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Laminated root rot: ecology relationship and stand productivity impacts in coastal Douglas-fir ecosystems.. Beale, Jeffrey D. 1992-12-31

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LAMINATED ROOT ROT: ECOLOGICAL RELATIONSHIPS AND STAND PRODUCTIVITY IMPACTS IN COASTAL DOUGLAS-FIR ECOSYSTEMS OF BRITISH COLUMBIA by JEFFREY DAVID BEALE B.Sc., Lakehead University, Ontario, 1980  A THESIS 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 the required standard  THE UNIVERSITY OF BRITISH COLUMBIA AUGUST, 1992 © Jeffrey David Beale, 1992  In presenting this thesis in partial fulfillment 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 representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.  Department of Forestry The University of British Columbia 2075 Wesbrook Place Vancouver, B.C., Canada V6T 1W5 August 10, 1990  ii ABSTRACT  The effect of laminated (Phellinus) root rot in coastal second growth Douglas-fir ecosystems of southeastern Vancouver Island was investigated in 139 ecosystems centered around 215 growth and yield permanent sample plots (PSP's). The impact of Phellinus root rot on stand productivity was estimated from PSP records and a percent  basal area reduction (%BAR) parameter estimate determined using variable-radius plot sampling. The methods involved ecological site characterization to Biogeoclimatic Ecosystem Classification system units, sampling of root rot incidence and severity, and calculation of root rot damage intensity, sampling of old growth stand conditions (stand density and species composition), determination of second growth stand origin, and summaries of second growth stand conditions and growth and yield from the 30 to 35 yr long PSP records.  Phellinus root rot was present in 87% of the 139 sample survey units, while the  incidence in the PSP's was 37%. Root rot damage intensity varied significantly between the CDFmm and CWHxm subzones, respectively 5.94% and 11.11%. Generally, root rot did not vary much between subzone variants, plant alliances and associations and site associations. The highest root rot intensity was in the mesic conditions and tailed off in the drier and fresher soil moisture regimes. Only stand density (stems/ha) of old growth Douglas-fir and western hemlock were significantly and negatively correlated with Phellinus root rot intensity. It appears that the nearly two-times greater density of old  growth Douglas-fir in the CWHxm contributed to nearly double the damage intensity, as  iii compared to the CDFmm subzone. Trends in the old growth species composition indicated a natural, host-pathogen dynamic equilibrium is part of the ecosystem. That is, Phellinus kills-out susceptible, shade intolerant pioneer seral species by creating infection  center gaps, making way for less-susceptible, shade tolerant mid-late seral species. In doing so, the inoculum is reduced but rarely eliminated, therefore keeping it in equilibrium with its hosts. Elevation (m asl), slope (%), mineral soil pH and coarse fragment content (% by volume) varied substantially between subzones, and likely play some role in the disease incidence and damage intensity. No other ecological site or stand attributes were correlated to damage intensity.  Second growth stand attributes (stand density (stems/ha), relative density and species composition) had similar distribution patterns to damage intensity, although no meaningful relationships were determined in multiple linear regression models. Second growth less- to non-susceptible host species composition was found to increase by about 4% in Phellinus infected conditions (both in temporary variable-radius %BAR sampling and PSP's) compared to a 0.2% increase in healthy conditions, strongly suggesting Phellinus acts as a biogenic successional agent that induces species shifts as part of the  host-pathogen equilibrium.  Root rot damage intensities were highest in second growth stands of wildfire origin, followed by logged-only, then logged-and-slashburned stands. Although stand origin class ages varied significantly (they were positively related to the damage intensity) there was an indication that logging and logging-and-slashburning could have played a role in  iv reducing the inoculum levels particularly through fire.  Ecosystem hazard and Phellinus risk were approximated for ecosystem units on the basis of root rot incidence and intensity estimates. The site association taxon model (climax species potential) was found to accurately estimate Phellinus risk when the host Phellinus susceptibility patterns are considered in light of the postulated host-pathogen  dynamic equilibrium.  Within a set of comparable PSP's, yield reductions ranged up to 30% depending on stand age and damage intensity. Yield models estimated volume reductions to range between 4.97 - 8.86% at age 80, or site height 35 m, depending on ecological and age 10 stand density stratifications. Percent basal area reductions ranged from 8.25 - 9.99%, depending on the species and diameter class stratification of the estimate parameter.  Management at the stand and forest level must consider the pervasive nature of Phellinus root rot. The significant ecological role the disease appears to play in inducing  a species shift, has direct implications on the use of less-susceptible species in control and management strategies. Stand yield impacts appear to be substantial enough to warrant treatment where timber production objectives are primary.  v TABLE OF CONTENTS  ABSTRACT ^  ii  TABLE OF CONTENTS ^  v  LIST OF TABLES ^  xii  LIST OF FIGURES ^  xv  LIST OF APPENDICES ^  xxiii  LIST OF SYMBOLS ^  xxiv  ACKNOWLEDGEMENTS ^  xxix  1.0 INTRODUCTION ^  1  2.0 LAMINATED ROOT ROT BIOLOGY AND BEHAVIOUR ^ 4 3.0 A REVIEW OF SITE ECOLOGICAL RELATIONSHIPS OF PHELLINUS WEIRII ^ 4.0 MEASUREMENT OF ROOT ROT INCIDENCE AND DAMAGE -- A REVIEW ^  8 14  4.1^A Review of Measurement Methods ^  14  4.2^Evaluation and Selection of an Appropriate Root Rot Measurement Method ^  17  5.0 STUDY LOCATION ^  23  5.1^Geography ^  23  5.2^Physiography ^  23  5.3^Soil Parent Materials--Surficial Deposits ^  25  5.4^Climate ^  26  5.5^Forest Cover and Stand History--Past and Present ^ 28 6.0 METHODS ^  30  vi 6.1^General Approach ^  30  6.2 Sampling Design ^  30  6.3^Measurement Of Root Rot Incidence, Severity and Calculation of Intensity ^  36  6.3.1 Calculation Methods For %BAR Percent Basal Area Reduction Variables ^ 6.3.2 Assumptions Of The Percent Basal Area Reduction (%BAR)Sampling Method ^ 6.3.2.1^Expected Relationships For the %BAR Damage Intensity Parameter ^ 6.3.2.2 Testing The Homogeneity Of Species Compositions And The Decomposition Of The %BAR Severity Parameters ^ 6.4^Ecological Assessments ^ 6.4.1 Site: Physiography ^ 6.4.2 Site: Vegetation ^ 6.4.3 Site: Soils ^ 6.4.4 Site: Forest Floor ^ 6.4.5 Site: Old Growth Stand History ^ 6.4.5.1^Old Growth Stand History (Stand Density & Species Composition) Estimates Using FixedRadius Plots ^  42 44 44 45 47 47 48 48 49 49  50  6.5^Site: Stand Mensuration ^  51  6.6^Laboratory Analyses And Data Preparation ^  51  6.6.1 Mineral Soil And Forest Floor Physical Properties ^ 6.6.2 Mineral Soil And Forest Floor Chemical Properties ^ 6.6.3 Conversion Of Chemical Property Concentrations To Kg/ha ^ 6.6.4 PSP Stand Attribute Data Preparation ^ 6.6.5 Classification Of Root Rot Damage Intensity (%BAR) ^ 6.6.6 Ecological Classification ^ 6.6.6.1^Climate Classification ^ 6.6.6.2^Vegetation Classification ^ 6.6.6.3^Site Classification ^  52 54 54 55 58 61 62 62 69  ^ ^  vii 70  6.7^Statistical Analysis Methods ^  74  7.0 RESULTS AND DISCUSSION ^ 7.1^Data Structure And General Relationships ^  74  7.1.1 Assessment Of The Percent Basal Area Reduction (%BAR) Variable And It's Components ^ 7.1.2 Incidence-Severity, Severity-Intensity and Incidence-Intensity Relationships ^ 7.1.3 Stand-Based:Land-Based Incidence and Land-Based:Stand-Based Relationships ^ 7.1.4 Comparison Of The %BAR Survey Data To The Permanent Sample Plot (PSP) Data ^ 7.2^Ecological Relationships of Phellinus weirii ^  74 79 84 86 89  7.2.1 Describing Phellinus Root Rot Variability in Relation to Biogeoclimatic Units ^  89  7.2.1.1^Zonal (Climatic) Classification And Disease Variability ^  90  7.2.1.1.1 Disease Incidence Variability: ^ Zonal Classification 7.2.1.1.2 Disease Intensity Variability: ^ Zonal Classification  90 91  7.2.1.2^Vegetation Classification And Disease Variability ^ 94 7.2.1.2.1 Disease Incidence Variability: Vegetation Classification ^ 7.2.1.2.2 Disease Intensity Variability: Vegetation Classification  94 ^. 95  7.2.1.3^Site Classification And Disease Variability ^ 98 7.2.1.3.1 Disease Incidence Variability: Site Classification ^  98  7.2.1.3.2 Disease Intensity Variability: Site Classification ^  99  7.2.2 Correlation Of Ecological Parameters And Phellinus Root Disease ^  100  viii 7.3 Stand History And Tree Species Dynamics ^ 7.3.1 Old Growth Stand Conditions and Phellinus Variability 7.3.1.1^Old Growth Stand Conditions and Phellinus Root Rot Variability Between BARS-Damage Intensity Classes (DIC's) ^ 7.3.1.2 Old Growth Stand Conditions and Phellinus Root Rot Variability Between Subzones ^ 7.3.1.3 Old Growth Stand Conditions and Phellinus Variability Between Plant Alliances and Plant Associations ^ 7.3.1.4 Old Growth Stand Conditions and Phellinus Root Rot Variability Between Plant Alliances And Plant Associations ^ 7.3.1.5 Old Growth Stand Density and Species Composition and Second Growth Phellinus Damage Intensity ^  105 105  108 110 111 111 113  7.3.2 Second Growth - Phellinus Variability Relationships ^ 117 7.3.2.1 7.3.2.2 7.3.2.3 7.3.2.4 7.3.2.5 7.3.2.6  Second Growth Stand Conditions and Phellinus Variability in the BARS Damage Intensity Classes ^ 119 Second Growth Stand Conditions and Phellinus Variability in the Subzones at PSP Establishment 119 Second Growth Stand Conditions and Phellinus Variability in the Subzone Variants at PSP Establishment 122 Second Growth Stand Conditions and Phellinus Variability in the Plant Alliances 124 Second Growth Stand Condition and Phellinus Variability in the Plant Associations 125 Second Growth Stand Conditions and Phellinus Variability in the Site Association 126  7.3.3 Second Growth Species Dynamics  130  7.3.3.1 Second Growth Species Dynamics Estimates from Variable-Radius Plot Sample (%BAR Survey) Data ^ 131 7.3.3.1.1 Comparison of Species Compositions by Disease Condition ^ 7.3.3.1.2 Species Composition Shifts Stratified By Disease Condition And Diameter Limit Classes ^  131 133  ^ ^  ix 7.3.3.1.3 Comparison of Non-susceptible Species Compositions Between Diameter Classes and Disease Condition ^ 7.3.3.2 Second Growth Species Dynamics From the PSP's Measurement Record ^ 7.3.4 Stand History: Fire And Logging ^  136 138 141  7.4 Phellinus Root Rot - Ecological and Stand History Models ^  145  7.5 Phellinus Root Rot Growth And Yield Reduction Relationships ^  150  7.5.1 Damage Appraisal of Phellinus Root Rot on Growth and Yield of Second Growth Douglas-fir Ecosystems ^ 150 7.5.1.1^Growth and Yield Comparisons Within Selected Installations ^ 7.5.1.2 Yield Comparisons Using the Chapman-Richards Non-Linear Growth (VAC) Model ^ 7.5.1.3^Growth and Yield--Site Height Models for the Whole PSP Dataset ^ 7.5.1.3.1 Growth Models ^ 7.5.1.3.2 Yield Models ^  151 160 162 162 168  7.5.1.4^Growth and Yield: All Data, Stratified by Stand Density Classes (stems/ha at age 10 yr) ^ 171 7.5.1.4.1 Stand Density (Less than 1 000 stems/ha): ^ 171 7.5.1.4.1.1^Yield Models ^  171  7.5.1.4.2 Stand Density (1 000-1 999 stems/ha): ^ 173 7.5.1.4.2.1^Yield Model ^  173  7.5.1.4.3 Stand Density (2 000-4 999 stems/ha): ^ 174 7.5.1.4.3.1^Yield Models ^ 8.0 DISCUSSION ^  174 176  8.1^Damage Incidence - Severity - Intensity Relationships ^ 177  x 8.2 The Relationship Between PSP's and the %BAR Sample Survey Data ^  187  8.3^Phellinus Root Rot Variability in Relation to Ecological Site Factors and BEC Units ^  180  8.3.1 Disease Incidence ^ 8.3.2 Disease Intensity ^ 8.3.3 Site Ecological Factors ^  180 181 182  8.4^Phellinus Root Rot Variability in Relation to Old Growth and Second Growth Conditions and BEC Units ^  185  8.4.1 Phellinus Root Rot Variability in Relation to Old Growth Densities and Species Composition Across BEC Units ^ 187 8.4.2 Second Growth Stand Conditions ^ 191 8.4.2.1^Second Growth Species Dynamics: and Variability Radius Plot Sample Surveys ^ 193 8.4.2.2 Second Growth Species Dynamics: Permanent Sample Plot Records ^ 194 8.4.2.3 Summary and the Natural, Host-Pathogen Dynamic Equilibrium Model ^ 197 8.5^The Effects of Stand History (Logging and Burning) on the Behaviour of Phellinus Root Rot ^ 8.5.1 Evaluating the Effects of Logging on Phellinus wierii Survival ^ 8.5.2 Evaluating the Effects of Slashburning on Phellinus wierii Survival ^ 8.5.3 Evaluating the Effects of Wildfire on Phellinus wierii Survival ^  199 200 202 204  8.6^Predicting Biogeoclimatic Unit Phellinus Root Rot Hazard and Risk^207 8.6.1 Phellinus Root Rot and the BEC Site Association Taxon Model ^ 8.6.2 Multiple Regression Models ^ 8.6.3 Phellinus Root Rot - Coastal Douglas-fir Ecosystems Hazard (Susceptibility) and Risk Classification for Southeastern Vancouver Island ^ 8.7^Damage Appraisal of Phellinus Root Rot on Growth and Yield of Second Growth Douglas-fir Ecosystems ^  207 211 212 214  ^  xi 8.7.1 Yield Comparisons within Selected Installations ^ 214 8.7.2 Yield Comparisons using the Chapman-Richards Model ^ 215 8.7.3 Growth and Yield Comparisons Over Site Height ^ 215 215 8.7.3.1^All data-No Stratification ^ 8.7.3.2^Growth and Yield All Data, Stratified by Stand Density Classes (stems/ha at age 10 yr) ^ 218 8.7.3.2.1 Less than 1 000 stems/ha ^ 8.7.3.2.2 1 000-1 999 stems/ha ^ 8.7.3.2.3 2 000-4 999 stems/ha ^  218 218 219  9.0 SUMMARY ^  221  10.0 RECOMMENDATIONS FOR MANAGEMENT ^  227  10.1 Damage Appraisal ^ 10.2 Stand And Forest Level Prescriptions ^ 11.0 REFERENCES ^  227 229 231  ^  xii  LIST OF TABLES  ^1^Summary of Key Climatic Properties within the Study Area ^  27  2^Stand Origins by Subzone Variant ^  29  3^Matrix of Damage Severity Variables and Consequent %BAR Damage Intensity Parameters ^  42  4^Expected Relationships Between %BAR Damage Intensity Parameters ^  45  5^Decomposition of %BAR Damage Severity Variables to Subcomponents ^  46  6^Vegetation Environment Analysis ^  64  7^Hierarchical Synopsis of Vegetation Units Distinguished in the Study Area ^  65  8^Diagnostic Species Correlations to PCA Axis Scores ^ 67 9^Hierarchical Synopsis of the BEC Site Units Distinguished in this Study ^  70  10^Descriptive Statistics for Study-Wide Estimates of Phellinus Root Rot Incidence, Severity, Severity Sub-Components and Intensity ^  77  11^Pearsons Correlation Matrix of Phellinus Root Rot Survey Sampling Variables and Parameters ^  81  12^Incidence - Intensity Relationships (BARS/NSBAR) as a Function of Stand (DSWP) and Land (DSPT) Based Incidence ^  84  13^Phellinus Root Rot Incidence by Zonal Classification Units ^  91  14^Distribution Of Phellinus Root Rot in Subzones and Variants as Estimated via the BARS Damage Intensity Classification ^  92  ^ ^  XIII 15^Phellinus Root Rot Incidence by Plant Classification Units ^  95  16^Distribution Of Phellinus Root Rot in Plant Alliances and Plant Associations Estimated via the BARS Damage Intensity Classification ^  96  17^Phellinus Root Rot Incidence by Site Classification Units ^  98  18^Distribution Of Phellinus Root Rot by Site Associations as Estimated via the BARS Damage Intensity Classification ^ 99 19^Pearsons Correlation Matrices ^  102  20^Actual Soil Moisture and Nutrient Regimes for Site Associations in the Study Area ^  103  21^Descriptive Statistics for Old Growth Stems/ha and Species Composition by Various Classifications ^ 107 22^Descriptive Statistics for Second Growth Species Composition and Stand Densities and Volume Estimates by Various Classifications ^  118  23^Descriptive Statistics for Species Composition by Disease Condition ^  132  24^Species Composition Ratio Variable Definitions ^  134  25^Comparison of Non-Susceptible Species Composition (NSSppC) ^137 26^Mean Differences in Tolerant, Intermediate and Resistant (TIRD) Species Composition Proportion in PSP's Between First and Last Measurements ^  141  27^Multiple Regression and Ancova Models for Percent Basal Area Reduction (BARS) - Susceptible Species, Greater than the Sample Diameter Limit ^  148  28^Percent Change of Stand Variables Between First and Last Measurements Spanning 30 to 35 yr ^  152  29^Descriptive Stand Statistics for Combined Site Associations; FdHw-Salal and HwFd-Kindbergia Mean and (Standard Deviation)^161  xiv ^  161  30  Chapman-Richards Volume - Total Age Growth Model Statistics  31  All-PSP's Growth Model "Independent" of Root Rot Effects on Site Height Measurements ^  164  All-PSP's Growth Model - Healthy Condition "Dependent" of Root Rot Effects on Site Height Measurements ^  166  All-PSP's Growth Model - Infected Condition "Dependent" of Root Rot Effects on Site Height Measurements ^  166  34  All-PSP's Yield Model  ^  169  35  Stand Density (< 1 000 stems/ha) Yield Model ^  172  36  Stand Density (< 1 000-1 999 stems/ha) Yield Model  ^  173  37  Stand Density (< 2 000-4 999 stems/ha) Yield Model ^  174  38  Phellinus Root Rot Hazard and Risk Classification for Douglas-Fir Ecosystems on S.E. Vancovuer Island ^  213  32 33  ^  XV  UST OF FIGURES ^1^Study Area and Biogeoclimatic Unit Map ^ 2^Sample Survey Layout Centered about a Permanent Sample Plot ^  24 35  3^Healthy Variable-Radius Plot Condition  ^40  4^Infected Variable-Radius Plot Condition  ^41  5^Percent Basal Area Reduction (BARS) - Total Age Scatterplot Classified by BARS-Damage Intensity Classes  ^61  6^Boxplots of BARS Classified by the BARS-Damage Intensity Classes  ^61  7^95% Confidence Ellipses of the PCA Ordination Scores of Six Vegetation Units Identified in the Study ^  68  8^Histogram of the Four Percent Basal Area Reduction Parameter Estimates All-Samples  ^75  9^Histogram of the Mean Tree Counts by Species Susceptibility and Disease Condition  ^78  10^Incidence-Severity Relationship of Stand-Based Incidence (DSWP), to Damage Severities (NSSEV) and (SEVS) ^  79  11^Incidence-Severity Relationship of Land-Based Incidence (DSPT), to Damage Severities (NSSEV) ^ and (SEVS)  79  12^Severity-Intensity Relationship of All-Species Damage Severity (NSSEV), to Damage Intensity ^ (NSBAR)  80  13^Severity-Intensity Relationship of Susceptible Species (>_12.0/17.5 cm) Damage Severity (SEVS), ^ to Damage Intensity (BARS)  80  xvi 14^Incidence-Intensity Relationship of Stand-Based Incidence (DSWP), to (NSBAR) and (BARS) Damage Intensities  ^80  15^Incidence-Intensity Relationship of Land-Based Incidence (DSPT), to (NSBAR) and (BARS) Damage Intensities  ^80  16^Boxplots of Damage Severity (NSSEV) Classified by BARS-Damage Intensity Classes ^  82  17^Boxplots of Damage Severity (SEVS) Classified by BARS-Damage Intensity Classes ^  82  18^Boxplots of Land-Based Disease Incidence (DSPT) Classified by BARS-Damage Intensity Classes ^  83  19^Boxplots of Stand-Based Disease Incidence (DSWP) Classified by BARS-Damage Intensity Classes  ^83  20^Stand:Land-Based Incidence Relationship  ^86  21^Land:Stand-Based Incidence Relationship  ^86  22^Boxplots of Land-Based Disease Incidence (DSPT) Classed by Absence and Presence of Disease Incidence in PSP's  ^87  23^Boxplots of Stand-Based Disease Incidence (DSWP) Classed by Absence and Presence of Disease ^ Incidence in PSP's 24^Boxplots of Damage Intensity (NSBAR) Classed by Absence and Presence of Disease Incidence in PSP's  88  ^88  25^Boxplots of Damage Intensity (BARS) Classed by Absence and Presence of Disease Incidence in PSP's ^ 88 26^Boxplots of % Basal Area Reduction-BARS by Subzone ^ 93 27^Boxplots of the % Basal Area Reduction-BARS by Subzone Variant  ^94  28^Boxplots of % Basal Area Reduction-BARS by Plant Alliances  ^97  xvii 29^Boxplots of % Basal Area Reduction-BARS by ^ Plant Association  97  Area Reduction-BARS by 30^Boxplots of % Basal ^ Site Association  100  31^Percent (%) Coarse Fragment Content (by volume) by Site Association ^  104  32^Elevation (m) asl by Site Association ^  104  33^Slope (%) by Site Association ^  104  34^Fine Fraction (<2 mm) Mineral Soil Bulk Density (g/cm 3 ) by Site Association ^  104  35^Mineral Soil Percent Porosity by Site Association ^ 105 36^Mineral Soil pH by Site Assocation ^  105  37^Boxplots of Old Growth Douglas-fir and western hemlock stems/ha (SPHFH) Classified by BARS-Damage Intensity Classes ^  109  38^Boxplots of Old Growth western red cedar stems/ha (SPHCW) Classified by BARS-Damage Intensity Classes ^ 109 39^Boxplots of Old Growth Douglas-fir and western hemlock Species Composition (COMPFH) Classified by BARS-Damage Intensity Classes ^  109  40^Boxplots of Old Growth western red cedar Composition (COMPCW) Classified by BARS-Damage Intensity Classes ^ 109 41^Old Growth Douglas-fir and western hemlock stems (stumps)/ha by Site Association ^  112  42^Old Growth western red cedar stems (stumps)/ha by Site Association ^  112  43^Old Growth Douglas-fir and western hemlock Species Compositions by Site Association ^  112  44^Old Growth western red cedar Species Compositions by Site Association ^  112  xviii  45^A Two-Dimensional Contour Plot Illustrating the Relationship Between Damage Intensity (BARS) and Old Growth Stand Density (stems/ha) of Douglas-fir and western hemlock (SPHFH) and western red cedar (SPHCW) ^ 115 46^A Three-Dimensional Plot Illustrating the Relationship Between Damage Intensity (BARS) and Old Growth Stand Density (stems/ha) of Douglas-fir and western hemlock (SPHFH) and western red cedar (SPHCW) ^  116  47^First PSP Measure Fd, Bg and Hw (Susceptible and Intermediate) Species Composition by Subzone ^ 120 48^First PSP Measure PI, Pw, Cw and Deciduous (Resistant) Species Composition by Subzone ^  120  49^Back-Estimated stems/ha, 4.0 cm at Reference Age 10 yr by Subzone ^  121  50^Back-Estimated Basal Area (m 2/ha), >4.0 cm at Reference Age 10 yr by Subzone ^  121  51^Back-Estimated Curtis' Relative Density, >4.0 cm at Reference Age 10 yr by Subzone ^  121  52^First PSP Measure of Fd, Bg and Hw (Susceptible and Intermediate) Species Composition by Subzone Variant ^ 122 53^First PSP Measure of PI, Pw, Cw and Deciduous (Resistant) Species Composition by the Subzone Variant ^  122  54^Back-Estimated stems/ha, >4.0 cm at Reference Age 10 yr by Subzone Variant ^  123  55^Back-Estimated Basal Area (m 2 /ha), >4.0 cm at Reference Age 10 yr by Subzone Variant ^  123  56^Back-Estimated Curtis' Relative Density, >4.0 cm at Reference Age 10 yr by Subzone Variant ^  124  57^First PSP Measure of Fd, Bg and Hw (Susceptible and Intermediate) Species Composition by Site Association ^ 127 58^First PSP Measure of PI, Pw, Cw and Deciduous (Resistant) Species Composition by Site Association ^  127  xix 59^Back-Estimated stems/ha, .4.0 cm at Reference Age 10 yr by Site Association ^  128  60^First PSP Measure of stems/ha, 4.0 cm at Reference Age 10 yr by Site Association ^  128  61^Back-Estimated Basal Area (m 2 /ha), >4.0 cm at Reference Age 10 yr by Site Association ^  129  62^First PSP Measure of Basal Area (m 2 /ha), >4.0 cm at Reference Age 10 yr by Site Association ^  129  63^Back-Estimated Curtis' Relative Density, ,4.0 cm at Reference Age 10 yr by Site Association ^  129  64^First PSP Measure of Curtis' Relative Density, >4.0 cm at Reference Age 10 yr by Site Association ^  130  65^Second Growth Species Composition for Healthy Stand Conditions  132  66^Second Growth Species Composition for Infected Stand Conditions ^  132  67^Non-Susceptible Species Composition by Diameter Limit Disease Condition and Subzone ^  135  68^Susceptible Species Composition by Diameter Limit Disease Condition and Subzone ^  135  69^Changes in Non-Susceptible and Susceptible Species Composition ^  135  70^Net Changes in Non-Susceptible Second Growth Species Composition over 30 to 35 yr in Healthy and Infected PSP's ^ 139 71^Net Changes in Susceptible Second Growth Species Composition over 30 to 35 yr in Healthy and Infected PSP's ^  139  72^Boxplots of % Basal Area Reduction-BARS Classified by Three Stand Origins; Wildfire (BURN), Logged-Only (LOG) ^ and Logged and Slashburning (LOG & BURN) 143  )0(  73^Boxplots of % Basal Area Reduction-BARS Classified by Three Stand Origins; Wildfire (BURN), and Logged and Slashburned (LOG & BURN) ^  143  74^Boxplots of Total Age Classified by Three Stand Origins ^ 144 75^Boxplots of Total Age Classified by Two Stand Origins ^ 144 76^Predicted % Basal Area Reduction - BARS, at Total Age 80 yr by Subzone ^  149  77^Predicted % Basal Area Reduction - BARS at Total Age 80 yr by Subzone Variant ^  149  78^Predicted % Basal Area Reduction - BARS at Total Age 80 yr by Plant Alliances ^  149  79^Predicted % Basal Area Reduction - BARS at Total Age 80 yr by Plant Association ^  149  80^Predicted % Basal Area Reduction-BARS at Total Age 80 yr by Site Association ^  150  81^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 218 and 219) ^  153  82^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 218 and 219) ^  154  83^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 2007, 2008, and 2009) ^  155  84^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 2007, 2008, and 2009) ^  156  85^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 160, 161, and 162) ^  157  86^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 348 and 349) ^  158  87^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 158 and 159) ^  159  ^ ^  xxi 88^Chapman-Richards Volume-Age Curves (4.0 cm) Comparing Healthy ( ) and Phellinus Root Rot Infected (---) Stand Conditions for the Combined FdHw-Salal and HwFd-Kindbergia s.a.'s  162  89^All-PSP's Growth Model; Healthy PSP Scatterplot and Growth Function ^  164  90^All-PSP's Growth Model; Infected PSP Scatterplot and Growth Function ^  165  91^All-PSP's Growth Model; Comparitive Growth Function Plots Healthy (_) and Infected PSP's (---) ^  165  92^All-PSP's Growth Model; Healthy PSP Scatterplot and Growth Function ^  167  93^All-PSP's Growth Model; Infected PSP Scatterplot and Growth Function ^  167  94^All-PSP's Growth Model; Comparitive Growth Function Plots (_) Healthy, and (---) Infected ^  168  95^All-PSP's Yield Model; Healthy PSP Scatterplot and Yield Function ^  170  96^All-PSP's Yield Model; Infected PSP Scatterplot and Yield Function ^  170  97^All-PSP's Yield Model; Comparitive Yield Function Plots Healthy^Infected (---) PSP's ^  170  98^Yield Models for Stand Density < 1 000 stems/ha at Age 10 yr for Healthy (___) and Infected (---) PSP Conditions ^  172  99^Yield Models for Stand Density 1 000 - 1 999 stems/ha at Age 10 yr for Healthy (_) and Infected (---) ^ PSP Conditions  174  100^Yield Models for Stand Density 2 000 - 4 999 stems/ha at Age 10 yr for Healthy^and Infected (---) ^ PSP Conditions  175  UST OF APPENDICES  A^Soil Chemical Concentration to Kg/ha ^  242  B  243  Curtis' Relative Stand Density ^  C^Species List ^  244  D  Vegetation Summary Table ^  247  E  Spectral Analysis: Soil Moisture and Soil Nutrient ^ 251  F^Relationship Of Site And Stand Ecological Variables To The Site Association Identified in this Study ^ G  253  Illustrations of Selected Variables from Pearsons Correlations ^ 264  xxiv UST OF SYMBOLS Topographic Variables  Name^ Description^Measurement Unit(s) ELEV^Elevation above sea level SLOPE^Percent slope ASPECT^Slope aspect^  Azimuth degrees  Mineral Soil Variables cm ROOTDP^Major rooting zone depth^ cm RRLADP^Root restricting layer depth^ cm MOTDP^Depth to mottling zone^ cm SEEPDP^Depth to seepage^ CF20^Coarse fragment content by volume (ocular)^(% or porportion of volume) for soil depth 10 to 30 cm horizon^ MSBDT^Total bulk density (coarse & fine fraction) ^ g/cm2 g/cm2 MSBDF^Bulk density (fine fraction, <2 mm)^ PORF^Percent porosity (% volume occupied by air spaces) SMR^Relative soil moisture regime categorical 0 to 9 values (Klinka et a/. 1984)^ SNR$^Relative soil nutrient regime categorical A-E values (Klinka et a/. 1984)^ MSPH^Soil hydrogenion activity (pH) for 10 to 30 cm horizon MSC^Percent carbon (c) content for 10 to 30 cm horizon MSN^Percent nitrogen (N) content for 10 to 30 cm horizon MSCN^Carbon: Nitrogen ratio for 10 to 30 cm horizon ^proportion MEQCA^Milliequivalents calcium (Ca) for 10 to 30 cm horizon^ meq MEQMG^Milliequivalents magnesium (Mg) for 10 to 30 cm horizon^ meq MEQK^Milliequivalents pottassium (K) for 10 to 30 cm horizon^ ppm MSMN^Mineralizable nitrogen for 10 to 30 cm horizon ^Kg/ha MSMNK^Mineralizable nitrogen for 10 to 30 cm horizon  Forest Floor Variables ^ FFPH^Hydrogen ion activity FFC^Percent carbon (c) FFN^Percent nitrogen (N) ^ FFMN^Mineralizable nitrogen  pH ppm  xxv Old Growth Minsuration Variables SPHFH SPHCW COMPFH COMPCW  Stems or stumps/ha of Douglas-fir and western hemlock Stems or stumps/ha of western red cedar Proportion of total species composition-Douglas-fir and western hemlock Proportion of total species composition western red cedar  Sample plot mean Sample plot mean (proportion 1-1.0)  Stand (History) Origin Variables ^ BURN ^Wildfire origin LOG ^Logged-only origin LOG & BURN Logged and slashburn origin  Second Growth Mensuration Variables (PSP-based) FSUS/LSUS FINT/LINT FRES/LRES FDEC/LDEC FSUSINT FRESDEC FST4/LST4 FBA4/LBA4 FVL4/LVL4 CRD1 CRD87 CRDINCR AGE1 AGE87 SI RRIN STHGHT STHGHT2  First/Last measurment; highly susceptible species (Fd, Bg) composition First/Last measurement; intermediately susceptible species (Hw) composition First/Last measurement; resistant species (PI, Pw & Cw) composition First/Last measurement; deciduous species (Ar, Mb, Dr, Bi, Dw, Ch, 0g, Ac) Sum of FSUS and FINT species composition Sum of FRES and FDEC species composition First/Last measurement; stems/ha z4.0 cm dbh diameter limit First/Last measurement; basal area/ha z4.0 cm dbh diameter limit First/Last measurement; volume/ha z4.0 cm dbh diameter limit Curtis' relative density, at first measurement Curtis' relative density at last measurement Change in relative density first to last measurement Total age (years since germination) at first measurement Total age (years since germination) adjusted to 1987 Douglas-fir site index; top height at breastheight age 50 (Bruce 1981) Phellinus root rot incidence inside permanent sample plots Site height, average total height of site trees (sampling procedures vary but often largest diameter trees disease and damage-free) Site height squared for linear modelling purposes  Proportion of Total Composition (PropTC) PropTC PropTC PropTC PropTC PropTC (stems/ha) (m 2 /ha) (m 3 /ha) 1-20 1-20 1-20 yr yr m  m  xxvi DSTHGHT1 VOL4 DVOL4  m/yr  Annual site height increment Gross volume (24.0 cm) per hectare; observed and predicted Annual volume increment (24.0 cm) per hectare, per year; observed and predicted  m 3 /ha m 3 /ha/yr  Phellinus Root Rot Survey Statistics %BAR S N NS NSBAR SBAR BARS BARNS NSSEV  SSEV  SEVS  SEVNS  SALH NALH NSALH  SALI NALI NSALI  Percent basal area reduction, due to root rot, from the healthy condition Susceptible species to Phellinus root rot (Fd, Bg and Hw) "Non-susceptible" species to Phellinus root rot (PI, Pw, Cw & Deciduous spp.) "Non-susceptible" and susceptible species NS species %BAR statistic for 24.0 cm dbh diameter limit variable-radius samples S species %BAR statistic for 24.0 cm dbh diameter limit variable-radius samples S species %BAR statistic for 12.0/17.5 cm dbh diameter limit variable-radius samples NS species %BAR statistic for 12.0/17.5 cm dbh diameter limit variable-radius samples NS species, 24.0 cm diameter limit, %BAR statistic for severity, or relative basal area reduction between healthy and infected variable-radius basal area estimates within a 25-plot sample survey, (see also Table 3) S species, 24.0 cm diameter limit, %BAR statistic for severity, or relative basal area reduction between healthy and infected variable-radius basal area estimates within a 25-plot sample survey, (see also Table 3) S species, 12.0/17.5 cm diameter limit, %BAR statistic for severity, or relative basal area reduction, (see also Table 3) NS species, *12.0/17.5 cm diameter limit, %BAR statistic for severity, or relative basal area reduction, (see also Table 3) Severity sub-component; 24.0 cm diameter limit healthy condition, susceptible species Severity sub-component; 24.0 cm diameter limit healthy condition, non-susceptible species Severity sub-component; 24.0 cm diameter limit healthy condition, non-susceptible and susceptible species Severity sub-component; 24.0 cm diameter limit infected condition, susceptible species Severity sub-component; 24.0 cm diameter limit infected condition, non-susceptible species Severity sub-component; 24.0 cm diameter limit infected condition, non-susceptible and susceptible species  /  00  (sample plot mean %) (sample plot mean %) (sample plot mean %) (sample plot mean %)  ^  )ocvii SLSH^Severity sub-components; 24.0 cm <12.0/17.5 cm diameter limit, healthy condition, susceptible species NLSH^Severity sub-components; 24.0 cm <12.0/17.5 cm diameter limit, healthy condition, non-susceptible species NSLSH^Severity sub-components; 24.0 cm <12.0/17.5 cm diameter limit, healthy condition, non-susceptible and susceptible species SLSI^Severity sub-components; 24.0 cm <12.0/17.5 cm diameter limit, infected condition, susceptible species NLSI^Severity sub-components; 24.0 cm <12.0/17.5 cm diameter limit, infected condition, non-susceptible species NSLSI^Severity sub-components; 24.0 cm <12.0/17.5 cm diameter limit, infected condition, non-susceptible and susceptible species SGRH^Severity sub-components; 12.0/17.5 cm diameter limit, healthy condition, susceptible species NGRH^Severity sub-components; 12.0/17.5 cm diameter limit, healthy condition, non-susceptible species NSGRH^Severity sub-components; 12.0/17.5 cm diameter limit, healthy condition, non-susceptible and susceptible species SGRI^Severity sub-components; 212.0/17.5 cm diameter limit, infected condition, susceptible species NGRI^Severity sub-components; 12.0/17.5 cm diameter limit, infected condition, non-susceptible species NSGRI^Severity sub-components; 212.0/17.5 cm diameter limit, infected condition, non-susceptible and susceptible species N1^24.0 cm, non-susceptible species-only, healthy condition species composition N2^24.0 cm, non-susceptible species-only, infected condition species composition S1^24.0 cm, susceptible species-only, healthy condition species composition S2^24.0 cm, susceptible species-only, infected condition species composition N3^12.0/17.5 cm, non-susceptible species-only, healthy condition species composition N4^12.0/17.5 cm, non-susceptible species-only, infected condition species composition S3^12.0/17.5 cm, susceptible species-only, healthy condition species composition S4^12.0/17.5 cm, susceptible species-only, infected condition species composition  xxviil DNBA^k4.0 cm, non-susceptible species-only species composition, net of infected DSBA^24.0 cm, susceptible species only species composition, net of infected DBAN^212.0/17.5 cm, non-susceptible species-only species composition, net of infected DBAS^k12.0/17.5 cm, susceptible species-only species composition, net of infected  Biogeoclimatic Ecosystem Classification Variables SUBZONE^CDFmm, CWHxm VARIANT^CDFmm, CWHxm1, mx2 PORD^Plant alliance p.all.^Plant alliance: Full (and abbreviated) names were; Pseudotsuga-Mahonia (PseudMahonia), Tsuga-Mahonia (same), and Thuja-Tiarella (Thuja-Tiarlea)  PASS^Plant association p.a.^Plant association: Full (and abbreviated) names were; Pseudotsuga-Arbutus (PseudArbutus), Pseudosuga-Gaultheria (PseudGaulthr), Tsuga-Mahonia (TsugaMahonia), and Pseudotsuga-Achlys (PseudoAchlys)  SASS^Site association s.a.^Site association: Full (and abbreviated) names were; Fd-Salal (Fsalal), FdBg-Oregon grape (FBoregrape), FdHw-Salal (FHsalal), HwFdKindbergia (HFKindberg), and Cw-Foamflower (CWfoamflowr)  Tree Species (Common Names) Fd^Douglas-fir Bg^grand fir Hw^western hemlock Cw^western red cedar PI^Iodgepole pine Pw^western white pine Ar^Pacific madrone (arbutus) Og^Garry oak Ch^cherry Dw^Flowering dogwood Bi^paper birch Mb^bigleaf maple Dr^red alder Ac^back cottonwood  ACKNOWLEDGEMENTS  I extend my gratitude to my thesis committee members, Dr. B.J. (Bart) van der Kamp, Dr. K. (Karel) Klinka, Dr. P.L. (Peter) Marshall and Dr. W.J. (Bill) Bloomberg for their encouragement and guidance. A very special thanks to MacMillan Bloedel Limited, Woodlands Service staff, Nanaimo, B.C. for providing access to your PSP's and ecological data, environmental laboratory and your collective guidance; especially Janna Kumi, Dr. Kim Iles, Bill Wilson, Steven Northway, Ian Turner, Jim Loucks, Dr. Nick Smith, Bill Beese, Arlene Gamel and the laboratory staff. And, to Canadian Pacific Products Limited staff, Saanichton B.C., Vlad Korelus and Keith Tudor, thanks for access to your PSP's. To the field crews that endured a hot summer of data collection; Peter Dragunas for soils and forest floor sampling, Al Ohs for Phellinus root rot sampling, and Kat Palmer, Bryce Bancroft and Gordon Butt for ecological characterization -- thank you! This study would not have been possible without significant moral and financial support from my B.C. Ministry of Forests managers and staff advisors, Peter Ackhurst, Russ Hughes, Paul Wood, Dr. Bob DeBoo, Dr. John Muir and Dave Gilbert. To my colleagues in the Vancouver Forest Region Forest Sciences section, thanks for your enduring advice and commaraderie -- especially Bob Green. Finally, I extend my deepest and sincere thanks to my wife Jane, for her encouragement, love and assistance, you have shed true light on the meaning of committment and discipline, and to Caitlin Rose, our new daughter who is obsessed with statistical graphics displays, what a joy to work with you! Financial support in part was obtained from the Canada and B.C. Forest Resources Development Agreement I 1985-1990 - Provincial Direct Delivery Pest Control.  1 1.0 INTRODUCTION  Laminated root rot, a forest root disease caused by the pathogen Phellinus weirii (Murr.) Gilbertson, is considered to be the most damaging pest of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) second growth coastal forests of western North  America (Childs 1963, Childs and Shea 1967, Wallis 1976). The effects of the disease are variable, but generally stand density and productivity falls below that of healthy stands over time (Mounce et al. 1940, Bier and Buckland 1947, Childs 1970, Johnson and Wallis 1972, Nelson 1980, Bloomberg and Reynolds 1985).  Forest pathologists have observed the incidence and intensity, of several root diseases to vary with certain forest, ecological and environmental site factors (Buckland et al. 1954, Williams and Marsden 1982, Hobbs and Partridge 1979, Reynolds and  Bloomberg 1982, Whitney 1978, Shields and Hobbs 1978, Bloomberg and Beale 1985, Nilsen 1983, Huse 1983, MacDonald et al. 1987a, 1987b, Wilks et al. 1985), although causal relationships are difficult to quantify and confirm (Childs 1963, 1970). The possibility of patterns in the laminated (or Phellinus) root rot pathosystem of second growth coastal Douglas-fir ecosystems in British Columbia is of interest to forest pathologists, ecologists, silviculturists and forest managers alike. A study directed to assess whether ecological site factors influence or control the Phellinus root rot pathosystem, and to assess the impact on forest productivity would aid management of the coastal Douglas-fir ecosystem and provide direction for Phellinus root rot control research efforts.  2 British Columbia's ecosystem-based forest management using the system of Biogeoclimatic Ecosystem Classification (Pojar et al. 1987), provides the ideal basis for investigating and reporting on the epidemiological patterns of laminated root rot and its effects on forest productivity in second growth Douglas-fir ecosytems.  Specifically the objectives of the study are:  To assess Phellinus root rot damage intensity levels in coastal Douglas-fir  (1)  ecosystems, particularly at the site classification level of the Biogeoclimatic Ecosystem Classification (BEC) system; To develop a Phellinus root rot hazard and risk classification for BEC system  (2)  units in coastal Douglas-fir ecosystems; (3)  To develop a model of the relationships between Phellinus root rot and significant ecological, site and stand history factors in coastal Douglas-fir ecosystems;  (4)  To determine the effects on forest productivity attributable to Phellinus root rot in coastal Douglas-fir ecosystems through examination of species dynamics, stand growth and yield, and root rot damage intensity estimates;  (5 )  ^  To compare two Phellinus root rot sampling methods; (1) fixed-radius and (2)  variable-radius plots;  3 (6)  To compare two damage expression terms (1) land-area diseased, and (2) stand-parameter diseased and;  (7)  To compare two disease dynamics relationships (1) incidence-severity and (2) incidence-intensity.  4 2.0 LAMINATED ROOT ROT BIOLOGY AND BEHAVIOUR  Disease development begins in a newly regenerating stand when healthy roots of susceptible tree species contact infected stumps and roots (inoculum sources) from the previous stand (Wallis and Reynolds 1965). Host tree species in coastal Douglas-fir ecosystems are in order of decreasing susceptibility: grand fir (Abies grandis (Dougl.) Lindl.), Douglas-fir, western hemlock (Tsuga heterophylla Bong.), lodgepole pine (Pinus contorta var. latifolia Engelm.), and western white pine (Pinus monticola Dougl.). Western  red cedar (Thuja plicata Donn. ex D. Don in Lamb) and all deciduous tree species are resistant to P. weirii (Wallis 1976, Hadfield 1985). Subsequent tree to tree infection spread occurs radially between living trees through root contacts. Both ectotrophic and endotrophic mycelial spread are involved in P. weirii transmission. Ectotrophic spread is more frequent and successful compared to endotrophic spread and is relatively unaffected by site or root factors (Bloomberg and Reynolds 1982). Fungal advancement continues proximally and distally along the roots causing decay and root death, resulting in reduced water and nutrient uptake and weakened structural support to the tree. Dependent upon tree age at infection, crown symptoms may appear 1 to 15 years later, with time-to-mortality inversely related to the tree's age at initiation of infection (Wallis 1976). Crown symptoms associated with laminated root rot are reduced terminal leader and branch growth, chlorotic and thinning foliage, and frequently observed distress cone crops (Mounce et al. 1940). The length of time until death or windthrow of crown symptomatic trees is variable, but is probably no greater than 15 years (Bloomberg 1983).  5 Laminated root rot is first manifested in young stands in one-to-two-tree infection centers five to twenty years after stand establishment. In coastal stands, laminated root rot appears to have a random, but often aggregated pattern of fairly discrete infection centers (Childs 1963, 1970, Foster and Johnson 1963). The likelihood of infection centers coalescing increases with stand age (Foster and Johnson 1967, Wallis and Reynolds 1965). Infection centers expand radially at approximately 30 cm/yr ± 15 cm (Childs 1970, Nelson and Hartman 1975, Bloomberg 1984, McCauley and Cook 1980). Trees infected around the advancing perimeter of infection centers undergo increment reduction for several years before death. Continuing infection in stands of susceptible species will gradually reduce susceptible species stand density (basal area and stems per hectare) through tree growth reduction, usually followed by premature mortality and/or windthrow, leaving stands poorly stocked, often with non-productive openings (infection centers). Significant stand productivity losses, in terms of tree mortality, basal area, volume and periodic increment have been demonstrated in second growth Douglas-fir (Mounce et al. 1940, Bier and Buckland 1947, Buckland et al. 1954, Gillette 1975, Ford 1977, Bloomberg and Wallis 1979, Nelson 1980, Thies 1983, Bloomberg and Reynolds 1985).  Differential resistance to damage of Douglas-fir, first reported by Buckland et al. (1954), has important implications in damage detection and appraisal, and in the creation of inoculum sources. Trees showing no resistance are said to have "no formation of callus tissue or adventitious roots, as the disease spreads too rapidly for healing or compensation to occur", (Buckland et al. 1954). Such trees are killed by fungal growth solely in the sapwood. Trees showing resistance usually exhibit no visible distress  6 symptoms as the decay of roots is compensated for by callus tissue production and adventitious roots, thus maintaining vigour with marginal structural support, (Bloomberg and Hall (1986) have observed this phenomena in 30 to 40 yr old Doulgas-fir stands on Vancouver Island. "Resistant trees" can essentially maintain normal growth in protected, well stocked stand conditions for many years. It is still unknown if the differential resistance reported by Buckland et al. (1954) is genetically controlled (e.g., tree-age-atinfection response), environmentally site controlled, or a combination of these factors. The mechanisms which impede fungal development and decay actually aid in the pathogens' persistence in stumps and roots for periods exceeding 50 years. However, "resistant tree" inoculum likely poses a less serious threat as inoculum sources because the lower infected stump volume is usually well contained inside the callused stump (Buckland et al. 1954). These or other resistance mechanisms also aid in masking above-ground symptoms, which confounds disease detection and damage assessments.  