UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

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

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata


831-ubc_1992_fall_beale_jeffrey.pdf [ 10.42MB ]
JSON: 831-1.0075207.json
JSON-LD: 831-1.0075207-ld.json
RDF/XML (Pretty): 831-1.0075207-rdf.xml
RDF/JSON: 831-1.0075207-rdf.json
Turtle: 831-1.0075207-turtle.txt
N-Triples: 831-1.0075207-rdf-ntriples.txt
Original Record: 831-1.0075207-source.json
Full Text

Full Text

We accept this thesis as conformingthe required standardLAMINATED ROOT ROT:ECOLOGICAL RELATIONSHIPS AND STAND PRODUCTIVITYIMPACTS IN COASTAL DOUGLAS-FIR ECOSYSTEMS OFBRITISH COLUMBIAbyJEFFREY DAVID BEALEB.Sc., Lakehead University, Ontario, 1980A THESIS IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE STUDIES(DEPARTMENT OF FORESTRY)THE UNIVERSITY OF BRITISH COLUMBIAAUGUST, 1992© Jeffrey David Beale, 1992In presenting this thesis in partial fulfillment of the requirements for an advanced degreeat the University of British Columbia, I agree that the Library shall make it freely availablefor reference and study. I further agree that permission for extensive copying of thisthesis for scholarly purposes may be granted by the Head of my Department or by hisrepresentatives. It is understood that copying or publication of this thesis for financialgain shall not be allowed without my written permission.Department of ForestryThe University of British Columbia2075 Wesbrook PlaceVancouver, B.C., CanadaV6T 1W5August 10, 1990iiABSTRACTThe effect of laminated (Phellinus) root rot in coastal second growth Douglas-firecosystems of southeastern Vancouver Island was investigated in 139 ecosystemscentered around 215 growth and yield permanent sample plots (PSP's). The impact ofPhellinus root rot on stand productivity was estimated from PSP records and a percentbasal area reduction (%BAR) parameter estimate determined using variable-radius plotsampling. The methods involved ecological site characterization to BiogeoclimaticEcosystem Classification system units, sampling of root rot incidence and severity, andcalculation of root rot damage intensity, sampling of old growth stand conditions (standdensity and species composition), determination of second growth stand origin, andsummaries of second growth stand conditions and growth and yield from the 30 to 35yr long PSP records.Phellinus root rot was present in 87% of the 139 sample survey units, while theincidence in the PSP's was 37%. Root rot damage intensity varied significantly betweenthe CDFmm and CWHxm subzones, respectively 5.94% and 11.11%. Generally, root rotdid not vary much between subzone variants, plant alliances and associations and siteassociations. The highest root rot intensity was in the mesic conditions and tailed off inthe drier and fresher soil moisture regimes. Only stand density (stems/ha) of old growthDouglas-fir and western hemlock were significantly and negatively correlated withPhellinus root rot intensity. It appears that the nearly two-times greater density of oldgrowth Douglas-fir in the CWHxm contributed to nearly double the damage intensity, asiiicompared to the CDFmm subzone. Trends in the old growth species compositionindicated a natural, host-pathogen dynamic equilibrium is part of the ecosystem. That is,Phellinus kills-out susceptible, shade intolerant pioneer seral species by creating infectioncenter gaps, making way for less-susceptible, shade tolerant mid-late seral species. Indoing so, the inoculum is reduced but rarely eliminated, therefore keeping it in equilibriumwith 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 thedisease incidence and damage intensity. No other ecological site or stand attributes werecorrelated to damage intensity.Second growth stand attributes (stand density (stems/ha), relative density andspecies composition) had similar distribution patterns to damage intensity, although nomeaningful relationships were determined in multiple linear regression models. Secondgrowth less- to non-susceptible host species composition was found to increase by about4% in Phellinus infected conditions (both in temporary variable-radius %BAR samplingand PSP's) compared to a 0.2% increase in healthy conditions, strongly suggestingPhellinus acts as a biogenic successional agent that induces species shifts as part of thehost-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 originclass ages varied significantly (they were positively related to the damage intensity) therewas an indication that logging and logging-and-slashburning could have played a role inivreducing the inoculum levels particularly through fire.Ecosystem hazard and Phellinus risk were approximated for ecosystem units onthe 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 hostPhellinus susceptibility patterns are considered in light of the postulated host-pathogendynamic equilibrium.Within a set of comparable PSP's, yield reductions ranged up to 30% dependingon stand age and damage intensity. Yield models estimated volume reductions to rangebetween 4.97 - 8.86% at age 80, or site height 35 m, depending on ecological and age10 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 ofPhellinus root rot. The significant ecological role the disease appears to play in inducinga species shift, has direct implications on the use of less-susceptible species in controland management strategies. Stand yield impacts appear to be substantial enough towarrant treatment where timber production objectives are primary.vTABLE OF CONTENTSABSTRACT ^  iiTABLE OF CONTENTS ^  vLIST OF TABLES  xiiLIST OF FIGURES ^  xvLIST OF APPENDICES  xxiiiLIST OF SYMBOLS ^  xxivACKNOWLEDGEMENTS  xxix1.0 INTRODUCTION ^  12.0 LAMINATED ROOT ROT BIOLOGY AND BEHAVIOUR ^ 43.0 A REVIEW OF SITE ECOLOGICAL RELATIONSHIPSOF PHELLINUS WEIRII ^  84.0 MEASUREMENT OF ROOT ROT INCIDENCE ANDDAMAGE -- A REVIEW ^  144.1^A Review of Measurement Methods ^  144.2^Evaluation and Selection of an Appropriate RootRot Measurement Method ^  175.0 STUDY LOCATION ^  235.1^Geography  235.2^Physiography ^  235.3^Soil Parent Materials--Surficial Deposits ^  255.4^Climate ^  265.5^Forest Cover and Stand History--Past and Present ^ 286.0 METHODS ^  30vi6.1^General Approach ^  306.2 Sampling Design  306.3^Measurement Of Root Rot Incidence, Severityand Calculation of Intensity ^  366.3.1 Calculation Methods For %BAR Percent BasalArea Reduction Variables ^  426.3.2 Assumptions Of The Percent Basal Area Reduction(%BAR)Sampling Method  446.3.2.1^Expected Relationships For the%BAR Damage Intensity Parameter ^  446.3.2.2 Testing The Homogeneity Of SpeciesCompositions And The DecompositionOf The %BAR Severity Parameters ^  456.4^Ecological Assessments ^  476.4.1 Site: Physiography  476.4.2 Site: Vegetation ^  486.4.3 Site: Soils  486.4.4 Site: Forest Floor  496.4.5 Site: Old Growth Stand History ^  496.4.5.1^Old Growth Stand History (Stand Density &Species Composition) Estimates Using Fixed-Radius Plots ^  506.5^Site: Stand Mensuration  516.6^Laboratory Analyses And Data Preparation ^  516.6.1 Mineral Soil And Forest Floor Physical Properties ^ 526.6.2 Mineral Soil And Forest Floor Chemical Properties  546.6.3 Conversion Of Chemical Property ConcentrationsTo Kg/ha ^  546.6.4 PSP Stand Attribute Data Preparation ^  556.6.5 Classification Of Root Rot Damage Intensity (%BAR) ^ 586.6.6 Ecological Classification ^  616.6.6.1^Climate Classification  626.6.6.2^Vegetation Classification ^  626.6.6.3^Site Classification ^  69vii^6.7^Statistical Analysis Methods ^  707.0 RESULTS AND DISCUSSION  747.1^Data Structure And General Relationships ^  747.1.1 Assessment Of The Percent Basal Area Reduction(%BAR) Variable And It's Components ^  747.1.2 Incidence-Severity, Severity-Intensity andIncidence-Intensity Relationships  797.1.3 Stand-Based:Land-Based Incidence andLand-Based:Stand-Based Relationships ^  847.1.4 Comparison Of The %BAR Survey Data To ThePermanent Sample Plot (PSP) Data  867.2^Ecological Relationships of Phellinus weirii ^  897.2.1 Describing Phellinus Root Rot Variabilityin Relation to Biogeoclimatic Units ^  897.2.1.1^Zonal (Climatic) ClassificationAnd Disease Variability ^  907. Disease Incidence Variability:Zonal Classification  ^907. Disease Intensity Variability:Zonal Classification  917.2.1.2^Vegetation Classification And Disease Variability ^ 947. Disease Incidence Variability:Vegetation Classification ^  947. Disease Intensity Variability:Vegetation Classification  ^. 957.2.1.3^Site Classification And Disease Variability ^ 987. Disease Incidence Variability:Site Classification ^  987. Disease Intensity Variability:Site Classification ^  997.2.2 Correlation Of Ecological Parameters And PhellinusRoot Disease ^  100viii7.3 Stand History And Tree Species Dynamics ^  1051057.3.1 Old Growth Stand Conditions and Phellinus Variability7.3.1.1^Old Growth Stand Conditions and PhellinusRoot Rot Variability Between BARS-DamageIntensity Classes (DIC's) ^  1087.3.1.2 Old Growth Stand Conditions and PhellinusRoot Rot Variability Between Subzones ^ 1107.3.1.3 Old Growth Stand Conditions and PhellinusVariability Between Plant Alliances andPlant Associations ^  1117.3.1.4 Old Growth Stand Conditions and PhellinusRoot Rot Variability Between Plant AlliancesAnd Plant Associations ^  1117.3.1.5 Old Growth Stand Density and SpeciesComposition and Second Growth PhellinusDamage Intensity ^  1137.3.2 Second Growth - Phellinus Variability Relationships ^ 1177. GrowthVariability in theSecond GrowthVariability in theEstablishmentSecond GrowthVariability in theEstablishmentSecond GrowthVariability in theSecond GrowthVariability in theSecond GrowthVariability in theStand Conditions and PhellinusSubzone Variants at PSPStand Conditions and PhellinusPlant Alliances  Stand Condition and PhellinusPlant Associations  Stand Conditions and PhellinusSite Association  Stand Conditions and PhellinusBARS Damage Intensity Classes ^ 119Stand Conditions and PhellinusSubzones at PSP1197.3.3 Second Growth Species Dynamics1221241251261307.3.3.1 Second Growth Species Dynamics Estimates fromVariable-Radius Plot Sample (%BAR Survey) Data ^ 1317. Comparison of Species Compositionsby Disease Condition ^  1317. Species Composition Shifts StratifiedBy Disease Condition And Diameter LimitClasses ^  133ix7. Comparison of Non-susceptible SpeciesCompositions Between Diameter Classes^and Disease Condition ^  1367.3.3.2 Second Growth Species Dynamics From the PSP'sMeasurement Record ^  1387.3.4 Stand History: Fire And Logging  1417.4 Phellinus Root Rot - Ecological and StandHistory Models ^  1457.5 Phellinus Root Rot Growth And Yield ReductionRelationships ^  1507.5.1 Damage Appraisal of Phellinus Root Rot on Growthand Yield of Second Growth Douglas-fir Ecosystems ^ 1507.5.1.1^Growth and Yield Comparisons Within SelectedInstallations ^  1517.5.1.2 Yield Comparisons Using the Chapman-RichardsNon-Linear Growth (VAC) Model ^  1607.5.1.3^Growth and Yield--Site Height Models forthe Whole PSP Dataset ^  1627. Growth Models  1627. Yield Models ^  1687.5.1.4^Growth and Yield: All Data, Stratified byStand Density Classes (stems/ha at age 10 yr) ^ 1717. Stand Density (Less than 1 000 stems/ha): ^ 1717.^Yield Models ^  1717. Stand Density (1 000-1 999 stems/ha): ^ 1737.^Yield Model ^  1737. Stand Density (2 000-4 999 stems/ha): ^ 1747.^Yield Models ^  1748.0 DISCUSSION  1768.1^Damage Incidence - Severity - Intensity Relationships ^ 1778.2 The Relationship Between PSP's and the %BAR SampleSurvey Data ^  1878.3^Phellinus Root Rot Variability in Relation to EcologicalSite Factors and BEC Units ^  1808.3.1 Disease Incidence  1808.3.2 Disease Intensity ^  1818.3.3 Site Ecological Factors  1828.4^Phellinus Root Rot Variability in Relation to Old Growth andSecond Growth Conditions and BEC Units ^  1858.4.1 Phellinus Root Rot Variability in Relation to Old GrowthDensities and Species Composition Across BEC Units ^ 1878.4.2 Second Growth Stand Conditions ^  1918.4.2.1^Second Growth Species Dynamics: andVariability Radius Plot Sample Surveys ^  1938.4.2.2 Second Growth Species Dynamics: PermanentSample Plot Records ^  1948.4.2.3 Summary and the Natural, Host-PathogenDynamic Equilibrium Model  1978.5^The Effects of Stand History (Logging and Burning) on theBehaviour of Phellinus Root Rot ^  1998.5.1 Evaluating the Effects of Logging onPhellinus wierii Survival ^  2008.5.2 Evaluating the Effects of Slashburning onPhellinus wierii Survival  2028.5.3 Evaluating the Effects of Wildfire onPhellinus wierii Survival ^  2048.6^Predicting Biogeoclimatic Unit Phellinus Root Rot Hazard and Risk^2078.6.1 Phellinus Root Rot and the BEC Site AssociationTaxon Model ^  2078.6.2 Multiple Regression Models ^  2118.6.3 Phellinus Root Rot - Coastal Douglas-fir EcosystemsHazard (Susceptibility) and Risk Classification forSoutheastern Vancouver Island ^  2128.7^Damage Appraisal of Phellinus Root Rot on Growth and Yieldof Second Growth Douglas-fir Ecosystems ^  214xxi8.7.1 Yield Comparisons within Selected Installations ^ 2148.7.2 Yield Comparisons using the Chapman-Richards Model ^ 2158.7.3 Growth and Yield Comparisons Over Site Height  215^^All data-No Stratification ^  2158.7.3.2^Growth and Yield All Data, Stratifiedby Stand Density Classes (stems/ha at age 10 yr) ^ 2188. Less than 1 000 stems/ha ^  2188. 1 000-1 999 stems/ha  2188. 2 000-4 999 stems/ha  2199.0 SUMMARY ^  22110.0 RECOMMENDATIONS FOR MANAGEMENT ^  22710.1 Damage Appraisal ^  22710.2 Stand And Forest Level Prescriptions ^  22911.0 REFERENCES ^  231xiiLIST OF TABLES^1^Summary of Key Climatic Properties within theStudy Area ^  27^2^Stand Origins by Subzone Variant ^  293^Matrix of Damage Severity Variables and Consequent%BAR Damage Intensity Parameters ^  424^Expected Relationships Between %BAR DamageIntensity Parameters ^  455^Decomposition of %BAR Damage Severity Variablesto Subcomponents ^  466^Vegetation Environment Analysis ^  647^Hierarchical Synopsis of Vegetation UnitsDistinguished in the Study Area ^  658^Diagnostic Species Correlations to PCA Axis Scores ^ 679^Hierarchical Synopsis of the BEC Site Units Distinguishedin this Study ^  7010^Descriptive Statistics for Study-Wide Estimates ofPhellinus Root Rot Incidence, Severity, SeveritySub-Components and Intensity ^  7711^Pearsons Correlation Matrix of Phellinus Root RotSurvey Sampling Variables and Parameters ^  8112^Incidence - Intensity Relationships (BARS/NSBAR)as a Function of Stand (DSWP) and Land (DSPT) BasedIncidence ^  8413^Phellinus Root Rot Incidence by Zonal ClassificationUnits ^  9114^Distribution Of Phellinus Root Rot in Subzonesand Variants as Estimated via the BARS Damage IntensityClassification ^  92XIII^15^Phellinus Root Rot Incidence by Plant ClassificationUnits ^  9516^Distribution Of Phellinus Root Rot in PlantAlliances and Plant Associations Estimated via the BARSDamage Intensity Classification ^  9617^Phellinus Root Rot Incidence by Site ClassificationUnits ^  9818^Distribution Of Phellinus Root Rot by Site Associationsas Estimated via the BARS Damage Intensity Classification ^ 9919^Pearsons Correlation Matrices ^  10220^Actual Soil Moisture and Nutrient Regimes for SiteAssociations in the Study Area ^  10321^Descriptive Statistics for Old Growth Stems/ha andSpecies Composition by Various Classifications ^  10722^Descriptive Statistics for Second Growth SpeciesComposition and Stand Densities and VolumeEstimates by Various Classifications ^  11823^Descriptive Statistics for Species Compositionby Disease Condition ^  13224^Species Composition Ratio Variable Definitions ^  13425^Comparison of Non-Susceptible Species Composition (NSSppC)^13726^Mean Differences in Tolerant, Intermediate and Resistant(TIRD) Species Composition Proportion in PSP's BetweenFirst and Last Measurements ^  14127^Multiple Regression and Ancova Models for Percent BasalArea Reduction (BARS) - Susceptible Species, Greaterthan the Sample Diameter Limit ^  14828^Percent Change of Stand Variables Between First andLast Measurements Spanning 30 to 35 yr ^  15229^Descriptive Stand Statistics for Combined Site Associations;FdHw-Salal and HwFd-Kindbergia Mean and (Standard Deviation)^161xiv30 Chapman-Richards Volume - Total Age Growth Model Statistics ^ 16131 All-PSP's Growth Model "Independent" of Root Rot Effectson Site Height Measurements ^ 16432 All-PSP's Growth Model - Healthy Condition "Dependent" ofRoot Rot Effects on Site Height Measurements ^ 16633 All-PSP's Growth Model - Infected Condition "Dependent" ofRoot Rot Effects on Site Height Measurements ^ 16634 All-PSP's Yield Model ^ 16935 Stand Density (< 1 000 stems/ha) Yield Model ^ 17236 Stand Density (< 1 000-1 999 stems/ha) Yield Model ^ 17337 Stand Density (< 2 000-4 999 stems/ha) Yield Model 17438 Phellinus Root Rot Hazard and Risk Classification for Douglas-FirEcosystems on S.E. Vancovuer Island ^ 213XVUST OF FIGURES^1^Study Area and Biogeoclimatic Unit Map ^  24^2^Sample Survey Layout Centered about a PermanentSample Plot ^  353^Healthy Variable-Radius Plot Condition  ^404^Infected Variable-Radius Plot Condition  ^415^Percent Basal Area Reduction (BARS) - Total AgeScatterplot Classified by BARS-Damage Intensity Classes  ^616^Boxplots of BARS Classified by the BARS-DamageIntensity Classes  ^617^95% Confidence Ellipses of the PCA OrdinationScores of Six Vegetation Units Identified inthe Study ^  688^Histogram of the Four Percent Basal AreaReduction Parameter Estimates All-Samples  ^759^Histogram of the Mean Tree Counts by SpeciesSusceptibility and Disease Condition  ^7810^Incidence-Severity Relationship of Stand-BasedIncidence (DSWP), to Damage Severities(NSSEV) and (SEVS) ^  7911^Incidence-Severity Relationship of Land-BasedIncidence (DSPT), to Damage Severities (NSSEV)and (SEVS)  ^7912^Severity-Intensity Relationship of All-SpeciesDamage Severity (NSSEV), to Damage Intensity(NSBAR)  ^8013^Severity-Intensity Relationship of SusceptibleSpecies (>_12.0/17.5 cm) Damage Severity (SEVS),to Damage Intensity (BARS)  ^80xvi14^Incidence-Intensity Relationship of Stand-BasedIncidence (DSWP), to (NSBAR) and (BARS) DamageIntensities  ^8015^Incidence-Intensity Relationship of Land-BasedIncidence (DSPT), to (NSBAR) and (BARS) DamageIntensities  ^8016^Boxplots of Damage Severity (NSSEV) Classifiedby BARS-Damage Intensity Classes ^  8217^Boxplots of Damage Severity (SEVS) Classified byBARS-Damage Intensity Classes ^  8218^Boxplots of Land-Based Disease Incidence (DSPT)Classified by BARS-Damage Intensity Classes ^  8319^Boxplots of Stand-Based Disease Incidence (DSWP)Classified by BARS-Damage Intensity Classes  ^8320^Stand:Land-Based Incidence Relationship  ^8621^Land:Stand-Based Incidence Relationship  ^8622^Boxplots of Land-Based Disease Incidence (DSPT)Classed by Absence and Presence of DiseaseIncidence in PSP's  ^8723^Boxplots of Stand-Based Disease Incidence (DSWP)Classed by Absence and Presence of DiseaseIncidence in PSP's  ^8824^Boxplots of Damage Intensity (NSBAR) Classedby Absence and Presence of Disease Incidencein PSP's  ^8825^Boxplots of Damage Intensity (BARS) Classed byAbsence and Presence of Disease Incidence in PSP's ^ 8826^Boxplots of % Basal Area Reduction-BARS by Subzone ^ 9327^Boxplots of the % Basal Area Reduction-BARS by SubzoneVariant  ^9428^Boxplots of % Basal Area Reduction-BARS byPlant Alliances  ^97xvii29^Boxplots of % Basal Area Reduction-BARS byPlant Association^ 9730^Boxplots of % Basal Area Reduction-BARS bySite Association^ 10031^Percent (%) Coarse Fragment Content (by volume)by Site Association ^  10432^Elevation (m) asl by Site Association ^  10433^Slope (%) by Site Association ^  10434^Fine Fraction (<2 mm) Mineral Soil Bulk Density(g/cm3) by Site Association ^  10435^Mineral Soil Percent Porosity by Site Association ^ 10536^Mineral Soil pH by Site Assocation ^  10537^Boxplots of Old Growth Douglas-fir and western hemlockstems/ha (SPHFH) Classified by BARS-Damage IntensityClasses ^  10938^Boxplots of Old Growth western red cedar stems/ha(SPHCW) Classified by BARS-Damage Intensity Classes ^ 10939^Boxplots of Old Growth Douglas-fir and western hemlockSpecies Composition (COMPFH) Classified by BARS-DamageIntensity Classes ^  10940^Boxplots of Old Growth western red cedar Composition(COMPCW) Classified by BARS-Damage Intensity Classes ^ 10941^Old Growth Douglas-fir and western hemlock stems(stumps)/ha by Site Association ^  11242^Old Growth western red cedar stems (stumps)/haby Site Association ^  11243^Old Growth Douglas-fir and western hemlock SpeciesCompositions by Site Association ^  11244^Old Growth western red cedar Species Compositionsby Site Association ^  112xviii45^A Two-Dimensional Contour Plot Illustrating theRelationship Between Damage Intensity (BARS) and OldGrowth Stand Density (stems/ha) of Douglas-fir andwestern hemlock (SPHFH) and western red cedar (SPHCW) ^ 11546^A Three-Dimensional Plot Illustrating the RelationshipBetween Damage Intensity (BARS) and Old Growth StandDensity (stems/ha) of Douglas-fir and western hemlock(SPHFH) and western red cedar (SPHCW) ^  11647^First PSP Measure Fd, Bg and Hw (Susceptible andIntermediate) Species Composition by Subzone ^  12048^First PSP Measure PI, Pw, Cw and Deciduous (Resistant)Species Composition by Subzone ^  12049^Back-Estimated stems/ha, 4.0 cm at Reference Age 10 yrby Subzone ^  12150^Back-Estimated Basal Area (m 2/ha), >4.0 cm at ReferenceAge 10 yr by Subzone ^  12151^Back-Estimated Curtis' Relative Density, >4.0 cm atReference Age 10 yr by Subzone ^  12152^First PSP Measure of Fd, Bg and Hw (Susceptible andIntermediate) Species Composition by Subzone Variant ^ 12253^First PSP Measure of PI, Pw, Cw and Deciduous (Resistant)Species Composition by the Subzone Variant ^  12254^Back-Estimated stems/ha, >4.0 cm at Reference Age 10 yrby Subzone Variant ^  12355^Back-Estimated Basal Area (m2/ha), >4.0 cm at ReferenceAge 10 yr by Subzone Variant ^  12356^Back-Estimated Curtis' Relative Density, >4.0 cm atReference Age 10 yr by Subzone Variant ^  12457^First PSP Measure of Fd, Bg and Hw (Susceptible andIntermediate) Species Composition by Site Association ^ 12758^First PSP Measure of PI, Pw, Cw and Deciduous (Resistant)Species Composition by Site Association ^  127xix59^Back-Estimated stems/ha, .4.0 cm at Reference Age 10 yrby Site Association ^  12860^First PSP Measure of stems/ha, 4.0 cm at Reference Age10 yr by Site Association ^  12861^Back-Estimated Basal Area (m2/ha), >4.0 cm at ReferenceAge 10 yr by Site Association ^  12962^First PSP Measure of Basal Area (m 2/ha), >4.0 cm atReference Age 10 yr by Site Association ^  12963^Back-Estimated Curtis' Relative Density, ,4.0 cm atReference Age 10 yr by Site Association ^  12964^First PSP Measure of Curtis' Relative Density, >4.0cm at Reference Age 10 yr by Site Association ^  13065^Second Growth Species Composition for Healthy StandConditions     13266^Second Growth Species Composition for Infected StandConditions ^  13267^Non-Susceptible Species Composition by Diameter LimitDisease Condition and Subzone ^  13568^Susceptible Species Composition by Diameter LimitDisease Condition and Subzone ^  13569^Changes in Non-Susceptible and Susceptible SpeciesComposition ^  13570^Net Changes in Non-Susceptible Second Growth SpeciesComposition over 30 to 35 yr in Healthy and Infected PSP's ^ 13971^Net Changes in Susceptible Second Growth SpeciesComposition over 30 to 35 yr in Healthy andInfected PSP's ^  13972^Boxplots of % Basal Area Reduction-BARS Classified byThree Stand Origins; Wildfire (BURN), Logged-Only (LOG)and Logged and Slashburning (LOG & BURN)  ^143)0(73^Boxplots of % Basal Area Reduction-BARS Classified byThree Stand Origins; Wildfire (BURN), and Logged andSlashburned (LOG & BURN) ^  14374^Boxplots of Total Age Classified by Three Stand Origins ^ 14475^Boxplots of Total Age Classified by Two Stand Origins ^ 14476^Predicted % Basal Area Reduction - BARS, at TotalAge 80 yr by Subzone ^  14977^Predicted % Basal Area Reduction - BARS at TotalAge 80 yr by Subzone Variant ^  14978^Predicted % Basal Area Reduction - BARS at TotalAge 80 yr by Plant Alliances ^  14979^Predicted % Basal Area Reduction - BARS at TotalAge 80 yr by Plant Association ^  14980^Predicted % Basal Area Reduction-BARS at TotalAge 80 yr by Site Association ^  15081^Productivity Comparisons in Douglas-fir Growth andYield Installations (PSP 218 and 219) ^  15382^Productivity Comparisons in Douglas-fir Growth andYield Installations (PSP 218 and 219) ^  15483^Productivity Comparisons in Douglas-fir Growth andYield Installations (PSP 2007, 2008, and 2009) ^  15584^Productivity Comparisons in Douglas-fir Growth andYield Installations (PSP 2007, 2008, and 2009) ^  15685^Productivity Comparisons in Douglas-fir Growth andYield Installations (PSP 160, 161, and 162) ^  15786^Productivity Comparisons in Douglas-fir Growth andYield Installations (PSP 348 and 349) ^  15887^Productivity Comparisons in Douglas-fir Growth andYield Installations (PSP 158 and 159) ^  159xxi^88^Chapman-Richards Volume-Age Curves (4.0 cm)Comparing Healthy ( ) and Phellinus Root RotInfected (---) Stand Conditions for the CombinedFdHw-Salal and HwFd-Kindbergia s.a.'s   16289^All-PSP's Growth Model; Healthy PSP Scatterplotand Growth Function ^  16490^All-PSP's Growth Model; Infected PSP Scatterplotand Growth Function ^  16591^All-PSP's Growth Model; Comparitive Growth FunctionPlots Healthy (_) and Infected PSP's (---)^ 16592^All-PSP's Growth Model; Healthy PSP Scatterplotand Growth Function ^  16793^All-PSP's Growth Model; Infected PSP Scatterplotand Growth Function ^  16794^All-PSP's Growth Model; Comparitive Growth FunctionPlots (_) Healthy, and (---) Infected ^  16895^All-PSP's Yield Model; Healthy PSP Scatterplot andYield Function ^  17096^All-PSP's Yield Model; Infected PSP Scatterplot andYield Function ^  17097^All-PSP's Yield Model; Comparitive Yield FunctionPlots Healthy^Infected (---) PSP's ^  17098^Yield Models for Stand Density < 1 000 stems/ha atAge 10 yr for Healthy (___) and Infected (---)PSP Conditions ^  17299^Yield Models for Stand Density 1 000 - 1 999 stems/haat Age 10 yr for Healthy (_) and Infected (---)PSP Conditions^ 174100^Yield Models for Stand Density 2 000 - 4 999 stems/haat Age 10 yr for Healthy^and Infected (---)PSP Conditions^ 175UST OF APPENDICESA^Soil Chemical Concentration to Kg/ha ^  242B Curtis' Relative Stand Density ^  243C^Species List ^  244D Vegetation Summary Table ^  247E Spectral Analysis: Soil Moisture and Soil Nutrient ^ 251F^Relationship Of Site And Stand Ecological Variables ToThe Site Association Identified in this Study ^  253G Illustrations of Selected Variables from Pearsons Correlations ^ 264xxivUST OF SYMBOLSTopographic VariablesName^ Description^Measurement Unit(s) ELEV^Elevation above sea levelSLOPE Percent slopeASPECT^Slope aspect^ Azimuth degreesMineral Soil VariablesROOTDP^Major rooting zone depth^ cmRRLADP Root restricting layer depth cmMOTDP^Depth to mottling zone cmSEEPDP Depth to seepage^ cmCF20^Coarse fragment content by volume (ocular)^(% or porportion offor soil depth 10 to 30 cm horizon^ volume)MSBDT^Total bulk density (coarse & fine fraction) g/cm2MSBDF Bulk density (fine fraction, <2 mm) g/cm2PORF^Percent porosity (% volume occupied by airspaces)SMR Relative soil moisture regime categoricalvalues (Klinka et a/. 1984)^ 0 to 9SNR$^Relative soil nutrient regime categoricalvalues (Klinka et a/. 1984) A-EMSPH^Soil hydrogenion activity (pH) for 10 to 30cm horizonMSC Percent carbon (c) content for 10 to 30 cmhorizonMSN^Percent nitrogen (N) content for 10 to 30 cmhorizonMSCN^Carbon: Nitrogen ratio for 10 to 30 cm horizon^proportionMEQCA Milliequivalents calcium (Ca) for 10 to 30 cmhorizon^ meqMEQMG^Milliequivalents magnesium (Mg) for 10 to 30cm horizon meqMEQK^Milliequivalents pottassium (K) for 10 to 30cm horizon^ ppmMSMN^Mineralizable nitrogen for 10 to 30 cm horizon^Kg/haMSMNK Mineralizable nitrogen for 10 to 30 cm horizonForest Floor VariablesFFPH^Hydrogen ion activity^ pHFFC Percent carbon (c)FFN Percent nitrogen (N)FFMN^Mineralizable nitrogen^ ppmOld Growth Minsuration VariablesSPHFHSPHCWCOMPFHCOMPCWStems or stumps/ha of Douglas-fir and westernhemlockStems or stumps/ha of western red cedarProportion of total species composition--Douglas-fir and western hemlockProportion of total species composition -western red cedarSample plot meanSample plot mean(proportion 1-1.0)Stand (History) Origin VariablesBURN^Wildfire originLOG Logged-only originLOG & BURN^Logged and slashburn originSecond Growth Mensuration Variables (PSP-based)xxvFSUS/LSUSFINT/LINTFRES/LRESFDEC/LDECFSUSINTFRESDECFST4/LST4FBA4/LBA4FVL4/LVL4CRD1CRD87CRDINCRAGE1AGE87SIRRINSTHGHTSTHGHT2First/Last measurment; highly susceptiblespecies (Fd, Bg) compositionFirst/Last measurement; intermediatelysusceptible species (Hw) compositionFirst/Last measurement; resistant species(PI, Pw & Cw) compositionFirst/Last measurement; deciduous species(Ar, Mb, Dr, Bi, Dw, Ch, 0g, Ac)Sum of FSUS and FINT species compositionSum of FRES and FDEC species compositionFirst/Last measurement; stems/ha z4.0 cmdbh diameter limitFirst/Last measurement; basal area/ha z4.0 cmdbh diameter limitFirst/Last measurement; volume/ha z4.0 cmdbh diameter limitCurtis' relative density, at first measurementCurtis' relative density at last measurementChange in relative density first to lastmeasurementTotal age (years since germination) at firstmeasurementTotal age (years since germination) adjustedto 1987Douglas-fir site index; top height at breast-height age 50 (Bruce 1981)Phellinus root rot incidence inside permanentsample plotsSite height, average total height of site trees(sampling procedures vary but often largestdiameter trees disease and damage-free)Site height squared for linear modellingpurposesProportion of TotalComposition (PropTC)PropTCPropTCPropTCPropTCPropTC(stems/ha)(m2/ha)(m3/ha)1-201-201-20yryrmmDSTHGHT1VOL4DVOL4Annual site height incrementGross volume (24.0 cm) per hectare; observedand predictedAnnual volume increment (24.0 cm) perhectare, per year; observed and predictedm/yrm3/ham3/ha/yrxxviPhellinus Root Rot Survey Statistics%BARSNNSNSBARSBARBARSBARNSNSSEVSSEVSEVSSEVNSSALHNALHNSALHSALINALINSALIPercent basal area reduction, due to root rot,from the healthy conditionSusceptible 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 speciesNS species %BAR statistic for 24.0 cm dbhdiameter limit variable-radius samplesS species %BAR statistic for 24.0 cm dbhdiameter limit variable-radius samplesS species %BAR statistic for 12.0/17.5 cmdbh diameter limit variable-radius samplesNS species %BAR statistic for 12.0/17.5 cmdbh diameter limit variable-radius samplesNS species, 24.0 cm diameter limit, %BARstatistic for severity, or relative basalarea reduction between healthy and infectedvariable-radius basal area estimates withina 25-plot sample survey, (see also Table 3)S species, 24.0 cm diameter limit, %BARstatistic for severity, or relative basalarea reduction between healthy and infectedvariable-radius basal area estimates withina 25-plot sample survey, (see also Table 3)S species, 12.0/17.5 cm diameter limit, %BARstatistic for severity, or relative basalarea reduction, (see also Table 3)NS species, *12.0/17.5 cm diameter limit,%BAR statistic for severity, or relativebasal area reduction, (see also Table 3)Severity sub-component; 24.0 cm diameter limithealthy condition, susceptible speciesSeverity sub-component; 24.0 cm diameter limithealthy condition, non-susceptible speciesSeverity sub-component; 24.0 cm diameter limithealthy condition, non-susceptible andsusceptible speciesSeverity sub-component; 24.0 cm diameter limitinfected condition, susceptible speciesSeverity sub-component; 24.0 cm diameter limitinfected condition, non-susceptible speciesSeverity sub-component; 24.0 cm diameter limitinfected condition, non-susceptible andsusceptible species0/0(sample plot mean %)(sample plot mean %)(sample plot mean %)(sample plot mean %))ocviiSLSH^Severity sub-components; 24.0 cm <12.0/17.5cm diameter limit, healthy condition,susceptible speciesNLSH^Severity sub-components; 24.0 cm <12.0/17.5cm diameter limit, healthy condition,non-susceptible speciesNSLSH^Severity sub-components; 24.0 cm <12.0/17.5cm diameter limit, healthy condition,non-susceptible and susceptible speciesSLSI^Severity sub-components; 24.0 cm <12.0/17.5cm diameter limit, infected condition,susceptible speciesNLSI^Severity sub-components; 24.0 cm <12.0/17.5cm diameter limit, infected condition,non-susceptible speciesNSLSI^Severity sub-components; 24.0 cm <12.0/17.5cm diameter limit, infected condition,non-susceptible and susceptible speciesSGRH^^Severity sub-components; 12.0/17.5 cmdiameter limit, healthy condition,susceptible speciesNGRH^Severity sub-components; 12.0/17.5 cmdiameter limit, healthy condition,non-susceptible speciesNSGRH^Severity sub-components; 12.0/17.5 cmdiameter limit, healthy condition,non-susceptible and susceptible speciesSGRI^Severity sub-components; 212.0/17.5 cmdiameter limit, infected condition,susceptible speciesNGRI^Severity sub-components; 12.0/17.5 cmdiameter limit, infected condition,non-susceptible speciesNSGRI^Severity sub-components; 212.0/17.5 cmdiameter limit, infected condition,non-susceptible and susceptible speciesN1^24.0 cm, non-susceptible species-only,healthy condition species compositionN2 24.0 cm, non-susceptible species-only,infected condition species compositionS1^24.0 cm, susceptible species-only, healthycondition species compositionS2 24.0 cm, susceptible species-only, infectedcondition species compositionN3^12.0/17.5 cm, non-susceptible species-only,healthy condition species compositionN4 12.0/17.5 cm, non-susceptible species-only,infected condition species compositionS3^12.0/17.5 cm, susceptible species-only,healthy condition species compositionS4 12.0/17.5 cm, susceptible species-only,infected condition species compositionxxviilDNBA^k4.0 cm, non-susceptible species-only speciescomposition, net of infectedDSBA^24.0 cm, susceptible species only speciescomposition, net of infectedDBAN^212.0/17.5 cm, non-susceptible species-onlyspecies composition, net of infectedDBAS^k12.0/17.5 cm, susceptible species-onlyspecies composition, net of infectedBiogeoclimatic Ecosystem Classification VariablesSUBZONE^CDFmm, CWHxmVARIANT CDFmm, CWHxm1, mx2PORD^Plant alliancep.all. Plant alliance: Full (and abbreviated) nameswere; Pseudotsuga-Mahonia (PseudMahonia),Tsuga-Mahonia (same), and Thuja-Tiarella(Thuja-Tiarlea)PASS^Plant associationp.a. Plant association: Full (and abbreviated) nameswere; Pseudotsuga-Arbutus (PseudArbutus),Pseudosuga-Gaultheria (PseudGaulthr),Tsuga-Mahonia (TsugaMahonia), andPseudotsuga-Achlys (PseudoAchlys)SASS^Site associations.a. Site association: Full (and abbreviated) nameswere; Fd-Salal (Fsalal), FdBg-Oregon grape(FBoregrape), FdHw-Salal (FHsalal), HwFd-Kindbergia (HFKindberg), and Cw-Foamflower(CWfoamflowr)Tree Species (Common Names)Fd^Douglas-firBg grand firHw western hemlockCw^western red cedarPI Iodgepole pinePw western white pineAr^Pacific madrone (arbutus)Og Garry oakCh cherryDw^Flowering dogwoodBi paper birchMb bigleaf mapleDr^red alderAc back cottonwoodACKNOWLEDGEMENTSI extend my gratitude to my thesis committee members, Dr. B.J. (Bart) van derKamp, Dr. K. (Karel) Klinka, Dr. P.L. (Peter) Marshall and Dr. W.J. (Bill) Bloomberg fortheir 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, environmentallaboratory 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 andthe 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 forsoils 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 financialsupport 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 mycolleagues in the Vancouver Forest Region Forest Sciences section, thanks for yourenduring advice and commaraderie -- especially Bob Green.Finally, I extend my deepest and sincere thanks to my wife Jane, for herencouragement, love and assistance, you have shed true light on the meaning ofcommittment and discipline, and to Caitlin Rose, our new daughter who is obsessed withstatistical graphics displays, what a joy to work with you!Financial support in part was obtained from the Canada and B.C. Forest ResourcesDevelopment Agreement I 1985-1990 - Provincial Direct Delivery Pest Control.11.0 INTRODUCTIONLaminated 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 NorthAmerica (Childs 1963, Childs and Shea 1967, Wallis 1976). The effects of the disease arevariable, but generally stand density and productivity falls below that of healthy standsover time (Mounce et al. 1940, Bier and Buckland 1947, Childs 1970, Johnson and Wallis1972, Nelson 1980, Bloomberg and Reynolds 1985).Forest pathologists have observed the incidence and intensity, of several rootdiseases to vary with certain forest, ecological and environmental site factors (Bucklandet al. 1954, Williams and Marsden 1982, Hobbs and Partridge 1979, Reynolds andBloomberg 1982, Whitney 1978, Shields and Hobbs 1978, Bloomberg and Beale 1985,Nilsen 1983, Huse 1983, MacDonald et al. 1987a, 1987b, Wilks et al. 1985), althoughcausal relationships are difficult to quantify and confirm (Childs 1963, 1970). Thepossibility of patterns in the laminated (or Phellinus) root rot pathosystem of secondgrowth coastal Douglas-fir ecosystems in British Columbia is of interest to forestpathologists, ecologists, silviculturists and forest managers alike. A study directed toassess whether ecological site factors influence or control the Phellinus root rotpathosystem, and to assess the impact on forest productivity would aid management ofthe coastal Douglas-fir ecosystem and provide direction for Phellinus root rot controlresearch efforts.2British Columbia's ecosystem-based forest management using the system ofBiogeoclimatic Ecosystem Classification (Pojar et al. 1987), provides the ideal basis forinvestigating and reporting on the epidemiological patterns of laminated root rot and itseffects on forest productivity in second growth Douglas-fir ecosytems.Specifically the objectives of the study are:(1) To assess Phellinus root rot damage intensity levels in coastal Douglas-firecosystems, particularly at the site classification level of the BiogeoclimaticEcosystem Classification (BEC) system;(2) To develop a Phellinus root rot hazard and risk classification for BEC systemunits in coastal Douglas-fir ecosystems;(3) To develop a model of the relationships between Phellinus root rot andsignificant ecological, site and stand history factors in coastal Douglas-firecosystems;(4) To determine the effects on forest productivity attributable to Phellinus root rotin 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.42.0 LAMINATED ROOT ROT BIOLOGY AND BEHAVIOURDisease development begins in a newly regenerating stand when healthy roots ofsusceptible tree species contact infected stumps and roots (inoculum sources) from theprevious stand (Wallis and Reynolds 1965). Host tree species in coastal Douglas-firecosystems are in order of decreasing susceptibility: grand fir (Abies grandis (Dougl.)Lindl.), Douglas-fir, western hemlock (Tsuga heterophylla Bong.), lodgepole pine (Pinuscontorta var. latifolia Engelm.), and western white pine (Pinus monticola Dougl.). Westernred cedar (Thuja plicata Donn. ex D. Don in Lamb) and all deciduous tree species areresistant to P. weirii (Wallis 1976, Hadfield 1985). Subsequent tree to tree infectionspread occurs radially between living trees through root contacts. Both ectotrophic andendotrophic mycelial spread are involved in P. weirii transmission. Ectotrophic spreadis more frequent and successful compared to endotrophic spread and is relativelyunaffected by site or root factors (Bloomberg and Reynolds 1982). Fungal advancementcontinues proximally and distally along the roots causing decay and root death, resultingin 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 (Wallis1976). Crown symptoms associated with laminated root rot are reduced terminal leaderand branch growth, chlorotic and thinning foliage, and frequently observed distress conecrops (Mounce et al. 1940). The length of time until death or windthrow of crownsymptomatic trees is variable, but is probably no greater than 15 years (Bloomberg 1983).5Laminated root rot is first manifested in young stands in one-to-two-tree infectioncenters five to twenty years after stand establishment. In coastal stands, laminated rootrot appears to have a random, but often aggregated pattern of fairly discrete infectioncenters (Childs 1963, 1970, Foster and Johnson 1963). The likelihood of infection centerscoalescing increases with stand age (Foster and Johnson 1967, Wallis and Reynolds1965). Infection centers expand radially at approximately 30 cm/yr ± 15 cm (Childs 1970,Nelson and Hartman 1975, Bloomberg 1984, McCauley and Cook 1980). Trees infectedaround the advancing perimeter of infection centers undergo increment reduction forseveral years before death. Continuing infection in stands of susceptible species willgradually 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 andperiodic 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, Bloombergand 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 creationof inoculum sources. Trees showing no resistance are said to have "no formation ofcallus tissue or adventitious roots, as the disease spreads too rapidly for healing orcompensation to occur", (Buckland et al. 1954). Such trees are killed by fungal growthsolely in the sapwood. Trees showing resistance usually exhibit no visible distress6symptoms as the decay of roots is compensated for by callus tissue production andadventitious roots, thus maintaining vigour with marginal structural support, (Bloombergand Hall (1986) have observed this phenomena in 30 to 40 yr old Doulgas-fir stands onVancouver Island. "Resistant trees" can essentially maintain normal growth in protected,well stocked stand conditions for many years. It is still unknown if the