RELATIONSHIPS BETWEEN SITE INDEX OF SITKA SPRUCE (Picea sitchensis (Bong.) Carr.) AND MEASURES OF ECOLOGICAL SITE QUALITY IN THE EASTERN QUEEN CHARLOTTE ISLANDS by AUDREY FRANCES PEARSON B.Sc.(Honours) University of British Columbia, 1983
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (DEPARTMENT OF FORESTRY)
We accept this thesis as conforming to the required standard
THE UNIVERSITY OF'BFÛTISH COLUMBIA
OCTOBER 1992
© Audrey Frances Pearson, 1992 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.
Department
The University of British Columbia Vancouver, Canada
Date
DE-6 (2/88) ABSTRACT
Relationships between measures of ecological site quality and height growth of Sitka spruce [Picea sitchensis (Bong.) Carr), measured as site index (metres at 50 years at breast height), were investigated for the eastern Queen Charlotte Islands using multiple linear regression techniques. Temperature and light were assumed to be similar in a region of similar climate, expressed as biogeoclimatic subzone, and the investigation focused on measures of moisture and nutrients. This study tested the hypothesis that the classification units of biogeoclimatic ecosystem classification system are good indirect estimates of ecological site quality so are able to predict forest growth.
Fifty-five plots were chosen for study to represent a wide range in ecological site quality and geographic variation. Individual measures of moisture and nutrients were represented as categorical variables; soil moisture and nutrient regime; and continuous variables; soil nutrients and predicted moisture deficit in combination with soil physical measures. Soil moisture and nutrient regimes were represented as spectra of indicator species groups. The synoptic effect of individual measures of moisture and nutrients were expressed as site associations and assumed to be represented by plant associations. The relationship between the categorical and continuous variables for soil nutrients was also examined.
The most successful models used the BEC classification units; soil moisture regime, soil nutrient regime and site association. Multiple linear regression models using either soil moisture plus soil nutrient regimes or site associations were equally successful in explaining variation in (ln) site index (adjusted R2 = 0.80; 12 = 0.79). However, the former should be considered the Ill superior model for predictive purposes since the variance of the latter model was not homogeneous. The soil nutrient regime model was as successful in explaining variation in site index as the best continuous model (mineral soil mineralizable N plus forest floor mineralizable N, extractable calcium and potassium) (adjusted = 0.40 versus 0.42). The soil moisture regime model was more successful than those using continuous variables (adjusted = 0.45 versus no relationship). The lack of relationship was attributed to inaccuracy of the water balance models due to their lack of calibration for this location and forest type. There was a moderate relationship with site index and plant associations (adjusted 2^ = 0.49); however vegetation is subject to change with succession over time so would be difficult to apply for predictive purposes. Indicator species groups showed no relationship with site index, likely due to the paucity of understorey species from canopy closure and intense browsing by introduced deer.
The soil nutrient regime classification was well supported by a discriminant analysis using soil chemical nutrients which correctly classified the plots in 83% of the cases, on average. The soil moisture regime classification could not be tested since the models used to estimate moisture deficit in this study were considered too inaccurate. Continuous synoptic versus categorical models could also not be compared for the same reason.
It was concluded that the units of the biogeoclimatic ecosystem classification were good indirect measures of ecological site quality and good predictors of forest growth. The equations generated in this study need to be tested with independent data and further investigation is necessary to determine moisture relationships for Sitka spruce. TABLE OF CONTENTS
Abstract ii Table of Contents iv List of Tables v List of Figures vi List of Appendices vi List of Symbols .vli Acknowledgements viii CHAPTER 1 INTRODUCTION 1 1.1 Objectives 1 1.2. Literature Review 3 1.3 The Approach 9 CHAPTER 2. THE STUDY AREA 11 2.1 Physiography 11 2.2 Geology and Surficial Materials 11 2.3 Climate 12 2.4 Forest Vegetation 15 CHAPTER 3 MATERIALS AND METHODS 18 3.1 Field Methods 18 3.2 Laboratory Analysis 20 3.3 Water Balance 21 3.4 Ecological Analysis and Classification 23 3.4.1 Vegetation Analysis and Classification 23 3.4.2 Site Analysis and Classification 24 3.5 Relationships Between Site Index and Ecological Variables 25 CHAPTER 4 RESULTS 27 4.1 Vegetation Classification 27 4.2 Soil Moisture Analysis 32 4.3 Soil Nutrient Analysis 34 4.3.1 Cluster Analysis for Soil Nutrients and Soil Nutrient Regimes 37 4.3.2 Discriminant Analysis for Soil Nutrients and Soil Nutrient Regimes ... 39 4.4 Site Classification 42 4.5 Relationships Between Sitka Spruce Site Index and Ecological Site Quality .47 CHAPTER 5 DISCUSSION 55 CHAPTER 6 CONCLUSIONS 63 LITERATURE CITED .64
APPENDICES 72 LIST OF TABLES
1) Selected climatic characteristics for the CWHwhl variant 13 2) Diagnostic combination of species for plant associations 28 3) Means and standard deviations for selected environmental characteristics for plant associations 30 4) Means and standard deviations for selected characteristics by soil moisture regimes 33 5) Means and standard deviations for selected characteristics by soil nutrient regimes 35 6) Mean and standard deviations for Sitka spruce site index by soil moisture and soil nutrient regime 36 7) Group membership predicted by discriminant analysis 41 8) Constants and coefficients of classification functions for soil nutrient regimes determined by discriminant analysis 41 9) Means and standard deviations for selected characteristics for site associations 43 10) Models for regression of Sitka spruce site index and selected categorical and continuous variables 48 11) Stability of linear multiple regression nutrient equations .49 LIST OF FIGURES
1) Location of the study plots in the Queen Charlotte Islands 19 2) 80% confidence ellipses for principal component scores for plant alliances along first two principal component axes 29 3) Box plots of Sitka spruce site index by plant association 31 4) Cluster analysis for soil nutrients and soil nutrient regimes 38 5) Discriminant analysis for soil nutrients and soil nutrient regimes 40 6) Mean Sitka spruce site index for site associations 44 7) Mean Sitka spruce site index for beach side, ocean-spray affected site associations 45 8) Box plots of Sitka spruce site index by site association 46 9) Predicted versus actual site index and residual analysis for soil moisture plus soil nutrient regime regression model 51 10) Predicted versus actual site index and residual analysis for site association regression model 52
11) Edatopic grid for Sitka spruce site index 54
LIST OF APPENDICES
Appendix A Alphabetical list of plant species 72 Appendix B Reciprocal averaging iterations for vegetation classification .... 73 Appendix C 80% confidence ellipses for principal component analysis of plant associations 79 Appendix D Pearson correlation matrix for soil nutrients 82 LIST OF SYMBOLS Classification BEC biogeoclimatic ecosystem classification SMR soil moisture regime SD slightly dry F fresh M moist VM very moist W wet SNR soil nutrient regime P poor M medium R rich VR very rich Soil Variables FF forest floor MS mineral soil TOTC total carbon TOTN total nitrogen MINN mineralizable nitrogen P phosphorus S sulphur Ca extractable calcium Mg extractable magnesium K extractable potassium SUMCAT sum of extractable calcium, magnesium and potassium CN carbon/nitrogen ratio PH pH Vlll
ACKNOWLEDGEMENTS
I am very grateful to my committee members, Gary Bradfield and Peter Marshall, for being my guides to the world of statistics, an area 1 have developed a dangerous fondness for.
Thanks also go to Rob Pollock formerly of the B.C. Forest Service, Del Williams of the B.C. Forest Service, Ron Bronstein formerly of Western Forest Products, Sue Craven of Fletcher Challenge and John Duncan of MacMillan Bloedel for their kind assistance in my field work on the Islands. John Barker of Western Forest Products was also a valuable information source. 1 am grateful for the funding provided by the B.C. Forest Service. Special thanks go to Diane Hanson (nee Tamis), my trusty field assistant who measured the dozens of trees, dug the meters of soil pits and weighed the kilos of rocks cheerfully and competently. My office mate, Gordon Kayahara, has been my invaluable companion. Reid Carter was always ready with helpful advice. Qingli Wang and Gaofeng Wang helped keep the laughter flowing. Xie- Xie Peng-you!
And to everyone who makes the Islands a very special place for me, How-a, Thank you. And to my Mother, who cheerfully paid whatever bills my education presented to her - 1 couldn't have done it without you! Following my heart has never been constrained for the mere lack of money. For that, 1 am very deeply grateful. CHAPTER 1 INTRODUCTION
1.1 Objectives
One of foresters' great preoccupations is quantifying forest productivity and therefore the amount of wood that the landbase is potentially capable of producing. Forest productivity, as an ecosystem process, usually refers to the accumulation of photosynthate by the canopy, its allocation into tissue, and losses through respiration and consumption by herbivores (Waring and Schlesinger 1985). Foresters are virtually always only concerned with wood production, i.e. the amount of biomass or volume accumulated in the stem and its rate of increase (Spurr and Barnes 1980).
Direct assessments of productivity are very difficult since insufficient long-term studies exist for reliable data sets (Daubermire 1976; Spurr and Barnes 1980). Height growth, however, can be measured indirectly as site index, the height of dominant trees at a reference age, and inferences made to volume (Monserud 1984). Therefore, site index serves an indirect measure of volume. Site quality has two similar but not identical meanings in the literature. Carmean (1975) used it to mean the "productive capacity" of the land for growing trees, i.e., an index of performance which is usually measured as site index. Spurr and Barnes (1980) defined site quality as "the sum total of all of the factors affecting the capacity to produce forests", such as climate and edaphic factors. Since the terms site index and site quality are used interchangeably in the literature and it is not always obvious whether the investigator is referring to the productive capacity of the site, the factors which determine the latter, or height of trees at a reference age. To alleviate the confusion. Carter and Klinka (1991) chose to define site quality following Spurr and Barnes (1980) as "ecological site quality" which is equivalent to the earlier concept of an "operational environment", the phenomena that affect a plant during its life cycle (Mason and Langeheim 1957).
The phenomena that effect the plant during its life cycle and, therefore its groAvth, are ultimately a function of five factors that cannot be replaced - temperature, light, moisture, nutrients and mechanical forces (Livingston and Shreve 1921; Hills 1952; Major 1963; Waring and Major 1964; Bakuzis 1969; Krajina 1969). Therefore areas of similar temperature, light, moisture, nutrients and mechanical forces should have similar potentials for growth. These factors are also difficult to measure directly, so they too estimated indirectly.
The biogeoclimatic ecosystem classification system (BEC) is based on the concept of an operational environment, although mechanical forces are generally not considered. Temperature and light are estimated through delineating regions of similar climate (subzones) (Pojar et al. 1987). A site association consist of all sites that have similar or equivalent physical properties and the same vegetation potential (op cit). Therefore, the BEC variables are potentially good indicators of ecological site quality and good predictors of forest productivity. The objective of this study was to test the ability of the biogeoclimatic classification system's measures of ecological site quality to predict growth of Sitka spruce [Picea sitchensis (Bong.) Carr.) measured as site index (m @ 50yr at breast height age). 1.2 Literature Review
Bole productivity is virtually always represented by site index, the height of dominant trees at a reference age (Spurr 1952; Hagglund 1981; Monserud 1984). While there are certainly caveats with respect to the use of site index (Monserud 1984), dominant trees are generally a reliable index of stand volume since height growth is closely related to volume growth and is unaffected by stand density (Monserud 1984; Thrower 1989).
There have been hundreds of studies that have attempted to find relationships between site index and measures of ecological site quality. Comprehensive reviews have been published by Carmean (1975) and Hagglund (1981). Measures can be characterized as biotic and abiotic factors and are often used in combination, such as in a classification system (Spurr and Barnes 1980). Biotic factors include units of vegetation classification systems and frequency of indicator species. Abiotic factors include measures of climate, soil properties and topography. Techniques generally involve using the chosen measures of ecological site quality as independent variables in regression analyses and/or testing for differences between proposed classes of variables of interest (Hagglund 1981). No one variable or approach has been consistently successful, and results vary widely with tree species and ecosystem.
Indicator plant analysis is based on the concept that plants serve as "phytometers", which integrate the sum of environmental factors experienced by vegetation on the site (Spurr and Barnes 1980). Plants that occur only on a narrow range of environmental conditions can indicate that condition e.g., high available nitrogen, low soil moisture. Daubermire (1976) reviewed the use of vegetation to assess productivity and concluded that indicator plants are effective measures of ecological site quality (e.g., Cajander 1926; Rowe 1956; McLean and Bolsinger 1973). Other authors caution that understorey plants in the forest do not necessarily only reflect site since they are not always independent of other factors, such as stand density, disturbance and past history (Spurr and Barnes 1980; Spies and Barnes 1985). Forest trees may also be more deeply rooted than understorey species; therefore, they can be influenced by different factors (Carmean 1975). Indicator plants may also only be applicable in areas that have not been harvested or otherwise severely altered by humans (Spurr and Barnes 1980; Schonau 1988).
Green et al. (1989) found a good relationship between site index of second-growth Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and indicator species groups for climate, soil moisture and soil nitrogen. However, Green (1989) found a poor relationship between western redcedar site index [Thiy'a plicata (Donn exD. Don in Lamb.) and indicator species groups in the Queen Charlotte Islands, which he attributed to poor understorey development due to canopy closure and browsing pressure from introduced deer.
Climate parameters used to represent ecological site quality have included latitude, longitude, growing degree days, temperature, wind, precipitation and évapotranspiration (Hagglund 1981). The first four variables are often used as means of stratifying by geographic area or similar climate (e.g., Farr and Harris 1979; 1983). Climate on a local scale is often expressed by topographic variables such as aspect, elevation and slope (e.g., Worrell and Malcolm 1990a).