As stand infection continues, reduction of P.weirii inoculum sources may occur in several ways: (1) windthrow and uprooting of inoculum; (2) amount and viability of ectotrophic P. weirii declines to near zero in forty years on old growth Douglas-fir stump and root systems (Hansen 1979); (3) succession to more disease tolerant tree species such as western hemlock, western red cedar, western white pine, lodgepole pine and many deciduous species. (Wallis 1976, McCauley and Cook 1980, Cook 1980, Williams and Marsden 1982, Dickman and Cook 1989); (4) tree resistance factors (Buckland et al. 1954); (5) reduction of root contacts over time due to stand density-related self-thinning and premature disease-related windthrow (Buckland et al. 1954); (6) activity of  7 antagonistic soil microbiological factors (Childs 1970, Rose et al. 1980, Hutchins and Li 1981, Hutchins and Rose 1984); (7) genetic variation of P. weirii clones in terms of pathogenic virulence hypothesised by Childs (1963, 1970), and demonstrated by Hansen et al. (1983).  The persistence and potential for carry-over into subsequent rotations is largely dependent on the presence, number, size and spatial distribution of inoculum sources (Childs 1970, Tkacz and Hansen 1982, Hansen et al. 1983). The role of basidiospore spread and infection in the disease epidemiology has been considered, but is not thought to be large (Childs 1970). Clearly basidiospores must play a role in at least long distance spread and infection to account for the pathogens extensive range, however infrequent or inefficient the means (Dickman and Cook 1989). Attempts to inoculate intact and wounded trees and stumps with basidiospores have failed (Nelson 1971).  In summary, the epidemiology of Phellinus root rot disease intensification and persistence, must consider clonal genetic variation of the pathogen (Hansen et al. 1983, Angwin 1985), mechanisms of tree resistance and disease persistence, the combined effects of site history (tree species succession, harvesting and fire patterns), stand attributes such as density and species compositions, and various ecological site conditions. Apparently the disease is under the influence of site factors, in fact it can be considered a disease of the site (Hadfield 1985). These factors are of utmost importance in assessing the hazard (stand susceptibility) and risk (probability and intensity of damage) of disease on forest sites.  8 3.0 A REVIEW OF SITE ECOLOGICAL RELATIONSHIPS OF PHELLINUS WEIRII  Evidence for the effect of site factors on the spread and persistence of P. weirii has been variable but generally indicates some correlations and relationships. Bier and Buckland (1947) report that Phellinus root disease occurred as abundantly on good as on poor sites of second growth Douglas-fir on southeastern Vancouver Island. Later, Buckland et al. (1954) observed that extensive disease spread appeared related to dense growth of shallow roots and high frequency of root contacts; root contact frequency rises from 20 to 60 years then drops-off. They also observed the most rapid spread and heaviest damage was in good, well stocked sites 20 to 60 yr old. Shallow soils induce shallow radiating root systems with a high frequency of root contacts, and therefore significantly contributes to disease spread and damage. Sites with deep, dry soils have lower disease spread rates since the roots are small, fibrous, and grow downwards, minimizing root contact frequency (Buckland et al. 1954). Childs (1970) reported that laminated root rot was common from sea level upwards, on good to poor sites, and on many kinds of soils from deep loarns to gravels, but with no distinction between any geographic or site variables. Childs (1970) further stated that beyond stand history, stand density would most significantly affect the level of the pathogen on a site. More recently, Reynolds and Bloomberg (1982) reported the probability of intertree root contact is positively related to increasing d.b.h., percent slope, soil gravel content and stand density, while inversely related to rooting depth. Bloomberg (1990) has shown stand density to be positively related to disease spread rates in long-term monitoring plots.  9 In Oregon, experiments to assess the effects of soil factors (pH, nitrogen, moisture, temperature, bulk density and microorganisms) on P. weirii inoculation and ectotrophic growth response, suggest that soil environment does influence the rate of mycelial growth (Hansen et al. 1983, Angwin 1985). Several key responses of P. weirii to adjusted site factors found by Angwin (1985), summarised below, tend to support some site factor control on P. weirii. Maximum inoculation success and mycelial growth was observed at 15% soil water content, decreasing with higher or lower water content. Similarly with soil temperature, maximum growth, and to a lesser degree inoculation success peaked at 20 to 25 ° C levels, dropping by more than 50% for higher and lower soil temperatures. Increasing soil pH affected mycelial growth positively to pH 6.0 then dropped. It should be noted that soil pH above 5.5 stimulates bacteria and actinomycetes, some possibly antagonistic to P. weirii, and below pH 5.5 mineralization of immobilized nitrogen to ammonium is reduced, (the nitrogen form ammonium is useable by P. weirii). Finally, additions of urea-nitrogen fertilizer strongly reduced mycelia! growth, possibly indicating a positive effect on host vigor, or antagonistic microorganisms and/or the indirect effect of lowering soil pH. All these factors, among others, are likely involved in the success and growth of P. weirii.  In a large scale root disease survey of coastal second growth Douglas-fir in B.C., laminated root rot damage intensity was found to vary significantly between biogeoclimatic subzone variants, forest cover type group and site quality (Beale 1987). Disease intensity was also strongly correlated with stand age and site index. The wetter maritime Coastal Douglas-fir subzone variant (CDFb), now CWHxm1, was 30 to 50% more severely infected  10 than the drier Coastal Western Hemlock subzone variant (CWHa1), now CWHxm2. The Douglas-fir inventory type group was infected at more than double the intensity of the FirHemlock type group. Disease intensity predictions for the Douglas-fir type group estimate the Medium site quality to have the greatest intensity at harvest ages (80 yr, plus), followed closely by the Good sites and distantly by the Poor sites (Beale 1987).  In another study of root disease survey data, Bloomberg and Beale (1985) reported P. weirii infection intensity varied significantly (p<.10) between several ecological site unit  classifications in the CDFb (now CWHxm1) subzone variant. Significantly lower damage intensity was observed in forest cover types containing western red cedar and lodgepole pine, and less so with types containing western hemlock. Several other root disease pathosystems, discussed below, are illustrative of these types of patterns too.  In northern Idaho, the occurrence of P. weirii and Armillaria meilea (Vahl. ex Fr.) Quel. sensu lato, is directly related to the occurrence of Douglas-fir and grand fir, and inversely related to elevation (Williams and Marsden 1982, Hobbs and Partridge 1979). Furthermore, significant slope-aspect, soil-aspect and habitat type-age interactions are important predictors for estimating the probability of root disease center occurrences (Williams and Marsden 1982). In northern Idaho, the incidence of pathogenic Armillaria spp. decreases as habitat type productivity increases, and further depends on specific  combinations of habitat type and stand development history (McDonald et al. 1987a and 1987b). In otherArmillaria spp. pathosystems, sites found to be most susceptible are low in pH (Singh 1983); low in pH, N, P, and Ca and high in K (Shields and Hobbs 1979); and  11 have high soil surface compaction (Ono 1970).  Whitney (1978), reported that tomentosus root rot (lnonotus (Polyporus) tomentosus (Fr.) Teng.) and Armillaria mellea, sensu lato, are positively affected by high soil moisture and fine soil texture. Van Groenewoud (1956) found I. tomentosus infection centers were in association with near-surface impermeable soil layers, or sandy soil veneers over very well-drained subsoils, causing more shallow-rooting than in healthy portions of stands. He also found that I. tomentosus infection centers are associated with acidic soil pH; centers were never found in soil pH greater than 7.0, and the damage was most severe in soils down to pH 4.5. Van Groenewoud (1956), concluded that the white spruce (Picea glauca (Moench.)) Voss.-Hylocomium-Calliergonella plant association is the most  conducive to tomentosus root disease development. Low organic horizon pH, low nutrient content of the major rooting zone, and high stand density was common to the stand disease caused by I. tomentosus in spruce forests of northern Saskatchewan (Van Groenwoud and Whitney 1969). Spread of black stain root disease (Leptographium wageneri Goheen & Cobb (anamorph Verticladiella wageneri Kend.)) in the ponderosa  pine (Pinus ponderosa Laws.) pathosystem of the Sierra Nevada in California is strongly related to high soil water potential, indicating spread is favoured on wetter sites (Wilks et al. 1985).  In Norway, Huse (1983), reported that the frequency of annosus butt rot, causal pathogen Heterobasidion annosum (Fr.) Bref., on Norway spruce (Picea abies (L.) Karst.), is positively correlated to tree diameter, increasing maturity class, soil depth, clay content  12 and slope, while negatively correlated to forest floor thickness and the extent of soil podsolization. The frequency of butt rot increases from poorer to richer site plant communities. In a similar Norwegian study of H. annosum butt rot frequency on older aged Norway spruce, no relationship with site class was found (Nilsen 1983). Nilsen (1983), also reported that butt rot was apparently more frequent in the Melico-piceetum typicum vegetation type (moderately poor site type), in good-drainage soils (they appear  to be mesic), and in the 150 to 300 m elevation band. No strong relationships were found between butt rot frequency and soil chemical properties and pH (Nilsen 1983).  In summary, it is apparent from observations in the laminated root rot pathosystem and other related root rot pathosystems, that some combination of ecological factors either influences, controls, responds to, or is spuriously correlated with disease incidence and intensity patterns. Similarly, tree species susceptibility to infection, and past and present stand compositions (which are related to ecosystem-specific tree species successional chronosequences) are also shown to reflect the damage intensity of P. weirii, as also appears to be the case for other root rot pathosystems. By extension, the  demonstrated reductions to forest productivity by P. weirii may also vary by some combination of ecological criteria or classification.  The literature suggests further studies are needed to understand Phellinus root rot damage and its effects on productivity, and its ecological role in different coastal Douglasfir ecosystems. Such a study should consider evaluating the effects and interactions of ecosystems and their component variables, stand origin, and past and present stand  13 conditions (species composition and stand density (stems/ha, basal area/ha) on Phellinus root rot epidemiology.  Furthermore, in B.C. there is a critical and legal requirement to assess stand hazard (stand susceptibility) and pest risk (probability and intensity of damage) during certain phases of forest management to aid silvicultural decision making. Also, it is increasingly important to accurately reflect the effects of Phellinus root rot on growth and yield for stand and forest level planning, as the competition for scarce forest resources increases.  In order to achieve the study objectives, a sensitive and accurate estimator of root rot damage incidence, severity and intensity must be used to evaluate the relationship of Phellinus root rot to ecosystem site components, ecosystem classification taxon, stand  history, past and present stand attributes, stand dynamics' parameters and its effects on forest productivity. The question of an appropriate damage intensity estimator is dealt with in the following section.  14 4.0 MEASUREMENT OF ROOT ROT INCIDENCE AND DAMAGE — A REVIEW  4.1 A Review of Measurement Methods  Measuring the effects of root rot on forest productivity remains a difficult task because there appears to be a lack of standardized and acceptable disease measurement terminology and sampling methodologies, that can provide accurate damage estimates with a direct (or even indirect) linkage to forest inventories and/or growth and yield projection systems. Furthermore, disease estimates are prone to large measurement errors because of difficult diagnostics due to highly variable tree susceptibilities and symptom expression, the decay and disappearance of killed trees over time, and observor experience.  Disease measurement terms such as incidence, intensity, frequency, severity and prevalence have been poorly defined and used interchangeably, in practice and in the literature, thus creating confusion in measurements and interpretation. To lend clarity to the selection of appropriate measurement terms consider that pests in general, and root rots in particular, have clumped distribution patterns of infection centers, with varying levels of damage within infection centers. It follows then, that measurement terms and sampling methods should attempt to describe/estimate the degree of infection center coverage, and the degree of damage within infection centers--which is analogous to,  "How much root rot is there, and how is it affecting the crop?" A third measurement term  15 might be used to describe the combined-term overall damage condition in a sample population. Seem (1984), eloquently addresses these three disease measurement  concepts in light of historical definitions and usage, and concludes with three pathometry (disease and damage measurement) definitions for:  (i)  degree of infection center coverage,  (ii)  degree of damage within infection center areas, and  (iii)^overall damage condition for a sample population.  The degree of infection center coverage is best described by the term incidence, which is defined as the proportion (0 to 1) or percentage (0 to 100) of diseased entities within a sampling unit (Seem 1984). Incidence is measured as present or absent (a binomial or quantal response), (Zadoks and Sehein 1979). Incidence is quickly and easily measured and generally more accurate and reproducible than other quantitative measures (Horsfall and Cowling 1978). These features of incidence measurement make it a favoured measure for detection and enumerations of disease spread patterns (Pearce 1976).  The degree of damage within infection centers is defined as severity, or the quantity of disease affecting entities within a sampling unit (Seem 1984). Severity also can be expressed as a proportion or percentage.  16 The overall damage estimate is defined by Seem (1984) as intensity, which is a product of incidence and severity (Moore 1953).  In Seem's (1984) definitions he stated that "... an entity is the plant part or plant population that is measured. A sampling unit is a group of entities that form a single composite or average measure. This distinction must be made when incidence and severity are compared because disease incidence on a single entity cannot be compared to severity." Relationships between incidence and severity can only be derived when entities are grouped into sampling units that are defined in a spatial hierarchy. For example, the spatial hierarchical levels for this study might be (from high to low spatial resolution); root, tree, fixed-radius plot/variable-radius plot (prism sweep), 1 ha sample survey, biogeoclimatic site series, site association, variant, subzone and zone. Sampling units are used to define the spatial hierarchical level in which incidence and severity measures are made. The sampling unit hierarchy is also necessary because an incidence measure at one level can become a severity measure at a higher level. As an example, incidence may be measured as the proportion of trees infected, and severity as the average number of roots infected per tree -- the sampling unit is in both cases the tree. If then, the incidence were measured as the proportion of 0.01 ha plots infected (the sampling unit is now 0.01 ha plots) the severity measure would become the number of infected trees in 0.01 ha sample plots.  Measurement of root rot incidence, severity and intensity generally falls into one of two damage expression estimates; (1) land-area-diseased per unit area (e.g., proportion  17 or percent), and (2) stand-parameter-diseased per unit area (e.g., stems/ha, basal area/ha or volume/ha) (Filip 1980). The choice of the sampling unit and sampling entity will determine the damage expression term, or conversely the choice of damage expression, based on end use objectives, will determine the sampling units and entities. Filip (1980) recognized four survey sampling methods for estimating land-area-diseased: (i) fixed-radius plots, (ii) variable-radius plots, (iii) line-intercept (Bloomberg et al. 1980), and (iv) aerial photography; and two sampling methods for estimating stand-parameterdiseased (e.g., inventory plots, growth and yield plots): (i) fixed-radius plots, and (ii) variable-radius plots.  4.2 Evaluation and Selection of an Appropriate Root Rot Measurement Method  The pathometry terms incidence, severity and intensity can be used as criteria for evaluating land-area and stand-parameter-diseased measurement expressions.  The land-area-diseased expressions appear best used for incidence, although given various spatial hierarchy sampling units (transects, swaths or plots), damage severity and intensity estimates can be derived. A disadvantage of the land-area-diseased expression is that it only samples land (Is it infected or not infected?), without providing a direct linkage measure or expression relating to stand attributes/parameters. This is unfortunate since land-area-diseased can only be determined by assessing disease incidence by looking at trees, the very parameter used for estimating stand-parameter-  18 diseased damage expressions.  The stand-parameter-diseased expression, given the same spatial hierarchy sampling unit restrictions, can provide incidence, severity and intensity estimates. Incidence is assessed on the basis of disease occurrence on a sampled stand parameter (trees), and severity is quantified on the basis of disease expression on some stand parameter (density or volume), with intensity multiplicatively derived from the two. The standparameter-diseased expression term provides a direct linkage to forest inventory and/or growth and yield. Interestingly, if incidence is also simultaneously determined on a landarea-diseased basis, incidence-severity, and incidence-intensity relationships (land-based to stand-based severity, or intensity) can be determined. The latter relationship could potentially simplify sampling methods and reduce costs, by estimating disease intensity from cheaper and easier to perform land- or stand-based disease incidence sampling.  Selection of a disease sampling method should then be based on; (a) the desired terms of disease expression, land- and/or stand-parameter-diseased, (b) the sampling objectives (e.g., general detection, stand or forest level damage appraisal, silvicultural treatment decision making or silvicultural treatment layout), (c) sampling costs, and (d) ease of use. Other sampling method factors to be considered are: (i) surveyors' proficiency in detection, diagnosis and delineation of disease conditions, (ii) variability of the sampling methods (usually not known), and (iii) the efficiency of the sampling method to estimate a land-area or stand-parameter-diseased.  19 Recall that the stated objectives of this thesis (see pg. 2) require an efficient and accurate sampling method to assess incidence and damage in land- and stand-based expressions so that ecological and stand dynamics relationships, and stand productivity responses to Phellinus root rot can be determined. A short review of various root rot sampling methods in use today follows.  The intersection length sampling method for root rots (Bloomberg et al. 1980) is a  flexible set of survey options for making land-area-diseased assessments which can include a mapping option. It has been used extensively for disease incidence sampling in coastal British Columbia (Bloomberg 1983, and Beale 1987). Essentially the proportion of the line/grid transect lengths in root disease centers, defined by above ground disease symptomology, is the estimate of the land-area diseased (incidence or intensity estimate) per sample block. No estimate of disease severity is made using this method. Filip (1980), ranked this method second to fixed-radius plot sampling for accuracy, but twice as fast for assessing the land-area-diseased.  Fixed-radius plot sampling to estimate the land-area-diseased appears to be the most  accurate and compatible for sampling small, irregularly shaped areas (Filip 1980). Filip's fixed-radius plot sampling method required surveyors to estimate the area infected within plots. The method is definitely slow, but it is highly accurate. Alternatively, the sampling could be much faster by only estimating disease presence or absence. This method can also be used to estimate stand-parameter-diseased terms, but is known to be less efficient than the variable-radius plot sampling discussed below.  20 Variable-radius plot sampling was least accurate for estimating land-area-diseased  in Filip's (1980) study. However, simultaneous acquisition of stand-parameter-diseased data may offset the loss in accuracy of estimating the land-area-diseased using this method. Variable-radius plot sampling compared to fixed-radius plot sampling "is simpler...without sacrificing accuracy, reduces personal errors and provides a better balanced sample of the various diameter classes within the stand" (Dilworth and Bell 1979). In terms of simplicity "variable plot sampling does not require measurement of the plot radius or tree diameters to compute the basal area per hectare as with fixed-radius plots. Stem counts are made, with each tree contributing equally, without regard to diameter, to the basal area estimate" (Dilworth and Bell 1979). Similarly, disease conditions are tallied on a tree by tree basis. The principle of tree selection differs markedly between the two methods: Grosenbaugh (1952) states, "In (fixed-radius) plot sampling the probability of tree selection is proportional to tree frequency; in point sampling (variable-radius) it is proportional to tree basal area". These concepts are important for disease sampling because the impact of diseases on the basal area growing stock contributes the most to volume production. The latter can easily computed from sample-based basal area/ha which is then factored by a sampled/or known volume:basal area ratio (VBAR), (Dilworth and Bell 1979). Although variable-radius plots are ideally suited to estimate disease severity (i.e., if basal area or volume is the desired parameter), the notion of disease incidence is most easily captured by stratifying sample plots on the basis of disease presence/absence. The proportion of the plots infected, to any degree (one or more trees), is termed disease incidence.  21 Forest disease surveys using variable-radius plots to estimate damage to stand parameters have been successfully conducted in the Pacific Northwest (Goheen 1979, Goheen and Worrel 1979). On Vancouver Island, B.C., Blair et al. (1975) used variableradius plots in a sequential sampling scheme to estimate percent basal area infected in stands managed by MacMillan Bloedel Limited.  Several studies based on growth and yield permanent sample plots or stem analysis plots have also provided direct linkage to the effects of Phellinus root rot on stand  productivity (Mounce et al. 1940, Bier and Buckland 1947, Buckland et al. 1954, Johnson et al. 1972, Nelson 1980, Thies 1983, Bloomberg and Reynolds 1985, and Bloomberg  1990). Recognition of pest activities in growth and yield or forest inventory permanent sample plots (PSP's) could allow for comparison of: (a) root rot incidence and, (b) growth and yield rates between PSP's affected and not-affected by root rots, given comparable initial stand and site conditions. These types of comparisons are basic to the hypotheses being examined in this thesis. Generally, sets of growth and yield PSP's might be suitable for extrapolation of damage effects on local stand productivity, whereas forest inventory PSP's would more likely be representative of forest conditions.  Based on the discussion of disease measurement terminology and sampling methodology, I propose a new comprehensive approach to root rot sampling that will include: (a) ecological characterization and stratification of sampling units, (b) land-area and stand-parameter disease incidence estimates, (c) stand-parameter-diseased estimates for damage severity and intensity using two different diameter limits and, (d)  22 land-area-diseased incidence estimates for growth and yield permanent sample plots. This methodology would provide the basis for studying ecological and stand dynamics relationships, development of incidence-severity and incidence-intensity relationships, and provide a direct linkage measure to forest inventory and growth and yield for assessing stand productivity responses to Phellinus root rot.  23 5.0 STUDY LOCATION  5.1 Geography  The study was conducted on the southeastern half of Vancouver Island between Union Bay and Shawnigan Lake; on the eastern slopes and coastal plain of the Beaufort Mountains, and the lower elevations around Port Alberni and south along the Alberni Inlet. Some additional PSP's were sampled in the lower elevations at the west end of Cowichan Lake. The study area is illustrated in Figure 1, and is better defined by the southern twothirds of the shaded biogeoclimatic subzone variants in the figure.  5.2 Physiography  The study area lies within two major physiographic areas of the Western Canadian Cordilleran system: the Coastal Trough and the Outer Mountain Area.  The eastern portion of the study area lies within the Nanaimo Lowland of the Georgia Depression which rises to 600 m asl in the Vancouver Island Ranges, westwards from the Georgia Strait. The area is largely underlain by the Nanaimo Group of Upper Cretaceous sedimentary rocks (hard sandstone, conglomerate, shales and softer rocks), giving rise to differential erosion potential and soil parent materials. The Nanaimo Lowland was intensively glaciated during the Pleistocene, reducing relief and leaving behind a complex of glacial and glaciofluvial blankets and veneers (Holland 1976).  24  CDFmm Coastal Douglas-fir, moist maritime  'fr"• • •■  CWHxm1 Coastal Western Hemlock, eastern very dry maritime  •  CWHxm2 Coastal Western Hemlock, western very dry maritime  - -  Figure 1  Study area and biogeoclimatic unit map. Note, the area from just south of Courtenay southwards comprised the study location. Source: Ministry of Forests, Vancouver Forest Region, Burnaby, B.C.  25 The western portion of the study area lies within the the Insular Mountains, comprised of the Port Alberni Basin and the Vancouver Island Ranges. The Port Alberni Basin is a low lying area extending northwestward from Port Alberni about 40 km with widths between 8 and 13 km. Its elevational boundary runs to about the 300 m contour on the north, west and southern flanks of the basin. The eastern flank is bounded by the scarp of the western slopes of the Beaufort Range. The basin is also underlain by Upper Cretaceous sedimentary rocks (Holland 1976). A third but very small portion of the study area lies within the Vancouver Island Ranges. The ranges are composed of a heterogenous group of pre-Cretaceous sedimentary and volcanic rocks with numerous granitic batholiths that run on a northwest-southeast axis (Holland 1976). The combination of alpine, valley and continental ice-sheet glaciation during the Pleistocene has produced soil parent materials of a very heterogenous nature on southern Vancouver Island, particularly in the study area (Jungen et al. 1985).  5.3 Soil Parent Materials--Surficial Deposits  Two periods of glaciation 18,500 and 11,500 years ago resulted in extensive deposition of fluvial and morainal materials, as well as marine deposits (Jungen et al. 1985). Fluvial deposits consist of glaciofluvial and contemporary fluvial deposits; its materials are sorted and less heterogenous than morainal deposits (Howes and Kenk 1988). Common glaciofluvial landforms are old river terraces or raised estuaries composed of coarse sands to sandy gravels. Finer textured, fluvial and glaciofluvial deposits are found at lower elevations and near active rivers (Jungen et al. 1985). Glacial  26 morainal deposits are composed of an unsorted heterogenous mixture of sands, silt and clay with variable amounts and sizes of coarse fragments (Howes and Kenk 1988). Morainal deposits (tills) are generally very compacted, coarse textured and bouldery. Some finer textured tills derived from shales and volcanic rock, are found near Courtenay, Nanaimo and Port Alberni. Sandy tills are derived from sandstone bedrock and interglacial sands (Jungen et al. 1985). Marine deposits found at elevations below 100m in the Nanaimo Lowland (Jungen et al. 1985) were formed as a result of land submergence and uplifting following glaciation (Howes and Kenk 1988). Colluvial deposits, resulting from mass wasting (Howes and Kenk 1988) are usually found on, or at the base of, steep slopes or in areas of interspersed exposed bedrock. Colluvial deposits are composed of angular, nonuniform sized coarse materials ranging from bouldery to sandy gravel, thus generally well drained (Jungen et al. 1985).  5.4 Climate  The study area is completely within the rainshadow effect of the Olympic Mountain Ranges in western Washington State and the Vancouver Island Ranges to the west of the Alberni Basin and to the west of the Nanaimo Lowlands. Thus, the area is in a leeward position to the westerly prevailing moist Pacific weather systems. This is indicated in the naming of the regional climates (biogeoclimatic subzones) within B.C. (Klinka et al. 1984). The climate according to Koppen, as described by Trewartha (1968), ranges from Csb to Cfb. This indicates a mild, temperate, rainy climate with a cool summer, with a minor distinction of a dry summer (s), versus a no-distinct dry season (f). The dry summer  27 climate (Csb) is found near the Georgia Strait and the Alberni Basin, while the no-distinct dry season climate (Cfb), is primarily a feature of increasing elevation and westward geographic location. Klinka et al. (1979) reports that three distinct regional climates can be identified for the climatic gradient within the study area. These are listed by their revised BEC unit names (Banner et al. 1990) as:  CDFmm (CDFa1) =  Moist Maritime Coastal Douglas-fir subzone variant,  CWHxm1 (CDFb1) =  Eastern Very Dry Maritime Coastal Western Hemlock subzone variant,  CWHxm2 (CWHa2) =  Western Very Dry Maritime Coastal Western Hemlock subzone variant (see Fig. 1).  Further analysis of climatic data by (Courtin 1990) has confirmed the presence of three regional climates, which are a major basis for differentiating ecosystems in the biogeoclimatic ecosystem classification system (Klinka et a/. 1984; Pojar et al. 1987) (see Table 1).  TABLE 1^SUMMARY OF KEY CLIMATIC PROPERTIES WITHIN THE STUDY AREA (From Klinka et al. 1984) Climatic Property  Subzone Variant CDFmm  CWHxm1  CWHxm2  873  1215  2123  % Percipitation As Snow  6  9  7  Precipitation Of Driest Month (mm)  18  28  35  16.8  17.4  17.4  3550  3459  3360  Mean Annual Precipitation (mm)  °  Warmest Month Mean Temperature ( C)  °  Growing Degree Days > C  28 5.5 Forest Cover and Stand History—Past and Present  The study area is under an extensive cover of predominantly naturally regenerated, second growth, even-aged, Douglas-fir forests. These forests regenerated largely as a result of the forest industry which began at chemainus, B.C. in 1862. Expansion of the industry, particularly up and down the Vancouver Island coastline, occurred because of the construction of the Esquimalt and Nanaimo (E&N) Railroad in 1886 (Jungen et al. 1985). The coal mining industry, centered in and south of Nanaimo, also cut extensively from the local forest for mining timber peaking between 1901-1930's (Jungen et al. 1985). The historical progesssion of logging from the coastline westwards, across the Nanaimo Lowlands and into the Alberni Basin, is seen in the decreasing average PSP age across the biogeoclimatic subzone variants; CDFmm, CWHxm1, and CWHxm2, have an average age of 76, 70 and 59 years, respectively.  These second growth forests are generally pure Douglas-fir ( > 80% species composition by volume), with minor mixtures of western hemlock, western red cedar, grand fir, western white pine, lodgepole pine, red alder (Alnus rubra Bong.), bigleaf maple (Acer macrophyllum Pursh.), arbutus (Arbutus menziesii Pursh.), and Garry oak (Quercus ganyana Dougl.), depending on the ecological and historical conditions affecting stand  development. The second growth Douglas-fir forests are now in a mid-seral condition, with a mean age of 70 years. The majority of stands regenerated following harvest of predominantly old-growth Douglas-fir forests containing sizeable components of western red cedar and likely some western hemlock, depending on ecological site conditions and  29 Phellinus root rot history. Stand origins are difficult to ascertain, but it appears from the  PSP records that some stands regenerated after logging (L), others after logging and slashburning--prescribed or wildfire (LB), while a third group regenerated after wildfire with no known logging history (B). The study-wide ratio of these stand origins is 13:30:96 L:B:LB, (Table 2).  TABLE 2^STAND ORIGINS BY SUBZONE VARIANT Origin  Total  Subzone Variant CDFmm  CWHxm1  CWHxm2  Logged (L)  2  5  6  13  Burned (B)  9  17  4  30  Logged & Burned (LB)  19  38  39  96  Total  30  60  49  139  Note:^The Burned-origin has significantly greater incidence (p=.054) in the CDFmm and CWHxm1, and lower in the CWHxm2 than expected as estimated from a log-linear model; likelihood ratio chi-square statistic.  30 6.0 METHODS  6.1 General Approach  This study was observational in nature, and was aimed at collecting data and determining relationships between Phellinus root rot incidence, severity and intensity and the following datasets: growth and yield, ecological characteristics, stand history, and old growth and second growth stand attributes within the coastal Douglas-fir ecosystems of B.C.  6.2 Sampling Design  This study was focused on a set of 215 PSP's located on southeastern Vancouver Island, within the CDFmm, CWHxm1 and CWHxm2 biogeoclimatic subzone variants. With the exception of 11 PSP's near Cowichan Lake, the core set of 204 PSP's are the property of MacMillan Bloedel Limited. The PSP's are located on either Managed Forest Unit No. 19, or Tree Farm Licence No. 44 in several forest operations divisions: Cameron, Sproat, Northwest Bay, Chemainus and Shawnigan. The 11 PSP's near Cowichan Lake are on Managed Forest Unit No. 7, owned by Canadian Pacific Forest Products Limited.  Historically, most growth and yield PSP's were located to meet "normal forest" condition sampling objectives (i.e., avoiding disturbances such as poor stocking, rock outcrops, windthrow conditions and insect and disease damage (Iles 1987). In the past,  31 a small proportion of PSP's, about 15%, were established randomly (Northway 1990). Most natural, unmanaged Douglas-fir PSP's were established between 1955 to 1965, in 30 to 40 yr old stands. Both the date, and stand age at PSP establishment make it likely that some PSP's were located on infected sites. Root rots were not widely known or understood at that time, and the first major wave of root rot symptoms and mortality would have just begun in stands of those ages. The small area of the PSP's, generally 0.04ha, makes it difficult to assess root rot dynamics (not a study objective per se), but due to plot locating procedures, any root rot incidence in the plots is most likely due to incursion from nearby root rot centers rather than to PSP establishment in infection centers.  A vast amount of soils data had been collected in the past decade in many of the PSP's, which could aid in ecological correlation and classification work, saving significant soil sampling and analytical costs (Kumi 1987, and Beese 1987). Both owners of the PSP's knew that an undetermined but sizeable number of Douglas-fir PSP's were infected with Phellinus root rot, and that there was an opportunity cost to not determining the damage effects due to the disease. It was felt the value of 30 to 40 years of connected plot growth data on a fairly wide range of sites, in over 200 PSP's, should override any concern over using PSP's located for different objectives. Intensive stand-parameterdiseased root rot sampling as proposed, in such close proximity to the PSP's would also provide for the best possible linkage to growth and yield, and perhaps to the forest inventory, if the study results could be corroborated with large-scale root disease surveys  32 such as that reported by Beale (1987).  All unlogged, pure Douglas-fir (>80% by volume at PSP establishment), naturally regenerated (5% turned out to be planted), non-silviculturally treated PSP's in 30 to 120 year old stands were selected for sampling from MacMillan Bloedel's growth and yield database. The PSP's selected from Canadian Pacific Forest Products were selected similarly, but focused solely on very high site qualities only. This latter selection was an attempt to fill a gap in site association coverage that was recognized after field sampling and preliminary ecological analyses were done. Random selection of PSP's would have been considered if some level of site classification were known. PSP selection would then have been made equally or weighted proportionally to the representation of PSP's across the distribution of site associations/series or types, thereby reducing sampling costs. Since the biogeoclimatic classification was not available, all 215 PSP's were visited during the study.  The sampling design and procedures for all datasets were centered on a 1 ha sample of the contiguous ecosystem identified at each selected PSP location. A contiguous ecosystem was first defined at the site type level (Banner et al. 1990), but was found too impractical given significant soils variability. A more reliable and easier definition for the 1 ha sample survey was set at the site series level of the BEC system. The generalized sampling procedures were:  33 1.  Ecological characterization to the site series level was done in, or as close to the selected PSP as possible. This included description of vegetation, physiography, soils and forest floor. Aggregation of ecologically similar PSP's within growth and yield installations was done where appropriate.  2.  Sampling for root rot incidence and severity, and old growth stand history (stems/ha and species composition) was conducted simultaneously on a systematic, 20 m-grid interval (25 plots) within the contiguous ecosystem as identified at the PSP, with the PSP located centrally where the PSP location and ecotone allowed, (Fig. 2).  3.  At each of the 25 plot centres, root rot incidence was sampled using; (i) a land-area-diseased estimate based on 0.005 ha fixed-radius plots and, (ii) a stand-parameter-diseased estimate using variable-radius plots, (Fig. 2).  4.  At each of the 25 plot centres, root rot severity was sampled using the standparameter-diseased expression by establishing two separate diameter limit (>4.0 cm and >12.0/17.5 cm), same basal area factor variable-radius plots. Two diameter limits were used to sample ingrowth due to Phellinus root rot disturbances, (Fig. 2).  5.^At each of the 25 plot centres, old growth stand conditions (stems/ha and species composition) were estimated from 0.01 ha fixed-radius plots.  34 6.  Each PSP was assessed for incidence of Phellinus root rot, (Fig. 2).  7.  Permanent sample plot growth and yield data collected by the PSP owners, provided stand origin, mensuration and species dynamics data.  LEGEND:  PSP^= A Phellinus Infected Permanent Sample Plot; RRIN =1 = Phellinus Root Rot Centre  0^Healthy Fixed-Radius and VariableRadius Plot; DSPT =0, DSWP =0 0^Healthy Fixed-Radius and Infected Variable-Radius Plot; DSPT=O, DSWP =1 0^Infected Fixed-Radius Plot and Healthy Variable-Radius Plot; DSPT = 1, DSWP =0 0^Infected Fixed-Radius Plot and Infected Variable-Radius Plot; DSPT = 1, DSWP =1  ^ .1- -4  Figure 2  ^  Example Sampling Transect  Sample plot layout centered about a permanent sample plot within a contiguous, homogenous ecosystem as defined at the site association level. Under optimal conditions a 5 by 5 sample plot grid (25 point) layout was located. Example transects illustrate all possible disease incidence conditions; RRIN and DSPT/DSWP (0) = Healthy, (1) = Infected.  36 6.3 Measurement Of Root Rot Incidence, Severity and Calculation of Intensity  Incidence of Phellinus root rot was sampled for, estimated and expressed in three ways; one method for PSP's and two methods for the Percent Basal Area Reduction (%BAR) sample surveys:  Permanent Sample Plot--Phellinus Incidence  (1)  The presence/absence of Phellinus root rot was assessed in each PSP (a land-area-diseased expression, variable name is RRIN), and was used at the whole-study level as a measure of incidence, (Fig. 2). The PSP buffer extending 10 m, was assessed similarly, (variable name is RRBF).  %BAR Sample Surveys--Phellinus Incidence  (2)  Land-Area-Diseased Expression. The presence/absence of Phellinus root rot was assessed in each of 25 fixed-radius 0.005 ha plots. A plot was defined "infected" when the incidence of P. weirii was confirmed in the second growth stand component. The proportion of infected plots was termed incidence (variable name is DSPT).  37 (3)  ^  Stand-Parameter-Diseased Expression. The presence/absence of  Phellinus root rot was assessed in each of 25 variable-radius plots. A  plot was defined "infected" when one or more Phellinus "infected" trees (living or dead) were counted "in", (Figs. 2; and 4 see pg. 41). The proportion of plots infected with P. weirii was termed incidence (variable name is DSWP). Note, an incidence measure was made for each of the two separate diameter limit sweeps. Incidence was estimated simultaneously in the prism sweep and tally procedure for damage severity, discussed below.  Damage severity of Phellinus root rot was sampled for, estimated and expressed in one method for the Percent Basal Area Reduction (%BAR) sample surveys:  %BAR Sample Surveys--Phellinus Damage Severity  (1)^Two full-circle sweeps were made using the same basal are factor (baf) prism, to provide two "in" tree tallies; one each for >4.0 cm and >12.0/17.5 cm diameter limits. The >12.0 cm diameter limit was only used in stands where 7 to 11 trees could not be selected at the 17.5 cm limit (e.g., in young or poor quality stands). All "in" trees were tallied, except Phellinus infected trees, thus the tallies were always healthy trees only that were distinguished by Phellinus incidence (DSWP) notations and diameter limit classes (Fig. 2).  38 Two diameter limits were used to sample ingrowth due to Phellinus disturbance and to impose an artificial "age class" structure; the latter assumes that diameter is strongly related to age. This is borne out by the fact that tree counts and basal areas are generally inversely related to the diameter limit size. Thus, the larger and smaller diameter limits are analogous to an "older" and "younger" age class respectively. If all trees are truly even-aged then the size differences attributed to age are more likely due to sampling of dominant/codominant versus intermediate/suppressed crown class trees. Observations by forest pathologists consistently indicate the phenomena of new ingrowth into root rot centers that implies younger tree ages. Aging of ingrowth was not conducted in this study. The diameter class/age class stratification would enable a view into stand dynamics and variations in productivity response to disease condition.  Calculation of Phellinus damage severity parameters (and subsequent %BAR intensities) required one further post-sampling stratification to separate out the susceptible species (Douglas-fir, grand fir and western hemlock) from the less-to-non-susceptible species. This enabled a comparison of the "worst case" root rot damage scenario with lesser damage conditions. Following the calculation procedures outlined in 6.3.1 and illustrated in Figures 3 and 4, the severity parameter for a given diameter limit class and species susceptibility class combination  39 is determined for a sample survey (25 plots/ha), by calculating the basal area reduction of Phellinus infected prism sweeps relative to the basal area of healthy prism sweeps and expressed as a proportion. The severity parameter names are shown in Table 3, and severity subcomponent variables in Table 5.  