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Productivity of western larch in relation to categorical measures of climate, soil moisture, and soil… New, David Morley 1999

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PRODUCTIVITY O F W E S T E R N L A R C H IN R E L A T I O N T O C A T E G O R I C A L M E A S U R E S O F C L I M A T E , SOIL MOISTURE, AND SOIL NUTRIENTS by D A V E ) M O R L E Y N E W A THESIS S U B M I T T E D IN P A R T I A L F U L F I L M E N T OF T H E R E Q U I R E M E N T S F O R T H E D E G R E E O F M A S T E R OF S C I E N C E in T H E F A C U L T Y OF G R A D U A T E STUDIES Department of Forest Sciences We accept this thesis as conforming to the required standard T H E U N I V E R S I T Y OF BRIT ISH C O L U M B I A July 1999 © David Morley New, 1999 In p r e s e n t i n g t h i s t h e s i s i n p a r t i a l f u l f i l m e n t of the requirements f o r an advanced degree a t the U n i v e r s i t y of B r i t i s h Columbia, I agree t h a t the L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r r e f e r e n c e and study. I f u r t h e r agree that permission f o r extensive copying of t h i s t h e s i s f o r s c h o l a r l y purposes may be granted by the head of my department or by h i s or her r e p r e s e n t a t i v e s . I t i s understood t h a t copying or p u b l i c a t i o n of t h i s t h e s i s f o r f i n a n c i a l gain s h a l l not be allowed without my w r i t t e n permission. Department of The U n i v e r s i t y of B r i t i s h Columbia Vancouver, Canada Date ABSTRACT The relationship between western larch (Larix occidentalis Nutt.) productivity, as determined by site index, and estimates of climate, soil moisture and soil nutrients, as delineated within the Biogeoecosystem classification system, was examined. Data was collected from 315 even-aged stands throughout the range of western larch in B .C. Climate, expressed by subzone, showed a significant effect on the site index of western larch; however, significant differences occurred only between two groups: lower precipitation (Interior Douglas-fir) and higher precipitation (Interior Cedar - Hemlock and Montane Spruce ) subzones. The climatic growth optimum for western larch corresponded to the intermediate segment of a regional climatic gradient - wetter temperate climate - that is represented by the Dry Warm Interior Cedar -Hemlock subzone. In relation to edatopes, site index significantly increased from water-deficient to moist sites and decreased from moist to wet sites, and on water-deficient sites, it increased with increasing soil nutrient regime. On water-deficient sites, estimates of both actual soil moisture regime and soil nutrient regimes had both positive significant and consistent effects on site index. On non-water deficient sites, the effect of soil nutrient regime appeared to be marginal. A cross-validated prediction model based on actual soil moisture regime and soil nutrient regime accounted for 83% of the variation in site index of western larch suggesting that there is a strong correlation between ecological site classification and western larch productivity. The developed model, combined with the identification of actual soil moisture regime, and soil nutrient regime can be used to provide reliable site index predictions for western larch throughout its range in B.C. to a level of accuracy required for practical forest management. Key Words Larix occidentalis, biogeoclimatic subzone, actual soil moisture regime, soil nutrient regime, productivity, prediction, site index. TABLE OF CONTENTS A B S T R A C T ii T A B L E O F C O N T E N T S i i i L IST O F T A B L E S v L IST OF F I G U R E S vi i A C K N O W L E D G E M E N T S vi i i 1. I N T R O D U C T I O N 1 1.1 The Role of Western Larch 1 1.2 Site Index as the Measure of Forest Productivity 3 1.3 Estimating Site Index from Environmental Factors 3 1.4 A n Ecological Framework for Estimating Site Index 4 1.5 Objectives 6 2. T H E S T U D Y A R E A 8 3. M A T E R I A L S A N D M E T H O D S 10 3.1 Selection of Study Sites and Sampling 10 3.2 Estimates of Climate, Soil Moisture, and Soi l Nutrient Regimes 13 3.3 Analysis of the Relationship between Site Index and Climate 16 3.4 Analysis of Relationships between Site Index and Soil Moisture and Nutrient Regimes 18 4. R E L A T I O N S H I P S B E T W E E N SITE I N D E X A N D C L I M A T I C , SOIL M O I S T U R E A N D SOIL N U T R I E N T R E G I M E S 23 4.1. Relationships between Site Index and Climate 23 4.2 Relationships between Site Index and Soi l Moisture and Nutrient Regimes 26 4.2.1 The IDF Group 26 4.2.2 The I C H - M S Climate Group 27 4.3 Discussion 30 4.4 Conclusions 38 5. P R E D I C T I O N OF W E S T E R N L A R C H SITE I N D E X 40 5.1 Introduction 40 5.2 Methods 40 5.3 Results 42 5.4 Discussion 46 5.6 Conclusions 51 6. C O N C L U S I O N S 52 L I T E R A T U R E C I T E D 54 A P P E N D I X 1 59 A P P E N D I X 2 68 A P P E N D I X 3 71 A P P E N D I X 4 73 A P P E N D I X 5 74 iv L I S T O F T A B L E S Table 3.1 Means of selected climatic characteristics for the eight study subzones 1: Very Dry Hot IDF (IDFxh), Dry M i l d IDF (IDFdm), Moist Warm IDF (EDFmw), Dry Warm I C H (ICHdw), Moist Warm I C H (ICHmw), Moist Cool I C H (ICHmk), Dry M i l d M S (MSdm), and Dry Cool M S (MSdk). The means were calculated from the data obtained from Environment Canada (1982). Precipitation is in mm and temperature in 0 Celsius 11 Table 3.2 Number of study stands stratified according to the eight study subzones. Abbreviations for subzones as in Table 3.1 12 Table 3.3 Conversion table of relative to actual soil moisture regime (RSMRs to A S M R s ) for the eight study subzones. A S M R s of zonal sites are shown outlined and shaded. Abbreviations for subzones as in Table 3.1; abbreviations for A S M R s are: X D - extremely dry, E D - excessively dry, V D - very dry, M D - moderately dry, SD slightly dry, F - fresh, M -moist, V M - very moist, W - wet, V W - very wet 14 Table 3.4 Western larch site index means (in boldface), standard deviations (in parentheses) and number of stands on zonal sites stratified according to study subzone. Abbreviations for subzones as in Table 3.1 16 Table 3.5 Western larch site index means (in boldface), standard deviations (in parentheses) and number of stands on sites of poor and medium soil nutrient regime stratified by actual soil moisture regime and subzone. Abbreviations for subzones as in Table 3.1 17 Table 3.6 Number of stands and means (in bold print) and standard deviations (in parentheses) of western larch site index (m @ 50 yrs bh) of the 315 study sites stratified according to actual soil moisture and soil nutrient regimes 19 Table 3.7 Edatopic grids for the IDF group showing the number of stands sampled in each edatope and those edatopes selected in: (A) Block 1 for analysis and (B) Block 2 for analysis. Selected edatopes are shaded in grey. Two identical edatopic grids are presented in order to clearly show edatope selection for each block 20 Table 3.8 Edatopic grids for the I C H - M S group showing the number of stands sampled in each edatope and those edatopes selected in: (A) Block 1 for analysis and (B) Block 2 for analysis. Selected edatopes are shaded in grey. Two identical edatopic grids are presented in order to clearly show edatope selection for each block 21 v Table 4.1 Analysis of variance for mean site index on zonal sites in eight study subzones (a = 0.05) 24 Table 4.2 Western larch site index means on zonal sites stratified according to study subzone. Values in the same row with different lower case superscripts are significantly different (a = 0.05). Abbreviations for subzones as in Table 3.1 24 Table 4.3 Set 2: Analysis of variance for mean site index on moderately dry, poor and medium sites in seven study subzones (a = 0.05) 25 Table 4.4 Set 3: Analysis of variance for mean site index on slightly dry, poor and medium sites in seven study subzones (a = 0.05) 26 Table 4.5 Analysis of variance for mean site index in Block 1 of the IDF climatic group (a = 0.05) 27 Table 4.6 Analysis of variance for mean site index in Block 2 of the IDF group (a = 0.05) 27 Table 4.7 Analysis of variance for mean site index in Block 1 of the I C H - M S group (a = 0.05) 29 Table 4.8 Analysis of variance for mean site index in Block 2 of the I C H - M S group (a = 0.05) 29 Table 5.1 The A S M R S N R prediction model developed from the construction data set (n = 210) relating site index (m @ 50 yrs bh) of western larch to combinations of actual soil moisture and soil nutrient regimes 42 Table 5.2 The A S M R S N R prediction model developed from all site data (the combined construction and test data sets (n=315)) relating site index (m @ 50 yrs bh) of western larch to combinations of actual soil moisture and soil nutrient regimes 45 Table 5.3 Edatopic grid showing the A S M R S N R model (n=315) predicted site index values and ± 95% confidence interval (m), measured mean site index values (m @ 50 yr bh), and S IBEC mean site index values (m @ 50 yr bh) according to actual soil moisture and soil nutrient regimes. Sample sizes indicated refer only to the measured and predicted site index values 47 vi L I S T O F F I G U R E S Figure 2.1 Range of western larch in B .C. The location of major and minor sampling areas are indicated by the larger and smaller points, respectively 9 Figure 4.1 Bar graph of western larch site index on zonal sites in eight study subzones. Error bars are for standard deviation. Abbreviations for subzones as in Table 3.1 25 Figure 4.2 Means and standard error of means of western larch site index for (A) poor and medium hygrosequences in relation to very dry, moderately dry, and slightly dry A S M R s for Block 1 of the IDF group and (B) for poor, medium, and rich hygrosequences in relation to moderately dry and slightly dry A S M R s for Block 2 of the IDF group 28 Figure 4.3 Means and standard error of means of western larch site index for (A) poor, medium and rich hygrosequences in relation to very dry, moderately dry, and slightly dry A S M R s for Block 1 of the I C H - M S group and (B) for medium, and rich hygrosequences in relation to very dry, moderately dry, slightly dry, and fresh A S M R s for Block 2 of the I C H - M S group 31 Figure 5.1 Residual analysis for the A S M R S N R construction model (n = 210): (A) residual (m) versus predicted site index (m @ 50 yrs bh) and (B) measured versus predicted site index (m @ 50 yrs bh) 43 Figure 5.2 Prediction error for the A S M R S N R construction model in relation to measured site index (m @ 50 yrs bh) of the test data set (n=105) 45 Figure 5.3 Residual analysis for the A S M R S N R model (n=315). Measured versus predicted site index (m @ 50 yrs bh) 46 vi i A C K N O W L E D G E M E N T S I would first like to thank Dr. Karel Klinka who willingly accepted me as his student despite the fact that my performance outside a field based working relationship had not been proven. It has been through his constant support and encouragement that I am now able to acknowledge him here. To him I am most grateful. I am also much indebted to those belonging to the "Klinka Machine" each of who in their own way have aided me in achieving this end. I would like to thank my committee members, Dr. Les Lavkulich and Dr. Peter Marshall for their time and their counsel which were not solely confined to matters of science. Finally, I must thank my wife Allison most of all, she was a constant support and managed to hold our family unit together and maintain a semblance of home despite my mind and body often being elsewhere. 1. I N T R O D U C T I O N 1.1 The Role of Western L a r c h Western larch (Larix occidentalis Nutt.), a Western North American-Cordilleran species, is one of three larch species that occur in British Columbia. It is the only one of these larch that has timber values and silvical attributes that make it a very desirable component in a forest (Morrison 1991). Therefore, it would be beneficial to know what is the potential productivity of larch on all sites that are capable of supporting its growth. Western larch contributes to aesthetic and wildlife diversity of forests. Seasonal changes in foliage add a great deal to the aesthetic diversity of the stands in which larch is even a small component. Larch veterans contribute to wildlife diversity by providing habitat for pileated woodpeckers and other primary cavity nesters (McClelland 1992). Larch's leafless winter habit makes it less palatable as browse for foraging ungulates. This enables larch seedlings to develop in areas where other tree species are consistently browsed due to intense wildlife feeding. Pure stands of western larch are infrequent; most often it forms an admixture - usually a dominant stratum in mixed-species stands that have established after wildfires. The common associates with larch in continental temperate climates are lodgepole pine (Pinus contorta Dougl. ex Loud.), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), western hemlock (Tsuga heterophylla (Raf.) Sarg.), and western redcedar (Thuja plicata Donn D. Don) Spach). Hybrid spruce (Picea engelmannii Parry ex Engelmann x P. glauca (Moench) Voss) and subalpine fir (Abies lasiocarpa (Hook.) Nutt.) associate with larch in mild continental subalpine boreal climates. Within its relatively small range in the south-eastern part of the province, shade intolerant western larch is one of the fastest growing species, with the exception of some hardwoods, such as Populus species and paper birch (Betula papyrifera Marsh.). The only other conifer that can compete with larch is lodgepole pine and it is only for the first 50 years, after which long-lived larch (about 900 years) exceeds it in height growth (Feidler and Lloyd 1992). Its straight growth form and self-pruning make it a valuable timber crop species for lumber production. In addition, larch begins to produce high-density mature wood at 15 years of age while most other species require 40 years. Producing such wood quality at young ages will become desirable as old-growth stands are depleted and log size in second growth stands decreases (Jaquish et al. 1992). Although larch timber is usually marketed with or as Douglas-fir, its density and strength make it the strongest of the softwood species, and it is often used in manufacturing laminated beams and load bearing timbers (Keegan et al. 1992). Western larch has few serious damaging agents. The most common is dwarf mistletoe {Arceuthobium laricis (Piper) St. John) (Carlson et al. 1992). The larch sawfly (Pristiphora erichsonii Htg.), larch casebearer (Coleophora laricella Hbn.), and spruce budworm (Choristoneura spp.) all pose a threat, but attacks rarely result in mortality (Carlson et al. 1992). In terms of resistance to Armillaria root disease (Armillaria ostoyae Ramagn. Herink), larch is seen as a viable alternative to Douglas-fir, especially on sites that are known to be infected and, due to environmental constraints, do not permit extensive site preparation (Morrison 1991). In order to maintain a component of resistant tree species on such sites, western larch is being planted within and beyond its present natural range. 