas of stronglydipping phyllites and slates. These processes havetransformed residuum into mixed congeliturbate orcongelifractate parent materials, which mask the geo-logic influence on gross soil morphology. As a resultof these climatically driven slope processes, most high-elevation soils of the southern Appalachians exhibittime and often lithologic discontinuities, with youngdeposits overlying older colluvium or highly weath-ered residuum on sideslopes. Horizonation has beenrestricted in these soils by weathering in a cold envi-ronment on unstable slopes, resulting in a relativelyshort period of profile development, limited amountsof chemical alteration, lateral subsurface movement ofsolutes, and ultimately weak expression of morpho-logical characteristics.

Because of the severe climate, remoteness, difficultterrain, and marginal economic value of land re-sources, few detailed studies of the nature and origin

Table 1. Field classification of soils from the Great Smoky Moun-tains (GSM), the Black Mountains (BM), and Mt. Rogers (MR)used in the analysis.

LocationClassificationTypic or Pachic HaplumbreptsUmbric DystrochreptsLithic HaplumbreptsTypic or Lithic HaplorthodsfTotal

GSM45

312

BM2

19

12

MR10

111

Total1651

1335

of these soils have been made and the relatively littleamount of soil survey work that has previously beenconducted in these areas has been done largely on areconnaissance basis (E'aniels et al., 1984, 1987;Springer, 1984). As a result, soil series concepts inthese areas traditionally have been poorly defined,making correct classification of many pedons belowthe family level impossible. Although soil survey ac-tivities in the southern mountains have increased inrecent years, few modern surveys to date have ade-quately defined mutually exclusive classes for distin-guishing soils derived from contrasting parentmaterials in the closed boreal forests at the highestelevations of the southern Appalachians.

Moreover, a key mechanism of forest response tostrong-acid anion loading associated with atmosphericdeposition has been reported to be accelerated leachingof basic nutrients from soils and potential increasedAl mobilization (Lucier and Haines, 1990). Soil min-eral weathering rates and SO^-adsorption propertieshave been implicated as important sensitivity criteriafor assessing base-cation depletion (Reuss and John-son, 1986). Knowledge of the type, amount, and geo-graphical distribution of weatherable soil minerals andsoil chemical properties is; therefore critical to under-standing the geochemical factors that may influenceacidification of soils and surface waters in the southernAppalachian spruce-fir ecosystem. Our objectives inthis study were to: (i) use multivariate statistical analy-sis to evaluate morphological, physical, chemical, andmineralogical differences between soils from each ofthree geologically distinct areas, (ii) determine the rel-ative effectiveness of these properties in differentiatingsoils, and (iii) describe the distribution of weatherablesoil minerals in relation to parent material at each site.

MATERIALS AND METHODSStudy Area

Soils were studied in each of three extensive areas of south-ern Appalachian spruce-fir forests: the Mt. Rogers NationalRecreation Area (Virginia), the Great Smoky Mountains Na-tional Park (Tennessee and North Carolina), and the BlackMountains (North Carolina) (Fig. 1). The Mt. Rogers studyarea is underlain by thick masses of upper Precambrian me-tavolcanic and metasedimen.tary rocks ranging in compo-

t These pedons meet the chemical criteria for a spodic horizon but lack distincteluvial/illuvial features.

Fig. 1. General location map of the Mt. Rogers (MR), Black Moun-tains (BM), and Great Smoky Mountains (GSM) study areas.

FELDMAN ET AL.: HIGH-ELEVATION FOREST SOILS OF THE SOUTHERN APPALACHIANS: I. 1631

sition from rhyplite to a complex association of interbeddedand interfingering arkosic sandstone, rhythmically layeredargillite, pebbly mudstone, tillite, and minor amounts of bas-alt (Rankin, 1967, 1970). Because rhyolite dominates in=50% of the area, soils tend to be greatly influenced by thepresence of fine-grained quartz and alkali feldspars. Mostrocks of the Mt. Rogers area have been influenced by at leastone episode of Paleozoic metamorphism and, as a result,small outcroppings of greenschist provide a source of chloriteand other ferromagnesian minerals in localized areas.

Rocks of the Great Smoky Mountains consist of a seriesof Precambrian clastic metasediments that intertongue ex-tensively in the high-elevation spruce-fir zone. The Thun-derhead Sandstone is a series of massive, thick-bedded rockscomposed largely of quartz and K-feldspars with lesseramounts of mica. The Anakeesta Formation consists of dark,silty and argillaceous, highly fractured sediments metamor-phosed to slate, phyllite, and schist. These rocks have finetextures and are composed largely of quartz, sodic plagio-clase, K-feldspars, muscovite, biotite, chlorite, and smallamounts of both Fe sulfides and free C (King et al., 1968).

