UBC Faculty Research and Publications

Quantitative characterization of field-estimated soil nutrient regimes in the subalpine coastal forest. Klinka, Karel; Splechtna, Bernhard E.; Chourmouzis, Christine 1999

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Scientia Silvica Extension Series, Number  21, 1999Quantitative Characterization of Field-estimated Soil Nutrient Regimesin the Subalpine Coastal ForestIntroductionSite classificationf inf  the  biogeoclimatic ecosystem classificationf system  is  based onf three  differenftiatinfg properties: climaticregimes (expressed by biogeoclimatic subzonfes or varianfts), soil moisture regimes (SMRs), anfd soil nfutrienft regimes (SNRs). ASNR represenfts a segmenft of a regionfal soil nfutrienft gradienft, i.e., soils which provide similar levels of planft-available nfutrienftsover a lonfg period. SNRs are idenftified inf the field usinfg a nfumber of easily observable soil morphological properties anfd infdicatorplanft species. However, we nfeed to knfow to what extenft soil nfutrienft properties support these infdirect field-estimates. There havebeenf several studies that quanftitatively characterize regionfal soil nfutrienft gradienfts inf differenft climatic regionfs, but nfo study hasyet beenf donfe inf the subalpinfe coastal forest (Mounftainf Hemlock zonfe). Inffluenfced by a maritime subalpinfe boreal climate, high-elevationf coastal soils differ from low-elevationf soils by havinfg a thicker forest floor anfd a higher organfic matter conftenft. Inf thestudy summarized here, relationfships betweenf soil chemical properties anfd field-estimated SNRs are examinfed anfd soil chemicalproperties anfd field-idenftified SNRs are related to the site infdex of Pacific silver fir (Abies amabilis (Dougl. ex Loud.) Forbes)- onfe of the major timber crop species inf the Coastal Westernf Hemlock anfd Mounftainf Hemlock zonfes.Study Stands and ProcedureStudy stanfds were selected across the enftire nfative ranfge of Pacific silver fir inf southwesternf British Columbia. The study areainfcluded Vanfcouver Islanfd anfd the adjacenft mainflanfd south of the linfe extenfdinfg from Port McNeill to Lillooet. The study stanfdshad regenferated nfaturally after a major disturbanfce (winfd, fire or clearcuttinfg) anfd were deliberately selected across the widestranfge of climate, soil moisture, anfd soil nfutrienft confditionfs. Elevationf was measured by a Thommenf pocket altimeter. The SMRanfd SNR of each stanfd were idenftified usinfg easily observable soil morphological properties anfd infdicator planfts. Site infdex (m @50 yr bh) for each plot was obtainfed from stem anfalysis (also see Scfientia Silvicfa Number 19).A total  of 79 stanfds inf  the  monftanfe anfd subalpinfe portionf of the  coastal forest were selected for the  study.  A  0.04  ha plot wasestablished inf each stanfd anfd a composite sample was takenf of the enftire forest floor anfd the first 30 cm of the minferal soil inf 12ranfdomly selected poinfts. The composite samples were air-dried, prepared for laboratory anfalysis, anfd anfalyzed for the followinfgnfutrienft properties: pH, total C (tC), total N (tN), minferalizable-N (minf-N), anfd extractable Ca (eCa), Mg (eMg), K (eK), P (eP),anfd S (eSO4-S). All properties were expressed as confcenftrationf onf a dry mass basis. To describe the quality of organfic matter anfdN-availability, C:N ratio was calculated.Samples were stratified onfly accordinfg to three field-idenftified SNRs (poor, medium, anfd rich), sinfce very poor anfd very rich siteswere inffrequenft  anfd did