2 1.2 Site Index as the Measure of Forest Productivity Tree growth depends on the ecosystems in which they are found. The ability to predict the growth of any tree species in any ecosystem is one of the prerequisites for sustainable forest management. Compared to other measures, site index (mean height of the largest diameter (dominant) trees in even-aged stands at a breast height reference age) is the most practical and widely used means of predicting tree growth, and estimating forest productivity (i.e., the capacity of a site to support the growth of a given tree species)(Spurr and Barnes 1980). However, the use of site index for estimating forest productivity is not always possible, such as in situations where the trees are suppressed in growth, damaged, not of a measurable age, or absent (e.g., Hagglund 1981, Monserud 1988, Monserud et al. 1990). In such situations, another method of estimating forest productivity must be used. 1.3 Estimating Site Index from Environmental Factors Methods that do not use direct growth measures for estimating forest productivity are referred to as indirect (Clutter et al. 1983). Indirect methods use individual or complex (synoptic) environmental factors or classes (taxa) delineated in various classifications or combinations of both. To predict site index, most indirect methods employ multiple regression techniques, with several selected categorical and/or continuous independent variables that have been found to be strongly related to site index (e.g., Jones 1969, Green et al. 1989, Klinka and Carter 1990, Chen et al. 1998). Success and portability of such predictions vary, depending on the selected variables. As a rule, predictions are accurate in specific, usually small areas; 3 however, accuracy is most often lost with increasing area of application because the influence of the selected variables diminishes due to compensating effects (Monserud et al. 1990). Furthermore, procedures for obtaining measures of environmental factors can be time demanding and may require time-consuming laboratory analyses (Klinka and Carter 1990). Therefore, there is a need for site index estimates from factors that have a direct influence on tree growth and that are easily measured in the field. 1.4 An Ecological Framework for Estimating Site Index A s a rule, each forest consists of a number of ecosystems (i.e., a number of stands and sites) that are distributed in a certain pattern in the landscape. A biologically viable and sustainable manipulation of ecosystems necessitates knowing what ecosystems are in the forest and their distribution pattern. Ecosystem classification provides a means for recognizing similar (and different) ecosystems, and hence, is a tool for applying ecosystem- or site-specific management. The system of biogeoclimatic ecosystem classification (BEC) (Krajina 1969, Pojar etal. 1987) is the most widely used classification system in British Columbia today (MacKinnon et al. 1992). This system, which has been used for about twenty years, has provided the ecological foundation for all silvicultural endeavours in the province. Site identification and hence, determination of ecological qualities of forest sites, is easily and consistently carried out with the aid of subzone maps and field keys. 4 In the BEC system, local ecosystems, represented either by plant communities or sites, are classified either according to their vegetation or environmental properties, and organized into a climatic superstructure using zonal classification (Pojar et al. 1987). Sites are classified, as well as identified, using three environmental characteristics: (1) climatic regime (represented by biogeoclimatic units), (2) soil moisture regime, (3) soil nutrient regime, and, if appropriate, by additional environmental factors or properties strongly affecting the development of vegetation (Pojar et al 1987). These three characteristics are, in fact, the primary factors influencing plant establishment, growth, and productivity (e.g., Major 1951). Therefore, site classification according to the BEC system is essentially an integration of the determinants of plant growth. The relationship of site productivity with these primary factors or delineated site units is implicit in the system. Several studies using climatic, soil moisture, and soil nutrient regimes as categorical or continuous variables were successful in predicting site index for a number of tree species in the province. For example, Green et al. (1989) successfully predicted the site index of coastal Douglas-fir using field estimates of climatic, soil moisture, and soil nutrient regimes. Klinka and Carter (1990) reported that catagorical models based on soil moisture and nutrient regimes were superior to analytical models in predicting coastal Douglas-fir site index, and concluded that indirect synoptic measures of climate, soil moisture, and soil nutrients were good predictors of Douglas-fir site index over a large area and provided useful estimates of the direct measures. Using field estimates of soil moisture, nutrient, and aeration regimes as predictors, Wang and Klinka (1996) developed a model that accounted for 87% of the variation in white spruce site index in the Sub-Boreal Spruce zone. Using soil moisture and nutrient regimes in a combination with selected climatic and topographic properties as independent variables, Chen et al. (1998) developed models for estimating site index of trembling aspen (Populus tremuloides Michx.) in the Boreal White and Black Spruce zone. The models developed provide precise predictions and their predictors are easily and consistently identified in the field. 1.5 Objectives In view of the importance of western larch, the goal of this study was to expand the efforts of past research to this species, for which no provincial height growth and site index functions and productivity - site relationships have been developed. Although there is considerable information about the silvical characteristics of larch (Schmidt et al. 1976, Fielder and L loyd 1992, K l inka et al. 1998), little is known about its potential productivity on sites on which it may grow. The results of this study wi l l increase our knowledge about the productivity of western larch in relation to sites as characterized by the B E C system, and wi l l contribute to the provincially co-ordinated Site Index - Biogeoclimatic Ecosystem Classification program. The study objectives were: (1) to estimate site index of western larch-dominated stands using the newly developed site index functions (Brisco 1999), (2) to describe the pattern and variation in mean site index in relation to field estimates of climatic, soil moisture, and soil nutrient regimes, and (3) to develop and validate models predicting larch site index using field estimates of climatic, soil moisture, and soil nutrient regimes, having in mind operational application. 6 These objectives were accomplished by sampling 315 stands located across the entire range of western larch in the province. Height growth functions developed by Brisco (1999) were used to determine site index of study stands located across a wide range of environmental conditions. Descriptive statistics were used to examine the relationship between site index and inferred climatic, soil moisture, and soil nutrient regimes. Inferential statistics were used to evaluate the strength of these relationships and site index predictions. To detect the relatively strongest climatic influence on height growth, the analysis of site index - climate relationships was based on the zonal concept (Pojar et al. 1987), so only stands on zonal sites were examined. In addition to examining separately the effect of soil moisture and nutrient regimes on site index, this study also examined the effect of their interaction on larch site index because this effect has not been addressed in the previous productivity - site studies. Unlike most previous studies, this study cross-validates the model developed for predicting larch site index (Neter et al. 1996). 7 2. THE STUDY AREA To cover the entire native range of western larch in British Columbia, the study area comprised the south-central and eastern part of the province ranging from 49° 03' to 50° 50' N latitude, from 115° 58' to 119° 25' W longitude, and from 395 to 1,590 m in elevation. The boundaries of the range are quite abrupt; larch does not occur or is very infrequent north of the Shuswap Lake (51° N latitude), west of the Okanagan River, and east of the Rocky Mountains (Thompson 1992) (Figure 2.1). In view of the large size of the area, the great variation in orography and geomorphology, and the history of disturbance, the study area features a complex pattern of ecosystems as climate, soils, and vegetation can vary considerably over a short distance. The climate of the area is continental and ranges from dry cool temperate (delineated by the Interior Douglas-fir (IDF) zone) to wet cool temperate (delineated by the Interior Cedar-Hemlock (ICH) zone) to mild subalpine boreal (delineated by the Montane Spruce (MS) zone) (Krajina 1969). Glaciated igneous, metamorphic, and sedimentary rocks, glacial landforms, and colluvium dominate the landscape, although areas of fluvial and lacustrine deposits are encountered. In the mountains, thick deposits of glacial till are restricted to valley bottoms and adjacent lower slopes. Soils vary in texture and range from Brunisols to Luvisols to Podzols (Canada Soil Survey Committee 1978) with Moder to Mor humus formation (Green et al. 1993). Forest communities in the study area are predominantly coniferous and vary in age, composition, and structure according to the variation in site and the history of disturbance. The 8 most common tree species are Douglas-fir, ponderosa pine (Pinus ponderosa Dougl. ex P. & C Lawson), lodgepole pine, and trembling aspen in the Interior Douglas-fir (IDF) zone; western hemlock, western redcedar, and paper birch in the Interior Cedar - Hemlock (ICH) zone; and lodgepole pine, hybrid spruce, and subalpine fir in the Montane Spruce (MS) zone. A more complete description of the study area is given in Holland (1976), Valentine et al. (1978), and Meidinger and Pojar (1991). 125° 120° 115° Figure 2.1 Range of western larch in B.C. The location of major and minor sampling areas are indicated by the larger and smaller points, respectively. 9 3. MATERIALS AND METHODS 3.1 Selection of Study Sites and Sampling The presence of western larch in the study area is variable, as it has depended primarily on the history of disturbance by wildfires. Larch forms most often a minor or major admixture in mid-seral, coniferous stands across a wide range of sites. In general, its presence decreases with increasing latitude, precipitation, and elevation (i.e., decreasing temperature). To obtain the widest possible range in site quality, study sites were deliberately selected across the entire spectrum of climatic, soil moisture, and soil nutrient conditions found within in the study area. Within the IDF zone, study sites were located in three subzones: Very Dry Hot IDF (IDFxh), Dry Mild IDF (IDFdm) and Moist Warm IDF (IDFmw). Within the ICH zone, study sites also were located in three subzones: Dry Warm ICH (ICHdw), Moist Warm ICH (ICHmw), and Moist Cool ICH (ICHmk). Within the MS zone, study sites were located in two subzones: Dry Mild MS (MSdm) and Dry Cool MS (MSdk) (Meidinger and Pojar 1991). A detailed description of climatic, vegetation, and soil characteristics for these subzones is given in Lloyd et al. (1990) and Braumandl and Curran (1992). Selected climatic characteristics of the subzones are given in Table 3.1. Within each subzone, study sites were located in naturally established, unmanaged, more or less even-aged, mid-seral, western larch-dominated stands, which developed after wildfires. The selected stands were uniformly stocked (from 60 to 90% tree layer cover), ranged considerably in age (from 35 to 158 years @ bh) and site index (from 6.9 to 29.3 m @ 50 yr bh) 1 0 Table 3.1 Means of selected climatic characteristics for the eight study subzones : Very Dry Hot IDF (IDFxh), Dry M i l d IDF (IDFdm), Moist Warm IDF (TDFmw), Dry Warm I C H (ICHdw), Moist Warm I C H (ICHmw), Moist Cool I C H (ICHmk), Dry M i l d M S (MSdm), and Dry Cool M S (MSdk). The means were calculated from data obtained from Environment Canada (1982). Precipitation is in mm and temperature in 0 Celsius Characteristic IDFxh IDFdm TDFmw ICHdw ICHmw ICHmk MSdm MSdk Mean annual precipitation 409 446 507 746 947 722 605 590 Mean precipi-tation May-September 163 193 210 246 297 333 199 229 Mean precipi-tation of the driest month 20 26 35 47 31 28 23 Mean precipi-tation of the wettest month 54 61 64 107 153 106 83 87 Mean annual temperature 6.0 5.0 6.1 7.5 6.6 4.2 3.0 3.4 Mean tempera-ture of the coldest month -7.6 -9.1 -6.7 -4.1 -5.5 -9.3 -8.3 -10.2 Mean tempera-ture of the warmest month 17.8 17.6 17.9 19.1 18.0 15.0 14.4 15.6 Frost free period (days) 111 105 108 144 134 ND 69 68 Degree days >5°C 1653 1528 1640 1881 1685 1241 991 1209 Two lowercase letters abbreviations used for subzones designate relative precipitation regime: x - very dry, d - dry, m - moist, w - wet, and v - very wet; and relative temperature regime: h - hot, w - warm, m - mild, k - cool, and c - cold (Meidinger and Pojar 1991). 