differentialresistance reported by Buckland et al. (1954) is genetically controlled (e.g., tree-age-at-infection response), environmentally site controlled, or a combination of these factors.The mechanisms which impede fungal development and decay actually aid in thepathogens' persistence in stumps and roots for periods exceeding 50 years. However,"resistant tree" inoculum likely poses a less serious threat as inoculum sources becausethe lower infected stump volume is usually well contained inside the callused stump(Buckland et al. 1954). These or other resistance mechanisms also aid in maskingabove-ground symptoms, which confounds disease detection and damage assessments.As stand infection continues, reduction of P.weirii inoculum sources may occur inseveral ways: (1) windthrow and uprooting of inoculum; (2) amount and viability ofectotrophic P. weirii declines to near zero in forty years on old growth Douglas-fir stumpand root systems (Hansen 1979); (3) succession to more disease tolerant tree speciessuch as western hemlock, western red cedar, western white pine, lodgepole pine andmany deciduous species. (Wallis 1976, McCauley and Cook 1980, Cook 1980, Williamsand 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-thinningand premature disease-related windthrow (Buckland et al. 1954); (6) activity of7antagonistic soil microbiological factors (Childs 1970, Rose et al. 1980, Hutchins and Li1981, Hutchins and Rose 1984); (7) genetic variation of P. weirii clones in terms ofpathogenic virulence hypothesised by Childs (1963, 1970), and demonstrated by Hansenet al. (1983).The persistence and potential for carry-over into subsequent rotations is largelydependent on the presence, number, size and spatial distribution of inoculum sources(Childs 1970, Tkacz and Hansen 1982, Hansen et al. 1983). The role of basidiosporespread and infection in the disease epidemiology has been considered, but is not thoughtto be large (Childs 1970). Clearly basidiospores must play a role in at least long distancespread and infection to account for the pathogens extensive range, however infrequentor inefficient the means (Dickman and Cook 1989). Attempts to inoculate intact andwounded trees and stumps with basidiospores have failed (Nelson 1971).In summary, the epidemiology of Phellinus root rot disease intensification andpersistence, must consider clonal genetic variation of the pathogen (Hansen et al. 1983,Angwin 1985), mechanisms of tree resistance and disease persistence, the combinedeffects of site history (tree species succession, harvesting and fire patterns), standattributes such as density and species compositions, and various ecological siteconditions. Apparently the disease is under the influence of site factors, in fact it can beconsidered a disease of the site (Hadfield 1985). These factors are of utmost importancein assessing the hazard (stand susceptibility) and risk (probability and intensity ofdamage) of disease on forest sites.83.0 A REVIEW OF SITE ECOLOGICAL RELATIONSHIPS OFPHELLINUS WEIRIIEvidence for the effect of site factors on the spread and persistence of P. weirii hasbeen variable but generally indicates some correlations and relationships. Bier andBuckland (1947) report that Phellinus root disease occurred as abundantly on good ason poor sites of second growth Douglas-fir on southeastern Vancouver Island. Later,Buckland et al. (1954) observed that extensive disease spread appeared related to densegrowth of shallow roots and high frequency of root contacts; root contact frequency risesfrom 20 to 60 years then drops-off. They also observed the most rapid spread andheaviest damage was in good, well stocked sites 20 to 60 yr old. Shallow soils induceshallow radiating root systems with a high frequency of root contacts, and thereforesignificantly contributes to disease spread and damage. Sites with deep, dry soils havelower disease spread rates since the roots are small, fibrous, and grow downwards,minimizing root contact frequency (Buckland et al. 1954). Childs (1970) reported thatlaminated root rot was common from sea level upwards, on good to poor sites, and onmany kinds of soils from deep loarns to gravels, but with no distinction between anygeographic or site variables. Childs (1970) further stated that beyond stand history, standdensity 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 ispositively 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 tobe positively related to disease spread rates in long-term monitoring plots.9In Oregon, experiments to assess the effects of soil factors (pH, nitrogen, moisture,temperature, bulk density and microorganisms) on P. weirii inoculation and ectotrophicgrowth 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 sitefactors found by Angwin (1985), summarised below, tend to support some site factorcontrol on P. weirii. Maximum inoculation success and mycelial growth was observedat 15% soil water content, decreasing with higher or lower water content. Similarly withsoil temperature, maximum growth, and to a lesser degree inoculation success peakedat 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 shouldbe noted that soil pH above 5.5 stimulates bacteria and actinomycetes, some possiblyantagonistic to P. weirii, and below pH 5.5 mineralization of immobilized nitrogen toammonium is reduced, (the nitrogen form ammonium is useable by P. weirii). Finally,additions of urea-nitrogen fertilizer strongly reduced mycelia! growth, possibly indicatinga positive effect on host vigor, or antagonistic microorganisms and/or the indirect effectof lowering soil pH. All these factors, among others, are likely involved in the successand 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 biogeoclimaticsubzone variants, forest cover type group and site quality (Beale 1987). Disease intensitywas also strongly correlated with stand age and site index. The wetter maritime CoastalDouglas-fir subzone variant (CDFb), now CWHxm1, was 30 to 50% more severely infected10than the drier Coastal Western Hemlock subzone variant (CWHa1), now CWHxm2. TheDouglas-fir inventory type group was infected at more than double the intensity of the Fir-Hemlock type group. Disease intensity predictions for the Douglas-fir type group estimatethe 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) reportedP. weirii infection intensity varied significantly (p<.10) between several ecological site unitclassifications in the CDFb (now CWHxm1) subzone variant. Significantly lower damageintensity was observed in forest cover types containing western red cedar and lodgepolepine, and less so with types containing western hemlock. Several other root diseasepathosystems, 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, andinversely related to elevation (Williams and Marsden 1982, Hobbs and Partridge 1979).Furthermore, significant slope-aspect, soil-aspect and habitat type-age interactions areimportant predictors for estimating the probability of root disease center occurrences(Williams and Marsden 1982). In northern Idaho, the incidence of pathogenic Armillariaspp. decreases as habitat type productivity increases, and further depends on specificcombinations of habitat type and stand development history (McDonald et al. 1987a and1987b). In otherArmillaria spp. pathosystems, sites found to be most susceptible are lowin pH (Singh 1983); low in pH, N, P, and Ca and high in K (Shields and Hobbs 1979); and11have 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 moistureand fine soil texture. Van Groenewoud (1956) found I. tomentosus infection centers werein association with near-surface impermeable soil layers, or sandy soil veneers over verywell-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 severein soils down to pH 4.5. Van Groenewoud (1956), concluded that the white spruce(Picea glauca (Moench.)) Voss.-Hylocomium-Calliergonella plant association is the mostconducive to tomentosus root disease development. Low organic horizon pH, lownutrient content of the major rooting zone, and high stand density was common to thestand disease caused by I. tomentosus in spruce forests of northern Saskatchewan (VanGroenwoud and Whitney 1969). Spread of black stain root disease (Leptographiumwageneri Goheen & Cobb (anamorph Verticladiella wageneri Kend.)) in the ponderosapine (Pinus ponderosa Laws.) pathosystem of the Sierra Nevada in California is stronglyrelated to high soil water potential, indicating spread is favoured on wetter sites (Wilks etal. 1985).In Norway, Huse (1983), reported that the frequency of annosus butt rot, causalpathogen Heterobasidion annosum (Fr.) Bref., on Norway spruce (Picea abies (L.) Karst.),is positively correlated to tree diameter, increasing maturity class, soil depth, clay content12and slope, while negatively correlated to forest floor thickness and the extent of soilpodsolization. The frequency of butt rot increases from poorer to richer site plantcommunities. In a similar Norwegian study of H. annosum butt rot frequency on olderaged 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-piceetumtypicum vegetation type (moderately poor site type), in good-drainage soils (they appearto be mesic), and in the 150 to 300 m elevation band. No strong relationships werefound between butt rot frequency and soil chemical properties and pH (Nilsen 1983).In summary, it is apparent from observations in the laminated root rot pathosystemand other related root rot pathosystems, that some combination of ecological factorseither influences, controls, responds to, or is spuriously correlated with disease incidenceand intensity patterns. Similarly, tree species susceptibility to infection, and past andpresent stand compositions (which are related to ecosystem-specific tree speciessuccessional 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, thedemonstrated reductions to forest productivity by P. weirii may also vary by somecombination of ecological criteria or classification.The literature suggests further studies are needed to understand Phellinus root rotdamage and its effects on productivity, and its ecological role in different coastal Douglas-fir ecosystems. Such a study should consider evaluating the effects and interactions ofecosystems and their component variables, stand origin, and past and present stand13conditions (species composition and stand density (stems/ha, basal area/ha) onPhellinus 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 certainphases of forest management to aid silvicultural decision making. Also, it is increasinglyimportant to accurately reflect the effects of Phellinus root rot on growth and yield forstand 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 rootrot damage incidence, severity and intensity must be used to evaluate the relationship ofPhellinus root rot to ecosystem site components, ecosystem classification taxon, standhistory, past and present stand attributes, stand dynamics' parameters and its effects onforest productivity. The question of an appropriate damage intensity estimator is dealtwith in the following section.144.0 MEASUREMENT OF ROOT ROT INCIDENCE ANDDAMAGE — A REVIEW4.1 A Review of Measurement MethodsMeasuring the effects of root rot on forest productivity remains a difficult taskbecause there appears to be a lack of standardized and acceptable diseasemeasurement terminology and sampling methodologies, that can provide accuratedamage estimates with a direct (or even indirect) linkage to forest inventories and/orgrowth and yield projection systems. Furthermore, disease estimates are prone to largemeasurement errors because of difficult diagnostics due to highly variable treesusceptibilities and symptom expression, the decay and disappearance of killed treesover time, and observor experience.Disease measurement terms such as incidence, intensity, frequency, severity andprevalence have been poorly defined and used interchangeably, in practice and in theliterature, thus creating confusion in measurements and interpretation. To lend clarity tothe selection of appropriate measurement terms consider that pests in general, and rootrots in particular, have clumped distribution patterns of infection centers, with varyinglevels of damage within infection centers. It follows then, that measurement terms andsampling methods should attempt to describe/estimate the degree of infection centercoverage, 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 term15might be used to describe the combined-term overall damage condition in a samplepopulation. Seem (1984), eloquently addresses these three disease measurementconcepts 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 entitieswithin a sampling unit (Seem 1984). Incidence is measured as present or absent (abinomial or quantal response), (Zadoks and Sehein 1979). Incidence is quickly and easilymeasured and generally more accurate and reproducible than other quantitativemeasures (Horsfall and Cowling 1978). These features of incidence measurement makeit a favoured measure for detection and enumerations of disease spread patterns (Pearce1976).The degree of damage within infection centers is defined as severity, or the quantityof disease affecting entities within a sampling unit (Seem 1984). Severity also can beexpressed as a proportion or percentage.16The overall damage estimate is defined by Seem (1984) as intensity, which is aproduct of incidence and severity (Moore 1953).In Seem's (1984) definitions he stated that "... an entity is the plant part or plantpopulation that is measured. A sampling unit is a group of entities that form a singlecomposite or average measure. This distinction must be made when incidence andseverity are compared because disease incidence on a single entity cannot be comparedto severity." Relationships between incidence and severity can only be derived whenentities are grouped into sampling units that are defined in a spatial hierarchy. Forexample, the spatial hierarchical levels for this study might be (from high to low spatialresolution); root, tree, fixed-radius plot/variable-radius plot (prism sweep), 1 ha samplesurvey, biogeoclimatic site series, site association, variant, subzone and zone. Samplingunits are used to define the spatial hierarchical level in which incidence and severitymeasures are made. The sampling unit hierarchy is also necessary because an incidencemeasure 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 theaverage 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 (thesampling unit is now 0.01 ha plots) the severity measure would become the number ofinfected trees in 0.01 ha sample plots.Measurement of root rot incidence, severity and intensity generally falls into one oftwo damage expression estimates; (1) land-area-diseased per unit area (e.g., proportion17or percent), and (2) stand-parameter-diseased per unit area (e.g., stems/ha, basalarea/ha or volume/ha) (Filip 1980). The choice of the sampling unit and sampling entitywill determine the damage expression term, or conversely the choice of damageexpression, 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-parameter-diseased (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 RotMeasurement MethodThe pathometry terms incidence, severity and intensity can be used as criteria forevaluating land-area and stand-parameter-diseased measurement expressions.The land-area-diseased expressions appear best used for incidence, although givenvarious spatial hierarchy sampling units (transects, swaths or plots), damage severity andintensity estimates can be derived. A disadvantage of the land-area-diseased expressionis that it only samples land (Is it infected or not infected?), without providing a directlinkage measure or expression relating to stand attributes/parameters. This isunfortunate since land-area-diseased can only be determined by assessing diseaseincidence by looking at trees, the very parameter used for estimating stand-parameter-18diseased damage expressions.The stand-parameter-diseased expression, given the same spatial hierarchy samplingunit restrictions, can provide incidence, severity and intensity estimates. Incidence isassessed on the basis of disease occurrence on a sampled stand parameter (trees), andseverity is quantified on the basis of disease expression on some stand parameter(density or volume), with intensity multiplicatively derived from the two. The stand-parameter-diseased expression term provides a direct linkage to forest inventory and/orgrowth and yield. Interestingly, if incidence is also simultaneously determined on a land-area-diseased basis, incidence-severity, and incidence-intensity relationships (land-basedto stand-based severity, or intensity) can be determined. The latter relationship couldpotentially simplify sampling methods and reduce costs, by estimating disease intensityfrom 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 desiredterms of disease expression, land- and/or stand-parameter-diseased, (b) the samplingobjectives (e.g., general detection, stand or forest level damage appraisal, silviculturaltreatment 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 ofthe sampling methods (usually not known), and (iii) the efficiency of the sampling methodto estimate a land-area or stand-parameter-diseased.19Recall that the stated objectives of this thesis (see pg. 2) require an efficient andaccurate sampling method to assess incidence and damage in land- and stand-basedexpressions so that ecological and stand dynamics relationships, and stand productivityresponses to Phellinus root rot can be determined. A short review of various root rotsampling methods in use today follows.The intersection length sampling method for root rots (Bloomberg et al. 1980) is aflexible set of survey options for making land-area-diseased assessments which caninclude a mapping option. It has been used extensively for disease incidence samplingin coastal British Columbia (Bloomberg 1983, and Beale 1987). Essentially the proportionof the line/grid transect lengths in root disease centers, defined by above ground diseasesymptomology, 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 twiceas fast for assessing the land-area-diseased.Fixed-radius plot sampling to estimate the land-area-diseased appears to be the mostaccurate and compatible for sampling small, irregularly shaped areas (Filip 1980). Filip'sfixed-radius plot sampling method required surveyors to estimate the area infected withinplots. The method is definitely slow, but it is highly accurate. Alternatively, the samplingcould be much faster by only estimating disease presence or absence. This method canalso be used to estimate stand-parameter-diseased terms, but is known to be lessefficient than the variable-radius plot sampling discussed below.20Variable-radius plot sampling was least accurate for estimating land-area-diseasedin Filip's (1980) study. However, simultaneous acquisition of stand-parameter-diseaseddata may offset the loss in accuracy of estimating the land-area-diseased using thismethod. Variable-radius plot sampling compared to fixed-radius plot sampling "issimpler...without sacrificing accuracy, reduces personal errors and provides a betterbalanced sample of the various diameter classes within the stand" (Dilworth and Bell1979). In terms of simplicity "variable plot sampling does not require measurement of theplot radius or tree diameters to compute the basal area per hectare as with fixed-radiusplots. Stem counts are made, with each tree contributing equally, without regard todiameter, to the basal area estimate" (Dilworth and Bell 1979). Similarly, diseaseconditions are tallied on a tree by tree basis. The principle of tree selection differsmarkedly between the two methods: Grosenbaugh (1952) states, "In (fixed-radius) plotsampling the probability of tree selection is proportional to tree frequency; in pointsampling (variable-radius) it is proportional to tree basal area". These concepts areimportant for disease sampling because the impact of diseases on the basal area growingstock contributes the most to volume production. The latter can easily computed fromsample-based basal area/ha which is then factored by a sampled/or known volume:basalarea ratio (VBAR), (Dilworth and Bell 1979). Although variable-radius plots are ideallysuited 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 thebasis of disease presence/absence. The proportion of the plots infected, to any degree(one or more trees), is termed disease incidence.21Forest disease surveys using variable-radius plots to estimate damage to standparameters have been successfully conducted in the Pacific Northwest (Goheen 1979,Goheen and Worrel 1979). On Vancouver Island, B.C., Blair et al. (1975) used variable-radius plots in a sequential sampling scheme to estimate percent basal area infected instands managed by MacMillan Bloedel Limited.Several studies based on growth and yield permanent sample plots or stem analysisplots have also provided direct linkage to the effects of Phellinus root rot on standproductivity (Mounce et al. 1940, Bier and Buckland 1947, Buckland et al. 1954, Johnsonet al. 1972, Nelson 1980, Thies 1983, Bloomberg and Reynolds 1985, and Bloomberg1990). Recognition of pest activities in growth and yield or forest inventory permanentsample plots (PSP's) could allow for comparison of: (a) root rot incidence and, (b) growthand yield rates between PSP's affected and not-affected by root rots, given comparableinitial stand and site conditions. These types of comparisons are basic to the hypothesesbeing examined in this thesis. Generally, sets of growth and yield PSP's might be suitablefor extrapolation of damage effects on local stand productivity, whereas forest inventoryPSP's would more likely be representative of forest conditions.Based on the discussion of disease measurement terminology and samplingmethodology, I propose a new comprehensive approach to root rot sampling that willinclude: (a) ecological characterization and stratification of sampling units, (b) land-areaand stand-parameter disease incidence estimates, (c) stand-parameter-diseasedestimates for damage severity and intensity using two different diameter limits and, (d)22land-area-diseased incidence estimates for growth and yield permanent sample plots.This methodology would provide the basis for studying ecological and stand dynamicsrelationships, development of incidence-severity and incidence-intensity relationships, andprovide a direct linkage measure to forest inventory and growth and yield for assessingstand productivity responses to Phellinus root rot.235.0 STUDY LOCATION5.1 GeographyThe study was conducted on the southeastern half of Vancouver Island betweenUnion Bay and Shawnigan Lake; on the eastern slopes and coastal plain of the BeaufortMountains, 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 CowichanLake. The study area is illustrated in Figure 1, and is better defined by the southern two-thirds of the shaded biogeoclimatic subzone variants in the figure.5.2 PhysiographyThe study area lies within two major physiographic areas of the Western CanadianCordilleran system: the Coastal Trough and the Outer Mountain Area.The eastern portion of the study area lies within the Nanaimo Lowland of the GeorgiaDepression which rises to 600 m asl in the Vancouver Island Ranges, westwards from theGeorgia Strait. The area is largely underlain by the Nanaimo Group of Upper Cretaceoussedimentary rocks (hard sandstone, conglomerate, shales and softer rocks), giving riseto differential erosion potential and soil parent materials. The Nanaimo Lowland wasintensively glaciated during the Pleistocene, reducing relief and leaving behind a complexof glacial and glaciofluvial blankets and veneers (Holland 1976).24 CDFmm Coastal Douglas-fir, moist maritimeCWHxm1 Coastal Western Hemlock, eastern very dry maritimeCWHxm2 Coastal Western Hemlock, western very dry maritime'fr"•-•-•■•Study area and biogeoclimatic unit map. Note, the area from just south of Courtenaysouthwards comprised the study location. Source: Ministry of Forests, VancouverForest Region, Burnaby, B.C.Figure 125The 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 AlberniBasin is a low lying area extending northwestward from Port Alberni about 40 km withwidths between 8 and 13 km. Its elevational boundary runs to about the 300 m contouron the north, west and southern flanks of the basin. The eastern flank is bounded by thescarp of the western slopes of the Beaufort Range. The basin is also underlain by UpperCretaceous sedimentary rocks (Holland 1976). A third but very small portion of the studyarea lies within the Vancouver Island Ranges. The ranges are composed of aheterogenous group of pre-Cretaceous sedimentary and volcanic rocks with numerousgranitic batholiths that run on a northwest-southeast axis (Holland 1976). Thecombination of alpine, valley and continental ice-sheet glaciation during the Pleistocenehas produced soil parent materials of a very heterogenous nature on southern VancouverIsland, particularly in the study area (Jungen et al. 1985).5.3 Soil Parent Materials--Surficial DepositsTwo periods of glaciation 18,500 and 11,500 years ago resulted in extensivedeposition of fluvial and morainal materials, as well as marine deposits (Jungen et al.1985). Fluvial deposits consist of glaciofluvial and contemporary fluvial deposits; itsmaterials are sorted and less heterogenous than morainal deposits (Howes and Kenk1988). Common glaciofluvial landforms are old river terraces or raised estuariescomposed of coarse sands to sandy gravels. Finer textured, fluvial and glaciofluvialdeposits are found at lower elevations and near active rivers (Jungen et al. 1985). Glacial26morainal deposits are composed of an unsorted heterogenous mixture of sands, silt andclay 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 andinterglacial sands (Jungen et al. 1985). Marine deposits found at elevations below 100min the Nanaimo Lowland (Jungen et al. 1985) were formed as a result of landsubmergence and uplifting following glaciation (Howes and Kenk 1988). Colluvialdeposits, resulting from mass wasting (Howes and Kenk 1988) are usually found on, orat the base of, steep slopes or in areas of interspersed exposed bedrock. Colluvialdeposits are composed of angular, nonuniform sized coarse materials ranging frombouldery to sandy gravel, thus generally well drained (Jungen et al. 1985).5.4 ClimateThe study area is completely within the rainshadow effect of the Olympic MountainRanges in western Washington State and the Vancouver Island Ranges to the west of theAlberni Basin and to the west of the Nanaimo Lowlands. Thus, the area is in a leewardposition to the westerly prevailing moist Pacific weather systems. This is indicated in thenaming 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 Csbto Cfb. This indicates a mild, temperate, rainy climate with a cool summer, with a minordistinction of a dry summer (s), versus a no-distinct dry season (f). The dry summer27climate (Csb) is found near the Georgia Strait and the Alberni Basin, while the no-distinctdry season climate (Cfb), is primarily a feature of increasing elevation and westwardgeographic location. Klinka et al. (1979) reports that three distinct regional climates canbe identified for the climatic gradient within the study area. These are listed by theirrevised BEC unit names (Banner et al. 1990) as:CDFmm (CDFa1) =CWHxm1 (CDFb1) =CWHxm2 (CWHa2) =Moist Maritime Coastal Douglas-fir subzone variant,Eastern Very Dry Maritime Coastal Western Hemlocksubzone variant,Western Very Dry Maritime Coastal Western Hemlocksubzone variant (see Fig. 1).Further analysis of climatic data by (Courtin 1990) has confirmed the presence ofthree regional climates, which are a major basis for differentiating ecosystems in thebiogeoclimatic ecosystem classification system (Klinka et a/. 1984; Pojar et al. 1987) (seeTable 1).TABLE 1^SUMMARY OF KEY CLIMATIC PROPERTIES WITHIN THE STUDY AREA(From Klinka et al. 1984)Climatic Property Subzone VariantCDFmm CWHxm1 CWHxm2Mean Annual Precipitation (mm) 873 1215 2123% Percipitation As Snow 6 9 7Precipitation Of Driest Month (mm) 18 28 35Warmest Month Mean Temperature (°C) 16.8 17.4 17.4Growing Degree Days > °C 3550 3459 3360285.5 Forest Cover and Stand History—Past and PresentThe study area is under an extensive cover of predominantly naturally regenerated,second growth, even-aged, Douglas-fir forests. These forests regenerated largely as aresult of the forest industry which began at chemainus, B.C. in 1862. Expansion of theindustry, particularly up and down the Vancouver Island coastline, occurred because ofthe 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 extensivelyfrom 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 NanaimoLowlands and into the Alberni Basin, is seen in the decreasing average PSP age acrossthe biogeoclimatic subzone variants; CDFmm, CWHxm1, and CWHxm2, have an averageage of 76, 70 and 59 years, respectively.These second growth forests are generally pure Douglas-fir ( > 80% speciescomposition 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 (Quercusganyana Dougl.), depending on the ecological and historical conditions affecting standdevelopment. 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 ofpredominantly old-growth Douglas-fir forests containing sizeable components of westernred cedar and likely some western hemlock, depending on ecological site conditions and29Phellinus root rot history. Stand origins are difficult to ascertain, but it appears from thePSP records that some stands regenerated after logging (L), others after logging andslashburning--prescribed or wildfire (LB), while a third group regenerated after wildfire withno known logging history (B). The study-wide ratio of these stand origins is 13:30:96L:B:LB, (Table 2).TABLE 2^STAND ORIGINS BY SUBZONE VARIANTOrigin Subzone Variant TotalCDFmm CWHxm1 CWHxm2Logged (L)Burned (B)Logged & Burned (LB)2919517386439133096Total 30 60 49 139Note:^The Burned-origin has significantly greater incidence (p=.054) in the CDFmm andCWHxm1, and lower in the CWHxm2 than expected as estimated from a log-linearmodel; likelihood ratio chi-square statistic.306.0 METHODS6.1 General ApproachThis study was observational in nature, and was aimed at collecting data anddetermining relationships between Phellinus root rot incidence, severity and intensity andthe following datasets: growth and yield, ecological characteristics, stand history, and oldgrowth and second growth stand attributes within the coastal Douglas-fir ecosystems ofB.C.6.2 Sampling DesignThis study was focused on a set of 215 PSP's located on southeastern VancouverIsland, within the CDFmm, CWHxm1 and CWHxm2 biogeoclimatic subzone variants. Withthe exception of 11 PSP's near Cowichan Lake, the core set of 204 PSP's are theproperty of MacMillan Bloedel Limited. The PSP's are located on either Managed ForestUnit 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 Lakeare 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, rockoutcrops, windthrow conditions and insect and disease damage (Iles 1987). In the past,31a 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, in30 to 40 yr old stands. Both the date, and stand age at PSP establishment make it likelythat some PSP's were located on infected sites. Root rots were not widely known orunderstood at that time, and the first major wave of root rot symptoms and mortalitywould have just begun in stands of those ages. The small area of the PSP's, generally0.04ha, makes it difficult to assess root rot dynamics (not a study objective per se), butdue to plot locating procedures, any root rot incidence in the plots is most likely due toincursion from nearby root rot centers rather than to PSP establishment in infectioncenters.A vast amount of soils data had been collected in the past decade in many of thePSP's, which could aid in ecological correlation and classification work, saving significantsoil sampling and analytical costs (Kumi 1987, and Beese 1987). Both owners of thePSP's knew that an undetermined but sizeable number of Douglas-fir PSP's were infectedwith Phellinus root rot, and that there was an opportunity cost to not determining thedamage effects due to the disease. It was felt the value of 30 to 40 years of connectedplot growth data on a fairly wide range of sites, in over 200 PSP's, should override anyconcern over using PSP's located for different objectives. Intensive stand-parameter-diseased root rot sampling as proposed, in such close proximity to the PSP's would alsoprovide for the best possible linkage to growth and yield, and perhaps to the forestinventory, if the study results could be corroborated with large-scale root disease surveys32such as that reported by Beale (1987).All unlogged, pure Douglas-fir (>80% by volume at PSP establishment), naturallyregenerated (5% turned out to be planted), non-silviculturally treated PSP's in 30 to 120year old stands were selected for sampling from MacMillan Bloedel's growth and yielddatabase. The PSP's selected from Canadian Pacific Forest Products were selectedsimilarly, but focused solely on very high site qualities only. This latter selection was anattempt to fill a gap in site association coverage that was recognized after field samplingand preliminary ecological analyses were done. Random selection of PSP's would havebeen considered if some level of site classification were known. PSP selection would thenhave been made equally or weighted proportionally to the representation of PSP's acrossthe distribution of site associations/series or types, thereby reducing sampling costs.Since the biogeoclimatic classification was not available, all 215 PSP's were visited duringthe study.The sampling design and procedures for all datasets were centered on a 1 hasample of the contiguous ecosystem identified at each selected PSP location. Acontiguous ecosystem was first defined at the site type level (Banner et al. 1990), but wasfound too impractical given significant soils variability. A more reliable and easierdefinition for the 1 ha sample survey was set at the site series level of the BEC system.The generalized sampling procedures were:331. Ecological characterization to the site series level was done in, or as close tothe selected PSP as possible. This included description of vegetation,physiography, soils and forest floor. Aggregation of ecologically similar PSP'swithin 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 asystematic, 20 m-grid interval (25 plots) within the contiguous ecosystem asidentified at the PSP, with the PSP located centrally where the PSP locationand ecotone allowed, (Fig. 2).3. At each of the 25 plot centres, root rot incidence was sampled using; (i) aland-area-diseased estimate based on 0.005 ha fixed-radius plots and, (ii) astand-parameter-diseased estimate using variable-radius plots, (Fig. 2).4. At each of the 25 plot centres, root rot severity was sampled using the stand-parameter-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 rotdisturbances, (Fig. 2).5.^At each of the 25 plot centres, old growth stand conditions (stems/ha andspecies composition) were estimated from 0.01 ha fixed-radius plots.346. 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 SamplePlot; RRIN =1= Phellinus Root Rot Centre0^Healthy Fixed-Radius and Variable-Radius Plot; DSPT =0, DSWP =00^Healthy Fixed-Radius and InfectedVariable-Radius Plot; DSPT=O, DSWP =10^Infected Fixed-Radius Plot and HealthyVariable-Radius Plot; DSPT = 1, DSWP =00^Infected Fixed-Radius Plot and InfectedVariable-Radius Plot; DSPT = 1, DSWP =1.1- -4^ Example Sampling TransectFigure 2^Sample plot layout centered about a permanent sample plot within a contiguous, homogenous ecosystem as defined at the siteassociation level. Under optimal conditions a 5 by 5 sample plot grid (25 point) layout was located. Example transects illustrateall possible disease incidence conditions; RRIN and DSPT/DSWP (0) = Healthy, (1) = Infected.366.3 Measurement Of Root Rot Incidence, Severity andCalculation of IntensityIncidence of Phellinus root rot was sampled for, estimated and expressed in threeways; 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 wasused at the whole-study level as a measure of incidence, (Fig. 2). ThePSP buffer extending 10 m, was assessed similarly, (variable name isRRBF).%BAR Sample Surveys--Phellinus Incidence(2) Land-Area-Diseased Expression. The presence/absence of Phellinusroot rot was assessed in each of 25 fixed-radius 0.005 ha plots. A plotwas defined "infected" when the incidence of P. weirii was confirmedin the second growth stand component. The proportion of infectedplots was termed incidence (variable name is DSPT).37(3)^Stand-Parameter-Diseased Expression. The presence/absence ofPhellinus root rot was assessed in each of 25 variable-radius plots. Aplot was defined "infected" when one or more Phellinus "infected" trees(living or dead) were counted "in", (Figs. 2; and 4 see pg. 41). Theproportion of plots infected with P. weirii was termed incidence(variable name is DSWP). Note, an incidence measure was made foreach of the two separate diameter limit sweeps. Incidence wasestimated simultaneously in the prism sweep and tally procedure fordamage severity, discussed below.Damage severity of Phellinus root rot was sampled for, estimated and expressed inone 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 cmdiameter limits. The >12.0 cm diameter limit was only used in stands where7 to 11 trees could not be selected at the 17.5 cm limit (e.g., in young or poorquality stands). All "in" trees were tallied, except Phellinus infected trees, thusthe tallies were always healthy trees only that were distinguished by Phellinusincidence (DSWP) notations and diameter limit classes (Fig. 2).38Two diameter limits were used to sample ingrowth due to Phellinusdisturbance and to impose an artificial "age class" structure; the latterassumes that diameter is strongly related to age. This is borne out bythe fact that tree counts and basal areas are generally inversely relatedto the diameter limit size. Thus, the larger and smaller diameter limitsare analogous to an "older" and "younger" age class respectively. If alltrees are truly even-aged then the size differences attributed to age aremore likely due to sampling of dominant/codominant versusintermediate/suppressed crown class trees. Observations by forestpathologists consistently indicate the phenomena of new ingrowth intoroot rot centers that implies younger tree ages. Aging of ingrowth wasnot conducted in this study. The diameter class/age class stratificationwould enable a view into stand dynamics and variations in productivityresponse to disease condition.Calculation of Phellinus damage severity parameters (and subsequent%BAR intensities) required one further post-sampling stratification toseparate out the susceptible species (Douglas-fir, grand fir and westernhemlock) from the less-to-non-susceptible species. This enabled acomparison of the "worst case" root rot damage scenario with lesserdamage conditions. Following the calculation procedures outlined in6.3.1 and illustrated in Figures 3 and 4, the severity parameter for agiven diameter limit class and species susceptibility class combination39is determined for a sample survey (25 plots/ha), by calculating thebasal area reduction of Phellinus infected prism sweeps relative to thebasal area of healthy prism sweeps and expressed as a proportion.The severity parameter names are shown in Table 3, and severitysubcomponent variables in Table 5.Note, prior to this study several pilot %BAR surveys were tested andcompared against operational root rot surveys in the Port AlberniForest District. Field and calculation methods were revised followingthe pilot sampling.40O 80 12O 97 *0O 3506O 4010OO 2^,-, 1L..) L.)11•LEGEND:0 = Susceptible > Diameter Limit - Healthy^■ = Non-Susceptible > Diameter Limit - Heathy0 = Susceptible < Diameter Umit - Healthy^^ = Non-Susceptible < Diameter Umit - Healthy* Plot Centre'IN' TREE COUNTS BY %BASAL AREA REDUCTION PARAMETER4.0 cm Tree No.'s ^12.0/17.5 cm Tree No.'sNSBAR (1 through 12) E = 12 BARNS (1,3,4,5,6,8,9,10,11,12) E = 9SBAR (1,2,3,5,6,7,8,9,10,12) E = 10^BARS (1,3,5,6,8,9,10,12) E = 8Figure 3^Healthy variable-radius plot condition. Root rot incidence is null or DSWP=0.41140h 1 1or 10ci 150 17^0 18O 9E 80174 0 5010 6016* 01 1^ 2• 30 12LEGEND:0 = Susceptible > Diameter Umit - Healthy^■ = Non-Susceptible > Diameter Limit - Healthy0 = Susceptible < Diameter Limit - Healthy^^ = Non-Susceptible < Diameter Umit - Healthy* Plot CenterIN' TREE COUNTS BY %BASAL AREA REDUCTION PARAMETER4.0 cm Tree No.'s^ z 12.0/17.5 cm Tree No.'sNSBAR (2,3,5,6,9,11-18) E = 11 BARNS (3,5,6,8,9,11,16) E = 7SBAR (5,6,9,15,16,18) E = 6^ BARS (5,6,9,16) E = 4Figure 4^Infected variable-radius plot condition. Root rot incidence is positive or DSWP= 1. Notevisibly infected trees ( I ) 1, 4, 7, 10 and 13 are not counted as "in" trees in the samplingdesign described. Also, Tree 12 is considered "out".42TABLE 3^MATRIX OF DAMAGE SEVERITY VARIABLES ANDCONSEQUENT %BAR DAMAGE INTENSITY PARAMETERS (in Parentheses)Species Class Diameter LimitNon-susceptible &Susceptible speciesSusceptible species4.0 cm z 12.0/17.5 cmNSSEV (NSBAR)SSEV (SBAR)SEVNS (BARNS)SEVS (BARS)Non-susceptible speciesSusceptible species==western red cedar, white pine, shore pine, Sitka spruce, redalder, birch, cherry, flowering dogwood, cottonwood, arbutusand Garry oak.Douglas-fir, grand fir, and western hemlock.6.3.1^Calculation Methods For %BAR Percent Basal Area Reduction VariablesThe generic %BAR calculation follows:Intensity = (( Severity ) x ( Incidence )) x 100The %BAR calculation for SBAR, (Susceptible species only,^cm):% SBAR = ( SSEV X DSWP) X 100%SBAR - ( ^- BA',X (proportion DSWP) X 100BA hBAh - is the mean tree count multiplied by the samplestand Basal Area Factor (BAF) of the healthy/noninfected point sample sweeps; i.e., with noinfected "in" trees. For example, from Fig. 3 theSBAR tally is 10 trees, multiplied by BAF=8, equals 80 m 2 ;BA/ 1 - is the mean tree count multiplied by the sample standBasal Area Factor (BAF) of the infected point sample sweeps;i.e., with one or more infected "in" trees, and where "in"E.g.,trees are not counted in the tally. For example, from Fig. 4the 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 m2 ;DSWP - is the proportion of the point sample sweeps havingvisibly 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 outof 2, or 0.50;1 - Variables are severity subcomponents and are respectivelySAW and SALT in this example (See Table 5 pg. 46).SBAR = (  80m2 - 48m2  ) X 0.50 X 10080m 2SBAR = 20% Basal Area Reduction43446.3.2^Assumptions of the Percent Basal Area Reduction (%BAR) Sampling Method6.3.2.1^Expected Relationships For the %BAR Damage Intensity ParameterThe calculated %BAR damage intensity parameters shown in Table 4, illustrate theexpected relationships between the parameters. Parameters that account for non-susceptible plus susceptible species (NSBAR, BARNS), would in most cases have higherstem counts and basal areas as compared to variables that only account for susceptiblespecies (SBAR, BARS), since the former account for greater, near total tree speciesdiversity. The result is that NSBAR & BARNS reflect lower damage intensity as comparedto 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 mayreflect lower percent damage than the BARS & BARNS parameters.Sampling outside of the conditions found in this study (points (a)-(h) in Table 4), maytend to equilibrate the estimated relationships between %BAR parameter estimates, andmay even cause a reversal of the expected relationship.45TABLE 4^EXPECTED RELATIONSHIPS BETWEEN%BAR DAMAGE INTENSITY PARAMETERSNSBAR^s^s^sSBAR^s^sBARSBARNSSeveral very important assumptions are of concern in comparing Basal Area Reductionestimation methods. Each relationship above, is dependent upon the following generalizedsample stand conditions:a) stand species composition, (Douglas-fir > 70% by volume);b) site and ecological conditions, (BEC units; e.g., CDFmm, CWHxm)c) primarily naturally regenerated, unmanaged/nonspaced;d) mid-seral stand age, (approx. 