Note, prior to this study several pilot %BAR surveys were tested and compared against operational root rot surveys in the Port Alberni Forest District. Field and calculation methods were revised following the pilot sampling.  40  O  8  0 O  0  11  •  *  7  10  O  9  12  2^,-, 1  O  L..)^L.) O  6  0  4  O 0  3  5  LEGEND:  0 = Susceptible > Diameter Limit - Healthy ^■ = Non-Susceptible > Diameter Limit - Heathy 0 = Susceptible < Diameter Umit - Healthy ^^ = Non-Susceptible < Diameter Umit - Healthy * Plot Centre 'IN' TREE COUNTS BY %BASAL AREA REDUCTION PARAMETER  4.0 cm Tree No.'s  ^  NSBAR (1 through 12) E = 12  ^  SBAR (1,2,3,5,6,7,8,9,10,12) E = 10  12.0/17.5 cm Tree No.'s  BARNS (1,3,4,5,6,8,9,10,11,12) E = 9  ^  BARS (1,3,5,6,8,9,10,12) E = 8  Figure 3^Healthy variable-radius plot condition. Root rot incidence is null or DSWP=0.  41  0 12 016 14  0  c  i 15  or  0 10  h  11  0  17^  0  18  * 01 1 ^ 2  •  O  3  9  E  8  0  1  01 4  0  5  7  0  6  LEGEND: 0 = Susceptible > Diameter Umit - Healthy ^■ = Non-Susceptible > Diameter Limit - Healthy 0 = Susceptible < Diameter Limit - Healthy ^^ = Non-Susceptible < Diameter Umit - Healthy * Plot Center IN' TREE COUNTS BY %BASAL AREA REDUCTION PARAMETER 4.0 cm Tree No.'s^  z 12.0/17.5 cm Tree No.'s  NSBAR (2,3,5,6,9,11-18) E = 11^  BARNS (3,5,6,8,9,11,16) E = 7  SBAR (5,6,9,15,16,18) E = 6^  BARS (5,6,9,16) E = 4  Figure 4^Infected variable-radius plot condition. Root rot incidence is positive or DSWP= 1. Note visibly infected trees ( I ) 1, 4, 7, 10 and 13 are not counted as "in" trees in the sampling design described. Also, Tree 12 is considered "out".  42 TABLE 3^MATRIX OF DAMAGE SEVERITY VARIABLES AND CONSEQUENT %BAR DAMAGE INTENSITY PARAMETERS (in Parentheses) Diameter Limit  Species Class 4.0 cm Non-susceptible & Susceptible species  NSSEV (NSBAR)  SEVNS (BARNS)  SSEV (SBAR)  SEVS (BARS)  Susceptible species Non-susceptible species  =  Susceptible species  =  z 12.0/17.5 cm  western red cedar, white pine, shore pine, Sitka spruce, red alder, birch, cherry, flowering dogwood, cottonwood, arbutus and Garry oak. Douglas-fir, grand fir, and western hemlock.  6.3.1^Calculation Methods For %BAR Percent Basal Area Reduction Variables  The generic %BAR calculation follows: Intensity = (( Severity ) x ( Incidence )) x 100  The %BAR calculation for SBAR, (Susceptible species only, ^cm): % SBAR = ( SSEV X DSWP) X 100 %SBAR  -  (  ^-  BA ,  BA h  '  X (proportion DSWP) X 100  BA h - is the mean tree count multiplied by the sample stand Basal Area Factor (BAF) of the healthy/ noninfected point sample sweeps; i.e., with no infected "in" trees. For example, from Fig. 3 the SBAR tally is 10 trees, multiplied by BAF=8, equals 80 m 2 ;  BA / 1 - is the mean tree count multiplied by the sample stand Basal Area Factor (BAF) of the infected point sample sweeps; i.e., with one or more infected "in" trees, and where "in"  43 trees are not counted in the tally. For example, from Fig. 4 the SBAR tally is 6 trees,multiplied by BAF=8, equals 48 m 2 ;  BAF - is the stand sample Basal Area Factor, eg., BAF 8 is 8 m 2 ; DSWP - is the proportion of the point sample sweeps having visibly infected "in" trees in the tally--infected "in" trees were not tallied in this study. For example, from Fig. 3 and 4, the proportion of infected point samples is 1 out of 2, or 0.50; 1 - Variables are severity subcomponents and are respectively  SAW and SALT in this example (See Table 5 pg. 46). E.g., SBAR =  ( 80m 2 - 48m 2 ) 0.50 X 100 X 80m 2  SBAR = 20% Basal Area Reduction  44 6.3.2^Assumptions of the Percent Basal Area Reduction (%BAR) Sampling Method  6.3.2.1^Expected Relationships For the %BAR Damage Intensity Parameter  The calculated %BAR damage intensity parameters shown in Table 4, illustrate the expected relationships between the parameters. Parameters that account for nonsusceptible plus susceptible species (NSBAR, BARNS), would in most cases have higher  stem counts and basal areas as compared to variables that only account for susceptible species (SBAR, BARS), since the former account for greater, near total tree species  diversity. The result is that NSBAR & BARNS reflect lower damage intensity as compared to SBAR & BARS. Similarly, parameters with the lower diameter limit, >4.0 cm (NSBAR & SBAR), must have higher stem counts and basal areas than the higher diameter limits, >12.0 or 17.5 cm (BARS & BARNS), with the result that NSBAR & SBAR parameters may reflect lower percent damage than the BARS & BARNS parameters.  Sampling outside of the conditions found in this study (points (a)-(h) in Table 4), may tend to equilibrate the estimated relationships between %BAR parameter estimates, and may even cause a reversal of the expected relationship.  45 TABLE 4^EXPECTED RELATIONSHIPS BETWEEN %BAR DAMAGE INTENSITY PARAMETERS NSBAR  ^  s^s^s  SBAR^s^s BARS BARNS Several very important assumptions are of concern in comparing Basal Area Reduction estimation methods. Each relationship above, is dependent upon the following generalized sample stand conditions: a) b) c) d) e) f) g) h)  stand species composition, (Douglas-fir > 70% by volume); site and ecological conditions, (BEC units; e.g., CDFmm, CWHxm) primarily naturally regenerated, unmanaged/nonspaced; mid-seral stand age, (approx. 40 to 80 yr); moderate stand density (approx. 800-3 000 stems/ha) at 10 yr; variable plot sampling diameter limits (A.0 cm and 217.5 cm); intensity of root rot and, diameter class ingrowth rates  NOTE: Items in (parentheses) indicate the general conditions found in this study.  6.3.2.2^Testing The Homogeneity Of Species Compositions And The Decomposition Of The %BAR Severity Parameters  An assumption in stand selection was that species composition did not vary significantly between the ecological units, so that there was enough similarity in susceptible species composition to detect responses to Phellinus root rot with near-equal probability between BEC units using the %BAR damage intensity parameter estimates. In order to test the hypothesis of species composition similarity between subzone variants, site associations and site series, the %BAR severity parameters were decomposed to provide all possible combinations of species susceptiblity, diameter class and disease incidence status (Table 5). For testing, a new, third diameter class,  46 intermediate to the upper and lower classes was created through subtraction of the upper from the lower diameter classes. This process also provided the required %BAR components to assess species shifts/dynamics in response to Phellinus root rot.  TABLE 5^DECOMPOSITION OF %BAR DAMAGE SEVERITY VARIABLES TO SUBCOMPONENTS 24.0 cm Diameter Limit & Healthy Sweeps SALH = Susceptible spp. NALH = Non-susceptible spp. NSALH = Non-susceptible and Susceptible spp. 24.0 cm Diameter Limit & Infected Sweeps SALI = Susceptible spp. NALI = Non-susceptible spp. NSALI - Non-susceptible and Susceptible spp. 24.0 cm <17.5 cm Diameter Limit & Healthy Sweeps (determined by subtraction) SLSH = Susceptible spp. NLSH = Non-susceptible spp. NSLSH = Non-susceptible and Susceptible spp. 24.0 cm <17.5 cm Diameter Umit & Infected Sweeps (determined by subtraction) SLSI = Susceptible spp. NLSI = Non-susceptible spp. NSLSI = Non-susceptible and Susceptible spp. 12.0/17.5 cm Diameter Limit & Healthy Sweeps SGRH = Susceptible spp. NGRH = Non-susceptible spp. 12.0/17.5 cm Diameter Umit & Infected Sweeps SGRI = Susceptible spp. NGRI = Non-susceptible spp. NSGRI = Non-susceptible and Susceptible spp.  Healthy condition, susceptible (SALH) and non-susceptible (NALH) species compositions, .4.0 cm, are shown to be very stable about the all-samples means of 91.52% and 8.48%, respectively, when plotted across site associations (Figs. 65-66, pg. 132). The variability of susceptible and non-susceptible species across site associations  47 is minimal and not significant (p > .05); also see Table 22, p.119.  6.4 Ecological Assessments  Ecological assessments were done to characterize regional climate (geographic location), physiography, vegetation, soils, forest floor, tree and stand measurements. The integration of these climatic and site parameters enables classification of sample stands to levels of most interest in the Vegetation, Zonal (Climatic) and Site Classifications of the BEC system (Pojar et al. 1987).  In general, the procedure for ecological assessments at each PSP was to confirm the geographic location on the forest cover maps (1:20 000 scale) and then determine the indicated subzone variant (regional climate) from the biogeoclimatic unit map (1:500 000 scale), (Nuszdorfer et al. 1984). Subsequently, the sites' physiography, vegetation, soils and forest floor were described using the procedures outlined by Walmsley et al. (1980). Since this study has a strong operational basis, site description modifications lean towards the Site Diagnosis procedures described by Klinka et al. (1984) and Green et al. (1984).  6.4.1^Site: Physiography  Site physiography was described for the general site conditions identified at the PSP, which included elevation, slope position (crest, upper, middle, lower, toe or level), slope  48 shape (straight, concave, convex or irregular/hummocky), percent slope, and aspect.  6.4.2 Site: Vegetation  In a 0.04 ha releve plot, the percent cover of vegetation in 3 tree layers, 2 shrub layers, 1 herb layer and 1 moss layer was described and classed on the Domin-Krajina 6-class cover class scale, after Walmsley et al. (1980). The relevó plot usually conformed to the PSP dimensions, if not, then the most representative location near the PSP was described.  6.4.3 Site: Soils  Soils were described on the basis of two soil pits dug outside the PSP at representative locations (to avoid damage to the PSP), following the Canadian System of Soil Classification (CSSC 1978) and Walmsley et al. (1980). Physical soil parameters of particular interest were: rooting depth (cm); type of root restricting layer (none, fragmental, compacted, cemented, or rock); major rooting zone soil texture (2 mm); major rooting zone coarse fragment content (> 2 mm), root restricting layer depth (cm); depth to mottling (cm); depth to seepage water (cm); total and fine fraction bulk density (g/cm 3 )•  Two mineral soil bulk density samples of about 1 000 cm 3 were taken for the 10 to 30 cm depth, at each of the soil pits per PSP. Volume was measured by replacement  ^  49 with fine, dry playsand (after Klinka et al. 1981). Two soil chemistry samples were also taken from the 10 to 30 cm mineral soil depth and composited at the MacMillan Bloedel, Nanaimo environmental laboratory.  6.4.4^Site: Forest Floor  Forest floor described and classed to the Group level as described by Klinka et al. (1981). Separate forest floor samples were taken for bulk density and chemistry at four randomly located points within the PSP. At each point, two 225 cm 2 square blocks of forest floor were cut out to mineral soil depth. The first block at each point was used for forest floor chemistry, and the second block was measured for depth. Forest floor depth was based on the mean of four representative depths taken from each side. Samples for each of bulk density and chemistry were composited at the MacMillan Bloedel, Nanaimo environmental laboratory.  6.4.5^Site: Old Growth Stand History  Stand history was estimated from the PSP database, confirmed by ecological assessment, and further assessed via the fixed-radius plot old growth stump survey. Information available from the PSP data base pertained to logging and fire history, regeneration method and presence of old growth veteran trees. Old growth stump survey procedures involved estimating the old growth species composition and stems/ha by species. Only Douglas-fir and western red cedar were easily identifiable from stumps 30  50 to 120 years old. Western hemlock was extremely difficult to identify so long after harvest and consequently very few hemlock were tallied. Systematically located 0.01 ha (5.64 m fixed-radius) stump survey plots were established on the same 25-plot sampling grid as the %BAR survey.  6.4.5.1 Old Growth Stand History (Stand Density & Species Composition) Estimates Using Fixed-Radius Plots  Stems/ha were estimated separately for susceptible and non-susceptible species, Douglas-fir/western hemlock (SPHFH) and western red cedar (SPHCW), respectively, as follows:  FD + E HW) SPHFH =^X 100 No.Plots  SPHCW - (E CW) X 100 No.Plots Where:  ^  FD is Douglas-fir, HW is western hemlock, CW is western red cedar, No.PLOTS is the sample size and, 100 is the sample size expansion factor to 1 ha.  The old-growth species composition was calculated as follows:  E FH^ EC W COMPFH =^COMPCW -^ E FH + E CW^E FH + E CW Where:  ^  COMPFH and COMPCW are species compositions for Douglas-fir/western hemlock, and western red cedar, respectively.  51 6.5 Site: Stand Mensuration  Tree and stand measurement data were obtained exclusively from the PSP summary data files provided by MacMillan Bloedel Limited, Woodlands Services, in Nanaimo, BC, and Canadian Pacific Forest Products Limited, Saanichton Forestry Centre. No additional measurements were undertaken in PSP's or the surrounding sampling area. Note, that since the growth and yield remeasurement cycle is five years for both firms, the data is not all representative of the 1987 field season in which the rest of the study occurred.  6.6 Laboratory Analyses And Data Preparation  As part of previous ecological studies conducted by MacMillan Bloedel Limited on the PSP database, many of the mineral soil and forest floor physical and chemical property data required for this study were available (Kumi 1987, and Beese 1987). The soils data, stored on the BC Ministry of Environments' Soils Information System, was summarised for the 10 to 30 cm mineral soil depth and for the F and H layers of the forest floor.  Laboratory analyses for various mineral soil and forest floor physical and chemical properties collected as part of this study were conducted by the Environmental Lab. at the MacMillan Bloedel Limited, Woodlands Service's office in Nanaimo, BC.  52 6.6.1^Mineral Soil And Forest Floor Physical Properties  Bulk density for mineral soil and forest floor were of interest in order to estimate nutrient levels on a per hectare basis, and to provide an estimate of root penetrability of mineral soils. Soil porosity was calculated from the bulk density to provide an estimate of aeration and its possible effects on fungal (P.weirii) ecology.  Bulk density samples were air dried for up to 48 hours on trays after arrival at the laboratory. Samples were paper bagged and oven dried for 24 hr at 105°C. Total dry weights were then recorded. Samples were then separated into the coarse and fine fractions (>2 mm, and .2 mm, respectively) by passing the materials through a 2 mm sieve and the fractional weights' recorded. Since the organic content of the Brunisols and Podzols common to the study area are known to be low (CSSC 1978), only obvious organic matter was removed manually.  Mineral soil bulk density was calculated for the total sample and the fine fraction (coarse-fragment free), after Klinka et al. (1981). Coarse-fragment free volumes were calculated prior to bulk density calculations using the standard 2.65 g/cm 3 for estimating the volume of coarse fragments in the samples.  53 MSBDT (g / cm') = MSTOT / MSVOLtot MSBDF (g / cm 2 ) = MSFIN / MSVOLfin Where:  MSBDT is the total sample mineral soil bulk density (coarse and fine fractions); MSTOT is the dry weight of the total sample; MSVOLtot is the volume of the excavated, total soil bulk density sample; MSBDF is the fine fraction mineral soil bulk density; MSFIN is the dry weight of the fine fraction sample; and MSVOLfin is the estimated volume of the fine fraction.  Forest floor bulk density was calculated similarly, as follows:  FFBD (g / cm 3 ) = FFTOT / FFVOL Where:  FFBD is the forest floor, fine fraction bulk density; FFTOT is the four-sample composite dry weight of the forest floor, fine fraction; FFVOL is the four sample composite volume of the forest floor.  Mineral soil porosity, or the proportion of the soil volume occupied by air or water, was calculated for the fine fraction using the following formula from Armson (1977):  PORF = 1 - (MSBDF / particle density) Where:  PORF is the mineral soil fine fraction porosity; MSBDF is the mineral soil fine fraction bulk density; particle density is taken to be 2.65 g/cm 3 unless local data indicates other lower values (Armson 1977).  54 6.6.2^Mineral Soil And Forest Floor Chemical Properties  Mineral soil and forest floor chemical properties were composited and air dried prior to fine fraction mm) separation. Mineral soil samples were passed through a 2 mm sieve to obtain the fine fraction. Forest floor samples were coarse ground then fine ground, and sieved through a 2 mm screen to obtain the fine fraction. Mineral soil and forest floor pH were determined with a pH potentiometer probe in a 1:1, soil (or forest floor):water suspension (Peech 1965). Total carbon was determined using the WalkleyBlack wet oxidation method (Walkley 1947). Total nitrogen (N) was determined colorimetrically by Technicon Autoanalyzer II after the 60-mesh samples were digested in a semimicro Kjeldahl digest (Lavkulich 1981). Mineralizable nitrogen (N) was determined colorimetrically (by Technicon Autoanalyzer II) from a 1 N KCI extractant, following a 2-week (30°C) anaerobic incubation (Waring-Bremner method) (from Keeney 1982). Exchangeable cations (Ca, Mg and K) were determined by atomic absorption spectrophotometry preceded by the ammonium acetate (pH 7) extraction method (Black 1965).  6.6.3^Conversion Of Chemical Property Concentrations To Kg/ha  Conversion of nutrient concentrations in the <2 mm fraction to kg/ha was done to provide a better estimate of chemical nutrient availability to ecosystems. (Also, see Appendix A). Kilogram per hectare estimates are sensitive to bulk density and coarsefragment content estimates. Also, the measures provide a reference with which others  55 may wish to make comparisons.  6.6.4^PSP Stand Attribute Data Preparation  Some of the PSP summary data required manipulation to be meaningful for statistical analyses, particularly regarding species composition and shifts in species composition over time, and averaging procedures for PSP's. The following variables were summarised and/or calculated on the basis of first and last PSP measurement: Total age in 1987 Breast-height age, Species composition and percent composition (1ST, 2ND, & 3RD order), Stems/ha .>_4.0 cm, Basal area/ha >4.0 cm, Volume/ha^cm, Stems/ha .17.5 cm, Basal area/ha ?.17.5 cm, Volume/ha ?.17.5 cm, Curtis'relative density (metric) ?.4.0 cm, Site height, Site index (Bruce's, Reference age 50 yr).  The data manipulations done were:  (1)^Breast-height ages were converted to total ages using the age correction values contained in the PSP file. This was done to aid the interpretation of root rot spread and behaviour, because it is perceived to be simpler to comprehend on a volume--total age versus volume--breast height age basis.  56 (2)  Species compositions were classified into four laminated root rot susceptibility classes after Wallis (1976), Hadfield (1985) and Beale (1989a). Percent species compositions were recalculated and expressed as proportions for first and last measurements. Species Composition Classes used in this study were: Susceptible = Douglas-fir, and grand fir; Intermediate = western hemlock; Resistant= lodgepole pine, western white pine and western red cedar; Deciduous = arbutus bigleaf maple, red alder, birch and cherry.  (3)  Several new variables were calculated to describe the non-susceptible species classes (Intermediate and Resistant) at the First and Last measurements. The new variables (FTIR, LTIR and TIRD) aid evaluation of species shifts in relation to presence or absence of Phellinus root rot in PSP's. FTIR and LTIR are the sum of the Intermediate and Resistant species classes compositions, first and last measurements, respectively. TIRD is the difference between the first (FTIR) and last (LTIR) Intermediate and Resistant compositions.  Similarly, several new variables were calculated to describe the Susceptible species composition at the first and last measurements; FSUS and LSUS, respectively. SUSD is the difference between the first (FSUS) and last (LSUS) measurements of the Susceptible compositions. Deciduous compositions were not included due to little change over time and an unbalanced coverage across ecological units.  57 (4)  Stems/ha, basal area/ha, volume/ha and Curtis' relative density were backestimated to reference age 10 yr using a Stand Projection model developed by Nick Smith for MacMillan Bloedel Limited, Woodlands Services, Nanaimo (Smith 1990). Given the variable conditions at PSP establishment, a reference age was required for comparison and sorting of similar PSP's for statistical analyses. Age 10 was selected to more closely approximate stand conditions at establishment. Age 10 seemed to provide realistic back-estimations, whereas at age zero (0) the results became much more variable and less realistic. Also, the author felt that back-estimation of about 20 to 40 yr was a reasonable regress in time.  (5)  Many PSP's were established as part of larger growth and yield installations. In a number of cases, two or more PSP's fitting the study criteria were sampled from within the same installation, often resulting in one common ecological description, and one root rot sample survey; thus two or more PSP's for one contiguous, homogenous ecosystem. In terms of data preparation and summarization, two options were available: (a) use all PSP's and replicate the ecological/root rot survey and stand history data to match the number of PSP's, or (b) use unique ecological/root rot survey/stand history data and average the appropriate PSP data. The latter option was chosen after consulting MacMillan Bloedel as to the suitability of averaging PSP data (Wilson 1989, and Bloomberg 1990). All stand measurement variables were averaged to obtain their arithmetic mean. Relative density was  58 recalculated for the >4.0 cm class as follows:  Curtis' Relative Density = STAND-BA / DBHg Where:  ^  STAND_BA is the recalculated stand basal area (m2), and DBHg is the recalculated quadratic mean diameter. (see Appendix B)  6.6.5 Classification Of Root Rot Damage Intensity (%BAR)  Classification of continuous variables into categorical variables allows for a different view of the data and its relationships to other variables. Classification of a disease variable, such as BARS, into several meaningful damage intensity classes was done to discern ecological relationships. Where some independent ecological variables are highly variable, classification may improve the ability to search for relationships.  In order to determine meaningful damage intensity classes, the appropriate damage response estimate parameters had to be selected. Of the four %BAR estimators, only BARS provided a damage estimate for the larger diameter, susceptible species (of which the stands are primarily comprised). The other %BAR parameters included nonsusceptible species and/or trees below the average stand age diameter that may mask the relationship in question (i.e., because of ingrowth). Operationally, BARS is most likely to provide the best estimate of what foresters are most often considering during stand management or pre-harvest silviculture assessments in the study area---that is, Douglas-fir  59 trees of average or better volume and value. Furthermore, in trying to visualize various damage levels and make conditional damage intensity classifications, the author could not easily communicate or visualize the subtleties of the other damage estimators; therefore, BARS was selected as the most useful damage estimator.  Three BARS-Damage Intensity Classes (BARS-DIC's) were determined on the basis of personal experience, consultation with other root disease workers (Bloomberg 1990(a), and Reynolds 1990), and graphical review of the data structure which included BARS frequency distributions and normal probability plots. The classification resulted in two class limits separating Severe, Medium and Low classes. The upper limit has a classic growth curve shape, perhaps best described by a nonlinear function, while the lower limit is well described by a linear function. Class limits and within-class conditions were also corroborated on the basis of recent root rot simulation summaries conducted on the TASS-ROTSIM model (Bloomberg 1990(a)). The class limit functions were used to code BARS data for statistical analysis. The functions describing the BARS class limits are as follows:  BARS UPPER CLASS LIMIT:  Two linear functions mimic the two important phases identifiable in the proposed nonlinear function:  60 (i)  the rapid root rot growth/center expansion phase (30 to 60 yr) followed by a  (ii)  slower root rot growth trend to a near asymptotic level phase (61 to 100 years).  BARS Rapid Growth Phase Limit = -3 + 0.300 X (AGE87) BARS Asymptotic Phase Limit = 10 + 0.083 X (AGE87)  Two linear functions were used instead of one non-linear function because it was easier to estimate, and subsequently classify BARS data in Systat.  BARS LOWER CLASS LIMIT: BARS = 3 + 0.033 X (AGE87)  The BARS-Damage Intensity Classes are superimposed on a BARS-total age scatterplot, illustrated in Figure 5. BARS variability across the BARS-DIC limits is illustrated by boxplot (Fig. 6). For an explanation of boxplots, and how to read them see Tukey (1977), McGill et al. (1987), Titus (1987) and Wilkinson (1988).  61  Figure 5  Percent Basal Area Reduction (BARS) - total age scatterplot classified by the BARS-Damage Intensity Classes; (L) Low, (M) Medium and (S) Severe.  Figure 6  Boxplots of BARS classified by the BARS-Damage Intensity Classes (BARS-DIC's).  6.6.6^Ecological Classification  Ecological classification followed the methods for the system of biogeoclimatic ecosystem classification (BEC) outlined by Pojar et a/. (1987). Three ecological concepts: (ecological equivalence, polyclimax condition and zonal ecosystem), are the basic principles of the BEC system that effect the resultant classification and interpretation of study plots. Ecological classification methods involve classification of climate, vegetation and site, with a final integrative stage to organize similar ecosystems at local or regional levels into a natural taxonomic hierarchy (BEC units). These methods, as used in this study, are described separately below.  ^ ^  62 6.6.6.1^Climate Classification  Permanent sample plot database designations for subzone variant were checked against the biogeoclimatic subzone map. Final adjustments to PSP subzone variant classifications were done following vegetation analysis and the formulation of plant associations and subassociations using diagnostic combinations of plant species. The resultant number of homogeneous sample units per subzone variant was 30, 60 and 49 for CDFmm, CWHxm1 and CWHxm2, respectively.  6.6.6.2^Vegetation Classification  Vegetation data (lifeform, species, plant layer, and species significance) were error checked and recoded from the 6-class cover scale to the 10-class Domin-Krajina scale required for quantitative procedures (Klinka et al. 1989).  Vegetation classification was based on analysis of species presence (frequency) and significance (percent cover) to formulate floristically similar units in a natural taxonomic heirarchy. Diagnostic combinations of species differentiate floristic communities into plant associations--the basic unit of vegetation classification (Pojar et a/. 1987). The procedure is based on the Braun-Blanquet tabular method (Westhoff and van der Maarel 1980), a reciprocal averaging ordination technique (Gauch 1977), clarification of the floristic structure (exclusion of low <3% incidence species), application of professional judgement, principal components analysis, and formulation of diagnostic combinations  63 of species (DCS), according to Pojar et al. (1987). Plant associations, subassociation(s), alliance(s), and order(s) were named in accordance to the present classification heirarchy (Klinka 1990). The analytic procedure was based on a comprehensive vegetation and environmental data tabulation and summary computer program (Emmanuel 1989), lodged on the UBC computer. Principal components analyses were used on diagnostic species to detect relationships between environmental characteristics and vegetation units giving indication of units' relative moisture and nutrient condition. Spectral analysis also aided vegetation classification by relating plant species proportional distributions stratified by soil nutrient and soil moisture indicator values to conditionally named vegetation units (Klinka and Krajina 1986).  Naming of vegetation units for this study, followed the formulation of the Diagnostic Combination of Species (Table 6); this resulted in: two plant orders, three plant alliances, four plant associations and three plant subassociations, (after Pojar et al. (1987)), (Table 7).  A complete listing of plant species found in the study is in Appendix C. The vegetation summary table used in the formative stages of discerning floristic uniformity is in Appendix D.  64 TABLE 6  VEGETATION ENVIRONMENT ANALYSIS 1^2^3^4^5 $PA^$PG^$TM^$PAT^$PAA  Vegetation Unit Number of Plots Vegetation Units and Species  6 $PAP  Diagnostic^25^47^27^38^8  8  Value l^Presence class 2 and mean species significance 3  Pseudotsuga-Mahonia p.o. & p.all. Holodiscus discolor Rosa gymnocarpa  (d) (d,c)  IV^4^III^3 V 3^V 3  I^+ II^1  I^+ II^2  II^1  1 $Pseudotsuga-Arbutus p.a. III^3  I^+  (d)^II^1 (d,cd)^II^4 (d)^I^+ (d)^II^2  IV 2 V5 III^3 IV 3  V3 V5 IV 4 V5  IV 4 V5 IV 4 V5  HI^1 I^+  III^3  IV 3 I^+  II^+^II^+^IV^3 V 5^V 7^V 6 III 2^V 2^V 3  II^1 II^1  II^2 III^1 I^+  II^1^II^3 II^+^II^1 I^+  I^+ II^1  I^+ II^1  II^+^II^+ I^+ II^2^II^1^II^+  (d)  Arbutus menziesii 2 $Pseudotsuga-Gaultheria p.a. Achlys triphylla Hylocomium splendens Linnaea borealis Tsuga heterophylla  V4 IV 3 II^1 V4  IV 6 V6 III^3 V5  3 Tsuga-Mahonia p.o. & p.all., $Tsuga-Mahonia p.a.  Thuja-Tiarella p.o., Thuja-Achlys p.all., Pseudotsuga-Achlys p.a. Plagiomnium insigne Polystichum munitum Tiarella trifoliata  (d) (dd,cd) (d)  4 $Pseudotsuga-Achlys-Typic p.sa.  5 $Pseudotsuga-Achlys-Alnus p.sa. Alnus rubra (d,cd) Bromus vulgaris (d) Rubus spectabilis^ (d)  V5 IV 2 IV 3  I^1 II^1 I2  6 $Pseudotsuga-Achlys-Plagiomnium p.sa. Plagiomnium insigne Rhizomnium glabrescens Trientalis latifolia  (d) (d,c) (d)  IV 3 V4 IV 2  1 Species diagnostic values: d - differential, dd - dominant differential, cd - constant dominant, c - constant, is important companion (Pojar et al. 1987)  2 Presence classes as percent of frequency: I = 1-20, II + 21-40, III = 41-60, IV = 61-80, V = 81-100. If 5 plots or less, presence class is arabic value (1-5). 3 Species significance class midpoint percent cover and range: + = 0.2 (0.1 - 0.3), 1 = 0.7 (0.4 - 1.0), 2 = 1.6 (1.1 - 2.1), 3 = 3.6 (2.2 - 5.0), 4 = 7.5 (5.1 - 10.0), 5 = 15.0 (10.0 - 20.0), 6 = 26.5 (20.1 - 33.0), 7 = 41.5 (33.1 50.0), 8 = 60.0 (50.1 - 70.0), 9 = 85.0 (70.1 - 100).  65  TABLE 7^HIERARCHICAL SYNOPSIS OF VEGETATION UNITS DISTINGUISHED IN THE STUDY AREA VEGETATION UNIT Plant Order Plant Alliance Plant Association Plant Sub-association Pseudotsuga-Mahonia Pseudotsuga-Mahonia Pseudotsuga-Arbutus Pseudotsuga-Gaultheria Tsuga-Mahonia Tsuga-Mahonia Thuja-Tiarella Thuja-Achlys Pseudotsuga-Achlys Pseudotsuga-Achlys-Typic Pseudotsuga-Achlys-Alnus Pseudotsuga-Achlys-Plagiomnium Each vegetation unit is characterized by a typical diagnostic combination of species after Pojar  The six lowest level vegetation classification units (plant associations and subassociations), (Tables 6 and 7), are thought to be arranged mainly according to an increasing soil moisture gradient. This assumption was tested quantitatively via principal components analysis (PCA) of diagnostic species representative of the six units. The first two principal components accounted for 39.5% of total variance; 25% and 13.5% respectively for the first and second components. PCA component axis scores were then correlated to individual species (Table 8). To explain the correlations, 15 species with relatively strong positive and negative correlations were reviewed for relationships to environmental characteristics of each vegetation unit. Ordination of the PCA scores for the six vegetation units on the first two axes illustrates the units' discreteness represented  66 by the 95% confidence ellipses (Fig. 7). Although there is no separation of ellipses the trend along the moisture gradient illustrates the ecotonal relationship of these vegetation units. For the first component, high positive correlation coefficients for Polystichum munitum (.53), Achlys triphylla (.70), and Tsuga heterophylla (.60) (species indicative of  slightly to fresh soil moisture conditions), and high negative correlations of Holodiscus discolor (-.54) and Arbutus menziesii (-.40) (species indicative of very dry to moderately  dry soil moisture conditions), suggest that it adequately represents the soil moisture gradient. The second component is likely to represent the soil nutrient gradient as indicated by high positive correlations of Linnaea borealis (.41), Rosa gymnocarpa (.47), Hylocomium splendens (.65) (species indicative of soil nutrient poor to medium), and high  negative correlation of Polystichum munitum (-.49) and, Alnus rubra (-.38) (species indicative of soil nutrient medium to rich). The assumption that the vegetation units are arranged along the soil moisture and nutrient gradients is further supported by spectral analysis (Klinka and Krajina 1986, pg.104) of plant species compared to the vegetation units, seen by increasing proportions of drier site indicator species on drier vegetation units and vice versa on fresher vegetation units (see Appendix E).  67  DIAGNOSTIC SPECIES CORRELATIONS TO PCA AXIS SCORES  TABLE 8  Correlation1  Diagnostic Species Axis 1  Axis 2  HOLODIS4  Holodiscus discolor  -.5379  .2695  ROSAGYM4  Rosa gymnocarpa  -.2533  .4721  ARBUMEN1  Arbutus menziesii  -.4028  .0025  ACHLTRI6  Achlys triphylla  .7001  .1716  HYLOSPL7  Hylocomium splendens  .5497  .6512  LINNBOR6  Linnaea borealis  .6127  .4130  TSUGHET1  Tsuga heterophylla  .6004  -.4685  POLYMUN6  Polystichum munitum  .5315  -.4919  TIARTRI6  Tiarella trifoliata  .5187  -.0758  ALNURUB1  Alnus rubra  .1440  -.3805  BROMVUL6  Bromus vulgaris  .1939  .0833  RUBUSPE4  Rubus spectabilis  .2052  -.3016  PLAGINS7  Plagiomnium insigne  .3175  -.0966  RHIZGLA7  Rhizomnium glabrescens  .2795  -.0041  TRIELAT6  Trientalis latifolia  .2899  .4081  1^Correlations were significant at (  a^=.01)  68  95% CONFIDENCE ELLIPSES  0 In  O 2 O  U° LJ o ul  0  5  ^I -  I IPA 2 IPG 3 SIM 4 SPAT FAA 6 PAP  10.0^-51.0^0.0^5.0^10.0^15.0^20.0  FIRST PC AXIS SCORES  Figure 7  ^  95% Confidence Ellipses of the PCA ordination scores of six vegetation units identified in the study; (1) Pseudotsuga-Arbutus p.a., (2) Pseudotsuga-Gaultheria p.a., (3) TsugaMahonia p.a., (4) Pseudotsuga-Achlys-Typic p.sa., (5) Pseudotusga-Achlys-Alnus p.sa., and (6) Pseudotsuga-Achlys-Plagiomnium p.sa.  69 6.6.6.3^Site Classification  The site classification procedures organize ecosystems into groups similar in site quality, site productivity and potential climax vegetation regardless of present vegetation (Pojar et al. 1987, Banner et al. 1990). Site associations, the basic unit of the classification, are characterized by certain climax vegetation (or late successional stages) predicted to occur on a certain range of soil moisture and soil nutrient regimes within a range of regional climates represented by biogeoclimatic subzones (Pojar et al. 1987). Climatically uniform groups at the biogeoclimatic subzone or variant level within a site association represent site series. Critical soil and/or topographic properties within an association or series can be used to group ecosystems to the site type level. Site type level differentiating criteria are presently not finalized and are dependent upon management or research objectives.  Using the site classification procedure, I identified nine site associations and 14 site series (Table 9), (after Banner et al. 1990). Classification to the site type level was envisaged but the heterogeneity of sites precluded the option, particulary when considering soil properties that may affect the incidence and severity of Phellinus root rot. This was especially true in trying to establish meaningful groups in the data considering the generally homogenous edaphic and topographic features of the study area as a whole. Environmental properties summaries were produced (Emmanuel 1989), to assist in classifying ecosystems to site association and series, but are not presented in this thesis. The relationships of most continuous ecological, site, soil and stand variables to  70 the site associations identified in this study are seen in Appendix F.  TABLE 9^HIERARCHICAL SYNOPSIS OF THE SITE UNITS DISTINGUISHED IN THIS STUDY SITE UNIT  SITE UNIT  Site Association Site Series  Site Association Site Series  FdPI - Arbutus CDFmm - FdPI - Arbutus  FdHw - Salal * CWHxm1 - FdHw - Salal CWHxm2 - FdHw - Salal  Fd - Salal * CDFmm - Fd - Salal  HwFd - Kindbergia * CWHxm1 - HwFd - Kindbergia CWHxm1 - HwFd - Kindbergia  FdBg - Oregon grape * CDFmm - FdBg - Oregon grape  Fd - Swordfern CWHxm2 - Fd - Swordfern  FdPI - Cladina CWHxm1 - FdPI - Cladina  Cw - Foamfiower * CWHxm1 - Cw - Foamflower CWHxm2 - Cw - Foamfiower  *^Site Associations with rt6 sample p ots, and retained for further analyses.  6.7 Statistical Analysis Methods  Statistical analyses were conducted using the SYSTAT and SYGRAPH PC computer package (Wilkinson 1988). The observational nature of this study required extensive exploratory data analysis (Tukey 1977), which included transformations and graphical review of the dependent (root rot) parameters on the independent variables (ecological/mensurational) prior to numerical analyses.  Subsequently, Pearson correlations and tests of significance (Bonferroni's adjusted  71 probabilities) were reviewed for linear correlations at the 20% significance level ( a  =0.20). This relatively large  a  value was used for two reasons: (i) very large  ecological property variation and (ii) the presence of measurement errors.  Assumptions for linear regression were assessed with the following results:  (a)  Measurement of independent variables without error was impossible to confirm, but the work was done with skilled, well-trained field crews and laboratory analysts.  (b)  Homogeneity of variance (homoscedasticity) was evaluated via scatterplots, boxplots and examination of linear model residual plots; homoscedasticity was met for growth and yield data, but much less so for %BAR root rot parameters and ecological variables.  (c)  Linearity assumptions between dependent and independent variables were generally met, but many ecological and root rot variables/parameters patterns were extremely amorphous and ill-defined (many nonlinear intercorrelations were expected).  (d)  Normal distribution of variables was not met for root rot %BAR parameters (incidence and intensity) and second growth species composition surveyderived variables, but was met for the %BAR severity parameter, most ecological and growth and yield variables (checked with normal probability  72 plots).  The methods for evaluating linear model assumptions were taken from Weisburg (1985), Chatterjee and Price (1977), Miller (1986), Zar (1986), and Neter and Wasserman (1974). Root rot dependant variable distributions (%BAR variables) were positively skewed and leptokurtotic; this was due to few high root rot intensity estimates and many low to zero value estimates, resulting in non-normal distributions. Normal probability plots showed near normality for values greater than about 3% basal area reduction. Transformations to reduce heteroscedasticity and improve linearity such as arcsin square root of (%BAR/100) (Zar 1984), folded square root of the %BAR variables, (Tukey 1977), and Taylors power law transformation (Box and Cox 1964), provided no improvement. Some species dynamics variables appeared to have similar distributions, and as with %BAR variables were unresponsive to transformations. As a result, univariate analyses for ecological, stand history and species dynamics were conducted on untransformed variables.  Since two independent datasets were under study (%BAR sample survey estimates  vs. PSP's), graphical and tabular presentations were done to assess similarities between the two datasets, to aid inference and descriptive model building.  Disease incidence relationships were evaluated through tabular analysis of categorical variables, aided by log-linear models. Disease intensity relationships and species dynamics were evaluated generally through construction of multiple linear  73 regression models--for descriptive purposes.  74 7.0 RESULTS AND DISCUSSION  7.1 Data Structure And General Relationships  Study-wide, 153 unique ecological units (244 unique PSP's) were classified and summarized for all variables. Due to later observed inconsistencies, missing data for key variables, and lack of replication, the total sample size was shrunk to 139 ecological units (215 PSP's). The volume of statistics relating to this dataset was enormous, and only the more "significant" findings are reported in this section.  7.1.1^Assessment Of The Percent Basal Area Reduction (%BAR) Variable And It's Components  Of the four %BAR root rot parameter estimates, only two were generally considered for analysis and inference; NSBAR (all-species ?_4.0 cm) and BARS (susceptible species 17.5 cm), respectively representing the smallest and largest root rot damage intensity estimates on average (Fig. 8), and Table 10.  75  Figure 8 Histogram of the the four percent basal  area reduction parameter estimates allsamples mean and (standard error).  nsbar sbar^bars barns Percent Basal Area Reduction Measure  Recall, that one of the %BAR sampling method assumptions was the expected relationships of the %BAR intensity parameters would rank out as per those tabled in Table 4, p.45. The rankings of intensity parameters in Figure 8 match the expected relationships exactly.  The principles of the Percent Basal Area Reduction sampling method are illustrated in part in the following descriptive statistics (Table 10). Table 10 is composed of the components of the %BAR equations for the NSBAR and BARS parameters. Most of the parameters are graphed later in this section, but note the variation of all parameters. Students t-tests were conducted on the incidence, severity subcomponents, resultant severity and %BAR intensity parameters shown in Table 10. The mean estimates of landbased (DSPT) and stand-based (DSWP) incidence were significantly different (p = .000). The mean estimates of the non-susceptible species (NSALH/NSALI) were significantly  76 different (p = .000), as were the susceptible species (SGRH/SGRI), (p = .000). The severity parameters (NSSEV/SEVS) and the %BAR intensity were also significantly different (p = .005) and .0001, respectively).  TABLE 10^  DESCRIPTIVE STATISTICS FOR STUDY-WIDE ESTIMATES OF PHELLINUS ROOT ROT INCIDENCE, SEVERITY, SEVERITY SUB-COMPONENTS AND INTENSITY Intensity  Severity Subcomponents /  Seventy  Incidence  cm Suscept. Infected SGRI  .4.0 cm All Species NSBAR  2_17.5 cm Susceptible BARS  139  139  139  139  1.000  1.250  0.000  -1.900  -9.100  14.460  13.000  11.670  9.333  38.400  54.200  1.490  8.820  12.000  10.420  9.333  40.300  63.300  0.290  0.318  8.958  6.380  7.181  4.882  8.248  9.994  0.049  0.037  0.053  2.853  4.045  3.154  3.038  85.570  110.759  0.164  0.221  0.192  0.231  1.689  2.011  1.776  1.743  9.250  10.524  Standard Error  0.014  0.019  0.016  0.020  0.143  0.171  0.151  0.148  0.785  0.893  Skewness (G1)  0.975  0.641  -0.124  -0.341  0.480  0.728  -0.142  0.278  1.312  1.237  Kurtosis (G2)  0.185  -0.566  -0.621  0.485  0.166  1.471  0.292  0.199  1270  1.772  Coefficent of Variation  0.976  0.840  0.663  0.726  0.189  0.315  0.247  0.357  1.122  1.053  Land-Area DSPT  Stand-Parameter DSWP  ?A.0 cm All Species NSSEV  .17.5 cm Susceptible SEVS  4.0 cm All Spp. Healthy NSALH  4.0 cm All Spp. Infected NSALI  117.5 cm Suscept. Healthy SGRH  139  139  139  139  139  139  Minimum  0  0  -0.140  -0.490  5.64  Maximum  0.680  0.880  0.847  1.000  Range  0.680  0.880  0.987  Mean  0.168  0.263  Variance  0.027  Standard Dev.  No. of Cases  _17.5 2  The %BAR severity sub-components are raw "in" tree counts, yet to be factored by the prism' basal area factor (either 4, 6 or 8 m The %BAR severity sub-components are used in the %BAR calculation; Severity = (healthy minus the infected) / (healthy).  2 ).  