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Testing site index-site factor relationships for predicting lodgepole pine and interior spruce productivity.. Klinka, Karel; Kayahara, Gordon J.; Chourmouzis, Christine 1998-12-31

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Scientia Silvica Extension Series, Number  18, 1998Testing Site Index-Site Factor Relationships for Predicting LodgepolePine and Interior Spruce Productivity in Central British ColumbiaIntroductionKnowledge of the  potential productivity of a tree  species becomes especially important  when timber  production is  the  primarymanagement objective.  However, direct determination of potential productivity is often not possible. For example, in situationswhere the  site is  unstocked, stocked with trees  unsuitable for productivity measurement, or stocked with species other than  theone of interest.  In these cases, an indirect estimate using known characteristics of the site itself is required. Such estimates weremade using regression to model  site index with indirect measures of site quality for lodgepole pine (Pinus contorta) and interiorspruce  (Picea engelmannii ? P.  glauca)  in  the  Sub-Boreal Spruce (SBS)  zone of central BC.   We tested  the  utility of  theseproductivity relationship models for predicting the site index of lodgepole pine and interior spruce (Kayahara et al. accepted forpublication).MethodsThe model development plots  and the  test  plots were located  on sites  in  central British Columbia (latitude  51?40' to 55?05?N,longitude  119?30' to  128?25?W,  elevation 505 to  1060 m).   The plots were located  in  the  warmer montane boreal climate asdelineated by the SBS zone. The climate is characterized by seasonal extremes of temperature; severe, snowy winters; relativelywarm, moist, and short summers; and moderately high annual precipitation (481 to 1250 mm).In the  model development studies, site index  (height of specified dominant trees  at 50 years breast height age) was used as anestimate of productivity,  and was the dependent variable in  multiple regression  equations.   Subzones or  variants, soil moistureregime (SMR), soil nutrient regime (SNR), and soil aeration regime (SAR) (spruce only) were used in the form of dummy variablesas the independent variables (Table 1).Table 1. ?Best fit? regression models relating site index to combinations of soil moisture regimes, soil nutrient regimes, and soilaeration regimes used as ?dummy? variables.Lodgepole pineSI = 14.69 - 3.89(ED) - 0.27(VD) + 3.25(MD) + 5.46(SD) + 5.84(F) + 8.15(M) + 6.92(VM) - 2.29(P) - 1.57(M) - 0.8(R)adjusted R? = 0.84; standard error of the estimate = 1.60 m; SI = site index;  SMR: ED = extremely dry, VD =  very dry, MD =moderately dry,  SD= slightly dry, F = fresh, M =  moist, VM =  very moist; SNR: P = poor, M = medium, R = rich; the interceptrepresents wet and very rich.Interior spruceSI = 6.07 + 10.41(MDa) + 14.1(SDa) + 11.18(DFr) + 14.56(Fa) + 14.28(Ma) + 12.56(VMa) + 10.35(Mr) + 6.76(Wr) - 1.51(VP) -0.67(P) + 1.12(R) + 1.99(VR)adjusted R? = 0.90; standard error of estimate = 1.17 m; variable names as for lodgepole pine with the following additions: SAR: ?a? =adequate, ?r? = restricted; SMR: DF = dry to fresh, W = wet; SNRs: VR = very rich; the intercept represents wet and medium siteswith deficient aerationWe  sampled  an additional 180 lodgepole  pine and 89 interior  spruce  plots for  use as test  plots.   These plots were located  innaturally established, unmanaged, immature, even-aged, uniformly stocked (60-90% tree layer cover) stands, without a history ofdamage and suppression.  In  each stand,  a 20 x 20 m (0.04  ha) sample  plot was located  to  represent  an individual  ecosystemrelatively uniform in soil, understory vegetation, and stand characteristics. Subzones or variants were identified using the regionalfield guide.  Site SMR, SNR, and for spruce SAR classes were estimated.  The productivity of each stand was estimated by siteindex  (height  at 50 years breast height age). In  each sample  plot, the  five  largest  diameter (dominant)  trees  with no obviousevidence of growth abnormalities and damage were measured for age at breast height, and height. Site index was taken from heightgrowth tables for lodgepole pine for interior spruce.Scientia Silvicais published by the Forest Sciences Department,The University of British Columbia, ISSN 1209-952XEditor: Karel Klinka (klinka@interchange.ubc.ca)Research: Gordon Kayahara (gordon.kayahara@mnr.gov.on.ca)Production and design: Christine Chourmouzis (chourmou@interchange.ubc.ca)Financial support: Northwood Pulp and Timber Ltd. and the G.F. Dodd ChairFor more information contact: G. KayaharaCopies available from: www.forestry.ubc.ca/klinka  or K. Klinka, Forest Sciences Department, UBC,The site index test values for the combinations of SMR, SNR, and, for spruce, SAR, were compared to the values predicted usingthe ?best fit? regression models for pine and spruce. The regression models were tested for bias by measuring the difference inmean site index between the predicted and test values using a paired t-test.  Precision was measured by (1) the square root of theaverage squared deviation of the predicted from the test values, called the square root of the mean squared prediction error (root-MSPR); and (2) by a classification summary table. The latter procedure simply groups the test plots into classes whose site indexdiffered from the predicted site index by increasing one metre increments.ResultsThe regression model for lodgepole pine predicted an overall mean site index value that did not differ significantly (using a pairedt-test) from that of the test data set (mean difference of -0.1 m; p = 0.57; n = 180). However, the model lacked precision having aroot-MSPR  of 2.80 m (Figure  1). Only 56% of the  test  plots had differences from  the  predicted values of 2.0 m or less.    Theregression model for interior spruce  predicted an overall mean site index value that differed significantly (using a paired t-test)from that of the test data set (mean difference +2.9m; p < 0.001; n = 89). Similar to pine, the model lacked precision with a root-MSPR of 3.2. Only 44% of the test plots had differences from the predicted value of 2.0 m or less.Figure 1. Scatter plots comparing thepredicted to measured (from the testdata) site index for lodgepole pine (a)and interior spruce (b).  The dashed lineis the line of equal site indexPredicted site index (m)4 8 12 16 20 24 28Measured site index (m)481216202428Predicted site index (m)4 8 12 16 20 24 28Measured site index (m)481216202428DiscussionThe concern for a forest manager is that the model for lodgepole pine was unbiased and the model for interior spruce was biasedtowards  higher site  index,  and neither model was very precise.   Thus, autecological productivity relationships  developed forcentral British Columbia appear to have some practical utility when applied to the prediction of the mean site index for a given areafor lodgepole pine, but little utility for interior spruce. There appears to be little utility for either species for predicting site indexof individual  sites within an area. Forest managers requiring a site index  prediction tool  need to  decide whether the  degree ofaccuracy and precision provided by these models is acceptable.  If not, alternative approaches will need to be developed.ReferencesKayahara G.J., K. Klinka, and P.L. Marshall. 1998. Testingsite index - site factor relationships for predicting Pinuscontorta  and Picea engelmannii x glauca  productivityin central British Columbia. For. Ecol. Manage. 110: 141-150.(a) (b)

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