40 to 80 yr);e) moderate stand density (approx. 800-3 000 stems/ha) at 10 yr;f) variable plot sampling diameter limits (A.0 cm and 217.5 cm);g) intensity of root rot and,h) diameter class ingrowth ratesNOTE: Items in (parentheses) indicate the general conditions found in this study.^Testing The Homogeneity Of Species Compositions And The DecompositionOf The %BAR Severity ParametersAn assumption in stand selection was that species composition did not varysignificantly between the ecological units, so that there was enough similarity insusceptible species composition to detect responses to Phellinus root rot with near-equalprobability between BEC units using the %BAR damage intensity parameter estimates.In order to test the hypothesis of species composition similarity between subzonevariants, site associations and site series, the %BAR severity parameters weredecomposed to provide all possible combinations of species susceptiblity, diameter classand disease incidence status (Table 5). For testing, a new, third diameter class,46intermediate to the upper and lower classes was created through subtraction of the upperfrom the lower diameter classes. This process also provided the required %BARcomponents to assess species shifts/dynamics in response to Phellinus root rot.TABLE 5^DECOMPOSITION OF %BAR DAMAGE SEVERITYVARIABLES TO SUBCOMPONENTS24.0 cm Diameter Limit & Healthy SweepsSALH = Susceptible spp.NALH = Non-susceptible spp.NSALH = Non-susceptible and Susceptible spp.24.0 cm Diameter Limit & Infected SweepsSALI = 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 SweepsSGRH = Susceptible spp.NGRH = Non-susceptible spp.12.0/17.5 cm Diameter Umit & Infected SweepsSGRI = Susceptible spp.NGRI = Non-susceptible spp.NSGRI = Non-susceptible and Susceptible spp.Healthy condition, susceptible (SALH) and non-susceptible (NALH) speciescompositions, .4.0 cm, are shown to be very stable about the all-samples means of91.52% and 8.48%, respectively, when plotted across site associations (Figs. 65-66, pg.132). The variability of susceptible and non-susceptible species across site associations47is minimal and not significant (p > .05); also see Table 22, p.119.6.4 Ecological AssessmentsEcological assessments were done to characterize regional climate (geographiclocation), physiography, vegetation, soils, forest floor, tree and stand measurements. Theintegration of these climatic and site parameters enables classification of sample standsto levels of most interest in the Vegetation, Zonal (Climatic) and Site Classifications of theBEC system (Pojar et al. 1987).In general, the procedure for ecological assessments at each PSP was to confirmthe geographic location on the forest cover maps (1:20 000 scale) and then determinethe indicated subzone variant (regional climate) from the biogeoclimatic unit map (1:500000 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 modificationslean towards the Site Diagnosis procedures described by Klinka et al. (1984) and Greenet al. (1984).6.4.1^Site: PhysiographySite physiography was described for the general site conditions identified at the PSP,which included elevation, slope position (crest, upper, middle, lower, toe or level), slope48shape (straight, concave, convex or irregular/hummocky), percent slope, and aspect.6.4.2 Site: VegetationIn a 0.04 ha releve plot, the percent cover of vegetation in 3 tree layers, 2 shrublayers, 1 herb layer and 1 moss layer was described and classed on the Domin-Krajina6-class cover class scale, after Walmsley et al. (1980). The relevó plot usually conformedto the PSP dimensions, if not, then the most representative location near the PSP wasdescribed.6.4.3 Site: SoilsSoils were described on the basis of two soil pits dug outside the PSP atrepresentative locations (to avoid damage to the PSP), following the Canadian System ofSoil Classification (CSSC 1978) and Walmsley et al. (1980). Physical soil parameters ofparticular 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/cm3)•Two mineral soil bulk density samples of about 1 000 cm 3 were taken for the 10 to30 cm depth, at each of the soil pits per PSP. Volume was measured by replacement49with fine, dry playsand (after Klinka et al. 1981). Two soil chemistry samples were alsotaken from the 10 to 30 cm mineral soil depth and composited at the MacMillan Bloedel,Nanaimo environmental laboratory.^6.4.4^Site: Forest FloorForest 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 fourrandomly located points within the PSP. At each point, two 225 cm 2 square blocks offorest floor were cut out to mineral soil depth. The first block at each point was used forforest floor chemistry, and the second block was measured for depth. Forest floor depthwas based on the mean of four representative depths taken from each side. Samplesfor each of bulk density and chemistry were composited at the MacMillan Bloedel,Nanaimo environmental laboratory.6.4.5^Site: Old Growth Stand HistoryStand history was estimated from the PSP database, confirmed by ecologicalassessment, 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 surveyprocedures involved estimating the old growth species composition and stems/ha byspecies. Only Douglas-fir and western red cedar were easily identifiable from stumps 3050to 120 years old. Western hemlock was extremely difficult to identify so long after harvestand consequently very few hemlock were tallied. Systematically located 0.01 ha (5.64 mfixed-radius) stump survey plots were established on the same 25-plot sampling grid asthe %BAR survey. Old Growth Stand History (Stand Density & Species Composition) EstimatesUsing Fixed-Radius PlotsStems/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 100No.PlotsSPHCW - (E CW)  X 100No.PlotsWhere:^FD is Douglas-fir, HW is western hemlock,CW is western red cedar, No.PLOTS is the samplesize and, 100 is the sample size expansion factorto 1 ha.The old-growth species composition was calculated as follows:H^ E WCOMPFH =^COMPCW -^C E FHE + F E CW E FH + E CWWhere:^COMPFH and COMPCW are species compositionsfor Douglas-fir/western hemlock, andwestern red cedar, respectively.516.5 Site: Stand MensurationTree and stand measurement data were obtained exclusively from the PSP summarydata files provided by MacMillan Bloedel Limited, Woodlands Services, in Nanaimo, BC,and Canadian Pacific Forest Products Limited, Saanichton Forestry Centre. No additionalmeasurements were undertaken in PSP's or the surrounding sampling area. Note, thatsince the growth and yield remeasurement cycle is five years for both firms, the data isnot all representative of the 1987 field season in which the rest of the study occurred.6.6 Laboratory Analyses And Data PreparationAs part of previous ecological studies conducted by MacMillan Bloedel Limited onthe PSP database, many of the mineral soil and forest floor physical and chemicalproperty data required for this study were available (Kumi 1987, and Beese 1987). Thesoils data, stored on the BC Ministry of Environments' Soils Information System, wassummarised for the 10 to 30 cm mineral soil depth and for the F and H layers of theforest floor.Laboratory analyses for various mineral soil and forest floor physical and chemicalproperties collected as part of this study were conducted by the Environmental Lab. atthe MacMillan Bloedel Limited, Woodlands Service's office in Nanaimo, BC.526.6.1^Mineral Soil And Forest Floor Physical PropertiesBulk density for mineral soil and forest floor were of interest in order to estimatenutrient levels on a per hectare basis, and to provide an estimate of root penetrability ofmineral soils. Soil porosity was calculated from the bulk density to provide an estimateof 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 thelaboratory. Samples were paper bagged and oven dried for 24 hr at 105°C. Total dryweights were then recorded. Samples were then separated into the coarse and finefractions (>2 mm, and .2 mm, respectively) by passing the materials through a 2 mmsieve and the fractional weights' recorded. Since the organic content of the Brunisols andPodzols common to the study area are known to be low (CSSC 1978), only obviousorganic 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 werecalculated prior to bulk density calculations using the standard 2.65 g/cm 3 for estimatingthe volume of coarse fragments in the samples.53MSBDT (g / cm') = MSTOT / MSVOLtotMSBDF (g / cm 2 ) = MSFIN / MSVOLfinWhere: MSBDT is the total sample mineral soilbulk 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; MSBDFis the fine fraction mineral soil bulk density;MSFIN is the dry weight of the fine fractionsample; and MSVOLfin is the estimated volumeof the fine fraction.Forest floor bulk density was calculated similarly, as follows:FFBD (g / cm 3 ) = FFTOT / FFVOLWhere: FFBD is the forest floor, fine fractionbulk density; FFTOT is the four-samplecomposite dry weight of the forest floor,fine fraction; FFVOL is the four samplecomposite 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 fractionporosity; MSBDF is the mineral soil finefraction bulk density; particle densityis taken to be 2.65 g/cm3 unless local dataindicates other lower values (Armson 1977).546.6.2^Mineral Soil And Forest Floor Chemical PropertiesMineral soil and forest floor chemical properties were composited and air dried priorto fine fraction mm) separation. Mineral soil samples were passed through a 2 mmsieve to obtain the fine fraction. Forest floor samples were coarse ground then fineground, and sieved through a 2 mm screen to obtain the fine fraction. Mineral soil andforest floor pH were determined with a pH potentiometer probe in a 1:1, soil (or forestfloor):water suspension (Peech 1965). Total carbon was determined using the Walkley-Black wet oxidation method (Walkley 1947). Total nitrogen (N) was determinedcolorimetrically by Technicon Autoanalyzer II after the 60-mesh samples were digestedin a semimicro Kjeldahl digest (Lavkulich 1981). Mineralizable nitrogen (N) wasdetermined colorimetrically (by Technicon Autoanalyzer II) from a 1 N KCI extractant,following a 2-week (30°C) anaerobic incubation (Waring-Bremner method) (from Keeney1982). Exchangeable cations (Ca, Mg and K) were determined by atomic absorptionspectrophotometry preceded by the ammonium acetate (pH 7) extraction method (Black1965).6.6.3^Conversion Of Chemical Property Concentrations To Kg/haConversion of nutrient concentrations in the <2 mm fraction to kg/ha was done toprovide a better estimate of chemical nutrient availability to ecosystems. (Also, seeAppendix A). Kilogram per hectare estimates are sensitive to bulk density and coarse-fragment content estimates. Also, the measures provide a reference with which others55may wish to make comparisons.6.6.4^PSP Stand Attribute Data PreparationSome of the PSP summary data required manipulation to be meaningful for statisticalanalyses, particularly regarding species composition and shifts in species compositionover time, and averaging procedures for PSP's. The following variables were summarisedand/or calculated on the basis of first and last PSP measurement:Total age in 1987Breast-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 correctionvalues contained in the PSP file. This was done to aid the interpretation ofroot rot spread and behaviour, because it is perceived to be simpler tocomprehend on a volume--total age versus volume--breast height age basis.56(2) Species compositions were classified into four laminated root rot susceptibilityclasses after Wallis (1976), Hadfield (1985) and Beale (1989a). Percentspecies compositions were recalculated and expressed as proportions for firstand last measurements. Species Composition Classes used in this studywere: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 speciesclasses (Intermediate and Resistant) at the First and Last measurements. Thenew variables (FTIR, LTIR and TIRD) aid evaluation of species shifts in relationto presence or absence of Phellinus root rot in PSP's. FTIR and LTIR are thesum of the Intermediate and Resistant species classes compositions, first andlast 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 Susceptiblespecies 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 compositionswere not included due to little change over time and an unbalanced coverageacross ecological units.57(4) Stems/ha, basal area/ha, volume/ha and Curtis' relative density were back-estimated to reference age 10 yr using a Stand Projection model developedby Nick Smith for MacMillan Bloedel Limited, Woodlands Services, Nanaimo(Smith 1990). Given the variable conditions at PSP establishment, a referenceage was required for comparison and sorting of similar PSP's for statisticalanalyses. Age 10 was selected to more closely approximate stand conditionsat establishment. Age 10 seemed to provide realistic back-estimations,whereas at age zero (0) the results became much more variable and lessrealistic. Also, the author felt that back-estimation of about 20 to 40 yr wasa 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 weresampled from within the same installation, often resulting in one commonecological description, and one root rot sample survey; thus two or morePSP's for one contiguous, homogenous ecosystem. In terms of datapreparation and summarization, two options were available: (a) use all PSP'sand replicate the ecological/root rot survey and stand history data to matchthe number of PSP's, or (b) use unique ecological/root rot survey/standhistory data and average the appropriate PSP data. The latter option waschosen after consulting MacMillan Bloedel as to the suitability of averagingPSP data (Wilson 1989, and Bloomberg 1990). All stand measurementvariables were averaged to obtain their arithmetic mean. Relative density was58recalculated for the >4.0 cm class as follows:Curtis' Relative Density = STAND-BA / DBHgWhere:^STAND_BA is the recalculated standbasal area (m2), and DBHg is therecalculated 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 differentview of the data and its relationships to other variables. Classification of a diseasevariable, such as BARS, into several meaningful damage intensity classes was done todiscern ecological relationships. Where some independent ecological variables are highlyvariable, classification may improve the ability to search for relationships.In order to determine meaningful damage intensity classes, the appropriate damageresponse estimate parameters had to be selected. Of the four %BAR estimators, onlyBARS provided a damage estimate for the larger diameter, susceptible species (of whichthe stands are primarily comprised). The other %BAR parameters included non-susceptible species and/or trees below the average stand age diameter that may maskthe relationship in question (i.e., because of ingrowth). Operationally, BARS is most likelyto provide the best estimate of what foresters are most often considering during standmanagement or pre-harvest silviculture assessments in the study area---that is, Douglas-fir59trees of average or better volume and value. Furthermore, in trying to visualize variousdamage levels and make conditional damage intensity classifications, the author couldnot 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 basisof personal experience, consultation with other root disease workers (Bloomberg 1990(a),and Reynolds 1990), and graphical review of the data structure which included BARSfrequency distributions and normal probability plots. The classification resulted in twoclass limits separating Severe, Medium and Low classes. The upper limit has a classicgrowth curve shape, perhaps best described by a nonlinear function, while the lower limitis well described by a linear function. Class limits and within-class conditions were alsocorroborated on the basis of recent root rot simulation summaries conducted on theTASS-ROTSIM model (Bloomberg 1990(a)). The class limit functions were used to codeBARS data for statistical analysis. The functions describing the BARS class limits are asfollows:BARS UPPER CLASS LIMIT:Two linear functions mimic the two important phases identifiable in the proposednonlinear 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 100years).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 waseasier 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 agescatterplot, illustrated in Figure 5. BARS variability across the BARS-DIC limits isillustrated by boxplot (Fig. 6). For an explanation of boxplots, and how to read them seeTukey (1977), McGill et al. (1987), Titus (1987) and Wilkinson (1988).61Figure 5 Percent Basal Area Reduction(BARS) - total age scatterplotclassified by the BARS-DamageIntensity Classes; (L) Low, (M)Medium and (S) Severe.Figure 6 Boxplots of BARS classified by theBARS-Damage Intensity Classes(BARS-DIC's).6.6.6^Ecological ClassificationEcological classification followed the methods for the system of biogeoclimaticecosystem classification (BEC) outlined by Pojar et a/. (1987). Three ecological concepts:(ecological equivalence, polyclimax condition and zonal ecosystem), are the basicprinciples of the BEC system that effect the resultant classification and interpretation ofstudy plots. Ecological classification methods involve classification of climate, vegetationand site, with a final integrative stage to organize similar ecosystems at local or regionallevels into a natural taxonomic hierarchy (BEC units). These methods, as used in thisstudy, are described separately below.62^^Climate ClassificationPermanent sample plot database designations for subzone variant were checkedagainst the biogeoclimatic subzone map. Final adjustments to PSP subzone variantclassifications were done following vegetation analysis and the formulation of plantassociations and subassociations using diagnostic combinations of plant species. Theresultant number of homogeneous sample units per subzone variant was 30, 60 and 49for CDFmm, CWHxm1 and CWHxm2, respectively.^Vegetation ClassificationVegetation data (lifeform, species, plant layer, and species significance) were errorchecked and recoded from the 6-class cover scale to the 10-class Domin-Krajina scalerequired for quantitative procedures (Klinka et al. 1989).Vegetation classification was based on analysis of species presence (frequency) andsignificance (percent cover) to formulate floristically similar units in a natural taxonomicheirarchy. Diagnostic combinations of species differentiate floristic communities into plantassociations--the basic unit of vegetation classification (Pojar et a/. 1987). The procedureis based on the Braun-Blanquet tabular method (Westhoff and van der Maarel 1980), areciprocal averaging ordination technique (Gauch 1977), clarification of the floristicstructure (exclusion of low <3% incidence species), application of professionaljudgement, principal components analysis, and formulation of diagnostic combinations63of 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 andenvironmental data tabulation and summary computer program (Emmanuel 1989), lodgedon the UBC computer. Principal components analyses were used on diagnostic speciesto detect relationships between environmental characteristics and vegetation units givingindication of units' relative moisture and nutrient condition. Spectral analysis also aidedvegetation classification by relating plant species proportional distributions stratified by soilnutrient and soil moisture indicator values to conditionally named vegetation units (Klinkaand Krajina 1986).Naming of vegetation units for this study, followed the formulation of the DiagnosticCombination 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)), (Table7).A complete listing of plant species found in the study is in Appendix C. Thevegetation summary table used in the formative stages of discerning floristic uniformityis in Appendix D.TABLE 6 VEGETATION ENVIRONMENT ANALYSISVegetation UnitNumber of PlotsVegetation Units and Species1^2^3^4^5$PA^$PG^$TM^$PAT^$PAADiagnostic^25^47^27^38^8Value l^Presence class2 and mean species significance 36$PAP8Pseudotsuga-Mahonia p.o. & p.all.Holodiscus discolor (d) IV^4^III^3 I^+ I^+Rosa gymnocarpa (d,c) V 3^V 3 II^1 II^2 II^11 $Pseudotsuga-Arbutus p.a.Arbutus menziesii (d) III^3 I^+2 $Pseudotsuga-Gaultheria p.a.Achlys triphylla (d)^II^1 IV 2 V 3 IV 4 V 4 IV 6Hylocomium splendens (d,cd) II^4 V 5 V 5 V 5 IV 3 V 6Linnaea borealis (d)^I^+ III^3 IV 4 IV 4 II^1 III^3Tsuga heterophylla (d) II^2 IV 3 V 5 V 5 V 4 V 53 Tsuga-Mahonia p.o. & p.all., $Tsuga-Mahonia p.a.Thuja-Tiarella p.o., Thuja-Achlys p.all., Pseudotsuga-Achlys p.a.Plagiomnium insignePolystichum munitumTiarella trifoliata(d)(dd,cd)(d)HI^1I^+III^3 IV 3I^+II^+^II^+^IV^3V 5^V 7^V 6III 2 V 2^V 34 $Pseudotsuga-Achlys-Typic p.sa.5 $Pseudotsuga-Achlys-Alnus p.sa.Alnus rubra (d,cd) II^1 II^2 II^1 II^3 V 5 I^1Bromus vulgaris (d) II^1 III^1 II^+ II^1 IV 2 II^1Rubus spectabilis^ (d)6 $Pseudotsuga-Achlys-Plagiomnium p.sa.I^+ I^+ IV 3 I 2Plagiomnium insigne (d) II^+^II^+ IV 3Rhizomnium glabrescens (d,c) I^+ I^+ I^+ V 4Trientalis latifolia (d) II^1 II^1 II^2 II^1 II^+ IV 21 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 =8 = 60.0 (50.1 - 70.0), 9 = 85.0 (70.1 - 100).(33.1 -50.0),26.5 (20.1 - 33.0), 7 = 41.56465TABLE 7^HIERARCHICAL SYNOPSIS OF VEGETATION UNITSDISTINGUISHED IN THE STUDY AREAVEGETATION UNITPlant OrderPlant AlliancePlant AssociationPlant Sub-associationPseudotsuga-MahoniaPseudotsuga-MahoniaPseudotsuga-ArbutusPseudotsuga-GaultheriaTsuga-MahoniaTsuga-MahoniaThuja-TiarellaThuja-AchlysPseudotsuga-AchlysPseudotsuga-Achlys-TypicPseudotsuga-Achlys-AlnusPseudotsuga-Achlys-PlagiomniumEach vegetation unit is characterized by a typical diagnostic combination of species after PojarThe six lowest level vegetation classification units (plant associations and sub-associations), (Tables 6 and 7), are thought to be arranged mainly according to anincreasing soil moisture gradient. This assumption was tested quantitatively via principalcomponents analysis (PCA) of diagnostic species representative of the six units. The firsttwo 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 thencorrelated to individual species (Table 8). To explain the correlations, 15 species withrelatively strong positive and negative correlations were reviewed for relationships toenvironmental characteristics of each vegetation unit. Ordination of the PCA scores forthe six vegetation units on the first two axes illustrates the units' discreteness represented66by the 95% confidence ellipses (Fig. 7). Although there is no separation of ellipses thetrend along the moisture gradient illustrates the ecotonal relationship of these vegetationunits. For the first component, high positive correlation coefficients for Polystichummunitum (.53), Achlys triphylla (.70), and Tsuga heterophylla (.60) (species indicative ofslightly to fresh soil moisture conditions), and high negative correlations of Holodiscusdiscolor (-.54) and Arbutus menziesii (-.40) (species indicative of very dry to moderatelydry soil moisture conditions), suggest that it adequately represents the soil moisturegradient. The second component is likely to represent the soil nutrient gradient asindicated by high positive correlations of Linnaea borealis (.41), Rosa gymnocarpa (.47),Hylocomium splendens (.65) (species indicative of soil nutrient poor to medium), and highnegative correlation of Polystichum munitum (-.49) and, Alnus rubra (-.38) (speciesindicative of soil nutrient medium to rich). The assumption that the vegetation units arearranged along the soil moisture and nutrient gradients is further supported by spectralanalysis (Klinka and Krajina 1986, pg.104) of plant species compared to the vegetationunits, seen by increasing proportions of drier site indicator species on drier vegetationunits and vice versa on fresher vegetation units (see Appendix E).67TABLE 8 DIAGNOSTIC SPECIES CORRELATIONS TOPCA AXIS SCORESDiagnostic Species Correlation1Axis 1 Axis 2HOLODIS4 Holodiscus discolor -.5379 .2695ROSAGYM4 Rosa gymnocarpa -.2533 .4721ARBUMEN1 Arbutus menziesii -.4028 .0025ACHLTRI6 Achlys triphylla .7001 .1716HYLOSPL7 Hylocomium splendens .5497 .6512LINNBOR6 Linnaea borealis .6127 .4130TSUGHET1 Tsuga heterophylla .6004 -.4685POLYMUN6 Polystichum munitum .5315 -.4919TIARTRI6 Tiarella trifoliata .5187 -.0758ALNURUB1 Alnus rubra .1440 -.3805BROMVUL6 Bromus vulgaris .1939 .0833RUBUSPE4 Rubus spectabilis .2052 -.3016PLAGINS7 Plagiomnium insigne .3175 -.0966RHIZGLA7 Rhizomnium glabrescens .2795 -.0041TRIELAT6 Trientalis latifolia .2899 .40811^Correlations were significant at ( a^=.01)95% CONFIDENCE ELLIPSES0InI IPA2 IPG3 SIM4 SPAT0 5 FAA^I  6 PAP- 10.0^-51.0^0.0^5.0^10.0^15.0^20.0FIRST PC AXIS SCORESO2OU°LJ oul68Figure 7^95% Confidence Ellipses of the PCA ordination scores of six vegetation units identified inthe study; (1) Pseudotsuga-Arbutus p.a., (2) Pseudotsuga-Gaultheria p.a., (3) Tsuga-Mahonia p.a., (4) Pseudotsuga-Achlys-Typic p.sa., (5) Pseudotusga-Achlys-Alnus p.sa.,and (6) Pseudotsuga-Achlys-Plagiomnium p.sa.696.6.6.3^Site ClassificationThe site classification procedures organize ecosystems into groups similar in sitequality, site productivity and potential climax vegetation regardless of present vegetation(Pojar et al. 1987, Banner et al. 1990). Site associations, the basic unit of theclassification, 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 arange of regional climates represented by biogeoclimatic subzones (Pojar et al. 1987).Climatically uniform groups at the biogeoclimatic subzone or variant level within a siteassociation represent site series. Critical soil and/or topographic properties within anassociation or series can be used to group ecosystems to the site type level. Site typelevel differentiating criteria are presently not finalized and are dependent uponmanagement or research objectives.Using the site classification procedure, I identified nine site associations and 14 siteseries (Table 9), (after Banner et al. 1990). Classification to the site type level wasenvisaged but the heterogeneity of sites precluded the option, particulary whenconsidering 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 consideringthe generally homogenous edaphic and topographic features of the study area as awhole. Environmental properties summaries were produced (Emmanuel 1989), to assistin classifying ecosystems to site association and series, but are not presented in thisthesis. The relationships of most continuous ecological, site, soil and stand variables to70the site associations identified in this study are seen in Appendix F.TABLE 9^HIERARCHICAL SYNOPSIS OF THE SITE UNITSDISTINGUISHED IN THIS STUDYSITE UNITSite AssociationSite SeriesSITE UNITSite AssociationSite SeriesFdPI - ArbutusCDFmm - FdPI - ArbutusFd - Salal *CDFmm - Fd - SalalFdBg - Oregon grape *CDFmm - FdBg - Oregon grapeFdPI - CladinaCWHxm1 - FdPI - CladinaFdHw - Salal *CWHxm1 - FdHw - SalalCWHxm2 - FdHw - SalalHwFd - Kindbergia *CWHxm1 - HwFd - KindbergiaCWHxm1 - HwFd - KindbergiaFd - SwordfernCWHxm2 - Fd - SwordfernCw - Foamfiower *CWHxm1 - Cw - FoamflowerCWHxm2 - Cw - Foamfiower*^Site Associations with rt6 sample p ots, and retained for further analyses.6.7 Statistical Analysis MethodsStatistical analyses were conducted using the SYSTAT and SYGRAPH PC computerpackage (Wilkinson 1988). The observational nature of this study required extensiveexploratory data analysis (Tukey 1977), which included transformations and graphicalreview 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 adjusted71probabilities) 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 largeecological 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 toconfirm, but the work was done with skilled, well-trained field crews andlaboratory analysts.(b) Homogeneity of variance (homoscedasticity) was evaluated via scatterplots,boxplots and examination of linear model residual plots; homoscedasticitywas met for growth and yield data, but much less so for %BAR root rotparameters and ecological variables.(c) Linearity assumptions between dependent and independent variables weregenerally met, but many ecological and root rot variables/parameters patternswere extremely amorphous and ill-defined (many nonlinear intercorrelationswere expected).(d) Normal distribution of variables was not met for root rot %BAR parameters(incidence and intensity) and second growth species composition survey-derived variables, but was met for the %BAR severity parameter, mostecological and growth and yield variables (checked with normal probability72plots).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 positivelyskewed and leptokurtotic; this was due to few high root rot intensity estimates and manylow to zero value estimates, resulting in non-normal distributions. Normal probability plotsshowed near normality for values greater than about 3% basal area reduction.Transformations to reduce heteroscedasticity and improve linearity such as arcsin squareroot 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 analysesfor ecological, stand history and species dynamics were conducted on untransformedvariables.Since two independent datasets were under study (%BAR sample survey estimatesvs. PSP's), graphical and tabular presentations were done to assess similarities betweenthe two datasets, to aid inference and descriptive model building.Disease incidence relationships were evaluated through tabular analysis ofcategorical variables, aided by log-linear models. Disease intensity relationships andspecies dynamics were evaluated generally through construction of multiple linearregression models--for descriptive purposes.73747.0 RESULTS AND DISCUSSION7.1 Data Structure And General RelationshipsStudy-wide, 153 unique ecological units (244 unique PSP's) were classified andsummarized for all variables. Due to later observed inconsistencies, missing data for keyvariables, 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 themore "significant" findings are reported in this section.7.1.1^Assessment Of The Percent Basal Area Reduction (%BAR) Variable And It'sComponentsOf the four %BAR root rot parameter estimates, only two were generally consideredfor analysis and inference; NSBAR (all-species ?_4.0 cm) and BARS (susceptible species17.5 cm), respectively representing the smallest and largest root rot damage intensityestimates on average (Fig. 8), and Table 10.nsbar sbar^bars barnsPercent Basal Area Reduction Measure75Figure 8 Histogram of the the four percent basalarea reduction parameter estimates all-samples mean and (standard error).Recall, that one of the %BAR sampling method assumptions was the expectedrelationships of the %BAR intensity parameters would rank out as per those tabled inTable 4, p.45. The rankings of intensity parameters in Figure 8 match the expectedrelationships exactly.The principles of the Percent Basal Area Reduction sampling method are illustratedin part in the following descriptive statistics (Table 10). Table 10 is composed of thecomponents of the %BAR equations for the NSBAR and BARS parameters. Most of theparameters are graphed later in this section, but note the variation of all parameters.Students t-tests were conducted on the incidence, severity subcomponents, resultantseverity and %BAR intensity parameters shown in Table 10. The mean estimates of land-based (DSPT) and stand-based (DSWP) incidence were significantly different (p = .000).The mean estimates of the non-susceptible species (NSALH/NSALI) were significantly76different (p = .000), as were the susceptible species (SGRH/SGRI), (p = .000). The severityparameters (NSSEV/SEVS) and the %BAR intensity were also significantly different(p = .005) and .0001, respectively).TABLE 10^ DESCRIPTIVE STATISTICS FOR STUDY-WIDE ESTIMATES OFPHELLINUS ROOT ROT INCIDENCE, SEVERITY, SEVERITY SUB-COMPONENTSAND INTENSITYIncidence Seventy Severity Subcomponents / IntensityLand-AreaDSPTStand-ParameterDSWP?A.0 cmAll SpeciesNSSEV.17.5 cmSusceptibleSEVS4.0 cmAll Spp.HealthyNSALH4.0 cmAll Spp.InfectedNSALI117.5 cmSuscept.HealthySGRH2_17.5 cmSuscept.InfectedSGRI.4.0 cmAll SpeciesNSBAR2_17.5 cmSusceptibleBARSNo. of Cases 139 139 139 139 139 139 139 139 139 139Minimum 0 0 -0.140 -0.490 5.64 1.000 1.250 0.000 -1.900 -9.100Maximum 0.680 0.880 0.847 1.000 14.460 13.000 11.670 9.333 38.400 54.200Range 0.680 0.880 0.987 1.490 8.820 12.000 10.420 9.333 40.300 63.300Mean 0.168 0.263 0.290 0.318 8.958 6.380 7.181 4.882 8.248 9.994Variance 0.027 0.049 0.037 0.053 2.853 4.045 3.154 3.038 85.570 110.759Standard Dev. 0.164 0.221 0.192 0.231 1.689 2.011 1.776 1.743 9.250 10.524Standard Error 0.014 0.019 0.016 0.020 0.143 0.171 0.151 0.148 0.785 0.893Skewness (G1) 0.975 0.641 -0.124 -0.341 0.480 0.728 -0.142 0.278 1.312 1.237Kurtosis (G2) 0.185 -0.566 -0.621 0.485 0.166 1.471 0.292 0.199 1270 1.772Coefficentof Variation 0.976 0.840 0.663 0.726 0.189 0.315 0.247 0.357 1.122 1.053The %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 2).The %BAR severity sub-components are used in the %BAR calculation; Severity = (healthy minus the infected) / (healthy).12.5010.0a7.51115.00a2.5a0.0 NSALH NSALI SGRH SGRISpecies Susceptibility Sc Disease Condition78Perhaps the most important principle of the %BAR sampling method is basal areareduction itself. How often it occurs is simply an estimate of incidence. Basal areareduction is illustrated by comparing mean tree counts of healthy sweeps (e.g., NSALHor SGRH) to infected sweeps (e.g., NSALI or SGRI) respectively (Fig. 9). (Also refer toTable 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 about30% for both all-species and susceptible-only. Recall, that healthy and infected treecounts 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-variabilityencountered in portions of stands infected with Phellinus root rot.Figure 9 Histogram of the mean tree counts byspecies susceptible and disease condition;non-susceptible plus susceptible species,healthy and infected (NSALH and NSALI,respectively), and susceptible speciesonly, healthy and infected (SGRH andSGRI, respectively).797.1.2^Incidence-Severity, Severity-Intensity and Incidence-Intensity RelationshipsThe probability distributions of incidence, severity, and intensity were assessed fornormality with cumulative probability plots and histograms. Land (DSPT) and stand(DSWP) based incidence variables both appear non-normally distributed with manyhealthy, uninfected sample plots, fewer lightly infected, and very few severely infected(Figs. 10 and 11). The severity variables (NSSEV and SEVS) have near normaldistributions (Figs. 10 and 11). Root rot damage intensity parameters (NSBAR andBARS) also appear to be non-normal (Figs. 12, 13, 14 and 15), with high frequencies ofzero to low intensities, and decreasing frequencies of higher intensities.NSSEVI, I-%rSEVS1-n--..DSPT_•.^..^•^.......^.• •^-.^....^.........^•...^...^._•-.^_^•^••.Figure 10 Incidence-Severity relationship ofstand-based incidence (DSWP),on the y-axis, to damage severities(NSSEV) and (SEVS), on the x-axis. Note, the DSWP histogramhas been rotated 90° clockwise.Figure 11 Incidence-Severity relationship ofland-based incidence (DSPT), onthe y-axis, to damage severities(NSSEV) and (SEVS), on the x-axis. Note, the DSWP histogramhas been rotated 90° clockwise.80NSBARn n-,--1-1NSSEV'^•'^/...^%.:^•' ...'"'sr^.s.•r.Figure 12^Severity-Intensity relationship ofall-species damage severity(NSSEV), on the y-axis, todamage intensity (NSBAR), on thex-axis. Note, the NSSEVhistogram has been rotated 90°clockwise.BARSSEVSJ•.^. ..^.^.^.'I: -,.::::: -^•i •Figure 13^Severity-Intensity relationship ofsusceptible species (212.0/17.5 cm) damage severity (SEVS),on the y-axis, to damage intensity(BARS), on the x-axis. Note, theSEVS histogram has been rotated90° clockwise.NSBARTil n,-I-1 nBARSTl-n— ,,DSWP • ..• '^..^.^.^.•. _• •.^.....^.'• - •-.. r.•.4^'•:,^.......^" .^:^...;.."..^■ .^....a.....;. :a 7.a..:.: ..." —I— .C.^• FIII n •Figure 14^Incidence-Intensity relationship ofstand-based incidence (DSWP),on the y-axis, to (NSBAR) and(BARS) damage intensities, on they-axis. Note, the DSWP histogramhas been rotated 90° clockwise.NSBARIII n,-11 ,BARSTI-n-DSPT_1 h--1••.^...^.• •^•^•.^.....^...• 1.1.,.1.......''''"^. ^...^...^.-.-^••••^•.^..^....^-.^-:^. -• .:• . 1 g•.: • .:0.-:Figure 15^Incidence-Intensity relationship ofland-based incidence (DSPT), onthe y-axis, to (NSBAR) and(BARS) damage intensities, on they-axis. Note, the DSPT histogramhas been rotated 90° clockwise.81The incidence-severity variables shown in Figures 10 and 11, are significantly(p=.0001) and positively correlated with each other, but not strongly, with Pearsons rvalues ranging between .323 to .356 (Table 11).TABLE 11 PEARSONS CORRELATION MATRIX OFPHELLINUS ROOT ROT SURVEY SAMPUNGVARIABLES AND PARAMETERSBARS^NSBAR^DSPT^DSWP^NSSEV^SEVS AGE87BARS 1.00NSBAR .874^1.00DSPT .811^.792^1.00DSWP .836^.817^.923^1.00NSSEV .566^.510^.355^.356^1.00SEVS .602^.456^.342^.323^.864^1.00AGE87 .264^.274^.312^.324^.185^.202 1.00AGE87(p.'s) 1.035^.025^.004^.002^.617^.363All Pearsons correlations are signficant at the 99% level of significance;1 Note AGE87 p-values are indicated separately below AGE87 correlations.The severity-intensity relationships (NSSEV:NSBAR and SEVS:BARS) illustrated inFigures 12 and 13, show signifcant (p=.0001) and positive correlations between variablepairs; Pearsons r values were .510 and .602, respectively (Table 11). Severity displaysa 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 (theproportional 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.zOt.O1.