78 Perhaps the most important principle of the %BAR sampling method is basal area reduction itself. How often it occurs is simply an estimate of incidence. Basal area reduction is illustrated by comparing mean tree counts of healthy sweeps (e.g., NSALH or SGRH) to infected sweeps (e.g., NSALI or SGRI) respectively (Fig. 9). (Also refer to Table 5, p. 46.) These are the Severity-subcomponents referred to in Table 10, above. Healthy tree counts were significantly greater than infected counts (p =.0001) by about 30% for both all-species and susceptible-only. Recall, that healthy and infected tree counts were used to estimate basal area reduction "severity", calculated in section 6.3.1. The coefficients of variation (CV) for healthy (NSALH) sweeps and infected (NSALI) sweeps were 18.9% and 31.5%, respectively, indicating a larger sampling-variability encountered in portions of stands infected with Phellinus root rot.  12.5 0  Figure 9 Histogram of the mean tree counts by  10.0  species susceptible and disease condition; non-susceptible plus susceptible species, healthy and infected (NSALH and NSALI, respectively), and susceptible species only, healthy and infected (SGRH and SGRI, respectively).  a 7.5 111  0 a  5.0  2.5  a  0.0  NSALH NSALI SGRH SGRI Species Susceptibility Sc Disease Condition  ^  79 7.1.2^Incidence-Severity, Severity-Intensity and Incidence-Intensity Relationships  The probability distributions of incidence, severity, and intensity were assessed for normality with cumulative probability plots and histograms. Land (DSPT) and stand (DSWP) based incidence variables both appear non-normally distributed with many healthy, uninfected sample plots, fewer lightly infected, and very few severely infected (Figs. 10 and 11). The severity variables (NSSEV and SEVS) have near normal distributions (Figs. 10 and 11). Root rot damage intensity parameters (NSBAR and BARS) also appear to be non-normal (Figs. 12, 13, 14 and 15), with high frequencies of zero to low intensities, and decreasing frequencies of higher intensities.  NSSEV  SEVS  I,  DSPT  .^. .^•  •  .......^. • •^.^....^. _  Figure 10  Incidence-Severity relationship of stand-based incidence (DSWP), on the y-axis, to damage severities (NSSEV) and (SEVS), on the xaxis. Note, the DSWP histogram has been rotated 90° clockwise.  Figure 11  1-n--  I-%r  .. .^. ..^._•  ........^• -.^_^•^••.  ..  Incidence-Severity relationship of land-based incidence (DSPT), on the y-axis, to damage severities (NSSEV) and (SEVS), on the xaxis. Note, the DSWP histogram has been rotated 90° clockwise.  ^  80  BARS  NSBAR  n n ,--1 1 -  -  SEVS  NSSEV  .^ • .^. .^.^.^.  '^•  'I: -,.::::: -^• '^/...^%.:^• ' ...'"'sr^.s.•  i•  r . Figure 12  J  ^  Severity-Intensity relationship of all-species damage severity (NSSEV), on the y-axis, to damage intensity (NSBAR), on the x-axis. Note, the NSSEV histogram has been rotated 90° clockwise.  NSBAR  .  •  •  a.....;.  ."  —I—  III  Figure 14  ^  n  BARS  III n,-11 ,  DSPT  •  .^:^...;.." .^.... :a  a..:.:  ..  .C.^•  F  .  :^. -• .:  7.  • 1.1., .1....... ''''"  _  •  . 1 g•.: • . :0.-:  1 h--1  •  Incidence-Intensity relationship of stand-based incidence (DSWP), on the y-axis, to (NSBAR) and (BARS) damage intensities, on the y-axis. Note, the DSWP histogram has been rotated 90° clockwise.  -  .-.-^• •••^• .^..^.. ..^-.^-  • •^•^• .^. ... .^...  •. 4^'  TI n^. . .^ . .  .^. • . .^.  •-.. r.  '• • :,^.......^" ..^■  NSBAR  '^. .^.^.^ .• . _ .^....  .• .^.  Severity-Intensity relationship of susceptible species (212.0/ 17.5 cm) damage severity (SEVS), on the y-axis, to damage intensity (BARS), on the x-axis. Note, the SEVS histogram has been rotated 90° clockwise.  Tl-n— ,,  -  ^.  ^  BARS  Til n,-I 1 n •  DSWP  Figure 13  Figure 15  ^  Incidence-Intensity relationship of land-based incidence (DSPT), on the y-axis, to (NSBAR) and (BARS) damage intensities, on the y-axis. Note, the DSPT histogram has been rotated 90° clockwise.  81 The incidence-severity variables shown in Figures 10 and 11, are significantly (p=.0001) and positively correlated with each other, but not strongly, with Pearsons r values ranging between .323 to .356 (Table 11).  PEARSONS CORRELATION MATRIX OF PHELLINUS ROOT ROT SURVEY SAMPUNG VARIABLES AND PARAMETERS  TABLE 11  BARS^NSBAR^DSPT^DSWP^NSSEV^SEVS BARS  1.00  NSBAR  .874^1.00  DSPT  .811^.792^1.00  DSWP  .836^.817^.923^1.00  NSSEV  .566^.510^.355^.356^1.00  SEVS  .602^.456^.342^.323^.864^1.00  AGE87  .264^.274^.312^.324^.185^.202  AGE87 (p.'s) 1  .035^.025^.004^.002^.617^.363  1  AGE87  1.00  All Pearsons correlations are signficant at the 99% level of significance; Note AGE87 p-values are indicated separately below AGE87 correlations.  The severity-intensity relationships (NSSEV:NSBAR and SEVS:BARS) illustrated in Figures 12 and 13, show signifcant (p=.0001) and positive correlations between variable pairs; Pearsons r values were .510 and .602, respectively (Table 11). Severity displays a positive, quadratic, asymptotic behaviour when box plotted against the BARS-DIC's (Figs. 16 and 17). Although the variability is high, it appears that severity (the proportional difference between healthy and infected variable-radius plot tree counts) basal area stabilizes at about .50 or 50% basal area reduction in the Severe BARS-DIC.  82 Also, the Severe and Medium BARS-DIC's severity are not significantly different (p = .289), but are both significantly different from the Low BARS-DIC (p = .0001).  1.5  z  1.0  0.5 Ot. O  0.0  —0.5  LOW^MEDIUM SEVERE  LOW^MEDIUM SEVERE  Damage Intensity Class  Damage Intensity Class  Figure 16  ^  Boxplots of damage severity (NSSEV) classified by BARSDamage Intensity Classes.  Figure 17  ^  Boxplots of Damage severity (SEVS) classified by BARSDamage Intensity Classes.  The incidence-intensity relationships (DSPT or DSWSP vs NSBAR or BARS) illustrated in Figures 16-19 , show significant (p = .0001) and positive correlations between variables, with Pearsons r values ranging between .792 to .836 (Table 11). Incidence plotted against the BARS-DIC's shows a fairly strong linear relationship, with wide variation in all three classes, whether land-or stand-based incidence (Figs. 18 and 19). Simple linear regression incidence-intensity models indicate that between 63 and 70 percent of the BARS or NSBAR variability can be described by the disease incidence (Table 12). In both cases, stand-based incidence provides a better, slightly less variable estimate of disease  83 intensity compared to land-based incidence. This is not surprising, since the calculation for BARS/NSBAR intensity is partly based on the stand-based incidence.  LOW^MEDIUM SEVERE Damage Intensity Class  Figure 18  ^  Boxplots of land-based disease incidence (DSPT) classified by BARS-Damage Intensity Classes.  Figure 19  ^  Boxplots of stand-based disease incidence (DSWP) classified by BARS-Damage Intensity Classes.  Comparison of land-based incidence to stand-based damage intensity estimates is of particular interest in coastal forests where most root disease sampling has been done using the Intersection Length Method (a land-based disease incidence estimation method). For example, consider solving for BARS damage intensities of 5%, 10% and 15% using land-based disease incidence (DSPT) (see Table 12). Disease incidence levels (DSPT) that respectively equate to 5%, 10% and 15% damage intensity BARS are 7.25%, 17.0% and 26.5%. This suggests that land-based incidence estimates, respectively translate into 45%, 70% and 76% overestimates of the stand-based root rot damage  84 intensity.  TABLE 12^INCIDENCE - INTENSITY RELATIONSHIPS (BARS/NSBAR) AS A FUNCTION OF STAND (DSWP) AND LAND (DSPT) BASED INCIDENCE Simple Linear Equation  R2  SEE  5 and 15 %BAR Equivalencies of (DSWP) and (DSPT) 5 %BAR  15 %BAR  BARS = -.491 + 39.861DSWP BARS = 1.245 + 51.924DSPT  .70 .66  5.79 6.17  14% DSWP 7.25% DSPT  38.86% DSWP 26.50% DSPT  NSBAR = -0.756 + 34.233DSWP NSBAR = 0.737 + 44.578DSPT ^-  .67 .63  5.35 5.66  17% DSWP 9.5 DSPT  46.03% DSWP 32.00% DSPT  7.1.3 Stand-Based:Land-Based and Land-Based:Stand-Based Incidence Relationships  The variance between the stand-parameter-diseased (DSWP) and land-area-diseased (DSPT) incidence expressions is quite large (Table 10), although the linear correlation between the parameters is very high at 0.923 (Table 11). The theory behind the fixedradius and variable-radius sampling would only predict similar incidence estimates if "equivalent" areas were being sampled--this was not the case. The sampling efficiencies of the two expressions were evaluated in two ways: (1) by calculating a stand:land incidence ratio and plotting it over the land-based incidence; the assumption being that the land-based expression is the more accurate estimate and is therefore the baseline (Fig. 20); and (2) by calculating a land:stand incidence ratio and plotting it over the standbased incidence, the assumption being that the stand-based expression is the more  85 accurate estimate and is therefore the baseline (Fig. 21). Note, a constant of .001 was added to both expressions of incidence prior to calculating the ratios, in order to avoid zero-values. The size of the constant will affect the scale of plots but should not affect the subsequent ratios.  In Figure 20 the stand:Iand-based incidence ratio has 10 non-plotted values ranging from 21 to 251 with land-based incidence equal to 0.001. The stand-based expression is clearly very sensitive to Phellinus root rot incidence at the lower land-based levels (<.35 or 35%), and stabilizing at higher incidence levels. The results are not surprising since the land-based expression is based on 0.005 ha (3.99 m fixed-radius plots), and the variable-radius plots, while not sampling land-area, are effectively sampling an average area of 0.027 ha. The variable-radius plot mean percent area sampled was determined as follows: the mean tree dbh marginal sphere was estimated to be about 0.0038 ha (3.5 m radius) multiplied by the mean tree count of 7.18 trees/sweep for a percent area of 0.027 ha.  The land:stand-based incidence ratio has no missing values (Fig. 21). The landbased expression does not seem to be as sensitive at the lower (< .35) end of the standbased incidence, but does stabilize beyond about (> .35). The land-based incidence stabilization appears to be more variable than the stand-based stabilization baselined against stand-based incidence.  86 1.1  7.0  0 a  U a)  6  0  4.0  0.7  Opp 133 033  cfb  0  -0  o pp3 Cr  ^ °  0^0.3  a  0  00^0.1^02^0.3^0.4^0.5^0.6  ^  U]  e  0.1 ^  07  —0.1  Y°J 00  Land—Based Phellinus Incidence  Stand:Iand-based incidence relationship. Note, there are 10 non-plotted observations equal to zero ranging between 21 and 251 on the stand:based incidence ratio axis.  8  ^  o  ^  _^0930 0^o 2:1 cP 0^GI crp  ^ ^  ^  d' o  ^  ^  00 0  0.5 _0  0:)^2.5 - 0 D o ^ ^8 ° -0 - o o^cb^o 4  Figure 20  ^ ^  0 ^  —0.5  ^  0.9  5.5  -0^1.0 4  ^ 03 031113  ^  ^  0 o  09)  ^  ^ 0  ^  ^  ^ ljo  ^^  ^  °. °a 0  0 o^o°  0  000  ^ a  °30  ^  ^^^  ^  ^  0.2  ^  0.4  ^  0.6  ^  0.8  ^  1.0  Stand—Based Phellinus Incidence  Figure 21  Land:stand based incidence relationship.  7.1.4^Comparison Of The %BAR Survey Data To The Permanent Sample Plot (PSP) Data  The two data sets were compared to determine if there is a relationship between PSP-based Phellinus root rot incidence and its associated 1 ha %BAR sample survey root rot estimators (incidence or intensity), since there was no common root rot estimator for both data sets. The sole estimator of Phellinus root rot in the PSP's was a land-based incidence variable (RRIN, respectively 0 or 1 or absence/presence), and similarly so for the 10m PSP buffer variable (RRBF). Root rot estimators for the 1 ha %BAR sample surveys were land-and stand-based disease incidence estimators (DSPT and DSWP, respectively), and %BAR intensity estimators (BARS and NSBAR). The two data sets  87 were compared graphically and tested with Students t-tests.  Infected permanent sample plots (RRIN =1) were found to occur in %BAR sample surveys which had significantly greater levels of BARS, NSBAR, DSWP and DSPT compared to healthy, uninfected PSP's (RRIN =0) (all p-values <.018) (Figs. 22-25). Fivepercent BARS/NSBAR thresholds, and their stand- and land-based incidence equivalents (DSWP and DSPT) were estimated (see Table 12, p. 84) and plotted (Figs. 22-25). The 5% threshold corresponded approximately to the Low-Medium BARS-Damage Intensity Class boundary, and was consistently exceeded by the infected PSP's. In other words, 80% of the infected PSP's occurred in sample stands where Phellinus root rot exceeded 5% BARS/NSBAR, and had a corresponding BARS-Damage Intensity Classification of Medium to Severe.  Figure 22 Boxplots of land-based disease incidence (DSPT) classed by absence (0) and presence (1) of disease incidence in PSP's. The upper and lower y-axis limits (---) refer to the 5% BARS and NSBAR damage intensity threshold.  P. weirii Absence/Presence in PSP  88  Figure 23 Boxplots of stand-based disease incidence (DSWP) classed by absence (0) and presence (1) of disease incidence in PSP's. The upper and lower y-axis limits (---) refer to the 5% BARS and NSBAR damage intensity threshold.  P. weirii Absence/Presence in PSP  P. weirii Absence/Presence in PSP  P. weirii Absence/Presence in PSP  Figure 24  ^  Boxplots of damage intensity (NSBAR) classed by absence (0) and presence (1) of disease incidence in PSP's. The y-axis limits (---) refer to the 5% damage intensity threshold. Note, that 80% of the infected PSP's occur in stands with greater than 5% damage intensity.  Figure 25  ^  Boxplots of damage intensity (BARS) classed by absence (1) and presence (1) of disease incidence in PSP's. The y-axis limits (---) refer to the 5% damage intensity threshold. Note, that 80% of the infected PSP's occur in stands with greater than 5% damage intensity.  89 7.2 Ecological Relationships of Phellinus weirfi  7.2.1^Describing Phellinus Root Rot Variability in Relation to Biogeoclimatic Units  Phellinus root rot variability within biogeoclimatic unit classifications was evaluated by comparison of root rot incidence, severity (in some cases), and intensity.  Disease incidence (or presence) in plots was assessed from several different sampling unit populations:  a)  Phellinus root rot sample surveys (1 ha), n =139,  b)  the Permanent Sample Plots (0.04 ha), n =215 and n =204,  c)  fixed-radius plots (0.005 ha), a land-area-diseased estimates (variable =DSPT), n=3 475 in 139 sample surveys, and  d)  variable-radius plots (area-less but approximates 0.0263 ha), standparameter-diseased estimates (variable= DSWP), n =3 475 in 139 sample surveys.  Damage severity was only evaluated in the site associations (n =139). Damage intensity was assessed graphically via boxplots, followed up by Tukey's Honestly Significant Difference multiple comparison tests of the means across biogeoclimatic units. Intensity was also assessed by comparing BARS-DIC and tested with log-linear models (Wilkinson 1988).  90 7.2.1.1^Zonal (Climatic) Classification And Disease Variability  7.2.1.1.1^Disease Incidence Variability: Zonal Classification  Disease incidence based on the presence/absence of P. weirii in 1 ha sample survey plots did not vary much between subzones or variants (Table 13), but had a very high incidence at 86%-plus. Disease incidence in the PSP's, (n =215 or 204), indicated substantially more PSP's infected in the CWHxm unit(s) compared to the CDFmm.  Incidence estimates show that the CWHxm subzone had a 50%-plus greater incidence of root rot compared to the CDFmm; land-based significant at p = .051 and stand-based at p = .020, respectively. Note, the stand-based incidence estimates are 50 to60% greater than land-based incidence estimates. Incidence estimates for the variants have a similar pattern, with no significant differences between the CWHxm1 and xm2 variants (p = .968). The land-based incidence indicated weak differences between the CDFmm and CWHxm1 (p = .199) and CDFmm and CWHxm2 (p = .154) variants, whereas the stand-based estimate indicated a strongly significant difference between the CDFmm and CWHxm1 variants (p = .047), and only a weak difference between CDFmm and CWHxm2 (p = .154).  91 TABLE 13^PHELLINUS ROOT ROT INCIDENCE BY ZONAL CLASSIFICATION UNITS  BEC Unit  PSP Basis (.04 ha) n=204  PSP Basis (.04 ha) n =215  Sample Survey Basis (1 ha) n=139  Land BasedFixed Radius (.005 ha) Incidence (Std.Err.)  Stand BasedVariable Radius  N  Incidence (Std.Err.)  N  Incidence  N  Incidence  N  Incidence  N  CDFmm  30  .90  55  .25  55  .25  750  CWHxm  109  .86  160  .39  149  .41  2725  CDFmm  30  .90  55  .25  55  .25  750  .117 (.024) 750  CWHxm1  60  .87  96  .38  95  .39  1500  .179 (.021) 1500 .296 (.030)  CWHxm2  49  .86  64  .39  54  .44  1225  .187 (.026) 1225 .274 (.032)  Total/Mean  139  .87  215  .354  204  .368  3475  Subzone .117 (.024) 750  .180 (.030)  .183 (.016) 2725 .286 (.022)  Variant  .168  3475  .180 (.030)  .263  7.2.1.1.2 Disease Intensity Variability: Zonal Classification  Sample survey plot frequencies by BARS-DIC's, varied significantly between the CDFmm and CWHxm subzones (p = .016), as tested by log-linear models. The CDFmm had a substantially lower frequency of Severe damage as compared to the CWHxm subzone (Table 14).  Similarly, sample survey plot frequency by BARS-DIC varied significantly between the CDFmm, CWHxm1 and CWHxm2 subzone variants (p = .011). The significant difference was due to nearly double the frequency of Severely classed plots in the CWHxm1 as  92 compared to CWHxm2, and four to seven times the frequency in the CDFmm variant (Table 14).  TABLE 14^DISTRIBUTION OF PHELLINUS ROOT ROT IN SUBZONES AND VARIANTS AS ESTIMATED VIA THE BARS DAMAGE INTENSITY CLASSIFICATION Frequency of %BAR Sample Surveys with Phellinus Root Rot Subzone Variant  Subzone  BARS-Damage Intensity CDFmm  CWHxm  Total  CDFmm  CWHxm  CWHxm  Total  Low  18  40  58  18  20  20  58  Medium  10  36  46  10  17  19  46  Severe  2  33  35  2  23  10  35  TOTAL  30  109  139  30  60  49  139  Mean, disease intensity estimates (BARS), were compared by t-test and found to vary significantly (p=0.017) between subzones; the means are 5.94% and 11.11%, respectively for the CDFmm and CWHxm subzones (Fig. 26).  93  Figure 26  Boxplots of % Basal Area Reduction-BARS by subzone. Mean BARS damage intensity for the CDFmm and CWHxm subzones are respectively, 5.94% and 11.11%.  Similarly, mean disease intensity estimates (BARS) for subzone variants were compared using Tukey's HSD method. The CDFmm variant was significantly different from CWHxm1 (p=0.007), and from the CWHxm2 (p=.110); the variant means are respectively, 5.94%, 12.88% and 8.93%. No significant difference in mean BARS was seen between the CWHxm1 and CWHxm2 variants (p=.419) (Fig. 27).  94  Figure 27  Boxplots of the % Basal Area Reduction-BARS by subzone variant. Mean BARS damage intensity for the CDFmm, CWHxm1 and CWHxm2 variants are respectively, 5.94%, 12.88% and 8.93%.  7.2.1.2^Vegetation Classification And Disease Variability  7.2.1.2.1^Disease Incidence Variability: Vegetation Classification  Disease incidence based on sample survey plots (1 ha), did not vary much between plant alliances (p.all.'s) or plant associations (p.a.'s) (Table 15). Incidence differences between the Pseudotsuga-Mahonia and Tsuga-Mahonia p.all., and the PseudotsugaMahonia and Thuja-Achlys p.all. were nonexistent (p=.307) to moderately significant  (p=.096) for the land-based estimates, and similarly for the stand-based estimates (p=.149 to p =.107) (Table 15). There is no evidence of differences between the Pseudotsuga-Mahonia and the Thuja-Achlys p.all. (p=.975). Incidence differences  between the Pseudotsuga-Arbutus and Tsuga-Mahonia, and Pseudotsuga-Arbutus and Thuja-Foamflower p.a.'s are non-significant (p=.192) to moderately significant (p=.067)  95 for the land-based estimates, and for the stand-based estimates very weakly (p = .113) to moderately significant (p = .090) (Table 15).  TABLE 15^PHEWNUS ROOT ROT INCIDENCE BY PLANT CLASSIFICATION UNITS BEC Unit  Sample Survey Basis (1 ha) n=139  Land-Based Fixed Radius (.01 ha)  Stand-Based Variable Radius  N  Incidence  Incidence (Std. Error)  Incidence (Std. Error)  66 25 48  .86 .96 .83  .136 (.058) .192 (.029) .200 (.027)  .217 (.068) .312 (.040) .301 (.036)  23 43 25 48  .96 .81 .96 .83  .099 (.022) .156 (.025) .192 (.029) .200 (.027)  .172 (.029) .241 (.034) .312 (.040) .301 (.036)  Plant Alliances Pseudotsuga Mahonia Tsuga Mahonia Thuja Achlys -  -  -  Plant Associations Pseudotsuga Arbutus Pseudotsuga Gaultheria Tsuga Mahonia Pseudotsuga Achlys -  -  -  -  7.2.1.2.2 Disease Intensity Variability: Vegetation Classification  Sample survey plot frequency by BARS-DIC did not vary significantly between the three plant alliances (p=.304), or the four plant associations (p = .183) occurring in the study area. Interestingly, the driest plant association, Pseudotsuga-Mahonia, showed a similar pattern to the frequency in the CDFmm subzone (variant); they were in fact highly correlated. The Tsuga-Mahonia plant association paralleled the frequency pattern of the CWHxm1 variant, with a higher frequency in the Severe BARS-DIC than predicted by the log-linear model, though not as strongly. Overall, the sample survey plot frequency by  96 damage intensity class was inversely related to damage intensity (Table 16).  TABLE 16^DISTRIBUTION OF PHELLINUS ROOT ROT IN PLANT ALLIANCES AND PLANT ASSOCIATIONS ESTIMATED VIA THE BARS DAMAGE INTENSITY CLASSIFICATION Frequency of %BAR Sample Surveys with Phellinus Root Rot  BARS-Damage Intensity Class  Plant Alliance  Plant Association  PALL1 PALL2 PALL3 Total  PASS1 PASS2 PASS3 PASS4 Total  Low  32  7  19  58  12  20  7  19  58  Medium  20  8  18  46  9  11  8  18  46  Severe  14  10  11  35  2  12  10  11  35  TOTAL  66  25  48  139  23  43  25  48  139  Where: PALL1 = Pseudotsuga-Mahon'a, PALL2 = Tsuga-Mahonia and PALL3 = Thuja-Achlys. Where: PASS1 = Pseudotsuga-Arbutus, PASS2 = Pseudotsuga-Gaultheria, PASS3 = Tsuga -Mahonia and PASS4 = Pseudotsuga-Achlys.  Mean BARS estimates for plant alliances were compared using Tukey's HSD method with no significant differences (all p-values > .319); the means are 8.84%, 12.40% and 10.33%, for Pseudotsuga-Mahonia, Tsuga-Mahonia and Thuja-Achlys p.all.'s, respectively (Fig. 28).  97  Figure 28  Boxplots of % Basal Area Reduction-BARS by plant alliances. Note the trend of elevated %BARS in the TsugaMahonia mesic p.all. and tailing-off into the drier and fresher p.all's. Mean BARS intensities for the (1) Pseudotsuga-Mahonia, (2) TsugaMahonia, and (3) Thuja-Achlys, p.all.'s, are respectively 8.84%, 12.40% and 10.33%.  Likewise, mean BARS estimates for plant associations did not vary significantly (all p-values>.206); the means are 6.48%, 10.09%, 12.4% and 10.33% for PseudotsugaArbutus, Pseudotsuga-Gaultheria, Tsuga-Mahonia and Pseudotsuga-Achlys, respectively  (Fig. 29).  Figure 29  Boxplots of % Basal Area Reduction-BARS by plant association. Mean BAR intensities for the (1) Pseudotsuga-Arbutus, (2) Pseudotsuga-Gaultheria, (3) Tsuga-Mahonia and (4) Pseudotsuga-Achlys, p.a.'s, are respectively, 6.48%, 10.09%, 12.40% and 10.33%.  98 7.2.1.3^Site Classification And Disease Variability  7.2.1.3.1^Disease Incidence Variability: Site Classification  Disease incidence, based on sample survey plots (1 ha), did not vary much between site associations, although the Cw-Foamflower s.a. was much lower than other s.a.'s (Table 17). Disease incidence, based on either n =215 or n =204 PSP datasets, showed no differences between s.a.'s with the exception of the zonal site associations Fd-Salal and HwFd-Kindbergia which were weakly different (p=.118) (Table 17). Land-and standbased incidence indicate the general similarity of incidence across s.a.'s, with the exception of weak to moderate differences between Fd-Salal and FdHw-Salal, (landbased, p =.181 and stand-based, p =.058), and the Fd-Salal and HwFd-Kindbergia (landbased, p= .102 and stand-based, p = .075) (Table 17).  TABLE 17^ PHELLINUS ROOT ROT INCIDENCE BY SITE CLASSIFICATION UNITS  BEC Unit  PSP Basis (.04 ha) n=204  PSP Basis (.04 ha) n =215  Sample Survey Basis (1 ha) n=139  Land Based Stand Based Fixed Radius Variable (.01 ha) Radius (-.027 ha)  N  Incidence (Std. Error)  N  Incidence (Std. Error)  N  Incidence (Std. Error)  Fd-Salal  24  .88 (.0663)  40  .25 (.0685)  40  .25 (.0685)  .095 (.021)  .160 (.026)  FdBg-Oregon grape  6  1.00 ( 0 )  13  .23 (.1167)  13  .23 (.1167)  .202 (.081)  .262 (.113)  FdHw-Salal  36  .92 (.0452)  49  .36 (.0686)  49  .36 (.0686)  .189 (.026)  .312 (.037)  HwFd-Kindbergia  54  .89 (.0426)  75  .45 (.0574)  73  .46 (.0635)  .193 (.023)  .297 (.031)  Cw-Foamflower  19  .68 (.1070)  34  .26 (.0752)  25  .32 (.0933)  .141 (.045)  .205 (.053)  TOTAL/MEAN  139  Site Association  .87  .34  .36  Incidence (Std. Error)  .168  Incidence (Std. Error)  .263  99 7.2.1.3.2 Disease Intensity Variability: Site Classification  Sample survey plot frequency by BARS-DIC varied significantly between site associations (p=.057), using a log-linear model. Site associations (s.a.'s) of interest are the Fd-Salal, with a very high frequency of Low severity, and a low frequency of Severe plots and the FdHw-Salal and HwFd-Kindbergia s.a.'s which had a greater than expected frequency of Medium and Severe class plots. In contrast the Cw-Foamflower s.a. had a lower frequency of Medium and Severe plots than expected (Table 18).  TABLE 18^DISTRIBUTION OF PHELLINUS ROOT ROT BY SITE ASSOCIATIONS AS ESTIMATED VIA THE BARS DAMAGE INTENSITY CLASSIFICATION Frequency of %BAR Sample Survey with Phellinus Root Rot Site Associations SA 11  SA 13  SA 21  SA 22  SA 24  TOTAL  Low  15  3  12  18  10  58  Medium  8  2  10  20  6  46  Severe  1  1  14  16  3  35  TOTAL  24  6  36  54  19  139  BARS-Damage Severity Class  Where: SA11=CDFmm-Fd-Salal, SA13 =CDFmm-FdBg-Oregon grape, SA21 =CWHxm-FdHw-Salal, SA22 =CWHxm-HwFd-Kindbergia, SA24 = CWHxm-Cw-Foamflower  Mean BARS estimates for site associations were compared using Tukey's HSD method. Only Fd-Salal differed significantly from FdHw-Salal (p = .066), and was weakly different from HwFd-Kindbergia s.a. (p = .121). Mean BARS are 5.12%, 9.25%, 12.29%, 11.17% and 8.68% respectively, for the Fd-Salal, FdBg-Oregon grape, FdHw-Salal, HwFd-  100 Kindbergia and Cw Foamflower s.a.'s, (Fig. 30). Other BARS comparisons were clearly -  not different (all p-values > .796).  Figure 30 Boxplots of % Basal Area Reduction-BARS by site association. Mean BARS intensities for; Fd-Salal, FdBg-Oregon grape, FdHw-Salal, HwFd-Kindbergia, CwFoamflower, s.a.'s, are respectively, 5.12%, 9.25%, 12.29%, 11.17% and 8.68%.  7.2.2^Correlation Of Ecological Parameters And Phellinus Root Disease  Efforts to find functional relationships between ecological parameters, BEC units, continuous and categorical root rot variables were frought with extreme difficulty because of data heterogeneity (lack of pattern). Ecological parameter--Phellinus root rot relationships were examined using Pearsons correlation, scatterplots and boxplots.  A Pearsons correlation matrix illustrates the non-existent to very weak correlations of BARS to most ecological variables (Table 19). Only SPHFH had a significant correlation at (p < .20) with BARS. Coarse fragment content (CF20) and mineral soil bulk  101 density (MSBDT) were negatively correlated and porosity (PORF) was positively correlated with BARS. Strong outliers appeared to strengthen the Pearsons correlation for several mineral soils and properties, (e.g., mineralizable nitrogen (MSMN), and MeqMg, MeqCa and MeqK). These weak correlations were disregarded due to a known large sampling variance that could not be corrected with the insufficient sample sizes used in this study (Green 1989, pg. 51-59).  Scatterplots for several weakly correlated, and thought-to-be-important, soil physical and chemical variables (CF20, MSBDT, MSBDF, PORF, SLOPE, ROOTDEPTH, MSPH, MSC, MSN, MSMN and MEQCA), illustrate very weak trends with BARS (see Appendix J).  Correlations significant at the 20% level of significance andjor variables of interest, eight ecological variables were boxplotted against subzone variant. Moving from the CDFmm through to CWHxm1 and CWHxm2, slightly-decreasing trends for CF20, MSBDT, MSBDF, MSPH and MEQCA were seen, and slightly-increasing trends were seen for PORF, SLOPE and ROOTDP (see Appendix F for ecological variables plotted against s.a. and Appendix G).  Interestingly, some site ecology variables have distributions strikingly similar to BARS plotted by site association. (Refer to Figure 30, and Figures 31-36, also see Appendix F). Of course many of the variables are integrated (thus correlated) to some degree in  ^  TABLE 19^  PEARSON CORRELATION MATRICES  I^ BARS^AGE87 CF20 MSBDT MSBDF PORF ROOTDP SLOPE MSPH MSC MSN MSMN MEQCA MEQMG MEQK BARS AGE87 CF20 MSBDT MSBDF PORF ROOTDP SLOPE MSPH MSC MSN MSMN MEQCA MEQMG MEQK  II BARS FSUS FINT FRES FDEC AGE87 ST410 BA410 VL410 CRD410 SPHFH SPHCW COMPFH COMPCW  1.000 0.274* -0.133* 0.000 0.001 -0.001 -0.037 0.178* -0.100 -0.010 0.034 -0.125* 0.095* 0.172* 0.188*  1.000 -0.065 0.208 0.115 -0.115 -0.102 -0.127 -0.009 0.012 -0.036 0.039 -0.035 -0.047 0.015  1.000 0.513 0.022 -0.022 -0.018 0.094 -0.023 0.094 -0.179 -0.080 0.029 0.039 -0.005  1.000 0.605 -0.605 -0.024 0.083 0.230 0.068 0.040 -0.122 0.004 -0.117 -0.104  1.000 -1.003 -0.119 -0.105 0.098 -0.064 0.076 -0.104 0.053 -0.036 -0.065  1.000 0.119 0.105 -0.098 0.064 -0.076 0.104 -0.053 0.036 0.065  1.000 0.071 0.114 -0.096 -0.060 -0.190 -0.145 -0.131 0.067  1.000 0.102 -0.053 -0.164 -0.118 0.261 0.290 0.053  1.000 -0.087 -0.111 -0.045 0.068 -0.144 -0.144  1.000 0.314 0.087 0.300 0.182 0.027  BARS  FSUS  FINT  FRES  FDEC  AGE87  ST410  BA410  VL410  CRD410 SPHFH  SPHCW COMPFH  COMPCW  1.000 -0.690 -0.455 -0.509 -0.147 0.002 0.014 0.014 -0.005 -0.140 -0.021 -0.160 0.098  1.000 0.056 -0.004 0.011 -0.090 -0.011 0.033 -0.040 0.152 -0.002 0.168 -0.088  1.000 -0.158 0.192 0.230 0.114 -0.035 0.180 0.117 0.173 0.029 0.081  1.000 0.039 -0.089 -0.095 -0.023 -0.089 -0.026 -0.111 0.058 -0.141  1.000 -0.317 -0.197 -0.265 -0.250 -0448 -0.236 -0.029 -0.059  1.000 0.695 0.339 0.866 0.283 0.309 -0.059 0.189  1.000 0.889 0.945 0.252 0.300 0.006 0.181  1.000 0.758 0.215 0.214 0.049 0.108  1.000 0.272^1.000 0.347^0.274 -0.034^0.333 0.215^-0.081  1.000 -0361 0.699  1.000  1.000 -0.002 0.093 -0.079 -0.040 0.264* -0.087 0.028 0.044 -0.001 -0.344 0.036 -0.175* 0.265*  SELECTED VARIABLES:  1.000 0.087 0.057 0.103 0.105  1.000 0.194 0.193 0.090  1.000 0.879 0.408  1.000 -0.547  1.000 0.408  1.000  (I) Ecological and soils, (II) Ecological and stand variables.^ NOTE: Emboldend correlations are significant at (p5.0.20), Bonferronis, adjusted probability. ^ (*) indicates correlations of interest but not significant (p >0.20)^  1--. o iv  103 the site classification procedure (Pojar et al. 1987 and Banner et al. 1990), for example elevation and percent slope (Figs. 32 and 33). Note, the substantially lower elevation and percent slope conditions for the CDFmm compared to the CWHxm s.a. Values for percent coarse fragments, mineral soil bulk density and mineral soil pH, are greater in the CDFmm s.a.'s compared to the CWHxm s.a.'s (Figs. 31, 34 and 36, respectively), and soil porosity is lower in the CDFmm compared to the CWHxm s.a.'s, (Fig. 35). The soil physical attributes appear to be related to the actual soil moisture and nutrient regimes shown in Table 20. The coarser soils have moderately dry soil moisture regimes. Soil coarse fragment content in particular is used to estimate site soil moisture.  TABLE 20^ACTUAL SOIL MOISTURE AND NUTRIENT REGIMES FOR SITE ASSOCIATIONS IN THE STUDY AREA Fd-Salal  FdBg-Oregon grape  FdHw-Salal  HwFd-Kindbergia  Cw-Foamflower  Actual Soil Moisture Regime (SMR)  MD  MD  MD  SD/F  SD/F  Actual Soil Nutrient Regime (SNR)  VP/M  M/R  VP/M  VP/M  M/R  Where:^MD is moderately dry, SD is slightly dry, F is fresh; and VP is nutrient very poor, M is nutrient medium, and R is nutrient rich, from (Banner et al. 1990)  104  Site Association  Figure 31  Percent (%) coarse fragment content (by volume) by site association. Means L to R are 60, 55, 62, 43 and 40. Note higher CF20's are associated with moderately dry, actual soil moisture regimes (refer to Table 20).  Figure 32  Elevation (m) asl by site association. Means are L to R, 85, 79, 191, 194 and 143 m. Elevation corresponds well with subzones. The CW-Foamflower unit is likely lower due to lower slope positions.  2.0  3  0  1.7  V 1.4 00  0.5 0.2 ^eg,^ v 0,1‘0vir ysa ye^1.2N vegya yitS9'^ VIVW. Cti CO?'  Site Association  Figure 33  Slope (%) by site association. Means are L to R, 5.5, 2.2, 12.4, 10.9 and 7.6%. The CDF units correspond to the gentle coastal plain and CWHxm units to upland mid to lower slope conditions.  Figure 34  Fine fraction (<2 mm) mineral soil bulk density (g/cm 3) by site association. Means L to R; .828, 1.125, .766, 7.16 and .695.  105 90 80 E  E  N  70  y„, .3 60  0  0ca  50 40 0  30 123v s9vo ^tegS  2 veyika016'1.^viv ,  ^vsNa t'cli coesc tl° ,  Site Association  Figure 35  Mineral soil percent porosity by site association. Means L to R; 69, 58, 72, 73 and 74%. Note, porosity is inversely related to MSBDF, high bulk density equals low porosity.  Figure 36  Mineral soil pH by site assocation. Means L to R are; 5.62, 5.18, 5.23, 5.26, and 5.3.  7.3 Stand History And Tree Species Dynamics  Stand history and second growth tree species dynamics were estimated from the %BAR root rot survey, old growth stand history (stump) survey and the PSP data sets.  7.3.1^Old Growth Stand Conditions and Phellinus Variability  Childs (1970) said that the potential for disease carry-over and intensification into subsequent rotations is largely dependent upon the number, size and spatial distribution of inoculum sources. Childs further stated many small well-distributed inoculum sources  106 lead to greater damage than a few large aggregated inoculum sources, because the expanding peripheral (and highly infective) area is greater with the former. Tkacz and Hansen (1982) found current stand infection in second growth Douglas-fir to be related to the spatial distribution of the preceeding stand's diseased stumps. Because neither the stump inoculum size, spatial distribution, nor inoculum (stumps) infectivity were sampled in this study, and the fact that the stump survey data and disease incidence estimates are sample survey means, this could not be checked in this study. Instead, a picture of the old growth forest condition is presented, with a view to interpreting the possible cause and/or effect of susceptible and non-susceptible host species composition and stand density of previous stand conditions on the incidence and intensity of Phellinus root rot in todays second growth stands.  A tabulation of old growth stand conditions related to Phellinus root rot damage intensity (BARS) and various classifications of BARS-Damage Intensity, stand history/origins and biogeoclimatic units is presented in Table 21.  107 TABLE 21^DESCRIPTIVE STATISTICS FOR OW GROWTH STEMS/HA AND SPECIES COMPOSITION BY VARIOUS CLASSIFICATIONS Damage Intensity, Stand Origin, and Ecological Classification  %BAR  Stems/Ha  BARS  SPHFH I SPHCW  Mean Damage Intensity Low Medium Severe  Species Composition  COMPFH  COMPCW  Mean, (Standard Deviation), & [Mean]  l  0.98 10.18 24.68  142(96) 111(70) 87(64)  21(31) 15(24) 29(48)  .82 (.26) [.871] .86 (.24) [.881] .77 (.23) [.750]  .11^(.14) [ .129] .10 (.16) [.119] .21 (.24) [.250]  15.45 10.54 8.22 8.49  63(57) 127(104) 134( 80) 133( 82)  12(26) 10(16) 26(37) 24(35)  .77 (.34) [.840] .93 (.13) [.927] .82 (.24) [.838] .83 (.23) [ .847]  .10 (.17) .07 (.13) .15 (.19) .14 (.18)  Subzone CDFmm CWHxm  5.94 11.11  76(75) 129(82)  11(19) 24(37)  .75 (.35) [.864] .84 (.23) [.843]  .12 (.19) [.136] .13 (.18) [.157]  Subzone Variant CDFmm CWHxm 1 CWHxm2  5.94 12.88 8.93  76(75) 125(84) 134(79)  11(19) 34(45) 12(18)  .75 (.35) [.864] .78 (.25) [.786] .91^(.17) [.918]  .12 (.19) [.136] .19 (.21) [.214] .07 (.10) [.082]  Plant Alliance Pseudotsuga-Mahonia Tsuga-Mahonia Thuja-Achlys  8.83 12.40 10.33  104(89) 133 (88) 130(70)  18(30) 25 (43) 24(35)  .80 (.28) [.852] .81 (.29) [.842] .85 (.20) [.844]  .12 (.17) [.148] .16 (.24) [.158] .13 (.16) [.156]  Plant Association Pseudotsuga-Arbutus Pseudotsuga-Gaultheria Tsuga-Mahonia Pseudotsuga-Achlys  6.48 10.09 12.40 10.33  118(95) 96(85) 133(88) 130(70)  18(39) 18(23) 25(43) 24(35)  .85 (.25) [.867] .77 (.30) [ .842] .81 (.29) [.842] .85 (.20) [.844]  .11^(.16) .13 (.18) .16 (.24) .13 (.16)  [.183] [.158] [.158] [.156]  Site Association Fd-Salal FdBg-Oregon grape FdHw-Salal HwFd-Kindbergia Cw-Foamflower  5.12 9.25 12.29 11.17 8.68  86(81) 37(23) 127(85) 128(83) 138(75)  8(18) 24(19) 25(40) 26(39) 13(18)  .81 (.34) [.915] .51 (.28) [.607] .81 (.25) [.835] .82 (.24) [.831] .94 (.07) [.914]  .06 (.14) .32 (.20) .14 (.16) .16 (.21) .06 (.07)  [.085] [.393] [.165] [.169] [.086]  Stand Origin Wildfire Logged Logged & Burned Logged (Logged + Logged & Burned)  [Mean] 1 represents mean species composition of the mean stems/ha of old growth stand density.  [.16] [.073] [.162] [.153]  108 7.3.1.1^Old Growth Stand Conditions and Phellinus Root Rot Variability Between BARS-Damage Intensity Classes (DIC's)  Stems (or stumps) per hectare of Douglas-fir and western hemlock (SPHFH) are inversely related to the BARS-DIC's (Table 21, Fig. 37). Tukey's HSD tests, showed that the SPHFH in the Low DIC was very weakly different from the Medium (p=.127), and significantly different from the Severe DIC (p = .004). No mean differences were detectable between Medium and Severe BARS-DIC's (p=.378). The relationship of stems \ ha of western red cedar (SPHCW) to DIC's was not as clear, with no substantial differences between DIC's (all p-values > .163) (Table 21, Fig. 38). Interestingly, the SPHCW were greatest in the Low and Severe DIC's, perhaps as a result of a possible host-pathogen dynamic equilibrium.  The contribution of Douglas-fir and western hemlock to the species composition (COMPFH), did not vary significantly between BARS-DIC's (all p-values>.275), though a slightly lower COMPFH was apparent in the Severe DIC. This likely indicated the mortality effects of root rot (Table 21, Fig. 39). On the other hand, the contribution of western red cedar to the species composition (COMPCW) varied significantly between Low and Severe, and Medium and Severe DIC's (p = .016) and (p = .018), respectively, but not between Low and Medium DIC's (p=.402) (Table 21, Fig. 40). The sharp increase in western red cedar composition in the Severe BARS-DIC appears to indicate a shift to more resistant tree species in the presence of increasing levels of Phellinus root rot.  109  LOW^MEDIUM SEVERE BARS--Damage Intensity Class  Figure 37  Boxplots of old growth Douglas-fir and western hemlock stems/ha (SPHFH) classified by BARSDamage Intensity Classes.  Figure 38  Boxplots of old growth western red cedar stems/ha (SPHCW) classified by BARS-Damage Intensity Classes.  a. a.  i.o 0  4.J 1 0.6  E0 0.6  e, 0.4 0  g 02 0.0  6^ LOW^MEDIUM SEVERE  LOW^MEDIUM SEVERE  BARS--Damage Intensity Class  Figure 39  Boxplots of old growth Douglas-fir and western hemlock species composition (COMPFH) classified by BARS-Damage Intensity Classes.  BARS--Damage Intensity Class  Figure 40  Boxplots of old growth western red cedar composition (COMPCW) classified by BARSDamage Intensity Classes.  110 7.3.1.2^Old Growth Stand Conditions and Phellinus Root Rot Variability Between Subzones  The total old growth stems/ha of Douglas-fir and western hemlock (SPHFH) plus western red cedar (SPHCW) were significantly lower (p = .017) in the CDFmm as compared to the CWHxm, 87 vs 153 stems/ha, respectively (Table 21). This translates into the CDFmm having only 57% the old growth stand density of the CWHxm. The higher stand density in the CWHxm is likely attributable to the cooler, moister climate. Similarily, the stand density of SPHFH-only in the CDFmm is 58.9% that of the CWHxm. Interestingly, the mean species old growth species composition of Douglas-fir and western hemlock, (i.e., the susceptible and potential Phellinus weirii inoculum sources), for the CDFmm and CWHxm subzones were virtually identical at 86% and 84%, respectively (Table 21).  Old growth stand conditions at the subzone variant level were more varied within the CWHxm, and remained unchanged for the CDFmm (Table 21). Total stems/ha were slightly higher in the CWHxm1 compared to the CWHxm2. This was attributable to nearly three times the number of western red cedar stems, while stems/ha of Douglas-fir and western hemlock were slightly lower. This also dramatically affected the species compositions, with Douglas-fir/western hemlock (COMPFH) significantly lower (p = .018) and western red cedar significantly higher (p = .001) in the CWHxm1 compared to the CWHxm2. Notably, Phellinus root rot intensity (BARS) was greatest in the CWHxm1 compared to the CWHxm2.  ^ ^  111 7.3.1.3^Old Growth Stand Conditions and Phellinus Variability Between Plant Alliances and Plant Associations  Old growth stand density and species compositions did not vary significantly between p.all.'s (all p-values > .226) or p.a.'s (all p-values > .215). The only noticeable trend was a slightly lower stand density for both Douglas-fir/western hemlock and western red cedar in the drier Pseudotsuga-Mahonia p.all., and in the Pseudotsuga-Arbutus and Pseudotsuga-Gaultheria p.a.'s.  7.3.1.4^Old Growth Stand Conditions and Phellinus Root Rot Variability Between Site Associations  Old growth stand conditions varied considerably between site associations, very closely following the pattern of subzone and variants with the exception of the FdBgOregon grape s.a. Ecologically, it was surprising to see fewer stems/ha and lower  composition of western red cedar in the Cw-Foamflower s.a., where cedar is indicated to be a climax species (Fig. 42). Rather, the greatest stems/ha and species compositions of western red cedar were in s.a.'s with the highest BARS damage intensities, (i.e., FdBgOregon grape, FdHw-Salal and HwFd-Kindbergia) (Figs. 41-44).  112 200  a.  a.  0  cn  0  150 a  0  100  O  U  50  o  a  ve^‘e.N. -  Ye^ a eto^Abe Vac'^ A toeSite Association  Figure 41  Figure 43  ^  Old growth Douglas-fir and western hemlock stems (stumps)/ha by site association. Means L to R are; 86, 37, 127, 128 and 138.  Old growth Douglas-fir and western hemlock species compositions by site association. Means L to R are; .916, .607, .835, .831 and .914.  Figure 42  Figure 44  ^  Old growth western red cedar stems (stumps)/ha by site association. Means L to R are; 8, 24, 25, 26 and 13.  Old growth western red cedar species compositions by site association. Means L to R are; .063, .085, .393, .165, .169 and .086.  113 7.3.1.5^Old Growth Stand Density and Species Composition and Second Growth Phellinus Damage Intensity  The relationship between Phellinus root rot damage intensity and old growth stand density and species composition was also evaluated through contour plots and threedimensional plots. Phellinus damage intensity (BARS) isobars were calculated using a negative exponentially weighted smoothing technique (McLain 1974) and plotted with SYGRAPH (Wilkinson 1988) (Figs. 45 and 46). These figures illustrate the Phellinus root rot relationship between old growth stems/ha Douglas-fir/western hemlock (SPHFH) and stems/ha western red cedar (SPHCW) for the full range of data.  Figures 45 and 46 illustrate three conditions which may have led to (conducive), or reacted to increasing levels of root rot in the old growth stand conditions: (i) decreasing susceptible species density (SPHFH) and increasing density of non-susceptible species (SPHCW), see lower left and lower right contour lobes, (ii) increasing susceptible and non-susceptible density, with emphasis on the latter, see upper right contour lobe, and (iii) increasing non-susceptible density (SPHCW) with stable susceptible density (SPHFH), see the -10% BARS contour which indicates a 10% gain in basal area due to severe Phellinus root rot activity. Two general observations are relevant. First is that second  growth Phellinus root rot intensity appears related to decreasing SPHFH and increasing SPHCW. The high root rot intensities are generally related to high SPHFH (>200 sph) and moderate SPHCW (> 120 sph); or low SPHFH (<200 sph) and moderate to high SPHCW (> 100 sph). Secondly, the most striking feature of all the contour plots is a contour trough between two peaks of high root rot intensity. Arguably the contour trough  114 might be construed as an equilibrium zone.  115  Old Growth Stems/ha—Western Red Cedar  Figure 45  ^  A two-dimensional contour plot illustrating the relationship between damage intensity (BARS) and old growth stand density (stems/ha) of Douglas-fir and western hemlock (SPHFH) and western red cedar (SPHCW).  116  Figure 46  ^  A three-dimensional plot illustrating the relationship between damage intensity (BARS) and old growth stand density (stems/ha) of Douglas-fir and western hemlock (SPHFH) and western red cedar (SPHCW). Data points are represented by (p ) squares.  117 7.3.2^Second Growth - Phellinus Variability Relationships  The relationship of second growth stand conditions to the Phellinus root rot BARS intensities, BARS-DIC's and to biogeoclimatic units was evaluated using PSPestablishment (>4.0 cm) stand attribute data. The stand parameters evaluated were six species composition classes based on susceptibility to Phellinus root rot, (FSUS (Fd, Bg), FINT (Hw), FRES (Pw, PI, Cw) FDEC (Deciduous), FSUSINT (FSUS + FINT) and FRESDEC (FRES + FDEC)), and three stand density parameters, (stems/ha (ST410), basal area (m 2 /ha) (BA410) and Curtis' Relative Density (CRD410)). A summary of parameter sample size, means and standard deviations is given in Table 22; volume/ha (VoI410) is included for comparison only.  