UBC Faculty Research and Publications

Quantitative characterization of field-estimated soil nutrient regimes in the subalpine interior forest Klinka, Karel; Chen, Han Y. H.; Chourmouzis, Christine 1999

You don't seem to have a PDF reader installed, try download the pdf

Item Metadata

Download

Media
[if-you-see-this-DO-NOT-CLICK]
SSES022.pdf [ 132.5kB ]
Metadata
JSON: 1.0107282.json
JSON-LD: 1.0107282+ld.json
RDF/XML (Pretty): 1.0107282.xml
RDF/JSON: 1.0107282+rdf.json
Turtle: 1.0107282+rdf-turtle.txt
N-Triples: 1.0107282+rdf-ntriples.txt
Original Record: 1.0107282 +original-record.json
Full Text
1.0107282.txt
Citation
1.0107282.ris

Full Text

Scientia Silvica Extension Series, Number  22, 1999Quantitative Characterization of Field-estimated Soil Nutrient Regimesin the Subalpine Interior ForestIntroductionSite classificationf of the biogeoclimatic ecosystem classificationf system is based onf climatic regime (expressed by biogeoclimaticsubzonfe), soil moisture regime (SMR), anfd soil nfutrienft regime (SNR). A SNR represenfts a segmenft of a regionfal soil nfutrienftgradienft, i.e., a populationf of soils which provide similar levels of planft-available nfutrienfts over a lonfg period. SNR is idenftified infthe field usinfg a nfumber of easily observable soil morphological properties anfd infdicator planft species. However, we do nfot knfowthe extenft to which soil nfutrienft properties are supported by these infdirect field-estimates. There have beenf several studies thatquanftitatively characterized regionfal soil nfutrienft gradienfts inf differenft climatic regionfs (see Sciencia Silvica Number 21 forsubalpinfe coastal forests), but this has nfot beenf donfe inf the subalpinfe infterior forest (Enfgelmanfnf Spruce - Subalpinfe Fir (ESSF)zonfe) where soils are inffluenfced by a conftinfenftal subalpinfe boreal climate. Inf the study summarized here, relationfships betweenfsoil chemical properties anfd field-estimated SNRs are examinfed anfd soil chemical properties anfd field-idenftified SNRs are relatedto the site infdex of subalpinfe fir (Abfies lasiocarpa (Dougl. ex Loud.) Forbes) anfd Enfgelmanfnf spruce (Picea engelmannii Parry exEnfgelmanfnf) - two major timber crop species inf the ESSF zonfe.Study Stands and ProcedureStudy stanfds were selected across the  ESSF zonfe of British  Columbia. The study stanfds were deliberately selected across thewidest ranfge of climate, soil moisture, anfd soil nfutrienft confditionfs, anfd were all nfaturally regenferated, unfmanfaged, relatively evenf-aged, fully stocked, anfd inf the stem exclusionf stage of stanfd developmenft (ranfginfg from 40 to 200 years at breast height). The SNRof each stanfd was estimated usinfg anf heuristic procedure that inftegrates a nfumber of easily observable soil morphological propertiesanfd infdicator planfts. Site infdex (m @ 50 yr bh) for each plot was obtainfed from stem anfalysis (see Scientia Silvica Number 23).Inf each of the 155 study stanfds, a 0.04 ha plot was established anfd a composite sample was takenf of the enftire forest floor anfd thefirst 30 cm of the minferal soil from 15 ranfdomly selected poinfts. The samples were air-dried, prepared for chemical anfalysis, anfdanfalyzed for the followinfg nfutrienft 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 thequality of organfic matter anfd N-availability, C:N anfd minf-N:tN ratios were calculated.Samples were stratified infto five SNR classes by two methods: (1) a qualitative method, usinfg the heuristically derived field-estimates,anfd (2) a quanftitative method, usinfg the soil chemical anfalysis. The agreemenft