—0.5LOW^MEDIUM SEVEREDamage Intensity ClassLOW^MEDIUM SEVEREDamage Intensity Class82Also, 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).Figure 16^Boxplots of damage severity(NSSEV) classified by BARS-Damage Intensity Classes.Figure 17^Boxplots of Damage severity(SEVS) classified by BARS-Damage Intensity Classes.The incidence-intensity relationships (DSPT or DSWSP vs NSBAR or BARS) illustratedin Figures 16-19 , show significant (p = .0001) and positive correlations between variables,with Pearsons r values ranging between .792 to .836 (Table 11). Incidence plottedagainst the BARS-DIC's shows a fairly strong linear relationship, with wide variation in allthree classes, whether land-or stand-based incidence (Figs. 18 and 19). Simple linearregression incidence-intensity models indicate that between 63 and 70 percent of theBARS or NSBAR variability can be described by the disease incidence (Table 12). In bothcases, stand-based incidence provides a better, slightly less variable estimate of diseaseLOW^MEDIUM SEVEREDamage Intensity Class83intensity compared to land-based incidence. This is not surprising, since the calculationfor BARS/NSBAR intensity is partly based on the stand-based incidence.Figure 18^Boxplots of land-based diseaseincidence (DSPT) classified byBARS-Damage Intensity Classes.Figure 19^Boxplots of stand-based diseaseincidence (DSWP) classified byBARS-Damage Intensity Classes.Comparison of land-based incidence to stand-based damage intensity estimates isof particular interest in coastal forests where most root disease sampling has been doneusing the Intersection Length Method (a land-based disease incidence estimationmethod). For example, consider solving for BARS damage intensities of 5%, 10% and15% 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, respectivelytranslate into 45%, 70% and 76% overestimates of the stand-based root rot damage84intensity.TABLE 12^INCIDENCE - INTENSITY RELATIONSHIPS(BARS/NSBAR) AS A FUNCTION OF STAND (DSWP) AND LAND (DSPT)BASED INCIDENCESimple Linear 5 and 15 %BAR Equivalencies ofEquation R2 SEE (DSWP) and (DSPT)5 %BAR 15 %BARBARS = -.491 + 39.861DSWP .70 5.79 14% DSWP 38.86% DSWPBARS = 1.245 + 51.924DSPT .66 6.17 7.25% DSPT 26.50% DSPTNSBAR = -0.756 + 34.233DSWP .67 5.35 17% DSWP 46.03% DSWPNSBAR = 0.737 + 44.578DSPT^- .63 5.66 9.5 DSPT 32.00% DSPT7.1.3 Stand-Based:Land-Based and Land-Based:Stand-Based Incidence RelationshipsThe variance between the stand-parameter-diseased (DSWP) and land-area-diseased(DSPT) incidence expressions is quite large (Table 10), although the linear correlationbetween the parameters is very high at 0.923 (Table 11). The theory behind the fixed-radius and variable-radius sampling would only predict similar incidence estimates if"equivalent" areas were being sampled--this was not the case. The sampling efficienciesof the two expressions were evaluated in two ways: (1) by calculating a stand:landincidence ratio and plotting it over the land-based incidence; the assumption being thatthe 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 stand-based incidence, the assumption being that the stand-based expression is the more85accurate estimate and is therefore the baseline (Fig. 21). Note, a constant of .001 wasadded to both expressions of incidence prior to calculating the ratios, in order to avoidzero-values. The size of the constant will affect the scale of plots but should not affectthe subsequent ratios.In Figure 20 the stand:Iand-based incidence ratio has 10 non-plotted values rangingfrom 21 to 251 with land-based incidence equal to 0.001. The stand-based expressionis 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 surprisingsince the land-based expression is based on 0.005 ha (3.99 m fixed-radius plots), and thevariable-radius plots, while not sampling land-area, are effectively sampling an averagearea of 0.027 ha. The variable-radius plot mean percent area sampled was determinedas follows: the mean tree dbh marginal sphere was estimated to be about 0.0038 ha (3.5m radius) multiplied by the mean tree count of 7.18 trees/sweep for a percent area of0.027 ha.The land:stand-based incidence ratio has no missing values (Fig. 21). The land-based expression does not seem to be as sensitive at the lower (< .35) end of the stand-based incidence, but does stabilize beyond about (> .35). The land-based incidencestabilization appears to be more variable than the stand-based stabilization baselinedagainst stand-based incidence.7.0a5.564.0^0:)^2.5 - 0 D o ^ ^8 °-04 - o o^cb^o_^0930 0^oOpp cfb2:1 cP 0^GIo pp3crp0-0^1.0 133 033 Cr e4—0.500^0.1^02^0.3^0.4^0.5^0.6^07Land—Based Phellinus Incidence1.100.9^ 03 031113 ^^ ^^ ^ ^^ ^^ ^ ^0U 00 0 0 o 09) ^ ^a) 0.7 ^ 8^ ^ ljo ^^^00 o °. °a 00.5 _0 0 d' 0 o^o° 0^o^000^-00^0.3 ° °30 aa ^ ^U]0.1Y°J ^^^ ^—0.100^0.2^0.4^0.6^0.8^1.0Stand—Based Phellinus Incidence86Figure 20 Stand:Iand-based incidencerelationship. Note, there are 10non-plotted observations equal tozero ranging between 21 and 251on the stand:based incidence ratioaxis.Figure 21 Land:stand based incidencerelationship.7.1.4^Comparison Of The %BAR Survey Data To The Permanent Sample Plot (PSP)DataThe two data sets were compared to determine if there is a relationship betweenPSP-based Phellinus root rot incidence and its associated 1 ha %BAR sample survey rootrot estimators (incidence or intensity), since there was no common root rot estimator forboth data sets. The sole estimator of Phellinus root rot in the PSP's was a land-basedincidence variable (RRIN, respectively 0 or 1 or absence/presence), and similarly so forthe 10m PSP buffer variable (RRBF). Root rot estimators for the 1 ha %BAR samplesurveys were land-and stand-based disease incidence estimators (DSPT and DSWP,respectively), and %BAR intensity estimators (BARS and NSBAR). The two data setsP. weirii Absence/Presence in PSP87were compared graphically and tested with Students t-tests.Infected permanent sample plots (RRIN =1) were found to occur in %BAR samplesurveys which had significantly greater levels of BARS, NSBAR, DSWP and DSPTcompared to healthy, uninfected PSP's (RRIN =0) (all p-values <.018) (Figs. 22-25). Five-percent 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). The5% threshold corresponded approximately to the Low-Medium BARS-Damage IntensityClass 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 exceeded5% BARS/NSBAR, and had a corresponding BARS-Damage Intensity Classification ofMedium to Severe.Figure 22 Boxplots of land-based disease incidence(DSPT) classed by absence (0) andpresence (1) of disease incidence inPSP's. The upper and lower y-axis limits(---) refer to the 5% BARS and NSBARdamage intensity threshold.P. weirii Absence/Presence in PSPP. weirii Absence/Presence in PSP P. weirii Absence/Presence in PSP88Figure 23 Boxplots of stand-based disease incidence(DSWP) classed by absence (0) andpresence (1) of disease incidence inPSP's. The upper and lower y-axis limits(---) refer to the 5% BARS and NSBARdamage intensity threshold.Figure 24^Boxplots of damage intensity(NSBAR) classed by absence (0)and presence (1) of diseaseincidence in PSP's. The y-axislimits (---) refer to the 5% damageintensity threshold. Note, that80% of the infected PSP's occur instands with greater than 5%damage intensity.Figure 25^Boxplots of damage intensity(BARS) classed by absence (1)and presence (1) of diseaseincidence in PSP's. The y-axislimits (---) refer to the 5% damageintensity threshold. Note, that80% of the infected PSP's occur instands with greater than 5%damage intensity.897.2 Ecological Relationships of Phellinus weirfi7.2.1^Describing Phellinus Root Rot Variability in Relation to Biogeoclimatic UnitsPhellinus root rot variability within biogeoclimatic unit classifications was evaluatedby comparison of root rot incidence, severity (in some cases), and intensity.Disease incidence (or presence) in plots was assessed from several differentsampling 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, andd) variable-radius plots (area-less but approximates 0.0263 ha), stand-parameter-diseased estimates (variable= DSWP), n =3 475 in 139sample surveys.Damage severity was only evaluated in the site associations (n =139). Damageintensity was assessed graphically via boxplots, followed up by Tukey's HonestlySignificant 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).907.2.1.1^Zonal (Climatic) Classification And Disease Variability7.^Disease Incidence Variability: Zonal ClassificationDisease incidence based on the presence/absence of P. weirii in 1 ha sample surveyplots did not vary much between subzones or variants (Table 13), but had a very highincidence at 86%-plus. Disease incidence in the PSP's, (n =215 or 204), indicatedsubstantially more PSP's infected in the CWHxm unit(s) compared to the CDFmm.Incidence estimates show that the CWHxm subzone had a 50%-plus greaterincidence of root rot compared to the CDFmm; land-based significant at p = .051 andstand-based at p = .020, respectively. Note, the stand-based incidence estimates are 50to60% greater than land-based incidence estimates. Incidence estimates for the variantshave a similar pattern, with no significant differences between the CWHxm1 and xm2variants (p = .968). The land-based incidence indicated weak differences between theCDFmm and CWHxm1 (p = .199) and CDFmm and CWHxm2 (p = .154) variants, whereasthe stand-based estimate indicated a strongly significant difference between the CDFmmand CWHxm1 variants (p = .047), and only a weak difference between CDFmm andCWHxm2 (p = .154).91TABLE 13^PHELLINUS ROOT ROT INCIDENCE BY ZONALCLASSIFICATION UNITSSample Survey PSP Basis PSP Basis Land Based- Stand Based-BEC Unit Basis (.04 ha) (.04 ha) Fixed Radius Variable Radius(1 ha) n =215 n=204 (.005 ha)n=139N Incidence N Incidence N Incidence N Incidence N Incidence(Std.Err.) (Std.Err.)SubzoneCDFmm 30 .90 55 .25 55 .25 750 .117 (.024) 750 .180 (.030)CWHxm 109 .86 160 .39 149 .41 2725 .183 (.016) 2725 .286 (.022)VariantCDFmm 30 .90 55 .25 55 .25 750 .117 (.024) 750 .180 (.030)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 .168 3475 .2637. Disease Intensity Variability: Zonal ClassificationSample survey plot frequencies by BARS-DIC's, varied significantly between theCDFmm and CWHxm subzones (p = .016), as tested by log-linear models. The CDFmmhad a substantially lower frequency of Severe damage as compared to the CWHxmsubzone (Table 14).Similarly, sample survey plot frequency by BARS-DIC varied significantly between theCDFmm, CWHxm1 and CWHxm2 subzone variants (p = .011). The significant differencewas due to nearly double the frequency of Severely classed plots in the CWHxm1 as92compared to CWHxm2, and four to seven times the frequency in the CDFmm variant(Table 14).TABLE 14^DISTRIBUTION OF PHELLINUS ROOT ROT INSUBZONES AND VARIANTSAS ESTIMATED VIA THE BARS DAMAGE INTENSITY CLASSIFICATIONFrequency of %BAR Sample Surveys with Phellinus Root RotBARS-Damage Intensity Subzone Subzone VariantLowMediumSevereTOTALCDFmm CWHxm Total CDFmm CWHxm CWHxm Total181024036335846351810220172320191058463530 109 139 30 60 49 139Mean, disease intensity estimates (BARS), were compared by t-test and found tovary significantly (p=0.017) between subzones; the means are 5.94% and 11.11%,respectively for the CDFmm and CWHxm subzones (Fig. 26).93Figure 26 Boxplots of % Basal AreaReduction-BARS by subzone.Mean BARS damage intensity forthe CDFmm and CWHxmsubzones are respectively, 5.94%and 11.11%.Similarly, mean disease intensity estimates (BARS) for subzone variants werecompared using Tukey's HSD method. The CDFmm variant was significantly differentfrom CWHxm1 (p=0.007), and from the CWHxm2 (p=.110); the variant means arerespectively, 5.94%, 12.88% and 8.93%. No significant difference in mean BARS wasseen between the CWHxm1 and CWHxm2 variants (p=.419) (Fig. 27).94Figure 27 Boxplots of the % Basal AreaReduction-BARS by subzonevariant. Mean BARS damageintensity for the CDFmm,CWHxm1 and CWHxm2 variantsare respectively, 5.94%, 12.88%and 8.93%.^Vegetation Classification And Disease Variability7.^Disease Incidence Variability: Vegetation ClassificationDisease incidence based on sample survey plots (1 ha), did not vary much betweenplant alliances (p.all.'s) or plant associations (p.a.'s) (Table 15). Incidence differencesbetween the Pseudotsuga-Mahonia and Tsuga-Mahonia p.all., and the Pseudotsuga-Mahonia 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 thePseudotsuga-Mahonia and the Thuja-Achlys p.all. (p=.975). Incidence differencesbetween the Pseudotsuga-Arbutus and Tsuga-Mahonia, and Pseudotsuga-Arbutus andThuja-Foamflower p.a.'s are non-significant (p=.192) to moderately significant (p=.067)95for the land-based estimates, and for the stand-based estimates very weakly (p = .113) tomoderately significant (p = .090) (Table 15).TABLE 15^PHEWNUS ROOT ROT INCIDENCE BYPLANT CLASSIFICATION UNITSBEC UnitSample SurveyBasis (1 ha) n=139Land-Based FixedRadius (.01 ha)Stand-BasedVariable RadiusPlant AlliancesN Incidence Incidence(Std. Error)Incidence(Std. Error)66254823432548. (.058).192 (.029).200 (.027).099 (.022).156 (.025).192 (.029).200 (.027).217 (.068).312 (.040).301 (.036).172 (.029).241 (.034).312 (.040).301 (.036)Pseudotsuga -MahoniaTsuga -MahoniaThuja -AchlysPlant AssociationsPseudotsuga -ArbutusPseudotsuga -GaultheriaTsuga -MahoniaPseudotsuga -Achlys7. Disease Intensity Variability: Vegetation ClassificationSample survey plot frequency by BARS-DIC did not vary significantly between thethree plant alliances (p=.304), or the four plant associations (p = .183) occurring in thestudy area. Interestingly, the driest plant association, Pseudotsuga-Mahonia, showed asimilar pattern to the frequency in the CDFmm subzone (variant); they were in fact highlycorrelated. The Tsuga-Mahonia plant association paralleled the frequency pattern of theCWHxm1 variant, with a higher frequency in the Severe BARS-DIC than predicted by thelog-linear model, though not as strongly. Overall, the sample survey plot frequency by96damage intensity class was inversely related to damage intensity (Table 16).TABLE 16^DISTRIBUTION OF PHELLINUS ROOT ROT INPLANT ALLIANCESAND PLANT ASSOCIATIONS ESTIMATED VIA THEBARS DAMAGE INTENSITY CLASSIFICATIONFrequency of %BAR Sample Surveys with Phellinus Root RotPlant Alliance Plant AssociationBARS-Damage Intensity Class PALL1 PALL2 PALL3 Total PASS1 PASS2 PASS3 PASS4 TotalLowMediumSevere322014781019181158463512922011127810191811584635TOTAL 66 25 48 139 23 43 25 48 139Where: 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 methodwith no significant differences (all p-values > .319); the means are 8.84%, 12.40% and10.33%, for Pseudotsuga-Mahonia, Tsuga-Mahonia and Thuja-Achlys p.all.'s, respectively(Fig. 28).97Figure 28 Boxplots of % Basal AreaReduction-BARS by plantalliances. Note the trend ofelevated %BARS in the Tsuga-Mahonia mesic p.all. and tailing-offinto the drier and fresher p.all's.Mean BARS intensities for the (1)Pseudotsuga-Mahonia, (2) Tsuga-Mahonia, 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 (allp-values>.206); the means are 6.48%, 10.09%, 12.4% and 10.33% for Pseudotsuga-Arbutus, Pseudotsuga-Gaultheria, Tsuga-Mahonia and Pseudotsuga-Achlys, respectively(Fig. 29).Figure 29 Boxplots of % Basal AreaReduction-BARS by plantassociation. Mean BAR intensitiesfor the (1) Pseudotsuga-Arbutus,(2) Pseudotsuga-Gaultheria, (3)Tsuga-Mahonia and (4)Pseudotsuga-Achlys, p.a.'s, arerespectively, 6.48%, 10.09%,12.40% and 10.33%.987.2.1.3^Site Classification And Disease Variability7.^Disease Incidence Variability: Site ClassificationDisease incidence, based on sample survey plots (1 ha), did not vary much betweensite 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, showedno differences between s.a.'s with the exception of the zonal site associations Fd-Salaland HwFd-Kindbergia which were weakly different (p=.118) (Table 17). Land-and stand-based incidence indicate the general similarity of incidence across s.a.'s, with theexception of weak to moderate differences between Fd-Salal and FdHw-Salal, (land-based, p =.181 and stand-based, p =.058), and the Fd-Salal and HwFd-Kindbergia (land-based, p= .102 and stand-based, p = .075) (Table 17).TABLE 17^ PHELLINUS ROOT ROT INCIDENCE BYSITE CLASSIFICATION UNITSSample Survey PSP Basis PSP Basis Land Based Stand BasedBEC Unit Basis (.04 ha) (.04 ha) Fixed Radius Variable(1 ha) n =215 n=204 (.01 ha) Radiusn=139 (-.027 ha)Site N Incidence N Incidence N Incidence Incidence IncidenceAssociation (Std. Error) (Std. Error) (Std. Error) (Std. Error) (Std. Error)Fd-Salal 24 .88 (.0663) 40 .25 (.0685) 40 .25 (.0685) .095 (.021) .160 (.026)FdBg-Oregongrape6 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 .87 .34 .36 .168 .263997. Disease Intensity Variability: Site ClassificationSample survey plot frequency by BARS-DIC varied significantly between siteassociations (p=.057), using a log-linear model. Site associations (s.a.'s) of interest arethe Fd-Salal, with a very high frequency of Low severity, and a low frequency of Severeplots and the FdHw-Salal and HwFd-Kindbergia s.a.'s which had a greater than expectedfrequency of Medium and Severe class plots. In contrast the Cw-Foamflower s.a. had alower frequency of Medium and Severe plots than expected (Table 18).TABLE 18^DISTRIBUTION OF PHELLINUS ROOT ROT BYSITE ASSOCIATIONS ASESTIMATED VIA THE BARS DAMAGE INTENSITY CLASSIFICATIONFrequency of %BAR Sample Survey with Phellinus Root RotSite AssociationsBARS-Damage Severity Class SA 11 SA 13 SA 21 SA 22 SA 24 TOTALLow 15 3 12 18 10 58Medium 8 2 10 20 6 46Severe 1 1 14 16 3 35TOTAL 24 6 36 54 19 139Where: SA11=CDFmm-Fd-Salal, SA13 =CDFmm-FdBg-Oregon grape, SA21 =CWHxm-FdHw-Salal,SA22 =CWHxm-HwFd-Kindbergia, SA24 = CWHxm-Cw-FoamflowerMean BARS estimates for site associations were compared using Tukey's HSDmethod. Only Fd-Salal differed significantly from FdHw-Salal (p = .066), and was weaklydifferent 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-100Kindbergia and Cw-Foamflower s.a.'s, (Fig. 30). Other BARS comparisons were clearlynot different (all p-values > .796).Figure 30 Boxplots of % Basal Area Reduction-BARSby site association. Mean BARSintensities for; Fd-Salal, FdBg-Oregongrape, FdHw-Salal, HwFd-Kindbergia, Cw-Foamflower, 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 DiseaseEfforts to find functional relationships between ecological parameters, BEC units,continuous and categorical root rot variables were frought with extreme difficulty becauseof data heterogeneity (lack of pattern). Ecological parameter--Phellinus root rotrelationships were examined using Pearsons correlation, scatterplots and boxplots.A Pearsons correlation matrix illustrates the non-existent to very weak correlationsof BARS to most ecological variables (Table 19). Only SPHFH had a significantcorrelation at (p < .20) with BARS. Coarse fragment content (CF20) and mineral soil bulk101density (MSBDT) were negatively correlated and porosity (PORF) was positively correlatedwith BARS. Strong outliers appeared to strengthen the Pearsons correlation for severalmineral soils and properties, (e.g., mineralizable nitrogen (MSMN), and MeqMg, MeqCaand MeqK). These weak correlations were disregarded due to a known large samplingvariance 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 physicaland chemical variables (CF20, MSBDT, MSBDF, PORF, SLOPE, ROOTDEPTH, MSPH,MSC, MSN, MSMN and MEQCA), illustrate very weak trends with BARS (see AppendixJ).Correlations significant at the 20% level of significance andjor variables of interest,eight ecological variables were boxplotted against subzone variant. Moving from theCDFmm through to CWHxm1 and CWHxm2, slightly-decreasing trends for CF20, MSBDT,MSBDF, MSPH and MEQCA were seen, and slightly-increasing trends were seen forPORF, 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 BARSplotted by site association. (Refer to Figure 30, and Figures 31-36, also see AppendixF). Of course many of the variables are integrated (thus correlated) to some degree inTABLE 19^ PEARSON CORRELATION MATRICESI^ BARS^AGE87 CF20 MSBDT MSBDF PORF ROOTDP SLOPE MSPH MSC MSN MSMN MEQCA MEQMG MEQKBARSAGE87CF20MSBDTMSBDFPORFROOTDPSLOPEMSPHMSCMSNMSMNMEQCAMEQMGMEQK1.0000.274*-0.133*0.0000.001-0.001-0.0370.178*-0.100-0.0100.034-0.125*0.095*0.172*0.188*1.000-0.0650.2080.115-0.115-0.102-0.127-0.0090.012-0.0360.039-0.035-0.0470.0151.0000.5130.022-0.022-0.0180.094-0.0230.094-0.179-0.0800.0290.039-0.0051.0000.605-0.605-0.0240.0830.2300.0680.040-0.1220.004-0.117-0.1041.000-1.003-0.119-0.1050.098-0.0640.076-0.1040.053-0.036-0.0651.0000.1190.105-0.0980.064-0.0760.104-0.0530.0360.0651.0000.0710.114-0.096-0.060-0.190-0.145-0.1310.0671.0000.102-0.053-0.164-0.1180.2610.2900.0531.000-0.087-0.111-0.0450.068-0.144-0.1441.0000.3140.0870.3000.1820.0271.0000.0870.0570.1030.1051.0000.1940.1930.0901.0000.8790.4081.0000.408 1.000IIBARSFSUSFINTFRESFDECAGE87ST410BA410VL410CRD410SPHFHSPHCWCOMPFHCOMPCWBARS1.000-0.0020.093-0.079-0.0400.264*-0.0870.0280.044-0.001-0.3440.036-0.175*0.265*FSUS1.000-0.690-0.455-0.509-0.1470.0020.0140.014-0.005-0.140-0.021-0.1600.098FINT1.0000.056-0.0040.011-0.090-0.0110.033-0.0400.152-0.0020.168-0.088FRES1.000-0.1580.1920.2300.114-0.0350.1800.1170.1730.0290.081FDEC1.0000.039-0.089-0.095-0.023-0.089-0.026-0.1110.058-0.141AGE871.000-0.317-0.197-0.265-0.250-0448-0.236-0.029-0.059ST4101.0000.6950.3390.8660.2830.309-0.0590.189BA4101.0000.8890.9450.2520.3000.0060.181VL4101.0000.7580.2150.2140.0490.108CRD410 SPHFH1.000^0.272^1.0000.347^0.274-0.034^0.3330.215^-0.081SPHCW1.000-03610.699COMPFH1.000-0.547COMPCW1.000(I) Ecological and soils, (II) Ecological and stand variables.^ 1--.NOTE: Emboldend correlations are significant at (p5.0.20), Bonferronis, adjusted probability.^ o(*) indicates correlations of interest but not significant (p >0.20) ivSELECTED VARIABLES:103the site classification procedure (Pojar et al. 1987 and Banner et al. 1990), for exampleelevation and percent slope (Figs. 32 and 33). Note, the substantially lower elevation andpercent slope conditions for the CDFmm compared to the CWHxm s.a. Values forpercent coarse fragments, mineral soil bulk density and mineral soil pH, are greater in theCDFmm s.a.'s compared to the CWHxm s.a.'s (Figs. 31, 34 and 36, respectively), andsoil porosity is lower in the CDFmm compared to the CWHxm s.a.'s, (Fig. 35). The soilphysical attributes appear to be related to the actual soil moisture and nutrient regimesshown in Table 20. The coarser soils have moderately dry soil moisture regimes. Soilcoarse fragment content in particular is used to estimate site soil moisture.TABLE 20^ACTUAL SOIL MOISTURE AND NUTRIENT REGIMES FORSITE ASSOCIATIONS IN THE STUDY AREAFd-Salal FdBg-OregongrapeFdHw-Salal HwFd-Kind-bergiaCw-Foam-flowerActual SoilMoisture Regime(SMR)MD MD MD SD/F SD/FActual SoilNutrient Regime(SNR)VP/M M/R VP/M VP/M M/RWhere:^MD is moderately dry, SD is slightly dry, F is fresh; and VP is nutrient very poor, M isnutrient medium, and R is nutrient rich, from (Banner et al. 1990)Figure 31 Figure 32 Elevation (m) asl by siteassociation. Means are L to R,85, 79, 191, 194 and 143 m.Elevation corresponds well withsubzones. The CW-Foamflowerunit is likely lower due to lowerslope positions.Percent (%) coarse fragmentcontent (by volume) by siteassociation. Means L to R are 60,55, 62, 43 and 40. Note higherCF20's are associated withmoderately dry, actual soilmoisture regimes (refer to Table20).Figure 33 Slope (%) by site association.Means are L to R, 5.5, 2.2, 12.4,10.9 and 7.6%. The CDF unitscorrespond to the gentle coastalplain and CWHxm units to uplandmid to lower slope conditions.2.01.73V 1.4000.50.2Figure 34 Fine fraction (<2 mm) mineral soilbulk density (g/cm3) by siteassociation. Means L to R; .828,1.125, .766, 7.16 and .695.104Site Association0 ye^1.2N^eg,^virysa vegya yitS9' v 0,1‘0VIVW. Cti CO?'Site Association9080EENy„,.300ca7060504030vs9123- 2,vevo^vsN^tegS yika016'1.^viva t'cli coesc,  tl°Site Association0105Figure 35 Mineral soil percent porosity bysite association. Means L to R;69, 58, 72, 73 and 74%. Note,porosity is inversely related toMSBDF, high bulk density equalslow porosity.Figure 36 Mineral soil pH by siteassocation. Means L to Rare; 5.62, 5.18, 5.23, 5.26,and Stand History And Tree Species DynamicsStand 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 VariabilityChilds (1970) said that the potential for disease carry-over and intensification intosubsequent rotations is largely dependent upon the number, size and spatial distributionof inoculum sources. Childs further stated many small well-distributed inoculum sources106lead to greater damage than a few large aggregated inoculum sources, because theexpanding peripheral (and highly infective) area is greater with the former. Tkacz andHansen (1982) found current stand infection in second growth Douglas-fir to be relatedto the spatial distribution of the preceeding stand's diseased stumps. Because neitherthe stump inoculum size, spatial distribution, nor inoculum (stumps) infectivity weresampled in this study, and the fact that the stump survey data and disease incidenceestimates are sample survey means, this could not be checked in this study. Instead, apicture of the old growth forest condition is presented, with a view to interpreting thepossible cause and/or effect of susceptible and non-susceptible host species compositionand stand density of previous stand conditions on the incidence and intensity of Phellinusroot rot in todays second growth stands.A tabulation of old growth stand conditions related to Phellinus root rot damageintensity (BARS) and various classifications of BARS-Damage Intensity, standhistory/origins and biogeoclimatic units is presented in Table 21.107TABLE 21^DESCRIPTIVE STATISTICS FOR OW GROWTHSTEMS/HA AND SPECIES COMPOSITION BYVARIOUS CLASSIFICATIONSDamage Intensity, Stand %BAR Stems/Ha Species CompositionOrigin, and EcologicalClassificationBARS SPHFH I SPHCW COMPFH COMPCWMean Mean, (Standard Deviation), & [Mean] lDamage IntensityLow 0.98 142(96) 21(31) .82 (.26) [.871] .11^(.14) [ .129]Medium 10.18 111(70) 15(24) .86 (.24) [.881] .10 (.16) [.119]Severe 24.68 87(64) 29(48) .77 (.23) [.750] .21 (.24) [.250]Stand OriginWildfire 15.45 63(57) 12(26) .77 (.34) [.840] .10 (.17) [.16]Logged 10.54 127(104) 10(16) .93 (.13) [.927] .07 (.13) [.073]Logged & Burned 8.22 134( 80) 26(37) .82 (.24) [.838] .15 (.19) [.162]Logged (Logged + 8.49 133( 82) 24(35) .83 (.23) [ .847] .14 (.18) [.153]Logged & Burned)SubzoneCDFmm 5.94 76(75) 11(19) .75 (.35) [.864] .12 (.19) [.136]CWHxm 11.11 129(82) 24(37) .84 (.23) [.843] .13 (.18) [.157]Subzone VariantCDFmm 5.94 76(75) 11(19) .75 (.35) [.864] .12 (.19) [.136]CWHxm 1 12.88 125(84) 34(45) .78 (.25) [.786] .19 (.21) [.214]CWHxm2 8.93 134(79) 12(18) .91^(.17) [.918] .07 (.10) [.082]Plant AlliancePseudotsuga-Mahonia 8.83 104(89) 18(30) .80 (.28) [.852] .12 (.17) [.148]Tsuga-Mahonia 12.40 133 (88) 25 (43) .81 (.29) [.842] .16 (.24) [.158]Thuja-Achlys 10.33 130(70) 24(35) .85 (.20) [.844] .13 (.16) [.156]Plant AssociationPseudotsuga-Arbutus 6.48 118(95) 18(39) .85 (.25) [.867] .11^(.16) [.183]Pseudotsuga-Gaultheria 10.09 96(85) 18(23) .77 (.30) [ .842] .13 (.18) [.158]Tsuga-Mahonia 12.40 133(88) 25(43) .81 (.29) [.842] .16 (.24) [.158]Pseudotsuga-Achlys 10.33 130(70) 24(35) .85 (.20) [.844] .13 (.16) [.156]Site AssociationFd-Salal 5.12 86(81) 8(18) .81 (.34) [.915] .06 (.14) [.085]FdBg-Oregon grape 9.25 37(23) 24(19) .51 (.28) [.607] .32 (.20) [.393]FdHw-Salal 12.29 127(85) 25(40) .81 (.25) [.835] .14 (.16) [.165]HwFd-Kindbergia 11.17 128(83) 26(39) .82 (.24) [.831] .16 (.21) [.169]Cw-Foamflower 8.68 138(75) 13(18) .94 (.07) [.914] .06 (.07) [.086][Mean] 1 represents mean species composition of the meanstems/ha of old growth stand density.1087.3.1.1^Old Growth Stand Conditions and Phellinus Root Rot VariabilityBetween BARS-Damage Intensity Classes (DIC's)Stems (or stumps) per hectare of Douglas-fir and western hemlock (SPHFH) areinversely related to the BARS-DIC's (Table 21, Fig. 37). Tukey's HSD tests, showed thatthe SPHFH in the Low DIC was very weakly different from the Medium (p=.127), andsignificantly different from the Severe DIC (p = .004). No mean differences were detectablebetween Medium and Severe BARS-DIC's (p=.378). The relationship of stems \ ha ofwestern red cedar (SPHCW) to DIC's was not as clear, with no substantial differencesbetween DIC's (all p-values > .163) (Table 21, Fig. 38). Interestingly, the SPHCW weregreatest in the Low and Severe DIC's, perhaps as a result of a possible host-pathogendynamic 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 aslightly lower COMPFH was apparent in the Severe DIC. This likely indicated the mortalityeffects of root rot (Table 21, Fig. 39). On the other hand, the contribution of western redcedar to the species composition (COMPCW) varied significantly between Low andSevere, and Medium and Severe DIC's (p = .016) and (p = .018), respectively, but notbetween Low and Medium DIC's (p=.402) (Table 21, Fig. 40). The sharp increase inwestern red cedar composition in the Severe BARS-DIC appears to indicate a shift tomore resistant tree species in the presence of increasing levels of Phellinus root rot.Boxplots of old growth Douglas-firand western hemlock stems/ha(SPHFH) classified by BARS-Damage Intensity Classes.Boxplots of old growth westernred cedar stems/ha (SPHCW)classified by BARS-DamageIntensity Classes.Figure 37 Figure 38Figure 40Figure 39 Boxplots of old growth westernred cedar composition(COMPCW) classified by BARS-Damage Intensity Classes.Boxplots of old growth Douglas-firand western hemlock speciescomposition (COMPFH) classifiedby BARS-Damage IntensityClasses.LOW^MEDIUM SEVEREBARS--Damage Intensity Class109LOW^MEDIUM SEVEREBARS--Damage Intensity Classa.a.i.o04.J1 0.6E00.6e, 0.40g 026^LOW^MEDIUM SEVEREBARS--Damage Intensity Class0.01107.3.1.2^Old Growth Stand Conditions and Phellinus Root Rot VariabilityBetween SubzonesThe total old growth stems/ha of Douglas-fir and western hemlock (SPHFH) pluswestern red cedar (SPHCW) were significantly lower (p = .017) in the CDFmm ascompared to the CWHxm, 87 vs 153 stems/ha, respectively (Table 21). This translatesinto the CDFmm having only 57% the old growth stand density of the CWHxm. Thehigher 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 andwestern 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 theCWHxm, and remained unchanged for the CDFmm (Table 21). Total stems/ha wereslightly higher in the CWHxm1 compared to the CWHxm2. This was attributable to nearlythree times the number of western red cedar stems, while stems/ha of Douglas-fir andwestern hemlock were slightly lower. This also dramatically affected the speciescompositions, with Douglas-fir/western hemlock (COMPFH) significantly lower (p = .018)and western red cedar significantly higher (p = .001) in the CWHxm1 compared to theCWHxm2. Notably, Phellinus root rot intensity (BARS) was greatest in the CWHxm1compared to the CWHxm2.111^^Old Growth Stand Conditions and Phellinus Variability Between PlantAlliances and Plant AssociationsOld growth stand density and species compositions did not vary significantly betweenp.all.'s (all p-values > .226) or p.a.'s (all p-values > .215). The only noticeable trend wasa slightly lower stand density for both Douglas-fir/western hemlock and western red cedarin the drier Pseudotsuga-Mahonia p.all., and in the Pseudotsuga-Arbutus andPseudotsuga-Gaultheria p.a.'s.^Old Growth Stand Conditions and Phellinus Root Rot VariabilityBetween Site AssociationsOld growth stand conditions varied considerably between site associations, veryclosely following the pattern of subzone and variants with the exception of the FdBg-Oregon grape s.a. Ecologically, it was surprising to see fewer stems/ha and lowercomposition of western red cedar in the Cw-Foamflower s.a., where cedar is indicated tobe a climax species (Fig. 42). Rather, the greatest stems/ha and species compositionsof western red cedar were in s.a.'s with the highest BARS damage intensities, (i.e., FdBg-Oregon grape, FdHw-Salal and HwFd-Kindbergia) (Figs. 41-44).200a.a.cn 00150a100050OoUaYe^ve^‘e.N. -a eto AbeVac'^ A toe-Site AssociationFigure 41^Old growth Douglas-fir andwestern hemlock stems(stumps)/ha by site association.Means L to R are; 86, 37, 127, 128and 138.112Figure 42^Old growth western red cedarstems (stumps)/ha by siteassociation. Means L to R are; 8,24, 25, 26 and 13.Figure 43 Old growth Douglas-fir andwestern hemlock speciescompositions by site association.Means L to R are; .916, .607, .835,.831 and .914.Figure 44 Old growth western red cedarspecies compositions by siteassociation. Means L to R are;.063, .085, .393, .165, .169 and.086.1137.3.1.5^Old Growth Stand Density and Species Composition and SecondGrowth Phellinus Damage IntensityThe relationship between Phellinus root rot damage intensity and old growth standdensity and species composition was also evaluated through contour plots and three-dimensional plots. Phellinus damage intensity (BARS) isobars were calculated using anegative exponentially weighted smoothing technique (McLain 1974) and plotted withSYGRAPH (Wilkinson 1988) (Figs. 45 and 46). These figures illustrate the Phellinus rootrot relationship between old growth stems/ha Douglas-fir/western hemlock (SPHFH) andstems/ha western red cedar (SPHCW) for the full range of data.Figures 45 and 46 illustrate three conditions which may have led to (conducive), orreacted to increasing levels of root rot in the old growth stand conditions: (i) decreasingsusceptible species density (SPHFH) and increasing density of non-susceptible species(SPHCW), see lower left and lower right contour lobes, (ii) increasing susceptible andnon-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 severePhellinus root rot activity. Two general observations are relevant. First is that secondgrowth Phellinus root rot intensity appears related to decreasing SPHFH and increasingSPHCW. 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 highSPHCW (> 100 sph). Secondly, the most striking feature of all the contour plots is acontour trough between two peaks of high root rot intensity. Arguably the contour troughmight be construed as an equilibrium zone.114Old Growth Stems/ha—Western Red Cedar115Figure 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).116Figure 46^A three-dimensional plot illustrating the relationship between damage intensity(BARS) and old growth stand density (stems/ha) of Douglas-fir and westernhemlock (SPHFH) and western red cedar (SPHCW). Data points are representedby (p ) squares.1177.3.2^Second Growth - Phellinus Variability RelationshipsThe relationship of second growth stand conditions to the Phellinus root rot BARSintensities, BARS-DIC's and to biogeoclimatic units was evaluated using PSP-establishment (>4.0 cm) stand attribute data. The stand parameters evaluated were sixspecies composition classes based on susceptibility to Phellinus root rot, (FSUS (Fd, Bg),FINT (Hw), FRES (Pw, PI, Cw) FDEC (Deciduous), FSUSINT (FSUS + FINT) andFRESDEC (FRES + FDEC)), and three stand density parameters, (stems/ha (ST410),basal area (m2/ha) (BA410) and Curtis' Relative Density (CRD410)). A summary ofparameter 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 DENSITIESAND VOLUME ESTIMATES BY VARIOUS CLASSIFICATIONSDamage Intensity, Stand %BAROrigin, and Ecological Measured Species Composition at PSP Establishments (> 4.0 cm by volume) Estimated Stand Densities and Volume at 10 yr (>4.0 cm)ClassificationBARS FSUS^I FINT^1 FRES^I FDEC^I FSUSINT^I FRESDEC ST410^I BA410^1 CRD410^I VIAIOn Mean Mean and (Standard Deviation),Damage IntensityLow 58 0.98 .906(.110) .026(.054) .039(072) .027(066) .932(.098) .067(.095) 2 218(1 659) 7.96(2.97) 2.94(133) 23.93( 8.98)Medium 46 10.18 .868(.120) .062(.079) .017(047) .053(.0E3) .930(086) .070(.086) 2 074(1 324) 7.85(2.74) 2.92(1.10) 23.67( 8.41)Severe 35 24.68 .894(.137) .055(.110) .027(.048) .021(054) .949(072) .048(.064) 2 033( 953) 8.14(238) 3.00(0.93) 24.43( 8.19)Stand OriginWildfire 30 15.45 .871(136) .051(.079) .047(072) .029(.069) .922(.091) .076(.085) 1 812( 969) 7.98(3.13) 2.84(1.14) 22.73(10.47)Logged 13 1054 .881(.164) .073(151) .022(043) .017(.050) .954(082) .038(.060) 1 823( 870) 8.02(2.19) 2.80( .81) 24.46( 8.08)Logged & Burned 96 8.22 .898(110) .039(.067) .024(057) .038(073) .937(.088) .062(087) 2 262(1 540) 7.96(2.70) 3.00(1.20) 24.28( 8.12)Logged + (Logged & Burned) 109 8.49 .896(.177) .043(.081) .024(.055) .036(.071) .939(087) .060(.085) 2 210(1 480) 7.96(2.01) 2.98(1.16) 2430( 8.08)SubzoneCDFmm 30 5.94 .910(119) .004(.011) .037(.081) .046(.078) .915(.106) .083(.102) 1 777( 879) 7.29(2.67) 2.64( .92) 20.78( 7.76)CWHxm 109 11.11 .885(121) .056(.087) .026(052) .031(.068) 941(.081) .058(079) 2 219(1 492) 8.16(2.74) 3.03(1.20) 24.85( 8.58)Subzone VariantCDFmm 30 5.94 .910(.119) .004(.024) .037(.081) .046(.078) .915(.106) .083(.102) 1 777( 879) 7.29(2.67) 2.64( .92) 20.78( 2.76)CWHxml 60 12.88 .898(.112) .031(.062) .038(.064) .032(.075) .929(.093) .070(.090) 2 629(1 757) 8.50(333) 330(1.45) 24.70( 9.52)CWHxm2 49 8.93 .869(131) .088(102) .012(.029) .030(059) .957(062) .042(061) 1 718( 864) 7.62(1.66) 2.70( .68) 25.04( 7.29)Plant AlliancePseudotsuga-Mahonia 66 8.83 .930(087) .013(037) .025(.055) .031(066) .943(.078) .057(.078) 2 562(1 722) 8.24(3.10) 3.16(135) 22.92( 8.55)Tsuga-Mahonia 25 12.40 .892(141) .071(122) .021(043) .016(046) .963(.057) .037(.057) 2 128( 854) 8.20(1.97) 3.06( £4) 25.11( 5.91)Thuja-Achlys 48 1033 .834(.130) .076(.082) .037(072) .048(084) .910(107) .085(.101) 1 520( 759) 7.48(253) 2.59( .92) 24.83( 9.63)Plant AssociationPseudotsuga-Arbutus 23 6.48 .908(.089) .001(.006) .042(078) .049(073) .910(.088) .090(.088) 2 890(2 212) 933(4.18) 3.61(1.82) 25.76(11.09)Pseudotsuga-Gaultheria 43 10.09 .942(.085) .019(.044) .017(.036) .022(.060) .961(.065) .039(.065) 2 386(1 391) 7.66(2.16) 2.93( .96) 2L40( 6.48)Tsuga-Mahonia 25 12.40 .892(141) .071(122) .021(043) .016(046) .963(057) .037(057) 2 128( 854) 8.20(1.97) 3.06( .84) 25.11( 5.91)Pseudotsuga-Achlys 48 10.33 .834(.130) .076(.082) .037(072) .048(084) .910(.107) .085(101) 1 520( 758) 7.48(253) 259( .92) 24.83( 9.63)Site AssociationFd-Salal 24 5.12 .922(090) .000(000) .035(072) .043(067) .922(.090) 078(.090) 1 853( 950) 7.46(2.82) 2.71( 98) 20.81( 8.12)FdBg-Oregon grape 6 9.25 .863(.205) .022(053) .048(118) .057(120) .885(164) .105(148) 1 472( 429) 6.60(2.00) 2.38( .65) 20.65( 6.77)FdHw-Salal 36 12.29 .926(102) .032(.065) .028(.050) .014(047) .958(064) .042(464) 2 801(1 925) 835(3.40) 3.25(155) 22.29( 9.25)HwFd-Kindbergia 54 11.17 .875(120) .070(097) .027(.056) .025(.051) .945(074) .052(.068) 2 173(1 197) 8.16(2.51) 3.05(1.05) 25.41( 7.93)Cw-Foamflower 19 8.68 .834(139) .064(086) .019(048) .083(111) .897(115) .103(115) 1 252( 509) 7.77(1.94) 256( .66) 28.14( 8.06)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^^Second Growth Stand Conditions and Phellinus Variability in the BARSDamage Intensity ClassesVirtually no variation of susceptible (FSUSINT) or non-susceptible (FRESDEC)species compositions were seen between the BARS-DIC's.