TABLE 22^  DESCRIPTIVE STATISTICS FOR SECOND GROWTH SPECIES COMPOSMONAND STAND DENSITIES AND VOLUME ESTIMATES BY VARIOUS CLASSIFICATIONS  Damage Intensity, Stand Origin, and Ecological Classification  %BAR Measured Species Composition at PSP Establishments (> 4.0 cm by volume)  BARS  FSUS^I  FINT^1  FRES^I  FDEC^I  FSUSINT^I  Estimated Stand Densities and Volume at 10 yr (>4.0 cm)  ST410^I  FRESDEC  BA410^1  CRD410^I  VIAIO  n  Mean  Damage Intensity Low Medium Severe  58 46 35  0.98 10.18 24.68  .906(.110) .868(.120) .894(.137)  .026(.054) .062(.079) .055(.110)  .039(072) .017(047) .027(.048)  .027(066) .053(.0E3) .021(054)  .932(.098) .930(086) .949(072)  .067(.095) .070(.086) .048(.064)  2 218(1 659) 2 074(1 324) 2 033( 953)  7.96(2.97) 7.85(2.74) 8.14(238)  2.94(133) 2.92(1.10) 3.00(0.93)  23.93( 8.98) 23.67( 8.41) 24.43( 8.19)  Stand Origin Wildfire Logged Logged & Burned Logged + (Logged & Burned)  30 13 96 109  15.45 1054 8.22 8.49  .871(136) .881(.164) .898(110) .896(.177)  .051(.079) .073(151) .039(.067) .043(.081)  .047(072) .022(043) .024(057) .024(.055)  .029(.069) .017(.050) .038(073) .036(.071)  .922(.091) .954(082) .937(.088) .939(087)  .076(.085) .038(.060) .062(087) .060(.085)  1 812( 969) 1 823( 870) 2 262(1 540) 2 210(1 480)  7.98(3.13) 8.02(2.19) 7.96(2.70) 7.96(2.01)  2.84(1.14) 2.80( .81) 3.00(1.20) 2.98(1.16)  22.73(10.47) 24.46( 8.08) 24.28( 8.12) 2430( 8.08)  Subzone CDFmm CWHxm  30 109  5.94 11.11  .910(119) .885(121)  .004(.011) .056(.087)  .037(.081) .026(052)  .046(.078) .031(.068)  .915(.106) 941(.081)  .083(.102) .058(079)  1 777( 879) 2 219(1 492)  7.29(2.67) 8.16(2.74)  2.64( .92) 3.03(1.20)  20.78( 7.76) 24.85( 8.58)  Subzone Variant CDFmm CWHxml CWHxm2  30 60 49  5.94 12.88 8.93  .910(.119) .898(.112) .869(131)  .004(.024) .031(.062) .088(102)  .037(.081) .038(.064) .012(.029)  .046(.078) .032(.075) .030(059)  .915(.106) .929(.093) .957(062)  .083(.102) .070(.090) .042(061)  1 777( 879) 2 629(1 757) 1 718( 864)  7.29(2.67) 8.50(333) 7.62(1.66)  2.64( .92) 330(1.45) 2.70( .68)  20.78( 2.76) 24.70( 9.52) 25.04( 7.29)  Plant Alliance Pseudotsuga-Mahonia Tsuga-Mahonia Thuja-Achlys  66 25 48  8.83 12.40 1033  .930(087) .892(141) .834(.130)  .013(037) .071(122) .076(.082)  .025(.055) .021(043) .037(072)  .031(066) .016(046) .048(084)  .943(.078) .963(.057) .910(107)  .057(.078) .037(.057) .085(.101)  2 562(1 722) 2 128( 854) 1 520( 759)  8.24(3.10) 8.20(1.97) 7.48(253)  3.16(135) 3.06( £4) 2.59( .92)  22.92( 8.55) 25.11( 5.91) 24.83( 9.63)  Plant Association Pseudotsuga-Arbutus Pseudotsuga-Gaultheria Tsuga-Mahonia Pseudotsuga-Achlys  23 43 25 48  6.48 10.09 12.40 10.33  .908(.089) .942(.085) .892(141) .834(.130)  .001(.006) .019(.044) .071(122) .076(.082)  .042(078) .017(.036) .021(043) .037(072)  .049(073) .022(.060) .016(046) .048(084)  .910(.088) .961(.065) .963(057) .910(.107)  .090(.088) .039(.065) .037(057) .085(101)  2 890(2 212) 2 386(1 391) 2 128( 854) 1 520( 758)  933(4.18) 7.66(2.16) 8.20(1.97) 7.48(253)  3.61(1.82) 2.93( .96) 3.06( .84) 259( .92)  25.76(11.09) 2L40( 6.48) 25.11( 5.91) 24.83( 9.63)  Site Association Fd-Salal FdBg-Oregon grape FdHw-Salal HwFd-Kindbergia Cw-Foamflower  24 6 36 54 19  5.12 9.25 12.29 11.17 8.68  .922(090) .863(.205) .926(102) .875(120) .834(139)  .000(000) .022(053) .032(.065) .070(097) .064(086)  .035(072) .048(118) .028(.050) .027(.056) .019(048)  .043(067) .057(120) .014(047) .025(.051) .083(111)  .922(.090) .885(164) .958(064) .945(074) .897(115)  078(.090) .105(148) .042(464) .052(.068) .103(115)  1 853( 950) 1 472( 429) 2 801(1 925) 2 173(1 197) 1 252( 509)  7.46(2.82) 6.60(2.00) 835(3.40) 8.16(2.51) 7.77(1.94)  2.71( 98) 2.38( .65) 3.25(155) 3.05(1.05) 256( .66)  20.81( 8.12) 20.65( 6.77) 22.29( 9.25) 25.41( 7.93) 28.14( 8.06)  Mean and (Standard Deviation),  Where:^FSUS is Douglas-fir and grand fir, FINT is western hemlock; FRES is lodgepole pine, western whi e and western red cedar; FDEC is deciduous spp:; FSUS1NT =FSUS + FINT and FRESDEC =FRES + FDEC. Stand attributes are back-estimated to age 10 yr (z4.0 cm) are: stems/ha (ST410), basal area (m 2/ha) (BA410), Curtis' relative density (CRD410) and volume (m 3/ha) ('L410).  ^ ^  119 7.3.2.1^Second Growth Stand Conditions and Phellinus Variability in the BARS Damage Intensity Classes  Virtually no variation of susceptible (FSUSINT) or non-susceptible (FRESDEC) species compositions were seen between the BARS-DIC's.  7.3.2.2^Second Growth Stand Conditions and Phellinus Variability in the Subzones at PSP Establishment  No substantial difference in species compositions were detectable between the CDFmm and the CWHxm subzones. The FSUSINT compositions are within 3.6% of each other, and the FRESDEC compositions are within 2.5% of each other (Figs. 47 and 48, respectively). Note, that the susceptible species composition is slightly greater in the CWHxm subzone compared to the CDFmm, with the opposite conditions for the nonsusceptible species composition.  120  o 0.9  0.4  Va)  ut 0.8 E. z  up 0.3  0.7  E  0.6  0.1  O  •  •  CDFmm  0.0  CWHxm  CDFmm  Subzone  Subzone  Figure 47  ^  First PSP measure Fd, Bg and Hw (susceptible and intermediate) species composition by subzone. Means L to R are: .915 and .949, respectively for the CDFmm and CWHxm subzones.  H  CWHxm  Figure 48  ^  First PSP measure PI, Pw, Cw and Deciduous (resistant) species composition by subzone. Means L to R are: .083 and .058, respectively for CDFmm and CWHxm subzones.  All three measures of stand density (stems/ha, basal area/ha and Curtis' relative density) back-estimated to reference age 10 yr indicated a greater density condition in the CWHxm subzone compared to the CDFmm subzone, though none are significantly greater (p-values are respectively .123, .124 and .104) and (Figs. 49, 50 and 51, respectively). Phellinus root rot intensity appears to have the same pattern as stand density across the subzones (see Fig. 26, pg. 93).  121  Figure 49  Back-estimated stems/ha, k4.0 cm at reference age 10 yr by subzone. Means L to R are: 1 777 and 2 219, respectively for the CDFmm and CWHxm subzones.  Figure 50  Figure 51  Back-estimated basal area (nn2 /ha), k4.0 cm at reference age 10 yr by subzone. Means L to R are: 7.29 and 8.26, respectively for the CDFmm and CWHxm subzones.  Back-estimated Curtis' relative density, k4.0 cm at reference age 10 yr by subzone. Means L to R are: 2.64 and 3.03, respectively for the CDFmm and CWHxm subzones.  122 7.3.2.3^Second Growth Stand Conditions and Phellinus Variability in the Subzone Variants at PSP Establishment  Susceptible species compositions (FSUSINT) increased slightly from the CDFmm through the CWHxm1 and CWHxm2 subzone variants. The mean compositions are 91.5%, 92.9% and 95.7% respectively (Fig. 52 and Table 22). The relatively high level in the CWHxm2 is due to a substantially greater composition of western hemlock compared to the other subzone variants. Concomitant and opposite compositions are seen for the non-susceptible species (Fig. 53 and Table 22).  z 0  10  0 t.)0.9  cl 0.8 E.  rip 0.7  I h  E ' )  0.6  0.5  CDFmm CWHxml CWHxm2 Subzone Variant  Figure 52 First PSP measure of Fd, Bg and Hw (susceptible and intermediate) species composition by subzone variant. Means L to R are: .915, .929 and .957, respectively for the CDFmm, CWHxm1 and CWHxm2 subzone variants.  Figure 53  ^  First PSP measure of PI, Pw, Cw and Deciduous (resistant) species composition by the subzone variant. Means L to R are: .083, .070 and .042, respectively for the CDFmm, CWHxm1 and CWHxm2 subzone variants.  123 Measures of stand density back-estimated to age reference 10 yr do not display the same patterns across the variants as the species compositions. In fact, mean stand density measures are all lowest in the CDFmm variant, peak in the CWHxm1 and then fall to levels slightly above the CDFmm in the CWHxm2 variant (Figs. 54, 55 and 56 and Table 22). Phellinus root rot intensity appears to have the same pattern as stand density across the subzone variants (Fig. 27, pg. 94). The CWHxm1 back-estimated stems/ha was significantly different from the CDFmm and CWHxm2 variants (p=.012) and (p = .001).  CDFmm CWHxml CWHxm2 Subzone Variant  Figure 54  ^  Back-estimated stems/ha, A.0 cm at reference age 10 yr by subzone variant. Mean L to R are: 1 777, 2 629 and 1 718, respectively for the CDFmm, CWHxm1 and CWHxm2 subzone variants.  Figure 55  ^  Back-estimated basal area (m 2 /ha), A.0 cm at reference age 10 yr by subzone variant. Means L to R are: 7.29, 8.60 and 7.62, respectively for the CDFmm, CWHxm1 and CWHxm2 subzone variants.  124  Figure 56  Back-estimated Curtis' relative density, z4.0 cm at reference age 10 yr by subzone variant. Means L to R are; 2.64, 3.30, and 2.70, respectively for the CDFmm, CWHxm1 and CWHxm2 subzone variants.  The CWHxm1 back-estimated basal area/ha was moderately different from the CDFmm (p= .076) and CWHxm2 (p = .144). Similar to the stems/ha, back-estimated Curtis' relative density for the CWHxm1 was significantly different from the CDFmm (p = .023) and CWHxm2 (p = .014) variants.  7.3.2.4 Second Growth Stand Conditions and Phellinus Variability in the Plant Alliances  Susceptible species compositions (FSUSINT) rose from the Pseudotsuga-Mahonia p.all. (94.3%), peaked in the Tsuga-Mahonia p.all. (96.3%), and fell to (91.0%) in the Thuja-Achlys p.all. The pattern somewhat mimics the Phellinus intensity levels observed  (Table 22). Non-susceptible species compositions (FRESDEC) were opposite to the conditions described for the susceptible compositions (Table 22). Figures for the plant alliance relationships are not shown.  125 Measures of stand density were consistent across all parameters and were greatest in the Pseudotsuga-Mahonia p.all. and fell dramatically in the Thuja-Achlys p.all. (Table 22). No apparent trend between p.all.'s and Phellinus root rot intensity patterns was observed.  7.3.2.5 Second Growth Stand Condition and Phellinus Variability in the Plant Associations  Susceptible species compositions (FSUSINT) were lowest in the Pseudotsuga-Arbutus and Pseudotsuga-Achlys p.a., (both 91.0%) (Table 22), and were greatest in the Pseudotsuga-Gaultheria and Tsuga-Mahonia (96.1% and 96.3%, respectively) (Table 22).  The Pseudotsuga-Arbutus p.a. comprised the majority of the CDFmm-Fd-Salal s.a.'s samples, and also the majority of the CDFmm subzone/variant samples. No apparent trend between susceptible species composition and Phellinus root rot intensity was observed. Figures for the plant association relationships are not shown.  Measures of stand density are less consistent across the p.a.'s than across the p.all.'s (Table 22). Stems per hectare are greatest in the Pseudotsuga-Arbutus p.a. (2 890 sph) dropping consistently with increasing soil moisture conditon to the PseudotsugaAchlys p.ass. (1 520 sph) (Table 22). There was clearly no trend with the observed Phellinus root rot intensities and stems per hectare across the plant associations. Basal  area and Curtis' relative density measures vary across the plant associations, with no apparent relationships between density and Phellinus root rot intensities. However, if the driest plant association (Pseudostuga-Arbutus) is not considered, it appears that basal  126 area has a similar pattern to Phellinus root rot intensity (Table 22). Note that the Phellinus intensity peaked in the Tsuga-Mahonia p.a. as did the basal area, dropping to  near-equal values in the Pseudotsuga-Gaultheria and Pseudotsuga-Achlys p.ass's. Curtis' relative density had almost the same pattern as the basal area measures, but was slightly more difficult to interpret.  7.3.2.6 Second Growth Stand Conditions and Phellinus Variability in the Site Associations  The pattern of susceptible species compositions (FSUSINT) was not very consistent with the Phellinus root rot intensity when viewed within site associations (Fig. 57 and Table 22). However, when FSUSINT was viewed within a subzone (i.e., at the site series level), the susceptible species composition was positively correlated with Phellinus root rot intensity in the CDFmm units, and even more so within the CWHxm units. The extreme variability of FSUSINT in the FdBg-Oregon grape s.a. and the very small sample size (n = 6) confounds the interpretation in this unit. The opposite relationships are true for the non-susceptible species composition (Fig. 58 and Table 22).  127  Site Association  Figure 57  ^  First PSP measure of Fd, Bg and Hw (susceptible and intermediate) species composition by site association. Means L to R are: .922, .885, .958, .945 and .897, respectively for the Fd-Salal,  FdBg-Oregon grape, FdHw-Salal, HwFd-Kindbergia and CwFoamflower s.a.'s.  Figure 58  ^  First PSP measure of PI, Pw, Cw and Deciduous (resistant) species composition by site association. Means L to R are: .078, .105, .042, .052 and .103, respectively for the  Fd-Salal, FdBg-Oregon grape, FdHw-Salal, HwFd-Kindbergia and Cw-Foamflower s.a.'s.  As above, stand density measures viewed at the s.a. level were difficult to interpret, but viewed within the subzone, (at the site series) the relationships to Phellinus root rot intensity became clearer. Consistently the FdBg-Oregon grape s.a. was difficult to interpret for the reasons stated above.  Generally, stems/ha, basal area/ha and Curtis' relative density all appeared to be positively related to Phellinus root rot intensity across all s.a.'s, (refer to Fig. 30, pg. 98). This was particularly obvious in the CWHxm s.a.'s (Figs. 59, 60 and 61 and Table 22). Figures 62, 63 and 64 illustrate the stand density measures at PSP establishment, and  128 provide a comparison to the back-estimated measures to reference age 10 yr. Also, note the generally similar pattern of the PSP establishment stand conditions to the reference age 10 yr conditions.  Site Association  Figure 59  ^  Back-estimated stems/ha, z4.0 cm at reference age 10 yr by site association. Mean L to R are: 1 853, 1 472, 2 801, 2 173 and 1 252 for the Fd-Salal, FdBg-Oregon grape, FdHw-Salal, HwFdKindbergia and Cw-Foamflower s.a.'s.  Figure 60  ^  First PSP measure of stems/ha, 4.0 cm at reference age 10 yr by site association. Mean L to R are: 1 875, 1 379, 2 798, 2 001 and 1 072 for the Fd-Salal, FdBg-Oregon grape, FdHw-Salal, HwFdKindbergia and Cw-Foamflower s.a.'s.  129  Figure 61  Back-estimated basal area (m 2 /ha), z4.0 cm at reference age 10 yr by site association. Means L to R are: 7.46, 6.60, 8.35, 8.16 and 7.77 for the Fd-Salal, FdBg-  Figure 62  Oregon grape, FdHw-Salal, HwFdKindbergia and Cw-Foamflower s.a.'s.  First PSP measure of basal area (m 2 /ha), z4.0 cm at reference age 10 yr by site association. Means L to R are: 38.2, 38.1, 33.3, 32.7 and 36.9 for the Fd-Salal, FdBgOregon grape, FdHw-Salal, HwFdKindbergia and Cw-Foamflower s.a.'s.  Figure 63  Back-estimated Curtis' relative density, z4.0 cm at reference age 10 yr by site association. Means L to R are: 2.71, 2.38, 3.25, 3.05 and 2.56 for the Fd-Salal, FdBgOregon grape, FdHw-Salal, HwFdKindbergia and Cw-Foamflower s.a.'s.  130  Figure 64  First PSP measure of Curtis' relative density, k4.0 cm at reference age 10 yr by site association. Means L to R are: 9.1, 8.7, 9.0, 8.2 and 7.8 for the Fd-Salal, FdBg-Oregon-grape, FdHw-Salal, HwFd-Kindbergia and Cw-Foamflower s.a.'s.  7.3.3^Second Growth Species Dynamics  The basis for investigating species composition shifts is that susceptible species commonly appear to be replaced with less-susceptible species as the former dies out due to Phellinus root rot activity. Variable-radius plot sample data were used to estimate second growth species dynamics (shifts) between healthy and infected disease conditions by evaluating: (i) species compositions, (ii) species composition ratios, and (iii) nonsusceptible species composition stratified by diameter "age" class. Species compositions were based on the mean tree counts from the %BAR sample surveys. The results are also compared against species composition shifts observed in the PSP's. The three estimates were very comparable. Overall, a net gain of non-susceptible species composition (with a corresponding drop in susceptible species composition) in Phellinus infected stand conditions compared to healthy stand conditions.  131 7.3.3.1^Second Growth Species Dynamics Estimates from Variable-Radius Plot Sample (%BAR Survey) Data  7.3.3.1.1 Comparison of Species Compositions by Disease Condition  Species compositions were stratified by species susceptibility class and infection condition. Comparison of percent species compositions stratified by disease condition indicated a drop in susceptible species composition and an increase in non-susceptible species in infected portions of stands, (Table 23). In healthy stand conditions, the mean susceptible and non-susceptible species compositions were 91.52%, and 8.48%, respectively, (Fig. 65), whereas, in infected conditions, mean susceptible and nonsusceptible species compositions are 87.06%, and 12.94%, respectively, (Fig. 66). The difference in susceptible species composition between disease conditions (-4.46%), while significantly different by t-test (p = .000), only represents a 4.87% drop from its healthy level. This is in sharp contrast to the non-significant t-test differences (p = .274) for the non-susceptible composition (-4.46%), which actually represents a 52.6% relative gain in non-susceptible species composition from the healthy condition.  The high variability of non-susceptible species composition shifts in infected conditions is likely due to; (i) a low and scattered composition, and (ii) ingrowth rates are slow and highly light dependent, which is in turn dependent on the size of and length of time since canopy gaps were created (in this case due to Phellinus root rot). In contrast, the lower variability of susceptible species composition shifts is mainly due to mortality or premature windthrow that almost always follows the infection of trees by Phellinus root rot.  ^  132 TABLE 23^DESCRIPTIVE STATISTICS FOR SPECIES COMPOSITION BY DISEASE CONDITION Statistic  Non-Susceptible Species Healthy (NALH)  I^Infected (NALI)  Susceptible Species Healthy (SALH)  Infected (SALI)  Mean Tree Count  0.759a^0.839a  8.187b^5.646c  Standard Deviation  0.782^1.158  1.669^2.037  Standard Error  0.066^0.098  0.142^0.173  Spp. Composition  8.48^12.94  91.52^87.06  Absolute  +4.46  - 4.46  NALH is Non-susceptible spp., k4.0 cm, Healthy ^NALI is Non-susceptible spp., k4.0 cm, Infected SALH is Susceptible spp., k4.0 cm, Healthy ^SALI is Susceptible spp., z4.0 cm, Infected  Mean tree-counts followed by disimilar letters are significantly different at ( a^<.05).  Figure 65  Second growth species composition for healthy stand conditions. Non-susceptible species (NALH), (pines, western red cedar and deciduous spp.), susceptible species (SALH), (grand fir, Douglas-fir and western hemlock) have mean compositions of 8.48% and 91.52%, respectively (y-axis limits).  Figure 66  Second growth species composition for infected stand conditions. Non-susceptible species (NALH), (pines, western red cedar and deciduous spp.), susceptible species (SALH), (grand fir, Douglas-fir and western hemlock) have mean compositions of 12.95% and 87.06%, respectively (y-axis limits).  133 7.3.3.1.2 Species Composition Shifts Stratified By Disease Condition And Diameter Limit Classes  Ratios of non-susceptible-only, and susceptible-only to the combined total species composition, within a disease condition, and nested within diameter "age" limit classes, were compared. Two diameter limit classes were used as analogs of "age classes", assuming a close relationship between age and diameter. The larger diameter limit class (>_1 2.0/17.5 cm) may be analogous to an "older age" class comprised of mainly dominant and codominant crown class trees. The lower diameter limit class (.4.0 cm) may be analagous to a "younger age" class, which includes all ages ("younger and older"). The 4.0 cm class was comprised of dominants/codominants but also a greater amount of suppressed and intermediate crown class trees, some of which were assumed to be younger as a result of ingrowth in response to Phellinus root rot activity. Unfortunately a sharp distinction between classes was not possible using this analysis. Diameter limit tree tallies, basal area and species composition derivations are shown in Section 6.3.4.2 and Table 5, p. 46, and species composition ratio derivations are shown in Table 24, pg. 134.  Bar graphs of the mean species compositions, grouped on a subzone basis, showed a consistent trend of higher proportions of non-susceptible species in infected conditions (N4, N2), compared to healthy conditions (N3, N1) (Fig. 67). Note the greater proportions of non-susceptible species composition in the >4.0 cm diameter limit class (N2), compared to the ?_12.0/17.5 cm diameter limit class (N4) (Fig. 67). Virtually the opposite relationships existed for susceptible species compositions; they were reduced  134 in infected conditions (S4, S2) compared to healthy conditions (S3, S1) (Fig. 68). Again, the susceptible composition was greater in the 12.0/17.5 cm diameter limit class (S4) compared to the 4.0 cm diameter class (S1) (Fig. 68).  Net changes in species compositions (infected minus healthy) shown in Table 24, and illustrated in Figure 68, indicated a study-wide net increase in non-susceptible species composition of 4.4% with a corresponding net decrease of -2.6% in susceptible species composition. Variation due to diameter limit "time/age" is minor, while increases/decreases were larger in the CDFmm subzone compared to the CWHxm subzone.  TABLE 24^SPECIES COMPOSITION RATIO VARIABLE DEFINITIONS Diameter Limits (24.0 cm) "Younger, All-Age"  (12.0/17.5 cm) "Older Age"  Non-susceptible species: (Pines, Cedar and Deciduous) N1 = NALH / NSALH (Healthy) N2 = NALI / NSALI (Infected) DNBA = N2 - N1  N3 = NGRH / NSGRH (Healthy) N4 = NGRI / NSGRI (Infected) DBAN = N4 - N3  Susceptible species: (Douglas-fir, grand fir and hemlock) S1 = SALH / NSALH (Healthy) S2 = SALI / NSALI (Infected) DSBA = S2 - S1  S3 = SGRH / NSGRH (Healthy) S4 = SGRI / NSGRI (Infected) DBAS = S4 - S3  135  Figure 67  Non-susceptible species composition by diameter limit disease condition and subzone (see also Table 24). Note, the greater composition in the lower (4.0 cm) diameter limit and in the infected compared to the healthy conditions. Also, note the difference between consecutive pairs are shown in Fig. 69.  Figure 68  Susceptible species composition by diameter limit disease condition and subzone (see also Table 24). Note, the greater composition in the larger (212.0/17.5 cm) diameter limit and in the healthy compared to the infected conditions. Also, note the differences between consecutive pairs are shown in Fig. 57(c).  Figure 69 Changes in non-susceptible and susceptible species composition, as derived and defined in Table 22. Overall, the non-susceptible species change (DBAN/DNBA) was +4.4%, while susceptible species net change (DBAS/DSBA) was -2.6%.  136 7.3.3.1.3^Comparison of Non-susceptible Species Compositions Between Diameter Classes and Disease Condition  The basis for investigating non-susceptible species composition shifts between diameter classes was that susceptible species composition was thought to decrease nonsusceptible species composition increase in response to P.weirii activity relative to healthy conditions (and shown in sections 7.3.2.1.1 and 7.3.2.1.2). Continuing with this notion, the non-susceptible composition should be greater in a truly smaller "younger age" diameter class (>4.0 <12.0/17.5 cm) relative to a larger "older age" diameter class (>_1 2.0/17.5 cm) in the presence of root rot.  Comparison of non-susceptible species composition between diameter classes showed no significant differences between the larger (>_1 2.0/17.5 cm) and smaller (>4.0 < 12.0/17.5 cm) diameter classes, stratified by disease condition using paired t-tests, (n =139, a < 0.10). The population distributions were not normal, but Wilcoxon rank sign tests indicated similar results.  A further stratification by Phellinus root rot intensity (BARS < 20% vs. BARS20%; n =116 and 23, respectively) was tested, which implied older, more damaged stand conditions for BARS>20%, and thus more time to allow for diameter-measured shifts towards increased non-susceptible species composition, if in fact it occurs (Table 25).  137 TABLE 25^COMPARISON OF NON-SUSCEPTIBLE SPECIES COMPOSITION (NSSppC) Diameter Class & Disease Condition  Mean NSSppC Tree Count By Root Rot  BARS 220%  BARS <20%  BARS a20%  Mean Diff.  P.  NGRH vs. NGRI  .588 vs .577 as  .616 vs .475 cc  0.141  p=.533  NLSH vs. NLSI  .177 vs .152 bb  .114 vs .163 de  -0.050  p=.095  NALH vs. NALI  .765 vs .837 )0(  .730 vs .847 yy  -0.117  p=.119  NGRH^is Non-susceptible, 212.0/17.5 cm Diameter limit, Healthy, NGRI^is Non-susceptible, 212.0/17.5 cm Diameter limit, Infected, NALH^is Non-susceptible, 24.0 cm Diameter limit, Healthy, NALI^is Non-susceptible, 24.0 cm Diameter limit, Infected, NLSH^is Non-susceptible, 24.0 cm <12.0/17.5 cm Diameter limit, Healthy, NLSI^is Non-susceptible, 24.0 cm <12.0/17.5 cm Diameter limit, Infected. Probability of differences were determined using paired t-tests on the mean differences between the first and latter variables in each pair.^Pairs followed by the same letter are not significantly different at^a^<0.10.  The paired t-tests indicated no significant differences between non-susceptible species composition diameter-disease conditions for the < 20% BARS stratification (all pvalues > .401). Although no significant differences were detectable, it is notable that the mean infected non-susceptible species composition (NALI) was in fact greater than the mean healthy non-susceptible species composition (NALH), thus emphasizing a consistent pattern of non-susceptible species composition increases due to Phellinus root rot incidence. However, in the >20% BARS class, the smaller 'younger' diameter class had significantly greater non-susceptible species composition (p =0.095), in the infected condition (NLSI), compared to the healthy condition (NLSH). The larger 'older' diameterinfection condition classes (NGRH vs NGRI) were not significantly different (p=0.553). The results are consistent with the hypothesis. Since the sample stands are virtually pure  138 Douglas-fir, there was no expectation of unequal levels of non-susceptible species composition in the larger predominant tree cover (>12.0/17.5 cm). Consistent with disease dependent diameter class shifts, infected non-susceptible species composition was much lower in the larger diameter class than the healthy; and in the smaller diameter class, the infected non-susceptible species composition was much larger than in the healthy condition.  7.3.3.2 Second Growth Species Dynamics From the PSP's Measurement Record  Species composition shifts in the PSP data (4.0 cm) were analysed in order to determine if some time dependent corroboration of the survey data existed. The PSP's have been measured on average for 30 to 35 yrs. The non-susceptible species composition was based on the sum of Tolerant (hemlock), Intermediate (pines) and Resistant (cedar) species (variable name is TIR), calculated for the first and last PSP measurements available, respectively FTIR and LTIR. The change in TIR species was calculated, (LTIR-FTIR =variable TIRD), and compared between PSP disease condition (RRIN =0 or 1, healthy or infected) and BEC units using t-test (Figs. 70 and 71 and Table 26). Similarly, the susceptible species compositions, (FSUS and LSUS) and changes (SUSD), were calculated and tested. The inclusion of western hemlock in the nonsusceptible species class makes this test not directly comparable to the %BAR sample survey species classification previously discussed in section 7.3.3.1. The author has observed that a great deal of western hemlock regenerates in infection centre openings and that hemlock regeneration is uncommonly infected by Phellinus root rot. SUSD was  139 tested with and without western hemlock and found similar results as presented, but of a slightly lower and more variable magnitude.  P. weirii Absence/Presence in PSP  Figure 70  ^  Net changes in non-susceptible second growth species composition over 30 to 35 yr in healthy and infected PSP's. Nonsusceptible species are; western hemlock, western white and lodgepole pines and western red cedar.  P.weirii Absence/Presence  Figure 71  ^  Net changes in susceptible second growth species composition over 30 to 35 yr in healthy and infected PSP's. Susceptible species are; grand fir and Douglas-fir.  Overall, the mean increase in TIRD species composition in PSP's infected with P.weirii is 3.7%, netted to 3.5% after subtraction of the 0.2% TIRD gain indicated in  healthy PSP's (Table 26). The difference is highly significant (p = .000), whether from separate or pooled variance t-tests of the means. The mean change in susceptible species (SUSD) is opposite to non-susceptible species; in healthy PSP's, SUSD increased by 0.6%, and in infected PSP's, SUSD decreased by 2.1% for a net decrease of 1.5%.  140 The susceptible species composition changes are also highly significant (p =0.006).  Proportional increases in TIR species composition were largest in the zonal site associations, and in the CDFmm and CWHxm2 subzone variants as indicated by the change factor ((TIRD  RR1N  . i - TIRD  RRIN=0  ) x 100), (Table 26).  141 TABLE 26^MEAN DIFFERENCES IN TOLERANT, INTERMEDIATE AND RESISTANT (TIRD) SPECIES COMPOSITION PROPORTION IN PSP's BETWEEN FIRST AND LAST MEASUREMENTS Site Association  N  RRIN  TIRD  STD  SEM  Change Factor  Fd - Salal * Fd - Salal  30 10  0  0 .062  .034 .074  .006 .023  620.0  FdBg - Oregon grape FdBg - Oregon grape  10 3  0  .008 .030  .013 .010  .004 .006  3.75  FdHw - Salal FdHw - Salal  31 18  0  1  .005 .034  .013 .052  .002 .012  6.80  HwFd - Kindbergia * HwFd - Kindbergia  41 34  0 1  0 .041  .039 .063  .006 .011  410.0  Cw - Foamflower Cw - Foamflower  25 9  0 1  .004 .004  .013 .042  .003 .014  2.00  All Associations All Associations  139 76  0 1  .002 .037  .029 .059  .002 .007  18.50  CDFmm CDFmm  41 14  0 1  .002 .052  .030 .064  .005 .017  26.00  CWHxm1 CWHxm1  59 37  0 1  .005 .025  .022 .052  .003 .008  5.00  CWHxm2 CWHxm2  39 25  0 1  .004 .045  .036 .065  .006 .013  11.25  1  1  Subzone Variant  Where: RRIN =0, is Healthy and RRIN =1, is Infected. The largest changes in TIRD species composition appear to be in the zonal site associations *, and in the CDFmm and CWHxm2 subzone variants. The change Factor is [TIRD (sIRRIN= 1) TIRD (sIRRIN=0)1 x (100), where default TIRD values of .001 are used for real zero values. -  7.3.4^Stand History: Fire And Logging  The frequency of occurrence of stand origins shows that 69% of the 139 PSP's originated after logging- and-burning, 21.6% after wildfire, and 9.4% after logging. The  142 results are not surprising given that vast areas of eastern Vancouver Island were railway logged (Gold 1985), with many fires originating from the logging and railway operations. Fire intensity is presumed to have been high due to heavy slash loads from logging old growth (Leavitt 1913, 1915), and evidenced in thin and developing second growth mor/moder forest floors, commonly occurring burned-out old growth stumps (authors observations), and the photographic record (Gold 1985).  Tukey's HSD multiple comparisons of mean Phellinus root rot intensities (BARS), show wildfire origin PSP's (15.45%) to be significantly more infected than the logged-andburned (8.22%) origin PSP's, (p = .002), but not significantly more infected than the logged-only (10.54%) origin, (p=.314), (Fig. 72). A t-test of wildfire versus logged (logged, plus logged-and-burned) origins shows a significant difference between groups (15.45% vs. 8.49%), (p = .001), (Fig. 73).  143  BURN^LOG LOG&BURN Historical Stand Origin  Figure 72  Boxplots of % Basal Area Reduction-BARS classified by three stand origins; wildfire (BURN), logged-only (LOG) and logged and slashburning (LOG & BURN).  Figure 73  Boxplots of % Basal Area Reduction-BARS classified by three stand origins; wildfire (BURN), and logged and slashburned (LOG & BURN).  Interestingly, mean stand ages are significantly different between stand origins (p<.088) (Figs. 74 and 75). These age differences are likely the greatest factor in explaining variation of %BAR damage intensity between different stand origins, although other stand origin effects cannot be discounted.  144  BURN^LOG LOG&BURN Historical Stand Origin  Figure 74  ^  Boxplots of total age classified by three stand origins. Mean total ages for wildfire, logged-only and logged-and-burned are 90, 70 and 60 years old, respectively.  Figure 75  ^  Boxplots of total age classified by two stand origins. Mean total ages for wildfire and logged-only plus logged-and-burned are 90 and 63 years old, respectively.  No other ecological variables, except old growth stand density and species compositions, had any apparent relationship to stand history (origin) (Table 21, p. 107).  Old growth stems/ha of Douglas-fir and western hemlock (SPHFH) for stands of wildfire origin are less than half (63 stems/ha) that of the logged-only (127 stems/ha) and logged-and-burned (133 stems/ha) origins (Table 21). Mean stems/ha (SPHFH) are significantly different between wildfire and logged (p = .036), and wildfire and logged-andburned stand origins (p = .0001). Mean stems/ha of old growth western red cedar (SPHCW) were substantially greater in the logged-and-burned stands compared to wildfire origin stands, 25 vs 10 stems/ha (p = .113).  145 Similarly the wildfire versus logged origin stems/ha means were 63 vs. 133 stems/ha (p = .000) for SPHFH, and 12 vs. 24 stems/ha (p = .082) for SPHCW.  7.4 Phellinus Root Rot - Ecological and Stand History Models  Following on the exploratory data analyses discussed in 7.2.1 and 7.2.2, and multiple linear regression models were constructed to aid inference of the disease's behaviour, and to develop a disease hazard classification for subzone, variant, plant alliance, plant association, and site association units. All models included total stand age (AGE87) as a model term while several other models included terms for old growth stand history (stems/ha of Douglas-fir--(SPHFH), stems/ha of western red cedar (SPHCW), species compositon of western red cedar (COMPCW), percent slope (SLOPE), mineral soil coarse fragment content, (CF20), and mineral soil total bulk density (MSBDT) variables.  Multiple Linear Regression Models:  Multiple linear regression models were constructed for two reasons; (a) heterogeneity of slopes for subzone and site association, and (b) to develop a comparative basis for an all-biogeoclimatic units hazard rating classification. All models, with the exception of the plant alliance and association models, were highly significant (p=0.000) and independent variables were all significant (all p-values < 0.050). Ecosystem  146 classification unit-by-stand age (AGE87) interaction term models, with a constant, consistently accounted for low amounts of the variation in BARS, (R 2 =.153 to .179). (see models 1, 2, 4, 5 and 3 in Table 27). Regression through the origin (removal of the constant, or non-intercept models) produced near equivalent models that are no-less biologically correct than the intercept-models (see models A, B, C, D and E in Table 27 and respectively Figs. 76-80). These non-intercept models were used in the construction of the ecosystem hazard rating classification (Figs. 47-51). The addition of stand history variables, SPHFH and SPHCW, on the non-intercept model boosted explained variation to 27.1% and reduced the standard error (see model 9 in Table 27). Non-significant additions to model fit were made by including slope, soil porosity, bulk density and coarse fragment content (not shown in Table 27).  A stand history model, while only accounting for only 17.5% of the total variation, did indicate the tendency of lower old growth stand densities of Douglas-fir and western hemlock (SPHFH) being associated with higher levels of P. weirii, and increasing levels of western red cedar composition (COMPCW) associated with higher levels of P. weirii (Table 27). A second stand history model accounted for 13.6% total variation with old growth stems/ha of Doulgas-fir/western hemlock, and western red cedar (Table 27). The model responses corroborate the earlier Pearsons correlation values for stand history in relation to BARS.  The model describing the relationship between BARS and several "thought-to-beimportant" soil variables (CF20, MSBDT and SLOPE), indicated a very low explanation of  147 variability (R 2 = .083), with independent variables highly significant at (p<0.001) (Table 27).  Although none of the ecological or stand history variables contributed much in themselves to the explanation of root rot variability, the models describe disease behaviour reasonably for the domain of coastal Douglas-fir ecosystems studied, especially considering the wide ecological and root rot variability within the area and, the fact that the PSP data base was assumed to be biased towards healthier stand conditions than was found. Furthermore, the findings clearly match root rot intensity relationships found with subzone variants (Beale, 1987), and s.a.'s (Beale 1989b, unpubl. data) that were determined from extensive, designed root rot surveys using the intersection length sampling method.  TABLE 27^MULTIPLE REGRESSION PREDICTION MODELS FOR PERCENT BASAL AREA REDUCTION (BARS) - SUSCEPTIBLE SPECIES, GREATER THAN THE SAMPLE DIAMETER LIMIT R2  SEE  Subzone Models (A)^Predicted BARS = 0.075SUBZ2*AGE87 + 0.173SUBZ2*AGE87 (1)^Predicted BARS = -0.624 + 0.083SUBZ1*AGE87 + 0.182SUBZ2*AGE87  .153 .142  9.722 9.757  Variant Models (B)^Predicted BARS = 0.075VAR11*AGE87 + 0.184VARI2*AGE87 + 0.154VAR13*AGE87 (2)^Predicted BARS = 0.527 + 0.068VARI1*AGE87 + 0.177VARI2*AGE87 + 0.145VARI3*AGE87  .159 .160  9.719 9.754  Plant Alliance Models Predicted BARS = 0.130PALL1*AGE87 + 0.202PALL2*AGE87 + 0.150PALL3*AGE87 (C)  .096  10.078  Plant Association Models (D) Predicted BARS = 0.094PASS1*AGE87 + 0.147PASS2*AGE87 + 0.202PASS3*AGE87 + 0.150PASS4*AGE87  .110  10.039  Site Association Models (E)^Predicted BARS = 0.065SASS1*AGE87 + 0.117SASS2*AGE87 + 0.179SASS3*AGE87 + 0.190SASS4*AGE87 +  .179  9.678  .179  9.712  .251  9.314  .175 .136  9.631 9.852  0.119SASS5*AGE87  (3)^Predicted BARS = -0.831 + 0.0075SASS1*AGE87 + 0 .127SASS2*AGE87 + 0 .190SASS3*AGE87 + 0.202SASS4*AGE87 + 0.131SASS5*AGE87  (9)^Predicted BARS = 0.086SASSI*AGE87 + 0.115SASS2*AGE87 + 0.195SASS3*AGE87 + 0.209SASS4*AGE87 + 0.158SASS5*AGE87 + -0.032SPHFH + 0.046SPHCW Stand History Model (14) Predicted BARS = 13.004 - -0.041SPHFH + 14.074COMPCW Predicted BARS = 14.781 - 0.048SPHFH + 0.044SPHCW (15) Where: AGE87= Total Stand Age; SUBZ1=CDFmm, SUBZ2=CWHxm; VARI1=CDFmm, VARI2 =CWHxm1, VARI3=CWHxm2;  PALL1=Pseudotsuga-Mahonia, PALL2=Tsuga-Mahonia, PALL3 =Thuja-Achlys; PASS1=Pseudotsuga-Arbutus, PASS2=Pseudotsuga-Mahonia, PASS3 = Tsuga-Mahonia, PASS4 = Thuja-Foamflower; SASS1=CDFmm-Fd-Salal, SASS2 = CDFmmFdBg-Oregon grape, SASS3=CWHxm-FdHw-Salal, SASS4 = CWHxm-HwFd-Kindbergia, SASS5 = CWHxm-Cw-Foamflower; SPHFH = Stems/ha Fd & Hw; COMPCW= Species composition for old growth Cw; CF20= %Coarse fragment content by volume; MSBDT=Total bulk density (g/cm 3); and SLOPE=%Slope.  149  Figure 76  Figure 78  Predicted % Basal Area Reduction - BARS, at total age 80 yr by subzone. Note the variability indicated by the single standard error bars.  Predicted % Basal Area Reduction - BARS at total age 80 yr by plant alliances; Pseudotsuga-Mahonia, Tsuga-Mahonia, and Thuja-Tiarella p.all.'s, respectively.  Figure 77  Figure 79  Predicted % Basal Area Reduction - BARS at total age 80 yr by subzone variant.  Predicted % Basal Area Reduction - BARS at total age 80 yr by plant association; Pseudotsuga-Arbutus, Pseudotsuga-Gaultheria, TsugaMahonia and Pseudotsuga-Achlys  p.a.'s.  150 20 Lq  Figure 80  .5 15  Predicted % Basal Area Reduction-BARS at total age 80 yr by site association; Fd-Salal,  FdBg-Oregon grape, FdHw-Salal, HwFd-Kindbergia, Cw-Foamflower s.a.'s, respectively. 5  i.  a.  0  sivA eve 9:01^1.°-  oce^Ylkosese s a.beff' cs ioro•tw'sev ,  Site Association  7.5 Phellinus Root Rot Growth And Yield Reduction Relationships  7.5.1^Damage Appraisal of Phellinus Root Rot on Growth and Yield of Second Growth Douglas-fir Ecosystems  The growth and yield response to Phellinus root rot was compared in healthy and infected PSP's. The results were evaluated in several ways: (1) one-on-one comparisons of healthy and infected PSP's within unique growth and yield installations (i.e., very similar site and stand conditions), (2) group comparison of healthy and infected PSP's dissimilar stands growing on similar sites, and (3) group comparison of healthy and infected PSP's within stands of similar initial density, and broadly similar site/ecological conditions. Growth and yield responses were expressed as volume, or basal area, by age, and by site height. Site height removes the effect of site and age in cases where the PSP's  151 would not otherwise be comparable (Mitchell and Cameron 1985). In some comparisons total age was better than breast-height age for evaluating the timing of stand responses to root rot.  In comparison method (1), the effect of stand density has been removed from withininstallations evaluation, by grouping PSP's according to their estimated initial stand density (stems/ha at age 10 yr) (see section 7.5.1.1). Comparison method (2), used the Chapman-Richards volume over total age model (see section 7.5.1.2) and, a quadratic volume over site-height model (see section 7.5.1.3). Comparison method (3), a quadratic volume over site-height model was used (see section 7.5.1.4).  7.5.1.1 Growth and Yield Comparisons Within Selected Installations  Yield comparisons within selected PSP installations (near-identical stand and site conditions) illustrated some of the most dramatic and conclusive yield reductions due to Phellinus infection of PSP's, (Figs. 81-87). Volume and basal area are plotted against site  height, with the exception of Figures 81 and 83 which are plotted against breast-height age for comparison. Graphs based on height or age show similar trends. Infected PSP's in comparison to healthy PSP's show departures in periodic volume and basal area increment (Table 28) resulting in lower gross volume and basal areas after 30 to 35 years of measurement. Infected PSP's relative to healthy generally have, (i) higher mortality (up to 4.2 times), and (ii) retain only 40% to 80% of the basal area increment, and (iii) 69% to 83% of the volume growth due to Phellinus damage which was most evident in the  152 Severely infected PSP's (219 vs. 218; and 2007 vs. 2008 & 2009). The growth rates in 'light to moderately' infected PSP's appear to be growing at rates equal to or better than the healthy PSP's.  TABLE 28^PERCENT CHANGE OF STAND VARIABLES BETWEEN FIRST & LAST MEASUREMENTS SPANNING 30 to 35 yr. PSP  Site Association  Percent (%) Change  I^Disease Condition  Stems/ha  Basal Area (m2 /ha)  Volume 2 /ha) (n  219 218  Healthy Severely Infected "Infected, % of Healthy*  - 6.37 -26.81 [420%]  25.61 10.27 [40%]  38.76 27.50 [71%]  2007 2008 2009  Healthy Severely Infected Severely Infected "Infected, % of Healthy"  -33.37 -55.84 -52.11 [162%]  31.05 11.39 14.80 [42%]  45.69 32.56 35.07 [74%1  160 161 162  Healthy Healthy Moderately Infected "Infected, % of Healthy"  -50.61 -33.69 -24.74 [ 59%]  35.74 28.96 22.44 [69%]  61.95 58.17 41.50 [69%1  158 159  Healthy Lightly Infected "Infected, % of Healthy"  -53.85 -26.42 [ 49%]  39.78 29.45 [74%]  64.00 52.99 [83%]  HwFd-Kindbergia  349 348  Healthy Lightly Infected "Infected, % of Healthy"  -33.31 -28.81 [ 86%]  24.36 19.55 [80%1  36.22 30.15 [83%j  Fd-Salal  HwFd-Kindbergia  Cw-Foamflower  HwFd-Kindbergia  Negative values indicate a net loss while positive values indicate a net gain over the measurement period of 30 to 35 yr. * "Infected, % of Healthy", indicates the percent retained by the Infected (PSP(s) as compared to the baseline "Healthy" uninfected condition represented by "Healthy" PSP's within the same installation. Note: Percent (%) change values have been determined from the measured values. Those values are not presented.  153  Productivity Comparisons in Douglas—fir Growth G Yield Installations  Healthy vs Laminated Root Rot Infected Permanent Sample Plots Basal Area (e/ha 4.0 cm+) BO  70  60  50  40  30  eo10^ ^  20^  30  Site Height  40^  50  Volume (m e/ha 4.0 cm+) 1000  219 Healthy  BOO  ........ 218 Infected 600  400  200  10  Figure 81  20  ^  ^I 30  ^  Site Height  40  50  Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 218 and 219). All species z4.0 cm, "Severely" infected with Phellinus root rot.  154 Productivity Comparisons in Douglas-fir Growth & Yield Installations Healthy vs Laminated Root Rot Infected Permanent Sample Plots Basal Area (18 2/ha 4.0 cm+) SO-  70  50  50  40  30  2$  , 30  .  ^  .  .  ^  40^50^60 BH Age  .  70  1 80  Volume (m '/ha 4.0 cm+) 1000  219 Healthy  BOO  ..,.. 218 Infected  600  ...---.  400  200  10  .  