2ND - no data obtained. 11 and were without signs of obvious damage and no history of growth suppression. The presence or absence of possible growth suppression was examined visually on increment cores. The number of study stands by subzones is given in Table 3.2, and selected characteristics of study sites are compiled in Appendix 1. Each selected stand had a more or less uniform, upper tree layer which featured dominants of western larch. In each study stand, a 20 x 20 m (0.04 ha) sample plot was located to represent an individual ecosystem that was relatively uniform in stand, understory vegetation, and soil characteristics. In order to prevent bias in plot selection, the first plot within a candidate stand that met the predetermined criteria was chosen. Western larch productivity was estimated by site index (m @ 50 years bh). In each sample plot, the three largest diameter (dominant) larch trees, with no obvious evidence of growth abnormalities and damage, were measured for age at breast height, using an increment borer, and height, using a clinometer. The average height and breast height age for the measured trees at each plot was calculated, and the larch site index for each study plot was determined using the height growth functions developed by Brisco (1999). Table 3.2 Number of study stands stratified according to the eight study subzones. Abbreviations for subzones as in Table 3.1. IDFxh IDFmw IDFdm ICHmw ICHdw ICHmk MSdm MSdk 31 12 53 87 22 36 38 37 12 3.2 Estimates of Climate, Soil Moisture, and Soil Nutrient Regimes Site and vegetation characteristics of each study stand were described using the site diagnosis procedure of Green and Klinka (1994). Biogeoclimatic subzone, representing climatic regime, was identified using maps obtained from the Kamloops and Nelson regional offices of the BC Ministry of Forests. Each site was identified in the field to one of eight relative soil moisture regimes (RSMRs) and to one of five soil nutrient regimes (SNRs). Field identification of RSMRs and SNRs was based on keys, using a combination of topographic and soil morphological properties (Appendices 2 and 3). Despite the fact that each regional climate has a unique soil moisture gradient, the same RSMRs are used to describe soil moisture conditions of climatically different sites (Krajina 1969, Klinka et al. 1989). However, comparison of soil moisture conditions across a range of climates requires an absolute scale (i.e., conversion of RSMRs into actual soil moisture regimes (ASMRs)). The ASMR (a class of an actual soil moisture gradient) describes the average amount of soil water that is actually available for plant growth during the growing season. The gradient is composed of ten ASMRs, ranging from extremely dry (representing sites with a prolonged, chronic water deficit) to fresh (representing sites with no water deficit and surplus) to very wet (representing sites with a prolonged water excess) (Appendix 4). In this study, field-estimated RSMRs were converted to ASMRs using the criteria and methods proposed by Klinka et al. (1989) (Table 3.3, Appendix 4). First, moist, very moist, wet, and very wet ASMRs were determined according to the occurrence and depth of the growing-season groundwater table or gleyed soil horizon. Second, the actual 13 Table 3.3 Conversion table of relative to actual soil moisture regime (RSMRs to ASMRs) for the eight study subzones. ASMRs of zonal sites are shown outlined and shaded. Abbreviations for subzones as in Table 3.1; abbreviations for ASMRs are: X D -extremely dry, ED - excessively dry, V D - very dry, M D - moderately dry, SD slightly dry, F - fresh, M - moist, V M - very moist, W - wet, V W - very wet. RSMR JDFxh JDFdm IDFmw Subzone ICHdw ICHmw ICHmk MSdm MSdk Actual Soil Moisture Regime 0 EX V D V D V D V D V D V D V D 1 ED VD VD MD M D M D M D M D 2 VD VD V D M D M D M D M D M D 3 VI» \1D Ml) M> SD SI) SI.) SI) 4 Ml) \1D SD S D F F SI) SI) 5 SD SD SD F M M F F 6 F F-M F-M M V M V M M M 7 M - V M VM-W VM-W VM-W W W V M - W V M - W evapotranspiration/potential evapotranspiration ratio (Et/Emax), using the Energy/Soil Limited model of Spittelhouse and Black (1981), was calculated in each study subzone for sites having RSMRs of 4 (so called mesic or intermediate sites) to determine whether they were water-deficient or not. If they were found to be non-deficient, they were converted to fresh or moist ASMRs, depending on the presence or absence of a utilization phase in annual water balance, respectively; if they were found to be deficient, they were converted to the appropriate ASMRs according to the calculated E^Emax r a t i ° - Third, the remaining RSMRs (<4) were converted to water-deficient A S M R s according to the estimated water-holding capacity of the soils in the field and the correlations between R S M R s and A S M R s established in other studies (e.g. K l inka and Carter 1990, K l inka et al. 1996b, Kayahara et al. 1997). Whenever understory vegetation was well developed, conversions were verified by indicator plant analysis (Kl inka et al. 1989, Green and K l inka 1994). The soil moisture spectra of the study stands were compared to the standard spectra using V T A B R E P O R T E R (Emanuel 1998) and each study stand was assigned to one of the five water-deficient A S M R s . To some extent, A S M R s of rapidly to imperfectly drained soils imply soil aeration regimes (SARs), as soil aeration usually decreases with decreasing depth of groundwater table from moist to very wet sites depending on soil drainage. However, S A R s were not estimated in this study, and it was assumed that all the study stands had good or adequate aeration. The five SNRs (classes framed along a soil nutrient gradient) represent sites with the levels of plant-available soil nitrogen ranging from low levels (very poor SNR) to high levels (very rich SNR) . Although not quantified in this study, the quantitative characterization of SNRs in several other studies suggests a possible relationship between field-identified, qualitative and quantitative SNRs of the study sites (Appendix 5). Several of the study sites in the Rocky Mountains Trench featured calcareous soils, which should be recognized as a special S N R , and were not included in the study data set. As with A S M R s , whenever possible, the assignment of study sites into SNRs was augmented by indicator plant analysis (Kl inka et al. 1989, Green and K l inka 1994). 15 3.3 Analysis of the Relationship between Site Index and Climate Stands on zonal sites were analyzed to examine the influence of climate on the height growth of western larch, with minimum confounding effects of local topography and soils. The zonal concept (e.g., Hills 1952, Krajina 1969, Daubenmire 1976) presumes that the vegetation on sites that represent intermediate topographic and edaphic conditions, relative to existing extremes found within an area influenced by a specific regional climate, will reflect the influence of that climate on vegetation most strongly. The zonal sites occurred on flats, gently sloping heights of land, or mid-slopes where the influence of aspect was minimized, and had RSMRs of 3 and 4 and poor and medium SNRs. In total, 151 stands on zonal sites were selected for the analysis (Table 3.4). Table 3.4 Western larch site index means (in boldface), standard deviations (in parentheses) and number of stands on zonal sites stratified according to study subzone. Abbreviations for subzones as in Table 3.1. IDFxh IDFdm IDFmw ICHdw ICHmk ICHmw MSdk MSdm 18.9 18.8 21.4 22.4 20.9 20.8 20.8 21.6 (1.7) (2.1) (2.0) (1.1) (1.5) (1.8) (1.7) (1.8) n=12 n=34 n=3 n=4 n=19 n=44 n=18 n=17 Stands on sites with the same ASMR and SNR were analyzed to further examine the influence of climate on larch site index. The selected stands were on sites that all had poor and medium SNRs, and were either very dry (Set 1), moderately dry (Set 2), or slightly dry (Set 3) 16 (Table 3.5). In total, 213 stands were selected for the analysis. In both analyses, following calculation of the mean larch site index for each study subzone, a simple comparison was made to determine if the differences in the mean site index between any of the subzones were large enough to merit statistical testing. A difference of 2 meters (or 10%) or larger was considered to warrant testing. One-way analysis of variance (a = 0.05) and the Tukey pair-wise comparison test (a = 0.05)(Chambers et al. 1983) were used to detect the presence of differences in the mean site index between study subzones. Table 3.5 Western larch site index means (in boldface), standard deviations (in parentheses) and number of stands on sites of poor and medium soil nutrient regime stratified by actual soil moisture regime and subzone. Abbreviations for subzones as in Table 3.1. Actual Soil Subzone Moisture Regime IDFxh IDFdm IDFmw ICHdw ICHmk ICHmw MSdk MSdm Very Dry 14.6 14.4 ND 1 ND 14.6 14.7 ND 14.3 (Set 1) (2.4) (1.4) - - (0.8) (1.4) - (1.0) n=6 n=3 - - n=3 n=4 - n=3 Moderately Dry 18.9 18.9 ND 20.8 17.6 17.6 18.4 18.5 (Set 2) (1.7) (2.1) - (0.7) (1.7) (1.8) (1.6) (1.6) n=12 n=34 - n=3 n=7 n=ll n=12 n=8 Slightly Dry 21.7 18.9 ND 22.4 20.9 20.7 20.8 21.6 (Set 3) (0.4) (2.1) - (1.1) (1.5) (1.8) (1.7) (1.8) n=3 n=3 - n=4 n=18 n=44 n=18 n=17 1 ND - no data were obtained due to the absence or sporadic occurrence of western larch under some edaphic conditions. 17 3.4 Analysis of Relationships between Site Index and Soil Moisture and Nutrient Regimes Following identification of ASMR and SNR for each sample plot (Appendix 1), study stands were stratified according to edatopes (Table 3.6). Each edatope represents a combination of a particular RSMR or ASMR and SNR - i.e. a cell on a two-dimensional matrix (edatopic grid). It would be desirable for studies of site - productivity relationships to have equal representation of samples with respect to climatic and edaphic factors within the native range of western larch. However, this situation was not possible because certain combinations of climatic, soil moisture, and soil nutrient regimes did not occur or occurred rarely. The usual sequence of edatopes in a topographically diversified landscape proceeds in a diagonal direction from water-deficient and poor sites to water-surplus and rich sites. Such a sequence was present and sampled in the study area. Larch stands were poorly represented on excessively dry, moist, very moist, and wet ASMRs and on very poor and very rich SNRs (Table 3.6). To minimize the confounding effect of climate when examining the effect of ASMR and SNR on western larch site index, the entire data set of 315 stands was divided into two subsets (climatic groups) according to the result of the climate analysis. The stands from the IDF zone formed one group (IDF group) and those sampled in the ICH and MS zones formed the other group (ICH-MS group). To facilitate the analysis, the study stands were stratified by edatopes separately for each climatic group. The edatopic grids were used as a visual means for selecting stands for the analysis. For each climatic group, two blocks of edatopes (Block 1 and 2) were selected for the analysis. Each block consisted of two or three sequences of adjacent edatopes, with each sequence comprised of edatopes with the same SNR but different ASMRs (hygrosequences, Major 1951)(Table 3.7, 3.8). 18 Table 3.6 Number of stands, means (in bold print) and standard deviations (in parentheses) of western larch site index (m @ 50 yrs bh) for the 315 study sites, stratified according to actual soil moisture and soil nutrient regimes. Actual soil Soil nutrient regime moisture regime Very poor Poor Medium Rich Very rich Excessively dry 3 ND 1 ND ND ND 8 . 6 (2.21) -Very dry 6 14 8 2 ND 1 3 . 2 1 4 . 3 1 5 . 4 1 6 . 6 (1.36) (1.49) (1.47) (1.33) Moderately dry ND 45 44 6 ND 1 7 . 2 1 9 . 9 2 0 . 8 (1.15) (1.50) (2.77) Slightly dry ND 52 58 28 ND 1 9 . 9 2 1 . 9 2 3 . 7 0 (1.06) (1.72) (1.72) Fresh ND ND 18 11 5 2 4 . 2 2 4 . 1 2 5 . 6 (0.78) (1.67) (0.99) Moist ND ND 1 2 5 2 6 . 7 2 6 . 2 2 6 . 8 (NA) (0.08) (0.76) Very moist ND ND ND 4 1 2 0 . 6 1 8 . 7 (2.10) (NA 2) Wet ND ND ND 1 ND 2 0 . 4 (NA) ND - no site index data were obtained due to the absence or sporadic occurrence of western larch under some edaphic conditions. 1 9 2 N A - not available Table 3.7 Edatopic grids for the DDF group showing the number of stands sampled in each edatope and those edatopes selected in: (A) Block 1 for analysis and (B) Block 2 for analysis. Selected edatopes are shaded in grey. Two identical edatopic grids are presented in order to clearly show edatope selection for each block. (A) Actual Soil nutrient regime moisture Very poor Poor Medium Rich Very rich regime Excessively dry 1 Very dry 2 1111111 j ^ H | | j H | j | f Moderately dry 19 28 ' 4 Slightly dry 1 15 Fresh 1 1 1 Moist 1 3 Very moist 1 Wet (B) Actual Soil nutrient regime moisture Very poor Poor Medium Rich Very rich regime Excessively dry 1 Very dry 2 7 3 Moderately dry 19 • 28 . Y : •\ Slightly dry 1 . 