^Second Growth Stand Conditions and Phellinus Variability in theSubzones at PSP EstablishmentNo substantial difference in species compositions were detectable between theCDFmm and the CWHxm subzones. The FSUSINT compositions are within 3.6% of eachother, 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 theCWHxm subzone compared to the CDFmm, with the opposite conditions for the non-susceptible species composition.Va)ut 0.8E.zo•CDFmm• CWHxmSubzone0.4up 0.3CDFmmHCWHxmE0.10.0Subzone120Figure 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 andCWHxm subzones.Figure 48^First PSP measure PI, Pw, Cw andDeciduous (resistant) speciescomposition by subzone. MeansL to R are: .083 and .058,respectively for CDFmm andCWHxm subzones.All three measures of stand density (stems/ha, basal area/ha and Curtis' relativedensity) back-estimated to reference age 10 yr indicated a greater density condition in theCWHxm subzone compared to the CDFmm subzone, though none are significantlygreater (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 standdensity across the subzones (see Fig. 26, pg. 93).121Figure 49 Back-estimated stems/ha, k4.0cm at reference age 10 yr bysubzone. Means L to R are: 1 777and 2 219, respectively for theCDFmm and CWHxm subzones.Figure 50 Back-estimated basal area2(nn /ha), k4.0 cm at reference age10 yr by subzone. Means L to Rare: 7.29 and 8.26, respectively forthe CDFmm and CWHxmsubzones. Figure 51 Back-estimated Curtis' relativedensity, k4.0 cm at reference age10 yr by subzone. Means L to Rare: 2.64 and 3.03, respectively forthe CDFmm and CWHxmsubzones.CDFmm CWHxml CWHxm2Subzone Variantz0 100.90t.)cl 0.8E.rip 0.7I'h)E 0.60.51227.3.2.3^Second Growth Stand Conditions and Phellinus Variability in theSubzone Variants at PSP EstablishmentSusceptible species compositions (FSUSINT) increased slightly from the CDFmmthrough the CWHxm1 and CWHxm2 subzone variants. The mean compositions are91.5%, 92.9% and 95.7% respectively (Fig. 52 and Table 22). The relatively high level inthe CWHxm2 is due to a substantially greater composition of western hemlock comparedto the other subzone variants. Concomitant and opposite compositions are seen for thenon-susceptible species (Fig. 53 and Table 22).Figure 52 First PSP measure of Fd, Bg andHw (susceptible and intermediate)species composition by subzonevariant. Means L to R are: .915,.929 and .957, respectively for theCDFmm, CWHxm1 and CWHxm2subzone variants.Figure 53^First PSP measure of PI, Pw, Cwand Deciduous (resistant) speciescomposition by the subzonevariant. Means L to R are: .083,.070 and .042, respectively for theCDFmm, CWHxm1 and CWHxm2subzone variants.CDFmm CWHxml CWHxm2Subzone Variant123Measures of stand density back-estimated to age reference 10 yr do not display thesame patterns across the variants as the species compositions. In fact, mean standdensity measures are all lowest in the CDFmm variant, peak in the CWHxm1 and then fallto levels slightly above the CDFmm in the CWHxm2 variant (Figs. 54, 55 and 56 andTable 22). Phellinus root rot intensity appears to have the same pattern as stand densityacross the subzone variants (Fig. 27, pg. 94). The CWHxm1 back-estimated stems/hawas significantly different from the CDFmm and CWHxm2 variants (p=.012) and(p = .001).Figure 54^Back-estimated stems/ha, A.0cm at reference age 10 yr bysubzone variant. Mean L to R are:1 777, 2 629 and 1 718,respectively for the CDFmm,CWHxm1 and CWHxm2 subzonevariants.Figure 55^Back-estimated basal area(m2/ha), A.0 cm at reference age10 yr by subzone variant. MeansL to R are: 7.29, 8.60 and 7.62,respectively for the CDFmm,CWHxm1 and CWHxm2 subzonevariants.124Figure 56 Back-estimated Curtis' relativedensity, z4.0 cm at reference age10 yr by subzone variant. MeansL to R are; 2.64, 3.30, and 2.70,respectively for the CDFmm,CWHxm1 and CWHxm2 subzonevariants.The CWHxm1 back-estimated basal area/ha was moderately different from theCDFmm (p= .076) and CWHxm2 (p = .144). Similar to the stems/ha, back-estimatedCurtis' relative density for the CWHxm1 was significantly different from the CDFmm(p = .023) and CWHxm2 (p = .014) variants. Second Growth Stand Conditions and Phellinus Variability in the PlantAlliancesSusceptible species compositions (FSUSINT) rose from the Pseudotsuga-Mahoniap.all. (94.3%), peaked in the Tsuga-Mahonia p.all. (96.3%), and fell to (91.0%) in theThuja-Achlys p.all. The pattern somewhat mimics the Phellinus intensity levels observed(Table 22). Non-susceptible species compositions (FRESDEC) were opposite to theconditions described for the susceptible compositions (Table 22). Figures for the plantalliance relationships are not shown.125Measures of stand density were consistent across all parameters and were greatestin the Pseudotsuga-Mahonia p.all. and fell dramatically in the Thuja-Achlys p.all. (Table22). No apparent trend between p.all.'s and Phellinus root rot intensity patterns wasobserved. Second Growth Stand Condition and Phellinus Variability in the PlantAssociationsSusceptible species compositions (FSUSINT) were lowest in the Pseudotsuga-Arbutusand Pseudotsuga-Achlys p.a., (both 91.0%) (Table 22), and were greatest in thePseudotsuga-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.'ssamples, and also the majority of the CDFmm subzone/variant samples. No apparenttrend between susceptible species composition and Phellinus root rot intensity wasobserved. Figures for the plant association relationships are not shown.Measures of stand density are less consistent across the p.a.'s than across thep.all.'s (Table 22). Stems per hectare are greatest in the Pseudotsuga-Arbutus p.a. (2 890sph) dropping consistently with increasing soil moisture conditon to the Pseudotsuga-Achlys p.ass. (1 520 sph) (Table 22). There was clearly no trend with the observedPhellinus root rot intensities and stems per hectare across the plant associations. Basalarea and Curtis' relative density measures vary across the plant associations, with noapparent relationships between density and Phellinus root rot intensities. However, if thedriest plant association (Pseudostuga-Arbutus) is not considered, it appears that basal126area has a similar pattern to Phellinus root rot intensity (Table 22). Note that thePhellinus intensity peaked in the Tsuga-Mahonia p.a. as did the basal area, dropping tonear-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 slightlymore difficult to interpret. Second Growth Stand Conditions and Phellinus Variability in the SiteAssociationsThe pattern of susceptible species compositions (FSUSINT) was not very consistentwith the Phellinus root rot intensity when viewed within site associations (Fig. 57 andTable 22). However, when FSUSINT was viewed within a subzone (i.e., at the site serieslevel), the susceptible species composition was positively correlated with Phellinus rootrot intensity in the CDFmm units, and even more so within the CWHxm units. Theextreme variability of FSUSINT in the FdBg-Oregon grape s.a. and the very small samplesize (n = 6) confounds the interpretation in this unit. The opposite relationships are truefor the non-susceptible species composition (Fig. 58 and Table 22).Site AssociationFigure 57^First PSP measure of Fd, Bg andHw (susceptible and intermediate)species composition by siteassociation. Means L to R are:.922, .885, .958, .945 and .897,respectively for the Fd-Salal,FdBg-Oregon grape, FdHw-Salal,HwFd-Kindbergia and Cw-Foamflower s.a.'s.127Figure 58^First PSP measure of PI, Pw, Cwand Deciduous (resistant) speciescomposition by site association.Means L to R are: .078, .105, .042,.052 and .103, respectively for theFd-Salal, FdBg-Oregon grape,FdHw-Salal, HwFd-Kindbergia andCw-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 rotintensity became clearer. Consistently the FdBg-Oregon grape s.a. was difficult tointerpret for the reasons stated above.Generally, stems/ha, basal area/ha and Curtis' relative density all appeared to bepositively 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, andSite Association128provide a comparison to the back-estimated measures to reference age 10 yr. Also, notethe generally similar pattern of the PSP establishment stand conditions to the referenceage 10 yr conditions.Figure 59^Back-estimated stems/ha, z4.0cm at reference age 10 yr by siteassociation. Mean L to R are: 1853, 1 472, 2 801, 2 173 and 1 252for the Fd-Salal, FdBg-Oregongrape, FdHw-Salal, HwFd-Kindbergia and Cw-Foamflowers.a.'s.Figure 60^First PSP measure of stems/ha,4.0 cm at reference age 10 yr bysite association. Mean L to R are:1 875, 1 379, 2 798, 2 001 and 1072 for the Fd-Salal, FdBg-Oregongrape, FdHw-Salal, HwFd-Kindbergia and Cw-Foamflowers.a.'s.129Figure 61 Figure 62Back-estimated basal area(m2/ha), z4.0 cm at reference age10 yr by site association. Means Lto R are: 7.46, 6.60, 8.35, 8.16 and7.77 for the Fd-Salal, FdBg-Oregon grape, FdHw-Salal, HwFd-Kindbergia and Cw-Foamflowers.a.'s.First PSP measure of basal area(m2/ha), z4.0 cm at reference age10 yr by site association. MeansL to R are: 38.2, 38.1, 33.3, 32.7and 36.9 for the Fd-Salal, FdBg-Oregon grape, FdHw-Salal, HwFd-Kindbergia and Cw-Foamflowers.a.'s.Figure 63 Back-estimated Curtis' relativedensity, z4.0 cm at reference age10 yr by site association. MeansL to R are: 2.71, 2.38, 3.25, 3.05and 2.56 for the Fd-Salal, FdBg-Oregon grape, FdHw-Salal, HwFd-Kindbergia and Cw-Foamflowers.a.'s.130Figure 64 First PSP measure of Curtis'relative density, k4.0 cm atreference age 10 yr by siteassociation. Means L to R are:9.1, 8.7, 9.0, 8.2 and 7.8 for theFd-Salal, FdBg-Oregon-grape,FdHw-Salal, HwFd-Kindbergia andCw-Foamflower s.a.'s.7.3.3^Second Growth Species DynamicsThe basis for investigating species composition shifts is that susceptible speciescommonly appear to be replaced with less-susceptible species as the former dies out dueto Phellinus root rot activity. Variable-radius plot sample data were used to estimatesecond growth species dynamics (shifts) between healthy and infected disease conditionsby evaluating: (i) species compositions, (ii) species composition ratios, and (iii) non-susceptible species composition stratified by diameter "age" class. Species compositionswere based on the mean tree counts from the %BAR sample surveys. The results arealso compared against species composition shifts observed in the PSP's. The threeestimates were very comparable. Overall, a net gain of non-susceptible speciescomposition (with a corresponding drop in susceptible species composition) in Phellinusinfected stand conditions compared to healthy stand conditions.1317.3.3.1^Second Growth Species Dynamics Estimates from Variable-RadiusPlot Sample (%BAR Survey) Data7. Comparison of Species Compositions by Disease ConditionSpecies compositions were stratified by species susceptibility class and infectioncondition. Comparison of percent species compositions stratified by disease conditionindicated a drop in susceptible species composition and an increase in non-susceptiblespecies in infected portions of stands, (Table 23). In healthy stand conditions, the meansusceptible and non-susceptible species compositions were 91.52%, and 8.48%,respectively, (Fig. 65), whereas, in infected conditions, mean susceptible and non-susceptible species compositions are 87.06%, and 12.94%, respectively, (Fig. 66). Thedifference in susceptible species composition between disease conditions (-4.46%), whilesignificantly different by t-test (p = .000), only represents a 4.87% drop from its healthylevel. This is in sharp contrast to the non-significant t-test differences (p = .274) for thenon-susceptible composition (-4.46%), which actually represents a 52.6% relative gain innon-susceptible species composition from the healthy condition.The high variability of non-susceptible species composition shifts in infectedconditions is likely due to; (i) a low and scattered composition, and (ii) ingrowth rates areslow and highly light dependent, which is in turn dependent on the size of and length oftime 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 mortalityor premature windthrow that almost always follows the infection of trees by Phellinus rootrot.132TABLE 23^DESCRIPTIVE STATISTICS FOR SPECIES COMPOSITIONBY DISEASE CONDITIONStatistic Non-Susceptible Species Susceptible SpeciesHealthy (NALH) I^Infected (NALI) Healthy (SALH) Infected (SALI)Mean Tree CountStandard DeviationStandard ErrorSpp. CompositionAbsolute^0.759a^0.839a0.782 1.1580.066^0.0988.48 12.94+4.468.187b^5.646c1.669 2.0370.142^0.17391.52 87.06- 4.46NALH is Non-susceptible spp., k4.0 cm, Healthy^NALI is Non-susceptible spp., k4.0 cm, InfectedSALH is Susceptible spp., k4.0 cm, Healthy^SALI is Susceptible spp., z4.0 cm, InfectedMean tree-counts followed by disimilar letters are significantly different at ( a^<.05).Figure 65 Figure 66Second growth speciescomposition for healthy standconditions. Non-susceptiblespecies (NALH), (pines, westernred cedar and deciduous spp.),susceptible species (SALH),(grand fir, Douglas-fir and westernhemlock) have meancompositions of 8.48% and91.52%, respectively (y-axis limits).Second growth speciescomposition for infected standconditions. Non-susceptiblespecies (NALH), (pines, westernred cedar and deciduous spp.),susceptible species (SALH),(grand fir, Douglas-fir and westernhemlock) have meancompositions of 12.95% and87.06%, respectively (y-axis limits).1337. Species Composition Shifts Stratified By Disease Condition And Diameter LimitClassesRatios of non-susceptible-only, and susceptible-only to the combined total speciescomposition, 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 dominantand codominant crown class trees. The lower diameter limit class (.4.0 cm) may beanalagous to a "younger age" class, which includes all ages ("younger and older"). The4.0 cm class was comprised of dominants/codominants but also a greater amount ofsuppressed and intermediate crown class trees, some of which were assumed to beyounger as a result of ingrowth in response to Phellinus root rot activity. Unfortunatelya sharp distinction between classes was not possible using this analysis. Diameter limittree tallies, basal area and species composition derivations are shown in Section 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, showeda consistent trend of higher proportions of non-susceptible species in infected conditions(N4, N2), compared to healthy conditions (N3, N1) (Fig. 67). Note the greaterproportions 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 theopposite relationships existed for susceptible species compositions; they were reduced134in 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 speciescomposition of 4.4% with a corresponding net decrease of -2.6% in susceptible speciescomposition. Variation due to diameter limit "time/age" is minor, whileincreases/decreases were larger in the CDFmm subzone compared to the CWHxmsubzone.TABLE 24^SPECIES COMPOSITION RATIO VARIABLE DEFINITIONSDiameter 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 - N1N3 = NGRH / NSGRH (Healthy)N4 = NGRI / NSGRI (Infected)DBAN = N4 - N3Susceptible species: (Douglas-fir, grand fir and hemlock)S1 = SALH / NSALH (Healthy)S2 = SALI / NSALI (Infected)DSBA = S2 - S1S3 = SGRH / NSGRH (Healthy)S4 = SGRI / NSGRI (Infected)DBAS = S4 - S3135Figure 67 Figure 68Non-susceptible speciescomposition by diameter limitdisease condition and subzone(see also Table 24). Note, thegreater composition in the lower(4.0 cm) diameter limit and in theinfected compared to the healthyconditions. Also, note thedifference between consecutivepairs are shown in Fig. 69.Susceptible species compositionby diameter limit disease conditionand subzone (see also Table 24).Note, the greater composition inthe larger (212.0/17.5 cm)diameter limit and in the healthycompared to the infectedconditions. Also, note thedifferences between consecutivepairs are shown in Fig. 57(c).Figure 69 Changes in non-susceptible andsusceptible species composition, asderived and defined in Table 22. Overall,the non-susceptible species change(DBAN/DNBA) was +4.4%, whilesusceptible species net change(DBAS/DSBA) was -2.6%.1367.^Comparison of Non-susceptible Species Compositions BetweenDiameter Classes and Disease ConditionThe basis for investigating non-susceptible species composition shifts betweendiameter classes was that susceptible species composition was thought to decrease non-susceptible species composition increase in response to P.weirii activity relative to healthyconditions (and shown in sections and 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 classesshowed 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 signtests 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 standconditions for BARS>20%, and thus more time to allow for diameter-measured shiftstowards increased non-susceptible species composition, if in fact it occurs (Table 25).137TABLE 25^COMPARISON OF NON-SUSCEPTIBLESPECIES COMPOSITION (NSSppC)Diameter Class & Disease ConditionMean NSSppC Tree CountBy Root Rot BARS 220%NGRH vs. NGRINLSH vs. NLSINALH vs. NALIBARS <20% BARS a20% Mean Diff. P..588 vs .577 as.177 vs .152 bb.765 vs .837 )0(.616 vs .475 cc.114 vs .163 de.730 vs .847 yy0.141-0.050-0.117p=.533p=.095p=.119NGRH^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 betweenthe first and latter variables in each pair.^Pairs followed by the same letter are not significantlydifferent at^a^<0.10.The paired t-tests indicated no significant differences between non-susceptiblespecies composition diameter-disease conditions for the < 20% BARS stratification (all p-values > .401). Although no significant differences were detectable, it is notable that themean infected non-susceptible species composition (NALI) was in fact greater than themean healthy non-susceptible species composition (NALH), thus emphasizing aconsistent pattern of non-susceptible species composition increases due to Phellinus rootrot incidence. However, in the >20% BARS class, the smaller 'younger' diameter classhad significantly greater non-susceptible species composition (p =0.095), in the infectedcondition (NLSI), compared to the healthy condition (NLSH). The larger 'older' diameter-infection 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 pure138Douglas-fir, there was no expectation of unequal levels of non-susceptible speciescomposition in the larger predominant tree cover (>12.0/17.5 cm). Consistent withdisease dependent diameter class shifts, infected non-susceptible species compositionwas much lower in the larger diameter class than the healthy; and in the smaller diameterclass, the infected non-susceptible species composition was much larger than in thehealthy condition. Second Growth Species Dynamics From the PSP's MeasurementRecordSpecies composition shifts in the PSP data (4.0 cm) were analysed in order todetermine if some time dependent corroboration of the survey data existed. The PSP'shave been measured on average for 30 to 35 yrs. The non-susceptible speciescomposition was based on the sum of Tolerant (hemlock), Intermediate (pines) andResistant (cedar) species (variable name is TIR), calculated for the first and last PSPmeasurements available, respectively FTIR and LTIR. The change in TIR species wascalculated, (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 Table26). Similarly, the susceptible species compositions, (FSUS and LSUS) and changes(SUSD), were calculated and tested. The inclusion of western hemlock in the non-susceptible species class makes this test not directly comparable to the %BAR samplesurvey species classification previously discussed in section The author hasobserved that a great deal of western hemlock regenerates in infection centre openingsand that hemlock regeneration is uncommonly infected by Phellinus root rot. SUSD wasP. weirii Absence/Presence in PSP P.weirii Absence/Presence139tested with and without western hemlock and found similar results as presented, but ofa slightly lower and more variable magnitude.Figure 70^Net changes in non-susceptiblesecond growth speciescomposition over 30 to 35 yr inhealthy and infected PSP's. Non-susceptible species are; westernhemlock, western white andlodgepole pines and western redcedar.Figure 71^Net changes in susceptiblesecond growth speciescomposition over 30 to 35 yr inhealthy and infected PSP's.Susceptible species are; grand firand Douglas-fir.Overall, the mean increase in TIRD species composition in PSP's infected withP.weirii is 3.7%, netted to 3.5% after subtraction of the 0.2% TIRD gain indicated inhealthy PSP's (Table 26). The difference is highly significant (p = .000), whether fromseparate or pooled variance t-tests of the means. The mean change in susceptiblespecies (SUSD) is opposite to non-susceptible species; in healthy PSP's, SUSD increasedby 0.6%, and in infected PSP's, SUSD decreased by 2.1% for a net decrease of 1.5%.140The susceptible species composition changes are also highly significant (p =0.006).Proportional increases in TIR species composition were largest in the zonal siteassociations, and in the CDFmm and CWHxm2 subzone variants as indicated by thechange factor ((TIRD RR1N . i - TIRD RRIN=0) x 100), (Table 26).141TABLE 26^MEAN DIFFERENCES IN TOLERANT, INTERMEDIATE ANDRESISTANT (TIRD) SPECIES COMPOSITIONPROPORTION IN PSP's BETWEEN FIRST AND LAST MEASUREMENTSSite Association N RRIN TIRD STD SEM Change FactorFd - Salal * 30 0 0 .034 .006Fd - Salal 10 1 .062 .074 .023 620.0FdBg - Oregon grape 10 0 .008 .013 .004FdBg - Oregon grape 3 1 .030 .010 .006 3.75FdHw - Salal 31 0 .005 .013 .002FdHw - Salal 18 1 .034 .052 .012 6.80HwFd - Kindbergia * 41 0 0 .039 .006HwFd - Kindbergia 34 1 .041 .063 .011 410.0Cw - Foamflower 25 0 .004 .013 .003Cw - Foamflower 9 1 .004 .042 .014 2.00All Associations 139 0 .002 .029 .002All Associations 76 1 .037 .059 .007 18.50Subzone VariantCDFmm 41 0 .002 .030 .005CDFmm 14 1 .052 .064 .017 26.00CWHxm1 59 0 .005 .022 .003CWHxm1 37 1 .025 .052 .008 5.00CWHxm2 39 0 .004 .036 .006CWHxm2 25 1 .045 .065 .013 11.25Where: RRIN =0, is Healthy and RRIN =1, is Infected. The largest changes in TIRD speciescomposition appear to be in the zonal site associations *, and in the CDFmm and CWHxm2subzone variants.The change Factor is [TIRD (sIRRIN= 1) - TIRD (sIRRIN=0)1 x (100), where default TIRD values of .001are used for real zero values.7.3.4^Stand History: Fire And LoggingThe frequency of occurrence of stand origins shows that 69% of the 139 PSP'soriginated after logging- and-burning, 21.6% after wildfire, and 9.4% after logging. The142results are not surprising given that vast areas of eastern Vancouver Island were railwaylogged (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 oldgrowth (Leavitt 1913, 1915), and evidenced in thin and developing second growthmor/moder forest floors, commonly occurring burned-out old growth stumps (authorsobservations), 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-and-burned (8.22%) origin PSP's, (p = .002), but not significantly more infected than thelogged-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).BURN^LOG LOG&BURNHistorical Stand Origin143Figure 72 Boxplots of % Basal AreaReduction-BARS classified bythree stand origins; wildfire(BURN), logged-only (LOG) andlogged and slashburning (LOG &BURN).Figure 73 Boxplots of % Basal AreaReduction-BARS classified bythree stand origins; wildfire(BURN), and logged andslashburned (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 inexplaining variation of %BAR damage intensity between different stand origins, althoughother stand origin effects cannot be discounted.BURN^LOG LOG&BURNHistorical Stand Origin144Figure 74^Boxplots of total age classified bythree stand origins. Mean totalages for wildfire, logged-only andlogged-and-burned are 90, 70 and60 years old, respectively.Figure 75^Boxplots of total age classified bytwo stand origins. Mean totalages for wildfire and logged-onlyplus logged-and-burned are 90and 63 years old, respectively.No other ecological variables, except old growth stand density and speciescompositions, 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 ofwildfire origin are less than half (63 stems/ha) that of the logged-only (127 stems/ha) andlogged-and-burned (133 stems/ha) origins (Table 21). Mean stems/ha (SPHFH) aresignificantly different between wildfire and logged (p = .036), and wildfire and logged-and-burned 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 wildfireorigin stands, 25 vs 10 stems/ha (p = .113).145Similarly 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 ModelsFollowing on the exploratory data analyses discussed in 7.2.1 and 7.2.2, and multiplelinear regression models were constructed to aid inference of the disease's behaviour,and to develop a disease hazard classification for subzone, variant, plant alliance, plantassociation, and site association units. All models included total stand age (AGE87) asa 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), speciescompositon of western red cedar (COMPCW), percent slope (SLOPE), mineral soil coarsefragment content, (CF20), and mineral soil total bulk density (MSBDT) variables.Multiple Linear Regression Models:Multiple linear regression models were constructed for two reasons; (a) heterogeneityof slopes for subzone and site association, and (b) to develop a comparative basis foran all-biogeoclimatic units hazard rating classification. All models, with the exception ofthe plant alliance and association models, were highly significant (p=0.000) andindependent variables were all significant (all p-values < 0.050). Ecosystem146classification 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). (seemodels 1, 2, 4, 5 and 3 in Table 27). Regression through the origin (removal of theconstant, or non-intercept models) produced near equivalent models that are no-lessbiologically correct than the intercept-models (see models A, B, C, D and E in Table 27and respectively Figs. 76-80). These non-intercept models were used in the constructionof the ecosystem hazard rating classification (Figs. 47-51). The addition of stand historyvariables, SPHFH and SPHCW, on the non-intercept model boosted explained variationto 27.1% and reduced the standard error (see model 9 in Table 27). Non-significantadditions to model fit were made by including slope, soil porosity, bulk density and coarsefragment content (not shown in Table 27).A stand history model, while only accounting for only 17.5% of the total variation, didindicate the tendency of lower old growth stand densities of Douglas-fir and westernhemlock (SPHFH) being associated with higher levels of P. weirii, and increasing levelsof 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 oldgrowth stems/ha of Doulgas-fir/western hemlock, and western red cedar (Table 27). Themodel responses corroborate the earlier Pearsons correlation values for stand history inrelation to BARS.The model describing the relationship between BARS and several "thought-to-be-important" soil variables (CF20, MSBDT and SLOPE), indicated a very low explanation of147variability (R 2 = .083), with independent variables highly significant at (p<0.001) (Table27).Although none of the ecological or stand history variables contributed much inthemselves to the explanation of root rot variability, the models describe diseasebehaviour reasonably for the domain of coastal Douglas-fir ecosystems studied, especiallyconsidering the wide ecological and root rot variability within the area and, the fact thatthe PSP data base was assumed to be biased towards healthier stand conditions thanwas found. Furthermore, the findings clearly match root rot intensity relationships foundwith subzone variants (Beale, 1987), and s.a.'s (Beale 1989b, unpubl. data) that weredetermined from extensive, designed root rot surveys using the intersection lengthsampling method.TABLE 27^MULTIPLE REGRESSION PREDICTION MODELS FOR PERCENT BASAL AREA REDUCTION(BARS) - SUSCEPTIBLE SPECIES, GREATER THAN THE SAMPLE DIAMETER LIMITR2 SEESubzone Models(A)^Predicted BARS = 0.075SUBZ2*AGE87 + 0.173SUBZ2*AGE87 .153 9.722(1)^Predicted BARS = -0.624 + 0.083SUBZ1*AGE87 + 0.182SUBZ2*AGE87 .142 9.757Variant Models(B)^Predicted BARS = 0.075VAR11*AGE87 + 0.184VARI2*AGE87 + 0.154VAR13*AGE87 .159 9.719(2)^Predicted BARS = 0.527 + 0.068VARI1*AGE87 + 0.177VARI2*AGE87 + 0.145VARI3*AGE87 .160 9.754Plant Alliance Models(C) Predicted BARS = 0.130PALL1*AGE87 + 0.202PALL2*AGE87 + 0.150PALL3*AGE87 .096 10.078Plant Association Models(D) Predicted BARS = 0.094PASS1*AGE87 + 0.147PASS2*AGE87 + 0.202PASS3*AGE87 + 0.150PASS4*AGE87 .110 10.039Site Association Models(E)^Predicted BARS = 0.065SASS1*AGE87 + 0.117SASS2*AGE87 + 0.179SASS3*AGE87 + 0.190SASS4*AGE87 + .179 9.6780.119SASS5*AGE87(3)^Predicted BARS = -0.831 + 0.0075SASS1*AGE87 + 0 .127SASS2*AGE87 + 0 .190SASS3*AGE87 + .179 9.7120.202SASS4*AGE87 + 0.131SASS5*AGE87(9)^Predicted BARS = 0.086SASSI*AGE87 + 0.115SASS2*AGE87 + 0.195SASS3*AGE87 + 0.209SASS4*AGE87 + .251 9.3140.158SASS5*AGE87 + -0.032SPHFH + 0.046SPHCWStand History Model(14) Predicted BARS = 13.004 - -0.041SPHFH + 14.074COMPCW .175 9.631(15) Predicted BARS = 14.781 - 0.048SPHFH + 0.044SPHCW .136 9.852Where: 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 = CDFmm-FdBg-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/cm3); and SLOPE=%Slope. 149Figure 76 Predicted % Basal Area Reduction- BARS, at total age 80 yr bysubzone. Note the variabilityindicated by the single standarderror bars.Figure 77 Predicted % Basal Area Reduction- BARS at total age 80 yr bysubzone variant.Figure 78 Predicted % Basal Area Reduction- BARS at total age 80 yr by plantalliances; Pseudotsuga-Mahonia,Tsuga-Mahonia, and Thuja-Tiarellap.all.'s, respectively.Figure 79 Predicted % Basal Area Reduction- BARS at total age 80 yr by plantassociation; Pseudotsuga-Arbutus,Pseudotsuga-Gaultheria, Tsuga-Mahonia and Pseudotsuga-Achlysp.a.'s.sivA eve 9:01^1.°-oce^Ylk s a.beff' tw'sevosese, cs ioro•Site Association.5 15Lqi.a.2050Figure 80150Predicted % Basal AreaReduction-BARS at total age 80 yrby site association; Fd-Salal,FdBg-Oregon grape, FdHw-Salal,HwFd-Kindbergia, Cw-Foamflowers.a.'s, respectively.7.5 Phellinus Root Rot Growth And Yield Reduction Relationships7.5.1^Damage Appraisal of Phellinus Root Rot on Growth and Yield of SecondGrowth Douglas-fir EcosystemsThe growth and yield response to Phellinus root rot was compared in healthy andinfected PSP's. The results were evaluated in several ways: (1) one-on-one comparisonsof healthy and infected PSP's within unique growth and yield installations (i.e., very similarsite and stand conditions), (2) group comparison of healthy and infected PSP's dissimilarstands growing on similar sites, and (3) group comparison of healthy and infected PSP'swithin 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 bysite height. Site height removes the effect of site and age in cases where the PSP's151would not otherwise be comparable (Mitchell and Cameron 1985). In some comparisonstotal age was better than breast-height age for evaluating the timing of stand responsesto root rot.In comparison method (1), the effect of stand density has been removed from within-installations evaluation, by grouping PSP's according to their estimated initial standdensity (stems/ha at age 10 yr) (see section Comparison method (2), used theChapman-Richards volume over total age model (see section and, a quadraticvolume over site-height model (see section Comparison method (3), a quadraticvolume over site-height model was used (see section Growth and Yield Comparisons Within Selected InstallationsYield comparisons within selected PSP installations (near-identical stand and siteconditions) illustrated some of the most dramatic and conclusive yield reductions due toPhellinus infection of PSP's, (Figs. 81-87). Volume and basal area are plotted against siteheight, with the exception of Figures 81 and 83 which are plotted against breast-heightage for comparison. Graphs based on height or age show similar trends. Infected PSP'sin comparison to healthy PSP's show departures in periodic volume and basal areaincrement (Table 28) resulting in lower gross volume and basal areas after 30 to 35 yearsof measurement. Infected PSP's relative to healthy generally have, (i) higher mortality (upto 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 the152Severely 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 thanthe healthy PSP's.TABLE 28^PERCENT CHANGE OF STAND VARIABLESBETWEEN FIRST & LAST MEASUREMENTSSPANNING 30 to 35 yr.PSP I^Disease Condition Percent (%) Change Site AssociationStems/ha Basal Area Volume(m2/ha) (n2/ha)219 Healthy - 6.37 25.61 38.76218 Severely Infected -26.81 10.27 27.50 HwFd-Kindbergia"Infected, % of Healthy* [420%] [40%] [71%]2007 Healthy -33.37 31.05 45.692008 Severely Infected -55.84 11.39 32.56 Cw-Foamflower2009 Severely Infected -52.11 14.80 35.07"Infected, % of Healthy" [162%] [42%] [74%1160 Healthy -50.61 35.74 61.95161 Healthy -33.69 28.96 58.17 HwFd-Kindbergia162 Moderately Infected -24.74 22.44 41.50"Infected, % of Healthy" [ 59%] [69%] [69%1158 Healthy -53.85 39.78 64.00159 Lightly Infected -26.42 29.45 52.99 HwFd-Kindbergia"Infected, % of Healthy" [ 49%] [74%] [83%]349 Healthy -33.31 24.36 36.22348 Lightly Infected -28.81 19.55 30.15 Fd-Salal"Infected, % of Healthy" [ 86%] [80%1 [83%jNegative values indicate a net loss while positive values indicate a net gain over the measurementperiod of 30 to 35 yr.* "Infected, % of Healthy", indicates the percent retained by the Infected (PSP(s) as comparedto 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 valuesare not presented.BO7060504030BOO60040020010 20 5040Productivity Comparisons in Douglas—fir Growth G Yield InstallationsHealthy vs Laminated Root Rot Infected Permanent Sample PlotsBasal Area (e/ha 4.0 cm+)eo ^10 20^ 30Site HeightVolume (m e/ha 4.0 cm+)100040^ 50219 Healthy........ 218 Infected ^I ^30Site Height153Figure 81^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 218 and219). All species z4.0 cm, "Severely" infected with Phellinus root rot.7050504030,302$219 Healthy..,.. 218 Infected...---.154Productivity Comparisons in Douglas-fir Growth & Yield InstallationsHealthy vs Laminated Root Rot Infected Permanent Sample PlotsBasal Area (182/ha 4.0 cm+)SO-. ^ . ^ .40 50 60BH AgeVolume (m'/ha 4.0 cm+)100070. 180600400200BOO.3010I ^ I ^ I ^ . ^ e40 50 60 70 80BH AgeFigure 82^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 218 and219). All species z4.0 cm, "Severely" infected with Phellinus root rot.2007 Healthy• — 2008 Infected2009 Infected•10^ 20^30Site Height2007 Healthy-- 2000 Infected• 2009 Infected40BOO600400200• -JSO155Productivity Comparisons in Douglas-fir Growth & Yield InstallationsHealthy vs Laminated Root Rot Infected Permanent Sample PlotsBasal Area (ma/ha 4.0 cm+)oo7030ao t ^ •^ • ^  •10 20^ 30 40 soSite HeightVolume (m3/ha 4.0 cm+)i000-Figure 83^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.2007 Healthys-%-— 2000 Infected• 2009 Infected2007 HealthyMB Infected156Productivity Comparisons in Douglas—fir Growth & Yield InstallationsHealthy vs Laminated Root Rot Infected Permanent Sample PlotsBasal Area (et/ha 4.0 cm+)8070605010302110 30 40 soBH Age60 70^ BOVolume (ae/ha 4.0 cm+)1000—800600400200^ 1   ^30^40 50^60^ 70 80BH AgeFigure 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.ISO Healthy162 Healthy161 Infected160 HealthyHealthy61 Infected157Productivity Comparisons in Douglas-fir Growth & Yield InstallationsHealthy vs Laminated Root Rot Infected Permanent Sample PlotsBasel Ares (s2/ha 4.0 cm+)BO70a^t ^ a  ^I10^ 20 30 40^ 50Site HeightVolume (a3/ha 4.0 cm+)1000I   ^I- I ^ I  ^A20^ 30 40^ 50Site Height80060040020010Figure 85^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.158Productivity Comparisons in Douglas-fir Growth G Yield InstallationsHealthy vs Laminated Root rot Infected Permanent Sample PlotsBasal Area (ma/ha 4.0 cm+).0700^ I^ I^ 1 110 20 30 40^ 50Site HeightVolume (m3/ha 4.0 cm+)1000— BOO600349 Healthy.348 Infected40020010 20I^ I^I30 40 50Site HeightFigure 86^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 348 and349). All species k4.0 cm, "Light to Moderately" infected with Phellinus root rot.--------159Productivity Comparisons in Douglas-fir Growth G Yield InstallationsHealthy vs Laminated Root Rot Infected Permanent Sample PlotsBasal Area (e/ha 4.0 cm+)so70159 Healthy150 Directed20^10 20^ 30^ 40^ 50Site HeightVolume (me/ha 4.0 cm+)1000BOO151 HealthyInfected60040020^ 30^ 40^ 50Site HeightFigure 87^Productivity Comparisons in Douglas-fir Growth and Yield Installations (PSP 158 and159). All species k4.0 cm, "Moderately" infected with Phellinus root rot.1607.5.1.2^Yield Comparisons Using the Chapman-Richards Non-Linear Growth (VAC)ModelThe Chapman-Richards volume-age model curves could only be calculated for thecombined FdHw-Salal and HwFd-Kindbergia s.a.'s (nearly all of the CWHxm subzonePSP's!). Gross volume (>4.0 cm) was related to total age. Site index was not consideredbecause if was virtually identical in healthy and infected strata (Table 29). The modelestimates for healthy and infected plots fit the data very well (Table 30 and Fig. 88). Themagnitude of yield reduction and the shape of the Phellinus infected curve (i.e., yieldreduction increased gradually, virtually from regeneration) conforms well to observationsfrom field survey work (Fig. 88), and simulations using the TASS-ROTSIM model (author'sunpublished observations). The healthy and infected PSP's are estimated to have 732.9and 668 m3/ha, respectively at age 80 yr for a reduction in yield of 8.86%. Thesereductions are probably conservative because the infected PSP strata is 7.5 yr older andhas a mean site index of 1.2 m greater than the healthy strata, indicating that the infectedstrata in the absence of Phellinus root rot should have greater yields than the healthystrata.161TABLE 29^DESCRIPTIVE STAND STATISTICS FOR COMBINED SITEASSOCIATIONS; FdHw-Salal and HwFd-KindbergiaMEAN AND (STANDARD DEVIATION)Disease Condition Site Index AGE871 ST4102 BA4103 VL4104 NHealthy 27.22 61.38 2927 8.77 24.74 70( 5.61) (20.59) (1709) (2.82) ( 7.93)Infected 28.45 68.88 2417 8.16 23.75 53( 5.17) (22.61) (1657) (3.48) ( 3.48)Combined 27.75 64.62 2708 8.51 24.32 123( 5.44) (21.72) (1699) (3.12) ( 9.00)1^Stand age 19872^Stems/ha A.0 cm at 10 yr3^Basal area/ha (m2/ha) A.0 cm at 10 yr4^Volume/ha (m3/ha) at 10 yr k4.0 cmTABLE 30^CHAPMAN-RICHARDS VOLUME - TOTAL AGE GROWTHMODEL STATISTICS(A.0 cm Gross Volume)EcologicalUnitsDiseaseConditionSums ofSquares Regression CoefficientsB1 B2 B3FdHw-Salal andHwFd-KindbergiaHealthyInfectedAll PSP's864650.0934093.51819265.01143.236975.3081132.611.01232.02181.017461.4371.9711.713NB:^File=BGROMB.SYS.The Chapman-Richards Growth Model Equation, see below:Predicted VOL4 = b1(1-EXP(-b2 AGE))b3162Figure 88 Chapman-Richards volume-agecurves (k4.0 cm) comparinghealthy (___) and Phellinus rootrot infected (---)stand conditionsfor the combined FdHw-Salal andHwFd-Kindbergia s.a.'s in theCWHxm subzone. The healthyyield at 80yr is 732.9 m3/hacompared to 668.0 m3/ha forinfected yield, resulting in a 8.86%yield reduction.^Growth and Yield--Site Height Models for the Whole PSP DatasetGrowth and yield models were fit to PSP's after dropping several outlier PSP's withinitial site heights over 40 m and several other PSP's with unexplainable measurementinconsistencies. 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 theall-PSP's model, therefore only the all-PSP's models are presented. Growth ModelsA linear growth difference model was fit for annual volume increment as a functionof a constant, an annual site height increment term and a site height increment - PSPdisease condition interaction term. (Note, the PSP disease condition was a dummy163variable; healthy or infected). The growth model provided a reasonably good fit for thedata (Table 31). The site height increment - disease condition interaction term washighly significant (p = .000), indicating that disease condition is important in explaining thevariation in volume increment rates. Healthy and infected increment data with theirrespective growth model functions are shown in Figures 89 and 90. Healthy PSP's havesubstantially greater homogeneity compared to the infected PSP's. More negative growthpoints in the infected PSP group. This was also reflected in the regression modelintercept and larger standard error of the estimate. The plot of both growth modelfunctions without the data points illustrates noticeably lower growth rates of the infectedPSP's compared to the healthy PSP's, (Fig. 91). Infected PSP's grow more slowly by4.4% to 11.7% over the range of 0.1 to 0.9 m annual site height increment. Assumingthat site height measurement is independent of root rot effects in PSP's, the pattern ofvolume growth reduction indicates that site quality (site height) has a positive effect onvolume growth reduction. Note, that this assumption is built into the model by using theconstant. Furthermore, evidence of a positive site height effect on Phellinus intensity wasnot shown in the correlation testing nor in the relationships of Phellinus and the siteassociations. An alternative and more likely assumption, is that site height measurementis partially dependent on the effects of root rot (i.e., root rot has had negative, ordepressive, effects on site height measurements). This assumption was tested bystratifying the data by disease condition, and by not including constants in separatemodels estimated for each disease condition.DEP VAR: DVOL4^N: 952^MULTIPLE R: .568^SQUARED MULTIPLE R: .322ADJUSTED SQUARED MULTIPLE R:.321 STANDARD ERROR OF ESTIMATE: 5.223VARIABLE COEFFICIENT^STD ERROR STD COEF TOLERANCE T P(2 TAIL)CONSTANT 4.811^0.406 0.000 11.850 0.000DSTHGHT1 20.562 0.985 0.598 0.870 20.867 0.000DSTHGHT1*RRIN -3.019^0.787 -0.110 0.870 -3.835 0.000ANALYSIS OF VARIANCESOURCE SUM-OF-SQUARES DF MEAN-SQUARE F-RATIO PREGRESSION 12307.381 2 6153.690 225.591 0.000RESIDUAL 25886.922 949 27.278Annual Site Height Increment (m/yr)164TABLE 31 All-PSP's Growth Model independent' of Root Rot Effects on Site Height MeasurementsWhere:^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 ofroot rot in PSP's (absence/presence 0/1).Figure 89 All-PSP's growth model; healthy PSPscatterplot and growth function, (PredictedDVOL4=4.811 + 20.562 DSTHGHT1). Themodel assumes that site heightmeasurements are independent of root rotheight depression effects.•.• .. *. •^." • . •Annual Site Height Increment (m/yr)›, 40coc,) 30aEC)54aaO• —40200—10—20—30—0.1^0.1^0.3^0.5^0.7^0.9^11—0.3165Figure 90^All-PSP's growth model; infectedPSP scatterplot and growthfunction (Predicted DVOL4=4.811+ 17.543 DSTHGHT1). The modelassumes that site heightmeasurements are independent ofroot rot height depression effects.Figure 91 All-PSP's growth model;comparitive growth function plotshealthy (_) and infected PSP's (---).To examine this alternative hypothesis, two more models were estimated for thehealthy and infected PSP strata. The healthy PSP growth model (Table 32) has ahigher constant and greater volume increment rate than the infected PSP growthmodel (Table 33). Figures 92 and 93 illustrate the healthy and infected modelfunctions respectively plotted over the data. The effect of Phellinus root rot on siteheight increment is seen in that most of the negative and lower volume increments areat the lower end of the site height increments. In these cases it is hypothesized thatthe site height measurement trees are infected for many years before symptoms arereadily expressed. There is a high probabilty that some trees measured for siteheights in PSP's would be affected by root rot height depression before being166dropped for reference height measurements. If site height measurements are in factsomewhat dependent on the effects of Phellinus root rot, the volume growthreductions are then inversely related to site height increment, with growth reductionsranging 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 heightmeasurement dependent models are sensible because the volume increments forinfected PSP's must not be equal at low site height increments, unless ingrowth is sodramatic to make up for growth reduction and tree mortality - a generallyinconceivable condition.TABLE 32 All-PSP's Growth Model - Healthy Condition "Dependent of Root Rot Effects on Site HeightMeasurementsDEP VAR:^DVOL4^N: 593^MULTIPLE R: .592^SQUARED MULTIPLE R: .350ADJUSTED SQUARED MULTIPLE R: .349^STANDARD ERROR OF ESTIMATE: 4.643VARIABLE COEFFICIENT STD ERROR STD COEF TOLERANCE T P(2 TAIL)CONSTANT 5.322 0.476 0.000 11.182 0.000DSTHGHT1 19.488 1.092 0.592 1.000 17.843 0.000ANALYSIS OF VARIANCESOURCE SUM-OF-SQUARES DF MEAN-SQUARE F-RATIO PREGRESSION 6862.594 1 6862.594 318.362 0.000RESIDUAL 12739.552 591 21.556Where: Predicted DVOL4 is the annual volume increment, k4.0 cm (m 3/ha/yr),DSTHGHT1 is the annual site height increment (m/yr).DEP VAR: DVOL4^N: 359^MULTIPLE R: .529^SQUARED MULTIPLE R: .280ADJUSTED SQUARED MULTIPLE R: .278 STANDARD ERROR OF ESTIMATE: 6.055VARIABLE^COEFFICIENT^STD ERRORCONSTANT^4.120^0.722DSTHGHT1 18.920 1.605STD COEF TOLERANCE T^P(2 TAIL)0.000^ 5.707^0.0000.529 1.000^11.789 0.000ANALYSIS OF VARIANCESOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIO^PREGRESSION^5095.609^1^5095.609^138.982^0.000RESIDUAL^13088.985 357^36.664Annual Site Height Increment (m/yr)167TABLE 33 M-PSP's Growth Moded - Infected Condition "Dependent" of Root Rot Effects on Site HeightMeasurementsWhere: Predicted DVOL4 is the annual volume increment, A.0 cm (m 3/ha/yr), DSTHGHT1 is the annual site height increment (m/yr).Figure 92^All-PSP's growth model; healthyPSP scatterplot and growthfunction (Predicted DVOL4=5.322+ 19.488 DSTHGHT1). The modelassumes that site heightmeasurements are dependent onroot rot height depression effects.Figure 93^All-PSP's growth model; infectedPSP scatterplot and growthfunction (Predicted DVOL4=4.120+ 18.920 DSTHGHT1). Themodel assumes that site heightmeasurements are dependent onroot rot height depression effects.Figure 94Annual Site Height Increment (m/yr)168All-PSP's growth model;comparitive growth function plots(__) healthy, and (---) infected.The model assumes that siteheight measurements aredependent on root rot heightdepression effects. Yield ModelsA quadratic volume yield-site height model (?.4.0 cm), predicted an increasing yieldloss in Phellinus infected PSP's as compared to healthy PSP's. The model included avolume yield-PSP disease condition (healthy or infected dummy variable) interaction termwhich 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 rootrot conditions in explaining volume variation. The healthy model functions indicate thathealthy PSP's have a slightly higher yield than the infected PSP's, with the infected grouphaving 5.5% less volume at 35 m site height (or approximately 80 years of age). Healthyand infected PSP data and their respective yield model functions again illustrate thegreater homogeneity of the healthy PSP's compared to the infected PSP's (Figs. 95 and96), respectively. In Figure 97, the infected PSP curve visibly departs from the healthy169curve at 10 m site height, but in fact is departing right from the origin. This is accuratebiologically, and compares well with the Chapman-Richards volume-age function shownin an earlier section. The volume yield reductions are lower in the volume-site heightmodels compared to the Chapman-Richards. Site quality (index) was not considered inthe C-R volume-age functions. The yield models suggest that the yield increment ratesare not yet showing significant effects of mortality in the infected PSP's although thegrowth rates are slowing, as shown in the previous section.TABLE 34 All-PSP's Yield ModelMODEL CONTAINS NO CONSTANT.DEP VAR: VOL4^N: 952^MULTIPLE R: .977^SQUARED MULTIPLE R: .955ADJUSTED SQUARED MULTIPLE R: .955 STANDARD ERROR OF ESTIMATE: 91.948VARIABLE^COEFFICIENT STD ERROR STD COEF TOLERANCE T^P(2 TAIL)STHGHT^2.958^0.561^0.179^0.041^5.272^0.000STHGHT2^0.458 0.019 0.821 0.039 23.653 0.000STHGHT2*RRIN^-0.030^0.008^-0.033^0.613^-3.817^0.000ANALYSIS OF VARIANCESOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIO^PREGRESSION .171879E+09^3^.572932E+08^6776.702^0.000RESIDUAL^8023254.390 949^8454.430Where: Predicted VOL4 is the volume yield A.0 cm (m3/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).1200-c-o^1000.0• . /c)800/ •ef••cr600. .:.t7).