30  I ^ I ^ I ^ . ^ e 40^ 50^ 60^ 70^80  BH Age  Figure 82^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 218 and 219). All species z4.0 cm, "Severely" infected with Phellinus root rot.  155 Productivity Comparisons in Douglas-fir Growth & Yield Installations  Healthy vs Laminated Root Rot Infected Permanent Sample Plots Basal Area (m a/ha 4.0 cm+) oo  70  2007 Healthy  • — 2008 Infected 2009 Infected  30  ao t ^ 10^  20^  •^ •  30^  ^  40^  Site Height  •  so  Volume (m 3/ha 4.0 cm+) i000-  BOO  2007 Healthy  -- 2000 Infected • 2009 Infected  600  400  200  10^  Figure 83  ^  •^  20  30  Site Height  •  40  -  J  SO  Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 2007, 2008, and 2009). All species z4.0 cm, "Severely" infected with Phellinus root rot.  156 Productivity Comparisons in Douglas—fir Growth & Yield Installations Healthy vs Laminated Root Rot Infected Permanent Sample Plots Basal Area (e t/ha 4.0 cm+) 80  70  2007 Healthy 60  s-%-— 2000 Infected  • 2009 Infected  50  10  30  2110  30  so  40  BH Age  60  70^  BO  Volume (ae/ha 4.0 cm+) 1000—  800 2007 Healthy  MB Infected  600  400  200  ^  1  ^  30^ 40^ 50^ 60^ 70^ 80  BH Age  Figure 84^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 2007, 2008, and 2009). All species A.0 cm, "Severely" infected with Phellinus root rot.  157 Productivity Comparisons in Douglas-fir Growth & Yield Installations  Healthy vs Laminated Root Rot Infected Permanent Sample Plots Basel Ares (s 2/ha 4.0 cm+) BO  70  ISO Healthy 162 Healthy 161 Infected  a^t  10^  ^  30^ Site Height  20^  a  40^  ^I  50  Volume (a 3/ha 4.0 cm+) 1000  800  600 160 Healthy Healthy 61 Infected  400  200  I  10  Figure 85  20^  ^  ^I- I  ^  30^  I  40^  ^A  50  Site Height  Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 160, 161, and 162). All species a4.0 cm, "Moderately" infected with Phellinus root rot.  158 Productivity Comparisons in Douglas-fir Growth G Yield Installations  Healthy vs Laminated Root rot Infected Permanent Sample Plots Basal Area (ma/ha 4.0 cm+)  .0  70  0^ 10^  I^  20^  I^  30^  Site Height  1  40^  501  Volume (m3/ha 4.0 cm+) 1000—  BOO  349 Healthy  600  .348 Infected  400  200  10  Figure 86  20  ^  I^  30^  Site Height  I  ^I  40^  50  Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 348 and 349). All species k4.0 cm, "Light to Moderately" infected with Phellinus root rot.  159 Productivity Comparisons in Douglas-fir Growth G Yield Installations  Healthy vs Laminated Root Rot Infected Permanent Sample Plots Basal Area (e/ha 4.0 cm+) so  70  159 Healthy  150 Directed  20 ^ 10^  20^  30^  Site Height  40^  50  40^  50  Volume (me/ha 4.0 cm+) 1000  BOO  600  151 Healthy  Infected  400  --------  20^  Figure 87  ^  30^  Site Height  Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 158 and 159). All species k4.0 cm, "Moderately" infected with Phellinus root rot.  160 7.5.1.2^Yield Comparisons Using the Chapman-Richards Non-Linear Growth (VAC) Model  The Chapman-Richards volume-age model curves could only be calculated for the combined FdHw-Salal and HwFd-Kindbergia s.a.'s (nearly all of the CWHxm subzone PSP's!). Gross volume (>4.0 cm) was related to total age. Site index was not considered because if was virtually identical in healthy and infected strata (Table 29). The model estimates for healthy and infected plots fit the data very well (Table 30 and Fig. 88). The magnitude of yield reduction and the shape of the Phellinus infected curve (i.e., yield reduction increased gradually, virtually from regeneration) conforms well to observations from field survey work (Fig. 88), and simulations using the TASS-ROTSIM model (author's unpublished observations). The healthy and infected PSP's are estimated to have 732.9 and 668 m 3 /ha, respectively at age 80 yr for a reduction in yield of 8.86%. These reductions are probably conservative because the infected PSP strata is 7.5 yr older and has a mean site index of 1.2 m greater than the healthy strata, indicating that the infected strata in the absence of Phellinus root rot should have greater yields than the healthy strata.  161 TABLE 29^DESCRIPTIVE STAND STATISTICS FOR COMBINED SITE ASSOCIATIONS; FdHw-Salal and HwFd-Kindbergia MEAN AND (STANDARD DEVIATION) Disease Condition  Site Index  AGE871  ST4102  BA4103  VL4104  N  Healthy  27.22 ( 5.61)  61.38 (20.59)  2927 (1709)  8.77 (2.82)  24.74 ( 7.93)  70  Infected  28.45 ( 5.17)  68.88 (22.61)  2417 (1657)  8.16 (3.48)  23.75 ( 3.48)  53  Combined  27.75 ( 5.44)  64.62 2708 (1699) (21.72) 1^Stand age 1987  8.51 (3.12)  24.32 ( 9.00)  123  2^  Stems/ha A.0 cm at 10 yr Basal area/ha (m 2 /ha) A.0 cm at 10 yr 4^ Volume/ha (m 3 /ha) at 10 yr k4.0 cm  3^  TABLE 30^CHAPMAN-RICHARDS VOLUME - TOTAL AGE GROWTH MODEL STATISTICS (A.0 cm Gross Volume) Ecological Units FdHw-Salal and HwFd-Kindbergia  Disease Condition Healthy Infected All PSP's  Sums of Squares 864650.0 934093.5 1819265.0  Regression Coefficients B1  B2  B3  1143.236 975.308 1132.611  .01232 .02181 .01746  1.437 1.971 1.713  NB:^File=BGROMB.SYS. The Chapman-Richards Growth Model Equation, see below: Predicted VOL4 = b1(1-EXP(-b2 AGE))b3  162  Figure 88  Chapman-Richards volume-age curves (k4.0 cm) comparing healthy (___) and Phellinus root rot infected (---)stand conditions for the combined FdHw-Salal and HwFd-Kindbergia s.a.'s in the CWHxm subzone. The healthy yield at 80yr is 732.9 m 3 /ha compared to 668.0 m 3 /ha for infected yield, resulting in a 8.86% yield reduction.  7.5.1.3^Growth and Yield--Site Height Models for the Whole PSP Dataset  Growth and yield models were fit to PSP's after dropping several outlier PSP's with initial site heights over 40 m and several other PSP's with unexplainable measurement inconsistencies. Furthermore, growth and yield models for the FdHw-Salal, and HwFd-  Salal s.a.'s were found to be virtually identical (graphically) to models produced for the all-PSP's model, therefore only the all-PSP's models are presented.  7.5.1.3.1 Growth Models  A linear growth difference model was fit for annual volume increment as a function of a constant, an annual site height increment term and a site height increment - PSP disease condition interaction term. (Note, the PSP disease condition was a dummy  163 variable; healthy or infected). The growth model provided a reasonably good fit for the data (Table 31). The site height increment - disease condition interaction term was highly significant (p = .000), indicating that disease condition is important in explaining the variation in volume increment rates. Healthy and infected increment data with their respective growth model functions are shown in Figures 89 and 90. Healthy PSP's have substantially greater homogeneity compared to the infected PSP's. More negative growth points in the infected PSP group. This was also reflected in the regression model intercept and larger standard error of the estimate. The plot of both growth model functions without the data points illustrates noticeably lower growth rates of the infected PSP's compared to the healthy PSP's, (Fig. 91). Infected PSP's grow more slowly by 4.4% to 11.7% over the range of 0.1 to 0.9 m annual site height increment. Assuming that site height measurement is independent of root rot effects in PSP's, the pattern of volume growth reduction indicates that site quality (site height) has a positive effect on volume growth reduction. Note, that this assumption is built into the model by using the constant. Furthermore, evidence of a positive site height effect on Phellinus intensity was not shown in the correlation testing nor in the relationships of Phellinus and the site associations. An alternative and more likely assumption, is that site height measurement is partially dependent on the effects of root rot (i.e., root rot has had negative, or depressive, effects on site height measurements). This assumption was tested by stratifying the data by disease condition, and by not including constants in separate models estimated for each disease condition.  164 TABLE 31 All-PSP's Growth Model independent' of Root Rot Effects on Site Height Measurements  ^ DEP VAR: DVOL4^N: 952 MULTIPLE R: .568^SQUARED MULTIPLE R: .322 ADJUSTED SQUARED MULTIPLE R:.321 STANDARD ERROR OF ESTIMATE: 5.223 COEFFICIENT^STD ERROR  VARIABLE CONSTANT DSTHGHT1 DSTHGHT1* RRIN  STD COEF  TOLERANCE T  P(2 TAIL)  4.811^0.406 20.562^0.985  0.000 0.598  0.870  11.850 20.867  0.000 0.000  -3.019^0.787  -0.110  0.870  -3.835  0.000  ANALYSIS OF VARIANCE SOURCE  SUM-OF-SQUARES  DF  MEAN-SQUARE  F-RATIO  P  REGRESSION RESIDUAL  12307.381 25886.922  2 949  6153.690 27.278  225.591  0.000  Where:  ^  Predicted DVOL4 is the annual volume increment, z4.0 cm (m 3 /ha/yr), DSTHGHT1 is the annual site height increment (m/yr) and RRIN is the incidence of root rot in PSP's (absence/presence 0/1).  Figure 89 All-PSP's growth model; healthy PSP scatterplot and growth function, (Predicted DVOL4=4.811 + 20.562 DSTHGHT1). The model assumes that site height measurements are independent of root rot height depression effects.  Annual Site Height Increment (m/yr)  •  165  ›, 40 co  c,) 30 •  •.  .  20 .  a  E  C) 54  *.  •^.  " • . •  0 —10 —20  aa  —30  O—40—0.3  —0.1^0.1^0.3^0.5^0.7^0.9^11 Annual Site Height Increment (m/yr)  Figure 90  ^  All-PSP's growth model; infected PSP scatterplot and growth function (Predicted DVOL4=4.811 + 17.543 DSTHGHT1). The model assumes that site height measurements are independent of root rot height depression effects.  Figure 91 All-PSP's growth model;  comparitive growth function plots healthy (_) and infected PSP's (--).  To examine this alternative hypothesis, two more models were estimated for the healthy and infected PSP strata. The healthy PSP growth model (Table 32) has a higher constant and greater volume increment rate than the infected PSP growth model (Table 33). Figures 92 and 93 illustrate the healthy and infected model functions respectively plotted over the data. The effect of Phellinus root rot on site height increment is seen in that most of the negative and lower volume increments are at the lower end of the site height increments. In these cases it is hypothesized that the site height measurement trees are infected for many years before symptoms are readily expressed. There is a high probabilty that some trees measured for site heights in PSP's would be affected by root rot height depression before being  166 dropped for reference height measurements. If site height measurements are in fact somewhat dependent on the effects of Phellinus root rot, the volume growth reductions are then inversely related to site height increment, with growth reductions ranging between 17.33% to 7.48% for annual site height increments of 0.1 to 0.9 m (Figure 94, combined lines, no data, 2 constants). Intuitively, the site height measurement dependent models are sensible because the volume increments for infected PSP's must not be equal at low site height increments, unless ingrowth is so dramatic to make up for growth reduction and tree mortality - a generally inconceivable condition.  TABLE 32 All-PSP's Growth Model - Healthy Condition "Dependent of Root Rot Effects on Site Height Measurements  DEP VAR:^DVOL4^N: 593^MULTIPLE R: .592^SQUARED MULTIPLE R: .350 ADJUSTED SQUARED MULTIPLE R: .349 ^STANDARD ERROR OF ESTIMATE: 4.643 VARIABLE CONSTANT DSTHGHT1  COEFFICIENT 5.322 19.488  STD ERROR  STD COEF  0.476 1.092  0.000 0.592  TOLERANCE T  P(2 TAIL)  1.000  11.182 17.843  ANALYSIS OF VARIANCE SOURCE REGRESSION RESIDUAL  SUM-OF-SQUARES  DF  MEAN-SQUARE  F-RATIO  P  6862.594 12739.552  1 591  6862.594 21.556  318.362  0.000  Where: Predicted DVOL4 is the annual volume increment, k4.0 cm (m 3 /ha/yr), DSTHGHT1 is the annual site height increment (m/yr).  0.000 0.000  167 TABLE 33 M-PSP's Growth Moded - Infected Condition "Dependent" of Root Rot Effects on Site Height Measurements  ^ DEP VAR: DVOL4^N: 359^MULTIPLE R: .529^SQUARED MULTIPLE R: .280 STANDARD ERROR OF ESTIMATE: 6.055 ADJUSTED SQUARED MULTIPLE R: .278 VARIABLE^COEFFICIENT^STD ERROR  STD COEF TOLERANCE T^P(2 TAIL)  CONSTANT^4.120^0.722 DSTHGHT1^18.920^1.605  5.707^0.000 0.000^ 0.529^1.000^11.789 0.000  ANALYSIS OF VARIANCE SOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIO^P REGRESSION^5095.609^1^5095.609 RESIDUAL^13088.985^357^36.664  ^  138.982^0.000  Where: Predicted DVOL4 is the annual volume increment, A.0 cm (m 3 /ha/yr), DSTHGHT1  is the annual site height increment (m/yr).  Annual Site Height Increment (m/yr)  Figure 92  ^  All-PSP's growth model; healthy PSP scatterplot and growth function (Predicted DVOL4=5.322 + 19.488 DSTHGHT1). The model assumes that site height measurements are dependent on root rot height depression effects.  Figure 93  ^  All-PSP's growth model; infected PSP scatterplot and growth function (Predicted DVOL4=4.120 + 18.920 DSTHGHT1). The model assumes that site height measurements are dependent on root rot height depression effects.  168  Figure 94  All-PSP's growth model; comparitive growth function plots (__) healthy, and (---) infected. The model assumes that site height measurements are dependent on root rot height depression effects.  Annual Site Height Increment (m/yr)  7.5.1.3.2 Yield Models  A quadratic volume yield-site height model (?.4.0 cm), predicted an increasing yield loss in Phellinus infected PSP's as compared to healthy PSP's. The model included a volume yield-PSP disease condition (healthy or infected dummy variable) interaction term which is highly significant (p = .000) (Table 34). All model terms were significant (p = .000). The volume increment-disease condition interaction term indicated the importance of root rot conditions in explaining volume variation. The healthy model functions indicate that healthy PSP's have a slightly higher yield than the infected PSP's, with the infected group having 5.5% less volume at 35 m site height (or approximately 80 years of age). Healthy and infected PSP data and their respective yield model functions again illustrate the greater homogeneity of the healthy PSP's compared to the infected PSP's (Figs. 95 and 96), respectively. In Figure 97, the infected PSP curve visibly departs from the healthy  169 curve at 10 m site height, but in fact is departing right from the origin. This is accurate biologically, and compares well with the Chapman-Richards volume-age function shown in an earlier section. The volume yield reductions are lower in the volume-site height models compared to the Chapman-Richards. Site quality (index) was not considered in the C-R volume-age functions. The yield models suggest that the yield increment rates are not yet showing significant effects of mortality in the infected PSP's although the growth rates are slowing, as shown in the previous section.  TABLE 34 All-PSP's Yield Model  MODEL CONTAINS NO CONSTANT. ^ DEP VAR: VOL4^N: 952 MULTIPLE R: .977^SQUARED MULTIPLE R: .955 ADJUSTED SQUARED MULTIPLE R: .955 STANDARD ERROR OF ESTIMATE: 91.948 VARIABLE^COEFFICIENT STD ERROR STD COEF TOLERANCE T^P(2 TAIL) STHGHT^2.958^0.561^0.179^0.041^5.272^0.000 STHGHT2^0.458^0.019^0.821^0.039^23.653 0.000 STHGHT2* RRIN^-0.030^0.008^-0.033^0.613^-3.817^0.000  ANALYSIS OF VARIANCE SOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIO ^P ^ REGRESSION .171879E+09^ 3^.572932E+08^6776.702^0.000 RESIDUAL^8023254.390 949^8454.430  Where: Predicted VOL4 is the volume yield A.0 cm (m 3/ha), STHGHT is the site height, STHGHT2 is the site height squared and RRIN is the incidence of root rot in PSP's (absence/presence 0/1).  170  Figure 95 All-PSP's yield model; healthy PSP scatterplot and yield function (Predicted VOL4=2.958 STHGHT + 0.458 STHGHT2).  1200 co^10 00 .0  --  •  .  /  •  /  800 c) ef• •cr 7). >-•  600 .  400  •  t  ,  • •  a c) ;8.  .:.  •  -•':*•,(•"  :^•  200 •  0 ^--:••  • I  I  0^10^20^30^40  ^  50  Site Height (m)  Figure 96 All-PSP's yield model; infected PSP scatterplot and yield function (Predicted VOL4=2.958 STHGHT + 0.428STHGHT2).  Figure 97  ^  All-PSP's yield model; comparitive yield function plots healthy U infected (---) PSP's.  171 7.5.1.4^Growth and Yield: All Data, Stratified by Stand Density Classes (stems/ha at age 10 yr)  Several of these models were estimated to evaluate the effects of initial stand density (back-estimated to reference age 10 yr) on the volume and Phellinus root rot related damage impacts. Although a logical argument was presented previously to rationalize the differences observed between %BAR and PSP-based damage estimates, I felt that stand densities were confounding the picture. In the absence of PSP-based Phellinus damage intensity information, it was impossible to determine if the PSP's were really portraying the same damage as the %BAR surveys.  Quadratic volume-site height yield models were fit for each of the young (1 to 20 yr old)stand density classes (<1 000, 1 000 - 1 999 and 2 000 - 4 999 stems/ha, (Forest Productivity Councils of B.C., Sept. 1990).  7.5.1.4.1 Stand Density (Less than 1 000 stems/ha):  7.5.1.4.1.1^Yield Model  A quadratic yield model (Table 35) estimates yield reduction to be 8.25% for infected PSP's at a site height of 35 m (or approx. 80 yr), (Fig. 98).  172 Table 35  ^  Stand Density Class (< 1 000 stems/ha) Yield Model  DEP VAR: VOL4^N: 169^MULTIPLE R: .975^SQUARED MULTIPLE R: .950 ADJUSTED SQUARED MULTIPLE R: .949 STANDARD ERROR OF ESTIMATE: 95.699 VARIABLE^COEFFICIENT^STD ERROR^STD COEF^TOLERANCE^T P(2 TAIL) STHGHT^-2.275^1.760^-0.157^0.021^-1.292^0.198 STHGHT2^0.547^0.058^1.160^0.020^9.374 0.000 STHGHT2* RRIN^-0.039^0.017^-0.051^0.618^-2.293^0.023  ANALYSIS OF VARIANCE SOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIO ^ 3^9603739.352^1048.645^0.000 REGRESSION .288112E+08 ^ 166^9158.241 RESIDUAL^1520267.965  1200  Figure 98  1000  800 600  400  200  0  0^10^20^30^40 Site Height (m)  ^  50  Yield models for stand density < 1 000 stems/ha at age 10 yr for healthy (__) and infected (---) PSP conditions.  173 7.5.1.4.2 Stand Density (1 000 - 1 999 stems/ha):  7.5.1.4.2.1^Yield Model  A yield model (Table 36) estimated yield reduction to be 8.63% for infected PSP's at a site height of 35 m (or approx. 80 yr), (Fig. 99).  Table 36  ^  Stand Density Class (1 000 - 1 999 stems/ha) Yield Model  ^ DEP VAR: VOL4^N: 412 MULTIPLE R: .986^SQUARED MULTIPLE R: .973 ADJUSTED SQUARED MULTIPLE R: .972 STANDARD ERROR OF ESTIMATE:76.755 VARIABLE^COEFFICIENT^STD ERROR^STD COEF^TOLERANCE^T^P(2 TAIL) STHGHT^0.313^0.767^0.019^0.031^0.408^0.684 STHGHT2^0.546^0.025^0.998^0.031^21.470 0.000 STHGHT2* RRIN^-0.048^0.009^-0.052^0.642^-5.115^0.000  ANALYSIS OF VARIANCE SOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIO^P ^ REGRESSION .854946E+08 3^.284982E+08^4837.270^0.000 ^ RESIDUAL^2409575.929 409^5891.384  174  Figure 99  Yield models for stand density 1 000 - 1 999 stems/ha at age 10 yr for healthy (__) and infected (---) PSP conditions.  7.5.1.4.3^Stand Density (2 000 - 4 999 stems/ha):  7.5.1.4.3.1^Yield Model  A yield model (Table 37) estimated yield reduction to be 4.97% for infected PSP's at a site height of 35 m (or approx. 80 yr), (Fig. 100). Table 37  ^  Stand Density Class (2 000 - 4 999 stems/ha) Yield Model  DEP VAR: VOL4^N: 330^MULTIPLE R: .985 SQUARED MULTIPLE R: .969 ADJUSTED SQUARED MULTIPLE R: .969 STANDARD ERROR OF ESTIMATE: 75.079 VARIABLE^COEFFICIENT^STD ERROR^STD COEF^TOLERANCE^T^P(2 TAIL) STHGHT^2.284^0.725^0.129^0.056^3.149^0.002 STHGHT2^0.561^0.028^0.877^0.051^20.347 0.000 STHGHT2* RRIN^-0.026^0.013^-0.027^0.536^-2.069^0.039  ANALYSIS OF VARIANCE SOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIO ^P ^ REGRESSION .580895E+08 ^ 3^.193632E+08^3435.127^0.000 RESIDUAL^1843237.797 327^5636.813  175  Figure 100  Yield models for stand density 2 000 - 4 999 stems/ha at age 10 yr for healthy U and infected (---) PSP conditions.  176 8.0 DISCUSSION  Variable-radius plot sampling was successfully used to estimate Phellinus root rot damage intensity using a Percent Basal Area Reduction (%BAR) parameter estimate technique. Damage intensity estimates were a function of damage severity and incidence estimates. Damage severity, or the proportional difference between the mean basal areas of healthy and infected point samples within a sample survey, ranged between 0 and 1 with a mean of 0.30. Stand-based damage incidence, or the proportion of variable-radius plots in a sample survey expressing Phellinus root rot symptoms (presence/absence), had a mean incidence of 0.263. Land-based disease incidence, had a mean incidence of 0.168. Mean %BAR estimates ranged from 8.25% to 9.99%, depending on the species susceptibility and diameter classes used for the parameter estimate. Means and (standard deviations) were as follows for the four parameter estimates: NSBAR 8.25% (9.25%); SBAR 8.92% (10.22%); BARNS 9.44% (9.99%) and BARS 9.99% (10.52%). These parameter estimates appear very reasonable in light of my experience and other workers estimates of damage incidence, severity and intensity.  Mean %BARS estimates showed that the first assumption regarding the sampling methodology and the expected %BAR's relationships was correct. The healthy-only, susceptible species composition was 91.5%, with statistically non-significant variation across s.a.'s. This ensured an equal probability for detection and measurement of Phellinus root rot. Infected stand areas compared to healthy areas, sustained about a  30% reduction in stand density. Damage severity or relative basal area reduction, was  177 statistically significant and provided conclusive evidence for the effects of Phellinus root rot and the sensitivity of the %BAR parameter estimate technique. The coefficients of variation for severity were respectively, 31.5% and 18.9% for the infected and healthy areas. The greater basal area variation of infected areas should be taken into consideration in the future designs of forest health and forest inventories.  8.1 Damage Incidence - Severity - Intensity Relationships  Incidence-Severity Relationship: Land- and stand-based incidence of Phellinus root  rot increased with increasing damage severity. This was reasonable considering that both parameters are time-since-infection or stand age interdependent functions. That is, the frequency of stems expressing root rot symptoms contributes to increasing damage severity, which is related to increasing frequency of disease incidence as infection centers expand to affect more of the land or stand area.  Severity-Intensity Relationship: Damage severity increased with damage intensity and  then stabilized at about 0.50 (or 50% basal area reduction) even in the most severe damage intensity conditions. The asymptotic behaviour of severity appears to indicate a resiliency of forest productivity in the presence of Phellinus root rot, in that basal area reduction equilibrated with basal area replacement (the latter likely due in part to ingrowth of less-susceptible, shade tolerant species such as western red cedar, western white pine and western hemlock), and enhanced growth of residual trees due to reduced competition. The severity-intensity relationship provided some indication of the  178 mechanisms of the proposed host-pathogen dynamic equilibrium.  Incidence-Intensity Relationship: Damage intensity increased with increasing damage  incidence. Estimates of land- or stand-based incidence were highly correlated to damage intensity (r = .811 and r = .836, respectively). Linear prediction models for %BARS as a function of land-based incidence and stand-based incidence estimated incidence levels to be 0.5 to 2.6 times larger than observed intensity levels. This indicated that incidence estimates alone are inappropriate estimators of damage effects on stand productivity because they significantly overestimate over the %BARS range of 5 to 15%. In order to accurately appraise damage to stand productivity, sampling should include a measure of incidence and severity.  The data suggests that stand-based versus land-based sampling methods should be emphasized in forest productivity-disease impact studies in order to provide more accurate and meaningful estimates of damage to stand and forest productivity. Knowledge of land-based incidence and stand-based intensity relationships should provide direction to future root disease surveys that have forest productivity, damage appraisal objectives.  Incidence-intensity relationships may be particularly useful for increasing sampling efficiencies, in that if the relationship is well known (perhaps from previous sampling), then further disease sampling can be reduced to simply land-based incidence (binomial response) sampling, with stand-based damage intensity then estimated from the  179 appropriate incidence-intensity relationship model. Binomial incidence sampling methods are generally cheaper to conduct and more easily reproduce consistent results.  Incidence-Incidence Relationship: Both stand:land-based and land:stand-based  incidence ratios were plotted over land and stand-based incidence, respectively (Figs. 20 and 21). The stand-based incidence apparently over-estimated the land-based estimates, particularly at low land incidences. The corollary, is that the land-based estimate underestimated the stand-based incidence at low stand incidence. Both incidence estimates appeared to level out and predict more closely to a 1:1 ratio as incidence approached .35 to .40 or 35 to 40%. Although the linear correlation coefficient estimate for land-stand incidence is .923, the greatest variation was at low levels of incidence (which is a critical area for management decision making). This variance might easily be corrected if the "effective" sampling areas were more closely defined, rather than the land-based fixedradius plots set at 3.99 m or .005 ha, and the stand-based variable-radius mean tree critical distance radius at about .027 ha, (i.e., adjust BAF selection). Considering that the bases of the two incidence estimates are completely different, the estimates are remarkably similar, with the greater incidence captured by the stand-based method compared to the land-based method. A recommendation for further land-based incidence sampling is to increase the sample plot size to 0.010 ha, and select BAF's to more nearly approximate land-incidence areas sampled.  180 8.2 The Relationship Between PSP's And The %BAR Sample Survey Data  Eighty percent (80%) of the infected PSP's are in stands where Phellinus root rot exceeded 5% BARS or NSBAR, or that have a BARS-Damage Intensity Class of MediumSevere. This was an important finding because it meant that the PSP's could be considered fairly representative of the stands sampled in and around the PSP, and vice versa, thus simplifying interpretations between ecological and growth and yield impact data in the %BAR survey and the PSP data sets.  8.3^Phellinus Root Rot Variability in Relation to Ecological Site Factors and BEG Units  8.3.1^Disease Incidence  Disease incidence ranged widely between 10 and 100% depending on the sampling unit population, but generally did not vary widely within the biogeoclimatic units. Disease incidence appeared to be positively correlated with the sampling unit size, and seen across all biogeoclimatic units. For example, the mean disease incidence in site associations increased from: 16.8% using 0.005 ha fixed-radius plots, to 26.3% using variable-radius plots, to 36.8% using 0.04 ha PSP's, to 87.0% using 1 ha %BAR sample survey plots.  The mean incidence observed using fixed-radius plots (mean was 12.5%) was, about  181 the same as that obtained in the large-scale root disease survey using the intersection length method (Beale 1987). These two land-area-diseased expressions appear to estimate similarly, although the study areas are not directly comparable. Incidence estimates using fixed- and variable-radius plots showed similar estimates, with the latter always greater (or more sensitive, particularly at lower land-based incidence levels) due to its "apparently larger" sampling unit area, thereby increasing the probability of sampling a Phellinus root rot incident. Incidence estimates based on the 1 ha sample surveys (mean was 87%) also provided identical estimates to 20 ha sample surveys conducted in the large-scale root disease survey (mean was 87%) in 1982-83 (Beale 1987). This finding suggests that future root disease sampling designs can based on smaller sampling units and thereby maintain estimate fidelity, and gain additional ecological and stand condition homogeneity.  8.3.2^Disease Intensity  Disease intensity ranged from -10% to 55% basal area reduction (BARS estimates). Generally, the magnitude of percent basal area reduction (%BAR) estimates was dependent upon the combination of tree selection parameters. Thus, increasing the minimum tree sampling diameter limit from 4.0 cm to 12.0/17.5 cm and/or dropping non-susceptible species trees from the tree count increased the %BAR damage intensity estimates. In mid-seral stands, selection of larger diameter susceptible species increases the probability of sampling Phellinus root rot infected trees and hence the percent basal area reduction estimate rise. Note, that negative percent basal area reduction (e.g., -10%  182 BARS) reflected a possible, although not a commonly occurring damage condition, in which the effect of root rot on stand yield (basal area) is actually positive. These situations occurred where very early Phellinus mortality removed susceptible species followed by very quick ingrowth of non-susceptible species (usually western red cedar or deciduous).  8.3.3^Site Ecological Factors  The relationship of site ecological variables to Phellinus root rot damage intensity was weak and tenuous at best. Although no ecological variables were significantly correlated to Phellinus damage intensity, several variables showed interesting patterns with Phellinus BARS-Damage Intensity Classes (BARS-DIC's). Mineral soil pH and coarse fragment content were negatively related to BARS-DIC's, and slope was positively related to BARSDIC's. Similarly, coarse fragment content, mineral soil bulk density, porosity, pH, elevation and slope showed weak but interesting correlations to subzone, variant and site associations, which indicated the integrative nature of the site classification (Pojar et al. 1987 and Banner et al. 1990). Actual soil moisture was strongly related to mineral soil percent coarse fragment content (by volume), with the moderately-dry soils associated with the coarser soils in the Fd-Salal, FdBg-Oregon grape and FdHw-Salal s.a.'s, and slightly-dry soils associated with the HwFd-Kindbergia and Cw-Foamflower s.a.'s (Fig. 31 and Table 20). Interpreting the effect of any one of these variables to Phellinus root rot incidence, or intensity, outside of the integrative BEC system and the dominant stand  183 historical conditions provided little insight into the behaviour of Phellinus root rot. It appears that true differences in Phellinus behaviour may only be detected and interpreted in the framework of the system of biogeoclimatic ecosystem classification.  There was an apparent relationship of Phellinus root rot with the zonal or regional climate indicated by the subzone and variant models, and to a lesser degree, the soil moisture-nutrient gradient indicated by the site association model. Plant alliance and association models, though not significant, further suggested an apparent soil moisture relationship, within the resolution expected from lower precision plant-based classification units. The apparent moisture relationship with Phellinus root rot intensity is more quadratic, in that Phellinus root rot increased with actual moisture condition (class), and/or with the moisture-by-nutrient class to a maximum, then decreased (Table 20 and Figs. 76-80). The point at which root rot decreased was evident in: (a) the variant model at the CWHxm2 variant (Fig. 77); (b) the plant alliance model at the Thuja-Achlys p.all. (Fig. 78); (c) the plant association model at the Thuja-Foamfiower p.a. (Fig. 79); and (d) the site association model at the Cw-Foamflower s.a. (Fig. 80). Curiously, western red cedar was associated with lower root rot levels; perhaps indicative of conducive or reactive stand conditions? Root rot levels in the CDFmm (driest) subzone and variant, and the Fd-Salal s.a., are about half that of the comparable units; the CWHxm subzone, CWHxm1 variant, or the Fd-Hw-Sala) s.a., respectively, (Figs. 76, 77 and 80). These observations consistently hinted at actual moisture (Klinka 1984), as being a significant factor in root rot behaviour, since most sample plots were nutrient poor-to-medium in site  184 quality. However, that condition did not hold so firmly for the site association model, especially when other site and stand attributes were considered.  At the subzone level, five site ecological variables may have some bearing on Phellinus root rot incidence and intensity. Elevation, was significantly lower in the  CDFmm, corresponding to its units' classification (Fig. 32), and has a significantly drier climate than the CWHxm (see Table 1, page 27). Percent slope was also significantly lower in the CDFmm which corresponds to the gently sloping, Nanaimo lowland coastal plain (Fig. 33). Percent slope has been shown to positively effect host tree root contact probabilities and subsequent spread rates of Phellinus root rot (Bloomberg and Reynolds, 1982 and Reynolds and Bloomberg, 1982). Thus lower mean percent slope conditions in the CDFmm may be contributing to lower levels of root rot, and conversely in the CWHxm subzone. Mineral soil bulk density (fine fraction <2 mm), and its inverse parameter, soil porosity, were respectively, higher and lower, in the CDFmm compared to the CWHxm subzone (Fig. 34 and 35). It is possible that the denser, less porous soils of the CDFmm might impede root egress and stand density, and thereby reduce root contact probabilities and consequent infection spread rates. The lower soil porosity in the CDFmm may also negatively affect respiration of P. weirii, thereby reducing its ability to colonize hosts. Soil pH was also shown to be significantly greater in the CDFmm compared to the CWHxm (Fig. 36). The 0.3 to 0.4 pH difference may be attributable to a higher proportion of marine and sedimentary base-rich parent materials, and/or slightly higher deciduous tree species compositions in the CDFmm. Experiments on the effects  185 of soil pH have shown elevated and reduced pH to adversely effect the growth of P. weirii (Angwin 1985). Other workers have observed similar effects with P. weirii (Williams and Marsden 1978, Hobbs and Partridge 1978), with Armillaria mellea Singh (1980), and with 1. tomentosus (Van Groenewoud 1956, and Van Groenewoud and Whitney 1969). In  summary, certain site characteristics (elevation, percent slope, mineral soil bulk density, porosity and pH) appear sufficiently different between subzones to lend some logical explanation as to their possible effects on Phellinus root rot incidence and intensity behaviour.  8.4 Phellinus Root Rot Variability in Relation to Old and Second Growth Conditions and BEC Units  A number of old and second growth stand density and species composition attributes also appeared to lend some logical explanation to the patterns and behaviour of Phellinus root rot. Integral to the explanation of patterns is the concept of a natural, host-pathogen dynamic equilibrium, which enables time to be considered into the behavioural patterns and not just simply snap shot conditions measured in this study.  A significant case for a dynamic host-pathogen equilibrium has been established by relating present day Phellinus root rot damage in second growth forests with old growth and second growth species compositions, stand densities, stand dynamics, stand origins, and via incidence-severity relationships.  186 A dynamic host-pathogen equilibrium for Phellinus weirii in coastal Douglas-fir ecosystems is expected to function as follows. The naturally occurring Phellinus weirii is a functionally significant and important organism in coastal ecosystems where Douglasfir is a predominant tree species. Phellinus root rot behaves as a gap forming agent particularly in the early to mid-seral successional conditions (generally on the most Phellinus-susceptible species; grand fir and Douglas-fir, and to a lesser degree western hemlock enabling the more shade tolerant (and coincidentally less-susceptible or disease tolerant) often polyclimax tree species to occupy the site. As the susceptible host species increase in composition and density (i.e., root rot conduciveness increases) so does the ability of the pathogen to open up the stand (i.e., forming gaps). In most site associations, the chronosequence successional trend towards less-susceptible, polyclimax host species consequently acts to slow the disease' advance, hence there is an equilibrium between host and pathogen. The pathogen apparently does not have the ability to kill-off all species nor persist for periods longer than 50 years, while other lesssusceptible to tolerant species have the ability to ingress into infected site conditions. Other site, climatic, fire and biotic factors may eventually return the site back to pioneer to early-seral conditions suitable for the pathogens role in gap forming dynamics and to reactive the cycle from remnant Phellinus inocula sources. Logging activity in these ecosystems could also play a role in reducing or increasing Phellinus intensity, depending on the equilibrium phase that the old growth forest was in at the time of logging, and in doing so, return the stand to pioneer early-seral conditions.  187 The following subsections expand on the development of the dynamic host-pathogen equilibrium postulate through discussion of the old and second growth stand conditions as they related to the incidence, severity and intensity estimates of Phellinus root rot measured in second growth stands. The inter-relationships between these three factors are fundamental to the mechanisms of the dynamic host-pathogen equilibrium.  8.4.1^Phellinus Root Rot Variability in Relation to Old Growth Stand Densities and Species Compositions Across BEC Units  The dynamic host-pathogen equilibrium was postulated in part on the basis of the following old growth stand condition observations: (a) an inverse relationship between old growth stand density (stems/ha) of Douglas-fir and Phellinus damage intensity (or BARSDIC's), (b) old growth stand density (stems/ha) of western red cedar was greatest in the Low and Severe BARS-DIC's, and (c) old growth species compositions of Douglas-fir and western red cedar were, respectively, the lowest and greatest in the Severe BARS-DIC.  Old growth species compositions of Douglas-fir and western red cedar were lower and higher in the CWHxm1 compared to the CDFmm and the CWHxm2 subzone variants, respectively. Interestingly, the CWHxm1 variant had substantially higher Phellinus root rot damage intensity (12.88%) compared to 5.94% and 8.93%, for the CDFmm and CWHxm1 variants, respectively. The phenomena of lower susceptible host composition (Douglas-fir) countered by a higher non-susceptible host composition (western red cedar) at the variant level provided an indication of the dynamic host-pathogen equilibrium. This  188 phenomena at the high level of ecological classification tends to corroborate the observed trends of species dynamics in portions of stands infected with Phellinus root rot.  The potential old growth inoculum source composition (Douglas-fir/western hemlock species compositions--COMPFH) were shown to be virtually equal between the CDFmm and CWHxm subzones (86.4% and 84.3%, respectively). Because of this fact, the role of Douglas-fir/western hemlock stand density (stems/ha, SPHFH) was though to be very important in a comparison of Phellinus root rot intensity. Stand density of SPHFH was shown to parallel the mean Phellinus root rot intensity in second growth stands in the CDFmm and CWHxm subzones and their respective site associations. Old growth stand densities in the CDFmm (76 stems/ha) were 59% that of the CWHxm (129 stems/ha), which corresponded remarkably well with the mean BARS Phellinus root rot intensity in the CDFmm (5.94%), which was 53% that of the CWHxm (11.11%). Since the old growth species compositions were virtually identical at the subzone level, it appeared that old growth stand density may be a critical factor in the transmission and intensification of Phellinus root rot in present second growth stands. Note, a causal relationship could not  be established. In apparent contradiction to the subzone relationship, old growth stand density of Douglas-fir was negatively and significantly correlated (p = .081) to Phellinus damage intensity, while there was no significant correlation to stand density of western red cedar. If the dynamic host-pathogen equilibrium were true, then the negative correlation for old growth Douglas-fir may be explained if we assume the old growth forests were in a reactive phase of the equilibrium, that is, due to Phellinus-related  189 mortality and reduction of stand density. Similarly, old growth species composition of Douglas-fir was negatively correlated (non-significant, p = .265) to Phellinus damage intensity, and western red cedar was positively correlated (insignificant, p = .346).  Old growth stand density-Phellinus intensity relationships at the site association level were not as clear. Only a slight increase in stand density (stems/ha) of Douglas-fir from the drier to fresher site associations was observed. Stand density and species composition of western red cedar was notably higher in the site associations with the highest Phellinus root rot intensities; FdBg-Oregon grape, FdHw-Salal and HwFdKindbergia s.a.'s (Figs. 42 and 44). In the same site associations, the old growth species  composition of Douglas-fir was notably lower. Ecologically, it was surprising to see relatively fewer stems/ha and lower composition of western red cedar in the CwFoamflower s.a., a site association in which cedar is indicated to be a climax species (Fig.  42). It appeared that western red cedar may have responded, or reacted, to the presence of Phellinus root rot earlier in the old growth stand life, and become a significant equilibration component of the stand. The high root rot intensity in the FdBg-Oregon grape s.a. is counter-intuitive given the low Douglas-fir stand density and species  composition observed in that s.a. (Figs. 41 and 43). This may be explained, in part, by the fact that grand fir (Bg), (a very highly susceptible species, and an indicated component of the climax forest), decomposes very quickly, and was therefore absent in stump sampling.  190 Phellinus intensity contour plots (Figs. 45 and 46 in Section 7.3.1.5) illustrate the Phellinus root rot relationship with old growth stand density (stems/ha Douglas-fir  (SPHFH) and stems/ha western red cedar (SPHCW)). The contour plots indicated a trough between two peaks of moderate to severe Phellinus root rot damage intensity. The peaks indicated high numbers of Douglas-fir and western red cedar and low numbers of Douglas-fir and western red cedar, possibly, relating to conducive and reactive stand conditions for Phellinus root rot behaviour, respectively. The upper peak approximately corresponds to high SPHFH (>200/ha) and moderate SPHCW (>120/ha), while the lower peak approximately corresponds to low SPHFH (<200/ha) and moderate to high SPHCW (> 100/ha). The existence of the peaks and a trough in these figures further suggested the concept of a natural, dynamic host-pathogen equilibrium. Both peaks might be viewed as a conditions conducive for transmitting P.weirii to second growth forests; the upper peak is perhaps due to greater stand density of susceptible Douglas-fir, while the lower peak may be more the old growth reaction to P.weirii - related mortality. However, between the two peaks lay stand conditions where coastal Douglas-fir ecosystems appeared to approach equilibrium (very low damage intensity), over long periods of time.  In summary, the old growth stand conditions suggested that Phellinus root rot is likely responsive to increasing stand density of Douglas-fir/western hemlock (SPFH). That was most apparent between subzone variants. The host-pathogen equilibrium is also suggested by increasing stand density and species composition of non-susceptible  191 western red cedar, which generally corresponded to reduced density of susceptible species. Phellinus root rot appears to have contributed to a shift to western red cedar in the old growth, but not to the point of eliminating the disease. Without better knowledge of the duration of infection and inoculum distribution at the time of the second growth stand origin, it was impossible to confirm whether the old growth conditions are causal (conducive) or reactive to Phellinus root rot activity in the old growth. However, it is most likely that the old growth stand conditions were at least partially a result, or an effect, of some root rot induced succession of forest successional trends strongly related to P. weirii activity (i.e., an effect of the dynamic host-pathogen equilibrium) as well as climate, fire and ecologically induced succession.  8.4.2^Second Growth Stand Conditions  Second growth stand density and susceptible species compositon factors that appear related to Phellinus damage intensity were; (a) first PSP measurement stand density (stems/ha ?..4.0 cm), (b) back-estimated stand density (stems/ha, >4.0 cm at 10 yr), (c) back-estimated Curtis' Relative Density (_44.0 cm at 10 yr), and (d) first PSP measurement of Phellinus susceptible species composition Douglas-fir, grand fir and western hemlock >4.0 cm, FSUSINT).  