betweenf the two methods was determinfed by discriminfanftanfalysis. Site infdex - SNR relationfships were examinfed by multiple regressionf. Confsiderinfg the wide geographical ranfge anfd steeplocal climatic gradienft, regressionfs infcluded elevationf, latitude, anfd lonfgitude as infdepenfdenft variables.Results and DiscussionThe five field-estimated (qualitative) SNRs were similar to the five soil chemical derived SNRs (Table 1, Figure 1 anfd Figure 2).Soil nfutrienft properties varied alonfg the regionfal soil nfutrienft gradienft, but the differenfces were more pronfounfced inf the minferalsoil thanf inf the forest floor. Inf forest floor, pH, total N, minf-N, minf-N:tN ratio, anfd sum of eCa, eMg, anfd eK infcreased, anfd theC:N ratio decreased from very poor to very rich sites; however, other properties did nfot show anfy confsistenft relationfship with thefield-idenftified SNR. Inf the  minferal soil, total  N, minf-N, anfd minf-N:tN ratio infcreased,  anfd the  C:N ratio decreased from verypoor through very rich sites, infdicatinfg the presenfce of a steep, N-drivenf nfutrienft gradienft. pH, tC, anfd sum of eCa, eMg, anfd eKalso infcreased  from poor through  rich sites.Table 1.  Means and ? standard errors of the mean for measured forest floor and 0 - 30 cm mineral soil nutrient propertiesaccording to quantitatively classified groups. Values in the same row with same superscript are not significantly different(p > 0.05); variables without superscripts are not significantly different.G36G31G35G39G33 G33 G30 G35 G39G35G53G2bG16G17G18G19G36G31G35G39G33 G33 G30 G35 G39G35G37G52G57G44G4fG3G26G3GbG8GcG13G14G13G15G13G16G13G17G13G18G13G19G13G36G31G35G39G33 G33 G30 G35 G39G35G37G52G57G44G4fG3G31G3GbG8GcG13G14G15G36G31G35G39G33 G33 G30 G35 G39G35G26G1dG31G3G55G44G57G4cG52G13G14G13G15G13G16G13G17G13G18G13G19G13G36G31G35G39G33 G33 G30 G35 G39G35G30G4cG51G10G31G3GbG50G4aG3G4eG4aG10G14GcG13G14G13G13G15G13G13G16G13G13G17G13G13G18G13G13G19G13G13G1aG13G13G36G31G35G39G33 G33 G30 G35 G39G35G30G4cG51G10G31G3G1dG3G57G52G57G44G4fG3G31G3G55G44G57G4cG52G3GbG8GcG13G14G15G16G17G18G39G33 G33 G30 G35 G39G35G24G59G44G4cG4fG44G45G4fG48G3G33G3GbG50G4aG3G4eG4aG10G14GcG13G18G13G14G13G13G14G18G13G15G13G13G15G18G13G39G33 G33 G30 G35 G39G35G28G5bG57G55G44G46._G57G44G45G4fG48G3G36G3GbG50G4aG3G4eG4aG10G14GcG13G15G13G17G13G19G13G1bG13G14G13G13G39G33 G33 G30 G35 G39G35G36G58G50G3G52G49G3G48G5bG57G55G44G46._G57G44G45G4fG48G3G46._G44G57G4cG52G51G56G3GbG4aG3G4eG4aG10G14GcG13G18G14G13G14G18G15G13Class   A  B  C  D  E Number of stands  18 47 48 36 6        Forest floor       pH 4.2?0.1c 4.4?0.1bc 4.5?0.1b 4.9?0.1a 5.1?0.3a Total C (g kg-1)  47.3?2.0 44.1?1.2 45.7?0.9 42.9?1.1 43.1?2.5 Total N (g kg-1) 1.08?0.05c 1.28?0.04b 1.47?0.05ab 1.47?0.06ab 1.57?0.09a C:N ratio  44.4?1.7a 34.5?0.5b 31.9?0.8c 30.2?1.0cd 28.1?3.2d Mineralizable-N (mg kg-1) 289?54c 448?43b 603?43a 512?40ab 553?49ab Min-N:total N ratio  0.26?0.03c 0.33?0.03b 0.39?0.02a 0.34?0.02ab 0.35?0.03ab Extractable P (mg kg-1) 158?17ab 210?15a 121?12b 88?9c 92?33bc Extractable SO4-S (mg kg-1)  36.1?7.4 32.6?2.1 42.0?4.2 38.7?2.8 44.2?4.0 Sum of extractable Ca, Mg, and K (g kg-1) 4.1?0.8c 4.5?0.3c 5.7?0.4b 8.6?0.8a 9.3?1.8a        Mineral soil       pH 4.9?0.1b 4.9?0.1b 5.2?0.1ab 5.7?0.1a 5.5?0.1a Total C (g kg-1) 3.0?0.5c 3.0?0.2c 3.3?0.2c 4.5?0.5b 6.5?0.7a Total N (g kg-1)  0.08?0.01 0.12?0.01 0.17?0.01 0.29?0.02 0.41?0.04 C:N ratio  27.0?1.2 22.8?0.6 20.2?0.5 18.3?0.9 16.2?1.2 Mineralizable-N (mg kg-1) 3.8?1.2d 9.5?0.9d 18.9?1.4c 54.1?3.2b 115.4?15.3a Min-N:total N ratio  0.05?0.01e 0.08?0.01d 0.12?0.01c 0.20?0.01b 0.29?0.04a Extractable P (mg kg-1) 45.4?5.9a 47.4?5.4a 18.3?3.4b 8.4?2.2c 4.8?1.1c Extractable SO4-S (mg kg-1)  8.5?1.6 7.4?0.9 14.0?2.0 8.4?1.3 7.2?1.4 Sum of extractable Ca, Mg, and K (g kg-1) 0.3?0.1c 0.4?0.1c 0.9?0.2b 4.4?1.4a 3.1?0.7a  Figure 1.  