The majority of studies concerned with quantifying and predicting forest growth have focused on soil parameters, both chemical and physical properties (Carmean 1975; Hagglund 1981). Measures such as soil taxonomic unit, drainage class, and depth of rooting, are often used (op cit.). Usually these alone are insufficient to explain a large amount of variation in site index and analjrtical variables such as soil nutrients or soil moisture content are needed. Carter and Klinka (1990) found promising results with a model of mineralizable N and moisture deficit as predicted by the Energy/Soil Limited Water Balance Model (Spittlehouse and Black 1981) and site index of Douglas-fir. Green (1989) found a good relationship between western redcedar site index and total N and volumetric moisture content.
However, relationships involving nutrients are unsuccessful when applied over large areas and require laboratory analysis (Com and Pluth 1984). Monserud et al. (1990) found that soil factors, both chemical and physical properties, accounted for far less variation in Douglas-fir site index than the combination of elevation and either habitat type, longitude or precipitation. They attributed the poor result to site factor interactions over the very large study area or their failure to measure the true causes of productivity.
No generally accepted measure of soil moisture has been determined since it is not very straightforward to measure what the tree actually experiences (Broadfoot 1969; Stone 1978). Models that use climate data to determine growing season évapotranspiration and therefore moisture deficit such as Thornthwaite et al. (1957) and Spittlehouse and Black (1981) are potentially a means for overcoming the difficulty of field measurements.
Previous investigations into relationships of ecological site quality and productivity of Sitka spruce have occurred in southeast Alaska, the Pacific coast, and northern Great Britain where spruce from the Queen Charlotte Islands were planted as a timber tree after World War I. There has been a previous descriptive study in the Queen Charlotte Islands (Day 1957) and a study investigating the productivity of forests of the Masset Lowlands that indirectly examined Sitka spruce (Green 1989).
The first attempt to relate Sitka spruce site index to ecological measures occurred in Southeast Alaska. Stephens et al (1968) concluded that site index of Sitka spruce could be estimated from soil depth and drainage. They identified seven soil groups and estimated site index for each. Their mean site index values were then applied to the soil ecosystem classification system developed in the Tongass National Forest and used as a means of assessing available timber volume (Rogers 1988). Use of the classification system was discontinued in 1980 (Ford et al 1988)
Ford et aL(1988) examined the relationship of Sitka spruce site index in Southeast Alaska to four soil variables commonly used in classification: depth of mineral soil, soil drainage class, organic carbon content and coarse fragment content. They found only soil drainage and coarse fragment content to be significantly related to site index. However, there were no significant differences in site index between individual drainage or coarse fragment classes. They concluded that soil taxa were not sufficient to predict site index of Sitka spruce, in contrast to Stephens et al (1968).
Farr and Harris (1979; 1983) looked at the range of Sitka spruce along the Pacific coast. They tried to integrate the variables potentially important to growth into an expression of overall dominant climatic factors. They found that site index of Sitka spruce was strongly correlated with latitude and mean annual growing degree days. Site index decreased northward at the rate of approximately 0.8 m per degree of latitude. They considered that temperature considered that temperature was more important than moisture deficit, since the latter is not a concern in a climate where summer drought is rare.
In Great Britain, there have been several studies that have investigated relationships between climate and soil factors and productivity of Sitka spruce, measured as yield class (m^/ha/year). Worrell (1987) found there was a very smcdl difference in yield class attributable to soil types. Plots in which there was restricted rooting on wet, excessively clayey or shallow soils resulted in a lower potential yield class. Yield class was significantly correlated with year of planting so the effect of soil was confounded by siMcultural input which increased with time (i.e. sites planted later received more site preparation and silvicultural treatment). Therefore, results were inconclusive.
Blyth and Macleod (1981a) found that total N in the "humus" was strongly related to jàeld class. In a companion paper, Blyth and MacLeod (1981b) found that a small number of variables, including depth to mottling and total P of the humus layer were able to account for 60-86% of the variation in local yield class, but that the regression equations developed varied too greatly over short distances to be useful. They found physiographic and soil physical properties to be more useful for predictive purposes.
Worrell and Malcolm (1990a; 1990b) found that indices of temperature and windiness were strongly related to spruce 5àeld class. The amount of variation explained was only marginally improved by adding soil taxonomic class. Estimates of rainfall or potential water deficit showed no relationship.
Day (1957), in a descriptive study of ecological requirements of Sitka spruce in the Queen Charlotte Islands, concluded that dominance of spruce is largely associated with the site characters related to soil water supply and drainage. He concluded that spruce is dominant on sites where there is a large enough supply of aerated water. The better drained the site, the more spruce is predominant. As this condition changes, western hemlock [Tst^a heterophylla (Raf. Sarg.) and western redcedar become the dominants. Spruce is generally the better competitor on better sites, but on less productive sites, it becomes even with hemlock and cedar. Green (1989) found similar results for the Masset Lowlands.
Day also considered that spruce is favoured by relatively high nutrient status. Its presence along the sea shore is related to nutrient enrichment from sea spray, especially phosphorus. Spruce is associated with sites where a high degree of nitrification occurs. On poorly drained sites, nitrification is poor as is spruce growth.
Green (1989) examined variation in tree growth in imperfectly and poorly drained sites in the Masset Lowlands. He used site index of western recedar as his measure of productivity and concluded that the site index of the other tree species, including Sitka spruce, was correlated with it. He found a good relationship between cedar site index, total N and volumetric moisture content. Sitka spruce showed a different pattern than did western hemlock and lodgepole pine {Pinus contortavar. contorta Dougl ex. Loud). Spruce height growth was comparably slower on poor sites and much higher on better sites.
In terms of its autecology, Sitka spruce requires large amounts of phosphorous and balanced amounts of available calcium with magnesium (Krajina et al 1982). Its growth is greater when nitrates are more prevalent than ammonium compounds, but it will tolerate the latter. It has a high flood resistance, and can survive under prolonged but not permanent flooding. Sitka spruce is an indicator of nitrogen rich soils, and does not occur on moisture or nutrient deficient sites (Klinka et al 1989).
1.3 The Approach
The BEC system is potentially well suited to characterize ecological site quality (Green et al. 1989). The classification deals primarily with three ecosystem elements: climate, vegetation, and soil (including topography and parent materials) (Pojar et al. 1987). The recognized units thus result from a synthesis of vegetation, climate and soil data (op cit.). A site association consists of all sites that have similar or equivalent physical properties and the same vegetation potential. Therefore, site classification organizes ecosystems on the basis of more or less stable environmental attributes and according to the concept of ecological equivalence.
The objective of this study was to test the ability of BEC measures of ecological site quality to predict growth of Sitka spruce, measured as site index (m @ SCtyrs at breast height age). Since the study occurred within a region of similar climate represented as a biogeoclimatic subzone, temperature and light were considered constant and the investigation focused on measures of moisture and nutrients.
Individual measures of moisture and nutrients were represented as categorical variables; soil moisture and nutrient regime; and continuous variables; soil nutrients and moisture deficit (Thornthwaite et al. 1957; Spittlehouse and Black 1981) in combination with soil physical measures. Soil moisture and nutrient regimes were represented as spectra of indicator species groups. The synoptic effect of individual measures of moisture and nutrients were expressed as site associations and assumed to be represented by plant associations. The relationship between the categorical and continuous variables for soil nutrients was also examined. CHAPTER 2. THE STUDY AREA
The Queen Charlotte Islands are located off the coast of British Columbia between Alaska and Vancouver Island in the Coastal Western Hemlock (CWH) biogeoclimatic zone (Meidinger and Pojar 1991). This study was situated in the Submontane Wet Hypermaritime CWH variant (CWHwhl) which occurs on the drier leeward side of the Queen Charlotte Islands mountains.
2.1 Physiography
The Islands are divided into three distinct physiographic regions; the Masset Lowlands, the Skidegate Plateau, and the Queen Charlotte Ranges (Holland 1976). The Queen Charlotte Ranges are rugged and range in elevation from 1200 m to sea level. The Skidegate Plateau ranges from 840 m to sea level. The Masset Lowlands are less than 160 m in elevation. A distinctive group within the latter is the Argonaut plain, an extensive glacial outwash plain on the northeastern tip of Graham Island.
2.2 Geology and Surficial Materials
Three major bedrock types (volcanic, sedimentary and intrusive) occur on the Islands (Banner et al. 1983). Volcanic rocks are the major type underlying most of the productive forests of the Skidegate plateau and Queen Charlotte Ranges. The Masset Lowlands are underlain by sedimentary rocks. Intrusive bedrock largely occurs in the Queen Charlotte Ranges, which were not included in this study. Lewis (1982) divided the volcanics into hard and soft, in relation to their ability to weather. The hard volcanics are basalts, found largely in the Queen Charlotte Ranges. The softer volcanics are deeply fractured and chemically weathered. Sedimentary rocks, which include sandstone and conglomerate, release few nutrients. The finer-grained elastics (siltstones, shales and argillites) weather more easily and are more base-rich than the former. Areas of limestone do occur in the Islands, but not in the area of this study. It is difficult to determine the contribution of bedrock type to nutrient status, since its effects can be influenced by the nature and depth of the surficial material (op cit.).
Morainal and coUuvial materials are widespread in the forested environments (Banner et al 1983). Fluvial and organic materials also occur to a lesser extent, with marine, eolian and exposed bedrock the least common.
2.3 Climate
In the Koppen classification, the climate is Cfb (Pojar and Banner 1984, Trewartha 1968): mesothermal (C), with no distinct dry season (f) and a cool summer season with the temperature of the warmest month under 22^C (b). Selected climatic characteristics for stations occurring within the study area are presented in Table 1.
The climate of the Queen Charlotte Islands is determined by four factors (Williams 1968): (1) the proximity of the Pacific Ocean to the west, (2) the barrier of the mountains of the mainland to the east, (3) the rugged topography of the Islands, and (4) the behaviour of three prominent features of atmospheric circulation - the prevailing westerlies, the Aleutian Low and the North Pacific High. Table 1. Selected climatic characteristics for the CWHwhl variant^
Climate station Masset Tlell Sandspit Sewell Inlet
Mean annual
precipitation (mm) 1444 1152 1281 4168
Mean annual snowfall(%MAP) 6.1 5.3 6.7 35 Mean monthly precipitation May-Sept, (mm) 76.9 64.2 85.4 143.9
Mean monthly precipitation Oct.-April (mm) 150 111 142 473
Mean precipitation of the driest month (mm) 55 51 43 108
Mean precipitation of the wettest month (mm) 208 172 194 671
Mean annual temperature (^C) 7.6 7.4 7.9 7.6
Mean temperature of the warmest month (OC) 14.4 14.2 14.7 15.1
Mean temperature of the coldest month iPc) 1.4 1.3 2.0 1.1
^ Source : Environment Canada (1980) Much of t±ie air masses that move over the Charlottes are carried by the prevailing westerlies from the direction of the open ocean (since the ocean tends to be warmer than the land in winter and cooler than it in summer) Williams (1968). The mainland Coast Range protects the Islands from continental air masses, which are cold in winter and hot in summer. Winters are thus very mild and summers rather cool.
There is an orographic effect created by the Queen Charlotte Ranges. Moist air moving in from the ocean on the west is forced to rise thereby losing most of its moisture on the windward slopes. The precipitation is heavier on the westside of the Islands; this is more pronounced during the wettest (i.e., winter) months. This effect creates a large variation between the west and east coast.
The Aleutian Low is generated by the sharp temperature differences between the relatively warm ocean and the cold continental land mass during the part of the year when the sun is south of the equator. This unstable low pressure system dominates the weather beginning in late September. It creates a series of storms that give dense cloudiness, heavy precipitation and strong winds during the fall and Avinter.
The North Pacific High creates rather dry sunny conditions on the coast from mid-May to mid-September. Although the summers are still relatively cool and wet, warm, dry spells are not uncommon especially on the east coast. The moderating influence of the ocean has other effects. The number of growing degree days is high. Frost is rare. The snowpack if any, rarely persists, except in the higher elevations. Cloud cover and fog are frequent, so evapotranspirational demand even during the summer can be quite low. Variations in temperature are more closely related to degree of protection from the open ocean than latitude, and generally do not vary widely - daily, seasonally or annually.
Conifers are thought to be competitively favoured by mfld, wet growing conditions that persist year round with the possibility of photos5mthesis during the winter months (Waring and Franklin 1979). The climate in the Charlottes is an optimum one for conifers.
2.4 Forest Vegetation and Soils
The forests of Queen Charlotte Islands are described in numerous publications, such as Lewis (1982), Banner et ai. (1983), Banner and Pojar (1984) and Banner et al. (1989). This discussion is largely based on the last, most recent work.
Climatic climax or zonal ecosystems occur on mesic to mesotrophic (i.e., fresh and nutrient medium) sites, usually on middle and upper slopes, on moderately well-drained moralnal and colluvial deposits. Loamy Humo-Ferric and Ferro-Humic Podzols with Mor humus forms are the typical zonal soil. Typic Folisols and Dystric Brunisols also commonly occur.
Banner et al. (1989) identified edaphic climax forests on moist, productive sites, which have an intermittent or constant lateral flow of mineral seepage water providing a turnover of nutrients and sufficient soil aeration. These forests occur on active alluvial sites and middle to toe slopes of steep coUuvial or coUuviated morainal landforms. Mixing of soil from colluvial action or windthrow is also often characteristic. Banner et al (1989) considered these forests the most productive in the CWH on the Islands, especially on recently deposited alluvial material. Less productive spruce forests occur on stabilized sand dunes and rocky marine headlands, both of which are influenced by sea spray.