nfot support suitable stanfds. To evaluate the  potenftial of every sinfgle property to discriminfate betweenffield-estimated SNRs, anfalysis of varianfce anfd multiple comparisonf of meanfs (usinfg Bonfferronfi's adjustmenft) for each variablewere carried out. Prior to anfalysis, several variables had to be tranfsformed to meet the requiremenfts of homogenfeity of varianfceanfd nformality. To examinfe the ability of forest floor anfd minferal soil nfutrienft properties to discriminfate betweenf field-idenftifiedSNRs, we used stepwise, jack-knfifed discriminfanft funfctionf anfalysis. This procedure gave infformationf onf how well SNR groupmembership could be predicted by soil nfutrienft measures.Regressionf anfalysis was applied to examinfe the relationfship betweenf soil nfutrienft properties anfd site infdex of Pacific silver fir.Sinfce climate has a large inffluenfce  onf site infdex,  especially onf monftanfe sites, elevationf was always enftered as a covariate. Tominfimize the inffluenfce of SMR, we onfly used fresh anfd moist sites inf the anfalysis (nf = 42).ResultsIn  general, the  selected  nutrient properties showed  the  trends  of increase  or decrease along the  soil  nutrient gradient that  werereported in several previous SNR studies (Table 1, Figure 1). In order from poor to rich SNRs, the forest floor pH, tN, min-N, andthe sum of eCa, eMg, and eK (SEB) increased, and tC and C:N decreased, with pH, tC, and SEB separating rich sites from poor andmedium sites, and tN, C:N, and min-N separating poor sites from rich sites. In the same order, the mineral soil pH, tC, tN, min-N,and SEB increased  and C:N decreased, with min-N and SEB separating rich sites from poor and medium sites, and tN  and C:Nseparating rich sites from poor sites. The mean values for forest floor eP and eSO4-S did not show any differences.Table 1.   Means and standard errors of means for the measured forest floor and 0 - 30 cm mineral soil nutrient propertiesaccording to field-identified soil nutrient regimes. Values in the same row with same superscript are not significantlydifferent (alpha = 0.05); properties without superscripts do not show significant differences between soil nutrient regimes.*Variables have been transformed using natural logarithm or square root for the analysis.Discriminant function analysis using forest floor, mineral soil, and both forest floor and mineral soil properties showed (i) a weakbut significant relationship between forest floor nutrient properties and SNRs, and (ii) a moderately strong relationship betweenmineral soil nutrient properties and SNRs. Of the forest floor properties, pH, eCa, C:N, and tN loaded highly on the first axis; of themineral soil properties, min-N, eCa, and eMg loaded highly on the first axis, and eSO4-S loaded highly negatively on the second axis.The discriminant function analysis that  used  only forest  floor  properties  correctly allocated  48% of the samples  to  the field-estimated SNRs; the analysis based only on mineral soil properties correctly allocated 62% of the samples to the field-estimatedSNRs; and the analysis based on both forest floor and mineral soil properties correctly allocated 63% of the samples to the field-estimated SNRs. However,  after reclassification of incorrectly allocated samples according to the discriminant functions, 90% ofsamples were allocated correctly to field-estimated SNRs using the same set of variables (Figure 2).After adjusting for elevation, site index of Pacific silver fir was found to be significantly related to forest floor and mineral soil C:Nratios and tN, and to the forest floor min-N, with C:N of the forest floor and mineral soil showing the strongest relationship (Table2).  