7;;. f^^ Bf* -t4 15 Fresh l 1 1 Moist 1 3 Very moist 1 Wet 20 Table 3.8 Edatopic grids for the ICH-MS group showing the number of stands sampled in each edatope and those edatopes selected in: (A) Block 1 for analysis and (B) Block 2 for analysis. Selected edatopes are shaded in grey. Two identical edatopic grids are presented in order to clearly show edatope selection for each block. Actual Soil nutrient regime moisture Very poor Poor Medium Rich Very rich regime Excessively dry 2 Very dry 4 (IBiilBIIB Moderately dry • N i i i H B 2 Slightly dry 13 Fresh 18 10 4 Moist 1 1 2 Very moist 4 Wet 1 (B) Actual Soil nutrient regime moisture Very poor Poor Medium Rich Very rich regime Excessively dry 2 Very dry 4 7 5 . : Moderately dry 26 16 2 Slightly dry 51 • 51 13 Fresh 18 10 4 Moist 1 1 2 Very moist 4 Wet 1 21 Analysis of variance (a = 0.05) were used to determine whether ASMR, SNR, and the ASMR*SNR interaction had a significant effect on western larch site index. A significant interaction implies that the effects of soil moisture and soil nutrients on site index are not additive. Plotted site index data for each block were used to demonstrate the trend in site index in relation to change in ASMRs and/or SNRs. In view of missing and poorly represented edatopes, and the selection requirements, not all edatopes were included in the analysis. It was assumed that the sequences of edatopes excluded from the analysis would exhibit similar trends in site index as those as those that were analyzed. The SYSAT statistical package (1997 Version 7.0) was used to carry out all statistical tests. All figures relating to the statistical tests were produced using SIGMA PLOT (1997 Version 4.00). 22 4. R E L A T I O N S H I P S B E T W E E N S I T E I N D E X A N D C L I M A T I C , S O I L M O I S T U R E A N D S O I L N U T R I E N T R E G I M E S 4 . 1 . Relat ionships between Site Index and Cl imate Analysis of variance (a = 0.05) indicated that there was a significant change in western larch site index with biogeoclimatic subzone (Table 4.1). The Tukey's multiple range test (Table 4.2) showed that the significant differences occurred primarily between the IDF subzones and the other study subzones. There were no significant differences in site index between the IDFxh and IDFdm subzones, and among any of the ICH and MS subzones, with the Moist Warm IDF (IDFmw) subzone being not significantly different from any study subzone. A visual summary of the variation in site index on zonal sites in relation to the eight study subzones is given in Figure 4.1. The highest mean site index value occurred in the Dry Warm ICH (ICHdw) subzone, while the lowest values were associated with the Very Dry Hot IDF (IDFxh) and the Dry Mild IDF (IDFdm) subzones. The differences in western larch site index on zonal sites among subzones can be attributed either to the differences in precipitation (reflected in actual soil moisture conditions), temperature, or both. This comparison, which was based on zonal sites with RSMRs of 3 and 4 and poor and medium SNRs, suggests that the differences in site index could be explained by the differences in ASMRs because ASMRs of zonal sites varied with subzone (Table 3.3). However, this comparison, did not address the possible influence of temperature. 23 Table 4.1 Analysis of variance for mean site index on zonal sites in eight study subzones (a = 0.05). Source Sum of squares Degrees of freedom Mean square F-ratio P Subzone 175.90 7 25.13 7.61 0.00 Error 472.00 143 3.30 Table 4.2 Western larch site index means on zonal sites stratified according to study subzone. Values in the same row with different lower case superscripts are significantly different (a = 0.05). Abbreviations for subzones as in Table 3.1. IDFxh IDFdm IDFmw ICHdw ICHmk ICHmw MSdm MSdk 18.9b 18.8 b 21.4 a b 22.4a 20.9a 20.8a 21.6a 20.8a Comparison of western larch site index on very dry (Set 1), moderately dry (Set 2), and slightly dry (Set 3), poor and medium sites showed no significant change with subzone (Tables 4.3 and 4.4). Testing was not considered necessary for Set 1 because the spread in site index between subzones was only 0.4 meters (Table 3.5). Thus, the results of the comparisons of site index on sites with equivalent soil moisture and nutrient conditions between subzones suggest that height growth of larch is not temperature-limited. Taking into account the results of both comparisons, it appears that height growth of larch in the province is moisture, but not temperature, dependent. 24 30 o X u -a c B c/5 25 20 H 10 II -III I l i •HI > , ? P i IBI T [ ' I IDFxh IDFdm IDFmw ICHdw ICHmw ICHmk MSdm MSdk Biogeoclimatic subzone Figure 4.1 Bar graph of western larch site index on zonal sites in eight study subzones. Error bars are for standard deviation. Abbreviations for subzones as in Table 3.1. Table 4.3 Set 2: Analysis of variance for mean site index on moderately dry, poor and medium sites in seven study subzones (a = 0.05). Source Sum of squares Degrees of freedom Mean square F-ratio P Subzone 34.58 6 5.76 1.69 0.13 Error 272.70 80 3.41 25 Table 4.4 Set 3: Analysis of variance for mean site index on slightly dry, poor and medium sites in seven study subzones (a = 0.05). Source Sum of squares Degrees of freedom Mean square F-ratio P Subzone 32.13 6 5.36 1.80 0.11 Error 300.44 101 2.98 4.2 Relat ionships between Site Index and Soi l Moisture and Nutr ient Regimes 4.2.1 The IDF Group Analysis of variance (a = 0.05) for each of the two blocks of the selected edatopes for the IDF group, indicated that both ASMR and SNR had a significant effect on western larch site index (Tables 4.5 and 4.6). However, the ASMR*SNR interaction was not significant in either block (P = 0.13 for Block 1, P = 0.48 for Block 2), suggesting that the influence of soil moisture and nutrients on site index is consistent across the selected range of edatopes. Line graphs of site index means for five hygrosequences (poor and medium SNRs in Block 1 and poor, medium, and rich SNRs in Block 2) in relation to very dry, moderately dry, and slightly dry ASMRs in Block 1, and in relation to moderately dry and slightly dry ASMRs in Block 2 showed consistent trends in change of site index with edatopes (Figure 4.2 (A) and (B)). In general, western larch site index increased (1) regardless of SNR, with increasing soil moisture, with the rate of change being faster from very dry to moderately dry sites than from moderately dry to slightly dry sites, and, (2) regardless of ASMR, with increasing availability of soil nutrients (nitrogen). 26 Table 4.5 Analysis of variance for mean site index in Block 1 of the IDF climatic group (a = 0.05). Source Sum of squares Degrees of freedom Mean square F-ratio P S M R 95.04 2 47.52 23.98 0.00 S N R 44.17 1 44.17 22.29 0.00 S M R * S N R 2.91 2 1.46 0.74 0.48 Error 116.92 59 1.98 Table 4.6 Analysis of variance for mean site index in Block 2 of the IDF group (a = 0.05). Source Sum of squares Degrees of freedom Mean square F-ratio P S M R 17.82 1 17.84 6.88 0.01 S N R 62.70 2 31.35 12.11 0.00 S M R * S N R 10.82 2 5.41 2.09 0.13 Error 176.06 68 2.59 4.2.2 The I C H - M S Climate Group Analysis of variance (a = 0.05) for each of the two blocks of the selected edatopes for the I C H - M S group produced similar results as for the IDF group (Tables 4.7 and 4.8). The A S M R had a significant effect on western larch site index in both blocks, and the A S M R * S N R 27 (B) Figure 4.2 Means and standard error of means of western larch site index for (A) poor and medium hygrosequences in relation to very dry, moderately dry, and slightly dry A S M R s for Block 1 of the IDF group and (B) for poor, medium, and rich hygrosequences in relation to moderately dry and slightly dry A S M R s for Block 2 of the IDF group 28 interaction was not significant in either block (P = 0.17 for Block 1, P = 0.16 for Block 2) suggesting again that the influence of soil moisture and nutrients on western larch site index is consistent across the range of selected edatopes. Although significant in Block 1, the effect of S N R was marginally non-significant in Block 2 (P = 0.06). Table 4.7 Analysis of variance for mean site index in Block 1 of the I C H - M S group. Source Sum of squares Degrees of freedom Mean square F-ratio P S M R 426.30 2 213.15 108.39 0.00 S N R 111.28 2 55.64 28.29 0.00 S M R * S N R 12.71 4 3.17 1.62 0.17 Error 322.51 164 1.97 Table 4.8 Analysis of variance for mean site index in Block 2 of the I C H - M S group. Source Sum of squares Degrees of freedom Mean square F-ratio P S M R 378.61 4 94.65 40.40 0.00 S N R 8.63 1 8.63 3.68 0.06 S M R * S N R 15.77 4 3.94 1.68 0.16 Error 255.38 109 2.34 29 Line graphs of site index means for five hygrosequences (poor, medium, and rich SNRs in Block 1, and medium and rich SNRs in Block 2) in relation to very dry, moderately dry, and slightly dry A S M R s in Block 1, and in relation to very dry through fresh A S M R s in Block 2 showed consistent trends in change of site index with edatopes, with one exception (Figures 4.3 (A) and (B)). In general, western larch site index increased (1) regardless of S N R , with increasing soil moisture and, (2) regardless of A S M R , with increasing availability of soil nutrients (nitrogen), with the exception of fresh sites where there was little change in site index with change in S N R (Figure 4.3(B)). Non-significant variations in site index with S N R on fresh and moist sites are apparent in Table 3.6 which shows the pattern of site index for all study sites. 4.3 Discussion To determine the influence of climate (i.e., precipitation and temperature) on western larch productivity, a two-step analysis was employed. In the first step, site index on zonal sites was compared among eight study subzones (zonal climoseqence), and in the second step, comparison included three climosequences, each with specific A S M R . Differences in site index between zonal sites could be explained either by differences in precipitation (if reflected in actual soil moisture conditions) or in temperature, or both. If zonal sites under comparison vary in A S M R , then the differences in site index can be attributed to the differences in precipitation between study subzones. If sites with equivalent soil moisture conditions across different subzones do not vary in site index, then it can be concluded that larch site index is not influenced by temperature across the study subzones. 30 (A) 28 26 -12 -I , , r-VD MD SD Actual soil moisture regime (B) 26 14 -I 1 1 1 1 -VD MD SD F Actual soil moisture regime Figure 4.3 Means and standard error of means of western larch site index for (A) poor, medium and rich hygrosequences in relation to very dry, moderately dry, and slightly dry A S M R s for Block 1 of the ICH-MS group and (B) for medium, and rich hygrosequences in relation to very dry, moderately dry, slightly dry, and fresh A S M R s for Block 2 of the ICH-MS group. 31 In the B E C system, classes delineated at the regional level of integration through floristic differences in zonal vegetation (e.g., biogeoclimatic subzones), are expected to be climatically different. Conceptually, each subzone should represent a distinct segment of a regional climatic gradient. However, testing subzones for the strength of climatic differences is fraught with difficulties due to the lack of long-term climatic data and in making decisions at which point any particular difference in climatic characters becomes ecologically significant. Assuming that each of the eight study subzones represents a distinct regional ecosystem influenced by a different climate, then it could be inferred that differences in forest productivity (measured by site index on zonal sites) would exist among each subzone. This assumption could not be supported, albeit based on testing only one tree species, as significant differences in western larch site index occurred only between two groups of subzones. The IDFmw subzone was transitional between the groups in both site index and climatic characteristics (Tables 3.1 and 4.2). However, the small sample size (n=3) on zonal sites and large variation in site index within that sample, may have also resulted in the IDFmw subzone not being significantly different in site index from any of the other subzones. The arrangement of the subzones, based on significant differences in the site index of western larch, into the IDF climate group and the I C H - M S climate group seems to reflect differences in mean annual precipitation between the subzones. Climate records indicate that the three IDF subzones sampled have the lowest mean annual precipitation levels of any of the subzones sampled (Table 3.1), and consequently significantly lower mean site index values. As such, zonal sites in the IDF subzones are water-deficient (moderately dry) while in the I C H and 32 MS subzones they are fresh and slightly dry, respectively. Thus, larch growth on the IDF zonal sites is water-limited while on the ICH and MS zonal sites some factor other than water is limiting growth. A variety of temperature related measures (Table 3.1) appear to suggest that temperature has little effect on the site index of western larch among the subzones studied. Temperature does not account for the significant differences in site index between the IDF and MS/ICH subzones. The lack of significant differences in site index measures between the MS and ICH subzones, despite much cooler mean annual temperatures, cooler mean temperature warmest month and shorter frost free period in the MS subzones, indicates that temperature differences related to climate was not significantly affecting western larch site index. A number of observations and 1 2 planting trials (Newsome et al. 1992, Lloyd and Jaquish pers. comm .) have shown that western larch can remain very productive when planted well outside its natural range in locations, where growing season temperature and frost free periods are much cooler and shorter respectively. During data collection for this study, several sites were located within the lower limits of the ESSF zone; however, they were excluded from the analysis due to a small number of replicas per subzone. Compared to the study subzones, the climate of these ESSF subzones had a shorter growing season and colder growing-season temperatures. A simple comparison suggested that the western larch site index on these sites was very similar to that measured on sites with similar ASMR and SNR, but located in climates that are more temperate. 