Typical soils on active alluvial landforms include Dystric and Sombric Brunisols and Regosols with Mormoder, Rhizomoder, MuUmoder, and RhizomuU humus forms. Podzols with Humimors occur on inactive alluvial landforms. On moist, rich sites of colluvial or colluviated morainal deposits, soils are Dystric Brunisols cind Folisols with Humimor, Hydromor and Hydromoder humus forms.
Low productivity forests are found on imperfectly to poorly drained gently undulating to flat terrain, such as the Masset Lowlands. Non-tidal wetlands are also common, but rarely contain spruce unless it is on raised hummocks.
Sitka black-tail deer were introduced to the Islands in the early 1900s (Carl and Guiget 1972). They have severely altered the forest vegetation and depleted shrub and herb layers (Pojar and Banner 1984). Understorey species generally cannot persist upon stand closure (at age 25-35 years) because of severe competition, especially for light (Alaback 1982). The deer no doubt exacerbate this situation. A sparse understory on the forest floor with luxuriant growth on old stumps that are too high for the deer to reach is a common sight. Banner and Pojar (1984) caution that care must be taken in blaming the deer as the sole reason for a depauperate flora since overbrowsing varies in its severity and there is little pre-and post-deer vegetation data. CHAPTER 3. MATERIALS AND METHODS
3.1 Field Methods
Fifty-five stands of naturally regenerated second-growth Sitka spruce were chosen for study, representing a wide range of ecological site quality and geographic location (Fig. 1). In each stand, a 400m2 plot was selected that was 1) homogeneous with respect to soils and vegetation, 2) contained trees close to 50 years in age with dominants free of disease or damage, and 3) had not been altered by silvicultural practices.
Site description for each sample plot followed the standard methods used by the B.C. Forest Service (Walmsley et al. 1980). Elevation, slope position, aspect and gradient, ground surface cover by forest floor, deca5àng wood, mineral soil and coarse fragment/bedrock were described. Vegetation on each sample plot was described using the standard methods of the BEC system (Pojar et al. 1987). Nomenclature followed Taylor and MacBride (1977) for the vascular plants, and Crum et al. (1973) for the bryophytes. Percentage cover (on a significance scale) of each species was estimated. Percentage cover by recognized vegetation strata: (A (tree), B (shrub), C (herb) and D (moss)) was also estimated. Epiphytic species were not included.
In each plot, five dominant spruce trees were measured for age and height. Site index was determined from these measurements using site index curves for second growth Sitka spruce in the Queen Charlotte Islands (Barker and Goudie 1987). Four soil pits, randomly located within the plot, were dug. One was dug down to parent material or root restricting layer to determine depth of rooting zone, the presence of gleyed soil horizons and the depth to groundwater table. Rocks greater than 2 cm diamter were weighed for coarse fragment determination. Soils were described following the Canadian Soil Survey Committee (1987) and Dumanksi (1978). Humus forms were determined from Klinka et al (1981).
Samples for mineral soil and forest floor bulk density were collected from each of the four pits. The volume of soil removed was estimated by measuring the amount of water required to fill the hole created. Samples for chemical analysis of forest floor and of mineral soil for 0-50 cm and 50+ cm were taken from the centre of the three undisturbed sides of the soil pit. These were composited into one forest floor sample per plot and one mineral soil sample for 0-50 cm and 50+ cm per plot.
3.2 Laboratory Analysis
The mass component of bulk density was determined by weighing the mass of soil removed from the bulk density pit after oven-drying at 105°C to a constant mass. Physical variables (bulk density, coarse fragment content and mineral sofl porosity) were calculated following Klinka et al (1981). Sofl nutrient variables were expressed in concentration (% or ppm) and on a per hectcire basis, calculated following the equations given in Green (1989). After air drying to a constant mass, composite forest floor and mineral soil samples for chemical analysis were ground or crushed, if necessary, then passed through a 2 mm sieve. The coarse fraction (>2 mm) was weighed. Chemical analyses were undertaken on the fine fraction (<2 mm). Mineral soil and forest floor pH were determined with a pH meter after suspension in water 1:5 for mineral soil, and 1:20 for forest floor. Total C was measured using an Leco Induction Furnace and C analyzer (Bremner and Tabatabai 1971). Total N was measured colourmetrically using semimicro-Kjeldahl digestion and NH4 estimated with a Technicon Autoanalyzer (Anomymous 1976). Mineralizable N was measured colourmetrically with the autoanalyzer after an anaerobic incubation procedure (Powers 1980). Total P (forest floor) and extractable P (mineral soil) were estimated using the extraction procedure of Mehllch (1978). Total S for forest floor was determined using the Fisher Model 475 Sulphur Analyzer. Available SO4-S for mineral soil was extracted using an ammonium acetate extraction then reduced to sulphide by HI and the amount determined colourmetrically (Kowlenko and Lowe 1972). Extractable Ca, Mg, and K were determined AA^th a Morgan's solution of sodium acetate (pH 4.8) (Greweling and Peech 1980) and then measured by absorption spectrophometry (Price 1978). Textural analysis was determined by the pipette method, with sand content being determined by wet sieving (Lavkulich 1978).
3.3 Water Balance
Growing season water balance was calculated using the Energy/Soil- Limited Water Balance Model developed for Douglas-flr on the south-east of Vancouver Island (Spittlehouse and Black 1981). Thirty year climate normals (Environment Canada 1980) were used for solar radiation, average monthly temperature and total monthly precipitation. Percentage tree cover was used for the rain interception coefficient (the amount of rain intercepted by the canopy). A solar radiation coefficient of 0.85 (out of a maximum of 1.00) was chosen (Carter, pers. comm. 1). The lower value accommodates for fog which is frequent in the Islands in summer. Mineral soil rooting depth was used for rooting depth, except in the case of Lignohumimors where the depth of forest floor was used with the soil coefficient for clay. The model assumes no recharge due to subsurface flow from higher elevations and/or no restricted drainage (Carter and Klinka 1990) and therefore cannot be applied for sites with those conditions.
Actual évapotranspiration and moisture deficit were also calculated following Thornthwaite et al. (1957). Thirty year climate normals (Environment Canada 1980) were used for average monthly temperature and total monthly precipitation. Soil rooting depth and soil texture were used to determine the appropriate coefficients for water holding capacity from which actual évapotranspiration and moisture deficit were calculated.
1. Reid Carter, Resource Analysis/Evaluation Forester, Fletcher Challenge Canada Ltd. 3.4 Ecological Analysis and Classification
3.4.1 Vegetation Analysis and Classification
Plots were sorted into groups using Reciprocal Averaging (RA) in a progressive fragmentation fashion (Gauch 1977; Peet 1980). With each iteration, outliers were removed and the analysis performed again until there were no distinct groups remaining within the data. Tabular analysis was also used in the group formation process. The associations were then formed in a hierarchical classification following Pojar et al. (1987) using the VTAB computer programme (Emanuel 1985).
The classification was also examined using a centered, covariance Principal Component Analysis (PCA) (Gauch 1977). Degree of separation was examined at both the association and alliance level. Component scores were analyzed for Axes 1-4 and for all species, understory species, with rare species (those occurring only once) removed, and for diagnostic species identified in the classification.
Although the first PCA axis, by definition, explains the most variation in the data, it is possible that ubiquitous species are contributing to most of that variation, thus differences between groups may not be revealed on the first or second axis because they are mainly influenced by the more dominant, common species shared by all groups. Therefore the scores from the third and fourth axis were also explored. 3.4.2 Site Analysis and Classification
Soil nutrient regime (SNR) was estimated from the field key (Banner et al. 1990), based on soil morphological properties, assisted by spectral analysis of indicator plants (Emanuel 1985). Relative soil moisture (SMR) regime was estimated from the field key, then converted into actual moisture regime from the conversion table, based on biogeoclimatic subzone (Banner et al. 1990). Final assignment was further refined by spectral analysis. Using the actual soil moisture and nutrient regimes identified, site associations were determined from Banner et al. (1990). Determination of beach side, ocean-spray affected sites was guided by estimates of the area of edge influence of salt spray (Cordes 1972).
Since classification based on field estimates is potentially subject to observer bias or error, the degree of agreement between the soil chemical nutrient data and the SNR classification was explored. No independent measure of soil moisture was available to test the soil moisture regime classification. A Ward minimum variance cluster analysis (Ward 1963; Wilkinson 1990) was performed to reveal natural groupings in the data and whether they related to assigned classes. This type of clustering, also known as the sums of squares method, minimizes the total within group sums of squares about the group centroid (Gordon 1981). The relative branch lengths indicate the joining distance between pairs of clusters.
A discriminant analysis (DA) was used to test the ability of the nutrient data to correctly place each plot in the assigned soil nutrient regime (Wilkinson 1990). DA is a procedure for combining variables so that the resulting composites have optimal properties for distinguishing the groups in the smallest possible number of dimensions (Gittins 1979). It identifies the linear combinations of the responses which best separate the groups and the variables that contribute the most to separating the groups. DA requires that the input variables have homogeneous variances and are multivariate normal. Homogeneity of variances of input variables was tested Avith a Bartlett's test (Zar 1984, Wilkinson 1990). Multivariate normality was assumed.
3.5 Relationships Between Site Index and Ecological Variables
Using the conceptual model that plant growth is a function of soil moisture and nutrients, relationships between site index and categorical or continuous variables representing soil moisture and nutrients were explored using simple and multiple linear regression analysis. Relationships were also explored between spruce site index and vegetation; using both plant associations and spectra of indicator species groups, and site, represented as site associations. For linear regression, the variances must be homogeneous. The presence of heterogeneity was was tested using a Bartlett's test (Zar 1984)
Multiple regression analysis requires that the explanatory variables are not strongly interrelated (Chatterjee and Price 1977). If this condition is not met, then the result of the regression can be ambiguous since the coefficients estimated become very sensitive to slight changes in the data or addition or deletions of variables in the equation. Nutrients are often interrelated.
A Pearson correlation matrix was calculated for the sets of variables of interest (Wilkinson 1990) (Appendix D) and only variables that were not highly correlated were used in a stepwise regression. The stability of the nutrient equations generated was tested by randomly removing five plots and comparing the coefficients with the original equation. If the coefficients did not vary greatly, then the variables are not multicollinear (Chatterjee and Price 1977). CHAPTER 4 RESULTS
4.1 Vegetation Classification
The progressive fragmentation, in conjunction with the tabular analysis, resulted in ten floristically similar groups (Table 2; Appendix B). These groups were then organized into a hierarchical classification, consisting of two alliances each with five associations which were arranged on a diagnostic table (Table 2). Only the Picea-Calamagrostis, -Claytonia, -Gymnœarpium and -
Gaultheria associations were clearly distinct Avith the fragmentation technique (Appendix B). The other associations were recognized through tabular analysis.
Of all the combinations of species used to test the degree of separation of the vegetation groups with principal component analysis (all species, no rare species, diagnostic species, understoiy species), understory species at the alliance level showed the most separation, which was in itself marginal (Fig. 2; Appendix C). This lack of distinctness is further confounded at the association level by the small sample size of some of the associations. The third versus second, and fourth versus third axes showed no differences between groups, associations or alliances. Table 2. Diagnostic combination of species for plant associations distinguished in the study plots.
Vegstot ton unit 1 2 3 4 5 G 1 B â 10
Number of ptots Dlagnostlc l 5 1 B 3 G 16 G 7 Veaetatlon units and species va lue' Presence class' and mea n speci 'S ctqn f 1 cance'
PIcaa-Ga1lum ©11.
Gai fum tri fforum 2 8 IV 3.3
Picea-Calamogrost Is
Cel fimagrost is nuthaen» t s (d.cd) 5 9 5 Oicentra formosa (d.c) 5 * 5 Hypochocrfs radical a (d.c) 5 3 5 Rubus parviflorua (d.c) 5 5 Vicia americana (d.c) 5 5
PIcea-Claytonia a.
A\nu% rutyra (dd) 5 7 5 3 5 IV 10 11 7.0 m 1, -1 Cl aytoni a sltji rica (d.cd) 5 8 5 * .0 Gailum Bparine (d.c) 5 4.5 3 2 Ci»''L/m triflorufn (dd) S • 5 5 7,5 2,4 3 B IV J 3 Poa marc Ida (d.cd) 5 8.5 Polypodtum glycyrrhlza (d.cd) 5 6.5 Stfl1arla cri spa (d.c) 5 4,5
PIcaa-Rhytidiada1phus tr 1 questr js a .
Rhy(tdlade(pHus trtqufftrus (d)
PIcea-Gymnocarplum a.
Conocepha1um conicum (d.c) 5 1 1 I * 0 Cymnocsrplum dryopteris (d.c) 5 4 8 I ' 0 Hy 1 ocom lyjm spl cndens (d.c) I I 5,7 5 I 1 I I 2 8 5 8 IV 4 5 V 4 8 Osmorhtza chllensis (d.c) 5 3 8 1 0 PIagiomnlum Insigne (d.cd) 5 5 5 • 0 I 3 1 III 1 . 1 Rh 1 zomn 1 i/m g 1 abrescens (d.cd) I 1 5 5 5 7 III 3 0 3 5 IV a 2 III 3.3
Piceo-Polystichum a.
Dryopt er1 s expansa (d) 1 1 0 IV 3 0 ^ 0 iV Po 1 y^t 1 chum m\jn i t um (dd) 5 3.5 1 * 0 3 0 V 7 2 * .0 1 3 .0 Pter1dium equ1t1 nom (d) I I • G IV 3 0 I * .0 VaccInlum parvifollum (d) I * .0 IV 2 5 * 0 iir * 0
Plcea-Hylocomlum aM.