>-•ca)400••,-•':*•,(•" :^•••;8. 200•0  ^ --:•• • I I0^10^20^30^40^50Site Height (m)170Figure 95 All-PSP's yield model; healthy PSPscatterplot and yield function (PredictedVOL4=2.958 STHGHT + 0.458STHGHT2).Figure 96 All-PSP's yield model; infectedPSP scatterplot and yield function(Predicted VOL4=2.958 STHGHT+ 0.428STHGHT2).Figure 97^All-PSP's yield model; comparitiveyield function plots healthy Uinfected (---) PSP's.1717.5.1.4^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 relateddamage impacts. Although a logical argument was presented previously to rationalizethe differences observed between %BAR and PSP-based damage estimates, I felt thatstand densities were confounding the picture. In the absence of PSP-based Phellinusdamage intensity information, it was impossible to determine if the PSP's were reallyportraying the same damage as the %BAR surveys.Quadratic volume-site height yield models were fit for each of the young (1 to 20 yrold)stand density classes (<1 000, 1 000 - 1 999 and 2 000 - 4 999 stems/ha, (ForestProductivity Councils of B.C., Sept. 1990). Stand Density (Less than 1 000 stems/ha):^Yield ModelA quadratic yield model (Table 35) estimates yield reduction to be 8.25% for infectedPSP's at a site height of 35 m (or approx. 80 yr), (Fig. 98).120010008006004002000 0^10^20^30^40^50Site Height (m)Figure 98172Table 35^Stand Density Class (< 1 000 stems/ha) Yield ModelDEP VAR: VOL4^N: 169^MULTIPLE R: .975^SQUARED MULTIPLE R: .950ADJUSTED SQUARED MULTIPLE R: .949 STANDARD ERROR OF ESTIMATE: 95.699VARIABLE^COEFFICIENT^STD ERROR^STD COEF^TOLERANCE^T P(2 TAIL)STHGHT^-2.275^1.760^-0.157^0.021^-1.292^0.198STHGHT2^0.547 0.058 1.160 0.020 9.374 0.000STHGHT2*RRIN^-0.039^0.017^-0.051^0.618^-2.293^0.023ANALYSIS OF VARIANCESOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIOREGRESSION .288112E+08^3^9603739.352^1048.645^0.000RESIDUAL^1520267.965 166 9158.241Yield models for stand density < 1000 stems/ha at age 10 yr forhealthy (__) and infected (---) PSPconditions.1737. Stand Density (1 000 - 1 999 stems/ha):^Yield ModelA yield model (Table 36) estimated yield reduction to be 8.63% for infected PSP's ata site height of 35 m (or approx. 80 yr), (Fig. 99).Table 36^Stand Density Class (1 000 - 1 999 stems/ha) Yield ModelDEP VAR: VOL4^N: 412^MULTIPLE R: .986^SQUARED MULTIPLE R: .973ADJUSTED SQUARED MULTIPLE R: .972 STANDARD ERROR OF ESTIMATE:76.755VARIABLE^COEFFICIENT^STD ERROR^STD COEF^TOLERANCE^T^P(2 TAIL)STHGHT^0.313^0.767^0.019^0.031^0.408^0.684STHGHT2^0.546 0.025 0.998 0.031 21.470 0.000STHGHT2*RRIN^-0.048^0.009^-0.052^0.642^-5.115^0.000ANALYSIS OF VARIANCESOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIO^PREGRESSION .854946E+08 3^.284982E+08^4837.270^0.000RESIDUAL^2409575.929^409^5891.384174Figure 99 Yield models for stand density 1000 - 1 999 stems/ha at age 10 yrfor healthy (__) and infected (---)PSP conditions.^Stand Density (2 000 - 4 999 stems/ha):^Yield ModelA yield model (Table 37) estimated yield reduction to be 4.97% for infected PSP's ata site height of 35 m (or approx. 80 yr), (Fig. 100).Table 37^Stand Density Class (2 000 - 4 999 stems/ha) Yield ModelDEP VAR: VOL4^N: 330^MULTIPLE R: .985 SQUARED MULTIPLE R: .969ADJUSTED SQUARED MULTIPLE R: .969 STANDARD ERROR OF ESTIMATE: 75.079VARIABLE^COEFFICIENT^STD ERROR^STD COEF^TOLERANCE^T^P(2 TAIL)STHGHT^2.284^0.725^0.129^0.056^3.149^0.002STHGHT2^0.561 0.028 0.877 0.051 20.347 0.000STHGHT2*RRIN^-0.026^0.013^-0.027^0.536^-2.069^0.039ANALYSIS OF VARIANCESOURCE^SUM-OF-SQUARES^DF^MEAN-SQUARE F-RATIO^PREGRESSION .580895E+08 3^.193632E+08^3435.127^0.000RESIDUAL^1843237.797^327^5636.813Figure 100 Yield models for standdensity 2 000 - 4 999stems/ha at age 10 yr forhealthy U and infected(---) PSP conditions.1751768.0 DISCUSSIONVariable-radius plot sampling was successfully used to estimate Phellinus root rotdamage intensity using a Percent Basal Area Reduction (%BAR) parameter estimatetechnique. Damage intensity estimates were a function of damage severity and incidenceestimates. Damage severity, or the proportional difference between the mean basal areasof healthy and infected point samples within a sample survey, ranged between 0 and 1with a mean of 0.30. Stand-based damage incidence, or the proportion of variable-radiusplots 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 incidenceof 0.168. Mean %BAR estimates ranged from 8.25% to 9.99%, depending on the speciessusceptibility 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 otherworkers estimates of damage incidence, severity and intensity.Mean %BARS estimates showed that the first assumption regarding the samplingmethodology and the expected %BAR's relationships was correct. The healthy-only,susceptible species composition was 91.5%, with statistically non-significant variationacross s.a.'s. This ensured an equal probability for detection and measurement ofPhellinus root rot. Infected stand areas compared to healthy areas, sustained about a30% reduction in stand density. Damage severity or relative basal area reduction, was177statistically significant and provided conclusive evidence for the effects of Phellinus rootrot and the sensitivity of the %BAR parameter estimate technique. The coefficients ofvariation for severity were respectively, 31.5% and 18.9% for the infected and healthyareas. The greater basal area variation of infected areas should be taken intoconsideration in the future designs of forest health and forest inventories.8.1 Damage Incidence - Severity - Intensity RelationshipsIncidence-Severity Relationship:  Land- and stand-based incidence of Phellinus rootrot increased with increasing damage severity. This was reasonable considering that bothparameters are time-since-infection or stand age interdependent functions. That is, thefrequency of stems expressing root rot symptoms contributes to increasing damageseverity, which is related to increasing frequency of disease incidence as infection centersexpand to affect more of the land or stand area.Severity-Intensity Relationship: Damage severity increased with damage intensity andthen stabilized at about 0.50 (or 50% basal area reduction) even in the most severedamage intensity conditions. The asymptotic behaviour of severity appears to indicatea resiliency of forest productivity in the presence of Phellinus root rot, in that basal areareduction equilibrated with basal area replacement (the latter likely due in part to ingrowthof less-susceptible, shade tolerant species such as western red cedar, western white pineand western hemlock), and enhanced growth of residual trees due to reducedcompetition. The severity-intensity relationship provided some indication of the178mechanisms of the proposed host-pathogen dynamic equilibrium.Incidence-Intensity Relationship:  Damage intensity increased with increasing damageincidence. Estimates of land- or stand-based incidence were highly correlated to damageintensity (r = .811 and r = .836, respectively). Linear prediction models for %BARS as afunction of land-based incidence and stand-based incidence estimated incidence levelsto be 0.5 to 2.6 times larger than observed intensity levels. This indicated that incidenceestimates alone are inappropriate estimators of damage effects on stand productivitybecause they significantly overestimate over the %BARS range of 5 to 15%. In order toaccurately appraise damage to stand productivity, sampling should include a measure ofincidence and severity.The data suggests that stand-based versus land-based sampling methods should beemphasized in forest productivity-disease impact studies in order to provide moreaccurate and meaningful estimates of damage to stand and forest productivity.Knowledge of land-based incidence and stand-based intensity relationships shouldprovide direction to future root disease surveys that have forest productivity, damageappraisal objectives.Incidence-intensity relationships may be particularly useful for increasing samplingefficiencies, in that if the relationship is well known (perhaps from previous sampling), thenfurther disease sampling can be reduced to simply land-based incidence (binomialresponse) sampling, with stand-based damage intensity then estimated from the179appropriate incidence-intensity relationship model. Binomial incidence sampling methodsare generally cheaper to conduct and more easily reproduce consistent results.Incidence-Incidence Relationship:  Both stand:land-based and land:stand-basedincidence ratios were plotted over land and stand-based incidence, respectively (Figs. 20and 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 under-estimated the stand-based incidence at low stand incidence. Both incidence estimatesappeared 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-standincidence is .923, the greatest variation was at low levels of incidence (which is a criticalarea for management decision making). This variance might easily be corrected if the"effective" sampling areas were more closely defined, rather than the land-based fixed-radius plots set at 3.99 m or .005 ha, and the stand-based variable-radius mean treecritical distance radius at about .027 ha, (i.e., adjust BAF selection). Considering that thebases of the two incidence estimates are completely different, the estimates areremarkably similar, with the greater incidence captured by the stand-based methodcompared to the land-based method. A recommendation for further land-based incidencesampling is to increase the sample plot size to 0.010 ha, and select BAF's to more nearlyapproximate land-incidence areas sampled.1808.2 The Relationship Between PSP's And The %BAR Sample Survey DataEighty percent (80%) of the infected PSP's are in stands where Phellinus root rotexceeded 5% BARS or NSBAR, or that have a BARS-Damage Intensity Class of Medium-Severe. This was an important finding because it meant that the PSP's could beconsidered fairly representative of the stands sampled in and around the PSP, and viceversa, thus simplifying interpretations between ecological and growth and yield impactdata in the %BAR survey and the PSP data sets.8.3^Phellinus Root Rot Variability in Relation to Ecological Site Factors and BEGUnits8.3.1^Disease IncidenceDisease incidence ranged widely between 10 and 100% depending on the samplingunit population, but generally did not vary widely within the biogeoclimatic units. Diseaseincidence appeared to be positively correlated with the sampling unit size, and seenacross all biogeoclimatic units. For example, the mean disease incidence in siteassociations increased from: 16.8% using 0.005 ha fixed-radius plots, to 26.3% usingvariable-radius plots, to 36.8% using 0.04 ha PSP's, to 87.0% using 1 ha %BAR samplesurvey plots.The mean incidence observed using fixed-radius plots (mean was 12.5%) was, about181the same as that obtained in the large-scale root disease survey using the intersectionlength method (Beale 1987). These two land-area-diseased expressions appear toestimate similarly, although the study areas are not directly comparable. Incidenceestimates using fixed- and variable-radius plots showed similar estimates, with the latteralways greater (or more sensitive, particularly at lower land-based incidence levels) dueto its "apparently larger" sampling unit area, thereby increasing the probability of samplinga 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 conductedin the large-scale root disease survey (mean was 87%) in 1982-83 (Beale 1987). Thisfinding suggests that future root disease sampling designs can based on smallersampling units and thereby maintain estimate fidelity, and gain additional ecological andstand condition homogeneity.8.3.2^Disease IntensityDisease intensity ranged from -10% to 55% basal area reduction (BARS estimates).Generally, the magnitude of percent basal area reduction (%BAR) estimates wasdependent upon the combination of tree selection parameters. Thus, increasing theminimum tree sampling diameter limit from 4.0 cm to 12.0/17.5 cm and/or droppingnon-susceptible species trees from the tree count increased the %BAR damage intensityestimates. In mid-seral stands, selection of larger diameter susceptible species increasesthe probability of sampling Phellinus root rot infected trees and hence the percent basalarea reduction estimate rise. Note, that negative percent basal area reduction (e.g., -10%182BARS) reflected a possible, although not a commonly occurring damage condition, inwhich the effect of root rot on stand yield (basal area) is actually positive. Thesesituations occurred where very early Phellinus mortality removed susceptible speciesfollowed by very quick ingrowth of non-susceptible species (usually western red cedar ordeciduous).8.3.3^Site Ecological FactorsThe relationship of site ecological variables to Phellinus root rot damage intensity wasweak and tenuous at best. Although no ecological variables were significantly correlatedto Phellinus damage intensity, several variables showed interesting patterns with PhellinusBARS-Damage Intensity Classes (BARS-DIC's). Mineral soil pH and coarse fragmentcontent were negatively related to BARS-DIC's, and slope was positively related to BARS-DIC's. Similarly, coarse fragment content, mineral soil bulk density, porosity, pH,elevation and slope showed weak but interesting correlations to subzone, variant and siteassociations, 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 soilpercent coarse fragment content (by volume), with the moderately-dry soils associatedwith the coarser soils in the Fd-Salal, FdBg-Oregon grape and FdHw-Salal s.a.'s, andslightly-dry soils associated with the HwFd-Kindbergia and Cw-Foamflower s.a.'s (Fig. 31and Table 20). Interpreting the effect of any one of these variables to Phellinus root rotincidence, or intensity, outside of the integrative BEC system and the dominant stand183historical conditions provided little insight into the behaviour of Phellinus root rot. Itappears that true differences in Phellinus behaviour may only be detected and interpretedin the framework of the system of biogeoclimatic ecosystem classification.There was an apparent relationship of Phellinus root rot with the zonal or regionalclimate indicated by the subzone and variant models, and to a lesser degree, the soilmoisture-nutrient gradient indicated by the site association model. Plant alliance andassociation models, though not significant, further suggested an apparent soil moisturerelationship, within the resolution expected from lower precision plant-based classificationunits. The apparent moisture relationship with Phellinus root rot intensity is morequadratic, 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 andFigs. 76-80). The point at which root rot decreased was evident in: (a) the variant modelat 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 redcedar was associated with lower root rot levels; perhaps indicative of conducive orreactive 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). Theseobservations consistently hinted at actual moisture (Klinka 1984), as being a significantfactor in root rot behaviour, since most sample plots were nutrient poor-to-medium in site184quality. 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 onPhellinus root rot incidence and intensity. Elevation, was significantly lower in theCDFmm, corresponding to its units' classification (Fig. 32), and has a significantly drierclimate than the CWHxm (see Table 1, page 27). Percent slope was also significantlylower in the CDFmm which corresponds to the gently sloping, Nanaimo lowland coastalplain (Fig. 33). Percent slope has been shown to positively effect host tree root contactprobabilities and subsequent spread rates of Phellinus root rot (Bloomberg and Reynolds,1982 and Reynolds and Bloomberg, 1982). Thus lower mean percent slope conditionsin the CDFmm may be contributing to lower levels of root rot, and conversely in theCWHxm subzone. Mineral soil bulk density (fine fraction <2 mm), and its inverseparameter, soil porosity, were respectively, higher and lower, in the CDFmm comparedto the CWHxm subzone (Fig. 34 and 35). It is possible that the denser, less porous soilsof the CDFmm might impede root egress and stand density, and thereby reduce rootcontact probabilities and consequent infection spread rates. The lower soil porosity inthe CDFmm may also negatively affect respiration of P. weirii, thereby reducing its abilityto colonize hosts. Soil pH was also shown to be significantly greater in the CDFmmcompared to the CWHxm (Fig. 36). The 0.3 to 0.4 pH difference may be attributable toa higher proportion of marine and sedimentary base-rich parent materials, and/or slightlyhigher deciduous tree species compositions in the CDFmm. Experiments on the effects185of 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 andMarsden 1978, Hobbs and Partridge 1978), with Armillaria mellea Singh (1980), and with1. tomentosus (Van Groenewoud 1956, and Van Groenewoud and Whitney 1969). Insummary, certain site characteristics (elevation, percent slope, mineral soil bulk density,porosity and pH) appear sufficiently different between subzones to lend some logicalexplanation as to their possible effects on Phellinus root rot incidence and intensitybehaviour.8.4 Phellinus Root Rot Variability in Relation to Old and Second Growth Conditions andBEC UnitsA number of old and second growth stand density and species compositionattributes also appeared to lend some logical explanation to the patterns and behaviourof 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 thebehavioural patterns and not just simply snap shot conditions measured in this study.A significant case for a dynamic host-pathogen equilibrium has been established byrelating present day Phellinus root rot damage in second growth forests with old growthand second growth species compositions, stand densities, stand dynamics, stand origins,and via incidence-severity relationships.186A dynamic host-pathogen equilibrium for Phellinus weirii in coastal Douglas-firecosystems is expected to function as follows. The naturally occurring Phellinus weiriiis a functionally significant and important organism in coastal ecosystems where Douglas-fir is a predominant tree species. Phellinus root rot behaves as a gap forming agentparticularly in the early to mid-seral successional conditions (generally on the mostPhellinus-susceptible species; grand fir and Douglas-fir, and to a lesser degree westernhemlock enabling the more shade tolerant (and coincidentally less-susceptible or diseasetolerant) often polyclimax tree species to occupy the site. As the susceptible host speciesincrease in composition and density (i.e., root rot conduciveness increases) so does theability of the pathogen to open up the stand (i.e., forming gaps). In most siteassociations, the chronosequence successional trend towards less-susceptible,polyclimax host species consequently acts to slow the disease' advance, hence there isan equilibrium between host and pathogen. The pathogen apparently does not have theability to kill-off all species nor persist for periods longer than 50 years, while other less-susceptible 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 pioneerto early-seral conditions suitable for the pathogens role in gap forming dynamics and toreactive the cycle from remnant Phellinus inocula sources. Logging activity in theseecosystems could also play a role in reducing or increasing Phellinus intensity, dependingon the equilibrium phase that the old growth forest was in at the time of logging, and indoing so, return the stand to pioneer early-seral conditions.187The following subsections expand on the development of the dynamic host-pathogenequilibrium postulate through discussion of the old and second growth stand conditionsas they related to the incidence, severity and intensity estimates of Phellinus root rotmeasured in second growth stands. The inter-relationships between these three factorsare fundamental to the mechanisms of the dynamic host-pathogen equilibrium.8.4.1^Phellinus Root Rot Variability in Relation to Old Growth Stand Densities andSpecies Compositions Across BEC UnitsThe dynamic host-pathogen equilibrium was postulated in part on the basis of thefollowing old growth stand condition observations: (a) an inverse relationship between oldgrowth stand density (stems/ha) of Douglas-fir and Phellinus damage intensity (or BARS-DIC's), (b) old growth stand density (stems/ha) of western red cedar was greatest in theLow and Severe BARS-DIC's, and (c) old growth species compositions of Douglas-fir andwestern 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 lowerand higher in the CWHxm1 compared to the CDFmm and the CWHxm2 subzone variants,respectively. Interestingly, the CWHxm1 variant had substantially higher Phellinus rootrot damage intensity (12.88%) compared to 5.94% and 8.93%, for the CDFmm andCWHxm1 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. This188phenomena at the high level of ecological classification tends to corroborate the observedtrends of species dynamics in portions of stands infected with Phellinus root rot.The potential old growth inoculum source composition (Douglas-fir/western hemlockspecies compositions--COMPFH) were shown to be virtually equal between the CDFmmand CWHxm subzones (86.4% and 84.3%, respectively). Because of this fact, the roleof Douglas-fir/western hemlock stand density (stems/ha, SPHFH) was though to be veryimportant in a comparison of Phellinus root rot intensity. Stand density of SPHFH wasshown to parallel the mean Phellinus root rot intensity in second growth stands in theCDFmm and CWHxm subzones and their respective site associations. Old growth standdensities 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 inthe CDFmm (5.94%), which was 53% that of the CWHxm (11.11%). Since the old growthspecies compositions were virtually identical at the subzone level, it appeared that oldgrowth stand density may be a critical factor in the transmission and intensification ofPhellinus root rot in present second growth stands. Note, a causal relationship could notbe established. In apparent contradiction to the subzone relationship, old growth standdensity of Douglas-fir was negatively and significantly correlated (p = .081) to Phellinusdamage intensity, while there was no significant correlation to stand density of westernred cedar. If the dynamic host-pathogen equilibrium were true, then the negativecorrelation for old growth Douglas-fir may be explained if we assume the old growthforests were in a reactive phase of the equilibrium, that is, due to Phellinus-related189mortality and reduction of stand density. Similarly, old growth species composition ofDouglas-fir was negatively correlated (non-significant, p = .265) to Phellinus damageintensity, and western red cedar was positively correlated (insignificant, p = .346).Old growth stand density-Phellinus intensity relationships at the site association levelwere not as clear. Only a slight increase in stand density (stems/ha) of Douglas-fir fromthe drier to fresher site associations was observed. Stand density and speciescomposition of western red cedar was notably higher in the site associations with thehighest Phellinus root rot intensities; FdBg-Oregon grape, FdHw-Salal and HwFd-Kindbergia s.a.'s (Figs. 42 and 44). In the same site associations, the old growth speciescomposition of Douglas-fir was notably lower. Ecologically, it was surprising to seerelatively fewer stems/ha and lower composition of western red cedar in the Cw-Foamflower 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 thepresence of Phellinus root rot earlier in the old growth stand life, and become a significantequilibration component of the stand. The high root rot intensity in the FdBg-Oregongrape s.a. is counter-intuitive given the low Douglas-fir stand density and speciescomposition observed in that s.a. (Figs. 41 and 43). This may be explained, in part, bythe fact that grand fir (Bg), (a very highly susceptible species, and an indicatedcomponent of the climax forest), decomposes very quickly, and was therefore absent instump sampling.190Phellinus intensity contour plots (Figs. 45 and 46 in Section illustrate thePhellinus root rot relationship with old growth stand density (stems/ha Douglas-fir(SPHFH) and stems/ha western red cedar (SPHCW)). The contour plots indicated atrough 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 numbersof Douglas-fir and western red cedar, possibly, relating to conducive and reactive standconditions for Phellinus root rot behaviour, respectively. The upper peak approximatelycorresponds to high SPHFH (>200/ha) and moderate SPHCW (>120/ha), while thelower peak approximately corresponds to low SPHFH (<200/ha) and moderate to highSPHCW (> 100/ha). The existence of the peaks and a trough in these figures furthersuggested the concept of a natural, dynamic host-pathogen equilibrium. Both peaksmight be viewed as a conditions conducive for transmitting P.weirii to second growthforests; 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-firecosystems appeared to approach equilibrium (very low damage intensity), over longperiods of time.In summary, the old growth stand conditions suggested that Phellinus root rot islikely responsive to increasing stand density of Douglas-fir/western hemlock (SPFH). Thatwas most apparent between subzone variants. The host-pathogen equilibrium is alsosuggested by increasing stand density and species composition of non-susceptible191western red cedar, which generally corresponded to reduced density of susceptiblespecies. Phellinus root rot appears to have contributed to a shift to western red cedarin the old growth, but not to the point of eliminating the disease. Without betterknowledge of the duration of infection and inoculum distribution at the time of the secondgrowth stand origin, it was impossible to confirm whether the old growth conditions arecausal (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 aneffect, of some root rot induced succession of forest successional trends strongly relatedto P. weirii activity (i.e., an effect of the dynamic host-pathogen equilibrium) as well asclimate, fire and ecologically induced succession.8.4.2^Second Growth Stand ConditionsSecond growth stand density and susceptible species compositon factors thatappear related to Phellinus damage intensity were; (a) first PSP measurement standdensity (stems/ha ?..4.0 cm), (b) back-estimated stand density (stems/ha, >4.0 cm at 10yr), (c) back-estimated Curtis' Relative Density (_44.0 cm at 10 yr), and (d) first PSPmeasurement of Phellinus susceptible species composition Douglas-fir, grand fir andwestern hemlock >4.0 cm, FSUSINT).Second growth stand conditions (species compositions, stems/ha, basal area(m2/ha), and relative density) were seen to vary between site associations, and although192not significant at the 20% level, the patterns were so surprisingly similar to that ofPhellinus root rot intensity that they could not be ignored. (See Section, p. 125).For example, the susceptible species compositions (FSUS, FINT or FSUSINT) measuredat PSP establishment were highly correlated to the observed root rot levels within theCWHxm 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, thegreater the susceptible species composition, the greater the probability of Phellinus rootrot, with the exception of the FdBg-Oregon grape s.a. (possibly due to the small samplesize). Similarly, total stems/ha, (using either first measurement >4.0 cm, or the back-estimated to age 10 yr, .4.0 cm estimates), had virtually identical distribution patterns toPhellinus root rot BARS estimates (Figs. 59 and 60, p. 127). It appeared that standdensity (stems/ha), was playing a real and significant role in Phellinus behaviour similarto that reported by Bloomberg (1990). Interestingly, the basal area estimates did notshow similar relationships, (Figs. 61 and 62, p. 128), although basal areas were slightlygreater in the CWHxm s.a.'s compared to the CDFmm s.a.'s. Curtis' relative standdensity 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 factorin disease spread" appears to be true. Stand density of both the old and second growthwas closely related to Phellinus root rot intensity at least the subzones and siteassociations.1938.4.2.1^Second Growth Species Dynamics: Variable-Radius Plot SampleSurveysThe dynamics of the postulated host-pathogen equilibrium were shown to be anactive phenomena in the second growth by examining changes in species compositionstratified by species susceptibility, diameter and disease condition classifications. Twosources of data were examined; (a) variable-radius plot sample survey data, and (b) 30-35 years of PSP records. Variable-radius plot %BAR sample survey data were examinedin three ways.The results of all three analytical approaches indicated similar trends in therelationships: (a) non-susceptible species compositons, and tree counts (hence basalareas) were greater in Phellinus infected conditions by about 30% compared to healthyconditions, (b) there was a greater net increase in non-susceptible species compositionin Phellinus infected conditions (gained about 4.4%) compared to the susceptible speciescomposition (lost about 2.6-4.4%), and (c) there appeared to be an inverse relationshipbetween the amount of non-susceptible species composition and tree diameter sizeclasses and Phellinus damage intensity (i.e., the non-susceptible species compositionwas greater in the small diameter classes vs. large, that is dependent on Phellinusintensity). In (c) above, the non-susceptible species composition shift strengthenedabove 20% basal area reduction (BARS) confirming that the differences in non-susceptiblespecies composition levels were in part, time dependent, as root rot development is alsohighly time dependent. That is to say, the reduction of susceptible species was194countered by mensurational ingrowth (or a shift in species composition) of non-susceptible species in infected areas, which is more noticeable above the 20% BARSthreshold. This was because Phellinus susceptible species composition drops relativelyquickly, often episodically, while the subsequent ingrowth of non-susceptible species wasslower, and was in response to the increased light, heat and moisture conditions enablingsaplings to enter the 4.0 cm diameter limit class.The successful attempt to estimate species succession using diameter limits "ageclass", and/or root rot intensity conditions to stratify tree tallies made at one measurement period, while not perfect,did allow for some interpretation of species dynamics.^Second Growth Species Dynamics: Permanent Sample Plot RecordsChanges in non-susceptible and susceptible species compositions were examinedusing permanent sample plot (PSP) records (.4.0 cm) stratified by disease condition(incidence: absence/presence) and the first and last measurement (spanning 30 to 35yr).Non-susceptible species compositions increased by 3.7% in Phellinus infected PSP'sand 0.2% in healthy PSP's, for a net increase of 3.5%, while the susceptible speciescompositions decreased by 2.1% in Phellinus infected PSP's and 0.6% in healthy PSP's,195for a net decrease of 1.5%. Comparison of the PSP-based species composition shiftsshowed 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 fromthe %BAR sample survey data. Similarly, the PSP-based change in susceptible speciesof -2.1% was remarkably close to the -2.6% to -4.46% decreases seen in the variable-radius plot survey data (see Section The similarities between the PSP andsurvey data lends strength to the PSP's being representative of the broader forestcondition (that was sampled),-- perhaps having forest sampling implications beyond thatrequired for evaluating root rot and species dynamics.Changes in non-susceptible species composition over 30 to 35 yr measurementswas large. The greatest increases occurred in the two zonal site associations (Fd-Salaland HwFd-Kindbergia), by over two orders of magnitude compared to the other siteassociations. The increases appeared somewhat related to Phellinus intensities. Thereasons are unclear, but the following explanations are forwarded. The significantincrease in Phellinus-tolerant, intermediate and resistant (TIR) species in the HwFd-Kindbergia zonal s.a. may be explained by the climatic, ecological, successional, andperhaps pathological factors that are integrated to form the basis of site association taxonnames. The site association taxon would indicate that Douglas-fir is a predominant earlyseral species on HwFd-Kindbergia s.a.'s, with successional trends towards a stable climaxvegetation comprised of western hemlock and Douglas-fir. Furthermore, Douglas-fir,196although dominant in the climax condition, becomes secondary to western hemlock (thelatter substantial component of TIR species). Given the differential Phellinussusceptibilities 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 thegreatest incidence and intensity of Phellinus root rot, it is suggested that root rot isplaying a major biogenic successional role in reducing the early sera! component ofDouglas-fir to a lower secondary level, as suggested by the site association taxon. A lesseffective case can be built for the Fd-Salal s.a., but the fact remains that stand densitydoes drop by some means (suggested to be Phellinus root rot), and that there is anincrease in species diversity, or a drop in Douglas-fir composition, although the siteassociation remains essentially pure (>80%) with other minor species mixes (oftendeciduous).It is possible that the more moderate environmental site conditions (e.g., light, heator moisture) typical of zonal site associations may be contributing to the larger increasesin non-susceptible species. The CDFmm and Fd-Salal units are warmer and have lowerstand densities on average than the CWHxm2 and HwFd-Kindbergia units, although thelatter two units are significantly moister. Considering that root rot incidence and intensityestimates in the CDFmm unit are about half that of the CWHxm2, it follows that light andheat likely contribute more to the release and mensurational ingrowth of TIR species thanmoisture.1978.4.2.3^Summary of the Natural, Host-Pathogen Dynamic Equilibrium ModelA model to explain the dynamics of species compositions can be postulated from theecosystems sampled. In healthy, root rot free conditions, the proportion of non-susceptible to susceptible species remained fairly static, with a tendency to slightincreases in non-susceptible species typical of the chronosequences. In Phellinus rootrot infected conditions, there was a substantial reduction of susceptible species with a lowproportion (replacement rate) of small diameter class ingrowth. This was countered withhigher a proportion of non-susceptible species entering the lower diameter class ascompared to healthy conditions. In other words, healthy stands over time, shift towardsa 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 biogenicsuccessional agent removing Phellinus susceptible species (generally pioneer to earlyshade 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 increasingproportions of non-susceptible species would also have occurred in the old growthforests barring major climatic change. Although the old growth stump survey data wasnot dramatic in illustrating the root rot related successional pattern (i.e., increasing redcedar composition), the trend nevertheless appears strong enough to support thehypothesis of species dynamics as related to damage intensity, with strong implicationsof a dynamic host-pathogen equilibrium.198The similarity in forest successional trends in old and second growth coastalDouglas-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 towardsexplaining why Phellinus root rot has not covered the whole Douglas-fir forest ecosystemand brought it to a non-productive condition. Furthermore, the suggestion of a dynamichost-pathogen equilibrium provides forest managers and silviculturists with direct evidencethat management prescriptions using less-to-non-susceptible tree species, will aid inreducing long term damage to Phellinus root rot, although not necessarily maximizingstand or forest timber (volume) productivity.The existence of a dynamic host-pathogen equilibrium and very high level ofPhellinus root rot incidence in coastal Douglas-fir ecosystems (87% of 1ha samplesurveys, 35% of 0.04 ha PSP's and 26% of variable-radius plots), suggests that Phellinusroot rot is a common and significant biological factor in the ecological functioning of theseecosystems. As such, the significance of Phellinus root rot and the host-pathogenequilibrium in the coastal Douglas-fir ecosystems should be strongly considered in allaspects of forest resources management (be it stand-specific or forest level managementprescriptions) for timber, wildlife, recreation, or site productivity-potential assessments forland-use resource allocations.It is clear from this study that the role of "pathogens" in forested ecosystems needsto be more clearly evaluated so that our management of these ecosystems does not199create an imbalance in the natural, dynamic host(s)-pathogen(s) equilibrium(s).Management actions favouring disequilibrium, will cause either reduced stand/forestproductivity of the pathogens' preferred hosts, or require great efforts to eradicate thepathogen inoculum from the site. Allowing the dynamic equilibrium to occur naturally willreduce stand and forest productivity over time under volume maximization managementobjectives, but in doing so will provide a more tree species (bio-) diverse ecosystem. Abalanced "production-forest" management approach should strive to use the principlesof shade-tolerant species succession ingrowth into Phellinus infected sites (as seen in thedynamic host-pathogen equilibrium). This should be achieved by shifting to alternateless-susceptible species for regeneration and stand tending prescriptions, thus reducingthe amplitude of the host-pathogen equilibrium. Since stand density also appearsstrongly related to enhancing Phellinus root rot intensity, regeneration and stand tendingprescriptions should consider managing to lower densities, that are in principle lessconducive to Phellinus, and thereby reducing crop risk. And, as unlikely as it is that firewill be used as a post-harvest Phellinus control treatment, fire appears to have had someeffect in controlling Phellinus root rot, as seen in the next section.8.5 The Effects Of Stand History (Logging And Burning) On TheBehaviour Of Phellinus Root RotPresent stand age was shown to be significantly different (all p-values < .088) betweenstand origins (Figs. 74 and 75), with root rot intensities paralleling mean stand age. Inthis study, stand age was not considered to be significantly correlated to damage200intensity (BARS), but this correlation is highly suspicious, since in most other root diseasestudies, stand age is highly and positively correlated, and important for prediction ofdamage intensity. There seems little doubt that the effect of stand age on Phellinus rootrot intensity in this study is a positive one, albeit not strong, and therefore explains asubstantial amount of the variation in damage intensity. Beyond the substantial role standage plays, I have sought to establish if any ecological factors play a role in the variationof Phellinus root rot intensity between stand origins.Several questions arose that after some deliberation lead to some explanation of thePhellinus root rot trend seen in these stand histories. What elements of P. weirii controlare suggested by the logging-only origin as compared to stands of wildfire origin? Whatmight the effect of slashburning be compared to wildfire? Are interactions betweenlogging and slashburning suggested by the data? How do past and present standdensities and species compositions relate to present infection levels? The next severalsubsections will address these questions.8.5.1 Evaluating The Effects Of Logging On Phellinus weirii SurvivalThe fundamental difference between Phellinus infected stands that are logged versusnot logged (or wildfire killed) is that of cut stump surfaces. How does P.weirii survive toreinfect the planted or naturally regenerating crop, and what is its natural P. weirii decaycurve?201Phellinus weirii is not considered to be an aggressive competitor, and is not able tocolonize wood previously decomposed or inhabited by other microorganisms (Hansen1979). When a Phellinus colonized tree dies (or is logged), the fungus can remain viableectotrophically for 40 to 50 yr on old growth Douglas-fir (Hansen 1979), or over 100 yrwhen 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, therebyexcluding antagonistic microorganisms (Nelson 1964, 1967, 1973, 1975). Zone lines aredarkly pigmented sheets of swollen hyphal tissue (Nelson 1967). Li (1983) showed thepigment in P. weirii zone lines to be tryosine-melanin in nature, and confirmed that it isimportant 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 funguscommon in soil and wood. The antagonism of various Trichoderma spp. to P. weirii hasbeen demonstrated by Nelson (1964,1973,1975) and by Goldfarb (1985, 1986) and(Goldfarb et al. 1989). Nelson and Thies (1985, 1986) demonstrated the ability ofTrichoderma viride to colonize stump tops and with increasing success in stumps withadvanced decay. Goldfarb (1985, 1986) has shown Trichoderma spp. to be slow, butwell adapted invaders of P. weirii infected stumps and root systems, in nature. Theseworkers suggest that Trichoderma spp. are likely effective biocontrol candidates. Nelsonis continuing biocontrol research of P. weirii primarily with Trichoderma spp. using varioustypes of stump top-or-side delivery systems (Nelson and Thies 1985, 1986, and Nelson2021990). 