Higher elevations of the Black Mountains are underlainby coarse-grained metamorphic rocks including mainlyquartz-rich mica gneisses and schists of Precambrian age(Stuckey and Steel, 1953; Brobst, 1962). These rocks containlarge amounts of biotite, muscovite, sodic-calcic plagioclase,quartz, and a wide range of accessory minerals includinggarnet, epidote, staurolite, and kyanite, indicating a high de-gree of regional metamorphism.

The study area has a perudic moisture regime, with coolsummers and cold winters. Potential evapotranspirationrarely exceeds precipitation, which is distributed uniformlythroughout the year with a mean annual value of >2000mm (Shanks, 1954). Local intense thunderstorms causemuch monthly variation in these averages. Snowfall and can-opy drip from fog also significantly increase moisture, asmost of the higher summits are enshrouded in clouds =40%of the time (Smallshaw, 1953). Monthly mean temperaturesrange from about — 2 °C in January to 17 °C in July, witha mean annual value of 8 °C and an average frost-free periodof 133 d (Shanks, 1954). Soils in all study areas have frigidtemperature regimes (Lietzke and McGuire, 1987).

Field and Laboratory MethodsIn order to adequately represent soil properties across the

total landscape at each of the three study areas, samplingsites were separated into two groups based on topographicposition and exposure to prevailing northwesterly and wes-terly winds. Duplicate soil pits were excavated and sampledat both exposed and protected sites on each of the threedominant landforms occurring within the southern Appa-lachian Highlands: (i) ridges and convex upper shoulderslopes, (ii) mid-backslopes, and (iii) lower backslope/covepositions. Sampling sites were randomly selected from rep-resentative areas that adequately characterized the dominantgeomorphic and vegetational features of the landform basedon information generated from transects, trail-cut descrip-tions, and observation of landscape surface features. All siteshad a minimum of 50% spruce-fir vegetation in the over-story. Because only one suitable site was found to representprotected coves at the MR study area, one observation wasomitted from the analysis. Unequal sample size, however,does not affect the outcome of multiple discriminant analy-sis, the statistical technique employed (Klecka, 1980). Ateach sampling site, soil was described and classified accord-ing to standard procedures (Guthrie and Witty, 1982; SoilSurvey Staff, 1990).

Samples of genetic soil horizons were air dried, sieved toremove coarse fragments (>2 mm), and thoroughly mixed.Standard methods (Soil Survey Staff, 1984) were used in the

following analyses: Particle-size distribution was determinedby pipette analysis; organic C was estimated by dichromatetitration; exchangeable Al was measured by extraction in 1M KC1; CEC was calculated as the sum of neutral 1 MNH4OAc (pH 7.0) exchangeable bases plus total soil acidityas determined by the BaCl2-TEA (pH 8.2) method; organ-ically bound Fe and Al were determined by extraction insodium pyrophosphate. The ECEC was calculated as the sumof exchangeable bases plus KCl-extractable acidity. Reduc-tant-soluble Fe and Al were determined by the DCB methodof Mehra and Jackson (1960). Soil pH was measured in thesupernatant portion of a 1:1 soil/distilled water suspensionafter a 1-h equilibration period.

Pretreatments for mineralogical analysis included removalof organic matter with 30% (w/w) H2O2 buffered at pH 5with 1 MNaOAc (Kunze, 1965). All samples were treatedwith DCB to remove Fe-oxide coatings. Sand was separatedby wet sieving, dried, and ground for 5 min in a reciprocatingball mill. Silt and clay fractions were separated by centrif-ugation and decantation using 1 M Na2CO3 (pH 9.5) as adispersant. X-ray diffraction was used to determine clay-mineral suites present by analyzing oriented Mg-saturated,glycerol-solvated samples with no heat treatment and after4 h of heating at 110 °C, and also from K-saturated sampleswith no heat treatment and after heating for 4 h at 110, 300,and 550 °C. Semioriented smear mounts of the silt (0.002-0.05 mm) and ground sand (0.05-2.0 mm) fractions wereprepared by depositing =0.5 g onto a glass slide using dis-tilled H2O and a spatula. Samples were scanned at a fixedcounting time of 4s at 0.075° 26 step-' with a Diano XRD8300 AD x-ray diffractometer (Diano Corp., Woburn, MA)using CuKa radiation (20 mA, 40 kV) and a graphite crystalmonochrometer. Mineral quantities were entered into thestatistical analysis as the integrated intensities of their re-spective XRD peaks with the exception of kaolinite andgibbsite, which were recorded as percent by weight. Subsam-ples of the K-saturated clay fractions were also analyzed byDSC using a DuPont 1090 Thermal Analyzer (TA Instru-ments, New Castle, DE). Samples were heated from 50 to625 °C in a N2 atmosphere at a rate of 20 °C min-1. Kaoliniteand gibbsite were quantified by mass-equivalent calibrationof endothermic peak areas using poorly crystalline Georgiakaolinite and Reynolds synthetic gibbsite as standards.