1 . 1 IV 2 2 V 1 . 8 Blechnum spicant (d) 3 G 1 I 1 l I I 2 , 1 III l 5 III V 5 V 5.8 IV A 5 V 4 , 8 Hylocomlum sp/tfndens (d.cd) 5 1 1 I 2 8 IV 5 3 5 V G IV 1 . 3 V 2 V 5,7 Rftyf fdi ad« (phus ( oreus (d) 3 3 5 I I I 1 5 IV 5 . 1 V 5 V r. t V 7 IV 3 4 Tsuga heterophylI a (d.c) 3 3 5 IV 4 G IV 3 , 3 G 2 Pfcea-Gaultherla a.
Caultnerla shalI on (d.cd) 5 +.5
Picea-Thoja a.
ThuJs plI cat a
P1cea-Hy1ocomlura a.
5 3 5 IV 10 I! «5 V 2 8 1 • Ol V 7.o]lII 1.4 Alnus rutjra
PIcea-KIndborgla a. ^
I *.0 13.1 m 1.1 Plagi omnium Iniigne (d) 5 5 5 1 *0 J *.0 II +0 V 2.0 Polyst ichum muni turn (d.c) 5 3,5 1 « .0 3 • .0 V 7 2
'Spoclea diagnostic valuaa: d - dIfferontI a I. dd - dominant differential, cd - constant dominent, c - constant, le - important companion (Pojar st al. 1987).
•Presence classes as percent of frequency; I - 1-30. II - 21-40, 111 - 41-60, IV - 61-80. V - 81-100. If 5 plots or less, presence class Is araolc value (1-5).
'Species significance class midpoint percent cover and range: • • 0.2 (0 I - 0 3). i • 0 7 (0.4 - 1.0), 2 - 1.6 (1.1 - 2.1), 3 - 3.6 (2.2 - 50), 4 . 7.5 (5.1 - 100). 5 - 15.0 (10.1 - 20.0), 6 - 36.5 (30,1 - 33.0), 7 - 41.5 (33.1 - 50.0), 8 - 60.0 (50.1 - 70.0). 9 - 85.0 (70.1 - 100). Fig. 2. 80% confidence ellipses for principal component scores for plant alliances along first two principle axes. l=Picea-Galiam alliance, 2=Picea- Hylocomium alliance from Table 2. Synoptic characteristics for plant associations are presented in Table 3 and box plots of site index in Fig. 3. There is a trend of increasing site index within the associations, but it is not related to the order of the associations in Table 2.
Table 3. Means and standard deviations (in parentheses) for selected environmental characteristics for plant associations.
Plant 1 2 3 4 5 6 7 8 9 10 association ^ Number of plots 1 1 5 2 8 3 6 16 6 7
SS SI 21 32 23 33 31 23 27 37 32 35.1 (0.0) (0.0) (3.9) (4.2) (5.4) (7.2) (3.9) (3.7) (6.8) (3.4)
SMR SD SD SD-W F-M SD-W SD-M SD-W F-VM SD-VM SD-W
SNR M R P-VR M-R M-VR P-M MR M-VR P-VR R-VR
Gleying2 0 0 20 0 32 0 12 67 18 24.5 (0) (0) (0) (0) (0) (0) (0) (21) (0) (1)
Water table^ 0 0 0 0 45 0 54 10 66 35 (0) (0) (0) (0) (17) (0) (9) (21) (0) (16)
TOTN^ 7012 10918 4672 4748 10245 3878 5696 8635 8626 8519 (0) (0) (2930) (605) (3106) (1699) (1640) (3337) (2719) (5305)
MINN 106 109 127 113 199 120 157 175 192 180 (0) (0) (54) (23) (149) (25) (50) (68) (70) (80)
FFCN 51 35 40 36 36 52 40 42 39 27 (0) (0) (8) (18) (9) (7) (4) (8) (8) (6)
^ Plant associations from Table 2. 1-Calamagrostis, 2-Claytonia, 3-RhytidiadelphiLS, 4-Gymnocarpium, 5-Polystichum, 6-Gaulthena, 7-ThiLja, 8-Hylocomium, 9-Alnus, lO-Kindbergia ^ Depth to gleyed horizon (cm) measured from the soil surface. Average is based on plots where gleying occurred. Depth to water table (cm) measured from the mineral soil surface. Average is based on plots where water table occurred. ^ All nutrient values in kg/ha and combined forest floor and mineral soil unless otherwise indicated. 50 1 1 1 1 \ \ r
40
X CD TD - 30 o
20 L o
10 J _L J \ I L 637594 10 8
Plant association
Fig. 3. Box plots for Sitka spruce site index by plant association (PA) for associations with greater than one plot. Numbers designating PAs are explained in Table 2. 4.2 Soil Moisture Analysis
Selected characteristics for SMR are presented in Table 4. Site index shows a significant trend (p<0.05) of higher in F-M-VM and lower in SD and W SMRs. Soil nitrogen shows no significant trends but the means are highest in the moist sites and lowest in the wet sites. As would be expected, depth of gleying and depth to water table are lowest in VM and W.
Values generated from the Energy/Soil Limited Water Balance Model (Spittlehouse and Black 1981) are not presented. Model results could not be confidently applied due to poor weather data and the lack of calibration of model coefficients for the North Coast. This finding is further discussed in Section 4.5 and Chapter 5.
Values from the Thornthwaite model are also not presented (Thornthwaite et al. 1957). For the plots at Sewell Inlet, precipitation exceeded the actual potential évapotranspiration calculated by the model, therefore, no deficit occurred. The model calculated a growing season moisture deficit for all other plots of between 45 and 105 mm, giving an actual/potential évapotranspiration ratio between 70-85% and deficit for 3 months of the growing season. Excluding the plots where a water table was present so no deficit occurred, the Thornthwaite model would place all the remaining plots as moderately dry (Klinka et al. 1989), which is unlikely to be accurate. These results are discussed further in Chapter 5. Table 4. Means and standard deviations {in parentheses) for Sitka spruce site index and selected soil characteristics by soil moisture regimes. Rows with no values superscripted in the same row indicate no significant differences between means.
SMR Slightly Dry Fresh Moist Very Moist Wet Number of plots 14 11 15 10 5
SS SI 25.1^ 33.6^ 36.7^ 33.1^ 26.4A (5.0) (4.7) (3.5) (5.7) (4.4)
TOTNI 7077 8323 8705 8187 5006 (3349) (4121) (3444) (3869) (2833)
MINN 158 158 203 157 126 (60) (73) (111) (70) (41)
GLEYING^ oA QA 30B 40^ 30^ DEPTH (0) (0) (0) (7) (5)
WATER3 oA QA oA 44B 27B DEPTH (0) (0) (0) (27) (16)
ROOTING 57 55 65 50 26 DEPTH (21) (16) (29) 23 (15)
^ All nutrient values in kg/ha and combined forest floor and mineral soil. 2 Depth to gleyed horizon (cm) measured from the soil surface (average of where gleying occurred). Depth to water table (cm) measured from the mineral soil surface (average of plots where water table occurred). Values with different letters superscripted in the same row indicate significantly different means (P< 0.05, Tukey's test). 4.3 Soil Nutrient Analysis
Selected characteristics for soil nutrient regimes (SNR) are presented in Table 5. Site index increases significantly (p<0.05) from poor to very rich SNR. While not significantly different, the mean values of FFP and FFS are highest for the very rich sites. There is a trend of decreasing forest floor C/N and increasing total and mineralizable N with richer sites. In comparison with the classes proposed by Klinka et al (1989) for actual soil nutrient regimes, those identified in this study have much higher values for nitrogen (both total and mineralizable), while pH and forest floor C/N have a relatively smaller range. However, the classes of Klinka et al (1989) were developed for southwestern B.C. so direct comparisons are not likely applicable. Percentage of indicator species indicating nitrogen rich soils (NITR3) do increase with richer nutrient regimes, although it is not significant (p>0.05). Table 5. Means and standard deviations (in parentheses) for Sitka spruce site index and selected characteristics by soil nutrient regimes. Rows with no values superscripted in the same row indicate no significant differences between means.
SNR Poor Medium Rich Very Rich Number of plots 4 18 22 11
SS SI I9.5A 28.9^ 34.8^ 33.6^ (3.7) (5.8) (3.7) (6.2)
FFPH 4.0 4.1 4.1 4.2 (0.4) (0.4) (0.4) (0.4
FFCN 49A 43A 35B 32B (8) (7) (9) (4)
TOTN^ 2777A 5770^ 8811^ 10844^ (857) (1429) (3578) (3262)
MINN 87A 14lA I65A 243^ (7) (48) (62) (116)
FFP 414 444 437 652 (94) (288) (232) (258)
FFS 308 383 397 573 (188) (273) (215) (256)
SUMCAT 1115 2040 2287 2087 (763) (2136) (2550) (939)
NITR32 3 10 27 30 (5) (18) (34) (30)
^ All nutrient values combined forest floor and mineral soil in kg/ha unless prefaced by FF (forest floor only).
^ Spectral frequency of indicator species groups indicating nitrogen rich soils.
Values with different letters superscripted in the same row indicate significantly different means (P< 0.05, Tukey's test). The patterns of the combined soil moisture and nutrient regimes with respect to site index (Table 6) are similar to that of individual regimes (Tables 4 and 5). Site index increases with site richness. Moist sites show the highest site index followed by fresh and very moist sites.
Table 6. Means, standard deviations (in parentheses) and number of plots for Sitka spruce site index by soil moisture and nutrient regimes. Soil moisture Soil nutrient regime regime Poor Medium Rich Very Rich
Slightly Dry 16.5 22.6 30.3 28.5 (1.5) (2.1) (1.5) (0.6) n=2 n=5 n=3 n=4
Fresh 29.7 35.5 34 (2.1) (5.3) (0) n=3 n=6 n=2
Moist 34.6 37 41
(3.6) (0.7) (1)
n=7 n=5 n=3
Very moist 22 26 34.7 40 (0) (0) (3.0) (0) n=l n=l n=7 n=l
Wet 23 25 34 25 (0) (1.4) (0) (0) n=l n=2 n=l n=l 4.3.1 Cluster Analysis for Soil Nutrients and Soil Nutrient Regime
The Ward minimum variance cluster analysis (Wilkinson 1990) used FFpH, FFCN, combined forest floor and mineral sofl total N, mineralizable N, forest floor total S, and sum extractable cations (Ca, Mg, K) (Fig. 4). All nutrients were measured in kg/ha. These measures were chosen as they showed the most variability within the data and/or have been used to previously characterize SNR (Klinka et al. 1989).
The cluster analysis shows 4 broad groupings within the data which generally exhibit a trend from poor to very rich. There are however some exceptions. These were either extremely high or low in one nutrient with respect to other plots in the same SNR or they are at the boundary between classes. Generally, cluster analysis supports the SNR classification. DISTAWCE METRIC IS EUCLIDEAN DISTANCE WARD MINIMUM VARIANCE METHOD
TREK DIAGRAM 0.000 DISTANCES P P P M M h M M P R
R
R
R
R M M M
M H M R
R
R
R M H
M M H 1^ M M
R
R R 11- R
VR
R
VR
VR
VH
VR R R R M R R R •/R
•JH R •/R R VR VR Fig. 4. Dendrogram produced by cluster analysis for VR selected soil nutrient variables and soil nutrient regimes. P=poor. M=medium, R=rich, VR=very rich 4.3.2 Discriminant Analysis for Soil Nutrients and Soil Nutrient Regimes
Forest floor pH and C/N, mineral soil pH and C/N, forest floor P and S, and combined forest floor and mineral soil total N, mineralizable N and sum extractable cations (all in kg/ha) were used in the discriminant analysis. Nutrients in kg/ha were transformed to log (10) to homogenize the variances. These measures were chosen since values for individual nutrients in kg/ha or concentration could not be transformed to make the variances homogeneous. Of all the variables tested, only log total N, log mineralizable N, and FFCN contributed significantly to the discrimination (p< 0.05). The canonical correlations of the derived functions were 0.87 for the first function and 0.34 for the second. The first function is, therefore, more strongly related to the nutrient regime classes than the second. With 80% confidence ellipses, there is overlap between the groups (Fig. 5) but the centroids are all significantly different from each other (p < 0.01 Wilks' Lambda F-statistic). The ellipse was omitted for the poor group because of too few observations.
As with the cluster analysis, the cases of where the proposed classification did not agree with the DA, the plots were located at class limits, and/or had one nutrient that was exceptionally low or high compared the other plots in the same class. Generally the DA supports the classification (Table 7). Standardized coefficient functions are presented in Table 8. Fig. 5. Ordination of the study plots as a function of the first canonical yanates (determined by discriminant analysis) showing 80% confidence el for soil nutrient regime classes. P=poor, M=medium, R=rich, V=very rich Table 7. Group membership predicted by discriminant analysis SNR P M R VR Total %Correctly classified P 3 1 0 0 4 75 M 0 17 1 0 18 94 R 0 4 16 2 22 73 VR 0 0 1 10 11 91 Total 3 22 18 12 55 83 (average)
Table 8. Constants and coefficients of classification functions for soil nutrient regimes determined by discriminant analysis.