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. weiriiobserved in logged versus wildfire originated stands may, in part, be due to a slowdisplacement of P. weirii by stump top invading antagonistic Trichoderma spp.8.5.2 Evaluating The Effects Of Slashburning (& Logging) On Phellinus weiriiSurvivalSurvival 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 columncondition (% hollowness) in stands logged 50 years previously. Notably, there is noindication of fire history in Hansen's (1979) study. Tkacz and Hansen (1982) have shownthe dependence of second growth root disease distribution on the previous rotation, andinterestingly their (1982) study sites were burned after harvest.From personal observations, and discussion with colleagues (Reynolds 1990, andBloomberg 1990a), it is very common to find old growth stumps completely burned outto a root bark shell in stands of logged-and-burned origin. The intensity and uncontrolledduration 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 decaycolumns. The assumption made in the logging-and-burning scenario is that fire enteredthe stump/root system via the cut stump surface and burned out the root's woodytissues, likely baking ectotrophic P. weirii to temperatures beyond its tolerance, thus203sharply reducing its surface area and infective viability. The upper limit to thermaltolerance 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 theoccurrence 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 antagonisticmicroorganisms in soil (Parmeter 1977). Actinomycete populations have been shown toincrease sharply on severly burned Douglas-fir sites (Wright and Tarrant 1957). Theactinomycete, 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 concentrationfrom forest ash leachate, which has been shown to have a negative effect on the in vitrogrowth of Armillaria ostoyae (Reaves et al. 1984). Furthermore, fire ash leachates areknown to positively effect Trichoderma spp. which in turn reduce the growth andrhizomorph 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 (Nelson1964, 1969), extensive work is underway to investigate biological control methods usingantagonistic 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).204It is possible that soil cation concentrations were raised following fires in the oldgrowth logging slash. In that scenario, P. weirii survival and infectivity would be reduceddirectly by decay column burning, and possibly through enhanced soil cation effects onantagonistic fungi, thus lending explanation to why Phellinus root rot levels (BARS) arelowest in stands of logged-and-burned origin. As for wildfire origin stands, it was unlikelythey had similar fire intensities as compared to the logged-and-burned origin since totalstand densities of stems/ha Douglas-fir and western hemlock (SPHFH), and western redcedar (SPHCW) were half that of the logged-and-burned stands, and ground fuels weremost certainly less. Thus the impact of fire on Phellinus was likely less in wildfire originstands than in logged-and-burned stand origins, hence, the highest root rot intensitieswere in stands of wildfire origin.Although the mean Phellinus (BARS) intensities between logged-only and logged-and-burned was not much different, 10.54% and 8.49% respectively, the trend should notbe ignored. Note that beyond age, fire is the only factor that is different. Although aninteraction of slashburning and logging was likely, fire following old growth loggingappears to have had a significant effect in controlling P. weirii.8.5.3^Evaluating The Effects Of Wildfire On Phellinus weirii SurvivalIn wildfire origin stands there were of course no man-made stumps; thus, regardlessof the P. weirii infection condition in the old growth, wildfire might only reduce inoculum205loads in the odd natural stump, and possibly through changed soil chemistry and itseffects on fungi antagonistic to P. weirii (as postulated above). Studies on the fire andP. weirii ecology of mountain hemlock forests of Oregon suggest that P. weirii infestationsmay enhance the probability of wildfire because of increased fuel loadings and ladderfuels (Dickman and Cook 1989). In this study, wildfire origin stands had less than half theold growth stems/ha of Douglas-fir and western hemlock (SPHFH), more stems/ha ofwestern red cedar (SPHCW), and more than double the species composition of westernred cedar (COMPCW) than stands of other origins (Table 21, pg. 105). These facts,combined with the observed negative correlation between BARS damage intensities andSPHFH, and a positive correlation with COMPCW observed in this study, lends supportto 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 longertime, then older stands that are destroyed by fire are more likely to leave inoculumcapable of generating subsequent reinfestation than are younger stands". Stand densityis known to have dramatic effects on stem sizes (McArdle et al. 1961), and hence stumpand root systems. Since total old growth stems/ha in wildfire origin stands were lessthan half that of other origins, it is not only likely that the trees were larger in the wildfireorigin old growth forests, but if infected with P. weirii would pose a greater inoculumpotential.Based on the observations of Childs (1970), Tkacz and Hansen (1982) and this studydata, one can postulate that the distribution and infectivity of P. weirii inoculum sources206might be higher in wildfire origin stands as compared to the other origins, thus effectingthe higher levels of infection observed in todays second growth forests for the followingreasons: (1) a lower likelihood of wildfire directly affecting ectotrophic or endotrophicinocula on standing trees during a wildfire, (2) the increased probability of wildfire in areasof P. weirii infestation due to disease effects on stand structure/fuel loadings, (3) thelikelihood of larger stem and root systems due to lower stand density (wildfire SPHFH 63stems/ha vs. logged, logged-and-burned 130 stems/ha), therefore potentially larger andlonger-living inoculum sources, and (4) a lower likelihood of wildfire producing as muchash 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 antagonisticto P. weirii.In summary, the most likely explanation for the variability of Phellinus damageintensities (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 veryintense slashburning that followed much of the old growth (railway) logging, and its lethaleffects on Phellinus decay columns via the cut stump surface and to a lesser extent onbelow-ground stump and root surface ectotrophic P. weirii; (3) the absence of cut stumpsurfaces in stands of wildfire origin; (4) the likelihood of lower intensity fire at or close tothe ground level; (5) the stems/ha of Douglas-fir old growth stumps in wildfire originstands were half (63) that of the logged-origin stands (133), combined with the westernred cedar was less than half (12) that of the logged-origin stands (24), theoretically207indicating a more severe Phellinus damage intensity condition.The frequency of occurrence of stand origins for the 139 sample survey locationsshowed that 69% originated after logging-and-burning, 21.6% after wildfire, and 9.4% afterlogging-only. The origin frequencies, while not providing any particular explanation inthemselves, provided an interesting perspective to the Douglas-fir ecosystems onsoutheastern Vancouver Island.8.6 Predicting Biogeoclimatic Unit Phellinus Root Rot HazardAnd Risk8.6.1 Phellinus Root Rot and the Site Association Taxon ModelThe search for direct link correlations of site ecological variables to Phellinus root rotbehaviour appears very uncertain. With the variability of ecological variables andparameters so high, an interpretation of Phellinus relationships at the site association levelwas attempted using a combination of past and present stand characteristics, Phellinus-host susceptibilities, predicted vegetation succession patterns (chronosequences) that areconnoted in the site association taxon (names), and the concept of the natural, dynamichost-pathogen equilibrium. The relevancy of this approach is based on the fact thatvegetation is a good measure of an ecosystems' productive potential as seen in the useof indicator plant species for identification of ecosystems (Klinka et al. 1989), and thecorresponding major role of vegetation (plant) classification in the BEC system (Pojar et208al. 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 uniformclimate (Banner et al. 1990). For example, the Fd-Salal s.a. connotes a major forestcover of Douglas-fir with a salal ground cover as the potential climax vegetation. Thesuccessional phases a s.a. goes through to reach this predicted climax condition areknown as chronosequences (they are not clearly documented at this time).Consider the coastal Douglas-fir ecosystem as a whole. It is a complex of treespecies compositions of varying Phellinus-susceptibilities, in a variety of site and standconditions, shaped by a variety of fire and harvest histories, and is in varioussuccessional phases and pest conditions. The success of measuring these factors, theirinteractions and their relationships to Phellinus root rot incidence, severity and intensitylevels has not yet borne conclusive answers, as this study shows. Site classification, andthe site association taxon model in particular, provided the most integrative tool forcomprehending most of the sources of zonal (climate), plant and site variation, with thepossible 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 predictabilityof a s.a.'s chronosequence to propagate and maintain Phellinus inoculum, which is itselfbased on knowledge of the relative susceptibilities to P. weirii of the s.a. taxons' climaxtree species vegetation. Other factors such as fire and harvest history, and past and209present stand conditions (species composition and stand density), could serve to adjustthe scale of the relative incidence or intensity (risk) in order to compensate for standconditions believed to be conducive-to or reactive-to Phellinus root rot, within the contextof the dynamic host-pathogen equilibrium. Critical to determination of risk is the host-pathogen equilibrium phase of the preceding stand at the time of origin of the secondgrowth forests, (i.e., estimation of previous stand conditions as they relate to probabilitiesof Phellinus inoculum carry-over). These phases are: (1) conducive, or highly-susceptiblespecies phase, (2) reactive, or less-susceptible species phase, or (3) Phellinus root rotequilibrium phase. Over or underating the Phellinus risk is highly probable if the ratingis based on past conditions only; knowing the potential condition is the essence of riskrating on an ecological basis.The site association taxon model for Phellinus root rot behaviour is as follows. Siteassociations with grand fir (Bg) or Douglas-fir (Fd) as a leading species are consideredto be the most conducive and have the highest probability to propogate Phellinus rootrot to the upper limits of an implied dynamic host-pathogen equilibrium. Climax speciescombinations of lower susceptiblity to Phellinus (e.g., western hemlock (Hw), lodgepolepine (PI), western white pine (Pw) or western red cedar (Cw)) serve to impede or reduceroot rot levels. In this context, the site association model proves to be very accurate inpredicting 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 twosusceptible species. In the CWHxm variant the transition from FdHw-Salal to HwFd-210Kindbergia s.a. reduces the highly susceptible component (Douglas-fir) from a primary-major species to a secondary-major species, thus reducing the probability of sustainedhigh damage. The observed BARS sample estimates show this, but when adjusted foran age interaction, the HwFd-Kindbergia s.a. was predicted (regression model) to havemarginally higher Phellinus intensity. This phenomena could be possible because agreater Phellinus intensity may be required to kill-out the Douglas-fir component in orderto 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 ofsusceptible species in a climax condition. Notably, the Cw-Foamflower s.a. had arelatively low stand density (stems/ha) of old growth western red cedar, which is contraryto the s.a. taxon. It is very likely that this s.a. had not yet acheived its potential climaxforest conditions at the time of the old growth harvest as its condition appears to be ina very conducive, or highly susceptible species phase.In summary, a method for estimating the hazard and risk of Phellinus root rot for siteassociations has been demonstrated through the integration of, (i) present-day diseaseincidence 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 thevegetation potential within the BEC system of site classification. The implications forestimating biogeoclimatic unit pest hazard and risk in this manner is enormous, sincemuch of the knowledge exists today.2118.6.2^Multiple Regression ModelsEmpirically-based multiple regression models were estimated to provide primarily adescriptive, and secondarily a predictive behaviour of Phellinus root rot intensity withinbiogeoclimatic units in order to develop a disease hazard and risk classification (seeSection 8.6.3.). Models for each biogeoclimatic unit included a stand age-biogeoclimaticunit interaction term. Stand age, although not significantly correlated to Phellinus root rotintensity in this study, does appear to have some relationship to Phellinus, and isgenerally considered to be a strong determinant in detection, assessment and predictionof Phellinus behaviour. None of the root rot behaviour models described enough of thedamage intensity variation for reliable stand-level prediction, but all the models diddescribe the general relationships within and between the biogeoclimatic units reasonablywell. Generally, all the models were similar. Interestingly, the relative damage intensitiesare nearly identical to those observed in large-scale root disease surveys done by the BCForest Service in the same ecosystems in 1982-83 (Beale 1987). Furthermore, themodels appear to describe the disease behaviour reasonably and support theinterpretations of Phellinus root rot behaviour in the coastal Douglas-fir ecosystems visa vis the postulated dynamic host-pathogen equilibrium, and more importantly theobservations made by the author from field experience and other disease analyses (Beale1989b).All Phellinus intensity models for the biogeoclimatic units were compared at reference212age 80 yr (Figs. 76-80) and were used for development of an ecologically-based Phellinusroot rot hazard and risk classification, see Section Phellinus Root Rot - Coastal Douglas-fir Ecosystem Hazard (Susceptibility)and Risk Classification for Southeastern Vancouver IslandAfter a sober review of all the available data, models postulations, a firstapproximation of Phellinus root rot hazard and risk for Douglas-fir ecosystems ofsoutheastern Vancouver Island was made (Table 44). Recall that hazard is anassessment of tree, stand forest and environmental conditions which are conducive topest 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 afunction of risk. The classification presented is heavily based on professional judgementand 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 ecologicalvariables/characteristics and stand attributes could not be considered in the classification,although as discussed earlier, many if not all variables/attributes (biological, geologicaland climatalogical) appear to be well integrated into the biogeoclimatic units and thereforedo not need to be uniquely identified and accounted for.213TABLE 38^PHELLINUS ROOT ROT HAZARD AND RISKCLASSIFICATION FORDOUGLAS-FIR ECOSYSTEMS ON S.E. VANCOUVER ISLANDBEC UNITS Low Medium SeverePLANT ALLIANCEPseudotsuga-MahoniaTsuga-MahoniaThuja-AchlysXXXPLANT ASSOCIATIONPseudotsuga-ArbutusPseudotsuga-MahoniaTsuga-MahoniaThuja-FoamflowerXXXXSUBZONECDFmmCWHxmXXSUBZONE VARIANTCDFmmCWHxm1CWHxm2XXXSITE ASSOCIATIONCDFmm-Fd-SalalCDFmm-FdBg-Oregon grapeCWHxm-FdHw-SalalCWHxm-HwFd-KindbergiaCWHxm-Cw-FoamflowerXXXXX1^Predicted Incidence (stand-parameter-diseased basis) at 80 yrLow <.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 yrLow s6%Medium >6 s10%Severe >10%2148.7 Damage Appraisal Of Phellinus Root Rot On Growth And Yield Of Second GrowthDouglas-fir Ecosystems8.7.1^Yield Comparisons Within Selected InstallationsComparison of growth and yield within selected PSP installations (chosen for near-identical site and stand conditions at PSP establishment) indicated some dramaticreductions in the yield of Phellinus infected PSP's. Compared to healthy PSP's Phellinusinfected 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. Greaterreductions to stems/ha and lesser increments of basal area and volume were attributableto greater Phellinus damage intensity in the PSP's. Interestingly, the growth rates ininfected PSP's, except in the cases of the most severely infected PSP's, appeared to beequal to or better than the healthy PSP's. This response was likely attributable to twofactors: (i) increased light and moisture conditions for the residual (remaining) trees,perhaps similar to the Phellinus root rot related natural thinning responses reported byOren et al. (1985), and (ii) increased mensurational growth rates for the residual trees andingrowth rates of shade tolerant, non-susceptible species (i.e., the latter being part of the3.5% net response of Phellinus non-susceptible species ingrowth shown in Phellinusinfected PSP's, ...a resultant of the dynamic host-pathogen equilibrium discribed in thisstudy).2158.7.2^Yield Comparisons Using the Chapman-Richards VAC ModelYield models using the Chapman-Richards VAC growth model were calculated forthe combined site associations FdHw-Salal and HwFd-Kindbergia (nearly all of theCWHxm subzone PSP's). Model estimates for healthy and infected PSP conditions (>4.0cm), fitted yield expectations very well. The estimated yield reduction and the form of thePhellinus infected curve (increasing gradually, virtually from regeneration) conformed wellto observations from field and survey work, and simulations using the TASS-ROTSIMmodel. The healthy PSP yield at 80 yr is estimated at 732.9 m3/ha, and the infected PSPyield at 668 m 3/ha, for an 8.86% yield reduction.The multiple linear regression Phellinus intensity model estimated percent basal areareduction for site associations (all species, >4.0 cm; NSBAR) at 80 yr to be 12.68%, whichcompared well with the 8.86% yield reduction from the Chapman-Richards VAC.8.7.3^Growth and Yield Comparisons Over Site Height8.7.3.1^All Data-No StratificationA growth difference model predicted a positive linear relationship between annualvolume increment (>4.0 cm) and annual site height increment for healthy and Phellinusinfected PSP's. Infected PSP's grew more slowly by 4.4% to 11.7% over the range of 0.1216to 0.9 m annual site height increment, respectively. Assuming that site heightmeasurements are independent of root rot effects in PSP's, the pattern of growthreduction indicated that site quality has a positive effect on growth reduction. Analternative and more likely assumption is that, site height measurements were somewhatnegatively affected by Phellinus root rot. If so, then growth reductions were inverselyrelated 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 regularlyassess site height tree health, and attempt to have alternate measurement trees in or nearthe PSP to ensure the integrity of site height measurements.A quadratic volume-site height model (..4.0 cm) predicted an increasing yield loss inPhellinus infected PSP's compared to healthy PSP's. Losses began immediately followingregeneration, but rose noticeably from 4.0% at site height 10 m to 5.5% at 35 m siteheight (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 waslikely due to several factors: (i) the %BAR survey estimates excluded all livingsymptomatic trees (the mean %BAR severity parameter was .30 or 30% reduction),therefore the intensity of "basal area reduction damage" was greater than the actualvolume loss as measured in the PSP's, (ii) the generally "damage-free" establishmentconditions for growth and yield PSP's mean that Phellinus infection is in most cases217making slow moving (on average 0.3 m/yr) incursions into PSP's well after PSPestablishment, (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 ofincidence 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% or1/5th of the PSP's would have been measured in 1987 (the growth and yield data usedin this analysis is current to the 1989/90 dormant season); perhaps 66% of the PSP dataadequately reflects the root rot growth and yield conditions observed in 1987 %BARsurveys. A second explanation of the difference between the two estimates is that thequadratic 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 intialdensities (Mitchell and Cameron 1985). The condition mentioned in (i) above, is one ofthe most significant factors in the disparity between the PSP and %BAR estimates of yieldreduction. With these factors considered, the %BAR damage intensity estimates mightbe reduced to more closely approximate the percent basal area reductions estimatedfrom a yield curve analysis of the PSP data. The conclusion then, is that the %BARsampling method very nearly estimated the actual volumetric (PSP-measured) losses thatoccurred in the coastal Douglas-fir ecosystems of southeastern Vancouver Island. Thisalso suggests that the set of growth and yield PSP's examined as part of this studysatisfactorily reflected the damage intensity found in the areas immediately surroundingthe PSP's.218Given that one of the largest factors in explaining the differences between the %BARand the PSP-based estimates is the experimental error introduced in the %BAR samplingdesign (i.e., the exclusion of visibly symptomatic trees in the tree tallies), the authorsuggests that all symptomatic trees be counted in %BAR tree tallies. That would allowthe %BAR estimate to be made not only on standing healthy but also standing infectedbasal area and dead and downed infected, separately or combined. These changesshould be incorporated into future percent basal area reduction %BAR type samplingmethodologies. The use of site height in modelling suggested that yield models beconstructed for the three stand origin density classes.^Growth and Yield: All Data, Stratified by Stand Density Classes (stems/ha atage 10 yr)^Less than 1 000 stems/haA quadratic yield model estimated yield reduction to be 8.25% for infected PSP's ata site height of 35 m (or approx. 80 yr), which compared very well with the linearregression model estimate of percent basal area reduction (NSBAR) of 8.88%.^1 000 - 1 999 stems/haA yield model estimated yield reduction to be 8.63% for infected PSP's at a siteheight of 35 m (or approx. 80 yr), which compared very well with the linear regression219model estimate of percent basal area reduction of 8.96%.^2 000 - 4 999 stems/haA yield model estimated yield reduction to be 4.97% for infected PSP's at a siteheight of 35 m (or approx. 80 yr), which does not compare well with the linear regressionmodel estimate of percent basal area reduction of 11.28%. This variance is likely due toa smaller sample size and widely ranging intial densities and ages.The relevance of stratification into stand density classes was clearly borne out in theresults of the first two density classes. For establishment densities below 2 000 stems/hathe yield models were amazingly similar between stand density classes, and also providenear-identical estimates to the NSBAR percent basal area reduction estimates for thesestrata. 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 greatin the PSP's with high establishment densities, in part, because gap formation is beneficialto the residual trees and/or the shade tolerant less-susceptible species are achievingincreasing mensurational ingrowth rates.Overall, this study has conclusively demonstrated that modelling of growth and yieldPSP's to produce growth and yield functions that reflect disease incidence (i.e., presenceor absence of Phellinus root rot) and stand density strata is possible. Several analyses220have all demonstrated similar growth and yield reductions through simple PSP-to-PSPcomparisons, the Chapman-Richards volume-age growth models, quadratic volume-siteheight models and growth difference models. In all analyses the within strata variabilityis so great that statistically significant growth and yield differences are impossible todetect at this point in time, although the trend towards continuing and worsening yieldreductions appears set. Continued analyses of this sort should be conductedperiodically, with the following considerations: (i) ensure the highest degree of ecologicaland stand condition-at-establishment fidelity as possible (it does not matter that the standconditions 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 anyother damage condition) should be stratified on the basis of within-PSP severity orintensity. Furthermore, if forest level damage impact assessments are attempted usingthis technique, then ensure the appropriate proportional composition of healthy toPhellinus infected PSP's is made in the determination of a yield function. The mix ofPSP's should be proportional to the amount of damage severity or intensity observed inthe forest, as determined from PSP sized samples (.04 ha, and as of 1990, .10 ha), asincidence and intensity are sample size dependent.2219.0 SUMMARYVariable-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 intensityshowed that root disease incidence was a larger predictive factor of damage intensitythan 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 levelsof land-based incidence. The estimates were highly correlated (r. .82), but the estimatesonly stabilized when the incidence was above .35 (35%). Overall, the mean stand-basedincidence 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 variable-radius plot sampling), allowed diameter-dependent views of damage intensity and standdynamics 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 notexceed .5 (50%) basal area reduction. This would appear to indicate stand productivity222resilience in the presence of Phellinus root rot. Through a method of subtraction,Phellinus host susceptibility classification, and disease incidence stratification, it wasfound that the less- to non-susceptible species compositions (particularly in the smallerdiameter class) were greater at infected points than at healthy points. This also providedsome evidence of Phellinus-induced succession and led to the postulation of a natural,dynamic host-pathogen equilibrium. Similarly, the less- to non-susceptible speciescomposition in Phellinus-infected permanent sample plots (PSP's) was found to increaseby 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, 0.04ha PSP's to the 1 ha %BAR sample survey units. The incidence in the 1 ha units wasidentical to that found in 20 ha sample units in a separate root disease study in the samearea of Vancouver Island. It is likely that 1 ha samples are close to the minimumsampling size required to accurately estimate Phellinus root rot incidence at the forestlevel in coastal Douglas-fir ecosystems.Incidence - intensity linear relationships were shown to be fairly strong, and arepotentially a very effective way of estimating damage intensity from binomial(presence/absence) incidence estimates alone. However, both land- and stand-basedincidence estimates overestimated the observed intensity by 0.5 to 2.6 times. This resultstrongly suggests that stand damage intensity sampling is best if a measure of incidenceand severity are combined, such as in the %BAR parameter estimate technique.223Damage intensity varied between levels of the zonal (climatic), plant and siteclassifications of the system of Biogeoclimatic Ecosystem Classification (BEC). The mostsignificant differences in mean damage intensities were between the CDFmm and CWHxmsubzones, (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 theinterpretation of Phellinus root rot behaviour, but not necessarily for forest managementprescriptions.Stand origins were found to have significantly different damage intensities of Phellinusroot rot, but were strongly affected by stand age. Beyond the substantial age effect, itis postulated that the effects of logging, and logging followed by intense slashburningboth had an increasingly negative effect on the survival of Phellinus weirii ectotrophicinoculum sources in relation to the wildfire conditions.Some site ecological variables were found to vary significantly with the subzone andvariant units (elevation m asl, percent slope and mineral soil pH), but not with root rotintensity. 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 thebiogeoclimatic units. Notably, stems/ha of old growth Douglas-fir and western hemlockwere significantly and negatively correlated with Phellinus root rot intensity, which followedthe pattern of root rot variation between subzones as well. Old growth stems/ha andcomposition of western red cedar were generally greater in ecosystems with higherPhellinus root rot intensities. Distribution patterns for second growth stems/ha and224Curtis' relative density index strongly reflected the root rot distributions of the siteassociations. Patterns in the old growth and second growth stand density, speciescomposition and the dynamic behaviour of species in and out of the presence of root rotled to the postulation of a natural, dynamic host-pathogen equilibrium.A classification of Phellinus hazard and risk for all biogeoclimatic units investigatedwas 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 Douglas-fir ecosystems. The site association taxon (name) model was shown to accuratelydescribe, in relative terms, the site association hazard (susceptibility) and risk (probabilityof and estimated damage intensity) of Phellinus root rot. The s.a. taxon model wasviewed in the context of the host-pathogen dynamic equilibrium, host susceptibilities andnatural successional chronosequences and their relationship to effect conducive, reactiveor equilibrating stand conditions for the pathogen.Estimates of yield reduction (_4.0 cm) due to incidence of Phellinus root rot ingrowth and yield PSP's were quite variable, but depended upon infection severity. In acarefully selected set of comparable PSP's, infected compared to healthy PSP's sufferedgreater reductions of stems/ha (49-420%) and retained (40-80%) less of the accruedbasal area and (71-83%) volume. Interestingly, the infected PSP growth rates haveremained equal to or better than the healthy PSP's except in the most severely infectedconditions. Yield reduction estimates for the FdHw-Salal and HwFd-Kindbergia combineds.a.'s using the Chapman-Richards growth model indicated an 8.86% reduction in volume225at 80 yr. In comparison, the yield reduction estimated for all PSP's using a quadraticmodel was 5.5% at site height 35 m (approx. 80 yr). Similarly, yield reductions for standdensity 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 andgenerally showed some growth reductions in infected PSP's ranging between 7-17% overthe annual site height increments of 0.1 to 0.9 m/yr. There appeared to be a site heightmeasurement dependence on Phellinus root rot incidence, meaning that site height treeheight growth was impacted by the disease--a situation that requires closer evaluationand correction if it really exists. Certainly the growth and yield analytical techniquesapplied to a PSP database stratified simply on the basis of Phellinus incidence has shownconclusively and dramatically the effects of the disease on stand dynamics andproductivity. A more careful evaluation is recommended following more accurateestimates 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%. Someadjustments to the %BAR survey procedure to account for the symptomatic treecontribution to productivity would reduce the %BAR estimate and bring yet closeragreement between the two productivity damage estimates. Furthermore, it is recognizedthat even with 35 to 37% of the PSP's infected, the proportion of infected PSP's does notfully 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 remarkablysimilar, suggesting both damage appraisal methods should be pursued further forPhellinus root rot and other damaging pest agents. The %BAR parameter estimationtechnique proved to be a sensitive, and flexible sampling method that enabled a wealth226of statistics and interpretations to be made for producivity and ecological questions alike.227^10.0^RECOMMENDATIONS FOR MANAGEMENT^10.1^Damage AppraisalThe greater sampling variation found in Phellinus infected areas, combined with thepervasive incidence of infected areas should be taken into account in designing futureforest and pest inventories.One hectare sample survey units were found to estimate incidence equally well as20 ha samples conducted in a separate study. A minimum sample size for estimating theincidence of Phellinus root rot at the forest level is suggested to be 1 ha.Continued field testing of the Percent Basal Area Reduction parameter estimatesampling technique should be carried out in other forest types for other pests (particularlyroot 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 calculateresults, and (b) it simply provides stand-parameter-diseased estimates of stand damage,rather than conducting land-area-diseased estimates then converting land-basedestimates to stand-based parameters. Further estimation of incidence-intensityrelationships is encouraged in order to build on the efficiencies of incidence sampling, ascompared with incidence-plus-severity sampling to estimate a stand-based damageintensity parameter.228Incidence-Intensity relationships appear to be an effective way to reduce samplingto only incidence, once the incidence-intensity relationships are well known for a stratum(e.g., biogeoclimatic unit, forest cover, age class, management regime). Incidencesampling is easier to conduct, cheaper and more easily produces repeatably consistentestimates.On average, Phellinus root rot was shown to reduce volume yields from comparablehealthy conditions by about 8.25% to 8.86%. Growth and yield in the permanent sampleplots appeared to be partially compensated for by enhanced growth of residual trees andingrowth of tolerant and resistant species. This condition was particularly noticeable inlight to moderate damage intensities. The magnitude and trends of damage intensityobserved from this set of permanent sample plots was surprisingly close to what I andother root disease workers have observed over the years. The negative effect on heightgrowth of site height measurement trees in PSP's should be investigated and alternatemeasurement trees be selected where warranted.A closer examination of permanent sample plots (both growth and yield andinventory 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 incorporatedin the development of growth and yield models and forest level yield analysisdeterminations.22910.2^Stand and Forest Level PrescriptionsHazard and risk of Phellinus root rot in the CDFmm and CWHxm biogeoclimatic unitswas estimated. The greatest risk was predicted to be in the CWHxm subzone, andnotably in the FdHw-Salal and HwFd-Kindbergia s.a.'s.The site association taxon model was shown to accurately predict the hazard andrisk of Phellinus root rot when considered in the context of; (i) a natural, dynamic host-pathogen equilibrium, (ii) host susceptibilities to Phellinus root rot, and (iii) the naturalsuccession chronosequences. The potential for estimating the hazard and risk for otherdamaging agents (e.g., pests) using this method is enormous, and should be pursuedas much of the knowledge needed for this is already available.Several other factors may be useful in estimating site risk: (i) Phellinus root rot wasweakly, but positively correlated to percent slope; (ii) Phellinus root rot damage intensityappears 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 hada positive effect on todays second growth Phellinus damage intensity.Mortality of primarily Douglas-fir in Phellinus infected sites was counteracted bysubstantial species composition shifts to less-susceptible to resistant host trees (westernhemlock, lodgepole and western white pine, western red cedar and deciduous).230The density and species composition shifts seen in the presence of Phellinus rootrot strongly suggests that management prescriptions consider lower stand densities andmore consideration of less-susceptible to resistant species as a means of reducingdamage, hazard and risk to ecosystem health.The natural, dynamic host-pathogen equilibrium suggests that stand productivity ofhighly susceptible species seres will only be reduced to an unknown maximum beforetolerant and resistant species ingress the Phellinus infected sites. It further suggests thatmanagement prescriptions should minimize the risk of stand disequilibrium conditions bynot creating Phellinus-conducive stand conditions. To do otherwise will increase loss ofstand productivity of highly susceptible species, and in all likelihood require expensiveprograms to eradicate the pathogen from the site and thereby re-equilibrate the site.23111.0^REFERENCESAngwin, 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 TheirImplications for Management in the Vancouver Forest Region. B.C. Ministry ofForests, For. Serv. Int. Rpt. PM-V-10, Burnaby, BC., V5G 4L9.Beale, J.D. 1989(a). Management Guidelines for Laminated Root Rot in the VancouverForest Region. FRDA Memo No. 108, BC Min. For., Victoria, BC, 12p.Beale, J.D. 1989(b) (unpubl. data). Applied Linear Regression Methods: Modelling theBehaviour and Epidemiology of Laminated Root Rot in Coastal Douglas-firEcosystems. File Report, BC Min. of Forests, Vancouver Forest Region, 4595Canada Way, Burnaby, BC, V5G 4L9, 72p.Beese, W., pers. comm. 1987. Forest Ecologist, Woodlands Services, MacMillan BloedelLimited, 65 Front St., Nanaimo, BC. V9R 5H9Black, C.A. 1965. Methods of Soil Analysis Part I Physical and Mineralogical Properties,Including Statistics of Measurements and Sampling. Madison: American Society ofAgronomy, Vol. 2. pp 891-896.Blair, H.A., C.H. Driver and E.C. Packee. 1975. A Survey of Disease Impact on IntensivelyManaged Immature Conifer Forest (Root and Butt Rot Survey). MacMillan BloedelLimited, Project Plan No. 721-1, 2, 3. 40p.Bloomberg, W.J. 1984. A Ground Survey Method For Estimating Loss Caused By Phellinusweirii Root Rot. 111. Simulation of Disease Spread and Impact. BC-R-7, 25p. Can.For. Serv./Pac. For. Res. Ctr., Victoria, B.C.Bloomberg, W.J. 1990. Effect of stand conditions on advance of Phellinus weirii in Douglas-fir plantations. Phytopathology 80:553-559.232Bloomberg, W.J., and A.A. Hall. 1986. Effect of laminated root rot on relationships betweenstem growth and root system size, morphology, and spatial distribution in Douglas-fir. For. Sci 32:202-219.Bloomberg, W.J., and J.D. Beale. 1985. Relationship of ecosystems to Phellinus weirii RootRot on Southern Vancouver Island. In: Proceedings of the 33rd Western InternationalForest Disease Work Conference; Olympia WA.; p. 20-28.Bloomberg, W.J., P.M. Cumberbirch, and G.W. Wallis. 1980. A Ground Survey Method ForEstimating Loss Caused by Phellinus weirii Root Rot. II. Survey Procedures and DataAnalysis. Can. For. Serv., Pac. For. Res. Ctr., BC-R-4, Victoria, B.C. 44p.Bloomberg, W.J., and G. Reynolds. 1982. Factors affecting transfer and spread of Phellinusweirii mycelium roots of second-growth Douglas-fir. Can. J. For. Res. 12:424-427.Bloomberg, W.J., and G. Reynolds. 