Direct measures of forest floor nutrient properties stratified according to field-estimated SNRs. Error bar is one standarderror of the mean. VP, P, M., R, and VR are very poor, poor, medium, rich , and very rich, respectively.G36G31G35G39G33 G33 G30 G35 G39G35G53G2bG16G17G18G19G36G31G35G39G33 G33 G30 G35 G39G35G37G52G57G44G4fG3G26G3GbG8GcG13G14G15G16G17G18G19G1aG1bG36G31G35G39G33 G33 G30 G35 G39G35G37G52G57G44G4fG3G31G3GbG8GcG13G11G13G13G11G14G13G11G15G13G11G16G13G11G17G13G11G18G36G31G35G39G33 G33 G30 G35 G39G35G26G1dG31G3G55G44G57G4cG52G13G14G13G15G13G16G13G17G13G36G31G35G39G33 G33 G30 G35 G39G35G30G4cG51G10G31G3GbG50G4aG3G4eG4aG10G14GcG13G15G13G17G13G19G13G1bG13G14G13G13G14G15G13G14G17G13G36G31G35G39G33 G33 G30 G35 G39G35G30G4cG51G10G31G1dG57G52G57G44G4fG3G31G3G55G44G57G4cG52G3GbG8GcG13G14G15G16G17G36G31G35G39G33 G33 G30 G35 G39G35G24G59G44G4cG4fG44G45G4fG48G3G33G3GbG50G4aG3G4eG4aG10G14GcG13G15G13G17G13G19G13G1bG13G14G13G13G36G31G35G39G33 G33 G30 G35 G39G35G28G5bG57G55G44G46._G57G44G45G4fG48G3G36G3GbG50G4aG3G4eG4aG10G14GcG13G18G14G13G14G18G15G13G15G18G16G13G36G31G35G39G33 G33 G30 G35 G39G35G36G58G50G3G52G49G3G48G5bG57G55G44G46._G57G44G45G4fG48G3G46._G44G57G4cG52G51G56G3GbG4aG3G4eG4aG10G14GcG13G15G17G19G1bComparison of the quantitative and qualitative methods showed that 111 (71.6%) of the sampled sites fell into the same SNR, 40(25.8%) samples fell to the adjacent SNR, and 4 (2.6%) samples were apart by two or more SNR classes. There was no discrepancybetween very rich SNR, however, there were discrepancies between the other SNR classes. For example, of the 52 sites that werefield-estimated as having medium SNR, approximately 40% were placed by multivariate analysis into poorer (A or B) or richer (D)classes, indicating a disagreement between the qualitative and quantitative methods. Further, of the 48 sites classified by multivariateanalysis into class C (medium SNR), approximately 30% were estimated in the field as having very poor, poor, or rich SNRs.Three types of linear regression models were developed for each study species: (1) the SNR model using field-identified SNRs asdummy variables (Equations [1] and [4]), (2) the model using classes derived from multivariate analysis as dummy variables (Equa-tions [2] and [5]), and (3) the analytical model using direct measures of soil nutrient properties as continuous variables (Equations[3] and [6]) (Table 2). All models were significant (p <0.001) and indicated the presence of strong relationships between site indexand climatic and soil nutrient variables. As expected, site index decreased with increasing elevation, latitude, and longitude,  andincreased with increasing levels of plant-available soil nutrients.Regardless of study  species,  the correlation between site  index and independent  variables determined by each model was quitesimilar. The SNR models had somewhat stronger relationships with site index than the class models (adjusted R2 ranged from 0.41to  0.52  for  the  SNR models compared to 0.41 to  0.47  for  the  class models); however,  the  analytical models had the  strongestrelationships compared to the SNR and class models (adjusted R2 ranged from 0.47 to 0.65) (Table 2).Figure 2.  