Second growth stand conditions (species compositions, stems/ha, basal area (m 2 /ha), and relative density) were seen to vary between site associations, and although  192 not significant at the 20% level, the patterns were so surprisingly similar to that of Phellinus root rot intensity that they could not be ignored. (See Section 7.3.2.6, p. 125).  For example, the susceptible species compositions (FSUS, FINT or FSUSINT) measured at PSP establishment were highly correlated to the observed root rot levels within the CWHxm s.a.'s, (Pearson r-values were respectively: .96, -.63 and .96, but not tabled). The same does not hold true for the CDFmm s.a.'s (Table 21, p. 105). Generally, the greater the susceptible species composition, the greater the probability of Phellinus root rot, with the exception of the FdBg-Oregon grape s.a. (possibly due to the small sample size). Similarly, total stems/ha, (using either first measurement >4.0 cm, or the backestimated to age 10 yr, .4.0 cm estimates), had virtually identical distribution patterns to Phellinus root rot BARS estimates (Figs. 59 and 60, p. 127). It appeared that stand  density (stems/ha), was playing a real and significant role in Phellinus behaviour similar to that reported by Bloomberg (1990). Interestingly, the basal area estimates did not show similar relationships, (Figs. 61 and 62, p. 128), although basal areas were slightly greater in the CWHxm s.a.'s compared to the CDFmm s.a.'s. Curtis' relative stand density measures (reference age 10) did show patterns similar to Phellinus root rot (Figs. 63 and 64, p. 129). Evidently Childs' (1970) comment of "that beyond stand history" (i.e., of disease presence/absence), "stand density would likely be the most important factor  in disease spread" appears to be true. Stand density of both the old and second growth was closely related to Phellinus root rot intensity at least the subzones and site associations.  193 8.4.2.1^Second Growth Species Dynamics: Variable-Radius Plot Sample Surveys  The dynamics of the postulated host-pathogen equilibrium were shown to be an active phenomena in the second growth by examining changes in species composition stratified by species susceptibility, diameter and disease condition classifications. Two sources of data were examined; (a) variable-radius plot sample survey data, and (b) 3035 years of PSP records. Variable-radius plot %BAR sample survey data were examined in three ways.  The results of all three analytical approaches indicated similar trends in the relationships: (a) non-susceptible species compositons, and tree counts (hence basal areas) were greater in Phellinus infected conditions by about 30% compared to healthy conditions, (b) there was a greater net increase in non-susceptible species composition in Phellinus infected conditions (gained about 4.4%) compared to the susceptible species composition (lost about 2.6-4.4%), and (c) there appeared to be an inverse relationship between the amount of non-susceptible species composition and tree diameter size classes and Phellinus damage intensity (i.e., the non-susceptible species composition was greater in the small diameter classes vs. large, that is dependent on Phellinus intensity). In (c) above, the non-susceptible species composition shift strengthened above 20% basal area reduction (BARS) confirming that the differences in non-susceptible species composition levels were in part, time dependent, as root rot development is also highly time dependent. That is to say, the reduction of susceptible species was  194 countered by mensurational ingrowth (or a shift in species composition) of nonsusceptible species in infected areas, which is more noticeable above the 20% BARS threshold. This was because Phellinus susceptible species composition drops relatively quickly, often episodically, while the subsequent ingrowth of non-susceptible species was slower, and was in response to the increased light, heat and moisture conditions enabling saplings to enter the 4.0 cm diameter limit class.  The successful attempt to estimate species succession using diameter limits "age class", and/or root rot intensi ty conditions to stratify tree tallies made at one measurement period, while not perfect, did allow for some interpretation of species dynamics.  8.4.2.2^Second Growth Species Dynamics: Permanent Sample Plot Records  Changes in non-susceptible and susceptible species compositions were examined using permanent sample plot (PSP) records (.4.0 cm) stratified by disease condition (incidence: absence/presence) and the first and last measurement (spanning 30 to 35 yr).  Non-susceptible species compositions increased by 3.7% in Phellinus infected PSP's and 0.2% in healthy PSP's, for a net increase of 3.5%, while the susceptible species compositions decreased by 2.1% in Phellinus infected PSP's and 0.6% in healthy PSP's,  195 for a net decrease of 1.5%. Comparison of the PSP-based species composition shifts showed striking similarities to variable-radius %BAR sample survey data. PSP-based TIR (tolerant, intermediate and resistant) species increased by 3.7% over about 30 yrs, compared to the 4.4% to 4.46% increases in non-susceptible species composition from the %BAR sample survey data. Similarly, the PSP-based change in susceptible species of -2.1% was remarkably close to the -2.6% to -4.46% decreases seen in the variableradius plot survey data (see Section 12.3.3.1). The similarities between the PSP and survey data lends strength to the PSP's being representative of the broader forest condition (that was sampled),-- perhaps having forest sampling implications beyond that required for evaluating root rot and species dynamics.  Changes in non-susceptible species composition over 30 to 35 yr measurements was large. The greatest increases occurred in the two zonal site associations (Fd-Salal and HwFd-Kindbergia), by over two orders of magnitude compared to the other site associations. The increases appeared somewhat related to Phellinus intensities. The reasons are unclear, but the following explanations are forwarded. The significant increase in Phellinus-tolerant, intermediate and resistant (TIR) species in the HwFdKindbergia zonal s.a. may be explained by the climatic, ecological, successional, and perhaps pathological factors that are integrated to form the basis of site association taxon names. The site association taxon would indicate that Douglas-fir is a predominant early seral species on HwFd-Kindbergia s.a.'s, with successional trends towards a stable climax vegetation comprised of western hemlock and Douglas-fir. Furthermore, Douglas-fir,  196 although dominant in the climax condition, becomes secondary to western hemlock (the latter substantial component of TIR species). Given the differential Phellinus susceptibilities to infection of Douglas-fir and western hemlock (Wallis 1976, Childs 1963, 1970, Filip and Schmidt 1979), and the fact that the HwFd-Kindbergia s.a. had the greatest incidence and intensity of Phellinus root rot, it is suggested that root rot is playing a major biogenic successional role in reducing the early sera! component of Douglas-fir to a lower secondary level, as suggested by the site association taxon. A less effective case can be built for the Fd-Salal s.a., but the fact remains that stand density does drop by some means (suggested to be Phellinus root rot), and that there is an increase in species diversity, or a drop in Douglas-fir composition, although the site association remains essentially pure (>80%) with other minor species mixes (often deciduous).  It is possible that the more moderate environmental site conditions (e.g., light, heat or moisture) typical of zonal site associations may be contributing to the larger increases in non-susceptible species. The CDFmm and Fd-Salal units are warmer and have lower stand densities on average than the CWHxm2 and HwFd-Kindbergia units, although the latter two units are significantly moister. Considering that root rot incidence and intensity estimates in the CDFmm unit are about half that of the CWHxm2, it follows that light and heat likely contribute more to the release and mensurational ingrowth of TIR species than moisture.  197 8.4.2.3^Summary of the Natural, Host-Pathogen Dynamic Equilibrium Model  A model to explain the dynamics of species compositions can be postulated from the ecosystems sampled. In healthy, root rot free conditions, the proportion of nonsusceptible to susceptible species remained fairly static, with a tendency to slight increases in non-susceptible species typical of the chronosequences. In Phellinus root rot infected conditions, there was a substantial reduction of susceptible species with a low proportion (replacement rate) of small diameter class ingrowth. This was countered with higher a proportion of non-susceptible species entering the lower diameter class as compared to healthy conditions. In other words, healthy stands over time, shift towards a higher composition of non-susceptible species as the ecological model would indicate, but at a less dramatic rate than in infected stands. Phellinus root rot acts as a biogenic successional agent removing Phellinus susceptible species (generally pioneer to early shade intolerant seral species) and favouring their replacement with Phellinus tolerant, intermediate and resistant (often shade tolerate, late-seral to poly-climax species). Assuming that this ecological process is consistent over time, shifts to increasing proportions of non-susceptible species would also have occurred in the old growth forests barring major climatic change. Although the old growth stump survey data was not dramatic in illustrating the root rot related successional pattern (i.e., increasing red cedar composition), the trend nevertheless appears strong enough to support the hypothesis of species dynamics as related to damage intensity, with strong implications of a dynamic host-pathogen equilibrium.  198 The similarity in forest successional trends in old and second growth coastal Douglas-fir ecosystems suggests a natural, dynamic host-pathogen equilibrium is at play. A host-pathogen equilibrium of this order and time-scale would go a long way towards explaining why Phellinus root rot has not covered the whole Douglas-fir forest ecosystem and brought it to a non-productive condition. Furthermore, the suggestion of a dynamic host-pathogen equilibrium provides forest managers and silviculturists with direct evidence that management prescriptions using less-to-non-susceptible tree species, will aid in reducing long term damage to Phellinus root rot, although not necessarily maximizing stand or forest timber (volume) productivity.  The existence of a dynamic host-pathogen equilibrium and very high level of Phellinus root rot incidence in coastal Douglas-fir ecosystems (87% of 1ha sample  surveys, 35% of 0.04 ha PSP's and 26% of variable-radius plots), suggests that Phellinus root rot is a common and significant biological factor in the ecological functioning of these ecosystems. As such, the significance of Phellinus root rot and the host-pathogen equilibrium in the coastal Douglas-fir ecosystems should be strongly considered in all aspects of forest resources management (be it stand-specific or forest level management prescriptions) for timber, wildlife, recreation, or site productivity-potential assessments for land-use resource allocations.  It is clear from this study that the role of "pathogens" in forested ecosystems needs to be more clearly evaluated so that our management of these ecosystems does not  199 create an imbalance in the natural, dynamic host(s)-pathogen(s) equilibrium(s). Management actions favouring disequilibrium, will cause either reduced stand/forest productivity of the pathogens' preferred hosts, or require great efforts to eradicate the pathogen inoculum from the site. Allowing the dynamic equilibrium to occur naturally will reduce stand and forest productivity over time under volume maximization management objectives, but in doing so will provide a more tree species (bio-) diverse ecosystem. A balanced "production-forest" management approach should strive to use the principles of shade-tolerant species succession ingrowth into Phellinus infected sites (as seen in the dynamic host-pathogen equilibrium). This should be achieved by shifting to alternate less-susceptible species for regeneration and stand tending prescriptions, thus reducing the amplitude of the host-pathogen equilibrium. Since stand density also appears strongly related to enhancing Phellinus root rot intensity, regeneration and stand tending prescriptions should consider managing to lower densities, that are in principle less conducive to Phellinus, and thereby reducing crop risk. And, as unlikely as it is that fire will be used as a post-harvest Phellinus control treatment, fire appears to have had some effect in controlling Phellinus root rot, as seen in the next section.  8.5 The Effects Of Stand History (Logging And Burning) On The Behaviour Of Phellinus Root Rot  Present stand age was shown to be significantly different (all p-values < .088) between stand origins (Figs. 74 and 75), with root rot intensities paralleling mean stand age. In this study, stand age was not considered to be significantly correlated to damage  200 intensity (BARS), but this correlation is highly suspicious, since in most other root disease studies, stand age is highly and positively correlated, and important for prediction of damage intensity. There seems little doubt that the effect of stand age on Phellinus root rot intensity in this study is a positive one, albeit not strong, and therefore explains a substantial amount of the variation in damage intensity. Beyond the substantial role stand age plays, I have sought to establish if any ecological factors play a role in the variation of Phellinus root rot intensity between stand origins.  Several questions arose that after some deliberation lead to some explanation of the Phellinus root rot trend seen in these stand histories. What elements of P. weirii control  are suggested by the logging-only origin as compared to stands of wildfire origin? What might the effect of slashburning be compared to wildfire? Are interactions between logging and slashburning suggested by the data? How do past and present stand densities and species compositions relate to present infection levels? The next several subsections will address these questions.  8.5.1 Evaluating The Effects Of Logging On Phellinus weirii Survival  The fundamental difference between Phellinus infected stands that are logged versus not logged (or wildfire killed) is that of cut stump surfaces. How does P.weirii survive to reinfect the planted or naturally regenerating crop, and what is its natural P. weirii decay curve?  201 Phellinus weirii is not considered to be an aggressive competitor, and is not able to  colonize wood previously decomposed or inhabited by other microorganisms (Hansen 1979). When a Phellinus colonized tree dies (or is logged), the fungus can remain viable ectotrophically for 40 to 50 yr on old growth Douglas-fir (Hansen 1979), or over 100 yr when it is enclosed by bands of resin-soaked wood (Buckland et al. 1954, Wallis 1976), or for shorter periods when it establishes zone lines around colonized areas, thereby excluding antagonistic microorganisms (Nelson 1964, 1967, 1973, 1975). Zone lines are darkly pigmented sheets of swollen hyphal tissue (Nelson 1967). Li (1983) showed the pigment in P. weirii zone lines to be tryosine-melanin in nature, and confirmed that it is important for the prolonged survival of P. weirii in infested stumps and roots. Li (1983) also observed the zone line pigment to inhibit two microorganisms antagonistic to P. weirii (Bacillus spp. (Hutchins 1980), and Streptomyces griseoloalbus (Rose et al. 1980))  but, he did not find it inhibitory to Trichoderma viride Pers., an antagonistic fungus common in soil and wood. The antagonism of various Trichoderma spp. to P. weirii has been demonstrated by Nelson (1964,1973,1975) and by Goldfarb (1985, 1986) and (Goldfarb et al. 1989). Nelson and Thies (1985, 1986) demonstrated the ability of Trichoderma viride to colonize stump tops and with increasing success in stumps with  advanced decay. Goldfarb (1985, 1986) has shown Trichoderma spp. to be slow, but well adapted invaders of P. weirii infected stumps and root systems, in nature. These workers suggest that Trichoderma spp. are likely effective biocontrol candidates. Nelson is continuing biocontrol research of P. weirii primarily with Trichoderma spp. using various types of stump top-or-side delivery systems (Nelson and Thies 1985, 1986, and Nelson  202 1990). Observations from the literature consistently point to the antagonistic effect of T. viride and its biocontrol potential. This suggested that the lower levels of P. weirii  observed in logged versus wildfire originated stands may, in part, be due to a slow displacement of P. weirii by stump top invading antagonistic Trichoderma spp.  8.5.2 Evaluating The Effects Of Slashburning (& Logging) On Phellinus weirii Survival  Survival of infective P. weirii has been shown to decrease to near zero over 50 years (Hansen 1979), but with only a weak positive correlation to stump size and decay column condition (% hollowness) in stands logged 50 years previously. Notably, there is no indication of fire history in Hansen's (1979) study. Tkacz and Hansen (1982) have shown the dependence of second growth root disease distribution on the previous rotation, and interestingly their (1982) study sites were burned after harvest.  From personal observations, and discussion with colleagues (Reynolds 1990, and Bloomberg 1990a), it is very common to find old growth stumps completely burned out to a root bark shell in stands of logged-and-burned origin. The intensity and uncontrolled duration of much of the past railway logging slashburns likely provided some level of P. weirii control, with increasing effectiveness in stumps with sizeable P.weirii decay  columns. The assumption made in the logging-and-burning scenario is that fire entered the stump/root system via the cut stump surface and burned out the root's woody tissues, likely baking ectotrophic P. weirii to temperatures beyond its tolerance, thus  203 sharply reducing its surface area and infective viability. The upper limit to thermal tolerance for P. weirii is in the range of 39 ° C plus (Nelson and Fay 1974, Angwin 1985), which burning root systems would generally exceed by an estimated 50 to 100 ° C (Parminter 1991). In support of this postulation, Wright and Tarrant (1958) found the occurrence of ectotrophic mycorrhizae decreased on logged and burned Douglas-fir sites, with ectotrophic mycorrhizal recovery depths increasing with burn severity.  Prescribed fire occurrence is also known to affect populations of antagonistic microorganisms in soil (Parmeter 1977). Actinomycete populations have been shown to increase sharply on severly burned Douglas-fir sites (Wright and Tarrant 1957). The actinomycete, S.griseoloalbus, has been shown to be antagonistic to P.weirii (Rose et al. 1980). Reaves (1985), showed that prescribed fire increased the soil cation concentration from forest ash leachate, which has been shown to have a negative effect on the in vitro growth of Armillaria ostoyae (Reaves et al. 1984). Furthermore, fire ash leachates are known to positively effect Trichoderma spp. which in turn reduce the growth and rhizomorph formation of A. ostoyae (Reaves et al. 1990). Since several Trichoderma spp. antagonistic to P. weirii are known to occur in coastal Douglas-fir forests soils (Nelson 1964, 1969), extensive work is underway to investigate biological control methods using antagonistic Trichoderma spp. in the U.S. Pacific Northwestern states, (Nelson 1981, Goldfarb 1985, 1986, 1989a, 1989b and Nelson and Thies 1985, 1986 and Nelson 1990).  204 It is possible that soil cation concentrations were raised following fires in the old growth logging slash. In that scenario, P. weirii survival and infectivity would be reduced directly by decay column burning, and possibly through enhanced soil cation effects on antagonistic fungi, thus lending explanation to why Phellinus root rot levels (BARS) are lowest in stands of logged-and-burned origin. As for wildfire origin stands, it was unlikely they had similar fire intensities as compared to the logged-and-burned origin since total stand densities of stems/ha Douglas-fir and western hemlock (SPHFH), and western red cedar (SPHCW) were half that of the logged-and-burned stands, and ground fuels were most certainly less. Thus the impact of fire on Phellinus was likely less in wildfire origin stands than in logged-and-burned stand origins, hence, the highest root rot intensities were in stands of wildfire origin.  Although the mean Phellinus (BARS) intensities between logged-only and loggedand-burned was not much different, 10.54% and 8.49% respectively, the trend should not be ignored. Note that beyond age, fire is the only factor that is different. Although an interaction of slashburning and logging was likely, fire following old growth logging appears to have had a significant effect in controlling P. weirii.  8.5.3^Evaluating The Effects Of Wildfire On Phellinus weirii Survival  In wildfire origin stands there were of course no man-made stumps; thus, regardless of the P. weirii infection condition in the old growth, wildfire might only reduce inoculum  205 loads in the odd natural stump, and possibly through changed soil chemistry and its effects on fungi antagonistic to P. weirii (as postulated above). Studies on the fire and P. weirii ecology of mountain hemlock forests of Oregon suggest that P. weirii infestations  may enhance the probability of wildfire because of increased fuel loadings and ladder fuels (Dickman and Cook 1989). In this study, wildfire origin stands had less than half the old growth stems/ha of Douglas-fir and western hemlock (SPHFH), more stems/ha of western red cedar (SPHCW), and more than double the species composition of western red cedar (COMPCW) than stands of other origins (Table 21, pg. 105). These facts, combined with the observed negative correlation between BARS damage intensities and SPHFH, and a positive correlation with COMPCW observed in this study, lends support to probable high P.weirii infection levels in the old growth forests preceeding the wildfires. Dickman and Cook (1989) also suggest that, "if larger roots harbor mycelia for a longer time, then older stands that are destroyed by fire are more likely to leave inoculum capable of generating subsequent reinfestation than are younger stands". Stand density is known to have dramatic effects on stem sizes (McArdle et al. 1961), and hence stump and root systems. Since total old growth stems/ha in wildfire origin stands were less than half that of other origins, it is not only likely that the trees were larger in the wildfire origin old growth forests, but if infected with P. weirii would pose a greater inoculum potential.  Based on the observations of Childs (1970), Tkacz and Hansen (1982) and this study data, one can postulate that the distribution and infectivity of P. weirii inoculum sources  206 might be higher in wildfire origin stands as compared to the other origins, thus effecting the higher levels of infection observed in todays second growth forests for the following reasons: (1) a lower likelihood of wildfire directly affecting ectotrophic or endotrophic inocula on standing trees during a wildfire, (2) the increased probability of wildfire in areas of P. weirii infestation due to disease effects on stand structure/fuel loadings, (3) the likelihood of larger stem and root systems due to lower stand density (wildfire SPHFH 63 stems/ha vs. logged, logged-and-burned 130 stems/ha), therefore potentially larger and longer-living inoculum sources, and (4) a lower likelihood of wildfire producing as much ash and cation enhanced leachate (compared to post old growth harvest slash burns), which may negatively affect P. weirii by it's beneficial effects of ash on fungi antagonistic to P. weirii.  In summary, the most likely explanation for the variability of Phellinus damage intensities (wildfire (15.45%), logged-only (10.54%), and logged-and-burned (8.22%)), are: (1) the significant and positive differences in present stand ages; (2) the effect of the very intense slashburning that followed much of the old growth (railway) logging, and its lethal effects on Phellinus decay columns via the cut stump surface and to a lesser extent on below-ground stump and root surface ectotrophic P. weirii; (3) the absence of cut stump surfaces in stands of wildfire origin; (4) the likelihood of lower intensity fire at or close to the ground level; (5) the stems/ha of Douglas-fir old growth stumps in wildfire origin stands were half (63) that of the logged-origin stands (133), combined with the western red cedar was less than half (12) that of the logged-origin stands (24), theoretically  207 indicating a more severe Phellinus damage intensity condition.  The frequency of occurrence of stand origins for the 139 sample survey locations showed that 69% originated after logging-and-burning, 21.6% after wildfire, and 9.4% after logging-only. The origin frequencies, while not providing any particular explanation in themselves, provided an interesting perspective to the Douglas-fir ecosystems on southeastern Vancouver Island.  8.6 Predicting Biogeoclimatic Unit Phellinus Root Rot Hazard And Risk  8.6.1 Phellinus Root Rot and the Site Association Taxon Model  The search for direct link correlations of site ecological variables to Phellinus root rot behaviour appears very uncertain. With the variability of ecological variables and parameters so high, an interpretation of Phellinus relationships at the site association level was attempted using a combination of past and present stand characteristics, Phellinushost susceptibilities, predicted vegetation succession patterns (chronosequences) that are connoted in the site association taxon (names), and the concept of the natural, dynamic host-pathogen equilibrium. The relevancy of this approach is based on the fact that vegetation is a good measure of an ecosystems' productive potential as seen in the use of indicator plant species for identification of ecosystems (Klinka et al. 1989), and the corresponding major role of vegetation (plant) classification in the BEC system (Pojar et  208 al. 1987). Site association taxon (names) define potential climax vegetation (i.e.,  predominant tree(s) and ground cover) for a site that is characterized by a uniform climate (Banner et al. 1990). For example, the Fd-Salal s.a. connotes a major forest cover of Douglas-fir with a salal ground cover as the potential climax vegetation. The successional phases a s.a. goes through to reach this predicted climax condition are known as chronosequences (they are not clearly documented at this time).  Consider the coastal Douglas-fir ecosystem as a whole. It is a complex of tree species compositions of varying Phellinus-susceptibilities, in a variety of site and stand conditions, shaped by a variety of fire and harvest histories, and is in various successional phases and pest conditions. The success of measuring these factors, their interactions and their relationships to Phellinus root rot incidence, severity and intensity levels has not yet borne conclusive answers, as this study shows. Site classification, and the site association taxon model in particular, provided the most integrative tool for comprehending most of the sources of zonal (climate), plant and site variation, with the possible exception of Phellinus root rot.  An attempt to predict the relative Phellinus root rot incidence and intensity (risk) using the site association taxon model, is further predicated on the long term predictability of a s.a.'s chronosequence to propagate and maintain Phellinus inoculum, which is itself based on knowledge of the relative susceptibilities to P. weirii of the s.a. taxons' climax tree species vegetation. Other factors such as fire and harvest history, and past and  209 present stand conditions (species composition and stand density), could serve to adjust the scale of the relative incidence or intensity (risk) in order to compensate for stand conditions believed to be conducive-to or reactive-to Phellinus root rot, within the context of the dynamic host-pathogen equilibrium. Critical to determination of risk is the hostpathogen equilibrium phase of the preceding stand at the time of origin of the second growth forests, (i.e., estimation of previous stand conditions as they relate to probabilities of Phellinus inoculum carry-over). These phases are: (1) conducive, or highly-susceptible species phase, (2) reactive, or less-susceptible species phase, or (3) Phellinus root rot equilibrium phase. Over or underating the Phellinus risk is highly probable if the rating is based on past conditions only; knowing the potential condition is the essence of risk rating on an ecological basis.  The site association taxon model for Phellinus root rot behaviour is as follows. Site associations with grand fir (Bg) or Douglas-fir (Fd) as a leading species are considered to be the most conducive and have the highest probability to propogate Phellinus root rot to the upper limits of an implied dynamic host-pathogen equilibrium. Climax species combinations of lower susceptiblity to Phellinus (e.g., western hemlock (Hw), lodgepole pine (PI), western white pine (Pw) or western red cedar (Cw)) serve to impede or reduce root rot levels. In this context, the site association model proves to be very accurate in predicting Phellinus root rot (see Fig. 30, pg. 98). Within a subzone variant (e.g., CDFmm), the Fd-Sala/ had lower root rot levels than the FdBg-Oregon grape with two susceptible species. In the CWHxm variant the transition from FdHw-Salal to HwFd-  210 Kindbergia s.a. reduces the highly susceptible component (Douglas-fir) from a primary-  major species to a secondary-major species, thus reducing the probability of sustained high damage. The observed BARS sample estimates show this, but when adjusted for an age interaction, the HwFd-Kindbergia s.a. was predicted (regression model) to have marginally higher Phellinus intensity. This phenomena could be possible because a greater Phellinus intensity may be required to kill-out the Douglas-fir component in order to achieve the HwFd climax forest cover condition predicted by the site association taxon. The Cw-Foamflower s.a. will sustain even lower root rot levels due to low levels of susceptible species in a climax condition. Notably, the Cw-Foamflower s.a. had a relatively low stand density (stems/ha) of old growth western red cedar, which is contrary to the s.a. taxon. It is very likely that this s.a. had not yet acheived its potential climax forest conditions at the time of the old growth harvest as its condition appears to be in a very conducive, or highly susceptible species phase.  In summary, a method for estimating the hazard and risk of Phellinus root rot for site associations has been demonstrated through the integration of, (i) present-day disease incidence and intensity, (ii) principles of a host-pathogen behaviour, (iii) a postulated, natural, dynamic host-pathogen equilibrium (conducive, reactive and equibria phases), and (iv) the predicted patterns of ecological succession (chronosequences) to the vegetation potential within the BEC system of site classification. The implications for estimating biogeoclimatic unit pest hazard and risk in this manner is enormous, since much of the knowledge exists today.  211 8.6.2^Multiple Regression Models  Empirically-based multiple regression models were estimated to provide primarily a descriptive, and secondarily a predictive behaviour of Phellinus root rot intensity within biogeoclimatic units in order to develop a disease hazard and risk classification (see Section 8.6.3.). Models for each biogeoclimatic unit included a stand age-biogeoclimatic unit interaction term. Stand age, although not significantly correlated to Phellinus root rot intensity in this study, does appear to have some relationship to Phellinus, and is generally considered to be a strong determinant in detection, assessment and prediction of Phellinus behaviour. None of the root rot behaviour models described enough of the damage intensity variation for reliable stand-level prediction, but all the models did describe the general relationships within and between the biogeoclimatic units reasonably well. Generally, all the models were similar. Interestingly, the relative damage intensities are nearly identical to those observed in large-scale root disease surveys done by the BC Forest Service in the same ecosystems in 1982-83 (Beale 1987). Furthermore, the models appear to describe the disease behaviour reasonably and support the interpretations of Phellinus root rot behaviour in the coastal Douglas-fir ecosystems vis a vis the postulated dynamic host-pathogen equilibrium, and more importantly the  observations made by the author from field experience and other disease analyses (Beale 1989b).  All Phellinus intensity models for the biogeoclimatic units were compared at reference  212 age 80 yr (Figs. 76-80) and were used for development of an ecologically-based Phellinus root rot hazard and risk classification, see Section 8.6.3.  8.6.3 Phellinus Root Rot - Coastal Douglas-fir Ecosystem Hazard (Susceptibility) and Risk Classification for Southeastern Vancouver Island  After a sober review of all the available data, models postulations, a first approximation of Phellinus root rot hazard and risk for Douglas-fir ecosystems of southeastern Vancouver Island was made (Table 44). Recall that hazard is an assessment of tree, stand forest and environmental conditions which are conducive to pest infestation without reference to probability (i.e., hazard is a measure of susceptibility). Risk is the probability and predicted intensity of an infestation. Hazard is in part a  function of risk. The classification presented is heavily based on professional judgement and a crude classification of stand-based disease incidence (<.25 is Low-Medium and ?_.25 is Medium-Severe), and damage intensity based on prediction of BARS at 80 yr is, (5_6% is Low, > 6 .10% is Medium, and > 10% is Severe). Individual ecological variables/characteristics and stand attributes could not be considered in the classification, although as discussed earlier, many if not all variables/attributes (biological, geological and climatalogical) appear to be well integrated into the biogeoclimatic units and therefore do not need to be uniquely identified and accounted for.  213  TABLE 38^PHELLINUS ROOT ROT HAZARD AND RISK CLASSIFICATION FOR DOUGLAS-FIR ECOSYSTEMS ON S.E. VANCOUVER ISLAND Low  BEC UNITS  Medium  Severe  PLANT ALLIANCE X  Pseudotsuga-Mahonia Tsuga-Mahonia Thuja-Achlys  X X  PLANT ASSOCIATION X  Pseudotsuga-Arbutus Pseudotsuga-Mahonia Tsuga-Mahonia Thuja-Foamflower  X X X  SUBZONE X  CDFmm CWHxm  X  SUBZONE VARIANT CDFmm CWHxm1 CWHxm2  X X X  SITE ASSOCIATION CDFmm-Fd-Salal CDFmm-FdBg-Oregon grape CWHxm-FdHw-Salal CWHxm-HwFd-Kindbergia CWHxm-Cw-Foamflower  X X X X X  1^Predicted Incidence (stand-parameter-diseased basis) at 80 yr Low <.25 or 25% Medium <.25 or 25% / z.25 or 25% Severe k.25 or 25% Predicted Damage Intensity (stand-based %Basal Area Reduction BARS) at 80 yr Low s6% Medium >6 s10% Severe >10%  214 8.7 Damage Appraisal Of Phellinus Root Rot On Growth And Yield Of Second Growth Douglas-fir Ecosystems  8.7.1^Yield Comparisons Within Selected Installations  Comparison of growth and yield within selected PSP installations (chosen for nearidentical site and stand conditions at PSP establishment) indicated some dramatic reductions in the yield of Phellinus infected PSP's. Compared to healthy PSP's Phellinus infected PSP's generally have mortality (up to 4.2 times greater), retain only 40% to 80% of the basal area increment and retain only 69% to 83% of the volume increment. Greater reductions to stems/ha and lesser increments of basal area and volume were attributable to greater Phellinus damage intensity in the PSP's. Interestingly, the growth rates in infected PSP's, except in the cases of the most severely infected PSP's, appeared to be equal to or better than the healthy PSP's. This response was likely attributable to two factors: (i) increased light and moisture conditions for the residual (remaining) trees, perhaps similar to the Phellinus root rot related natural thinning responses reported by Oren et al. (1985), and (ii) increased mensurational growth rates for the residual trees and ingrowth rates of shade tolerant, non-susceptible species (i.e., the latter being part of the 3.5% net response of Phellinus non-susceptible species ingrowth shown in Phellinus infected PSP's, ...a resultant of the dynamic host-pathogen equilibrium discribed in this study).  215 8.7.2^Yield Comparisons Using the Chapman-Richards VAC Model  Yield models using the Chapman-Richards VAC growth model were calculated for the combined site associations FdHw-Salal and HwFd-Kindbergia (nearly all of the CWHxm subzone PSP's). Model estimates for healthy and infected PSP conditions (>4.0 cm), fitted yield expectations very well. The estimated yield reduction and the form of the Phellinus infected curve (increasing gradually, virtually from regeneration) conformed well  to observations from field and survey work, and simulations using the TASS-ROTSIM model. The healthy PSP yield at 80 yr is estimated at 732.9 m 3 /ha, and the infected PSP yield at 668 m 3 /ha, for an 8.86% yield reduction.  The multiple linear regression Phellinus intensity model estimated percent basal area reduction for site associations (all species, >4.0 cm; NSBAR) at 80 yr to be 12.68%, which compared well with the 8.86% yield reduction from the Chapman-Richards VAC.  8.7.3^Growth and Yield Comparisons Over Site Height  8.7.3.1^All Data-No Stratification  A growth difference model predicted a positive linear relationship between annual volume increment (>4.0 cm) and annual site height increment for healthy and Phellinus infected PSP's. Infected PSP's grew more slowly by 4.4% to 11.7% over the range of 0.1  216 to 0.9 m annual site height increment, respectively. Assuming that site height measurements are independent of root rot effects in PSP's, the pattern of growth reduction indicated that site quality has a positive effect on growth reduction. An alternative and more likely assumption is that, site height measurements were somewhat negatively affected by Phellinus root rot. If so, then growth reductions were inversely related to site height increment, with growth reductions ranging between 17.33% to 7.48% for annual site height increments of 0.1 to 0.9 m.  A recommendation for growth and yield programs would be to carefully and regularly assess site height tree health, and attempt to have alternate measurement trees in or near the PSP to ensure the integrity of site height measurements.  A quadratic volume-site height model (..4.0 cm) predicted an increasing yield loss in Phellinus infected PSP's compared to healthy PSP's. Losses began immediately following  regeneration, but rose noticeably from 4.0% at site height 10 m to 5.5% at 35 m site height (or approximately 80 yr). The 5.5% yield reduction seemed low compared to the .4.0 cm, all-species NSBAR basal area reduction survey estimate of 8.28%, but it was likely due to several factors: (i) the %BAR survey estimates excluded all living symptomatic trees (the mean %BAR severity parameter was .30 or 30% reduction), therefore the intensity of "basal area reduction damage" was greater than the actual volume loss as measured in the PSP's, (ii) the generally "damage-free" establishment conditions for growth and yield PSP's mean that Phellinus infection is in most cases  217 making slow moving (on average 0.3 m/yr) incursions into PSP's well after PSP establishment, (iii) Phellinus incidence appears to be sampling unit plot size dependent, and since the PSP's are smaller (.04 ha) than sample surveys (1 ha), the probability of incidence and hence intensity is lower in the PSP's compared to the %BAR surveys, and (iv) the %BAR surveys were all conducted during the summer of 1987, while only 20% or 1/5th of the PSP's would have been measured in 1987 (the growth and yield data used in this analysis is current to the 1989/90 dormant season); perhaps 66% of the PSP data adequately reflects the root rot growth and yield conditions observed in 1987 %BAR surveys. A second explanation of the difference between the two estimates is that the quadratic volume-site height model is constructed over a wide range of stand densities. Volumes across sites and ages are only considered equivalent for stands of similar intial densities (Mitchell and Cameron 1985). The condition mentioned in (i) above, is one of the most significant factors in the disparity between the PSP and %BAR estimates of yield reduction. With these factors considered, the %BAR damage intensity estimates might be reduced to more closely approximate the percent basal area reductions estimated from a yield curve analysis of the PSP data. The conclusion then, is that the %BAR sampling method very nearly estimated the actual volumetric (PSP-measured) losses that occurred in the coastal Douglas-fir ecosystems of southeastern Vancouver Island. This also suggests that the set of growth and yield PSP's examined as part of this study satisfactorily reflected the damage intensity found in the areas immediately surrounding the PSP's.  218 Given that one of the largest factors in explaining the differences between the %BAR and the PSP-based estimates is the experimental error introduced in the %BAR sampling design (i.e., the exclusion of visibly symptomatic trees in the tree tallies), the author suggests that all symptomatic trees be counted in %BAR tree tallies. That would allow the %BAR estimate to be made not only on standing healthy but also standing infected basal area and dead and downed infected, separately or combined. These changes should be incorporated into future percent basal area reduction %BAR type sampling methodologies. The use of site height in modelling suggested that yield models be constructed for the three stand origin density classes.  8.7.3.2^Growth and Yield: All Data, Stratified by Stand Density Classes (stems/ha at age 10 yr)  8.7.3.2.1^Less than 1 000 stems/ha  A quadratic yield model estimated yield reduction to be 8.25% for infected PSP's at a site height of 35 m (or approx. 80 yr), which compared very well with the linear regression model estimate of percent basal area reduction (NSBAR) of 8.88%.  8.7.3.2.2^1 000 - 1 999 stems/ha  A yield model estimated yield reduction to be 8.63% for infected PSP's at a site height of 35 m (or approx. 80 yr), which compared very well with the linear regression  219 model estimate of percent basal area reduction of 8.96%.  8.7.3.2.3^2 000 - 4 999 stems/ha  A yield model estimated yield reduction to be 4.97% for infected PSP's at a site height of 35 m (or approx. 80 yr), which does not compare well with the linear regression model estimate of percent basal area reduction of 11.28%. This variance is likely due to a smaller sample size and widely ranging intial densities and ages.  The relevance of stratification into stand density classes was clearly borne out in the results of the first two density classes. For establishment densities below 2 000 stems/ha the yield models were amazingly similar between stand density classes, and also provide near-identical estimates to the NSBAR percent basal area reduction estimates for these strata. Beyond 2 000 stems/ha (actually 2 300) the data were few and scattered, dramatically effecting the analysis. It is conceivable that the damage is not yet so great in the PSP's with high establishment densities, in part, because gap formation is beneficial to the residual trees and/or the shade tolerant less-susceptible species are achieving increasing mensurational ingrowth rates.  Overall, this study has conclusively demonstrated that modelling of growth and yield PSP's to produce growth and yield functions that reflect disease incidence (i.e., presence or absence of Phellinus root rot) and stand density strata is possible. Several analyses  220 have all demonstrated similar growth and yield reductions through simple PSP-to-PSP comparisons, the Chapman-Richards volume-age growth models, quadratic volume-site height models and growth difference models. In all analyses the within strata variability is so great that statistically significant growth and yield differences are impossible to detect at this point in time, although the trend towards continuing and worsening yield reductions appears set. Continued analyses of this sort should be conducted periodically, with the following considerations: (i) ensure the highest degree of ecological and stand condition-at-establishment fidelity as possible (it does not matter that the stand conditions today are different as long as they started from near-identical site quality, species composition and stand density conditions), (ii) Phellinus infected PSP's (or any other damage condition) should be stratified on the basis of within-PSP severity or intensity. Furthermore, if forest level damage impact assessments are attempted using this technique, then ensure the appropriate proportional composition of healthy to Phellinus infected PSP's is made in the determination of a yield function. The mix of  PSP's should be proportional to the amount of damage severity or intensity observed in the forest, as determined from PSP sized samples (.04 ha, and as of 1990, .10 ha), as incidence and intensity are sample size dependent.  