SNR P M R VR
Constant -608.03 -674.05 -722.90 -770.24 FFC/N 0.12 -0.08 -0.32 -0.44 LGTOTN 235.65 251.63 264.00 268.48 LGMINN -47.59 -44.21 -44.91 -40.14 4.4 Site Classification
Thirteen site associations were distinguished following Banner et al. (1990). Selected characteristics by site association are presented in Table 9, and for site index in Fig. 6-8. There is a trend of increasing site index with rich-very rich and fresh-moist-very moist site associations (Fig. 8). The nutrient values are less clear (Table 9). The slightly dry rich-very rich associations (11 and 5) have as much total N as 6, but site index is less. Mineralizable N is highly variable. A Tukey's test (Zar 1984) was attempted to test for significant differences in site index among the site associations (SA). However, the results were too difficult to interpret. While the extremities are significantly different (SA 10,4,11 versus 2,7,6,9 in Table 9), the associations in the middle overlap. The site index for SA8 is not significantly different from any other association, likely due to its high standard deviation. A box plot of site index versus site association was chosen instead for presentation (Fig. 8). Associations with only one plot were not included. Table 9. Mccins and standard deviations (in parentheses) for selected characteristics by site associations in order of increasing average site index. Site association^ 10* 13 3 11* 4 12* 5 8 1 2 7 6 9 Number of plots 1 5 1 1 3 1 7 2 3 7 8 14 2
SS SI 15.0 22 22.0 21 24.3 26.0 29.3 29.5 29.7 34.6 35.4 36.6 38.0 (0) (2.5) (0) (0) (1.5) (0) (1.4) (6.4) (2.1) (3.7) (3.4) (4.1) (2.8)
SMR SD* SD VM SD-F* W M-VM* SD-F W F M VM F-M HB SNR P-M* P-M P-M R-VR* P-M P-M R-VR R-VR P-M P-M R-VR R-VR R-VR
FFPH 4.5 4.1 3.7 4.0 3.9 4.2 4.0 4.6 4.2 3.9 4.3' 4.0 4.1 (0) (0.3) (0) (0) (0.1) (0) (0.3) (0.4) (0.1) (0.5) (0.5) (0.3) (0.4)
FFCN 59 39 41 51 45 45 33 31 46 49 38 33 33 (0) (8.4) (0) (0) (1) (0) (9) (5) (4) (5) (8) (9) (2)
Total N2 2448 4044 4030 7012 4051 5980 9914 6441 5716 6569 8982 10068 9025 (0) (1289) (0) (0) (1821) (0) (1598) (4315) (1200) (1454) (3919) (4265) (754)
Mineralizable N 91 123 91 107 110 48 199 149 171 155 179 203 169 (0) (21) (0) (0) (32) (0) (57) (54) (32) (59) (59) (124) (56)
1 Site association codes: (*=beach association) 1-HwSs-Lanky moss,2-CwHw-Blueberry,S-CwYc-Goldthread, A-PlYc-Sphagnum, 5-CwSs-Swordfem, 6-CwSs-Foamflower, 7-CwSs-ConocephaIum,8-CwSs-Skunk cabbage. 9-Ss-Uly-of-the-valley, 10*-Ss-Salal, 11*-Ss-Reedgrass, 12* Ss-Swordfem, 13 Ss-Salal ^Nutrient values in kg/ha combined forest floor and mineral soil unless otherwise indicated. Wet Hypermaritime Coastal Western Hemlock(CWHwh) Subzone SOIL NUTRIENT REGIME P M R VR
SD Cw-Ss-Swordfern
Hw-Ss-Lanky Moss 29(1.5) n=5
F 30(2 1) n=3 Cw-Ss-Foamf lower Cw-Hw-Blueberry 37(4,1) n=14
ISs-Lily-of-^the-valleyl* M 138(2 &) n=?!* 35(3.7) n=7 Ifloodplain-high bench!'
Cw-Yc-(3oldthreaci Cw-Ss-Conocephalum
VM 22(0) n=1 35(3.4) n=8
Pl-Yc-Sphagnum Cw-Ss-Skunk cabbage
W 24(1,6) n=4 30(6.4) n=2
Fig. 6. Mean Sitka spruce site index, standard deviations in parentheses and number of plots for site associations. n=number of plots. Site associations from Banner et al. (1990). Wet Hypermaritime Coastal Western Hemlock(CWHwh) subzone Beach associations SOIL NUTRIENT REGIME P M R VR
Ss-î 3alal
SD 21(3.7 ') n=6 Ss-Reedgrass LU 1 29(5.6) n=2 (3 LU ce LU F ce Z)
o Ss-Swordfern M
o 26(0) n=1 CO < ^ VM O<
W
Fig. 7. Average Sitka spruce site index, standard deviations in parentheses, and number of plots for beach side, ocean-spray affected site associations. Site associations from Banner et al. (1990). 40 -
X CD 30 CD
90 L
10 10 4 11 5 8 1 2 7 6 9
Site association
Fig. 8. Box plots of Sitka spruce site index for site associations with greater than one plot. See Table 9 for explanation of site association numbers. 4.5 Relationships between Sitka Spruce Site Index and Measures of Ecological Site guality
There was a very weak relationship between spruce site index and individual continuous soil variables. Mineral soil TOTN, MINN, forest floor Mg, and K (logged to homogenize the variances) showed a weak relationship with site index (adjusted = 0.25, 0.10, 0.27, and 0.23, respectively). TOTC, C/N, pH, Ca, FFP and S showed no relationship with site index. MSP could not be transformed to make the variances homogeneous. Soil physical variables (coarse fragment content, rooting depth and mineral soil porosity) also showed no relationship with site index.
Multiple linear regression equations were more successful than single variable models in explaining variation in site index (Table 10). Soil nutrients were able to explain 35-45% of the variance in site index, either as continuous or categorical variables or principal component analysis scores. The nutrient analytical models (models 3 and 4) and the categorical model (model 2) are roughly equivalent. Various permutations of principal component analysis scores of nutrient data (covariance versus correlation; data in concentration versus kg/ha) were essentially equivalent in explaining in the range of site index.
The variables that were successful in discriminating the SNR classes (LOGTOTN, LOGMINN and FFCN) could not be used together in a multiple regression model since the nitrogen terms are highly correlated (Appendix D). Adding FFCN to either LOGTOTN or LOGMINN did not increase the amount of variation explained by either nutrient used alone. Table 10. Models for regression of Sitka spruce site index on selected continuous and categorical variables, n = 55
1) SI = 29.352 - 8.352(PA1)1 + 2.648(PA2) - 6.352(PA3) + 3.648(PA4) + 1.523(PA5) - 6.352(PA6) - 2.532(PA7) + 7.148(PA8) + 2.648(PA9)
adjusted = 0.49 SEE = 4.6 m.
2) SI = 33.636 - 14.136(P)2 - 4.747(M) + 1.182(R)
adjusted R^ = 0.40 SEE = 5.0 m.
3) SI = -20.703 + 19.3(LOGTOTN)3 - O.OKLOGK)*
adjusted R^ = 0.37 SEE = 5.1 m
4) SI = 29.567 + 0.0I2(FFMINN)5 + 0.002(FFCA)6
- 18.923(LQGFFK)'' + 3.793(LOGMSMINN)8
adjusted R^ =.42 SEE = 4.9 m
5) SI = 27.884 - 2.772(SD)9 + 5.034(F) + 8.341(M)
+ 5.256(VM)
adjusted R^ = 0.45 SEE= 4.8 m.
6) Ln(SI)10 = 3.351 . 0.179(SD)11 + 0.048(F) + 0.174(M) + 0.039(VM) - 0.294(P) - 0.025(M) + 0.157(R) adjusted R^ = 0.80 SEE = 0.1 m. l212 = 0.79 Empirical SEE 13 = 7.8m. 7) Ln(SI) = 3.296 + 0.093(SA1)14 + 2.242(SA2) - 0.205(SA3) - 1.105(SA4) + 0.080(SA5) + 0.300(SA6) + 0.266(SA7) + 0.077(SA8) + 0.340(SA9) - 0.588{SA10) - 0.251(SA11) -0.038(SA12)
adjusted R^ = 0.80 SEE = 0.1 m. I^ = 0.79 Empirical SEE = 6.0m.
^ Dummy variables representing plant associations from Table 2. ^ Dummy variables representing soil nvitrient regimes. combined forest floor and mineral soil total nitrogen in kg/ha (loglO) ^ combined forest floor and mineral soil potassium in kg/ha (loglO) ^ forest floor mineralizable nitrogen in ppm ^ forest floor calcium in ppm ^ forest floor potassium in ppm (log 10) ^ inineral soil mineralizable nitrogen in ppm (loglO) ^ Dummy variables representing soil moisture regimes, ^"^natural log of site index ^ ^ Dummy variables as in 1 and 10 above. Empirical R^, an estimated based on antilog values. ^•^ Estimated standard error of tlie estimate, based on antilog values- 1^ Dummy variables representing site associations. Symbols explained in Table 9. Since nutrients are often interdependent which could potentially violate the assumptions of multiple linear regression analysis, the two chemical nutrient equations (models 3 and 4) were tested for multicoUinearity. Five plots from the data set were randomly removed and the coefficients examined for any large differences (Table 11). If large differences were found, the equations were unstable, a sign of multicoUinearity (Chatterjee and Price 1977). The equations developed were relatively stable when five plots were randomly deleted and, therefore, were not multicoUinear (Table 11).
Table 11. Coefficients and R-^ for best multiple linear regressions of continuous nutrient variables for all plots and 5 plots removed.
All Plots 5 Plots Removed
Variable Coefficient R2 Coefficient R2
FF+MS (kgha) (model 3) CONSTANT -20.703 .37 -19.367 .35 LOGTOTN 19.300 18.363 LOGTOTK -10.010 -9.026
Concentration (model 4) CONSTANT 66.166 .42 66.271 .44 FFMINN 0.012 0.010 FFCA 0.002 0.002 LOGFFK -18.923 -19.091 LOGMSMINN 3.793 4.148
SMR was the only successful moisture variable, with an adjusted R"^ of 0.45 (model 5). Models using depth to gleying and depth to water table and water deficit predicted by the water balance models (Thornthwaite et al. 1957; Spittlehouse and Black 1981) showed no relationship with site index.
A further examination of the Energy/Soil Limited Water Balance Model
(Spittlehouse and Black 1981) results showed that the major discrepancy between slightly dry sites predicted by the model versus those of the SMR key occurred consistently in two conditions. First, the model did not predict a moisture deficit for the beach sand dune sites which are very coarsely textured and rapidly drained but have a large rooting depth.
Second, the model predicted a deficit for some fresh sites in the interior of the Islands, near Moresby Camp and south of Juskatla (Fig. 1). Climate data from the nearest stations, Sandspit and Masset, are unlikely to be applicable to these sites since the inlets receive more precipitation than the coast. Sewell Inlet is at the head of an inlet abutted by the San Cristoval Ranges at the border between the CWHwh and CWHvh subzones and has a climate record for only 7 years (Environment Canada 1980). Therefore the data from the Sewell Inlet station could not be used as a substitute. It is unlikely that the model developed for south coast Douglas-fir, is applicable in the Queen Charlottes, a point that is further elaborated upon in Chapter 5.
For models that expressed a combination of moisture and nutrients, both the SMR+SNR model (model 6) and the site association model (model 7) were equally successful in explaining the most variation in the logarithm of site index (adjusted R2 = 0.80) with similar MSE (0.466 and 0.432 respectively).
However, the variances of the site association model were not homogeneous even when logged as compared to the SMR+SNR model (Fig. 9 and 10). The categorical regression of plant associations was the most successful vegetation model (adjusted R'^=0.49). Models using vegetation PCA scores only explained 30% of site index variation. There was no relationship between site index and frequency of indicator species groups for soil moisture or soil nitrogen. 3,0 3,5 4,0 ACTUAL LN (SITE INDEX)
20 25 30 35 40 ESTIMATED LN (SITE INDEX)
Fig. 9. Predicted versus actual values of ln(site index) and residual versus estimated values of ln(site index) for model 6 (soil moisture plus soil nutrient regime). Adjusted = 0.80 30 3 5 4 0 ACTUAL LN (SITE INDEX)
-0.2 2.5 3,0 3.5 4.0
ESTIMATED LN (SITE INDEX)
Fig. 10. Predicted versus actual values of ln(site index) and residual versus estimated \jalue of ln(site index) for model 7 (site association). Adjusted R-^ = 0.80 The relationship between SNR, SMR and site index can also be expressed graphically with clines of site index on an edatope (a grid of SMR and SNR) seasu Krajina (1969) (Fig. 11). Model 6 (SMR and SNR) was used with least squared smoothing to construct the clines. The grid reveals that the optimum growth for Sitka spruce is in the region of moist and very rich sites, which concurs with Klinka et al (1989). This differs with Krajina (1969) which considered that very moist and very rich were the optimum conditions for spruce. Fig. 11. Edatopic grid showing Sitka spruce site index in relation to soil nutrient and soil moisture regimes. P=poor, M=medium, R=rich, VR=veiy rich; SD=slightly dry, F=fresh, M=moist, VM=very moist, W=wet. CHAPTER 5. DISCUSSION
The objective of this study was to test the ability of biogeoclimatic ecosystem classification units to represent ecological site quality and be able to predict growth of Sitka spruce, measured as site index. For BEC variables to be good predictors, they must explain a significant amount of variation in site index and be equal or superior in that ability with respect to the analogous continuous measures. Therefore relationships between categorical and continuous variables were also investigated.
The BEC classification units were successful at predicting site index of Sitka spruce. The best models developed in this study used either soil moisture plus nutrient regime or site association. Both had an adjusted R-^ = 0.80 (I-^ = 0.79), and similar MSEs although the variance of the site association model was not homogeneous; therefore it should not be used for predictive purposes. This result concurs Avith previous studies that have used units of the BEC system to predict site index for second-growth Douglas-fir and found them to be successful (Green et al 1989; Klinka and Carter 1990).