Site  index was not significantly  related to  the mineral soil min-N. Field-estimated  SNRs explained a similar  proportion ofvariation of site index as the direct soil nutrient measures (Table 2). When adjusted to the mean elevation of 1103 m, site index ofPacific silver fir on fresh and moist sites increased from 12.3 m on poor sites to 18.2 m on rich sites (Table 3).Soil nutrient regime  Poor  Medium  Rich Number of samples  23  35  21       Forest floor       pH 3.9?0.1a  4.0?0.1a  4.3?0.1b Total C (g kg-1) 446?4.3b  439?5.1b  412?9.5a Total N (g kg-1) 7.7?0.4a  9.3?0.4ab  10.1?0.6b C:N ratio  64.7?5.8b  51.6?3.2ba  44.7?3.5a Mineralizable-N (mg kg-1)*  124?6a  158?13ab  172?17b Extractable SO4-S(mg kg-1) 59?4 57?3 55?4 Extractable P (mg kg-1) 90?8 84?7 87?10 Sum of extractable Ca, Mg, and K (g kg-1) (SEB)*  3.6?0.3a  3.9?0.3a  5.4?0.6b Mineral soil       pH 4.6?0.1ba  4.5?0.1a  4.8?0.1b Total C (g kg-1) 45.7?5.6 55.3?5.2 63.9?7.4 Total N (g kg-1)* 1.7?0.3a  2.8?0.5ab  4.5?1.1b C:N ratio  34.2?3.5b  28.4?1.9ba  22.4?2.3a Mineralizable-N (mg kg-1)* 9.1?1.8a  15.3?2.2a  33.3?4.9b Extractable SO4-S (mg kg-1) 8.0?0.7a  10.3?0.6b  8.8?0.5ab Extractable P (mg kg-1)* 17?4 10?2 15?4 Sum of extractable Ca, Mg, and K (g kg-1) (SEB)*  0.14?0.04a  0.16?0.03a  0.59?0.15b G3G33G52G52G55G3 G30G48G47G4cG58G50 G35G4cG46._G4bG29G52G55G48G56G57G3G49G4fG52G52G55G3G53G2bG13G14G15G16G17G18G33G52G52G55G3 G30G48G47G4cG58G50 G35G4cG46._G4bG29G52G55G48G56G57G3G49G4fG52G52G55G3G57G26G3GbG4aG3G4eG4aG10G14GcG13G14G13G13G15G13G13G16G13G13G17G13G13G18G13G13G33G52G52G55G3 G30G48G47G4cG58G50 G35G4cG46._G4bG29G52G55G48G56G57G3G49G4fG52G52G55G3G57G31G3GbG4aG3G4eG4aG10G14GcG13G15G17G19G1bG14G13G14G15G33G52G52G55G3 G30G48G47G4cG58G50 G35G4cG46._G4bG29G52G55G48G56G57G3G49G4fG52G52G55G3G50G4cG51G10G31G3GbG50G4aG3G4eG4aG10G14GcG13G15G13G17G13G19G13G1bG13G14G13G13G14G15G13G14G17G13G14G19G13G14G1bG13G15G13G13G33G52G52G55G3 G30G48G47G4cG58G50 G35G4cG46._G4bG29G52G55G48G56G57G3G49G4fG52G52G55G3G26G1dG31G3G55G44G57G4cG52G13G14G13G15G13G16G13G17G13G18G13G19G13G1aG13G1bG13G33G52G52G55G3 G30G48G47G4cG58G50 G35G4cG46._G4bG30G4cG51G48G55G44G4fG3G56G52G4cG4fG3G57G31G3GbG4aG3G4eG4aG10G14GcG13G14G15G16G17G18G19G36G52G4cG4fG3G51G58G57G55G4cG48G51G57G3G55G48G4aG4cG50G48G33G52G52G55G3 G30G48G47G4cG58G50 G35G4cG46._G4bG30G4cG51G48G55G44G4fG3G56G52G4cG4fG3G50G4cG51G10G31G3GbG50G4aG3G4eG4aG10G14GcG13G18G14G13G14G18G15G13G15G18G16G13G16G18G17G13G36G52G4cG4fG3G51G58G57G55G4cG48G51G57G3G55G48G4aG4cG50G48G33G52G52G55G3 G30G48G47G4cG58G50 G35G4cG46._G4bG30G4cG51G48G55G44G4fG3G56G52G4cG4fG3G26G1dG31G3G55G44G57G4cG52G13G18G14G13G14G18G15G13G15G18G16G13G16G18G17G13G36G52G4cG4fG3G51G58G57G55G4cG48G51G57G3G55G48G4aG4cG50G48G33G52G52G55G3 G30G48G47G4cG58G50 G35G4cG46._G4bG30G4cG51G48G55G44G4fG3G56G52G4cG4fG3G36G28G25G3GbG4aG3G4eG4aG10G14GcG13G11G13G13G11G14G13G11G15G13G11G16G13G11G17G13G11G18G13G11G19G13G11G1aG13G11G1bFigure 1.  Direct measures of selected forest floor and mineral soil nutrient properties stratified according to field estimated soilnutrient regimes. Error bars indicate the standard errors of the mean.Figure 2.  