Statistical AnalysisSoil and site properties that effectively separated the ob-

servations into areas of different bedrock lithology were de-termined by multiple discriminant analysis. The principalobjectives of this procedure were to: (i) assign objects orobservations into mutually exclusive groups based on a setof independent variables, (ii) determine whether group cen-troids are statistically different, and (iii) determine contri-butions of predictor variables to discrimination of groups.The approach finds the best linear combination of variablesthat will discriminate between a priori groups by maximizingbetween-group variance relative to within-group variance(Dillon and Goldstein, 1984). The linear combination for adiscriminant function:

Z = [1]where nx — discriminant coefficients and Xx = independentvariables, transforms the original m measurements on anindividual observation into a single discriminant score, Z.Group centroids represent the average discriminant scorefor all individuals in a group. The hypothesis of no signifi-cant difference between group centroids was tested by Wilks'A statistic and the degree of relatedness between groups andcalculated functions was assessed by canonical correlationcoefficients (Klecka, 1980). The effectiveness of the discrim-

1632 SOIL SCI. SOC. AM. J., VOL. 55, NOVEMBER-DECEMBER

Table 2. Definitions of symbols and units of measurement used inthe analyses.

Symbol Definition and unit of measurementSAND 0.05- to 2.0-mm fraction (g kg-' soil)SILT 0.002- to O.OS-mm fraction (g kg-' soil)CLAY <0.002-mm fraction (g kg-' soil)SOLUM Solum thickness (cm)EPI Epipedon thickness (cm)OA Oa horizon thickness (cm)HIT Hue of the matrixVA Value of the matrixCH Chroma of the matrixpH pH (-log [HI)H* BaCl2-TEA, pH 8.2, exchangeable acidity (cmoU kg"1)FE Dithionite-citrate-bicarbonate-extractable Fe (% as free

Fe)SPODIC Meets chemical criteria for spodic horizonfOC Acid-dichromate-digestible organic C (g kg"1)SBAS 2 NH4OAc, pH 7, extractable Ca2*, Mg2*, and K* (crnoU

kg-)CEC Cation-exchange capacity as SBAS + H* (cmolc kg'1

soil)ECEC Cation-exchange capacity as SBAS + KCl-extractable

AP* (cmo^ kg-')BSS Base saturation of the CEC (%)BSE Base saturation of the ECEC (%)ALSAT KCl-extractable A13» (% of CEC)KSAT NH4OAc, pH 7, extractable K* (% of CEC)MGSAT NH4OAc, pH 7, extractable Mg2* (% of CEQCASAT NH4OAc, pH 7, extractable Ca2* (% of CEC)CQTZ Clay-fraction quartz (intensity 4.24 A x-ray diffraction

[XRD] peak)CGIBB Clay-fraction gibbsite (% determined by differential

scanning calorimetry [DSC])CKAOL Clay-fraction kaolinite (% determined by DSC)CMICA Clay-fraction mica (intensity 10.0 A XRD peak)C21 Clay-fraction expansible 2:1 phyllosilicates (2 intensity

12-14 A XRD peaks)SIQTZ Silt-fraction quartz (intensity 4.24 A XRD peak)SIMICA Silt-fraction mica (intensity 10.0 A XRD peak)SIKAOL Silt-fraction kaolinite (% determined by DSC)SIGIBB Silt-fraction gibbsite (% determined by DSC)SIPLAG Silt-fraction plagioclase (intensity 3.18 A XRD peak)SIKSPAR Silt-fraction K-feldspar (intensity 3.24 A XRD peak)SI21 Silt-fraction expansible 2:1 phyllosilicates (2 intensity