1 Dennis L l o y d , B . C . Ministry of Forests, Kamloops Forest Region., Phone:(250) 828-4131 33 Further examination of the effect of climate on the site index of western larch was carried out by comparing the mean site index of similar edatopes between a number of subzones (Table 3.5, 4.3, and 4.4). Testing seems to indicate that temperature plays a very insignificant role in influencing the site index of western larch within its range in B.C. Climate and ASMR are not independent of one another. By maintaining a similar ASMR across subzones, the effect of variable precipitation is eliminated and thus the effect of temperature on the site index of western larch should be seen. Large differences in growing season temperature and length do exist between subzones (Table 3.1); however, these differences do not appear to manifest themselves in site index measures. Therefore, within the range of western larch in B.C., one could speculate that significant change in larch productivity, due to climate differences, would only be expected when those differences were in annual precipitation. The reason for the lack of significant difference in site index of western larch between each subzone cannot be resolved with certainty. It may be that either the subzones of each climatic group were not climatically different or that climatic differences among them were minor and without a measurable effect on larch site index. A plausible explanation for the similarity of site index between the IDF subzones or among the ICH and MS subzones could be in the similarity in actual evapotranspiration. Vascular plant activity and hence, forest productivity, should increase with increasing actual evapotranspiration, which depends on temperature and soil water (i.e., precipitation) (Major 1951). Either temperature and precipitation differences among subzones of each climatic group are so minor that they do not 2 Barry Jaquish, Kalamalka Research Station, B . C . Ministry of Forests, Vernon, B . C . , Phone:(250) 549-4477 34 exert a significant influence on forest growth via actual evapotranspiration, or compensating effects between precipitation and temperature result in similar actual evapotranspiration. Zonal sites in one subzone with a higher growing-season temperature (i.e., higher potential evapotranspiration) but lower precipitation may have the same or similar actual evapotranspiration (i.e., potential productivity) as those in another subzone with a lower temperature (i.e., lower potential evapotranspiration) but higher precipitation. Thus, due to the presence of a weak actual evapotranspiration gradient among the subzones of each of the IDF and ICH-MS groups, it would suffice to describe the climatic effect on western larch site index in the study area by low and high precipitation strata. Wang and Klinka (1996), who studied the variation in site index of lodgepole pine, interior spruce, and subalpine fir on zonal sites in ten subzones of the Sub-Boreal Spruce zone, came to a similar conclusion. The distribution of study stands according to soil moisture and nutrient regime shows that an abundance of stands (199 of the total 315 sampled) occurred on sites of moderately dry and slightly dry ASMR and poor or medium SNR (Table 3.6). This suggests, as does the literature, that western larch is a species of moderate ecological amplitude (Fielder and Lloyd 1992, Schmidt and Shearer 1992, Klinka et al. 1998). In view of the distribution of the study sites (Table 3.6), testing the effects of ASMR, SNR and their interaction on western larch site index could be done only for the central portion of the edatopic grid (Figures 4.2, 4.3, Tables 3.7, 3.8). Thus, the assumption had to be made that the variation in site index outside the sampled region would exhibit similar trends. However, this assumption may not be valid for water-surplus sites on which the effects of SNR on site 35 index appeared to have decreased (Table 3.6). Excluding very moist and wet sites from further investigations is of little practical significance, as such sites are infrequent in the study area and larch grows on these sites very infrequently and less productively compared to other tree species. The maximum site index (about 26 m) of western larch occurred on moist sites, albeit with only minor differences between medium, rich, and very rich sites (Table 3.6). The minimum mean site index value was found on excessively dry and very poor sites. Site index sharply decreased with increasing water surplus from moist through wet sites; however, a small sample size prevented testing this trend. Spitzer and Stark (1982) reported that the growth of western larch was superior on rapidly drained sites compared to sites with restricted rooting zones and slow water percolation. Whether larch occurs infrequently on these sites because of the lack of wildfires or competition or because it poorly tolerates soil water surplus needs to be resolved in other studies. Site index also decreased with decreasing soil moisture from moist through excessively dry sites, and within any water-deficient A S M R , it increased with increasing S N R (Table 3.6, Figures 4.2 and 4.3). A general, though not significant, trend of an increasingly positive effect of increasing S N R on site index with increasing A S M R in the range from very dry to slightly dry sites was also detected. The edaphic conditions supporting the optimum growth and the pattern of change in larch site index in relation to edatopes are similar to those reported for several other tree species (see Green et al. 1989; Kl inka and Carter 1990; Wang et al. 1994; Wang and Kl inka 1996; Kayahara and Pearson 1996; Chen etal. 1998). The rate of increase or decrease in site index of western larch along a soil moisture gradient was generally greater than the rate of increase along a soil nutrient gradient, with the 36 positive effect of soil moisture changing into a negative one from moist through wet sites, as soil aeration decreased with increasing water surplus. For example, site index increased by about 4 m from very dry to moderately dry sites, about 3 m from moderately dry to slightly dry sites, and only to about 2 m from slightly dry through moist sites compared to about less than 3 m increase from poor to rich sites within the water-deficient region of the edatopic grid (Table 3.6). The greater influence of soil moisture than soil nutrients on forest growth was also reported in other studies (see Green et al. 1989; Klinka and Carter 1990; Wang et al. 1994; Wang and Klinka 1996; Kayahara and Pearson 1996; Chen et al. 1998). The substantial increase in site index values from the very dry to the moderately dry moisture regime (Figures 4.2 (A) and 4.3 (A and B)), may be reflective of western larch's inability to control water use. At some point between these two ASMR values there seems to be a threshold, below which the amount of water necessary for vigorous western larch growth is not available and height is severely reduced. Above the threshold enough soil moisture is available for western larch to realize its full growth potential. To achieve a rate of height growth sufficient to remain competitive, western larch must assimilate large amounts of carbon and as a result has high photosynthetic rates. This characteristic along with an inability to control stomata openings, results in high water use by western larch compared to its associated species (Higgens et al. 1987). This greater water demand may, in part, account for the absence of larch on dry sites and its abundance on mesic sites (Gower and Richards 1990, Gower et al. 1992). This hypothesis seems to be supported by the distribution of sample plots acquired in this study (Table 3.6). 37 An exception to the effect of SNR on site index (P = 0.06) occurred in Block 2 of the ICH-MS climatic group (Table 4.8, Figure 4.3(B)). Although the P was close to being significant and the effect of the ASMR*SNR interaction was insignificant, examination of Table 3.6 and Figure 4.3(B) suggested that on sites without a water deficit or surplus (i.e., fresh and moist sites) there is only a marginal or no increase in site index with increasing SNR. Despite the small sample size for moist sites, it appears that the growth response of western larch to an increasing supply of soil nutrients (nitrogen) is not consistent across the entire soil moisture gradient and differs with respect to soil moisture conditions (i.e., on water-deficient sites the response is significant while the fresh and moist there is little or no growth response). It may be that once all water and essential nutrient needs are met, the growth of larch does not benefit from increased nutrient supply due to the efficient use of nitrogen. 4.4 Conclusions Climate, represented by biogeoclimatic subzones, had a significant effect on site index of western larch; however, due to compensating effects between temperature and precipitation, significant differences occurred only between two groups: lower-precipitation (Interior Douglas-fir) and higher-precipitation (Interior Cedar-Hemlock and Montane Spruce) subzones. Thus the climatic effect on larch productivity was detectable above the subzone or even zone level of generalization (i.e., the productivity gradient does not coincide with the climatic gradient described by biogeoclimatic units). The climatic growth optimum corresponded to the intermediate segment of a regional climatic gradient - wetter temperate climate - that is 38 represented by the Dry Warm Interior Cedar-Hemlock subzone. In relation to edatopes, site index significantly increased from water-deficient to moist sites and decreased from moist to wet sites, and on water-deficient sites, it increased with increasing soil nutrient regime. On water-deficient sites, estimates of both actual soil moisture regime and soil nutrient regimes had both positive significant and consistent effects on site index, but the effect of soil nutrient regime appeared to be marginal on non-water deficient sites. 39 5. P R E D I C T I O N O F W E S T E R N L A R C H S I T E I N D E X 5.1 Introduct ion It was determined in Chapter 4 that any noticeable change in the site index of western larch due to climate was largely a reflection of a change in precipitation and not in temperature. Precipitation and ASMR are not independent of each other and, as such, the effect of climate on site index is accounted for, or represented by, ASMR. Hence, the insignificant differences seen in Chapter 4 when comparing site index on similar edatopes stratified by subzone. Therefore, climate, represented by zone or subzone (or as it was stratified in Chapter 4) into climate group) will not be considered as a prediction term in model development. It was also determined that the interaction of ASMR and SNR (ASMR*SNR) had no significant effect on the site index of western larch. Therefore it will not be included as a prediction term in model development either. Based on these findings, the focus of this section is on the construction and cross-validation of a single additive model using ASMR and SNR as prediction terms for estimating the site index of western larch in B.C. 5.2 Methods A model was constructed using eight soil moisture regime classes and five soil nutrient regime classes as determined in the BEC system. These thirteen independent, categorical variables were coded as dummy variables (Chatterjee and Price 1977). Site index was used as the dependent variable. A cross-validation technique (Neter et al. 1996) was used to validate the 40 model. This required that data be systematically split into two sets prior to model development; one set for model development, called the construction data set (n=2/3 total n), and one set for model validation, called the test data set (n= 1/3 total n). The general linear model, using the least squares procedure, was used to determined the line of best fit for the construction data set. The resulting construction model was initially tested for lack of fit by examining plotted residuals. If no lack of fit was evident, the construction model was then validated. The model validation procedure involved testing the construction model for bias and precision using the test data set. The construction model was tested for bias by examining the differences in site index between the predicted site index values of the construction model and the measured site index values of the test data set (i.e., error terms) using a paired t-test. Model precision was estimated by comparing the square root of the average squared deviation of the predicted test values (i.e., the root mean square prediction error - root-MSPR) and the mean square error (MSE) of the construction model. The function of this test is to assess the relative predictive capability of the regression model (Neter et al. 1996). Fol lowing model validation, the test data set and construction data set were recombined so that the entire data set was used to produce a final prediction model. Residuals of this final prediction model were plotted to examine for lack of fit. In order to assess the practical utility of predicted site index values, the ±95% confidence interval for mean site index estimates, and the ±95% confidence interval for the standard error of the estimate, also called the prediction interval, were calculated. Regression analysis for producing the model and confidence interval calculations were 41 performed using the SAS statistical package (1996 Version 6.12). The SYS AT statistical package (1997 Version 7.0) was used to carry out all other statistical tests. All figures relating to the statistical tests were produced using SIGMA PLOT (1997 Version 4.00). 5.3 Results A total of 210 plots were used in developing a model for predicting the site index of western larch (Table 5.1). The coefficient of determination (R2) indicated that the developed model explained 84% of the variation in site index due to ASMR and SNR. The standard error of the estimate (SEE) was 1.5 m. Visual inspection of plotted residuals indicated that no significant lack of fit existed between the fitted regression line and the data from which it was derived (Figure 5.1). Table 5.1 The ASMR SNR prediction model developed from the construction data set (n = 210) relating site index (m @ 50 yrs bh) of western larch to combinations of actual soil moisture and soil nutrient regimes. SI= 7.9 + 0.0(ED) + 4.8(VD) + 8.4(MD) + 10.8(SD) + 12.5(F) + 14.1(MST) + 7.3(VM) + 7.4(W) + 0.0(VP) + 1.1 (P) + 3.4(M) + 4.5(R) +4.7(VR) R 2 = 0.84 SEE = 1.5 MSE = 2.1 The intercept represents ED (excessively dry) and VP (very poor). 