Carter and Klinka (1990) who found a good relationship with Douglas-fir site index and water deficit as predicted by the Energy Limited Water Balance Model (Spittlehouse and Black 1981). This study showed no relationship between site index and water deficit as predicted by the model. A similar study investigating ecological site quality and site index relationships for western hemlock on the west coast of Vancouver Island also had the same result (Kayahara 1992). There are two sources of problems with the water balance model. First is the input data for weather which consists of monthly time steps of 30 year climate normals. Second are the coefficients used to calculate model parameters and the assumptions made about when évapotranspiration becomes soil moisture limited. Spittlehouse (1981) concluded that it was "obvious" that the model parameters needed to be calibrated for different species and ecosystems before the model could be applied outside the range of south coast Douglas-fir ecosystems where it was developed. The study of Carter and Klinka (1990) occurred within the range for which the model was developed.
The model is especially sensitive to precipitation which is essentially its driving variable (D. Spittlehouse pers. comm. ^) The climate stations in the Charlottes are in the driest sunniest part of the Islands and are not representative of the range of rainfall patterns that occur. Frequently the east coast (Sandspit and Tlell) will be clear and sunny while it is raining or foggy in Skidegate Inlet. The fresh sites predicted by the model as slightly dry were located away from the east coast. For stands not along the east coast, the forest is likely experiencing more rainfall than the weather data would predict. In the Vancouver Island study (Kayahara 1992), for sites that were considered to be dry or slightly dry by morphological characteristics, the model predicted no deficit which Kayahara attributed to very high precipitation in the climate data. Finally, daily rather than monthly time steps may be more appropriate; however, an investigation of this was beyond the scope of this study.
David Spittlehouse, Research Scientist, B.C. Ministry of Forests Research Branch. Several coefficients are used in the model (Spittlehouse and Black 1981; Carter and Klinka 1990). Soil depth and coefficients for texture are used to calculated available water storage capacity. Evaporation is calculated using the Priestley-Taylor coefficient which is based on tree physiology. The model assumes that évapotranspiration is energy limited, until soil water reaches 60%, then it becomes water limited.
It is unknown whether the solar coefficient chosen sufficiently accounted for fog, a common phenomenon in the Charlottes and likely an important component of water balance. It is also unknown whether an evaporation coefficient for Douglas-fir can be applied to Sitka spruce and whether the assumption of évapotranspiration becoming water limited at 60% of soil water capacity is valid.
The coefficients used for soil are probably accurate since they were measured empirically in the field and are based on soil texture which is independent of other factors. Water characteristics as a function of texture class are probably relatively consistent (A. Black pers. comm. The model did not predict a deficit for the very coarse textured rapidly drained soils on the beach side sand dunes. These forests occur in an exceptional edaphic situation and may be difficult to incorporate in a model. It is possible that aside from such edaphic situations, (i.e., forests growing on beach dunes and bedrock), trees do not experience a moisture deficit on the coast.
Andy Black. Professor, Dept. of Soil Science, U.B.C. The Thornthwaite model (Thornthwaite et al. 1957) is also inadequate for describing water balance. It derives potential évapotranspiration from temperature, latitude and precipitation and its accuracy is a function of the climate data and therefore may be suspect. The most serious problem occurs with the calculation of actual évapotranspiration which is based on coefficients for combinations of rooting depth, soil texture and vegetative cover. There are no appropriate coefficients for second-growth forests. The only coefficients given for forests assume a minimum rooting depth of 1.5 m. Rooting depth in this study ranged from 0.25 to 1.00 m. The only coefficients with appropriate rooting depths given were for agricultural crops which have different moisture behaviour from closed canopy second-growth forests. The higher transpiration and evaporative loss associated with food crops in bare soils in comparison to forests likely accounts for all the sites being classified as moderately dry, which is not realistic.
An independent field based measure of soil moisture is required to assess the performance of its categorical equivalent, soil moisture regime, in predicting site index, to give an independent evaluation of the relative soil moisture key and actual conversion table (Banner et al 1990) and provide more accurate, site specific climate data for the Charlottes. The Energy- Limited Water Balance model does predict deficits for some sites in the Charlottes. However, they show no relationship with site index. Inspection of the results indicates that the soil moisture regimes are more realistic in terms of their assessment of moisture status, but that conclusion is not supported by any data. Therefore, the possibility still exists that the model is correct and the key is wrong with respect to moisture deficit. It is beyond the ability of this author to recommend specific techniques, but instruments do exist that can measure both the soil moisture content, its change over the growing season and rainfall on the site (A. Black pers. comm.). With the poor climate data, even if the coefficients were properly Ccilibrated, the model may still be inadequate.
Klinka and Carter (1990) concluded that categorical variables were slightly better than their analytical counterparts in explaining variation in Douglas-fir site index. It is difficult to say whether this study supports this finding since there was no valid analytical variables for soil moisture, therefore no analytical model of moisture and nutrients could be developed. Analytical models may be valuable for describing Sitka spruce site index and the poor results obtained in this study may be due to a poor representation of soil water balance.
The SNR model was not clearly superior to the analytical multiple regression models using soil nutrients. However, the former does have the advantage of being more easily measured in the field. Also, given the variability in nutrient equations reported in previous studies (e.g. Blyth and MacLeod 1981b) the SNR model may be more reliable and applicable over a wider range of conditions.
In terms of nutrient studies, previous work has found nitrogen to be important (Blyth and MacLeod 1981b; Green 1989). This study concurs with that finding, which is not surprising since nitrogen is considered the most limiting factor for any conifer in the Pacific Northwest (Radwan et ai 1986). The importance of C/N ratio and FFpH is no doubt related to their effect on nitrogen availability. Considering the autecology of spruce, Mg, Ca, and P are also potentially significant (Krajina et al. 1982). Blyth and Macleod (1981b) found "humic" P to be important. This study found no relationship with FFP and no conclusions could be made about MSP since the variances were not homogeneous. Ca was shown to be related to site index, and K rather than Mg. Due to the correlation of the cations (Appendix D), regression models could not be constructed with both Ca and Mg and the models with Ca and K performed the best. With the interrelationships between the cations, Mg is likely indirectly represented. With respect the studies in Great Britain,(e.g.Blyth and Macleod 1981a; 1981b) caution should be applied in extrapolating results to North America. Sitka spruce in Britain is an exotic, planted and managed under an intensive silvicultural regime and therefore may be established on sites where it might not occur in its native conditions.
Previous studies on productivity of Sitka spruce, as reviewed in Chapter 1, have had inconclusive results as to the most important factors for spruce growth. Some studies found a weak relationship to soil physical and taxonomic variables (Stephens et al 1968; Worrell 1987; Ford et al 1988), but such models have performed poorly. This study found no relationships with soil physical variables.
The SNR classification was well supported by the discriminant analysis (DA), and to a lesser extent the cluster analysis. The DA indicated that of all the nutrient variables, only total N, mineralizable N and forest floor C/N ratio are important in discriminating the classes. These variables were found previously to be important in delineating soil nutrient regimes (Courtin et al 1988). However, this study found fewer variables useful in the discrimination than the latter study. There were no independent data to assess the accuracy of the soil moisture key. As stated previously, this is essential. Until such a calibration is done, the key should be applied with caution. This study was conducted in second-growth forests in order to limit the error in predicting site index (metres @ 50 years breast height age) However, for forest management purposes results need to be applicable to other successional stages. Green et al. (1989) felt that their model for immature Douglas-fir using site variables was applicable to other successional stages, since site units are identified by environmental variables which remain relatively constant over time. These models are therefore stronger than the ones based solely on current vegetation on the site which change with successional processes.
There were no relationships between indicator species groups and site index. This result contrasts with Green et al. (1989) which found an of 0.87 with frequency of indicator species groups and second-growth Douglas-fir site index. Forests at or near canopy closure have poorly developed understorey (and indicator) flora (Klinka et al. 1989) and the lowest species diversity (Harris 1984). In the Queen Charlottes, this lack of understory is further exacerbated by the intense browsing pressure of introduced deer. In addition, immature Douglas-fir stands may remain more open so there is more light for understory species. Green (1989) found a poor functional relationship with indicator species groups and site index of western redcedar in the Charlottes which he attributed to a closed canopy and the "non-site" variation of deer browsing. The predominant influence on understorey vegetation in the Charlottes is most likely not site characteristics. Rather, it is intense deer browsing. Therefore any relationships with vegetation will be marginal at best, such as the degree of separation of plant alliances and associations.
The PCA shows that the plant associations are weakly distinguished. Vegetation occurs on a continuous gradient, therefore some overlap could be expected. While it is admittedly arbitrary to place more weight on tabular analysis, given the paucity of the understorey, it is justified. Franklin et al. (1982) also found PCA a poor tool for delineating plant communities in comparison to tabular analysis.
Use of site index as a measure of growth potential of the moisture and nutrient conditions of a site assumes that height growth only reflects those site conditions and other factors such as mechanical forces are a constant. In the case of the beach ecosystems, this assumption may not be true in that height growth may actually be more influenced by mechanical forces i.e. wind. Trees may allocate more energy to diameter growth and windfirmness, than to height growth in comparison to non-beach sites. This is further confounded by loss of height growth from broken tops, which is often difficult to detect when determining site index in a dense second-growth stands. Barker reported encountering broken tops in his stem analysis study (J. Barker pers. comm.*^). Blyth and Macleod (1981a) also found this to be the case. Since the site association classification recognizes beach ecosystems as distinct, it is likely able to account for this phenomenon and variation in mechanical forces.
John Barker. Research Scientist, Western Forest Products Ltd. CHAPTER 6. CONCLUSIONS
1) The classification units of the biogoeclimatic ecosystem classification system are good predictors of site index of Sitka spruce and therefore good indirect measures of ecological site quality. Multiple linear regression models using either soil moisture plus soil nutrient regime or site association explained the most variation in site index of the models developed in this study (adjusted = 0.80 for ln(site index; 1^ = 0.79). The former model is the best for predictive purposes since the variances of the latter were not homogeneous.
2) The soil nutrient regime classification, based on qualitative field descriptors, was well supported by the analjrtical soil nutrient measures. A discriminant analysis using combined forest floor and mineral soil log total N, log mineralizable N and forest floor C:N ratio was able to classify the plots correctly in, 83% of the cases, on average. There was no suitable soil moisture analytical values to test the soil moisture regime classification.
3) Forest soil moisture relationships still need to be determined for Sitka spruce in the Charlottes. Neither water balance model used in this study
(Thornthwaite et ai 1957; Spittlehouse and Black 1981) were adequate. It is stiU unclear to what degree spruce growth is influenced by moisture deficit and the soil moisture regime classification needs to be assessed with independent data. Due to the inadequate representation of climate in the Charlottes from the location of weather stations, field measurements in the forest stand are likely necessary. LITERATURE CITED
Alaback, P. B. 1982. Dynamics of understorey biomass in Sitka spruce- western hemlock forests of southeast Alaska. Ecology 63:1932-1948.
Anonymous. 1976. Techicon Autoanalyzer. II. Methodology: individual/simultaneous determination of nitrogen and/or phosphorus in BD acid digest. Industrial Method. No. 329/74W/A, Technicon Corp., Tanytown, NY.
Banner, A., R.N. Green, K. Klinka, D.S. McLennan, D.V. Meidinger, F.C. Nuszdorfer and J. Pojar. 1990. Site classification for coastal British Columbia: a first approximation. B.C. Min. For., Victoria 2pp. (a coloured pamphlet).
Bcinner, A., J. Pojar, J.W. Schwab and R. Trowbridge. 1989. Vegetation and soils of the Queen Charlotte Islands: recent impacts on development pp. 261-280 In: G.G.E. Scudder and N. Gessler (eds.) The Outer Shores. Queen Charlotte Islands Museum Press, 1989.
Banner, A., J. Pojar, and R. Trowbridge. 1983. Ecosystem classification of the Coastal Western Hemlock Zone, Queen Charlotte Island Subzone (CWHg), Prince Rupert Forest Region, British Columbia. B.C. Min. For. Res. Branch, Smithers B.C. Draft Report. 255pp.
Barker, J. E. and J. W. Goudie. 1987. Site index curves for Sitka spruce. B.C. For. Serv. Br. Unpubl. Rep., Vancouver, B.C.
Bakuzis, E.V. 1969. Forestry viewed in a ecosytem perspective, pp. 189-258. In: G.M. Van Dyne, (ed.) The Ecosystem Concept in Natural Resource Management. Academic Press, New York.
Blyth, J.F. and D.A. McLeod. 1981a. Sitka spruce (Picea sitchensis) in northeastern Scotland. I: Relationships between site factors and growth. Forestry 54:41-62.
Blyth, J.F. and D.A. McLeod. 1981b. Sitka spruce (Picea sitchensis) in northeastern Scotland. II: Yield prediction by regression analysis. Forestry 54:63-73.
Bremner, J. and M.A. Tabatabai. 1971. Use of automated combustion techniques for total carbon, total nitrogen, and total sulfur analysis of soils, pp. 1-16 in L.M. Walsh (ed.). Instrumental Methods for Analysis of Soils and Plant Tissue. Soil Sci. Soc. Am., Mad., Wis. Canadian Soil Survey Committee. 1987. The Canadian system of soil classification. Can. Dept. Agric. Publ. 1646. 164pp.
Cajander, A.K. 1926. The theory of forest types. Acta. For. Fenn., 2(3) 11-108
Carmean, W.H. 1975. Forest site quality evaluation in the United States. Adv. Agron. 27: 209-269.
Carl, G.C. and C. J. Guiget. 1972. Alien Animals in British Columbia. Brit. Col. Prov. Mus. Handb. 14. Victoria, B. C. 103pp.
Carter, R.E. and K. Klinka. 1991. Use of ecological site classification in the prediction of forest productivity and response to fertilization, unpubl. rept. 11pp.
Carter, R.E. and K. Klinka. 1990. Relationships between growing-season soil water-deficit, mineralizable soil nitrogen and site index of coastal Douglas-fir. For. Ecol. Manage. 30:301-311
Carter, R.E. and K. Klinka. 1988. Douglas-fir fertilization decision-making for industrial use: an establishment report. Faculty of Forestry, Univ. of B.C., Vancouver, B.C. 33pp.