12-14 A XRD peaks)SIINT Silt-fraction interstratified phyllosilicates (2 intensity

> 14 A XRD peaks)SQTZ Sand-fraction quartz (intensity 4.24 A XRD peak)SMICA Sand-fraction mica (intensity 10.0 A XRD peak)SPLAG Sand-fraction plagioclase (intensity 3.18 A XRD peak)SKSPAR Sand-fraction K-feldspar (intensity 3.24 A XRD peak)S21 Sand-fraction expansible phyllosilicates (2 intensity 12-___________14 A XRD peaks)________________t The ratio of pyrophosphate (Py)-extractable Fe plus Al to percentage of

clay >0.2 and the ratio of Py-Fe plus Py-Al to DCB-Fe plus DCB-A1 >0.5.

inant functions in classifying observations into mutually ex-clusive groups was determined by using a split-sample cross-validation technique, which reduces upward bias introducedby classifying the same individuals used in computing thefunctions (Klecka, 1980; Dillon and Goldstein, 1984). In thesplit-sample method, original observations from each groupwere randomly divided into two subsamples: an analysissample to derive the discriminant function, and a hold-outsample used to classify individuals.

Variables initially selected for the analysis included phys-ical, chemical, mineralogical, and morphological soil prop-erties (Table 2). In order to compare sampling units, datagenerated from genetic soil horizons of each pedon werecoded into three layers, based on the following criteria: Layer1 consisted of data obtained from the surface mineral ho-rizon; Layer 2 was composed of the weighted average of dataobtained from genetic horizons comprising the uppermostdiagnostic subsurface horizon (e.g., Bwl plus Bw2, etc., ho-rizons comprising a cambic diagnostic horizon); and Layer3 consisted of data from the deepest horizon sampled, usu-

ally restricted by a dense gravelly horizon at a depth of 50to 90 cm. Multiple discriminant analysis was performed oneach respective layer to examine differences between studyareas. Separate analyses were also performed using exposureclass and landfprm, respectively, as grouping variables. Fi-nally, we examined the nature of differences between layersacross all observations.

Each analysis included a preliminary data screening toreduce the number of variables under consideration and toassess the statistical distribution of the selected variables.Computation of a Shapiro-Wilk W statistic (Afifi and Clark,1984) and interpretation of histograms and normal proba-bility plots were used to determine that a logarithmic trans-formation of the data was p referred over no transformationto achieve marginal normali ty and variance equality for eachof the observed variables. All variables were transformedusing the log transformation with the exception of pH, sinceit already represented the log of a concentration and wasapproximately normally distributed. Analysis of variancewas used to identify soil and site properties that did not differsignificantly (a = 0.05[*])between at least two groups. Var-iables that had similar distributions were considered to con-tribute very little to the overall discrimination betweengroups and were therefore eliminated as differentiae. A step-wise selection procedure was then used to determine the bestsubset of remaining variables for derivation of discriminantfunctions. Successive stepwise additions or deletions of var-iables from the system were based on their effect on thediscrimination achieved by variables already entered, takinga' — 0.15 as the fixed significance level associated with thepartial F statistic for entry (Costanza and Afifi, 1979). Sta-tistical analyses were performed using programs of the Sta-tistical Analysis System (SAS Institute, 1985) and theBiomedical Statistical System (Dixon, 1983).

RESULTS AND DISCUSSIONModel Derivation and Validation

The independent variables retained by the ANOVAprocedure for each analysis included soil propertiesthat differed significantly (P < 0.05) between studyareas (Table 3). Of these variables, a smaller subsetwas retained by the forward stepwise selection pro-cedure, which further eliminated variables not usefulin discriminating between groups. The remaining var-iables included only soil mineralogical and chemicalproperties, which, together with summary statistics,are presented in Table 4. Because significant differ-ences in soil physical and morphological propertieswere not observed, they were eliminated in the earlystages of initial data screening. Similarity in theseproperties, including epipedon and solum thickness,soil color and texture, pH, and DCB-extractable Fecontent, suggests that soils in all study areas developedunder uniform weathering conditions in relativelyequal periods of time, resulting in conformity in degreeof profile development despite notable differences inparent material.