42 (A) (B) 3 0 15 20 Predicted site index (m ) 30 ® e 10 15 20 25 Predicted site index (m @ 50 yrs bh)) Figure 5.1 Residual analysis for the ASMR SNR construction model (n = 210): (A) residual (m) versus predicted site index (m @ 50 yrs bh) and (B) measured versus predicted "site index (m @ 50 yrs bh). 4 3 When predicted values were compared to measured values on the test data set (n=105), the construction model proved to be unbiased in predicting site index. A paired t-test (a = 0.05) showed the regression model predicted a site index that did not differ significantly from the mean site index of the test data set. The mean difference was 0.09 m between the predicted and measured site index, with p = 0.55. Visual inspection of plotted prediction error values also indicated that no bias in site index prediction existed (Figure 5.2) and showed that the model estimates of the test data site index were usually within ±3 metres of the actual measured values. The calculated MSPR was 2.2 m. The 0.1 metre difference in value between the MSPR and MSE for the construction model indicate that the MSE (2.1m) of the model likely gives a reasonable estimate of the predictive ability of the model (Neter et al. 1996). Once the model has been validated it is customary to use the entire data set for final model construction (Neter et al 1996). Re-combining the test and construction data sets for estimating a final prediction model resulted in a model that explained 83% of the variance in site index (Table 5.2.). The final model showed virtually no change in R , SEE or MSE values when compared to the construction data set. Visual inspection of plotted residuals indicated no significant lack of fit existed between the fitted regression line and the data from which it was derived (Figure 5.3). Calculated confidence intervals for the mean are shown in Table 5.3. The prediction interval results are not shown. The estimated values for mean site index of any given edatope have a 95% confidence interval of less than 1 m, except at edatope extremes and when the sample size is small. 44 -4 I-5 10 15 20 25 30 Measured site index (m @ 50 yrs bh) 35 Figure 5.2 Prediction error for the A S M R S N R construction model in relation to measured site index (m @ 50 yrs bh) of the test data set (n=105). Table 5.2 The A S M R S N R prediction model developed from all site data (the combined construction and test data sets (n=315)) relating site index (m @ 50 yrs bh) of western larch to combinations of actual soil moisture and soil nutrient regimes. SI= 8.6 + 0.0(ED) + 4.6(VD) + 8.1(MD) + 10.6(SD) + 12.3(F) + 13.9(MST) + 7 .5(VM) + 7.7(W) + 0.0(VP) + 0.7(P) + 2.9(M) + 4.1(R) +4.4(VR) R 2 = 0.83 S E E = 1.5 M S E = 2.2 The intercept represents E D (excessively dry) and V P (very poor). 45 -6 I 1 1 1 1 5 10 15 20 25 30 Predicted site index (m ) Figure 5.3 Residual analysis for the ASMR SNR model (n=315). Measured versus predicted site index (m @ 50 yrs bh). 5.4 Discussion The final ASMR SNR model (n = 315) accounted for a high proportion of the variation in site index, indicting that even though the representation of some ASMRs and SNRs was limited, a strong relationship exists between western larch site index and estimates of soil moisture and soil nutrients. As expected, the results for western larch concur with those of similar studies which also showed a strong relationship between the site index of selected tree species and estimates of soil moisture and nutrients as delineated within the working framework of the BEC system (Klinka and Carter 1990, Wang 1992, Wang and Klinka 1996, Chen etal 1998). 46 Table 5.3 Edatopic grid showing the ASMR SNR model (n=315) predicted site index values and ± 95% confidence interval (m), measured mean site index values (m @ 50 yr bh), and SH3EC mean site index values (m @ 50 yr bh) according to actual soil moisture and soil nutrient regimes. Sample sizes indicated refer only to the measured and predicted site index values. Actual soil Statistic Soil nutrient regime moisture parameters Very poor Poor Medium Rich Very rich regime . Excessively dry n 3 0 0 0 0 Predicted 8.6±1.7 9.2 11.5 12.7 13.0 Measured 8.6 ND 1 ND ND ND SIBEC NA 2 NA NA NA NA Very dry n 6 14 8 2 0 Predicted 13.2+0.6 13.8+0.8 16.1+0.9 17.3+0.7 17.5 Measured 13.2 14.3 15.4 16.6 ND SIBEC NA 18.0 17.0 18.6 NA Moderately dry n 0 45 44 6 0 Predicted 16.7 17.4+0.5 19.6+0.5 20.8+0.5 21.1 Measured ND 17.2 19.9 20.8 ND SIBEC NA 19.6 18.8 20.1 23.7 Slightly dry n 0 52 58 28 0 Predicted 19.2 19.9+0.3 22.2+0.5 23.3+0.7 23.6 Measured ND 19.9 21.9 23.7 ND SIBEC NA 23.4 23.0 21.0 19.2 Fresh n 0 0 18 11 5 Predicted 20.9 21.5 23.8+0.5 25.0+0.6 25.2+1.1 Measured ND ND 21.9 23.7 25.6 SIBEC NA 22.9 23.7 21.9 27.0 Moist n 0 0 1 2 5 Predicted 22.5 23.1 25.4+1.3 26.6±1.3 26.8+1.1 Measured ND ND 26.5 26.2 26.8 SIBEC NA 18.9 24.8 24.1 20.0 Very moist n 0 0 0 4 1 Predicted 16.1 16.7 19.0 20.2+1.3 20.4+2.9 Measured ND ND ND 20.6 18.7 SIBEC NA NA 22.3 22.5 25.2 Wet n 0 0 0 1 0 Predicted 16.3 17.0 19.2 20.4+1.6 20.7 Measured ND ND ND 20.4 ND SIBEC NA NA NA NA NA N D - no data were obta ined due to the absence or sporadic occurrence o f western l a rch under some edaphic cond i t i ons . 2 N A - not ava i l ab le 47 The small difference in value between MSE of the construction model and the calculated MSPR suggested the construction model is unbiased. The similar SEE values between the model developed from the construction data set and the final model indicates that the construction data set was sufficiently large so that the addition of data did not seem to improve the prediction ability of the final model. The general trend of increasing site index with increasing ASMR and SNR, and the lack of a significant ASMR*SNR interaction on site index was established in Chapter 4. Considering this, the prediction model developed here may be used to predict the site index of western larch in some edatopes where no sampling occurred (Table 5.3). The lack of sampling within various combinations of ASMR and SNR likely reflects the natural distribution of western larch and a preference for particular sites. However, this does not mean that western larch does not, or could not, exist on sites which were not sampled, or that these sites do not occur in nature. Thus, having an estimate of western larch site index for such sites would be beneficial until such time as adequate sampling of those sites can occur. However, the extrapolated values must be used -with due caution as these estimates go beyond the range of the data and the trends established in Chapter 4 are based on a limited series of edatopes. The preferred method for model validation is through the collection of new, independent data (Bruce and Wensel 1987, Neter et al. 1996, Kayahara et al. 1998). However, this was not practical or feasible within this study. The cross-validation technique used here, withholding a portion of the original data set in order to test model predictive ability, is really an attempt to simulate the replication of the study (Neter et al 1996). Despite the short comings of this 48 technique, it does result in a model whose relative predictive ability is known. However, the model's usefulness can only be fully determined when tested against a reliable, independent data set. A n effort was made to ensure that sampling occurred throughout the entire range of western larch in B .C . Within this range, the only subzones not sampled, that commonly have western larch growing in them, are two subzones of the E S S F zone (ESSFwm, ESSFdk) and the very small ICHxw subzone. Although western larch may be found in small numbers in other subzones on particular sites, the general population of western larch was sampled. Thus, the A S M R S N R model that was developed provides site index estimates that wi l l be applicable throughout most of the natural range of western larch in B .C. In order to examine the usefulness of the A S M R S N R model, estimates for field application, the mean ±95% confidence interval and the ±95% prediction interval for the predicted site index value of each fi l led edatope were calculated. The results of mean confidence interval calculations (Table 5.3) suggest that predicted mean site index values provide an acceptable level of accuracy when estimating the mean site of western larch for a given area. The prediction interval (not shown) (i.e., the range of possible site indices accompanying a predicted site index for a single site at a confidence level of 95%) is so wide, generally ± 3 m, that there is l ikely little practical use for such predictions. A similar study, in which the confidence limits and prediction intervals were used to evaluate the practical usefulness of estimated site index values for western redcedar, showed comparable results (Kayahara et al. 1997). A n independent data set of 182 plots was acquired from S IBEC. S IBEC is an acronym 49 for site index biogeoecosystem classification system. It is the title given to a current project undertaken by the B.C. Ministry of Forests who are correlating site index data for the major tree species in B.C. to site units of the BEC system. The assignment of a site index value to a site unit of the BEC system (in SIBEC's case to the site series level of the BEC system) is meant to provide practicing foresters with an easily attainable estimate of site index when height and age can not be used. The independent data set acquired from SIBEC was used by SJJBEC to produce site index values for western larch which were correlated to site units at the site series level. The data set contained combinations of ASMR, SNR, and the associated site index for western larch. Due to the variety of sources used by SIBEC to compile the data set, it was considered unreliable and so was not used to test the predictive ability of the ASMR SNR model. However, a comparison of mean site index values of the SIBEC data and predicted values from the ASMR SNR model for various edatopes was made (Table 5.3). The comparison indicated that the SIBEC data tends to underestimate the site index of western larch among the moist, nutrient medium to very rich edatopes while overestimating site index on very dry, nutrient poor through rich edatopes. Assuming the site index values generated in this study are reasonably accurate, the apparent bias in the SIBEC data set implies a similar bias will also exist in the SIBEC site index values currently used by forest practitioners for site index estimates of western larch at the site series level. The confidence placed in the SIBEC estimates of site index, called a reliability rating, are applied by Forest Service regional ecologists (MOF 1997). The reliability of estimates of site index for western larch is generally rated low to medium among the subzones in which it occurs. The exception is the ICHmw and the IDFdm subzones where the rating is high. The pooling of 50 data complied for this study with that of the SJJBEC data set will help to improve the reliability of SJJBEC site index estimates in all but a few of the subzones occupied by western larch. 5.6 Conclusions Despite the limited representation of some ASMRs and SNRs, the large amount of variation in site index accounted for by the model indicates a strong relationship exists between western larch site index and estimates of soil moisture and soil nutrients. The results of the cross-validation indicate that the ASMR SNR model is unbiased and has an acceptable level of precision when predicting the site index of western larch. Until such time as a reliable, independent data set is available, it is reasonable to assume that the site index of western larch can be accurately predicted throughout the majority of its range in B.C. using this model. The model may also be used to extrapolate site index values for western larch in some of the edatopes where no sampling occurred. However, extrapolated values must be used with caution. For practical purposes, predicted mean site index values provide an acceptable degree of accuracy and level of confidence needed for estimating the mean site of western larch for a given area. A simple comparison of predicted mean site index values generated here with SIBEC site index means, calls into question the accuracy of site index values currently used by forest practitioners for estimating western larch site index at the site series level. 51 6. C O N C L U S I O N S This study expands the efforts of past research in using categorical measures of ecological site quality, as delineated within the working framework of the BEC system, to explain the variation in site index of major tree species in B.C. Climate, represented by biogeoclimatic subzones, had a significant effect on site index of western larch; however, due to compensating effects between temperature and precipitation, the climatic effect on larch productivity was only detectable above the subzone or even zone level of generalization. The climatic growth optimum for western larch corresponded to the intermediate segment of a regional climatic gradient - wetter temperate climate - that is represented by the Dry Warm Interior Cedar - Hemlock subzone. In relation to edatopes, site index significantly increased from water-deficient to moist sites and decreased from moist to wet sites, and on water-deficient sites, it increased with increasing soil nutrient regime. On water-deficient sites, estimates of both actual soil moisture regime and soil nutrient regimes had both positive significant and consistent effects on site index, however, on non-water deficient sites, the effect of soil nutrient regime appeared to be marginal. The model developed in this study, combined with the identification of ASMR and SNR, can be used to provide reliable site index predictions for western larch to a level of accuracy required for practical forest management throughout its range in B.C. The reliable prediction of western larch site index through the identification of ASMR and SNR, suggests that there is a strong correlation between ecological site classification and forest productivity. Testing the 52 model against a reliable, independent data set would enhance the confidence that can be placed in the predictive ability of the model. The relationships developed here, between the BEC system and the site index of western larch, challenges the validity of some of the existing western larch site index values correlated to site series through the SIBEC program. 