Chatterjee, S. and B. Price. 1977. Regression analysis by example. Wiley- Interscience, New York. 228 pp.
Corns, l.G.W. and D.J. Pluth. 1984. Vegetational indicators as independent variables in forest growth prediction in west-central Alberta, Ccinada. For. Ecol. Man. 9:13-25.
Cordes, L.D. 1972. An ecological study of the Sitka spruce forest on the west coast of Vancouver Island. Phd. thesis, Univ. of British Columbia. 268pp.D
Courtin, P.J., K. Klinka, M.C. Feller, and J.P. Demaerschalk. 1988. An approach to quantitative classification of nutrient regimes of forest soils. Can. J. Bot. 66:2640-2653.
Crum, H.A., W.C. Steere and L.E. Anderson. 1973. A new list of mosses of North America north of Mexico. The Biyologist 76:85-130.
Daubermire, R.F. 1976. The use of vegetation in assessing the productivity of forest lands. Bot. Rev. 42:115-143. Day, W.R. 1957. Sitka spruce in British Columbia - a study in forest relationships. Forestry Ccommission bulletin No. 28, Imperial Forestry Institute, Oxford.
Dillon, W.R. and M. Goldstein. 1984. Multivariate Analysis: Methods and Applications. John and Wiley and Sons. New York. 587pp.
Dumanski, J. (ed). 1978. Manual for describing soils in the field; The Canadian Soil Information System (CanSIS). Land Resource Institute, Agric. Can. Ottawa, Ont. 92pp.
Emanuel, J. 1985. A vegetation classification program. Unpublished manuscript. Faculty of Forestry, Univ. of B.C. 19pp.
Environment Canada 1980. Canadian Climate Normals, 1951-1980, British Columbia. Atmospheric Environment Service. Downsview, Ont. 268pp.
Farr, W. A. and A.S. Harris. 1979. Site index of Sitka spruce along the Pacific coast related to latitude and temperature. For. Sci. 25:145-153.
Farr, W. A. and A.S. Harris. 1983. Site index research in the western hemlock-Sitka spruce forest type in coastal Alaska. In: Ballard R. and S.P.Gessel, (eds). I.U.F.R.O. S5miposlum on forest site and continuous productivity. Proceedings of a symposium, Aug. 22-28 1982; Seattle WA. Gen. Tech. Rep. PNW-163, U.S.D.A. Pac. Nor. West. Res. and Exp. Station.
Ford, E.D., W.A. Farr and C. Lu-Ping. 1988. Preliminary analysis of four soil variables and their relation to site index of Sitka spruce in Southeast Alaska, pp. 84-89 In: Slaughter, C. W. and T. Gasbarro (eds.) Proceedings of the Alaska forest soil productivity workshop; April 28-30; Anchorage AK. Gen. Tech. Rep. PNW-219. U.S.D.A. Pac. Nor. West. Res. and Exp. Station.
Franklin, J.F., W.H. Moir, M.A. Hemstrom, S.E. Greene and B.G. Smith 1988. The forest communities of Mount Rainier National Park. Scientific Monograph. Ser. No. 19. USDI Nat. Park Serv. Washington D.C.
Gauch, H.G. 1977. ORDIFLEX - A flexible computer program for four ordination techniques: weighted averages, polar ordination, principal components analysis, and reciprocal averaging. Release B. Ecology and Systematics, Cornell University, Ithaca, New York. 185pp.
Gittins, R. 1979. Ecological applications of canonical analysis, pp. 309-353 In: L. Orloci, C.R. Rao and W. M. Stiteler (eds.) Multivariate Methods in Ecological Work. Burton Md: International Co-operative. Gordon, A. D. 1981. Classification. Methods for the exploratory analysis of multivariate data. Chapman and Hall, New York.
Green, R.N. 1989. Site - forest productivity relationships and their management implications in coast lowland ecosystems of east Graham Island, Queen Charlotte Islands. MSc. thesis, Univ. of British Columbia. 169pp.
Green, R.N., P.L. Marshall and K. Klinka. 1989. Estimating site index of Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) from ecological variables in southwestern British Columbia. For. Sci. 35:50-63.
Greweling, T. and M. Peech. 1960. Chemical soil tests. Cornell. Agric. Exp. Sta. Bull. 960.
Hagglund, B. 1981. Evaluation of forest site productivity. Comm. For. Bureau, For. Abstracts 42(11): 515-527.
Harris, L.D. 1984. The Fragmented Forest. Univ. of Chicago Presss. 211pp.
Hills, G.A. 1952. The classification of evaluation of site for forestry. Res. Rep. 24, Ontario Dept. of Lands and Forests, Toronto, Ont. 41pp.
Holland, S.S. 1976. Landforms of British Columbia. Bulletin 48, B.C. Dept. of Mines and Petroleum Resources, Victoria, B.C. 138pp.
Kayahara, G. J. 1992. An investigation of relationships between western hemlock ecosystems, ecological site quality and productivity in the Coastal Western Hemlock zone of B.C. MSc. thesis, Univ. of British Columbia. 127pp.
Klinka, K., and R.E. Carter. 1990. Relationships between site index and synoptic environmental factors in immature coastal Douglas-fir stands. For. Sci. Vol. 36(3):815-830
Klinka, K., V.J. Krajina, A. Ceska and A.M. Scagel. 1989. Indicator Plants of Coastal British Columbia. Univ. of British Columbia Press, Vancouver B.C. 287pp.
Klinka, K., R.N. Green, R.L. Trowbridge and L.E. Lowe. 1981. Taxonomic Classification of Humus Forms in Ecosystems of British Columbia. First Approximation. Land Manage. Rep. 8. B.C. Ministry of Forests, Victoria B.C. 54pp. Kowalenko, G.D. and L.E. Lowe. 1972. Observations on the bismuth sulphide colourmetric precedure for sulphate analysis in soil. Comm. in Soil. Sci. Plant Anal. 3:79-86.
Krajina, V.J., K. Klinka, and J. Worrall. 1982. Ecological characteristics of trees and shrubs of British Columbia. Fac. of For., Univ. of British Columbia, Vancouver, B.C. 131 pp.
Krajina, V.J.and R.C. Brooke 1969 . Ecology of forest trees in British Columbia. Ecol. West. N. Am. 2(19) : 1-146.
Lavkulich, L.M. 1978. Methods manual. Pedology Laboratory, Soil Sci. Dept., Univ. British Columbia, Vancouver, B.C. 224 pp.
Lewis, T. 1982. The Ecosystems of Tree Farm License 24, Queen Charlotte Islands, B.C.. A contract report to Western Forest Products Ltd., Vancouver, B.C. 191pp.
Livingston, B.C. and F. Shreve. 1921. The distribution of vegetation in the United Stats, as related to climatic conditions. Carnegie Inst. Wash. Publ. 285:1-585.
Major, J. 1963. A climatic index to vascular plant activity. Ecol. 44: 485- 498.
Mason, H.L. and J.H. Langeheim. 1957. Language analysis and the concept of environment. Ecol. 38:325-339.
McLean, CD. and C.L. Bolsinger. 1973. Estimating Dunning's site index from plant indicators. USDA Forest Serv. Res. Note PNW-152, Pac. Nor. West. Range and Exp. Station, Portland OR
Mehlich, A. 1978. New extractant for soil test evaluation of phosphorus, magnesium, calcium, sodium, manganese and zinc. Comm. Soil Sci. Plant Anal. 9: 477-4922
Meidinger, D.V. and J. Pojar. 1988. Biogeoclimatic and ecoregion units of the Prince Rupert Forest Region. Map published by the B.C. Ministry of Forests and Lands Research Branch, Victoria B.C.
Monserud, R.A., U. Moody, and D.W. Breuer. 1990. A soil-site study for inland Douglas-fir. Can. J. For. Res. 20:686-695. Monserud, R.A. 1984. Problems with site index: An opinionated review, pp. 167-180 In: J. G. Bockheim (ed.) Forest land classification: experience, problems and perspectives. Proc. North Central Forest Soils Committee symposium. NCR-102, Univ. of Wisconsin, U.S.D.A. For. Serv. North Central For. Exp. Stn.
Peet, R.K. 1980. Ordination as a tool for analyzing complex data set. Vegetatio 42:171-4.
Pojar, J., K. Klinka, and D.V. Meidinger. 1987. Biogeoclimatic ecosystem classification in British Columbia. For. Ecol. Manage. 22:119-154.
Pojar, J. and A. Banner. 1984. Old-growth forests and introduced deer on the Queen Charlotte Islands, British Columbia, pp. 247-257. In: W.R. Meehan, T.R. Merrel Jr. and T.A. Hanley (tech. eds.) Fish and Wildlife Relationships in Old-growth Forests: Proc. Symp. (Juneau, Alaska, 12-15 April 1982). Amer. Inst. Fish. Res. Biol.
Powers, R.F. 1980. Mineralizable soil nitrogen as an index of nitrogen availability to forest trees. Soil Sci. Soc. Am. J. 44: 1314-1320.
Price, W.J. 1978. Anal5^c atomic absorption spectrophotometry. Heydon and Son Ltd., London. 239 pp.
Radwan, M.A. and Brix. 1986. Nutrition of Douglas-fir. In: CD. Oliver, D.P. Hanley and J.D. Johnson (eds.) Douglas-fir: Stand management for the future. College of Forest Resources, Univ. Washington, Contrib. 55, 388pp.
Rogers, G.L. 1988. Estimations of site index in forest inventories of coastal Alaska—and other confessions, pp. 77-80 In: Slaughter, C. W. and T. Gasbarro (eds.) Proceedings of the Alaska forest soil productivity workshop; April 28-30; Anchorage AK. Gen. Tech. Rep. PNW-219. U.S.D.A. Pac. Nor. West. Res. and Exp. Station.
Rowe,J.S. 1956. Uses of undergrowth plant species in forestry. Ecol. 37(3): 462-473
SAS Institute Inc. 1982. SAS User's Guide: Statistics, 1982 edition. Caiy, NC. SAS Institute Inc.
Schonau, A.P.G. 1988. Problems in using vegetation or soil classification in determining forest site quality, pp 3-11 In: Cole, D.W. and S.P. Gessel. Forest Site Evaluation and Long-term Productivity. Univ. of Washington Press. 196pp. Spies, T.A. and B.C. Barnes. 1985. A multifactor ecological classification of the northern hardwood and conifer ecosystems at Sylvania Recreation Area, Upper Pennisula, Michigan. Can J. For. Res. 15:949-960.
Spittlehouse, D.L. 1981. Measuring and modelling évapotranspiration from Douglas-fir stands. Phd. thesis. Univ. of British Columbia.
Spittlehouse, D.L. and T.A. Black. 1981. A growing season water balance model applied to two Douglas-fir stands. Water Resour. Res. 17: 1651- 1656.
Spurr, S. H. 1952. Forest Inventory. Ronald Press, Co., New York. 476pp.
Spurr, S. H., and B. V. Barnes 1980. Forest ecology (3rd edition). John Wiley & Sons, New York.
Stephens, F.R., C. R. Gass, and R. F. Billings. 1968. Soils an site index in southeast Alaska. Report No. 2 of the Soil-site Index Administrative Study, Alaska region. U.S.D.A, Forest Service 17pp.
Taylor, R.L. and B. MacBiyde. 1977. Vascular plants of British Columbia. Tech. Bull. No. 4, Univ. of B.C. Press, Vancouver, B.C. 659pp.
Thornthwaite, C.W., J.R. Mather and D.B. Carter. 1957. Instructions and tables for computing potential évapotranspiration and the water balance. Publ. Climatol. Vol. 10, No. 3, Drexel Inst. Technol.,Lab. Climatol., Centerton, New Jersey
Stone, E.L. 1978. A critique of soil moisture - site productivity relationships. pp.41-52 In: W.E. Balmer. (Ed.) Soil moisture - site productivity symposium, proceedings, 1-3 Nov. 1977. M5atle Beach, SC. USDA Forest Service, Atlanta GA
Thrower, J.S. 1989. Site quality evaluation using site index. A presentation to the Silvicultural Institute of B.C. Module III training course at Surrey B.C., March 8, 1989. 11pp.
Trewartha, G.T. 1968. An Introduction to Climate. 4th edition. McGraw-Hill, New York.
Walmsley, M., G. Utzig, T. Void, D. Moon, and J. van Barnveld (eds.). 1980. Describing ecosystems in the field. B.C. Min. Env., RAB Technical Paper 2, B.C. Min. For., Land Management Rep. No. 7, Victoria, B.C. Ward, J.H. 1963. Hierarchical groupings to optimize an objective function. J. Amer. Stat. Assoc. 58:236-244.
Waring, R. H. and W. H. Schlesinger. 1985. Forest Ecosystems Concepts and Management. Academic Press, London. 340pp.
Waring, R. H. and J. F. Franklin. 1979. Evergreen coniferous forests of the Pacific Northwest. Science 204(4400): 1380-1386.
Waring, R.H. and J. Major. 1964. Some vegetation of the California coastal redwood region in relation to gradients of moisture, nutrients, light and temperature. Ecol. Monog. 34(2) pp. 167-215
Williams, G. 1968. Climate of the Queen Charlotte Islands, pp. 15-54. In Calder. J. and R. Taylor. 1968. Flora of the Queen Charlotte Islands, Part 1. Systematics of the vascular plants. Res. Branch. Can. Dept. of Agric. Monograph No. 4. Part 1. 659pp.
Wilkinson, L. 1990. SYSTAT. The system for statistics. SYSTATInc, Evanston. 111.