The canonical functions calculated in each of theanalyses were statistically significant, as measured bytransformation of Wilks' A to a multivariate F statisticfor testing group differences (Table 5). Canonical cor-relation coefficients ranged fromr* = 0.85 to 0.99 ineach analysis, indicating that the calculated functionsare indeed valid predictors of observed group differ-ences. The model was further validated by comparingclassification accuracy relative to chance. The pro-

FELDMAN ET AL.: HIGH-ELEVATION FOREST SOILS OF THE SOUTHERN APPALACHIANS: I. 1633

Table 3. Variables retained after analysis of variance and probabilitylevels associated with the hypothesis of no significant differencebetween study areas for Layers 1 to 3.

PropertytSANDSILTCLAYSOLUMEPIOAHUVACHpHH*FESPODICOCSBASCECECECBSSBSEALSATKSATMGSATCASATCQTZCGIBBCKAOLCMICAC21SIQTZSIMICASIKAOLSIGIBBSIPLAGSIKSPARSI21SIINTSQTZSMICASPLAGSKSPARS21

Layer 1

6.0001"*0.0001*"0.44980.08590.47760.0025**0.99990.40520.17700.21280.0120*0.11290.0005***0.0060**0.0232*0.0134**0.33730.0001***0.30620.0029**0.09620.0092"0.0001*"0.0184*0.0161*0.10620.0030"0.06520.22530.0001***0.0010***0.46950.0001*"0.0001*"0.0001*"0.0024**0.0001*"0.0001***0.0001***0.0001"*0.0001***

P> F

Layer 2

0.0001*"0.0001*"0.0008***0.08590.47760.0025**0.30920.21740.26700.95450.0010***0.54520.0005***0.0007***0.0003***0.0011"0.41810.0003***0.38980.0003***0.10430.0126**0.0001"*0.08440.0326*0.0013"0.0440*0.0033"0.16250.0001***0.0161**0.34610.0001***0.0001***0.0001***0.0130"0.0001***0.0001"*0.0001"*0.0001*"0.0097"

Layer 30.0002***0.0001***0.0484*0.08590.47760.0025**0.0005***0.46430.0326*0.57380.0027**0.33510.0005*"0.0151**0.13260.0028**0.73560.0001***0.05090.0027**0.20480.14960.0001***0.06230.0020**0.0006***0.06850.0001***0.0085**0.0001"*0.0026**0.23570.0001***0.0001*"0.0001***0.14910.0001***0.0001***0.0001***0.0001***0.0094**

*,**,*" Significant at the P = 0.05, 0.01, and 0.001 levels, respectively.t See Table 2 for definitions of symbols used to designate soil properties.

pprtional chance criterion, or the percentage of indi-viduals that would be correctly classified by chance,weighted for unequal sample size, is:

cro = p\ [2]where Cpro = proportional chance criterion and px —proportion of individuals in group .x. For analysis ofLayers 1 to 3, the proportional chance criterion of eachanalysis is:

Cpro = (0.34)2 + (0.34)2 + (0.31)2 = 0.35. [3]Results of the classification procedure indicate that the97 to 100% accuracy achieved by the model is sub-stantially higher than the 35% accuracy attributable tochance alone (Table 6).

InterpretationResults for analysis of Layers 1 to 3 show clear sep-

aration of groups, illustrating distinct differences be-tween soils of each study area (Fig. 2). The firstdiscriminant function, which accounts for 76 to 89%of the total discriminating power in each analysis (Ta-ble 5), is the primary source of difference between soils

Table 4. Variables selected by the forward stepwise discriminantanalysis for each layer in order of addition to the model, summarystatistics, and partial F statistics for variable entry into the finalmodel for soils from the Great Smoky Mountains (GSM), theBlack Mountains, (BM), and Mt. Rogers (MR).

GSM

Propertyt

SIMICASQTZBSSSIPLAGSPLAGSI21

SIMICASQTZBSSSPLAGSI21MGSATCASATALSATCGIBBSKSPAR

SQTZSPLAGALSATSKSPARSIQTZ

/>> Ft

0.00010.00010.00010.00540.01730.0386

0.00070.00010.00010.00010.02460.04070.04050.02250.04320.0114

0.00010.00010.01220.00010.0009

Mean

183.7115.5

1.853.827.419.5

150.8103.6

1.824.554.20.20.8

11.212.01.6

99.923.824.8

1.841.7

SD§

Layer 1105.420.30.5

30.824.536.7

Layer 275.522.90.6

21.536.40.10.2

10.210.72.0

Layer 325.6

2.625.32.6

33.4

BM

Mean

96.850.80.8

42.879.7

102.9

103.748.80.7

71.3166.8

0.10.3

21.58.21.0

49.778.810.3

1.045.9

SD

64.812.40.3

29.624.374.8

49.714.70.6

21.365.10.10.36.68.70.1

13.017.110.10.1

22.4

MR

Mean

4.492.3

1.35.61.01.0

6.184.40.71.03.20.10.2

11.23.3

20.8

84.31.0

11.321.191.7

SD

10.220.10.5

10.10.10.1

10.38.30.40.15.00.10.25.35.0

14.0

16.30.05.5

13.245.4

t See Table 1 for definitions of symbols used to designate soil properties.$ Probability levels associated with no significant contribution to the discrim-