Ordination of samples and centroids for poor, medium, and richsoil nutrient regimes according to the first two discriminant functions(based on both forest floor and mineral soil properties) followingreclassification of incorrectly assigned samples. Ninety percent of sampleswere correctly allocated to the soil nutrient regimes.G27G4cG56G46._G55G4cG50G4cG51G44G51G57G3G49G58G51G46._G57G4cG52G51G3G14G19G27G4cG56G46._G55G4cG50G4cG51G44G51G57G3G49G58G51G46._G57G4cG52G51G3G15G17G15G13G10G15G10G17G26G48G51G57G55G52G4cG47G56G35G4cG46._G4bG30G48G47G4cG58G50G33G52G52G55Scientia Silvica  is published by the Forest Sciences Department,The University of British Columbia, ISSN 1209-952XEditor: Karel Klinka (klinka@interchange.ubc.ca)Research: Bernhard Splechtna (bsplechtna@utanet.at)Production and design: Christine Chourmouzis (chourmou@interchange.ubc.ca)Financial support: Forest Renewal British ColumbiaFor more information contact: Bernhard Splechtna (bsplechtna@utanet.at)Copies available from:www.forestry.ubc.ca/klinka  orK. Klinka, Forest Sciences Department,UBC,3036-2424 Main Mall,Vancouver, BC,  V6T 1Z4Table 2.  Adjusted coefficients of determination (adj R2) and standard error ofestimates (SEE) for regression models of Pacific silver fir site index on elevation, soilnutrient properties, and SNRs (all represented by dummy variables). All models aresignificant at p < 0.001 (n = 42), all coefficients are significant (alpha=0.05).Table 3.  Marginal means of site index adjusted tothe mean elevation of 1103 m of stands on fresh andmoist sites stratified according to SNRs. SE isstandard error of the mean and n is the number ofsamples.DiscussionThe results of discriminant function analysis suggest it may be appropriate to revise the key for estimating SNRs in the field on montaneand subalpine coastal sites, emphasizing the morphological properties of the mineral soil (such as acidity, the degree of leaching, andmineralogy). However, the relationships of Pacific silver fir site index to soil nutrient properties suggest that the properties whichexplain most of the  variation in  soil nutrients along the  regional soil nutrient gradient (or between field-estimated SNRs) do notsignificantly affect site index. Of the soil properties, the quality of forest floor, mineral soil organic matter (measured as C:N ratio), andtotal N appear to influence Pacific silver fir height growth most strongly. These relationships are likely a reflection of the distribution ofPacific silver fir fine roots in the forest floor. Field-estimated soil nutrient regimes explain a similar portion of variation in site index asdirect soil nutrient measures. Similar findings were reported for the continental subalpine forest. However, the portion of the variationin site index that can be explained by differential availability of soil nutrients is generally low on montane and subalpine sites comparedto the influence exerted by climatic changes along an elevation gradient. Thus, despite the relatively weak relationship between directsoil nutrient properties and field-estimated soil nutrient regimes found in high-elevation forests of British Columbia, using existing fieldkeys for estimating quality of forest sites in relation to tree growth seems justified.ReferenceSplechtna, B. and K. Klinka. 2000. Quantitative characterization of nutrient regimes of high-elevation forest soils in the southerncoastal region of British Columbia. Accepted for publication in Geoderma 00/11/10.Independent variables   Adj R2 SEE     Elevation 0.58 4.5 Elevation, forest floor mineralizable-N  0.60 4.3 Elevation, forest floor total N  0.65  4.1 Elevation, forest floor C:N ratio  0.65 4.0 Elevation, mineral soil total N  0.62 4.2 Elevation, mineral soil C:N ratio  0.66 4.1 Elevation, SNR  0.63  4.2 G3SNR Mean SE n   Poor 12.3 1.7 6 Medium 16.2 0.9 24 Rich 18.2 1.2 12 G3


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