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M . Lavkulich. 1978. The soil landscapes of British Columbia. Reprint 1981. Agriculture Canada and The British Columbia Ministry of the Enviroment. Victoria B.C. 197pp. Wang, G.G. and K. Klinka. 1996. Use of synoptic variables in predicting white spruce site index. Forest Ecology and Management 80: 95 - 105. Wang, Q., G.G. Wang, K.D. Coats, and K. Klinka. 1994. Use of sites factors to predict lodgepole pine and interior spruce site index in the Sub-Boreal Spruce zone. B.C. Min. For., Res. Branch, Victoria, B.C. Res. Note No. 114. Wang, Q. 1992. Ecological and height growth analysis of some sub-boreal immature lodgepole pine stands in central British Columbia. Ph. D. thesis. Fac. For., Univ. B.C. Vancouver, B.C. 207 pp. 58 A P P E N D I X 1 Selected Characterist ics of Study Stands # Stand Zone Subzone Species B H A Height SI A S M R S N R Elev. Aspect 1 9501 IDF dm L w 69 22.20 19.20 M D M 875 N 2 9502 IDF dm L w 49 20.90 21.09 M D M 870 Flat 3 9503 IDF dm L w 55 17.60 16.87 M D P 880 Flat 4 9504 IDF dm L w 56 18.00 17.11 M D P 900 N 5 9505 IDF dm L w 60 19.90 18.34 M D M 885 N 6 9506 IDF dm L w 61 21.90 20.02 M D M 880 Flat 7 9507 IDF dm L w 43 16.40 17.54 M D R 925 Rdge 8 9508 IDF dm L w 55 24.40 23.36 M D R 920 E 9 9509 IDF dm L w 70 28.40 24.36 S D R 975 E 10 9510 IDF dm L w 53 20.00 19.48 M D M 1000 Flat/Rdg 11 9511 IDF dm L w 56 21.60 20.52 M D M 995 N 12 9512 IDF dm L w 54 24.30 23.46 M D R 985 N 13 9513 IDF dm L w 56 19.60 18.63 M D • M 960 Flat 14 9514 IDF dm L w 57 20.30 19.14 M D M 965 R 15 9515 IDF dm L w 58 25.80 24.11 S D R 960 Gully 16 9516 IDF dm L w 99 23.30 17.14 V D P 1060 E 17 9517 IDF dm L w 101 26.30 19.13 M D P 1045 N 18 9518 IDF dm L w 93 26.90 20.30 M D M 1110 Flat 19 9519 M S dk L w 75 27.80 23.11 SD M 1115 N 20 9520 M S dk L w 72 25.70 21.78 S D P 1120 N 21 9521 M S dk L w 72 28.00 23.71 S D M 1110 N 22 9522 M S dk L w 65 27.10 24.04 SD M 1115 Rdge 23 9523 M S dk L w 72 32.50 27.50 M R 1100 Gully 24 9524 M S dk L w 56 22.00 20.90 SD P 1150 E 25 9525 M S dk L w 93 25.70 19.41 M D P 1120 Flat 26 9526 M S dk L w 59 20.50 19.03 SD M 1140 W 27 9527 M S dk L w 41 13.80 15.05 V D R 1125 W 28 9701 IDF mw L w 101 32.81 24.04 S D M 470 Flat 29 9702 IDF mw L w 96 33.40 24.78 S D M 460 Flat 30 9703 IDF mw L w 97 42.11 31.01 SD R 452 Flat 31 9706 I C H mk L w 56 24.93 23.68 SD M 825 N 32 9707 I C H mk L w 91 28.12 21.42 S D M 1215 W 33 9708 I C H mw L w 63 17.33 15.64 M D P 1080 N 34 9709 I C H dw L w 120 36.95 24.73 SD M 970 N 35 9711 I C H mw L w 73 26.60 22.39 SD M 1125 W 59 # Stand Zone Subzone Species B H A Height SI A S M R S N R Elev. Aspect 36 9712 I C H mw L w 50 21.86 21.86 S D M 1145 W 37 9713 M S dk L w 47 21.95 22.58 S D M 1095 S 38 9714 M S dk Lw 55 26.00 24.89 S D M 1195 N 39 9715 M S dk Lw 51 17.20 17.05 V D P 1200 N 40 9716 M S dk Lw 56 21.13 20.08 M D P 1190 W 41 9717 M S dk L w 56 16.63 15.82 M D P 1240 W 42 9718 ICH mk L w 71 26.83 22.88 F R 940 Flat 43 9719 I C H mk Lw 48 21.40 21.80 S D M 1140 Flat 44 9720 M S dk Lw 57 22.42 21.13 SD M 1035 N 45 9721 M S dk L w 53 25.23 24.57 SD R 1140 S 46 9722 M S dk L w 53 22.18 21.61 M D P 1140 S 47 9723 M S dk L w 97 28.45 21.05 M D M 1450 E 48 9732 I C H mw L w 87 32.43 25.17 S D M 1100 S 49 9735 I C H mw Lw 109 25.67 18.05 SD M 1515 S 50 9736 I C H mw Lw 76 27.70 22.89 SD M 1400 S 51 9739 I C H mw L w 151 23.40 14.34 SD P 1100 S 52 9741 I C H mw L w 47 23.62 24.29 F M 1100 Flat 53 9742 I C H mw L w 46 17.28 17.94 V M R 1100 Flat 54 9743 I C H mw L w 47 24.83 25.54 S D M 1070 Flat 55 9744 I C H mw L w 45 18.15 19.03 SD P 1080 W 56 9745 I C H mw Lw 45 13.05 13.66 V D M 1080 W 57 9746 M S dk Lw 65 22.72 20.18 SD P 1170 E 58 9747 M S dk L w 87 27.73 21.56 SD M 1155 Flat 59 9749 I C H mk L w 69 22.63 19.57 S D M 1050 N 60 9750 I C H mk L w 59 16.40 15.24 M D M 1280 S 61 9751 I C H mk L w 54 25.50 24.62 S D R 945 N 62 9752 IDF dm Lw 57 26.00 24.49 M D M 1000 E 63 97071 I C H mk Lw 82 26.87 21.46 SD M 1280 W 64 97072 I C H mk L w 90 31.68 24.21 SD M 1330 W 65 97073 I C H mk L w 89 26.85 20.67 SD M 1410 S 66 BA01 IDF xh L w 52 17.30 17.00 M D P 1070 w 67 B A 0 2 IDF xh Lw 53 24.00 23.37 S D R 1090 w 68 B A 0 4 IDF xh Lw 48 12.90 13.01 V D P 1120 N 69 B A 0 5 IDF xh L w 54 27.60 26.64 M V R 980 w 70 B A 0 7 IDF xh L w 49 14.20 14.33 V D M 1030 N 60 # Stand Zone Subzone Species B H A Height SI A S M R S N R Elev. Aspect 71 B A 1 0 IDF xh L w 54 17.20 16.62 M D P 1160 N 72 BA11 IDF xh L w 49 19.50 19.68 M D M 1175 N 73 B A 1 2 IDF xh L w 57 19.30 18.20 M D P 1150 N 74 B A 1 3 IDF xh L w 51 27.10 26.61 M V R 1120 N 75 B A M IDF xh Lw 53 22.80 22.21 S D M 1130 W 76 B A 1 5 IDF xh L w 52 11.10 10.91 V D V P 1135 E 77 B A 1 6 IDF xh L w 116 14.80 10.31 E D V P 1125 N 78 B A 1 7 IDF dm Lw 55 27.10 25.95 F V R 1200 Flat 79 B A 1 8 IDF dm L w 55 24.60 23.56 S D R 1200 Flat 80 B A 1 9 IDF dm L w 51 23.40 23.19 F R 1230 Flat 81 B A 2 0 IDF dm Lw 55 24.50 23.46 S D R 1230 W 82 BA21 IDF dm Lw 53 21.20 20.65 S D R 1240 W 83 B A 2 2 IDF dm Lw 56 24.70 23.46 SD R 1240 S 84 B A 2 3 M S dm Lw 54 27.10 26.16 F V R 1330 W 85 B A 2 4 M S dm Lw 59 26.20 24.30 SD M 1330 W 86 B A 2 5 M S dm Lw 50 20.20 20.20 SD P 1350 W 87 B A 2 6 M S dm L w 53 19.00 18.51 M D P 1370 N 88 B A 2 7 M S dm Lw 46 14.60 15.15 V D M 1420 W 89 B A 2 8 M S dm Lw 40 20.20 22.35 F R 1420 W 90 B A 2 9 IDF xh L w 53 22.30 21.72 M D M 1135 Flat 91 B A 3 0 IDF xh L w 57 15.50 14.63 V D M 1140 E 92 B A 0 6 IDF xh L w 56 23.60 22.42 S D M 990 N 93 B A 0 9 IDF xh L w 54 22.70 21.92 M D M 1100 S 94 KaOl IDF xh Lw 49 20.20 20.39 M D M 1250 S 95 Ka06 I C H mw Lw 62 27.60 25.02 F M 760 Flat 96 Ka07 I C H mw L w 71 32.00 27.25 F R 760 Flat 97 Ka08 I C H mw Lw 58 25.10 23.47 S D M 830 S 98 Ka09 I C H mw L w 78 28.20 23.03 S D P 780 Flat 99 K a l O I C H mw L w 118 31.70 21.44 SD P 945 W 100 K a i l I C H mw Lw 54 25.80 24.34 F M 1015 Rdge 101 K a l 2 I C H mw Lw 67 24.60 21.54 W R 1010 Flat 102 K a l 3 I C H mw L w 55 28.40 27.19 F R 1015 N 103 K a l 3 5 I C H mw L w 115 24.30 16.72 V D V P 920 W 104 K a l 3 6 I C H mw Lw 112 33.60 23.24 SD M 915 s 105 K a l 3 7 I C H mw Lw 119 34.00 22.88 SD M 910 N 61 # Stand Zone Subzone Species B H A Height SI A S M R S N R Elev. Aspect 106 K a l 3 8 I C H mw Lw 78 26.30 21.50 SD P 750 Flat 107 K a l 4 I C H mw L w 71 21.40 18.30 SD P 1120 Rdge 108 K a l 4 0 I C H mw L w 93 33.60 25.28 F M 780 S 109 Ka l41 I C H mw L w 92 35.30 26.68 F M 760 Flat 110 K a l 4 2 ICH mw L w 90 34.20 26.12 F M 760 Flat 111 K a l 4 4 I C H mw L w 85 32.70 25.64 F M 755 Flat 112 K a l 5 I C H mw L w 87 27.50 24.35 S D P 1120 E 113 K a l 6 I C H mw Lw 96 26.30 19.57 SD P 960 E 114 K a l 7 I C H mw Lw 117 33.40 22.65 F R 970 E 115 K a l 9 I C H mw L w 116 27.60 18.86 M D P 995 E 116 Ka20 I C H mw L w 116 31.76 21.64 SD M 1015 E 117 Ka21 I C H mw L w 63 24.70 22.24 S D P 665 W 118 Ka22 I C H mw L w 73 24.10 20.31 S D P 660 W 119 Ka23 ICH mw L w 71 25.40 21.66 S D P 660 W 120 Ka24 I C H mw Lw 71 27.70 23.61 SD M 665 W 121 Ka245 I C H mw Lw 82 22.60 18.09 M D P 710 W 122 Ka25 IDF mw Lw 67 29.30 25.63 SD R 430 N 123 Ka26 IDF mw Lw 56 25.70 24.41 SD R 410 N 124 Ka27 IDF mw Lw 60 17.50 16.14 V D M 400 Rdge 125 Ka28 IDF mw L w 58 18.20 17.03 M D R 400 Flat 126 Ka29 IDF mw L w 60 29.40 27.04 M R 395 N 127 Ka30 IDF mw L w 81 25.70 20.65 M D M 420 Flat 128 Ka31 IDF mw L w 78 29.50 24.08 S D R 500 S 129 Ka32 IDF mw L w 65 28.90 25.63 F M 660 S 130 Ka33 I C H mw L w 67 28.10 24.58 S D M 705 E 131 Ka34 I C H mw L w 64 27.50 24.57 F M 695 E 132 KE01 IDF xh L w 56 20.20 19.20 V M V R 1120 E 133 K E 0 2 IDF xh Lw 62 26.80 24.30 SD R 1130 W 134 KE03 IDF xh Lw 65 15.80 14.08 V D P 1175 W 135 K E 0 4 IDF xh Lw 54 23.20 22.40 SD M 1170 S 136 KE05 IDF xh L w 46 22.90 23.78 SD R 1175 w 137 K E 0 6 IDF xh L w 50 17.90 17.90 M D P 1210 w 138 KE07 IDF xh Lw 54 19.10 18.45 M D P 1205 N 139 KE08 IDF xh Lw 59 20.90 19.40 M D M 1220 w 140 K E 0 9 IDF dm Lw 116 32.80 22.34 M D M 720 Flat 62 # Stand Zone Subzone Species B H A Height SI A S M R S N R Elev. Aspect 141 K E 1 0 IDF dm L w 108 29.70 20.93 M D P 820 S 142 KE11 IDF dm L w 114 36.50 25.01 SD R 840 E 143 K E 1 2 IDF dm L w 113 33.50 23.08 M D M 920 E 144 K E 1 3 IDF dm Lw 53 22.50 21.92 M D M 1025 S 145 K E 1 4 IDF dm L w 57 17.70 16.70 M D P 1050 s 146 KE15 IDF dm L w 58 21.30 19.92 M D M 1040 s 147 K E 1 6 IDF dm Lw 63 15.00 13.56 V D P 1070 w 148 KE17 TDF dm Lw 54 23.10 22.31 S D M 1050 s 149 KE18 IDF dm Lw 49 17.80 17.96 M D P 1035 w 150 K E 1 9 M S dm Lw 55 25.90 24.80 S D M 1100 E 151 K E 2 0 M S dm Lw 53 27.60 26.87 M V R 1140 E 152 KE21 M S dm Lw 56 28.00 26.58 F V R 1140 E 153 K E 2 2 M S dm Lw 45 12.50 13.08 V D P 1220 s 154 KE23 M S dm Lw 45 12.30 12.87 V D V P 1205 w 155 K E 2 4 M S dm L w 53 25.20 24.54 F V R 1220 E 156 KE25 M S dm L w 51 23.60 23.39 F R 1200 E 157 K E 2 6 M S dm L w 53 20.40 19.87 M D M 1130 Flat 158 KE27 M S dm L w 52 20.60 20.24 M D M 1130 E 159 K E 2 8 M S dm L w 53 21.40 20.84 S D P 1130 E 160 K E 2 9 M S dm Lw 53 20.70 20.16 S D P 1135 N 161 K E 3 0 M S dm Lw 50 22.30 22.30 S D R 1200 E 162 KE31 M S dm Lw 49 22.50 22.70 SD M 1200 E 163 K E 3 2 M S dm Lw 49 16.10 16.24 M D P 1255 E 164 K E 3 3 M S dm Lw 52 24.10 23.68 SD M 1230 E 165 K E 3 4 M S dm Lw 54 20.60 19.89 SD P 1180 E 166 K E 3 5 M S dm Lw 58 22.00 20.57 S D P 1215 s 167 KE36 M S dm L w 50 14.10 14.10 V D M 1165 S 168 KE37 M S dm Lw 51 19.90 19.73 SD P 1165 N 169 KE38 M S dm L w 58 24.10 22.53 S D M 1155 E 170 K E 3 9 M S dm L w 55 20.80 19.93 S D P 1100 E 171 K E 4 0 M S dm Lw 53 22.70 22.11 S D R 1170 E 172 NeOl IDF dm Lw 86 23.30 18.25 M D P 860 N 173 Ne02 IDF dm Lw 87 26.30 20.46 M D M 850 N 174 Ne03 IDF dm Lw 94 28.00 21.02 M D M 850 N 175 Ne05 I C H mk L w 98 27.70 20.42 M D M 1360 N _ 63 # Stand Zone Subzone Species BHA Height SI ASMR SNR Elev. Aspect 176 Ne06 ICH mk Lw 69 27.00 23.32 SD M 1330 S 177 Ne07 ICH mk Lw 72 27.90 23.63 SD M 1330 S 178 Ne08 ICH mk Lw 68 18.80 16.40 V D P 1295 E 179 Ne09 ICH mk Lw 73 24.90 20.98 M D M 1275 N 180 NelO ICH mk Lw 72 26.20 22.20 SD P 1290 E 181 Nei 08 IDF dm Lw 91 30.20 22.98 M D M 960 Flat 182 Nei 09 IDF dm Lw 99 22.70 16.71 V D P 970 Flat 183 Nel l ICH mk Lw 80 29.20 23.56 V M R 1285 E 184 Nei 10 IDF dm Lw 74 20.60 17.28 M D P 960 Rdge 185 N e i l l IDF dm Lw 79 20.90 17.03 M D P 950 Flat 186 Nei 12 ICH mw Lw 137 34.50 21.78 SD P 1310 N 187 Nei 13 ICH mw Lw 134 23.50 15.12 V D VP 1410 W 188 Nei 14 IDF dm Lw 79 20.90 17.03 M D P 1100 E 189 Nell5 IDF dm Lw 72 22.10 18.75 SD P 1100 E 190 Nei 16 IDF dm Lw 82 19.20 15.41 V D VP 1110 E 191 Nei 17 IDF dm Lw 93 23.40 17.70 M D P 1090 E 192 Nei 18 MS dk Lw 116 21.40 14.71 V D VP 1135 N 193 Nei 19 MS dk Lw 58 25.10 23.47 SD M 1200 Flat 194 Nel2 ICH mk Lw 72 31.60 26.74 F R 1275 E 195 Nel21 MS dk Lw 67 23.30 20.42 SD P 1200 W 196 Nei 22 MS dk Lw 69 21.30 18.43 M D P 1280 W 197 Nei 23 MS dk Lw 71 24.10 20.56 SD P 1300 Rdge 198 Nei 23 MS dk Lw 71 25.20 21.49 M D M 1300 W 199 Nei 24 MS dk Lw 102 25.20 18.27 M D P 1295 Flat 200 Nei 25 MS dk Lw 94 28.00 21.02 SD P 1285 E 201 Nei 26 MS dk Lw 118 31.80 21.50 SD P 1270 E 202 Nei 27 MS dk Lw 75 24.30 20.23 M D M 1275 Flat 203 Nei 28 MS dk Lw 100 23.20 17.00 V D P 1255 E 204 Nei 29 MS dk Lw 103 29.20 21.02 SD P 1220 E 205 Nel3 ICH mk Lw 75 20.70 17.26 M D P 1285 S 206 Nei 30 MS dk Lw 101 31.90 23.13 SD M 1240 E 207 Nel31 MS dk Lw 78 29.70 24.24 SD R 1115 Gully 208 Nei 32 MS dk Lw 91 24.90 19.00 M D P 1375 E 209 Nel33 MS dk Lw 121 27.90 18.70 M D P 1445 S 210 Nei 34 MS dk Lw 73 21.70 18.31 M D P 1465 E 64 # Stand Zone Subzone Species B H A Height SI A S M R S N R Elev. Aspect 211 N e l 4 I C H mk L w 67 16.60 14.59 V D P 1285 Flat/Rdg 212 N e l 5 I C H mk L w 81 26.30 21.13 S D P 1135 E 213 N e l 6 I C H mk L w 86 24.00 18.79 M D P 1155 E 214 N e l 7 I C H mk L w 78 29.10 23.76 SD R 1035 E/Flat 215 N e l 8 I C H mk L w 86 26.10 20.42 M D M 1035 E 216 N e i 9 I C H mw L w 84 27.80 21.96 SD P 1130 E 217 Ne20 I C H mw L w 90 28.10 21.51 SD P 1120 Deprssn 218 Ne21 I C H mw L w 89 22.60 17.45 V D M 1150 N 219 Ne22 I C H mw L w 81 26.60 21.37 SD P 1150 E 220 Ne23 I C H mw L w 86 29.30 22.89 S D M 1140 E 221 Ne24 I C H mw L w 88 30.90 23.87 M D R 1150 E 222 Ne25 I C H mw L w 88 27.90 21.58 S D P 1000 E 223 Ne26 I C H mw L w 89 27.40 21.09 S D P 1200 W 224 Ne27 I C H mw L w 90 32.10 24.52 S D M 1190 W 225 Ne28 I C H mw L w 85 24.20 19.05 M D P 1220 Rdge 226 Ne29 I C H mw L w 87 28.80 22.38 S D P 1220 W 227 Ne30 I C H mw L w 81 29.40 23.59 S D P 1300 S 228 Ne31 I C H mw L w 77 26.50 21.78 M D M 1320 Rdge 229 Ne32 I C H mw L w 82 25.40 20.30 SD P 1330 W 230 Ne33 I C H mw L w 85 30.60 24.01 SD M 1325 W 231 Ne34 I C H mw L w 87 33.60 26.06 F M 1320 E 232 Ne35 I C H dw L w 116 35.00 23.81 SD R 595 Flat 233 Ne36 I C H dw L w 93 31.10 23.43 SD P 865 W 234 Ne37 I C H dw L w 106 37.00 26.20 F M 860 W 235 Ne38 I C H dm L w 93 34.30 25.81 F M 650 S 236 Ne39 I C H dm L w 84 35.10 27.66 F R 650 Rdge 237 Ne40 I C H mw L w 83 31.40 25.04 F M 820 W 238 Ne41 I C H mw L w 101 35.00 25.36 F M 880 W 239 Ne42 I C H mw L w 62 30.20 27.36 F M 920 S 240 Ne43 I C H mw L w 58 28.90 . 