ination between study areas at a = 0.05.§ Standard deviation.

Table 5. Parameters of the canonical discriminant functions and testsof significance for each analysis.

Canonicaldiscriminant

function

III

III

III

Relativepercentage!

75.624.4

87.312.7

88.611.4

Canonicalcorrelation Wilks' A

Layer 10.97500.9234Layer 20.98620.9181Layer 30.97400.8453

t Percentage of total variability accounted

0.00068

0.00281

0.01229

for by the function.

Significancelevel

0.0001

0.0001

0.0001

formed from volcanic parent materials of the MR areavs. soils formed from more highly metamorphosedand argillaceous parent materials of the BM and theGSM, respectively. The second discriminant function,expressing the remaining 11 to 24% of the total vari-ance, vertically distinguishes between soils formedfrom intermediate and acid crystalline rocks (BM andMR) and soils derived from less acid metasediments(GSM).

In order to examine how these groups differ, thevariables retained in the final model were projectedonto the space defined by the two discriminant func-tions for each analysis (Fig. 3). In doing so, each at-tribute vector points toward the group having the

1634 SOIL SCI. SOC. AM. J., VOL. 55, NOVEMBER-DECEMBER

Table 6. Classification of pedons into groups using all pedons and a split-sample (SS) cross-validation procedure for soils from the GreatSmoky Mountains (GSM), the Black Mountains (BM), and Mt. Rogers (MR).

GSMOriginal group

GSMBMMRTotal

GSMBMMRTotal

GSMBMMRTotal

Pedons

12121135

12121135

12121135

All

1200-

1200-

1100-

SS

1200-

1210-

1210-

PredictedBM

All

Layer 10

120-

Layer 20

120-

Layer 31

120-

groupMR Correct

SS

0120-

0110-

0110-

All

00

11-

0011-

0011-

SS All SS

0 - -0 - -

11 - -100 100

0 -0 - -

11 - -100 97.1

0 - -0 - -

11 - -97.1 97.1

highest mean level for that variable and away fromgroups having the lowest mean level. The length ofeach vector indicates the relative importance of eachvariable in discriminating between groups.

Visual inspection of the plots for Function 1 (Fig.3) indicates that this dimension primarily contrastsBM and GSM soils with those of MR on the basis ofthe types of weatherable minerals in the sand and siltfractions. Soils of BM and GSM are dominated bysand- and silt-size plagioclase, respectively, and byboth mica and its weathering products (SI21) in thesilt fraction. Soils of MR are more siliceous and arecharacterized by high levels of quartz and K-feldparslower in the profile, resulting from the influence ofrhyolite in that area.

The dominance of sand-size plagioclase in BM soilsreflects inheritance from the coarse-grained gneissesand schists in that area, in contrast to the fine-grainedmembers of the Anakeesta Formation in GSM wherethese feldspars are more common in the silt fraction.Mica grain size follows similar trends, but the reversesituation was observed for secondary mica weatheringproducts. The high content of these silt-size expansible2:1 phyllosilicates (SI21) in BM soils results from thelarge amount of biotite initially present in these coarse-grained rocks, compared with muscovite, which ismore resistant to weathering (Loughnan, 1969).

The relative increase in vector length of silt-sizeminerals toward BM and GSM in surface horizons notonly reflects inheritance from parent material, but alsosuggests that hillslope processes, windthrow of trees,and freeze-thaw activity have contributed to signifi-cant amounts of particle-size reduction in these areas.We believe that the upward fining observed in thesesoils is largely the result of mechanical abrasion andphysical weathering of surficial deposits derived fromlocal bedrock types and not a remnant of a formereolian mantle. If the silt-enriched mantle were derivedfrom a distant source, then it would be expected thatthe mineralogy of these layers would be similar, ir-respective of the underlying bedrock (Munn and

Spackman, 1991). The properties reported in Table 4for Layer 1, however, differ across the different bed-rock lithologies. Soils of the MR area would also beexpected to have a high content of mica and plagio-clase in the silt fraction, which was not observed.