221 9.0 SUMMARY  Variable-radius plot sampling and the percent basal area reduction (%BAR) parameter estimate proved to be an effective estimator of Phellinus root disease damage. The technique of sampling for incidence and severity to estimate %BAR damage intensity showed that root disease incidence was a larger predictive factor of damage intensity than severity.  Sampling for disease incidence (land-area-diseased vs. stand-parameter-diseased) showed the stand-based method to be more sensitive to damage incidence at low levels of land-based incidence. The estimates were highly correlated (r. .82), but the estimates only stabilized when the incidence was above .35 (35%). Overall, the mean stand-based incidence was .263 (26.3%) as compared to the mean land-based incidence of .168 (16.8%).  Sampling for disease severity (using two separate diameter limit tallies in the variableradius plot sampling), allowed diameter-dependent views of damage intensity and stand dynamics to be made. Damage intensity was always greater in the large diameter class (>_1 2.0/17.5 cm) compared to the4.0 cm diameter class. Mean damage severity was .30 (30%) basal area reduction at points incident with Phellinus relative to healthy points, although severity was highly variable within 1 ha sample survey units. A severity intensity relationship showed severity to increase with incidence, but on average it did not exceed .5 (50%) basal area reduction. This would appear to indicate stand productivity  222 resilience in the presence of Phellinus root rot. Through a method of subtraction, Phellinus host susceptibility classification, and disease incidence stratification, it was  found that the less- to non-susceptible species compositions (particularly in the smaller diameter class) were greater at infected points than at healthy points. This also provided some evidence of Phellinus-induced succession and led to the postulation of a natural, dynamic host-pathogen equilibrium. Similarly, the less- to non-susceptible species composition in Phellinus-infected permanent sample plots (PSP's) was found to increase by a much larger relative amount compared to the susceptible species in healthy PSP's.  Disease incidence estimates increased with sampling unit "effective sampling area"; from .005 ha fixed-radius, to "area-less" variable-radius (approximately .027 ha) plots, 0.04 ha PSP's to the 1 ha %BAR sample survey units. The incidence in the 1 ha units was identical to that found in 20 ha sample units in a separate root disease study in the same area of Vancouver Island. It is likely that 1 ha samples are close to the minimum sampling size required to accurately estimate Phellinus root rot incidence at the forest level in coastal Douglas-fir ecosystems.  Incidence - intensity linear relationships were shown to be fairly strong, and are potentially a very effective way of estimating damage intensity from binomial (presence/absence) incidence estimates alone. However, both land- and stand-based incidence estimates overestimated the observed intensity by 0.5 to 2.6 times. This result strongly suggests that stand damage intensity sampling is best if a measure of incidence and severity are combined, such as in the %BAR parameter estimate technique.  223 Damage intensity varied between levels of the zonal (climatic), plant and site classifications of the system of Biogeoclimatic Ecosystem Classification (BEC). The most significant differences in mean damage intensities were between the CDFmm and CWHxm subzones, (BARS intensities were respectively, 5.94% and 11.11%). Other differences, while not always statistically significant at the 10% or better level, were important to the interpretation of Phellinus root rot behaviour, but not necessarily for forest management prescriptions.  Stand origins were found to have significantly different damage intensities of Phellinus root rot, but were strongly affected by stand age. Beyond the substantial age effect, it is postulated that the effects of logging, and logging followed by intense slashburning both had an increasingly negative effect on the survival of Phellinus weirii ectotrophic inoculum sources in relation to the wildfire conditions.  Some site ecological variables were found to vary significantly with the subzone and variant units (elevation m asl, percent slope and mineral soil pH), but not with root rot intensity. Similarly, several old and second growth stand attributes (species composition, and stand density (basal area and stems/ha)) were found to vary with several of the biogeoclimatic units. Notably, stems/ha of old growth Douglas-fir and western hemlock were significantly and negatively correlated with Phellinus root rot intensity, which followed the pattern of root rot variation between subzones as well. Old growth stems/ha and composition of western red cedar were generally greater in ecosystems with higher Phellinus root rot intensities. Distribution patterns for second growth stems/ha and  224 Curtis' relative density index strongly reflected the root rot distributions of the site associations. Patterns in the old growth and second growth stand density, species composition and the dynamic behaviour of species in and out of the presence of root rot led to the postulation of a natural, dynamic host-pathogen equilibrium.  A classification of Phellinus hazard and risk for all biogeoclimatic units investigated was made on the basis of the %BAR sample survey damage incidence and intensity data. The classification should aid in planning and operational management of coastal Douglasfir ecosystems. The site association taxon (name) model was shown to accurately describe, in relative terms, the site association hazard (susceptibility) and risk (probability of and estimated damage intensity) of Phellinus root rot. The s.a. taxon model was viewed in the context of the host-pathogen dynamic equilibrium, host susceptibilities and natural successional chronosequences and their relationship to effect conducive, reactive or equilibrating stand conditions for the pathogen.  Estimates of yield reduction (_4.0 cm) due to incidence of Phellinus root rot in growth and yield PSP's were quite variable, but depended upon infection severity. In a carefully selected set of comparable PSP's, infected compared to healthy PSP's suffered greater reductions of stems/ha (49-420%) and retained (40-80%) less of the accrued basal area and (71-83%) volume. Interestingly, the infected PSP growth rates have remained equal to or better than the healthy PSP's except in the most severely infected conditions. Yield reduction estimates for the FdHw-Salal and HwFd-Kindbergia combined s.a.'s using the Chapman-Richards growth model indicated an 8.86% reduction in volume  225 at 80 yr. In comparison, the yield reduction estimated for all PSP's using a quadratic model was 5.5% at site height 35 m (approx. 80 yr). Similarly, yield reductions for stand density classes < 1 000, 1 000-1 999 and 2 000-4 999 stems/ha at reference age 10 yr, were 8.25%, 8.63% and 4.97%. Growth models for all-PSP's were extremely variable and generally showed some growth reductions in infected PSP's ranging between 7-17% over the annual site height increments of 0.1 to 0.9 m/yr. There appeared to be a site height measurement dependence on Phellinus root rot incidence, meaning that site height tree height growth was impacted by the disease--a situation that requires closer evaluation and correction if it really exists. Certainly the growth and yield analytical techniques applied to a PSP database stratified simply on the basis of Phellinus incidence has shown conclusively and dramatically the effects of the disease on stand dynamics and productivity. A more careful evaluation is recommended following more accurate estimates of Phellinus root rot intensities in the PSP's. The PSP-based yield reductions (4.97% - 8.86%) compared well with the %BAR (NSBAR) estimate of 8.25%. Some adjustments to the %BAR survey procedure to account for the symptomatic tree contribution to productivity would reduce the %BAR estimate and bring yet closer agreement between the two productivity damage estimates. Furthermore, it is recognized that even with 35 to 37% of the PSP's infected, the proportion of infected PSP's does not fully reflect the 87% incidence of Phellinus root rot found in the 1 ha sample survey units. Nevertheless, the effectiveness of both methods in estimating damage is remarkably similar, suggesting both damage appraisal methods should be pursued further for Phellinus root rot and other damaging pest agents. The %BAR parameter estimation  technique proved to be a sensitive, and flexible sampling method that enabled a wealth  226 of statistics and interpretations to be made for producivity and ecological questions alike.  ^  227 ^10.0^RECOMMENDATIONS FOR MANAGEMENT  10.1^Damage Appraisal  The greater sampling variation found in Phellinus infected areas, combined with the pervasive incidence of infected areas should be taken into account in designing future forest and pest inventories.  One hectare sample survey units were found to estimate incidence equally well as 20 ha samples conducted in a separate study. A minimum sample size for estimating the incidence of Phellinus root rot at the forest level is suggested to be 1 ha.  Continued field testing of the Percent Basal Area Reduction parameter estimate sampling technique should be carried out in other forest types for other pests (particularly root rots) to estimate the efficiencies of the method. The greatest advantages of the %BAR parameter estimate technique are that (a) it is easy to conduct and calculate results, and (b) it simply provides stand-parameter-diseased estimates of stand damage, rather than conducting land-area-diseased estimates then converting land-based estimates to stand-based parameters. Further estimation of incidence-intensity relationships is encouraged in order to build on the efficiencies of incidence sampling, as compared with incidence-plus-severity sampling to estimate a stand-based damage intensity parameter.  228 Incidence-Intensity relationships appear to be an effective way to reduce sampling to only incidence, once the incidence-intensity relationships are well known for a stratum (e.g., biogeoclimatic unit, forest cover, age class, management regime). Incidence sampling is easier to conduct, cheaper and more easily produces repeatably consistent estimates.  On average, Phellinus root rot was shown to reduce volume yields from comparable healthy conditions by about 8.25% to 8.86%. Growth and yield in the permanent sample plots appeared to be partially compensated for by enhanced growth of residual trees and ingrowth of tolerant and resistant species. This condition was particularly noticeable in light to moderate damage intensities. The magnitude and trends of damage intensity observed from this set of permanent sample plots was surprisingly close to what I and other root disease workers have observed over the years. The negative effect on height growth of site height measurement trees in PSP's should be investigated and alternate measurement trees be selected where warranted.  A closer examination of permanent sample plots (both growth and yield and inventory types) should be made to evaluate the relative incidence and intensity levels. The pervasive effects/impacts of pests such as Phellinus root rot should be incorporated in the development of growth and yield models and forest level yield analysis determinations.  229 10.2^Stand and Forest Level Prescriptions  Hazard and risk of Phellinus root rot in the CDFmm and CWHxm biogeoclimatic units was estimated. The greatest risk was predicted to be in the CWHxm subzone, and notably in the FdHw-Salal and HwFd-Kindbergia s.a.'s.  The site association taxon model was shown to accurately predict the hazard and risk of Phellinus root rot when considered in the context of; (i) a natural, dynamic hostpathogen equilibrium, (ii) host susceptibilities to Phellinus root rot, and (iii) the natural succession chronosequences. The potential for estimating the hazard and risk for other damaging agents (e.g., pests) using this method is enormous, and should be pursued as much of the knowledge needed for this is already available.  Several other factors may be useful in estimating site risk: (i) Phellinus root rot was weakly, but positively correlated to percent slope; (ii) Phellinus root rot damage intensity appears to be greatest on mesic sites, although this was not a consistent observation, and (iii) the old growth and second growth stand density (stems/ha) apppear to have had a positive effect on todays second growth Phellinus damage intensity.  Mortality of primarily Douglas-fir in Phellinus infected sites was counteracted by substantial species composition shifts to less-susceptible to resistant host trees (western hemlock, lodgepole and western white pine, western red cedar and deciduous).  230 The density and species composition shifts seen in the presence of Phellinus root rot strongly suggests that management prescriptions consider lower stand densities and more consideration of less-susceptible to resistant species as a means of reducing damage, hazard and risk to ecosystem health.  The natural, dynamic host-pathogen equilibrium suggests that stand productivity of highly susceptible species seres will only be reduced to an unknown maximum before tolerant and resistant species ingress the Phellinus infected sites. It further suggests that management prescriptions should minimize the risk of stand disequilibrium conditions by not creating Phellinus-conducive stand conditions. To do otherwise will increase loss of stand productivity of highly susceptible species, and in all likelihood require expensive programs to eradicate the pathogen from the site and thereby re-equilibrate the site.  231 11.0^REFERENCES Angwin, P.A. 1985. Environmental Factors Influencing The Rate Of Phellinus weirii (Murr.) Gilbertson in Young-Growth Douglas-fir. MSc. Thesis, Dep. Bot. and Plant Path. Oregon State Univ., Corvallis, OR., 107p. Armson, K.A. 1977. Forest Soils. Properties and processes. Univ. of Toronto Press, Toronto, Ont. Banner, A., R.N. Green, A. Inselberg, K. Klinka, D.S. McLennan, D.V. Meidinger, F.C. Nuszdorfer and J. Pojar. 1990. Site Classification For Coastal British Columbia. Prov. of B.C., Min. of Forests, Victoria, B.C., colour pamphlet, 6p. Beale, J.D. 1987. A Survey of the Incidence and Severity of Root Diseases and Their Implications for Management in the Vancouver Forest Region. B.C. Ministry of Forests, For. Serv. Int. Rpt. PM-V-10, Burnaby, BC., V5G 4L9. Beale, J.D. 1989(a). Management Guidelines for Laminated Root Rot in the Vancouver Forest Region. FRDA Memo No. 108, BC Min. For., Victoria, BC, 12p. Beale, J.D. 1989(b) (unpubl. data). Applied Linear Regression Methods: Modelling the Behaviour and Epidemiology of Laminated Root Rot in Coastal Douglas-fir Ecosystems. File Report, BC Min. of Forests, Vancouver Forest Region, 4595 Canada Way, Burnaby, BC, V5G 4L9, 72p. Beese, W., pers. comm. 1987. Forest Ecologist, Woodlands Services, MacMillan Bloedel Limited, 65 Front St., Nanaimo, BC. V9R 5H9 Black, C.A. 1965. Methods of Soil Analysis Part I Physical and Mineralogical Properties, Including Statistics of Measurements and Sampling. Madison: American Society of Agronomy, Vol. 2. pp 891-896. Blair, H.A., C.H. Driver and E.C. Packee. 1975. A Survey of Disease Impact on Intensively Managed Immature Conifer Forest (Root and Butt Rot Survey). MacMillan Bloedel Limited, Project Plan No. 721-1, 2, 3. 40p. 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Growth Loss in Douglas-fir Infected with Phellinus (Poria) weirii Root Rot. B.Sci. Thesis, University of B.C., Faculty of Forestry, 65p. Forest Productivity Councils of B.C. 1990. Minimum Standards for Establishment and Remeasurement of Permanent Sample Plots in B.C., B.C. Min. of For., Inventory Br., Victoria, B.C., 25p. Foster, R.E., and A.L.S. Johnson. 1963. Studies in Forest Pathology )0(V: Assessments of Pattern, Frequency Distribution, and Sampling of Forest Disease in Douglas-fir Plantations. Can. For. Serv. Publ. No. 1011. 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, NY, 185 p.  234 Gammell, A., pers. comm. 1990. Manager, Environmental Laboratory, Woodlands Services, MacMillan Bloedel Limited, 65 Front St., Nanaimo, BC. V9R 5H9. Gillette, W.D. 1975. Biology of Poria weirii sexuality and effect on height growth of Douglasfir. M.S. thesis, Univ. Wash. 43 p. (manuscript not seen) cited in Bloomberg and Reynolds, 1985. Goheen, D.J., and J.S. Worrell. 1979. Root Disease Survey of the Proposed Ten Tails Timber Sale, Quilcene Ranger District, Olympic National Forest. For. Insect and Dis. Mgmt., PNW Reg., For. Serv. USDA, Portland OR. 12p. Goheen, D.J. 1979. Root Disease Evaluation of the Skeleton Mountain Area, Medford District, Bureau of Land Mgmt., For. Insect and Dis. Mgmt., PNW Reg., For. Serv. USDA, Portland OR. 16p. Gold, W. 1985. Logging As It Was, A Pictorial History of Logging on Vancouver Island. Morris Publishing, Victoria, British Columbia, 255p. Goldfarb, B., E.E. Nelson and E.M. Hansen. 1985. Trichoderma spp.: Distribution in Phellinus weirii-infested stumps and roots and antagonism in vitro. In Proceedings of the Western Intl For. Dis. Work Conf., Olympia, WA, Sept. 24-27, 1985. p 100103. Goldfarb, B. 1986. 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Prentice-Hall, Inc., Englewood Cliffs, N.J.  242 Appendix A^Soil Chemical Concentration To Kg/ha  The following formulae were used to convert chemical concentration data to kg/ha for the 10-30 cm mineral soil horizon and, kg/ha for the forest floor. The formulae were previously described by Green (1989), based on Kabzems (1985), Roy (1984) and Lewis (1976).  CONVERSION FORMULAE FOR MINERAL SOILS (10-30cm HORIZON):  MinN kg/ha = 0.10 * MinN ppm * MSBDfin * 20 cm * (1 - CF) N kg/ha = 1000 * %N * MSBDfin * 20 cm * (1 - CF) C kg/ha = 1000 * %C * MSBDfin * 20 cm * (1 - CF) Ca kg/ha = 20.04 * Meq Ca * MSBDfin * 20 cm * (1 - CF) Mg kg/ha = 12.16 * Meq Mg * MSBDfin * 20 cm * (1 - CF) K kg/ha = 39.096 * Meq K * MSBDfin * 20 cm * (1 - CF) Where CF is the estimated proportion of mineral soil coarse-fragment content by volume. CONVERSION FORMULAE FOR FOREST FLOORS:  %C kg/ha = 444444.444 * 20 cm * FFBD * 0.01 * %C %N kg/ha = 444444.444 * 20 cm * FFBD * 0.01 * %N MinN kg/ha = 444444.444 * 20 cm * FFBD * (MinN ppm * 10 -6) Where the component 444444.444 is the forest floor sample size (225 cm2) plot expansion factor to 1 ha.  243 Appendix B^Curtis' Relative Stand Density Index  This appendix describes the procedure used to recalculate Curtis' Relative Stand Density Index for ecologically similar PSP's within a growth and yield installation. In most cases stand conditions were very similar within an installation though great variability occurred due to microtopography and/or pests.  Curtis' Relative Density (4.0cm) = Stand_BA / QDBH FULL FORMULA: CRD>4.0cm + =STAND BA/(SQRT(SQRT((STAND BA/STAN D STEMS)/0.00007854)))  STEPS TO ESTIMATE CURTIS' RELATIVE DENSITY: 1.  Calculate the average STAND_BA/HA and average STAND_STEMS/HA for ecologically similar PSP's within a growth and yield installation, then;  2.  Calculate MEAN BASAL AREA/TREE = (STAND_BA) - (STAND_STEMS);  3.  MEAN DIAMETER/TREE = SQRT((MEAN BASAL AREA/TREE)/ 0.00007854);  4.  QUADRATIC MEAN DIAMETER = SQRT(MEAN DIAMETER/TREE).  NOTE:^The factor '0.00007854' is the metric constant used to estimate cross-sectional areas of 'circular' trees (g) Where g=0.00007854 dbh 2 , (Husch et a/. 1972).  244 Appendix C^  Species List  Coniferous trees 1 ABIEGRA 2 PICESIT 3 PINUCON 4 PINUMON 5 PSEUMEN 6 THUJPLI 7 TSUGHET  Abies grandis Picea sitchensis Pinus contorta Pinus monticola Pseudotsuga menziesii Thuja plicata Tsuga heterophylla  (Dougl. ex D. Don) Lindl (Bong.) Carr. Dougl. ex Loud. Dougl. ex D. Don in Lamb (Mirb.) Franco Donn ex D. Don in Lamb. (Raf.) Sarg.  Broad-leaved trees 8 ACERMAC^ 9 ALNURUB^ 10 ARBUMEN^ 11 BETUPAP^ 12 CORNNUT^ 13 MALUFUS^ 14 POPUTRI^ 15 QUERGAR^  Acer macrophyllum^ Anus rubra^ Arbutus menziesii^ Betula papyrifera^ Comus nuttallii^ Malus fusca^ Populus trichocarpa^ Quercus ganyana^  Pursh Bong. Pursh Marsh. Audub. ex Tom Gray (Raf.) Schneid Torr. Gray ex Hook. Dougl. ex Hook.  Evergreen shrubs 16 ARCTUVA^ 17 CHIMMEN^ 18 CHIMUMB^ 19 GAULOVA^ 20 GAULSHA^ 21 HEDEHEL^ 22 ILEXAQU^ 23 MAHOAQU^ 24 MAHONER^ 25 PAXIMYR^ 26 VACCOVT^  Arctostaphylos uva-ursi^ Chimaphila menziesii^ Chimaphila umbellata^ Gaultheria ovatifolia^ Gaultheria shallon^ Hedera helix^ Hex aquifolium^ Mahonia aquifolium^ Mahonia nervosa^ Paxistima myrsinites^ Vaccinium ovatum^  (L.) Spreng. (R. Br. ex D. Don) Spren (L.) Barton Gray Pursh L. L. (Pursh) Nutt. (Pursh) Nutt. (Pursh) Raf. Pursh  Deciduous shrubs 27 ACERGLA^ 28 AMELALN^ 29 CORNSER^ 30 CORYCOR^ 31 HOLODIS^ 32 LONICIL^ 33 LONIHIS^ 34 LONIINV^ 35 MENZFER^ 36 OEMLCER^ 37 PRUNEMA^ 38 RHAMPUR^ 39 RIBEDIV^ 40 RIBELAC^ 41 ROSAGYM^ 42 RUBUIDA^ 43 RUBUPAR^ 44 RUBUSPE^ 45 RUBUURS^ 46 SALISCO^ 47 SALIX^ 48 SORBSIT^ 49 SYMPALB^ 50 SYMPHES^ 51 VACCOVL^ 52 VACCPAR^  Acer glabrum^ Amelanchier alnifolia ^ Comus sericea^ Corylus comuta^ Holodiscus discolor^ Lonicera ciliosa^ Lonicera hispidula^ Lonicera involucrata^ Menziesia ferruginea^ Oemleria cerasiformis^ Prunus emarginata^ Rhamnus purshianus^ Ribes divaricatum^ Ribes lacustn3^ Rosa gymnocarpa^ Rubus idaeus Rubus parvilflorus^ Rubus spectabilis^ Rubus ursinus^ Salix scouleriana^ Salix sp. Sorbus sitchensis^ Symphoricarpos albus^ Symphoricarpos hesperius^ Vaccinium ovalifolium^ Vaccinium parvifolium^  Torr. (Nutt.) Nutt. L. Marsh. (Pursh) Maxim. (Pursh) DC. (Lindl.) Dougl. ex Torr. (Richards.) Banks ex Spr Sm. (Tom) Gray ex Hook (Dougl. ex Hook.) Walp. DC. Dougl. (Pers.) Poir Nutt. in Torr. Gray Nutt. Pursh Cham. Schlecht. Barratt in Hook. M. J. Roem. (L.) Blake G.N. Jones Sm. in Rees Sm. in Rees  245 Deciduous shrubs - Cont'd  53 ADIAPED^ 54 ATHYFIL^ 55 BLECSPI^ 56 DRYOEXP^ 57 GYMNDRY^ 58 POLYMUN^ 59 PTERAQU^  Adiantum pedatum^ Athyrium filix-femina ^ Blechnum spicant^ Dryopteris expansa^ Gymnocarpium dryopteris^ Polystichum munitum^ Pteridium aquilinum^  L. (L.) Roth (L.) Roth (Pres1) Fraser-Jenkins (L.) Newm. (KauIt) Presl (I.) Kuhn in Decken  Graminoids  60 BROMUS^ 61 BROMVUL^ 62 CALACAN^ 63 CALARUB^ 64 CAREHEN^ 65 CAREX^ 66 DACTGLO^ 67 FESTOCC^ 68 FESTSUT^ 69 FESTUCA^ 70 LUZULA^ 71 LUZUMUL^ 72 LUZUPAR^ 73 MELISUB^  Bromus sp. Bromus vulgaris^ Calamagrostis canadensis^ Calamagrostis rubescens ^ Carex hendersonii^ Carex sp. Dactylis glomerata^ Festuca occidentalis^ Festuca subulata^ Festuca sp. Luzula sp. Luzula multiflora^ Luzula parviflora^ Melica subulata^  (Hook.) Shear (Michx.) Beauv. Buckl. Bailey L. Hook. Trin. in Bong.  (Retz.) Lej. (Ehrh.) Desv. (Griseb.) Scribn.  Herbs  74 ACHIMIL^ 75 ACHLTRI^ 76 ADENBIC^ 77 ANAPMAR^ 78 CLAYSIB^ 79 COPTASP^ 80 CORNCAN^ 81 CORNUNA^ 82 DISPHOO^ 83 FRAGVES^ 84 FRAGVIR^ 85 GALIBOR^ 86 GALITRI^ 87 GOODOBL^ 88 HIERALB^ 89 LATHNEV^ 90 LILICOL^ 91 LINNBOR^ 92 LISTCOR^ 93 LYSIAME^ 94 MEMTARV^ 95 MOEHMAC^ 96 MONEUNI^ 97 MYCEMUR^ 98 ORTHSEC^ 99 OSMOCHI^ 100 PYROASA^ 101 PYROPIC^ 102 RUBUPED^ 103 SMILSTE^ 104 STACCOO^ 105 STREROS^ 106 TIARLAC^ 107 TIARTRI^  Achillea millefolium^ Achlys triphylla^ Adenocaulon bicolor^ Anaphalis margaritacea^ Claytonia sibirica^ Coptis aspleniifolia^ Comus canadensis^ Comus unalaschkensis^ disporum hookeri^ Fragaria vesca^ Fragaria virginiana^ Galium boreale^ Galium triflorum^ Goodyera oblonifolia^ Hieracium albiflorum^ Lathyrus nevadensis^ Lilium columbianum^ Linnaea borealis^ Listera cordata^ Lysichitum americanum^ Mentha arvensis^ Moehringia macrophylla^ Moneses uniflora^ Mycelis muralis^ Orthilia secunda^ Osmorhiza chilensis^ Pyrola asarifolia^ Pyrola picta^ Rubus pedatus^ Smilacina stellata^ Stachys cooleyae^ Streptopus roseus^ Tiarella laciniata^ Tiarella trifoliata^  L. (Sm.) DC. Hook. (L.) Benth. in Benth. L. Salisb. L. Ledeb. (Torr.) Nicholson L. Duchesne L. Michx. Rat Hook. Wats. Hanson ex Baker L. (L.) R. Br. in Alt. Huft. St. John L. (Hook.) Fenzl (L.) Gray (L.) Dumort. (L.) House Hook. Am. Michx. Sm. in Rees Sm. (I.) Desf. Heller Michx. Hook. L.  246 Herbs - Continued 108 TIARUNI^ 109 TOLMMEN^ 110 TRIELAT^ 111 TRILOVA^ 112 URTIDIO^ 113 VIOLA^ 114 VIOLADU^ 115 VIOLGLA^ 116 VIOLORB^  Tiarella unifoliata^ Tolmiea menziesii^ Trientalis latifolia^ Trillium ovatum^ Urtica dioica^ Viola sp. Viola adunca^ Viola glabella^ Viola orbiculata^  Hook. (Pursh) Torr. Gray Hook. Pursh L. Sm. in Rees Nutt. in Torr. Gray Geyer ex Hook.  Paras. & saproph. 117 BOSCHOO^ 118 CORALLO^ 119 CORAMAC^ 120 CORAMER^ 121 HEMICON^ 122 HYPOLAN^ 123 MONOUNI^ 124 PTERAND^  Boschinakia hookeri^ Corallorhiza sp. Corallorhiza maculata^ Corallorhiza mertensiana^ Hemitomes congestum^ Hypopithys lanuginosa^ Monotropa uniflora^ Pterospora andromeda^  Raf. Bong. Gray (Michx.) Nutt. L. Nutt.  Mosses 125 DICRFUS^ 126 DICRSCO^ 127 HYLOSPL^ 128 ISOPELE^ 129 ISOTSTO^ 130 KINDORE^ 131 KINDPRA^ 132 LEUCMEN^ 133 PLAGINS^ 134 PLAGUND^ 135 PLEUSCH^ 136 POLYJUN^ 137 RHACCAN^ 138 RHIZGLA^ 139 RHYTLOR^ 140 RHYTROB^ 141 RHYTTRI^  Dicranum fuscescens^ Dicranum scoparium^ Hylocomium splendens^ lsopterygium elegans^ lsothecium stoloniferum^ Kindbergia oregana^ Kindbergia praelonga^ Leucolepis menziesii^ Plagiomnium insigne^ Plagiothecium undulatum^ Pleurozium schreberi^ Polytrichum juniperinum^ Rhacomitrium canescens^ Rhizomnium glabrescens ^ Rhytidiadelphus loreus^ Rhytidiopsis robusta ^ Phytidiadelphus triquetrus^  Turn. Hedw. (Hedw.) B.S.G. (Brid.) Lindb. Brid. (Sull.) Ochyra (Hedw.) Ochyra (Hook.) Steere ex L. Koc (Mitt.) Kop. (Hedw.) B.S.G. (Brid.) Mitt. Hedw. (Hedw.) Brid. (Kindb.) Kop. (Hedw.) Wamst (Hook.) Broth. (Hedw.) Warnst.  Liverworts 142 CALYMUE^ 143 MARCPOL^ 144 SCAPBOL^  Calypogeja muelleriana ^ Marchantia polymorpha^ Scapania bolanderi^  (Schiffn.) K. Mull. L. Aust.  Lichens 145 CLADCHL 146 CLADFUR 147 CLADRAN 148 ICMAERI 149 PELTAPH 150 PELTCAN 151 PELTIGE 152 PELTMEN  Cladonia chlorophaea Cladonia furcata Cladina rangiferina lcmadophila ericetorum Peltigera aphthosa Peltigera canina Peltigera sp. Peltigera membranacea  (Florke ex Somm.) Spreng (Huds.) Schrad. (L.) Harm. (L.) Zahlbr. (L.) Willd. (L.) Willd.  Walp.  (Ach.) NyL  247  Appendix D Vegetation Unit Number of Plots  Vegetation Summary Table 1 25  Acer glabrum Acer macrophyllum Achillea millefolium Achlys triphylla Adenocaulon bicolor  427  III 2.0 I +.0 II 3.1  II 2.7  II 2.1  II 1.4  II 2.1  12.3  I 2.0  III^1.7  III 2.6  IV 2.5  V3.9  IV 4.5  V 4.1  IV 6.2  11.1  11.6  11.4  II +.3  11.9  II^1.1  II 3.0  V 5.1  I +.0  I +.0 II 1.0 I +.0  I +.0  Alnus rubra  II^1.4  II 2.9  Amelanchier alnifolia  II 2.1  II +.8  Anaphalis margaritacea  I +.2  III 3.1  I +.0  I +.0 I +.2  Athyrium filix-femina  I +.0  Betu/a papyrifera  I +.0  I +.0 I +.0  I +.0  III^1.6  Calamagrostis canadensis  II^+.9  II^1.8  IV 2.4  II^1.4  I +.0 I +.3  I^+.1  Calypogeja muelleriana  I +.0 I +.0  Carex hendersonii Carex sp.  I +.0  Chimaphila menziesii  I +.0  Chimaphila umbellata  I +.0  Cladonia chlorophaea  I +.0  II^1.3 I +.5  I +.0  II +.3  I +.0  I +.0 II^1.5  II^1.6  I +.0  II^+.3  1 +.0  Cladonia furcata  I +.2  Claytonia sibirica  I +.0  Coptis aspleniifolia  II +.8  Corallorhiza sp.  I +.0 II^1.0  Corallorhiza mertensiana  II +.3  I +.0  I +.0  I +.0  Comus canadensis Comus nuttallii  II 3.5  I +.0 II^1.1  Calamagrostis rubescens  Corallorhiza maculata  I +.0  I +.0  Bromus sp.  Cladina rangiferina  II +.3  I +.0  Blechnum spicant  Bromus vulgaris  11.9  I +.0  Arctostaphylos uva-ursi  Boschniakia hookeri  6 8  11.1  II^1.2  Adiantum pedatum  Arbutus menziesii  5 8  Presence class and mean species significance  Species Abies grandis  348 ^27  I +.0 11^1.5  Comus sericea  I +.2  Comus unalaschkensis  I +.0  corylus comuta  I +.0  1 1.2  11.0 I +.0  I +.0  Dactylis glomerata  I +.0  Dicranum fuscescens  I +.0  I +.0  248 Dicranum scoparium  11.4  I +.0  I +.0  I +.0  I +.0  Disporum hooked  I +.0  Dryopteris expansa  1 +.0  II +.3  I +.0  11.0  I +.0 +.0  I^+.1  Festuca sp.  1 +.0  I +.4  I +.0  II^1.4  11 +.3  I +.0  Fragaria vesca  I +.0 II +.6  H +.8  II^1.1  IV 2.1  IV 4.6  I +.0  I +.0  Festuca occidentalis Festuca subulata  +.0  Fragaria virginiana Galium boreale  II 3.1  I +.0  I +.0  Galium triflorum Gaultheria ovatifolia  I +.8  11^1.3 III 3.8  Gaultheria shallon  V 7.6  V 7.7  V 7.3  V 6.7  Goodyera oblongifolia  11^1.0  11^1.0  II +.5  II +.2  I +.0  I +.0  Hemitomes congestum Hieracium albiflorum Holodiscus discolor  IV 4.5  Hylocomium splendens  II 4.0  Hypopithys Lanuginosa  I +.0  I +.0  I +.0  I +.0 I +.0  I +.0  V 5.6  V 5.1  V 5.4  I +.0  I +.0  III 3.9  1 +.2  I +.0 II +.3  I +.0  Isothecium stoloniferum V 6.2  V 6.6  V 7.5  1 +.0  11.1  I^+.1  V 6.9  V 7.1  I +.0  Leucolepis menziesii Lilium columbianum  I +.0  Linnaea borealis  I +.6  III 3.0  Listera cordata Lonicera ciliosa  II^1.1  I +.0  1 +.0  II +.8  11.3 IV 4.4  IV 4.5  I +.0  I +.0  I +.0  I +.0  1^+.1  Lonicera hispidula Lonicera involucrata  1 +.2  II^1.4  11.2  I +.0 II +.8  Luzula multiflora  1 +.0  I +.0 I +.0  Luzula parviflora Lysichitum americanum  1 +.0  Mahonia aquifolium  1 +.3  1 +.0  Mahonia nervosa  V 4.5  V 5.2  Malus fusca  I +.2  V 4.8  V 5.6  I^+.1  I +.0  V 4.4  IV 4.8 11.2  Marchantia polymorpha I +.5  11.2  1 +.0  Mentha arvensis Menziesia ferruginea  III 3.2  I +.5  Luzula sp.  Melica subulata  IV 5.3 II 5.0  Kindbergia praelonga Lathyrus nevadensis  V 6.0  II +.3  1 +.0  lsopterygium elegans Kindbergia oregana  IV 3.4  I +.0  Icmadophila ericetorum Ilex aquifolium  I +.0 II +.3  Gymnocarpium dryopteria Hedera helix  IV 4.8  1 +.0  I +.0  11.2  249 Moehringia macrophylla Moneses uniflora  I+.0  I+.0 +.0  1 +.0  Mycelis muralis  +.2  +.0  I1.6  Oemleria cerasiformis  +.0  I +.0  Monotropa uniflora  +.0  Orthilia secunda Osmorhiza chilensis  I +.0  1 +.0  I+.6  Paxistima myrsinites  +.0  I^1.1  Peltigera aphthosa  +.0  11.1  1 +.0  I +.0  I +.0  Peltigera canina  I +.0  I +.0 +.1  11^1.4  +.0  I +.0  1 +.0  1 +.2  I +.0  I+.0  I +.5  1^1.1  II +.3  1 +.0 I +.0  Picea sitchensis Pin us contorta  11.4  I +.5  Pin us monticola  1 +.0  1 +.5  I +.0  I +.0  II 1.2  I 1.2  I +.0 II +.7  Plagiothecium undulatum  I +.5  I +.0  Plagiomnium insigne  III 2.2  II +.3  IV 3.4  II^1.3  IV 3.2 II^+.3  Pleurozium schreberi Polytrichum juniperinum Polystichum munitum  1 +.0 HI 1.9  I +.0 III 3.4  IV 3.4  Prunus emarginata  11.6  Pseudotsuga menziesii  V 8.2  Pterospora andromeda  1 +.0  Pteridium aquilinum  V 3.4  Pyrola picta  1 +.0  Quercus ganyana  I +.0  Rhacomitrium canescens  11.4  Rhamnus purshianus  1 +.0  V 8.0  V 7.7  V 7.6  V 4.1  V 4.6  V 4.4  IV 2.8  IV 4.6  1 +.0  1 +.0  1 +.0  1 +.0  1 +.2  1 +.0  1 +.0 I +.0  II^1.6  II^1.8  Rhytidiopsis robusta  1 +.3  111.6  II +.5  HI 3.0  1 1.4  I +.0  V 3.5  1 +.0  1 +.0  II^1.1 1 +.0  I +.0  III 2.0 I +.0  1 +.2 II^1.5  I +.0  II^1.8  II 2.3 1 +.2  I +.1 I +.0  Rubus pedatus 1 +.0  Rubus spectabilis  Scapania bolanderi  V 4.0 I +.0  I +.1 V 3.3  Rubus idaeus  Salix sp.  III 2.0  1 +.0  Ribes lacustre  Salix scouleriana  1^+.1 1 +.0  Ribes divaricatum  Rubus ursinus  11.9  1 +.2  Rhizomnium glabrescens  II 2.7  V6.5  V 8.0  Rhytidiadelphus loreus  Rubus parviflorus  V7.4  V 8.1  1 +.0  Pyrola asarifolia  Rosa gymnocarpa  V 5.1 I +.0  Populus trichocarpa  Rhytidiadelphus triquetrus  I 2.7  +.0  Peltigera sp. Peltigera membranacea  11.2  1 +.0  + .0 I +.0  V 3.3  V 3.3  IV 2.8  1 1.3  I +.0  1 +.0 I +.0  IV 3.7  12.7  IV 3.3  V 3.1  IV 2.8  1 +.3 11^1.5  I +.0  250 Smilacina stellata  I +.0  I +.0  Sorbus sitchensis  1 +.0  1 +.0 1 +.0  Stachys cooleyae  I +.0  Streptopus roseus II 2.9  II 2.1  II 2.8  II 1.6  11.2  Symphoricarpos hesperius  1 1.6  II 2.3  I 1.6  1^+.1  I 1.2  Thuja plicata  V 4.3  V 4.6  IV 4.1  IV 4.9  HI 2.8  Symphoricarpos albus  Tiarella laciniata I +.0  Tiarella unifoliata  I +.0  I +.0  III 2.2  Trientalis latifolia Tsuga heterophylla  V 2.0 I +.0  II^1.0 I +.0 II 2.1  II^1.5 1 +.0 IV 3.7  II 2.3  II^1.4  11.4  I^+.1  V 5.0  V 5.8  II +.3  II +.3 V 4.6  I 2.4  Vaccinium ovatum  Viola adunca  V 4.5  V 2.7  I^+.1  1 +.0  1 +.0  I +.0  1 +.0  IV 3.1  IV 3.7  V 4.0  I +.0  I +.0  1 +.0  Viola glabella Viola orbiculata  V 5.3 II 5.0  Vaccinium ovalifolium  Viola sp.  IV 2.4  I 2.7  Urtica dioica  Vaccinium parvifolium  V 3.2  I +.0  Tolmiea menziesii Trillium ovatum  1 +.0  1 +.0  Tiarella trifoliata  V 5.7  I +.0  I +.0  IV 4.3  I +.0  Appendix E Spectral Analysis: Soil Moisture Regime  VEGETATION UNIT. Indicator species groups, Frequency %COVER IPST %COVER TOTAL  31%  PAGE 3  Spectra (based on percentage of total cover of indicator species and expressed as % frequency)  SOIL MOIST. REGIME:  NUMBER OF SPECIES TOTAL^IND^INDIFF^LRICLASS  81  53  28  (X)  SPseudotsuge-Arbutus p.a.  3^  2  0  141i 6% 1%  66%^  1%^26%^ SPseuectsuga-Gaultherla p.a. 30%  86  56  30  0  2  3^  18%  77%^  I4  STsuga-Mahonla p.a. 33%  74  49  25  0  1 1  I  3^  2  89%^  4%  86  59  27  0  1  21^  I^4^151  3^  8% 2%  87%^  3%  ^I  SPseudotsuga-Achlys-Aims p.sa. 31%  46  27  19  0  I  3 n^  ^4  ^I^ ^I  78%  1%  Wseudotsuga-Achlys-Plagicenium p.sa. 35%  66  46  20  0  121^ 2%  3  33%  SOIL MOISTURE REGIME: 1 2 3 4 5 6  Excess to very dry Very to moderately dry Moderately dry to fresh Fresh to very moist Very moist to wet Wet to very wet  4  36%^  4  6% 1%  SPseudotsuga-Achlys-Typic p.sa. 31%  IS  4% 1%  29%  5  Appendix E - Continued Spectral Analysis:  SOIL NUTRI. REGIME:  ^  Soil Nutrient Regime  At'  PAGE  Spectra (based on percentage of total cover of indicator species and expressed as % frequency)^ VEGETATION UNIT.Indicator species groups, FregJency (%)  %covER IPSt^NUMBER OF SPECIES  %COVER TOTAL^TOTAL IND 16,0/FF UNCLASS  SPseudotsuga-Arbutus p.a. 2  1  49%^81^65^16^0  ^I^  30%^  60%  3^  to%  $Pseudotsuga-Gaultheria p.a.  52%  86^71^15^0  1  2  63%  26%  I^3 11%  $Tsuga - Mahonta p.a. 41%  1  2  3  66%  17%  17%  74^60^14^0  Pseudotsuge-Achlys-Typic p.sa.  46%  2^  1  86^70^16^0  21%^  53% $Pseudotsuga-Achlys-Alms p.sa.  49%  3  46^35^11^0  12%^14%  74%  $Pseudbtsugs-Aahlys-Plagtomnium p. se.  62%  1  66^55^11^0  35%^  Soil Nutrient Regime:  1 2 3  N - Poor N - Medium N - Rich  2  3  17%  48%  3  26%  I  253 Appendix F  Relationship Of Site And Stand Ecological Variables To The Site Associations Identified In The Study Area  80  60  40  20  0  -20  vseNeN e ve^"1.t.N olse- .to.voct 40.0 vot^  10‘" cm-00°' SASS  Figure 1  Elevation (m as1). Means are L to R, 85, 79, 191, 194 and 143 m, corresponds well with subzones. The last unit is likely lower due to lower slope positions.  Figure 2  Slope (%). Means are L to R, 5.5%, 2.2%, 12.4%, 10.9% and 7.6%. The CDF units corresponding to gentle coastal plain and CWHxm to upland mid to lower slope conditions.  254 150  100 A  50  0  v  ve-Ve^v+N E'l c. eg,^ ow^egc 40,1cr yseX .030 xFK^1.0E6°.‘ SASS  Figure 3  Aspect (azimuth degrees). Means are L to R, 128, 134, 133, 148 and 161°.  Figure 4  Major rooting zone depth (cm). Means are L to R, 52,40, 49, 52 and 57 cm.  100 O  80  60  40  20  0 VtategaVe.0.6S-U'I.^0.00 11Y1411Ci tOCVV SASS  Figure 5  Root restricting layer depth (cm). Means L to R, 57, 40, 54, 58 and 59 cm. No substantial differences across site units, except in the FdBg-Oregon grape unit.  Figure 6  Depth to mottling (cm). Means L to R, 25, 20, 50, 42 and 45 cm.  255  Figure 7  Depth to seepage water (cm), was well associated with slightly dry soil moisture conditions.  3  Figure 8  Percent (%) coarse fragment content by volume. Means L to R are; 60, 55, 62, 43 and 40. Note higher CF20's are associated with moderately dry actual soil moisture regimes.  2.0  1.5 2 F  A  0:1 1.0 1 0.5  0  0.0 cse0-1 eV ° vowNeN -pet SW*sv ygoVe^‘6.11.a-^ clitoa  viagreVP'VefttssV'1,11.0eVe, 40,0 V.5V5X 9-1 0 1° clito  SASS  Figure 9  Gross total mineral soil bulk density (g/m 3). Means L to R are; 1.46, 1.58, 1.38, 1.25 and 1.19. Note the higher MSBDT in the CDFmm site units. The trend corresponds to CF20.  SASS  Figure 10  Fine fraction (<2 mm) mineral soil bulk density (g/m 3). Means L to R are; .828, 1.125, .766, .716 and .695. Note the similar trend to MSBDT.  256 90 80 70  14 0  60 50 40 0  30 e^A.03-._&bect tyytiv 1. 9-1 va Vs9 ^ voveg, VO-It'otitaalv'  SASS  Figure 11  Figure 13  Mineral soil percent porosity, Means are L to R; 69%, 58%, 71%, 73% and 74%. Note, porosity is inversely related to MSBDF; high bulk density equals low porosity.  Mineral soil pH. Means L to R are; 5.62, 5.18, 5.26 and 5.3. FdSalal is significantly different ( a =.05) from all other pH distributions.  Figure 12  Figure 14  Relative soil moisture regime. Means are L to R; 3, 3.7, 2.4, 3.8 and 4.3. The moderately dry site units are predominated by high coarse fragment contents and bulk densities.  Mineral soil total (%) carbon content. Means are L to R; 2.04, 2.24, 1.84, 1.91 and 2.50, ( a^=.10).  257  Figure 15  Mineral soil total (%) nitrogen content. Means are L to R; .12, .18, .08, .13 and .14, with no significant differences detected at  Figure 16  Mineral soil carbon: nitrogen ratio. Means L to R are; 21.14, 15.08, 23.42, 18.82 and 16.96.  ( a <.10).  Figure 17  Mineral soil calcium concentration (meg). Means are L to R; 2.41, 4.24, 2.33, 2.94 and 2.64, with no significant differences detected at ( a <.10).  Figure 18  Mineral soil magnesium concentration (meg). Means L to R are; .42, 1.06, .56, .70 and .63, with no significant differences were detected at ( a <.10).  258 1.5  0  1.0  0^0 0  0.5  0.0  0  e 'ON^f, va^ Vst` VVIS°- • a.106C VOV eg^  10 11.11'.0409.0' SASS  Figure 19  Figure 21  Mineral soil potassium concentration (meg). Means L to R are; .16, .36, .20, .22 and .15.  Mineral soil mineralizable nitrogen (Kg/ha). Means L to R are; 10.6, 28.4, 6.2, 13.1 and 15.7. Note the substantial increase of MSMNK across s.a.'s within the CDFmm and CWHxm subzones.  Figure 20  Figure 22  Mineral soil mineralizable nitrogen (ppm). Means L to R are: 17.5, 31.0, 11.6, 16.6 and 19.3. Note the substantial increase of MSMN across s.a.'s within CDFmm and CWHxm subzones.  Forest floor mineralizable nitrogen (ppm). Means L to R are; 335, 424, 292, 316 and 306.  259  Figure 23  Old growth Douglas/fir and western hemlock stems (stumps)/ha. Means L to R are; 86, 37, 127, 128 and 138.  Figure 24  Old growth western red cedar stems (stumps)/ha. Means L to R are; 8, 24, 25, 26 and 13, with no significant differences detected at ( a <.10).  2  1  0  0  1  xeN tvxv,ecg, tvo ^ lAss. Vs°123.vee eg,^ z v vov^ oilosit"  -c  SASS  Figure 25  Old growth Douglas-fir and western hemlock species compositions. Means L to R are; .812, .511, .807, .824 and .937. FdBg-Oregon grape is significantly different from all s.a.'s at (^<.10).  Figure 26  Old growth western red cedar species compositions. Means L to R are; .063, .323, .137, .158 and .063. FdBg-Oregon grape is significantly different from Fd-Salal and Cw-Foamflower at ( a <.10).  260  Figure 27  ^  First PSP measure of Fd & Bg (susceptible) species composition. Means L to R are; .922, .863, .926, .875 and .834. Note the decreasing trend to FSUS with increasing soil moisture and nutrient regime.  Figure 28  ^  First PSP measure of Hw (intermediate) species composition. Means L to R are; 0, .022, .032, .07 and .064. Note the increasing trend with increasing soil moisture and nutrient regime.  0.4  0.3  02  0 0  0  0  0 0  0  0  0.1  0  *  0.0  -0.1  vs23•93. ev,oveytwl2x.,,,axe' 1%,,i.xo'c  Vao  SASS  Figure 29  First PSP measure of PI, Pw and Cw (resistant) species composition. Means L to R are; .035, .048, .028, .027 and .019, with no significant differences detectable at ( a <.10).  Figure 30  First PSP measure of Deciduous (resistant) species composition. Means L to R are; .043, .057, .014, .025 and .083. Note the significantly lower FDEC in the FdHw-Salal and HwFd-Kindbergia compared to the Cw-Foamflower (^<.10).  261 1.5  0.0 0  0.5 0.4  1.0  0  0  0 0.5  0.3  0  02  0  0  * *  0.1  o 0  0.0 0.0  0.0,0 10.‘ VS81S1 egyae^ VAA.Sa^ •030V^  —0.1  Csti WI°  Last PSP measurement of Fd & Bg (susceptible) species composition. Means L to R are; .928, .867, .909, .870 and .857, with no significant differences  Last PSP measurement of PI, Pw & Cw (resistant) species composition. Means L to R are; .048, .075, .038, .035 and .020, with not significant differences detected at ( a <.10). Note the simlarity with FRES.  .c ,910-1 v ae^\a‘  ixesv VP°'^ clit0a•va  tkvxo  SASS  Figure 32  detected at ( a <.10). Note the similarity with FSUS.  Figure 33  ^  viaave%  SASS  Figure 31  0_  Last PSP measurement of Hw (intermediate) species compositions. Means L to R are; .003, .018, .043, .076 and .076. Fd-Salal is significantly different from HwFd-Kindbergia and CwFoamflower at ( a <.05). Note the similarity with FINT.  Figure 34  Last PSP measurement of deciduous species composition. Means L to R are; .021, .040, .009, .016 and .046. Note the similarity with FDEC.  261 A  Figure 35  First PSP measurement of density (stems/ha) A.Ocm. Means L to R are; 1 875, 1 379, 2 798, 2 001 and 1 072. FdHw-Salal is significantly different from Fd-Salal at (  a  Figure 36  First PSP measurement of basal area (m 2 /ha) A.Ocm. means are L to R; 38.2, 38.1, 33.3, 32.7 and 36.9, with no significant differences detected at (  <.05), FdBg-Kindbergia  a <  .10).  and Cw-Foamflower at ( a <.05). The latter two site units were also significantly different at (  Figure 37  a  <.05).  First PSP measurement of volume (m 3 /ha) k4.0cm. means L to R are; 319, 332, 246, 264 and 346, with no significant differences detected at (  a  <.10).  Figure 38  First PSP measurement of Curtis' relative density A.Ocm. means L to R are; 9.1, 8.7, 9.0, 8.2 and 7.8, with no significant differences detected at (  a  <.10).  262 20  0  10  a  z  0  10 N 9--^ veg'vaVeNe -pdbev 00'0 VsNta Vo OW' o toi*O"  c  SASS  Figure 39  Curtis' relative density increment first-last PSP measurment means L to R are; 1.6, 2.3, 2.5, 3.3 and 2.7, with Fd-Salal significantly lower than HwFd-Kindbergia at ( a  Figure 40  First PSP measurement of total age (yr). means L to R are; 45, 39, 39, 31 and 34, with Fd-Salal significantly greater in age than HwFd-Kindbergia at (  <.05).  a  <.05).  50  40  51 30  20  10  a e^N.23of°ooler selal CaDve c Y11‘52'^(Pevt colo c3 8Wv  SASS  Figure 41  Last PSP measurement (approx. 1987) of total age (yr). means L to R are; 76, 76, 69, 62 and 64, with Fd-Salal significantly greater in age than HwFd-Kindbergia at ( a  <.05).  Figure 42  Site index, Bruces Douglas-fir ref. age 50 at breast-height. means L to R are; 25.4, 29.8, 24.9, 31.2 and 36.1, the only site units not differing significantly at ( a <.05) are Fd-Salal, FdHw-Salal, FdBdOregon grape and HwFdKindbergia.  263 20  a 15  O  a 10  iyr  5  0  1 Foxl Ave v3.. oat 1.0.er ,It' Vov^ 1#1 1108i 0 e "  SASS  Figure 43  Estimated density (stems/ha), A.0 cm at reference age 10 yr. Means L to R are; 1 853, 1 472, 2 801, 2 173 and 1 252, with FdHwSalal significantly greater stems/ha than Fd-Salal (  a  Figure 44  Estimated basal area (m 2 /ha), A.0 cm at reference age 10 yr. Means L to R are; 74, 6.6, 8.4, 8.2 and 7.8, with no significant differences detected at (  <.05)  a <.10).  and Cw-Foamflower (^<.05), and HwFd-Kindbergia is significantly greater than CwFoamflower at (  a < .05).  60  50  0  40  ,14  30  20  10  0 s-,e^_A 2L1. S;1•13ovetr.r ,c1,15%,  tisbe^typ- 1 11:04-` clicmsc'  SASS  Figure 45  Estimated volume (m 3 /ha), cm at reference age 10 yr. Means L to R are; 21, 21, 22, 25 and 28, with Cw-Foamflower significantly different from Fd-Salal, ( and FdHw-Salal at (  a  <.05)  a < .10).  Figure 46  Estimated Curtis' relative density, cm at reference age 10 yr. Means L to R are; 2.7, 2.4, 3.2, 3.0 and 2.6, with no significant differences detected at (  a <.10).  ^  264 Illustration Of Selected Variables From Pearsons Correlation  Appendix G  10 ^ 0^20^40^60^80^100 Soil Coarse Fragment Content (XVol, 10-30cm)  Figure 1 (b)  Figure 1(a)  60 50 2  7! 40 I 30 20 10 0  20^40^60^80^100^120 Rooting Depth (cm)  Figure 1(c)  Figure 1(d) 60  3  50  50  40  .2 40  3°  30  20  20  i  10  -10 -10 0 10 20  30 40 50 60 70 BO  10  10^.. 30^40^50^60^70^BO^90  Percent (%) Slope  Figure 1(e) Figure 1(a f)  Percent (2) Soil Porosity  Figure 1(f) Scatterplots of several thought-to-be important soil physical variables against % Basal Area Reduction-BARS. Figs. 1(a) CF20, (b), MSBDF, (c) MSBDT, (d) ROOTDP, (e) SLOPE, and (f) PORF. Gaussian 80% confidence ellipses are for interpretive aid only. Note the lack of correlation for all variables with the possible exception of % coarse fragment content.  265 80 50  O  40  O  0^30 0  aq^20  .^• •  j  •  10 0 10 ^ ^ ^ 8 0 1^2^3^4^5^6^7 Soil Percent (2) Total Carbon  Figure 2(b)  Figure 2(a) 80 50 40 30 20 10 0 10  0 0^02^0.4^0.6^0.8  1.0  10 0^ 20^40^60^80^100 Soil Mineralizable Nitrogen (ppm)  Soil Percent (%) lbtal Nitrogen  Figure 2(d)  Figure 2(c)  10 ^ 0^5^10^15^20^25 Soil Exchangeable Bases--Calcium (meq)  Figure 2(e)  Figure 2(a-e)^Scatterplots of several thought-to-be important soil chemistry variables against % Basal Area Reduction-BARS; (a) MSPH, (b) MSC, (c) MSN, (d) MSMN and (e) MEQCA. Note the lack of correlation or trend, and outliers for virtually all variables.  

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