Direct measures of mineral soil nutrient properties stratified according to field-estimated SNRs. Error bar is one standarderror of the mean. VP, P, M, R, and VR are very poor, poor, medium, rich , and very rich, respectively.Scientia Silvicais published by the Forest Scienfces Departmenft,The Unfiversity of British Columbia, ISSN 1209-952XEditor and researcfh supervisor: Karel Klinka (klinka@intferchange.ubc.ca)Researcfhed and written by: H.Y.H. Chen (han.chen@mnr.gov.on.ca)Producftion and design: Christfine Chourmouzis (chourmou@intferchange.ubc.ca)Financfial support: Forestf Renewal Britfish ColumbiaFor more information cfontacft: H.Y.H. Chenwww.forestfry.ubc.ca/klinka  or (han.chen@mnr.gov.on.ca)Copies available from:K. Klinka, Forestf Sciences Departfmentf, 3041-2424 Main Mall,UBC, Vancouver, BC,  V6T 1Z4ConclusionsNitrogenf related variables (C:N, total N, minferalizable -N, anfd minferalizable-N:total N) inf the minferal soil were the nfutrienft propertiesthat segregated best amonfg soil nfutrienft regimes of both qualitative anfd quanftitative methods for subalpinfe boreal soils. However,assignfmenft of the study sites infto onfe of the five soil nfutrienft regimes varied betweenf the methods. Regardless of these differenfces,both qualitatively anfd quanftitatively derived soil  nfutrienft regimes  had a similar  accounftability for  the  variationf of site  infdex ofsubalpinfe fir anfd Enfgelmanfnf spruce. This similarity justifies the use of the quanftitative methods inf estimatinfg the ecological qualityof forest sites  inf  the subalpinfe infterior  forest.ReferencesChenf, H.Y.H., Klinfka, K., J. Fonfs, anfd P.V. Krestov. 1998. Characterizationf of nfutrienft regimes inf some conftinfenftal subalpinfe boreal soils.Canf. J. Soil Sci. 78: 467-475.Table 2.  Models for the regression of subalpine fir and Engelmann spruce site index (SI) on categorical continuous soil nutrientvariables. Abbreviations used are: ELE - elevation (m); LAT - N latitude (degrees and minutes in metric); LONG - W longitude(degrees and minutes in metric); P - poor, M - medium, R - rich, and VR - very rich are qualitative SNRs (based on field observ ablesoil morphological properties); A - class A, B - class B, C - class C, D - class D, and E - class E are quantitative SNRs (base d ondirect soil nutrient measures and multivariate analysis); ff - forest floor property, ms -  mineral soil property; tN - total N  (g/kg); eSO4 -S - extractable SO4-S (mg/kg); pH - acidity; C:N - C:N ratio; SEB = sum of extractable Ca, Mg, and K (mg/kg).Regression model  Adjusted R2 SEE p n  Subalpine fir      [1]  SI = 145.9 ? 0.013(ELE) ? 0.858(LAT) - 0.705(LONG) + 1.643(P) + 3.7(M) + 2.577(R) + 5.13(VR)  0.41 3.44 <0.001 101 [2]  SI = 145.9 ? 0.013(ELE) ? 0.858(LAT) - 0.705(LONG) + 1.643(B) + 3.7(C) + 2.577(D) + 5.13(E)  0.41 3.44 <0.001 101 [3]  SI = 125.2 - 0.011(ELE) - 0.927(LONG) + 4.877(tNff) - 0.057(eSO4-Sms) + 2.192(pHms) - 0.206(SEB)  0.47 3.26 <0.001 101  Engelmann Spruce    [4]  SI = 183. 8 ? 0.019(ELE) ? 1.147(LAT) - 0.705(LONG) + 3.26(M) + 0.71(R) + 1.94(VR)  0.52 3.04 <0.001 52 [5]  SI = 174.0 ? 0.018(ELE) ? 1.205(LAT) - 0.629(LONG) + 4.261(B) + 4.565(C) + 2.52(D) + 3.947(E)  0.47 3.19 <0.001 52 [6]  SI = 249.9 ? 0.021(ELE) ? 1.063(LAT) - 1.182(LONG) - 0.368(C/Nff) + 0.213(C/Nms) 0.078(eSO4-Sms) - 0.495(SEBms) 0.65 2.58 <0.001 52          

Cite

Citation Scheme:

    

Usage Statistics

Country Views Downloads
United States 12 0
Germany 4 0
China 4 0
Canada 1 0
City Views Downloads
Mountain View 5 0
Shenzhen 4 0
Unknown 4 0
Ashburn 4 0
University Park 1 0
Sunnyvale 1 0
Redmond 1 0
Ottawa 1 0

{[{ mDataHeader[type] }]} {[{ month[type] }]} {[{ tData[type] }]}
Download Stats

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.594.1-0107282/manifest

Comment

Related Items