In Layer 1, which was comprised of surface mineralhorizons, base saturation and sand-size quartz are theprimary sources of difference between soils of GSMand those of both BM and MR, which are similar withrespect to these two variables. The increase in the vec-tor length of SQTZ toward GSM is the result of soilssampled in the vicinity of the Thunderhead Sand-stone, a coarse-grained feldspathic metaquartzite. Basesaturation is also highest in GSM and is an importantdiscriminator in both Layers 1 and 2, where organic-matter decomposition and base cycling provide thebulk of plant-available n utrients in the sprue-fir eco-system. Higher base-saturation trends in GSM Layer1 horizons are also attributed to release of Na and Caby plagioclase weathering, and to K release resultingfrom mica alteration. The lack of significant amountsof weatherable minerals in Layer 1 horizons of MRsoils underscores the importance of nutrient biocy-cling and suggests that base-cation depletion may notbe readily offset by mineral weathering in surface soilhorizons in this area.

The analysis of samples from Layer 2, representingthe uppermost diagnostic subsurface horizon of eachpedon, resulted in trends that were similar to thoseobserved in Layer 1, indicating homogeneity of themixed parent material and similarity in the surfaceand near-surface weathering environment. Layers 1and 2 comprise the solum of most profiles and includethe zone of biological activity which, in contrast toLayer 3, is significantly influenced by organic matterand nutrient cycling. Base saturation and especiallyMg and Ca saturation of the exchange complex areimportant discriminators, in Layer 2, indicating thatthe greater availability of chlorite and silt-size plagio-clase in parent materials of the GSM is an importantsource of exchangeable Mg and Ca.

FELDMAN ET AL.: HIGH-ELEVATION FOREST SOILS OF THE SOUTHERN APPALACHIANS: I. 1635

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Fig. 2. Scatterplot of individual observations and group centroids(stars) along discriminant functions, illustrating differences be-tween study areas for Layers 1 to 3.

Layer 3 was defined as the lowest horizon sampledand was limited by high subsoil coarse-fragment con-tent, which ranged from about 50 to 85% by volume.This uniformly gravelly subsoil effectively restrictedroot penetration and significant amounts of biologicalactivity to depths of less than =75 cm. As a result,Layer 3 differences between study areas were restrictedprimarily to characteristics inherited from parent ma-terial. Sand-size quartz, plagioclase, and K-feldspar aregreatest in GSM, BM, and MR soils, respectively, and

Fig. 3. Relative contribution of the predictor variables in discrimi-nating between study areas. Variable symbols are defined in Table2.

account for the majority of Layer 3 variation betweenstudy areas, showing even greater importance as dis-criminators in this layer than they do in either Layer1 or 2. The absence of silt-size minerals as an impor-tant discriminator in Layer 3 suggests minimal influ-ence of freeze-thaw-induced particle-size reduction inlower horizons. The high content of silt-size quartz inLayer 3 of MR soils, rather than being an exception,is the result of the fine-grained nature of the rhyolitein that area.

Aluminum saturation of the exchange complex and

1636 SOIL SCI. SOC. AM. J., VOL. 55, NOVEMBER-DECEMBER

clay-fraction gibbsite are also important discrimina-tors lower in the profile of all pedons, particularly inGSM soils, indicating that subsoil horizons act as aconsiderable sink for Al. Gibbsite commonly increaseswith depth in the clay fraction of all soils and formsprimarily from the in situ geochemical weathering offeldspars in gravelly subsoil horizons. Podzolizationand cheluviation of Al from mineral A (and E) hori-zons have also been shown to be common pedogenicprocesses in these soils (Feldman et al., 1991). As crit-ical Al/organic chelate levels are exceeded at somedepth in the profile, Al may be rapidly released tosolution in amounts that represent oversaturation withrespect to gibbsite, resulting in precipitation of eithernoncrystalline or crystalline Al phases in the subsoil.Because adsorption sites on noncrystalline Al or Feoxides play a key role in restricting the mobility of804" in these soils (Jpslin et al., 1987), the pollution-induced export of cations from these soil systems maybe minimal. However, the inherently low base satu-ration and lack of significant amounts of both nutrient

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Fig. S. Relative contribution of the predictor variables used to assessmultivariate differences between layers. Variable symbols are de-fined in Table 2.

biocycling and silt-size weatherable minerals lower inthe profile suggests that these subsoil horizons mightotherwise be sensitive to accelerated base-cation de-pletion induced by anion loading from atmosphericdeposition.