27.00 S D R 950 S 241 Ne44 I C H mw L w 52 20.20 19.84 S D P 950 s 242 Ne45 I C H mw L w 62 28.10 25.47 SD R 940 S/Rdge 243 Ne46 I C H mw L w 60 24.40 22.46 S D M 590 E 244 Ne47 I C H mw L w 56 24.30 23.08 SD M 610 Flat 245 Ne48 I C H mw L w 53 23.80 23.18 S D M 600 E 65 # Stand Zone Subzone Species B H A Height SI A S M R S N R Elev. Aspect 246 Ne49 I C H mw L w 55 20.90 20.02 SD P 610 E 247 Ne50 I C H mw L w 59 25.50 23.65 S D M 620 E 248 Ne52 I C H dw L w 97 35.50 26.19 F R 660 N 249 Ne53 I C H mw L w 87 35.20 27.29 SD R 835 S 250 Ne54 I C H mw L w 82 26.70 21.33 M D M 1045 Rdge/W 251 Ne55 I C H mw L w 84 28.80 22.73 SD P 1055 E 252 Ne56 I C H dw L w 62 23.30 21.14 M D M 905 S 253 Ne57 I C H dw L w 66 20.80 18.36 V D R 935 S 254 Ne58 I C H dw L w 92 37.30 28.17 M M 1050 Gully 255 Ne59 I C H dw L w 93 29.50 22.23 M D M 1090 S 256 Ne60 I C H mw L w 103 35.10 25.20 F M 1115 S 257 Ne61 I C H mw L w 95 34.40 25.63 F M 1225 w 258 Ne62 I C H mw L w 94 28.40 21.32 S D P 1245 S 259 Ne63 I C H mw L w 100 34.10 24.82 F M 1250 S 260 Ne64 I C H dw L w 71 30.30 25.81 S D R 700 Flat 261 Ne65 I C H dw L w 55 27.10 25.95 S D R 720 Flat 262 Ne66 I C H dw L w 58 29.50 27.56 F V R 720 Flat 263 Ne67 I C H dw L w 63 27.20 24.50 SD M 740 Flat 264 Ne68 I C H dw L w 63 25.40 22.87 M D R 750 W 265 Ne69 I C H dw L w 80 28.70 23.17 M D M 810 Flat 266 Ne70 I C H dw L w 85 37.90 29.68 M V R 880 Deprssn 267 Ne71 I C H dw L w 87 33.60 26.06 F M 880 N 268 Ne72 I C H dw L w 60 23.90 22.00 S D P 960 N 269 Ne73 I C H dw L w 63 27.40 24.66 SD R 970 N 270 Ne74 I C H dw L w 55 24.20 23.17 V M R 960 N 271 Ne75 I C H mw L w 80 28.20 22.66 M D M 1090 N 272 Ne76 I C H mw L w 52 17.80 17.49 M D P 1100 N 273 Ne77 I C H mw L w 89 24.00 18.50 M D P 1090 N 274 Ne78 I C H mw L w 89 23.30 17.98 M D P 1180 W 275 Ne79 I C H mw L w 88 23.70 18.37 M D P 1260 W 276 Ne80 I C H mw L w 108 30.20 21.27 V M R 1140 N 277 Ne81 I C H mw L w 107 26.80 19.00 SD P 1145 N 278 Ne82 I C H mw L w 72 25.10 20.76 SD P 1120 N 279 Ne83 I C H mw L w 70 19.70 16.95 V D M 1080 R 280 Ne84 I C H mw L w 71 21.20 18.13 M D P 1075 S 66 # Stand Zone Subzone Species B H A Height SI A S M R S N R Elev. Aspect 281 Ne85 I C H mk Lw 77 27.50 22.59 S D P 960 Flat 282 Ne87 I C H mk Lw 92 9.00 7.03 E D V P 1000 R 283 Ne88 I C H mk Lw 70 12.40 10.70 E D V P 980 N 284 Ne89 I C H mk L w 69 18.10 15.69 V D P 970 N 285 Ne90 I C H mk L w 60 25.80 23.75 SD M 990 W 286 Ne91 I C H mk L w 74 25.90 21.67 S D P 1050 E 287 Ne92 I C H mk L w 57 23.50 22.14 S D P 1130 E 288 Ne93 I C H mk L w 56 22.20 21.09 S D P 1190 W 289 Ne94 I C H mk Lw 65 21.50 19.10 M D P 1240 N 290 Ne95 I C H mk Lw 56 23.60 22.42 SD M 1310 W 291 Ne96 I C H mk Lw 59 24.00 22,52 SD P 1370 E 292 Ne97 I C H mk L w 56 19.50 18.54 SD P 1470 N 293 OK01 IDF dm L w 84 24.10 19.07 M D P 1390 N 294 OK02 IDF dm L w 88 26.70 20.66 M D M 1380 N 295 OK03 IDF dm Lw 86 32.30 25.18 S D R 1380 N 296 OK04 IDF dm Lw 86 28.70 22.42 M D M 1360 W 297 OK05 IDF dm Lw 88 30.00 23.18 SD M 1360 W 298 OK06 IDF dm L w 82 23.90 19.12 M D P 1390 N 299 OK07 IDF xh Lw 35 20.70 24.33 M V R 1170 W 300 OK08 IDF dm L w 53 22.10 21.53 M D M 1540 N 301 O K 1 0 M S dm L w 50 17.70 17.70 M D P 1470 W 302 OK11 M S dm L w 55 24.40 23.36 F R 1430 W 303 OK12 M S dm L w 53 22.30 21.72 S D M 1450 N 304 OK13 M S dm Lw 52 19.10 18.76 M D P 1530 S 305 OK14 M S dm Lw 102 31.10 22.47 M D M 1585 w 306 OK14n M S dm L w 108 26.30 18.57 M D P 1580 w 307 OK15 M S dm L w 50 19.10 19.10 SD P 1520 S 308 OK16 M S dm L w 82 31.80 25.36 S D M 1460 N 309 OK17 M S dm L w 79 29.50 23.94 S D M 1590 N 310 O K I 8 M S dm Lw 69 29.20 25.20 S D M 1590 N 311 OK19 IDF xh L w 53 20.80 20.26 M D M 950 N 312 OK20 IDF xh L w 39 14.00 15.62 V D P 1120 W 313 OK21 IDF xh Lw 57 18.60 17.54 M D P 1160 N 314 OK22 IDF dm L w 81 29.20 23.43 M D M 1390 N 315 OK23 IDF xh L w 78 17.10 14.05 V D P 800 N 67 APPENDIX 2 The Key to Identification of Relative Soil Moisture Regimes (after Greene et al. 1994) This key is designed to assist in identifying relative soil moisture regimes using observable edaphic feature during site assessment (diagnosis). Attempt always to check the results against plant indicators. Explanation of the Key Ridge crest: distinctly shaped height of land; usually with a convex slope shape. Upper slope: the convex-shaped, upper (water-shedding) portion of a mesoslope. Middle slope: the portion between the upper and lower portion of a mesoslope; the slope shape is usually straight. Lower slope: the concave-shaped, lower (water-receiving) portion of a mesoslope. It includes toe slopes which are nearly level areas directly below and adjacent to the lower slope. Flat: any level area excluding toe slopes; the surface shape is horizontal with no significant aspect Depression: any area that is concave-shaped (water-collecting); usually in a flat or subdued topography. Soil depth (= potential rooting depth): depth from the ground surface to a root restricting layer such as bedrock' strongly compacted or strongly cemented materials; or permanent, stagnant water table. Particle size coarse: sandy (sandy or loamy sand) with >35% coarse fragment content by volume, or loamy (sandy loam, loam, or sandy clay loam) with >70% coarse fragment volume. Particle size fine: silty (silt loam or silt) or clayey (silty clay loam, clay loam, sandy clay, silty clay, or clay) with <35% coarse fragment volume. Floodplain: post-glacial alluvial deposits bordering rivers and streams, still under the influence of periodic flooding. Gleyed layer or mottles: one or more soil horizons >10cm thick that have developed under poor drainage, waterlogging, and permanent or periodic reduction of iron and other elements. Typically, they feature mottles- many distinct and prominent, orange to red spots or blotches interspersed in a predominantly dull yellow, gray, blue, or olive coloured soil matrix. 6 8 Appendix 2 (continued) Key to Identification of Relative Soil Moisture Regimes l a ridge crest or upper slope3 2 lb. other slope positions 6 2a soil depth <40cm 3 2b soil depth >40cm 5 3a exposed bed > 50% 0 3b exposed bedrock <50% 4 4a particle size coarse and forest floor <20cm 1 4b particle size fine or forest floor >20cm 2-3 4c other soils 1-2 5a particle size coarse and forest floor <20cm 2 5b other soils 2-3 6a middle slopes" 7 6b other slope positions 12 7a water table seepage, or mottles present 8 7b water table seepage, or mottles absent 9 8a water table seepage, or mottles > 60 cm deep 5 8b water table seepage, or mottles< 60 cm deep 6 9a soil depth <40cm 10 9b soil depth >40cm 11 10a particle size coarse and forest floor <20cm 2 10b particle size fine and forest floor >20cm 4 10c other soils 2-3 I la particle size coarse and forest floor <20cm 2 II b particle size fine 5 1 lc other soils 3-4 12a lower slopes 13 12b other slope positions 17 13a water table seepage, or mottles present 14 13b water table seepage, or mottles absent 5 14a water table or seepage<30 cm deep 15 14b water table seepage >30 cm deep 16 15a particle size coarse and forest floor <20cm 6 15b particle size fine and forest floor >20cm 7 16a water table seepage, or mottles >60cm deep 5 16b particle size coarse and forest floor <60cm deep 6 69 Appendix 2 (continued) Key to Identification of Relative Soil Moisture Regimes 17a flat (s lope < 5 % ) 18 17b. depression --2S 18a f loodp la in • 19 18b other soils 22 19a water table or mottles present 20 19b water table or mottles absent 21 20a water table or mottles <30cm deep 7 20b water table or mottles >60 c m deep 5 20c other soils 6 21a part icle size coarse 3-4 21b other soils 5 22a part icle size f ine 23 22b other soils 25 23a water table or mottles present and soi l depth >60 c m 5 23b other soi ls 24 24a water table or mottles <20 c m deep 7C 24b water table or mottles >35 c m deep 5C 24c other soils 6C 25a water table or mottles present 26 25b water table or mottles absent 27 26a water table or mottles <30cm deep 7 26b water table or mottles >60 c m deep 5 26c other soils 6 27a part icle size coarse 2-3 27b other soils 4 28a minera l soi l 29 28b other soils 32 29a part icle size f ine 30 29b other soils ." 7 32 30a water table or mottles present 31 30b water table or mottles absent 5 31a water table or mottles <20cm deep 7C 31b water table or mottles >20 c m deep 6C 32a water table <30cm deep 7 32b water table >60 c m deep 5 32c other soils 6 "Cons ide r a lower rank ing for steep slope gradients and warm-aspect s lopes. b A t t a c h suff ix 1, m, or h for f loodp la in bench height: 1 ( low bench) , m (middle bench) , h (high bench) , e.g. 6 m . ° A t t a c h suf f ix f for strongly f luctuat ing water table, e.g., 5f. T h e soi l aeration regime of sites with a strongly f luctuat ing water table is always restricted or deficient. 7 0 APPENDIX 3 Key to Identification of Soil Nutrient Regimes (after Greene et al. 1994). This key is designed to assist in identifying soil nutrient regimes using observable edaphic features during site assessment (diagnosis). Attempt always to check the results against plant indicators. Explanation of the Key Wet soil: soil that has the growing-season water table within between 0 and <30 cm of the ground surface. Forest floor: all organic materials (L, F, and H horizons) on the mineral soil surface. Organic materials friable: well decomposed organic materials that dry out sufficiently during the growing season that they will have a crumbly consistency. Mor: humus form composed of L, F, and H horizons; the Fm horizon is matted and contains abundant fungal mycelia. Particle size coarse: sandy (sandy or loamy sand) with >35% coarse fragment content by volume, or loamy (sandy loam, loam, or sandy clay loam) with >70% coarse fragment volume. Soil dark coloured: soil that has high organic matter content, indicated by dark, chocolate-brown colours (Munsell notation value for moist colours<4). Soil light-coloured: soil that has a very low organic matter content, indicated by very pale colours(Munsell notation of value for moist colours>6). Soil depth (= potential rooting depth): depth from the ground surface to a root restricting layer such as bedrock; strongly compacted or strongly cemented material; or permanent, stagnant water table. Soil colluvial: soil derived from colluvial parent materials. 71 Appendix 3 (continued) The key to the identif ication of soil nutrient regimes (SNRs) Symbols for SNRs are: V P - very poor, P - poor, M - medium, R - rich, and V R - very rich. la soil wet 2 lb. soil not wet 9 2a forest floor >20cm .....3 2b forest floor <20cm 6 3a materials poorly decomposed V P 3b materials not poorly decomposed 4 4a materials well decomposed 5 4b materials not well decomposed P - M 5a materials friable V R 5b materials not friable R 6a Mor humus form 7 6b Other humus forms than Mor 8 7a Ae horizon>2cm or particle size coarse V P 7b Ae horizon<2cm or particle size not coarse P 8a soil dark coloured or Ah horizon >5cm R - V R 8b soil not dark coloured or Ah horizon <5cm M 9a soil depth <30cm 10 9b soil depth >30cm 13 10a Mor humus form 11 10b Other humus forms than Mor 12 1 la soil light-coloured V P 1 lb soil not light-coloured P - M 12a soil dark coloured or Ah horizon >5cm R 12b soil not dark coloured or Ah horizon <5cm M 13a Mor humus form or Ae horizon >2cm 14 13b Other humus forms than Mor Ae horizon <2cm 19 14a particle size coarse 15 14b particle size not coarse 17 15a soil light-coloured V P 15b soil not light-coloured 16 16a soil colluvial or dark-coloured R 16b soil not colluvial or dark-coloured P - M 17a soil light-coloured P 17b soil not light-coloured 18 18a seepage present R 18b seepage not present M 19a soil dark coloured or Ah horizon >5cm 20 19b soil not dark coloured or Ah horizon <5cm 21 20a particle size coarse R 20b particle size not coarse V R 21a soil light-coloured and particle size coarse M 21b soil not light-coloured and particle size not coarse R 72 APPENDIX 4 Tentative classification of actual soil moisture regimes (ASMRs) based on annual water balance and the depth of the growing-season groundwater table (from Klinka et al. 1998). Characteristic ASMR la. Rooting-zone groundwater absent during the growing season 2a. Water deficit occurs (soil-stored reserve water is used and drought begins if current precipitation is insufficient for plant needs) 3a. Deficit >7 months or AET/PET 1 <0.30 extremely dry (XD) 3b. Deficit >5 but <7 months or AET/PET <0.55 but >0.30 excessively dry (ED) 3c. Deficit >3 but <5 months or AET/PET <0.75 but >0.55 very dry (VD) 3d. Deficit > 1.5 but <3 months or AET/PET <0.90 but >0.75... moderately dry (MD) 3e. Deficit >0 but <1.5 month or AET/PET >0.90 slightly dry (SD) 2b. No water deficit occurs 4a. Utilization (and recharge) occurs (current need for water exceeds supply and soil-stored water is used) fresh (F) 4a. No utilization occurs (current need for water does not exceed supply) moist (M) lb. Rooting-zone groundwater present during the growing season 5a. Groundwater table >60 cm deep moist (M) 5b. Groundwater table >30 but <60 cm deep very moist (VM) 5c. Groundwater table >0 but <30 cm deep wet (W) 5d. Groundwater table at or above the ground surface very wet (VW) AET/PET — actual evapotranspiration/potential evapotranspiration ratio. 73 APPENDIX 5 M e a n values of soil properties used to characterize soil nutrient regimes of Br i t i sh Columbia 's soils (from Kl inka et al. 1998). Soil nutrient regime Property Very poor Poor Medium Rich Very rich (VP) (P) (M) (R) (VR) Forest floor p H 1 3.8 4.0 4.1 4.5 5.0 Forest floor C / N ratio1 ?3_ 42 20 21 Soil min-N (kg/ha) in drier C W H 2 <10 18 54 113 242 Soil min-N (kg/ha) in drier C W H 3 ISIIllliilS 13 25 47 176 Soil min-N (kg/ha) in SBPS and S B S 4 10 30 38 no Soil min-N (kg/ha) in S B S 5 29 43 hO 111 Mineral soil min-N (mg/kg) in drier N D 4.0 14 32 58 C W H 2 Mineral soil min-N (mg/kg) in drier 10 31 50 C W H 6 Mineral soil min-N (mg/kg) in wetter 23 34 29 70 N D C W H 7 Mineral soil min-N (mg/kg) in wetter 41 X5 172 322 C W H 8 Mineral soil min-N (mg/kg) in S B S 9 ~ T 3 1 5~ 17 47 181 Mineral soil min-N (mg/kg) in E S S F 9 S'Sllllilljl N D — not determined. 'Courtin et al. (1988). 2 Kabzems and K l inka (1987). 3 Carterand K l inka (1990) 4 Wang(1992). 5 Wang (1993) 6 K l i n k a and Carter (1990). 7Kayahara(1992). 8 Varga and K l inka (unpublished manus.) 9 K l i n k a etal. (1994). 1 0 K l i nka et al. (1998). 74 

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