Worrell, R. 1987. Predicting the productivity of Sitka spruce on upland site in Northern Britain. For. Comm. Bull. No. 72.
Worrell, R. and D.C. Malcolm 1990a. Productivity of Sitka spruce in northern Great Britain. 1. The effects of elevation and climate. Forestry 63(2)104- 117
Worrell, R. and D.C. Malcolm 1990b. Productivity of Sitka spruce in northern Great Britain. 2. Prediction from site factors. Forestry 63(2)119-128
Zar, J.H. 1984. Biostatistical analysis. 2nd edition. Prentice-Hall. Endglewood Cliffs, N.J. APPENDIX A. Alphabetical list of plant species
Alnus rubra Bong. red alder AthyriumJUix-feniina (L.) Roth lady fern
Blechnum spicant (L.) Roth deer fern
Calamagrostis nutkaensis (Presl) Steud. Pacific reedgrass Calypso bulbosa (L.) Oakes fairy-slipper Claytonia sibrica (L.) Siberian miner's lettuce Conocephalum conicum (L.) Llndb Coptis asplenifolia Salisb. goldthread
Dicentraformosa (Andr.) Walp. bleeding heart Dryopteris expansa (Presl) spiny wood fern Fraser-Jenkins
Galium aparine (L.) cleavers Galium, triflorum Mlchx. sweet-scented bedstraw Gaultheria shallon Pursh salal Goodyera oblongfolia Raf rattlesnake-plantain Gymnocarpum dryopteris (L.) Newm. oak fern
Hylocomium splendens (Hedw.)B.S.G. Hypochoeris radiacata (L.) hairy cat's ear
Listera caurina Piper northwestern twayblade Lysichitum americanum Hult.& St.John skunk cabbage
Maianthemum dilatatum (How.) Nels & Macbr. false lily-of-the-valley Moneses uniflora (L.) Gray single delight
Osmorhiza chilensis Hook. &Am. mountain sweet cicely
Peltigera aphthosa (L.) Willd. Plagiomnium insigne (Mitt.) Koponen Poa marcida Hitchc. Pogonatum alpinum (Hedw.) Roehl Polypodium glycyrrhiza (L.) licorice fern Polystichum munitum (Kaulf.) Presl sword fern Pteridium aqidinium (L.) Kuhn in Decken bracken fern
Rhizomnium glabrescens (Kindb.) Koponen Rhytidiadelphus loreus (Hedw.) Wamst. lanky moss Rhytidiadelphus triquestrus (Hedw.) Wamst. Rubus parvtflorus Nutt. thimblebeny
Stellaria crispa Cham. & Schlect crisp starwort Sphagnum spp.
Tiarella trifoliata L. foamflower
Vaccinium oualifolium Smith oval leaf blueberry Vaccinium parvijlorum Sm. in Ress red huckleberry Vicia americana Muhl. American vetch Appendix B. Ordination of reciprocal averaging plots scores for second versus first axis showing vegetation groups delineated through progressive fragmentation.
Small numbers represent plots. Large numbers represent plant associations from Table 2.
1 Ss-Calamagrostis 2 Ss-Claytonia 3 Ss-Rhytidiadelphus 4 Ss-Gymnocarpixum 5 Ss-Polystichum 6 Ss-Gaultheria 7 Ss-Thiya 8 Ss-Hylœomium 9 Ss-Alnus 10 Ss-Kindbergia Aii;.Lii.SA IX *xib) IS SM.U'ltS SCOIIES KlUU (IECIP»VE orin I 111 I 1 Oil 1 AXIS 1 OlllllMiTE (r AXISI IS S»I.1IMES SCOIIES f IIOI.I IIECll'ivE OIIDI lU 1 1 Oil 1 *XIS 2
!31 cn
rt) -I nr. o p
lllll222223]33]44<4<5555566666777778aOB6909090 Ui)24Ba0246aû246a02<6a02<6a02 I I (J) \ a> n o Cl \ r-t- nr. o 13 I 1 1 I II 2 II I I II III 2 I I I I I I III I III / I I / 11222223333344 ' " " " 2 ^ " " 0 2 < 6 a Q 2 4 6 a 0 2 4 5 a Q 2 4 G a 0 2 4 C 8 Û 2 4 6 0 0 2 4 6 B 0 2 4 C 8 n AUStl'JSA (X-AXISl ISSIMPIES SCORES FnOU REClPiVE OnDllUIIOII 1 AXIS 1 OflUIMAIE lY AXIS! IS SAMPLES SCORES (ROM RECIPAvE OROIII* I I Oil I AXIS- 2 lu9n8 I • 96 g 4 \ 92 90 \ es V 69 B4 B2 \ /1 80 1 78 7B U 72 6 —- 1 70 SB 66 64 62 60 5B 5B 54 1 \ 52 \ 50 I 48 \ 2 48 1 t 44 \ 1 1 1 42 1 1 1 n II 1 40 2 38 o 1 36 1 1 1 34 1 1 32 / 1 1 30 1 28 1 28 24 22 1 20 18 16 14 12 ID • D 6 4 2 1 1 1 1 1 I222223333344444555555BBB6777 7 7BBB8B99B9!) 0 0 .! 4 (i U 0 2 4 6 80246802468024680246802468024 680246802461 ) 0 AllSCISS» (X-AXISl IS SAMCI ES SCORES FROM PECIPAVE OnOIH»IIOtl I AXIS 1 OIIOINAIE lY-AXlSI IS SAMri ES SCORES FROM HECII'AVE OROItlAlIOM I AXIS 2 3 I o 12 I I I I 111 o I II? !=1 I n I I 11 I I I I I I 1222223333 .'<444<555ti066B6B777770OBBa9g999O 4(IU 0246nO246B0246d0248aO246B02168O246(jn2468O2468O Fift±i iteration - o c o) œ O) o ai ru C7) O m o CD 03 O 03 m - - to to Œ3 •» 113 nj ^ m 03 / s \ in ID \ in "«r in CM in o 1 - •a- IM -"^ / / . _ - \ ^ t\j œ - - If ru C3 oj nj ru O — OJ \ — C3 CO — fVJ ~ \ \ — O - OtDU3^ryoa)o-«'r>jO(3O*''^Oa)O'wruoc3O-''^'OCDo-»raO03cr-*ryooiO'W'ri;oœiO'vr^ OClCoalClClœo3aDa3C3^^'^'^'^'^oo^ûoo^nu^lnlnln'T-»^•v•v**l|-^'^'~'^ — — Appendix C 80% confidence ellipses for principal component analysis scores for plant associations. 80% confidence ellipses for plant associations with greater than 5 plots for first and second PCA axes (Y2 verses Yl) and second and third axes (Y3 verses Y2). Understorey species = all species occurring in the understorey. Diagnostic species = those species that are diagnostic for the plant association from Table 2. Numbers represent plant associations from Table 2. 1 Ss-Caiamagrostis 2 Ss-Claytonia 3 Ss-Rhytidiadelphus 4 Ss-Gymnocarpium 5 Ss-Polystichum 6 Ss-Gaultheria 7 Ss-Thuja 8 Ss-Hylocomium 9 Ss-Alnus 10 Ss-Kindbergia 80% confidence ellipses for principal component analysis scores for understorey species for second versus first axis and third versus second axes. 80% confidence elHpses for principal component analysis for diagnostic species for second versus first axis and third versus second axes. Appendix D Pearson correlation matrix for multiple linear regression equations Symbols used for individual forest floor and mineral soil nutrients in concentration FF forest floor MS mineral soil PH pH TN total nitrogen MNN mineralizable nitrogen P phosphorus S sulphur CA calcium MG magnesium K potassium LOGFMG log (10) forest floor magnesium LOGFK log (10) forest floor potassium LOGMSP log (10) forest floor phosphorus LOGMSMN log (10) mineral sofl mineralizable nitrogen Symbols used for combined forest floor and mineral soil nutrients in kg/ha TOTC total carbon TOTN total nitrogen TOTMN mineralizable nitrogen TOTP forest floor phosphorus TOTS forest floor sulphur SUMCAT sum calcium, magnesium and Dotassium FFPH forest floor pH MSPH mineral soil pH FFCN forest floor carbon to nitrogen ratio MSCN mineral sofl carbon to nitrogen ratio LGTOTN log (10) total nitrogen LGTOTP log (10) forest floor phosphorus LGTOTS log (10) forest floor sulphur LGSUMC log (10) sum calcium, magnesium and potassium FCfi immwÀL îms: FLOOR ÀÏD Kiim SOIL IVISIETS IH coHCEmiTios FFPB FTC îm îm 1.000 FTC 1.000 mt -0.307 0.371 1.000 nm o.in 0.071 0.<89 1.000 m Û.303 -0.358 0.3H 0.320 1.000 m -0.383 0.611 0.63< 0.333 5.052 îm 0.653 -0.300 -0.172 0.032 0.033 mfô 0.0<3 0.(03 0.168 -0.07< -0.312 m 0.00< 0.3U 0.223 0.137 -O.Û7< Nspa 0.363 -0.187 -«.322 -0.272 0.022 use 0.003 0.060 0.128 0.180 0.223 0.085 -0.008 0.215 0.269 0.368 0.115 0.231 HSIffl 0.056 -0.05< 0.083 0.331 -0.215 -0.253 -0.10( 0.15< 0.U5 0.126 0.015 -0.071 -0.132 0.0<5 0.23( 0.168 -0.002 -0.137 xsi: -0.068 -0.051 KSP -0.062 0.182 0.057 O.Hl H1.09( 0.184 MSS -0.133 -0.202 -fl.008 -0.351 0.010 0.<<7 0.201 LOGfîfô -0.003 0.2<5 -0.12( Locn; 0.337 0.211 -0.090 -0.039 -0.039 0.153 0.072 n ISS 0.306 LOGXSP D.031 -0.11! 0.071 LOOQffl Fîa FÎTC Fît KSPB ÎÎS 1.000 fîa -0.166 1.000 îm 0.(65 0.097 1.000 îîi 0.2(( -0.072 0.(91 1.000 -3.303 0.311 0.183 0.1(1 i.ooe KSC 0.03! -0.1J8 -0.321 -0.102 -0.3/6 KSVi 0.109 -O.U( -0.277 -0.11! -0.38( -0.091 -0.200 -0.323 -0.095 -0.26( KSCA -0.182 0.151 -0.115 -0.063 0.03! KSHG 0.271 0.162 0.612 0.073 0.090 KSI 0.391 -0.01! 0.607 0.165 -0.08! HSP O.IH -0.070 0.(78 0.388 0.010 HSS -0.163 -0.128 -^1.172 -0.082 -0.12! Locm; 0.(92 0.125 0.969 0.538 0.1(8 LOGfT 0.257 -0.150 0.(99 0.965 0.056 LOCXSP 0.17( -0.108 0.530 0.(11 0.05S LOGXSMI -0.115 -0.16( -0.(62 -0.319 -0.367 XSîffl KSMG KSC 1.000 KSTX • 0.328 1.000 0.181 0.776 l.OOO Ksa 0.525 0.(89 0.(01 1 000 HSMG 0.093 0.137 -0.070 0 312 1.000 KSI 0.211 0.285 0.092 0 069 0.866 KSP -O.075 -0.0(8 -0.0(7 0 003 0.230 .KSS 0.517 0.(95 0.(50 0 !7( 0.129 LOGFMC -0.3(7 -0.30( -3.337 -0 197 0.509 LOGFI -0.02( -0.033 -0.017 -0 059 0.132 LOGXSP -0.0(3 -0.038 -0.065 0 10( 0.296 LOGHSKil 0.735 0.801 0.876 0 252 -0.108 KSI KSP LOGKS? LOGXSM LOCXSP 1.000 LOGHSKÏ -O.303 1.000 mSOi CORKELATIOI «AÎSII FOB CMffllKD FOfiKT FLOÛfi À8D MIlESiL SOIL imiEÏTS lï IQ/U TOTC TOTÏ TOTM TOTP TOTS TOTC 1.000 TOTÏ 0.122 1.000 TOTM 0.(60 0.644 1.000 0.337 0.150 0.409 1.000 TOTS 0.540 0.238 0.352 0.736 1.000 smT 0.631 0.160 -0.049 -0.151 0.069 Ffpe -0.235 -0.041 0.051 -0.159 -0.236 KSPR -O.IM -0.121 H3.104 0.OJ4 -0.076 FfCH 0.0!5 -0.261 -0.166 -0.014 0.131 «SOf 0.253 -0.124 0.026 -0.062 O.OI! LCTOn 0.122 0.949 0.611 0.198 0.300 LGTOTWi 0.710 0.652 0.940 0.449 0.411 L(JTIW 0.270 0.073 0.349 0.905 0,564 LGTOTS 0.521 0.266 0.371 0.715 0.932 LGSIKC 0.111 0.255 0.020 -0.073 0.109 SUMQT FFPH «SPB FFd Hsa simt 1.000 ÏTPB 8.335 i.ÛOO NSPH 0.363 l.OOO FFCK e.225 -0.149 0.160 1.000 KSOI -0.341 -0.2!4 -0.314 0.153 1.000 LGTOTH 0.161 -0.047 -0.073 -0.295 -0.041 LCTOTîffl -O.067 -0.010 -0.092 -0.188 0.085 L'JTOT -1.¥J -0.270 0.046 -0.106 -0.0!0 LGTOTS -0.060 -0.326 -0.172 0.007 0.065 LGSUMC 0.903 0.366 0.464 0.180 -0.39Î LGTOTH LGTOIlf LGTOTP LGTOTS LGSUWC LGTOTI 1.000 LGTOTM 0.655 1.000 LGTOTP 0.106 Û.394 1.000 LGTOTS 0.317 0.439 0.633 1.000 LGSUMC 0.261 0.011 -0.269 -0.043 1.000