Significant Layer 1 differences between landformsare limited to clay content and horizon thickness, bothof which increase from ridges to mid-backslopes tocoves. Layer 2 variable;* that distinguished betweenlandforms include pH, clay-size mica, and silt-size ka-olinite, which show similar trends with respect to land-scape position. Layer 3 differences between landformsare limited to clay-size mica content, which also isgreatest in coves. These trends point to the importanceof slope processes in the transport of base cations andfine-earth materials as sediments become deeper, finer,and more well sorted after flowing downslppe ontosteeper gradients and more concave, depositional po-sitions in the landscape.

Differences between exposure classes are not signif-icant for all variables vdth the exception of ECEC,which is greater on exposed rather than protected sites,suggesting higher Al levels on these sites that are in-fluenced by longer periods of cloud interception andtherefore more acidic conditions (Mohnen, 1987).

From an analysis of multivariate differences be-tween layers across all observations, we conclude thatLayers 2 and 3 are nearly alike and that they differfrom Layer 1 mainly in terms of the first discriminantfunction, which accounts for 98% of the total disper-sion in the data (Fig. 4). Layer 1 differs from Layers2 and 3 mainly by having a larger amount of extract-able bases, lower pH levels, and lower matrix colorvalues (Fig. 5). These differences highlight the impor-tance of organic matter 1:0 soil physical and chemicalproperties and help distinguish between umbric epi-pedpns and cambic or spodic diagnostic subsurfacehorizons. Differences between layers are also attrib-utable to DCB-extractable Fe, which is associated withbiotite and chlorite weathering in near-surface hori-zons, and to clay-size gibbsite, which increases withdepth (Feldman et al., 1.991). Although soils in thisstudy exhibited a high degree of morphological simi-larity across all sampling areas, soil mineralogical andchemical differences between study areas were clearlygreater than subsoil variation within each pedon.

CONCLUSIONSAlthough physical and morphological properties

used to separate and classify these soils in the field didnot differ significantly for any of the pedons studied,the use of multiple discri minant analysis has resultedin the clear separation of soils into distinct groups.Because climate, vegetation, and time of soil forma-tion are similar at each study site, major differencesin soil properties were influenced primarily by char-acteristics inherited from parent materials with localdifferences resulting from topographic position. Soilsformed from siliceous metavolcanic parent materials(MR) were high in K-feldspar and quartz in both thesand and silt fractions. Soils derived from more highlymetamorphosed rocks (GSM and BM) had plagioclase

FELDMAN ET AL.: HIGH-ELEVATION FOREST SOILS OF THE SOUTHERN APPALACHIANS: I. 1637

as the dominant feldspar, were less quartzitic, and hadhigh contents of mica and 2:1 phyllosilicates.

The upward fining of particle size toward the surfaceof these soils is attributed to physical weathering pro-cesses, which are highly dependent on frost churningat these high elevations. Differences in soil propertiesbetween landforms, including soil depth, clay content,and pH, increased from convex ridges to linear back-slopes and concave landscape positions, reflecting theimportance of slope processes involved in the surfaceand subsurface transport of solutes and sediments. Thegeneral lack of weatherable minerals in soils of the MRarea and the diminished importance of nutrient bio-cycling in lower horizons of all soils indicates thataccelerated base-cation depletion attributable to long-term acid anion loading from atmospheric depositionmay not be readily compensated by mineral weath-ering. These results suggest that disturbances to theforest floor caused by fire, overgrazing, logging, or ero-sion may have a major impact on ecosystem resilienceduring stress.

ACKNOWLEDGMENTSWe wish to thank Mr. Joe Henderson, Ms. Kathleen New-

kirk, Dr. J.B. Campbell, Dr. W.L. Daniels, Dr. W.J. Ed-monds, and Dr. E.P. Smith for assistance and technicalsupport on this project. This research was supported byfunds provided by the North-eastern Forest Experiment Sta-tions' Spruce-Fir Research Cooperative within the jointUSEPA-U.S. Forest Service Forest Response Program. TheForest Response Program is part of the National Acid Dep-osition Assessment Program. This article has not been sub-ject to USEPA or Forest Service peer review and should notbe construed to represent the policies of either agency.