1985. Growth Loss and Mortality in Laminated Root RotInfection Centers in Second-Growth Douglas-fir on Vancouver Island. For. Sci.31:497-508.Bloomberg, W.J., and G.W. Wallis. 1979. Comparison of indicator variables for estimatinggrowth reduction associated with Phellinus weirii root rot in Douglas-fir plantations.Can. J. For. Res. 9:76-81.Bloomberg, W.J. pers. comm. Dec. 1990(a). Retired Senior Forest Pathologist with ForestryCanada and Consulting Pathologist; Victoria, BC.Bier, J.E. and D.C. Buckland. 1947. Relation of Research In Forest Pathology To TheManagement of Second Growth Forests I. Poria weirii Root Rot, An ImportantDisease Affecting Immature Stands of Douglas-fir. B.C. Lumberman, 31:49-66.Box, G.E.P., and D.R. Cox. 1964. An analysis of transformations. J. Royal Statist. Soc.B26:211-243.Buckland, D.C., A.C. Molnar, and G.W. Wallis. 1954. Yellow Laminated Root Rot ofDouglas-fir. Can. J. Bot. Vol. 32:69-81.Chatterjee, S. and B. Price. 1977. Regression Analysis by Example. John Wiley & Sons,toronto, ON. 228p.Childs, T.W. 1963. Poria weirii root rot. Phytopath. 53:1124-1127.Childs, T.W. 1970. Laminated root rot of Douglas-fir in western Oregon and Washington.USDA For. Serv. Res. Pap. PNW 102, 27p. Pac. NW For. & Range Exp. Sta.,Portland, OR.233Childs, T.W., and K.R. Shea. 1967. Annual losses from disease in Pacific Northwest forests.USDA For. Serv. Res. Bull, PNW-20.Cook, S.A. 1980. Stand Development In The Presence of a Pathogen, Phellinus weirii. In:Proceedings of the Forest Succession and Stand Development Research In TheNorthwest Symposium; Corvallis, OR.; p 159-170.Courtin, P. pers. comm. (unpublished data) 1990., Assistant Pedologist, B.C. Min. ofForests, Vancouver Forest Region, 4595 Canada Way, Burnaby, BC. V5G 4L9Dickman, A. and S. Cook. 1989. Fire and fungus in a mountain hemlock forest. Can. J. Bot.67:2005-2016.Dilworth, J.R., and J.F. Bell. 1979. Variable Probability Sampling - Variable Plot and Three-P.Published by: O.S.U. Book Stores, Inc., Corvallis, Oregon.Doliner, L.H., and J.H. Borden. 1984. Pesterms: A Glossary of Forest Pest ManagementTerms. Forest Pest Review Committee of British Columbia. Dep. of BiologicalSciences, Simon Fraser University, Burnaby, B.C.Emmanuel, J. 1989. Ecosystem Classification and Analysis. Univ. of B.C., Fac. of Forestry,Vancouver, B.C.Filip, G.M. 1980. Estimating Damage Caused by Root Diseases In Forested Areas. USDAFor. Serv. Rough Draft. Doc. # 1952A.Filip, G.M., and C.L. Schmidt. 1979. Susceptibility of native conifers to laminated root roteast of the Cascade range in Oregon and Washington. For. Sci. 25:261-265.Ford, B. 1977. 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 andRemeasurement 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 ofPattern, Frequency Distribution, and Sampling of Forest Disease in Douglas-firPlantations. 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 reciprocalaveraging. Release B, Ecology and Systematics, Cornell University, Ithaca, NY, 185p.234Gammell, 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 Douglas-fir. M.S. thesis, Univ. Wash. 43 p. (manuscript not seen) cited in Bloomberg andReynolds, 1985.Goheen, D.J., and J.S. Worrell. 1979. Root Disease Survey of the Proposed Ten TailsTimber 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, MedfordDistrict, 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 inPhellinus weirii-infested stumps and roots and antagonism in vitro. In Proceedingsof the Western Intl For. Dis. Work Conf., Olympia, WA, Sept. 24-27, 1985. p 100-103.Goldfarb, B. 1986. Trichoderma spp.: distribution in stumps infested with Phellinus weiriiand growth and antagonism in vitro. M.S. Thesis, Oregon State Univ., Corvallis. 78p. (manuscript not seen)Goldfarb, B., E.E. Nelson, and E.M. Hansen. 1989. Trichoderma species from Douglas-firstumps and roots infested with Phellinus weirii in the western Cascades of Oregon.Mycologia 81: 134-138.Goldfarb, B., E.E. Nelson, and E.M. Hansen. 1989. Trichoderma spp.: Growth Rates andAntagonism to Phellinus weirii in vitro. Mycologia 81: 375-381.Green, R.N., P.L. Marshall, and K. Klinka. 1989. Estimating site index of Douglas-fir(Pseudotsuga menziesii [Mirb.] Franco) from ecological variables in southwesternBritish Columbia. Forest Science 35:50:63.Green, R.N., 1989. Site - Forest Productivity Relationships And Their ManagementImplications In Coastal Lowland Ecosystems Of East Graham Island, QueenCharlotte Islands. MSc Thesis, Dpt. of Forestry, Univ. of B.C., Vancovuer, B.C., 169p.Green, R.N., P.J. Courtin, K. Klinka, R.J. Slaco and C.A. Ray. 1984. Site Diagnosis, TreeSpecies Selection, and Slashburning Guidelines For The Vancouver Forest Region.B.C. Min. of For., Land Mgmt. Rpt. No. 8, Prov. of B.C., Victoria, B.C.235Grosenbaugh, L.R. 1952. Plotless timber estimates-new, fast, easy. Journal of Forestry50:32-37.Hadfield, J.S. 1985. Laminated Root Rot: A Guide for Reducing and Preventing Losses inOregon and Washington Forests. USDA For. Serv., Pac. NW For. Pest Mgmt.,Portland, OR, 13p..Hansen, E.M. 1979. Survival of Phellinus weirii in Douglas-fir stumps after logging. Can. J.For. Res. 9:484-488.Hansen, E.M., K. Reynolds, and P. Angwin. 1983. Factors Influencing Spread of Phellinusweirii in Douglas-fir. Res. Pap., Oregon State University, Corvallis, OR., IUFRO Proc.,Melbourne, Australia.Hobbs, S.D., and A.D. Partridge. 1979. Wood decays, root rots, and stand compositionalong an elevation gradient. For. Sci. 25:31-42.Holland, S.S. 1976. Landforms of British Columbia: A Physiographic Outline. Bulletin 48,B.C. Dept. of Mines and Petro. Res., Prov. of B.C., Victoria, B.C. 138p & map.Howes, D.E., and E. Kenk (eds.) 1988. Terrain Classification System For British Columbia(Revised Edition)- MOE Manual 10, Recrea. Fish. Br., Min. of Envir. and Surv. & Res.Map. Br., Min. of Crown Lands, Prov. of B.C. 90p.Husch, B., C.J. Miller and T.W. Beers. 1972. Forest Mensuration. New York, Ronald PressCo. vii, 410pp.Huse, K.J. 1983. Forekomst Av Rate I Norsk Granskog, Frequency of butt rot in stands ofPicea abies (L.) Karst in Norway. Norsk Institutt For Skogforskning, 1432 As-NLH(Norway), Rapport 3/83, 39p.Hutchins, A.S. 1980. In vitro inhibition of root-rot pathogens Phellinus weirii, Armillariellame/lea, Fomes annosus, and Phytophthora cinnamomi by a newly isolated Bacillussp. Microb. Ecol. 6:253-259.Hutchins, A.S., and S.Y. Li. 1981. Relative capacities of filamentous and non-filamentousbacteria from two forest soils to inhibit Phellinus weirii in culture. Northw. Sci. 55:219-224.Hutchins, A.S., and S.L. Rose. 1984. The Variation in Antagonistic Streptomyces Populationsin Soils from Different Vegetation Types in Western Oregon. Northw. Sci. 58(4):249-255.Iles, K. pers. comm. 1987. Manager, Growth and Yield, Woodlands Services, MacMillanBloedel Limited, 65 Front St., Nanaimo, BC, V9R 5H9.236Jenny, H. 1980. The soil resource. Ecological Studies 37, Springer-Verlag, New York.377p.Johnson, A.L.S., G.W. Wallis, and R.E. Foster. 1972. Impact of root rot and other diseasesin young Douglas-fir plantations. The Forestry Chronicle, Vol. 48, No.6.Jungen, J.R., P. Sandborn, and P.J. Christie, 1985. Soils of Southeast Vancouver Island:Duncan-Nanaimo Area. MOE Tech. Rpt. 15., B.C. Min. of Envir./Min. of Agric. &Food, Prov. of B.C., Victoria, B.C. 188p.Jungen, J.R. 1985. Soils of Southern Vancouver Island. MOE Tech. Rpt. 17/Rpt. No. 44,B.C. Soil Survey, B.C. Min. of Envir., Prov. of B.C. 198p & 3 maps.Kabzems, R.D. 1985. Quantitative classification of soil nutrient regimes of somemesothermal Douglas-fir ecosystems. MSc. Thesis, Fac. of Forestry, Univ. of B.C.,Vancouver, B.C.Keeney, D.R. 1982. Nitrogen-Availability Indices. In Methods of Soil Analysis, Part 1, 2ndEd. (Page et al., eds.) Am. Soc. Agron. and Soil Sci. Soc. Am., Madison, Wisc. pp.711-733.Keser, N., and D. St.Pierre, 1973. Soils of Vancouver Island-A Compendium. B.C. For.Serv. Res. Note No.56, Prov. of B.C., Min. of For., Victoria, B.C. 005179 VA03Klinka, K., R.N. Green, P.J. Courtin, and F.C. Nuszdorfer. 1984. Site diagnosis, tree speciesselection, and slashburning guidelines for the Vancouver Forest region. B.C. Min.For. Land Mgmt. Rep. 25, Victoria, B.C.Klinka, K., F.C. Nuszdorfer, and L. Skoda. 1979. Biogeoclimatic units of central andsouthern Vancouver Island. B.C. Ministry of Forests, Victoria, B.C., 120 pp.Klinka, K., R.N. Green, R.L. Trowbridge, and L.E. Lowe. 1981. Taxonomic classification ofhumus forms in ecosystems of British Columbia. First approximation. LandManagement Rep. 8, B.C. Ministry of Forests, Victoria, B.C., 54 pp.Klinka, K., and V.J. Krajina. 1986. Ecosystems of the University of British ColumbiaResearch Forest, Haney, B.C. The University of British Columbia, Faculty of Forestry,Vancouver, B.C.Klinka, K., V.J. Krajina, A. Ceska and A.M. Scagel. 1989. Indicator of Coastal BritishColumbia, Univ. of British Columbia. Press, 288p.Klinka, K. pers. comm. 1990. Assistant Professor of Forest Ecology, Faculty of Forestry,Univ. of British Columbia, Vancouver, B.C.237Kummi, J. pers. comm. 1987. Forest Pathologist and Special Projects Forester, WoodlandsServices, MacMillan Bloedel Limited, 65 Front St., Nanaimo, BC. V9R 5H9.Lavkulich, L.M.^1981. University of British Columbia Methods Manual--PedologyLaboratory. 20p.Leavitt, C. 1913. Forest Protection in Canada, 1912., Commission of Conservation, Canada,Comm. on Forests, Bryant Press, Toronto, Ont.Leavitt, C. 1915. Forest Protection in Canada, 1915., Commission of Conservation,Canada, Comm. on Forests, Bryant Press, Toronto, Ont.Lewis, T. 1976. The till-derived podzols of Vancouver Island. Ph.D. Thesis. Dept. of Soil Sci.,Univ. of B.C., Vancouver, B.C.Li, C.Y. 1983. Melanin-like pigment in zone lines of Phellinus weirii colonized wood.Mycologia 75:562-566.Major, J. 1963. A climatic index to vascular plant activity. Ecology 44:485-498.McArdle, R.E., W.H. Meyer, and D. Bruce. 1961. The Yield Of Douglas Fir In The PacificNorthwest. Tech. Bull. 201, U.S.D.A. Dept. of Agric., Pac. Northwest For. & RngeExpt. Sta., For. Serv., 74p.MacDonald, G.I., N.E. Martin, and A.E. Harvey. 1987(a). Armillaria in the Northern Rockies:Pathogenicity and Host Susceptibility on Pristine and Disturbed Sites. USDA For.Serv. Res. Note, INT-371.MacDonald, G.I., N.E. Martin, and A.E. Harvey. 1987(b). Occurrence of Armillaria spp. inForests of the Northern Rocky Mountains. USDA For. Serv. Res. Note, INT-381.Mounce, I., J.E. Bier, and M.K. Nobles. 1940. A Root Rot of Douglas-fir Caused By Poriaweirii. Can. J. Bot. 18:522-533.McCauley, K.J., and S.A. Cook. 1980. Phellinus weirii infestation of two mountain hemlockforests in the Oregon Cascades. For. Sci. 26:23-29.McGill, R., J.W. Tukey and W.A. Larsen. 1978. Variation of Box Plots. Amer. Statistician32(1):12-16.Meidinger, D., E. Hamilton, and T. Fleming, 1987. Ecosystem Classification Program DataProcessing System User's Guide Version 2.0. B.C. Min. of Forests & Lands Res.Rpt. RR87006-HQ, Prov. of B.C., Victoria, B.C. 132p.Miller, R.G. 1986. Beyond ANOVA, basics of applied statistics. John Wiley & Sons, Toronto,ON. 317p.238Mitchell, K.J., and W.J. Bloomberg. 1986. Expanding concepts of growth and yieldmodeling to disease impacts and forest products. Pages 3-18 in: Proc. Can. For.Serv. 2nd Modeling or Workshop. Victoria, B.C., Can.Mitchell, K.J., and I.R. Cameron. 1985. Managed Stand Yield tables for Coastal Douglas-fir:Initial Density and Pre-Commercial thinning. Prov. of B.C., Min. of Forests, LandManagement Rep. No. 31, Victoria, B.C., 69p.Nelson, E.E. pers. comm. 1990. Senior Plant Pathologist, Pacific Northwest ResearchStation, USDA Forest Service, Corvallis, Oregon, 97331.Nelson, E.E. 1964. Some Probable Relationships of Soil Fungi and Zone Lines to Survivalof Poria weirii in Buried Wood Blocks. Phytopathology 54: 120-121 p.Nelson, E.E. 1969. Occurrence of Fungi Antagonistic to Poria weirii in a Douglas-fir ForestSoil in Western Oregon. For. Sci. 15(1):49-54.Nelson E.E. and Fay, H. 1974. Thermal Tolerance of Poria weirii. Can. J. For. Res. 4:288-290.Nelson, E.E. and W.G. Thies. 1985. Colonization of Phellinus weirii-infested stumps byTrichoderma viride: 1. Effect of isolate and inoculum base. Eur. J. For. Pathol. 15:425-431.Nelson, E.E. and W.G. Thies. 1986. Colonization of Phellinus weirii-infested stumps byTrichoderma viride: 2. Effects of Season of Inoculation and Stage of Wood Decay.Eur. J. For. Pathol. 16: 56-60.Nelson, E.E., and T. Hartman. 1975. Estimating spread of Poria weirii in a high-elevationmixed conifer stand. J. For. 73(3):141-142.Nelson, E.E. 1971. Invasion of freshly cut Douglas-fir stumps by Poria weirii. USDA For.Serv. Res. Note PNW 144, 5p. Pac. NW For. and Range Exp. Sta., Portland, OR.Nelson, E.E. 1967. Factors affecting survival of Poria weirii in small buried cubes ofDouglas-fir heartwood. For. Sci. 13:78-84.Nelson, E.E. 1973. Observations on the formation of zone lines in wood by Poria weirii.PNW For. & Range Exp. Sta. Res. Note PNW-210.Nelson, E.E. 1980. Laminated Root Rot Damage in a Younger Douglas-fir Stand. USDA For.Serv. PNW For. and Range Exp. Sta., Res. Note PNW-359.Neter, J., and W. Wasserman. 1974. Applied Linear Statistical Models. Regression, Analysisof Variance and Experimental Designs. Richard D. Irwin, Homewood, III. 842 pp.239Nilsen, P. 1983. The occurrence of rot in old stands of Norway Spruce (Picea abies [L.]Karst.), related to tree data and stand properties. Norsk lnstitutt For Skogforskning,1432 As-NLH (Norway), Rapport 2/83, 34p.Northway, S. pers. comm. 1990. Mensurationist/Resource Analyst, Corporate Forestry,MacMillan Bloedel Limited, 65 Front St., Nanaimo, BC. V9R 5H9.Nuszdorfer, F.C. Kassay, K.L. and A.M. Scagel, 1984. Biogeoclimatic subzones of theVancouver Forest Region. B.C. Min. of Forests, Burnaby, B.C., 1:500 000 colouredmap.Ono, K. 1970. Effect of soil conditions on the occurrence of Armillaria mellea root rot of theJapanese larch. Bulletin No. 22, Government Forest Experiment Station, Meguro,Japan, 208-219.Oren, R., W.G. Thies, and R.H. Waring. 1985. Tree vigour and stand growth of Douglas-firas influenced by laminated root rot. Can. J. For. Res. 15:985-988.Parmeter, J.R. 1977. Effects of fire on Pathogens. In H.A. Monney and C.E. Conrad (eds.)Proceedings of the Symposium of the Environmental Consequences of Fire and FuelManagement in Mediterranean Ecosystems, USDA For. Serv. Gen. Tech. Rep. WO-3,pp. 58-74.Peech, M. 1965. Hydrogen-Ion Activity. In Methods of Soil Analysis, Part 2. Black et al.(eds.). Amer. Soc. Agron., Madison, Wisc. pp. 914-926.Pojar, J., K. Klinka, and D.V. Meidinger. 1987. Biogeoclimatic ecosystem classification inBritish Columbia. For. Ecol. Manage., 22:119-154.Reaves, J.L., C.G. Shaw,lll, R.E. Martin, and J.E. Mayfield. 1984. Effects of ash leachateson growth and development of Armillaria mellea in culture. U.S. Dep. of Agr., For.Serv., Pac. Northwest For. and Range Exp. Sta., Res. Note PNW-418. 11p.Reaves, J.L. 1985. Interaction between Armillaria root disease, Trichoderma, andprescribed fire in a ponderosa pine forest. Atlanta University, Atlanta, Georgia.Ph.D. Dissertation. (manuscript not seen, ref. in Reaves et al. 1990)Reaves, J.L., C.G. Shaw, III, and J.E. Mayfield. 1990. The Effects of Trichoderma spp.Isolated from Burned and Non-Burned Forest Soils on the Growth and Developmentof Armillaria ostoyae in Culture. Northwest Sci. 64(1): 39-44.Reynolds, K.M., and W.J. Bloomberg. 1982. Estimating probability of intertree root contactin second-growth Douglas-fir. Can. J. For. Res. 12:493-498.Reynolds, G. pers. comm. 1990. Senior Forest Root Disease Technician, Forestry Canada,Pacific Forestry Centre, 506 W. Burnside Rd., Victoria, BC, V8Z 1 M5.240Rose, S.L., C.Y. Li, and A.S. Hutchins. 1980. A Streptomycete antagonist of Phellinus weirii,Fomes annosus and Phytophthora cinnamomi. Can. J. Microbiology 26:583-587.Roy, R.J.J. 1984. Ordination and classification of immature forest ecosystems in theCowichan Lake area, Vancouver Island. MSc. Thesis, Fac. of Forestry, Univ. of B.C.,Vancouver, B.C.Scagel, R., Y.A. El-Kassaby, and J. Emanuel. 1985. Assessing sample size and variablenumber in multivariate data, with specific reference to cone morphology variation ina population of Picea sitchensis. Can. J. Bot. 63:232-241.Seem, R.C. 1984. Disease Incidence and Severity Relationships. Ann. Rev. of Phytopathol.22:133-50.Shields, W.J., and S.D. Hobbs. 1979. Soil nutrient levels and pH associated with Armillariamellea on conifers in northern Idaho. Can. J. For. Res. 9(1):45-48.Simons, A. pers. comm. 1987. Regional Cruising Coordinator, B.C. Forest Service,Vancouver Forest Region, 4595 Canada Way, Burnaby, BC, V5G 4L9.Singh, R. 1983. Armillaria root rot: Influence of soil nutrients and pH on the susceptibilityof conifer species to the disease. Eur. J. For. Path. 13:92-101.Thies, W.G. 1983. Determination of growth reduction in Douglas-fir infected by Phellinusweirii. For. Sci. 29:305-315.Titus, S.J. 1987. A Graphic Approach to Summarizing Forestry Data. For. Chron. 347-350.Tkacz, B.M. and E.M. Hansen. 1982. Damage by Laminated Root Rot in Two SucceedingStands of Douglas-fir. J. For. 80:788-791.Trewartha, G.T. 1968. An Introduction to Climate (4th Edition). McGraw-Hill, New York, 408IDID.Tukey, J.W. 1977. Exploratory Data Analysis. Addison-Wesley Publishing Company.Van Groenewoud, H. 1956. A root disease complex in Saskatchewan white spruce. Can.Dept. Ag. Forest Biology div. Contribution No. 255.Van Groenewoud, H. and Whitney, R.D. 1969. White spruce mortality in Saskatchewan andManitoba. Pulp and Paper Magazine of Canada. April 4, 1969.Walkley, A. 1947. A critical examination of a rapid method for determining organic carbonin soils: Effect of variations in digestion conditions and of inorganic soil constituents.Soil Sci. 63:251-263.241Wallis, G.W. 1976. Phellinus (Poria) weirii root rot. Detection and management proposalsin Douglas-fir stands. Environ. Can., Can. For. Serv., For. Tech. Rep. No. 12, 16pp.Wallis, G.W., and G. Reynolds. 1965. The Initiation and Spread of Poria weirii Root Rot ofDouglas-fir. Can. J. Bot., Vol. 43, No.1.Walmsley, M., G. Utzig, T. Vold, D. Moon, and J. van Barneveld. 1980. Describingecosystems in the field. RAB Tech. Paper No.2, B.C. Min. of Environment and Min.of Forests, Prov. of B.C.Weisburg, S. 1985. Applied Linear Regression. John Wiley & Sons., Toronto, ON. 324p.Westhoff, V., and E. van der Maarel. 1980. the Braun-Blanquet approach. P. 287-399 inClassification of plant communities, R.H. Whittacker (ed.). Dr. W. Junk Publishers,The Hague, Netherlands.Whitney, R.D. 1978. Root Rot of spruce and balsam fir in northwestern Ontario. II. Casualfungi and site relationships. Environm. Can. Forestry Serv. Rept. 0-X-284, 42p.Williams, R.E., and M.A. Marsden. 1982. Modelling probability of root disease centeroccurrence in northern Idaho forests. Can. J. For. Res. 12:876-882.Wilks, D.S., P.L. Gersper and F.W. Cobb, Jr. 1985. Association of soil moisture with spreadof Ceratocystis wageneri in ponderosa pine disease centers. Plant Disease 69:206-208.Wilkinson, L. 1988. SYSTAT. The system for statistics. SYSTAT Inc., Evanston. III.Wilson, B. pers. comm. 1990. Growth and Yield Specialist, Woodlands Services, MacMillanBloedel Limited, 65 Front St., Nanaimo, BC, V9R 5H9.Wright, E., and R.F. Tarrant. 1957. Microbial soil properties after logging and slash burning.US For. Serv., Pac. Northwest For. Range Exp. Sta., Res. Notes 157.Wright, E., and R.F. Tarrant. 1958. Occurrence of mycorrhizae after logging andslashburning in the Douglas-fir type. US For. Serv., Pac. Northwest For. Range Exp.Sta., Res. Notes 160.Zar, J.H. 1984. Biostatistical analysis. 2nd edition. Prentice-Hall, Inc., Englewood Cliffs, N.J.242Appendix A^Soil Chemical Concentration To Kg/haThe following formulae were used to convert chemical concentration data to kg/hafor the 10-30 cm mineral soil horizon and, kg/ha for the forest floor. The formulae werepreviously 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 byvolume.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 * %NMinN kg/ha = 444444.444 * 20 cm * FFBD * (MinN ppm * 10-6)Where the component 444444.444 is the forest floor sample size (225 cm2) plotexpansion factor to 1 ha.243Appendix B^Curtis' Relative Stand Density IndexThis appendix describes the procedure used to recalculate Curtis' Relative StandDensity Index for ecologically similar PSP's within a growth and yield installation. In mostcases stand conditions were very similar within an installation though great variabilityoccurred due to microtopography and/or pests.Curtis' Relative Density (4.0cm) = Stand_BA / QDBHFULL FORMULA: CRD>4.0cm + =STAND BA/(SQRT(SQRT((STAND BA/STAND STEMS)/0.00007854)))STEPS TO ESTIMATE CURTIS' RELATIVE DENSITY:1. Calculate the average STAND_BA/HA and average STAND_STEMS/HA forecologically 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).244Appendix C^ Species ListConiferous trees1 ABIEGRA2 PICESIT3 PINUCON4 PINUMON5 PSEUMEN6 THUJPLI7 TSUGHETAbies grandisPicea sitchensisPinus contortaPinus monticolaPseudotsuga menziesiiThuja plicataTsuga heterophylla(Dougl. ex D. Don) Lindl(Bong.) Carr.Dougl. ex Loud.Dougl. ex D. Don in Lamb(Mirb.) FrancoDonn ex D. Don in Lamb.(Raf.) Sarg.Broad-leaved trees8 ACERMAC^ Acer macrophyllum^ Pursh9 ALNURUB Anus rubra^ Bong.10 ARBUMEN Arbutus menziesii Pursh11 BETUPAP^ Betula papyrifera Marsh.12 CORNNUT Comus nuttallii^ Audub. ex Tom Gray13 MALUFUS Malus fusca (Raf.) Schneid14 POPUTRI^ Populus trichocarpa Torr. Gray ex Hook.15 QUERGAR Quercus ganyana^ Dougl. ex Hook.Evergreen shrubs16 ARCTUVA^ Arctostaphylos uva-ursi^ (L.) Spreng.17 CHIMMEN Chimaphila menziesii (R. Br. ex D. Don) Spren18 CHIMUMB Chimaphila umbellata (L.) Barton19 GAULOVA^ Gaultheria ovatifolia^ Gray20 GAULSHA Gaultheria shallon Pursh21 HEDEHEL Hedera helix^ L.22 ILEXAQU^ Hex aquifolium L.23 MAHOAQU Mahonia aquifolium^ (Pursh) Nutt.24 MAHONER Mahonia nervosa (Pursh) Nutt.25 PAXIMYR^ Paxistima myrsinites (Pursh) Raf.26 VACCOVT Vaccinium ovatum^ PurshDeciduous shrubs27 ACERGLA^ Acer glabrum^ Torr.28 AMELALN Amelanchier alnifolia^ (Nutt.) Nutt.29 CORNSER Comus sericea L.30 CORYCOR^ Corylus comuta Marsh.31 HOLODIS Holodiscus discolor^ (Pursh) Maxim.32 LONICIL Lonicera ciliosa (Pursh) DC.33 LONIHIS^ Lonicera hispidula (Lindl.) Dougl. ex Torr.34 LONIINV Lonicera involucrata^ (Richards.) Banks ex Spr35 MENZFER Menziesia ferruginea Sm.36 OEMLCER^ Oemleria cerasiformis (Tom) Gray ex Hook37 PRUNEMA Prunus emarginata^ (Dougl. ex Hook.) Walp.38 RHAMPUR Rhamnus purshianus DC.39 RIBEDIV^ Ribes divaricatum Dougl.40 RIBELAC Ribes lacustn3^ (Pers.) Poir41 ROSAGYM Rosa gymnocarpa Nutt. in Torr. Gray42 RUBUIDA^ Rubus idaeus43 RUBUPAR Rubus parvilflorus^ Nutt.44 RUBUSPE Rubus spectabilis Pursh45 RUBUURS^ Rubus ursinus Cham. Schlecht.46 SALISCO Salix scouleriana^ Barratt in Hook.47 SALIX Salix sp.48 SORBSIT^ Sorbus sitchensis M. J. Roem.49 SYMPALB Symphoricarpos albus^ (L.) Blake50 SYMPHES Symphoricarpos hesperius G.N. Jones51 VACCOVL^ Vaccinium ovalifolium Sm. in Rees52 VACCPAR Vaccinium parvifolium^ Sm. in ReesDeciduous shrubs - Cont'd53 ADIAPED^ Adiantum pedatum^ L.54 ATHYFIL Athyrium filix-femina (L.) Roth55 BLECSPI Blechnum spicant (L.) Roth56 DRYOEXP^ Dryopteris expansa^ (Pres1) Fraser-Jenkins57 GYMNDRY Gymnocarpium dryopteris^ (L.) Newm.58 POLYMUN Polystichum munitum (KauIt) Presl59 PTERAQU^ Pteridium aquilinum (I.) Kuhn in DeckenGraminoids60 BROMUS^ Bromus sp.61 BROMVUL Bromus vulgaris^ (Hook.) Shear62 CALACAN Calamagrostis canadensis^ (Michx.) Beauv.63 CALARUB^ Calamagrostis rubescens Buckl.64 CAREHEN Carex hendersonii^ Bailey65 CAREX Carex sp.66 DACTGLO^ Dactylis glomerata L.67 FESTOCC Festuca occidentalis^ Hook.68 FESTSUT Festuca subulata Trin. in Bong.69 FESTUCA^ Festuca sp.70 LUZULA Luzula sp.71 LUZUMUL Luzula multiflora^ (Retz.) Lej.72 LUZUPAR^ Luzula parviflora (Ehrh.) Desv.73 MELISUB Melica subulata (Griseb.) Scribn.Herbs74 ACHIMIL^ Achillea millefolium^ L.75 ACHLTRI Achlys triphylla (Sm.) DC.76 ADENBIC Adenocaulon bicolor Hook.77 ANAPMAR^ Anaphalis margaritacea^ (L.) Benth. in Benth.78 CLAYSIB Claytonia sibirica L.79 COPTASP Coptis aspleniifolia Salisb.80 CORNCAN^ Comus canadensis^ L.81 CORNUNA Comus unalaschkensis Ledeb.82 DISPHOO disporum hookeri (Torr.) Nicholson83 FRAGVES^ Fragaria vesca^ L.84 FRAGVIR Fragaria virginiana Duchesne85 GALIBOR Galium boreale L.86 GALITRI^ Galium triflorum^ Michx.87 GOODOBL Goodyera oblonifolia Rat88 HIERALB Hieracium albiflorum Hook.89 LATHNEV^ Lathyrus nevadensis^ Wats.90 LILICOL Lilium columbianum Hanson ex Baker91 LINNBOR Linnaea borealis L.92 LISTCOR^ Listera cordata^ (L.) R. Br. in Alt.93 LYSIAME Lysichitum americanum^ Huft. St. John94 MEMTARV Mentha arvensis L.95 MOEHMAC^ Moehringia macrophylla (Hook.) Fenzl96 MONEUNI Moneses uniflora^ (L.) Gray97 MYCEMUR Mycelis muralis (L.) Dumort.98 ORTHSEC^ Orthilia secunda (L.) House99 OSMOCHI Osmorhiza chilensis^ Hook. Am.100 PYROASA Pyrola asarifolia Michx.101 PYROPIC^ Pyrola picta^ Sm. in Rees102 RUBUPED Rubus pedatus Sm.103 SMILSTE Smilacina stellata (I.) Desf.104 STACCOO^ Stachys cooleyae^ Heller105 STREROS Streptopus roseus Michx.106 TIARLAC Tiarella laciniata Hook.107 TIARTRI^ Tiarella trifoliata^ L.245Herbs - Continued108 TIARUNI^ Tiarella unifoliata^ Hook.109 TOLMMEN Tolmiea menziesii (Pursh) Torr. Gray110 TRIELAT Trientalis latifolia Hook.111 TRILOVA^ Trillium ovatum^ Pursh112 URTIDIO Urtica dioica L.113 VIOLA Viola sp.114 VIOLADU^ Viola adunca^ Sm. in Rees115 VIOLGLA Viola glabella Nutt. in Torr. Gray116 VIOLORB Viola orbiculata Geyer ex Hook.Paras. & saproph.117 BOSCHOO^ Boschinakia hookeri^ Walp.118 CORALLO Corallorhiza sp.119 CORAMAC Corallorhiza maculata Raf.120 CORAMER^ Corallorhiza mertensiana^ Bong.121 HEMICON Hemitomes congestum Gray122 HYPOLAN Hypopithys lanuginosa (Michx.) Nutt.123 MONOUNI^ Monotropa uniflora^ L.124 PTERAND Pterospora andromeda Nutt.Mosses125 DICRFUS^ Dicranum fuscescens^ Turn.126 DICRSCO Dicranum scoparium Hedw.127 HYLOSPL Hylocomium splendens (Hedw.) B.S.G.128 ISOPELE^ lsopterygium elegans^ (Brid.) Lindb.129 ISOTSTO lsothecium stoloniferum Brid.130 KINDORE Kindbergia oregana (Sull.) Ochyra131 KINDPRA^ Kindbergia praelonga^ (Hedw.) Ochyra132 LEUCMEN Leucolepis menziesii (Hook.) Steere ex L. Koc133 PLAGINS Plagiomnium insigne (Mitt.) Kop.134 PLAGUND^ Plagiothecium undulatum^ (Hedw.) B.S.G.135 PLEUSCH Pleurozium schreberi (Brid.) Mitt.136 POLYJUN Polytrichum juniperinum Hedw.137 RHACCAN^ Rhacomitrium canescens^ (Hedw.) Brid.138 RHIZGLA Rhizomnium glabrescens (Kindb.) Kop.139 RHYTLOR Rhytidiadelphus loreus (Hedw.) Wamst140 RHYTROB^ Rhytidiopsis robusta^ (Hook.) Broth.141 RHYTTRI Phytidiadelphus triquetrus (Hedw.) Warnst.Liverworts142 CALYMUE^ Calypogeja muelleriana^ (Schiffn.) K. Mull.143 MARCPOL Marchantia polymorpha L.144 SCAPBOL Scapania bolanderi Aust.Lichens145 CLADCHL146 CLADFUR147 CLADRAN148 ICMAERI149 PELTAPH150 PELTCAN151 PELTIGE152 PELTMENCladonia chlorophaeaCladonia furcataCladina rangiferinalcmadophila ericetorumPeltigera aphthosaPeltigera caninaPeltigera sp.Peltigera membranacea(Florke ex Somm.) Spreng(Huds.) Schrad.(L.) Harm.(L.) Zahlbr.(L.) Willd.(L.) Willd.(Ach.) NyL246Appendix D Vegetation Summary Table348 51825Vegetation UnitNumber of Plots 427 ^27Species Presence class and mean species significanceAbies grandisAcer glabrumAcer macrophyllumAchillea millefoliumAchlys triphyllaIII 2.0I +.0II 3.1I +.0II 1.0II^1.2I +.0II 2.1IV 2.511.112.3V3.9II 2.7I 2.0IV 4.5II 2.1III^1.7V 4.1II 1.4III 2.6IV 6.2Adenocaulon bicolor I +.0 11.1 11.6 11.4 II +.3 11.9Adiantum pedatum I +.0Alnus rubra II^1.4 II 2.9 II^1.1 II 3.0 V 5.1 11.9Amelanchier alnifolia II 2.1 II +.8 I +.2 I +.0Anaphalis margaritacea I +.0Arbutus menziesii III 3.1 I +.0Arctostaphylos uva-ursi I +.2Athyrium filix-femina I +.0 I +.0 II +.3Betu/a papyrifera I +.0 I +.0Blechnum spicant I +.0 I +.0 II 3.5Boschniakia hookeri I +.0 I +.0Bromus sp. I +.0Bromus vulgaris II^1.1 III^1.6 II^+.9 II^1.8 IV 2.4 II^1.4Calamagrostis canadensis I +.0Calamagrostis rubescens I +.3 I^+.1 I +.0Calypogeja muelleriana I +.0 II^1.3Carex hendersonii I +.5 I +.0Carex sp. I +.0 II +.3 I +.0Chimaphila menziesii I +.0 I +.0Chimaphila umbellata I +.0 II^1.5 II^1.6 I +.0 II^+.3Cladonia chlorophaea I +.0Cladonia furcata 1 +.0Cladina rangiferina I +.2Claytonia sibirica I +.0Coptis aspleniifolia II +.8Corallorhiza sp. I +.0Corallorhiza maculata II^1.0 II +.3 I +.0 I +.0Corallorhiza mertensiana I +.0Comus canadensis I +.0Comus nuttallii 11^1.5 1 1.2 11.0Comus sericea I +.2 I +.0 I +.0Comus unalaschkensiscorylus comutaI +.0I +.0 I +.0Dactylis glomerata I +.0Dicranum fuscescens I +.024768Dicranum scoparium 11.4 I +.0 I +.0 I +.0Disporum hooked I +.0Dryopteris expansa I +.0 II +.3Festuca occidentalis 11.0 I +.0 1 +.0 I +.0Festuca subulata +.0 I^+.1 II 3.1Festuca sp. 1 +.0 I +.4 I +.0 II^1.4 11 +.3 I +.0Fragaria vesca I +.0Fragaria virginiana +.0Galium boreale I +.0 II +.6 H +.8 II^1.1 IV 2.1 IV 4.6Galium triflorum I +.0 I +.8 11^1.3Gaultheria ovatifolia I +.0 I +.0Gaultheria shallon V 7.6 V 7.7 V 7.3 V 6.7 III 3.8 IV 4.8Goodyera oblongifolia 11^1.0 11^1.0 II +.5 II +.2 I +.0 I +.0Gymnocarpium dryopteria II +.3Hedera helix I +.0Hemitomes congestum I +.0 I +.0Hieracium albiflorum I +.0 I +.0Holodiscus discolor IV 4.5 III 3.9 I +.0 I +.0Hylocomium splendens II 4.0 V 5.6 V 5.1 V 5.4 IV 3.4 V 6.0Hypopithys Lanuginosa I +.0 I +.0Icmadophila ericetorum I +.0Ilex aquifoliumlsopterygium elegans1 +.2 1 +.0I +.0II +.3Isothecium stoloniferum I +.0 II +.3Kindbergia oregana V 6.2 V 6.6 V 7.5 V 6.9 V 7.1 IV 5.3Kindbergia praelonga II 5.0Lathyrus nevadensis 1 +.0 11.1 I^+.1Leucolepis menziesii I +.0 11.3 II +.8Lilium columbianum I +.0Linnaea borealis I +.6 III 3.0 IV 4.4 IV 4.5 II^1.4 III 3.2Listera cordata I +.0 I +.0Lonicera ciliosa II^1.1 I +.0 I +.0 I +.0Lonicera hispidula 1^+.1 I +.0 11.2Lonicera involucrata 1 +.0 1 +.2 I +.5Luzula sp. II +.8Luzula multiflora I +.0 1 +.0Luzula parviflora I +.0Lysichitum americanum 1 +.0Mahonia aquifolium 1 +.3 1 +.0Mahonia nervosa V 4.5 V 5.2 V 4.8 V 5.6 V 4.4 IV 4.8Malus fusca I +.2Marchantia polymorpha 11.2Melica subulata I +.5 I^+.1 I +.0 11.2Mentha arvensis 1 +.0Menziesia ferruginea 1 +.0 I +.0 11.2248Moehringia macrophylla + .0 1 +.0 11.2Moneses uniflora I +.0 I+.0 I+.0Monotropa uniflora +.0 1 +.0 I +.0Mycelis muralis +.2 +.0 I1.6 +.1 11^1.4 I 2.7Oemleria cerasiformis +.0 I +.0Orthilia secunda +.0 1 +.0 1 +.2 I +.0Osmorhiza chilensis I +.0 I+.6 I+.0 I +.5Paxistima myrsinites +.0 I^1.1Peltigera aphthosa +.0 11.1 1 +.0Peltigera canina I +.0 I +.0 I +.0 1^1.1 II +.3Peltigera sp. +.0Peltigera membranacea +.0 I +.0 1 +.0 1 +.0Picea sitchensis I +.0 I +.5Pin us contorta 11.4 I +.5 I +.0Pin us monticola 1 +.0 1 +.5 I +.0 I +.0 I 1.2Plagiomnium insigne II +.7 II +.3 IV 3.4Plagiothecium undulatum I +.0 II 1.2 III 2.2 II^1.3 IV 3.2Pleurozium schreberi II^+.3Polytrichum juniperinum 1 +.0 I +.0Polystichum munitum HI 1.9 III 3.4 IV 3.4 V 5.1 V7.4 V6.5Populus trichocarpa I +.0 11.9Prunus emarginata 11.6 1 +.2Pseudotsuga menziesii V 8.2 V 8.1 V 8.0 V 8.0 V 7.7 V 7.6Pterospora andromeda 1 +.0Pteridium aquilinum V 3.4 V 4.1 V 4.6 V 4.4 IV 2.8 IV 4.6Pyrola asarifolia 1 +.0 1 +.0Pyrola picta 1 +.0 1 +.0Quercus ganyana I +.0Rhacomitrium canescens 11.4Rhamnus purshianus 1 +.0 1 +.2 1 +.0 1^+.1 1 +.0 1 +.0Rhizomnium glabrescens 1 +.0 I +.0 1 +.0 V 4.0Rhytidiadelphus loreus I +.0 II^1.6 II^1.8 III 2.0 I +.0 III 2.0Rhytidiopsis robusta 1 +.3 111.6 II +.5 1 +.2 I +.0Rhytidiadelphus triquetrus II 2.7 HI 3.0 1 1.4 I +.0 II^1.5 I +.0Ribes divaricatum 1 +.0Ribes lacustre I +.1Rosa gymnocarpa V 3.3 V 3.5 II^1.1 II 2.3 II^1.8Rubus idaeus 1 +.0 1 +.2Rubus parviflorus 1 +.0 1 +.0 I +.1Rubus pedatus I +.0 1 +.0Rubus spectabilis 1 +.0 I +.0 IV 3.7 12.7Rubus ursinus V 3.3 V 3.3 IV 2.8 IV 3.3 V 3.1 IV 2.8Salix scouleriana 1 +.3Salix sp. 11^1.5 1 1.3 I +.0Scapania bolanderi I +.0249Smilacina stellataSorbus sitchensisStachys cooleyaeI +.0I +.0 1 +.0 1 +.01 +.0Streptopus roseus I +.0Symphoricarpos albus II 2.9 II 2.1 II 2.8 II 1.6 11.2Symphoricarpos hesperius 1 1.6 II 2.3 I 1.6 1^+.1 I 1.2Thuja plicata V 4.3 V 4.6 IV 4.1 IV 4.9 HI 2.8 V 5.7Tiarella laciniata 1 +.0 1 +.0Tiarella trifoliata I +.0 I +.0 III 2.2 V 2.0 V 3.2Tiarella unifoliata I +.0 I +.0Tolmiea menziesii I +.0Trientalis latifolia II^1.0 II^1.5 II 2.3 II^1.4 II +.3 IV 2.4Trillium ovatum I +.0 1 +.0 11.4 I^+.1 II +.3Tsuga heterophylla II 2.1 IV 3.7 V 5.0 V 5.8 V 4.6 V 5.3Urtica dioica I 2.7Vaccinium ovalifolium II 5.0Vaccinium ovatum I 2.4Vaccinium parvifolium IV 3.1 IV 3.7 V 4.0 V 4.5 V 2.7 IV 4.3Viola sp. I +.0 I +.0 I^+.1 1 +.0 1 +.0Viola adunca I +.0 1 +.0Viola glabella 1 +.0 I +.0Viola orbiculata I +.0 I +.0250PAGE 3SOIL MOIST. REGIME: Spectra (based on percentage of total cover of indicator species and expressed as % frequency)2 3^ I4 IS%COVER IPST%COVER TOTALNUMBER OF SPECIESTOTAL^IND^INDIFF^LRICLASS31% 81 53 28 030% 86 56 30 033% 74 49 25 031% 86 59 27 031% 46 27 19 035% 66 46 20 0VEGETATION UNIT. Indicator species groups, Frequency (X)SPseudotsuge-Arbutus p.a.2 3^ 141i1%^26%^ 66% 6% 1%1 2 118%STsuga-Mahonla p.a.3^ I 477% 4% 1%4% 89% 6% 1%SPseudotsuga-Achlys-Typic p.sa.1 21^ 3^ I^4^1513% 87% 8% 2%SPseudotsuga-Achlys-Aims p.sa.I n^ 3^I^4^I^5^I1% 78%Wseudotsuga-Achlys-Plagicenium p.sa.121^3 436%^ 29%2% 33%SPseuectsuga-Gaultherla p.a.Appendix ESpectral Analysis: Soil Moisture RegimeSOIL MOISTURE REGIME:1 Excess to very dry2 Very to moderately dry3 Moderately dry to fresh4 Fresh to very moist5 Very moist to wet6 Wet to very wetSOIL NUTRI. REGIME: Spectra (based on percentage of total cover of indicator species and expressed as % frequency)^ PAGE At'VEGETATION UNIT.Indicator species groups, FregJency (%)1 2^I^3^I49%^81^65^16^030% to%60%$Pseudotsuga-Gaultheria p.a.2^ 3121% 26%53%374%12%^14%2 348%35%^ 17%%covER IPSt^NUMBER OF SPECIES%COVER TOTAL TOTAL IND 16,0/FF UNCLASSSPseudotsuga-Arbutus p.a.1 263%$Tsuga-Mahonta p.a.26%1 266% 17%Pseudotsuge-Achlys-Typic p.sa.$Pseudotsuga-Achlys-Alms p.sa.$Pseudbtsugs-Aahlys-Plagtomnium p. se.I^352%11%341%17%46%49%162%86^71^15^074^60^14^086^70^16^046^35^11^066^55^11^0Appendix E - ContinuedSpectral Analysis:^Soil Nutrient RegimeSoil Nutrient Regime:1 N - Poor2 N - Medium3 N - RichAppendix F Relationship Of Site And Stand EcologicalVariables To The Site AssociationsIdentified In The Study Area253806040200-20vseNeN eve^"1.t.Nvot^olse- to.voct 40.010‘" cm-00°'SASSFigure 1 Figure 2 Slope (%). Means are L to R,5.5%, 2.2%, 12.4%, 10.9% and7.6%. The CDF unitscorresponding to gentle coastalplain and CWHxm to upland midto lower slope conditions.Elevation (m as1). Means are L toR, 85, 79, 191, 194 and 143 m,corresponds well with subzones.The last unit is likely lower due tolower slope positions.150100A500yseXE'l e ve-Ve^v+N.030 .c g,^vow^egc 40,1crxFK^1.0E6°.‘SASSAspect (azimuth degrees). Meansare L to R, 128, 134, 133, 148 and161°.Major rooting zone depth (cm).Means are L to R, 52,40, 49, 52and 57 cm.Figure 3 Figure 4Figure 5 Root restricting layer depth (cm).Means L to R, 57, 40, 54, 58 and59 cm. No substantial differencesacross site units, except in theFdBg-Oregon grape unit.Figure 6 Depth to mottling (cm). Means Lto R, 25, 20, 50, 42 and 45 cm.254OVtategaVe.0.6S-U'I.^0.0011Y1411Ci tOCVVSASS100200806040Figure 7 Depth to seepage water (cm), waswell associated with slightly drysoil moisture conditions.32FA10V.5V5XviagreVP'VefttssV'1,11.0eVe, 40,001° clito9-1SASSFigure 9 Gross total mineral soil bulkdensity (g/m3). Means L to R are;1.46, 1.58, 1.38, 1.25 and 1.19.Note the higher MSBDT in theCDFmm site units. The trendcorresponds to CF20.Figure 8 Percent (%) coarse fragmentcontent by volume. Means L to Rare; 60, 55, 62, 43 and 40. Notehigher CF20's are associated withmoderately dry actual soilmoisture regimes.2.01.50:1 eV° vowNeN -petygoVe^‘6.11.a-^SW*svclitoaSASSFigure 10 Fine fraction (<2 mm) mineral soilbulk density (g/m3). Means L toR are; .828, 1.125, .766, .716 and.695. Note the similar trend toMSBDT.255Figure 11 Figure 12 Relative soil moisture regime.Means are L to R; 3, 3.7, 2.4, 3.8and 4.3. The moderately dry siteunits are predominated by highcoarse fragment contents andbulk densities.Mineral soil percent porosity,Means are L to R; 69%, 58%, 71%,73% and 74%. Note, porosity isinversely related to MSBDF; highbulk density equals low porosity.256Mineral soil pH. Means L to Rare; 5.62, 5.18, 5.26 and 5.3. Fd-Salal is significantly different( a =.05) from all other pHdistributions.Mineral soil total (%) carboncontent. Means are L to R; 2.04,2.24, 1.84, 1.91 and 2.50,( a^=.10).Figure 13 Figure 140Vs9.19-1 vae^A.03-voveg,^._&bect tyytivVO-It'otitaalv'SASS908070014 60504030 257Figure 15 Mineral soil total (%) nitrogencontent. Means are L to R; .12,.18, .08, .13 and .14, with nosignificant differences detected atFigure 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 nosignificant differences detected atFigure 18 Mineral soil magnesiumconcentration (meg). Means L toR are; .42, 1.06, .56, .70 and .63,with no significant differences( a <.10). were detected at ( a <.10).`eva^'ON^f,VOV eg^VVIS°- • a.106C1011.11'.0409.0'SASSFigure 19 Mineral soil potassiumconcentration (meg). Means L toR are; .16, .36, .20, .22 and .15.Figure 20 Mineral soil mineralizable nitrogen(ppm). Means L to R are: 17.5,31.0, 11.6, 16.6 and 19.3. Notethe substantial increase of MSMNacross s.a.'s within CDFmm andCWHxm subzones.258Mineral soil mineralizable nitrogen(Kg/ha). Means L to R are; 10.6,28.4, 6.2, 13.1 and 15.7. Note thesubstantial increase of MSMNKacross s.a.'s within the CDFmmand CWHxm subzones.Forest floor mineralizable nitrogen(ppm). Means L to R are; 335,424, 292, 316 and 306.Figure 21 Figure 2200^000Figure 24Figure 23 Old growth Douglas/fir andwestern hemlock stems(stumps)/ha. Means L to R are;86, 37, 127, 128 and 138.Old growth western red cedarstems (stumps)/ha. Means L to Rare; 8, 24, 25, 26 and 13, with nosignificant differences detected at( a <.10).21010Figure 25 Old growth Douglas-fir andwestern hemlock speciescompositions. Means L to R are;.812, .511, .807, .824 and .937.FdBg-Oregon grape is significantlydifferent from all s.a.'s atFigure 26 Old growth western red cedarspecies compositions. Means L toR are; .063, .323, .137, .158 and.063. FdBg-Oregon grape issignificantly different from Fd-Salal259(^<.10).and Cw-Foamflower at ( a <.10).Vs°123.vee^xeNvov^ve zg,^lAss. tvxv,ecg, tvo-coilosit"SASS0.40.30 0002 000 000.1 0 *0.0-0.1vs23•93. ev,oveytwl2x.,,,axe'1%,,i.xo'cVaoSASS260Figure 27^First PSP measure of Fd & Bg(susceptible) species composition.Means L to R are; .922, .863, .926,.875 and .834. Note thedecreasing trend to FSUS withincreasing soil moisture andnutrient regime.Figure 28^First PSP measure of Hw(intermediate) speciescomposition. Means L to R are; 0,.022, .032, .07 and .064. Note theincreasing trend with increasingsoil moisture and nutrient regime.Figure 29 First PSP measure of PI, Pw andCw (resistant) speciescomposition. Means L to R are;.035, .048, .028, .027 and .019,with no significant differencesdetectable 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 thesignificantly lower FDEC in theFdHw-Salal and HwFd-Kindbergiacompared to the Cw-Foamflower(^<.10).Figure 31 Last PSP measurement of Fd &Bg (susceptible) speciescomposition. Means L to R are;.928, .867, .909, .870 and .857,with no significant differencesFigure 32 Last PSP measurement of Hw(intermediate) speciescompositions. Means L to R are;.003, .018, .043, .076 and .076.Fd-Salal is significantly differentfrom HwFd-Kindbergia and Cw-Figure 34Figure 33 Last PSP measurement of PI, Pw& Cw (resistant) speciescomposition. Means L to R are;.048, .075, .038, .035 and .020,with not significant differencesdetected at ( a <.10). Note thesimlarity with FRES.Last PSP measurement ofdeciduous species composition.Means L to R are; .021, .040, .009,.016 and .046. Note the similaritywith FDEC.detected at ( a <.10). Note thesimilarity with FSUS.261Foamflower at ( a <.05). Notethe similarity with FINT.0**VS81S1 egyae^10.‘•030V^VAA.Sa^0.0,0Csti WI°SASS1. 000.3 000200.1 o00.0 0_ ^—0.1.c,910-1 vae^\a‘viaave% VP°'^ixesvtkvxo clit0a•vaSASS261 AFirst PSP measurement of density(stems/ha) A.Ocm. Means L to Rare; 1 875, 1 379, 2 798, 2 001and 1 072. FdHw-Salal issignificantly different from Fd-Salalat ( a <.05), FdBg-Kindbergiaand Cw-Foamflower at ( a <.05).The latter two site units were alsosignificantly different at ( a <.05).First PSP measurement of basalarea (m2/ha) A.Ocm. means areL to R; 38.2, 38.1, 33.3, 32.7 and36.9, with no significantdifferences detected at ( a < .10).Figure 35 Figure 36First PSP measurement of volume(m3/ha) k4.0cm. means L to Rare; 319, 332, 246, 264 and 346,with no significant differencesdetected at ( a <.10).First PSP measurement of Curtis'relative density A.Ocm. means Lto R are; 9.1, 8.7, 9.0, 8.2 and 7.8,with no significant differencesdetected at ( a <.10).Figure 37 Figure 38az2010010Figure 39 Figure 40 First PSP measurement of totalage (yr). means L to R are; 45,39, 39, 31 and 34, with Fd-Salalsignificantly greater in age thanHwFd-Kindbergia at ( a <.05).Curtis' relative density incrementfirst-last PSP measurmentmeans L to R are; 1.6, 2.3, 2.5, 3.3and 2.7, with Fd-Salal significantlylower than HwFd-Kindbergia at( a <.05).504051 302010selal ae^N.23-CaDve c Y11‘52'^(Pevtof°ooler8Wv coloc3SASSFigure 41 Figure 42Last PSP measurement (approx.1987) of total age (yr). means Lto R are; 76, 76, 69, 62 and 64,with Fd-Salal significantly greaterin age than HwFd-Kindbergia at( a <.05).Site index, Bruces Douglas-fir ref.age 50 at breast-height. means Lto R are; 25.4, 29.8, 24.9, 31.2 and36.1, the only site units notdiffering significantly at ( a <.05)are Fd-Salal, FdHw-Salal, FdBd-Oregon grape and HwFd-Kindbergia.2620VsNta9--^NVo veg'vaVeNe -pdbev 00'0OW' co toi*O"SASSFigure 43 Estimated density (stems/ha),A.0 cm at reference age 10 yr.Means L to R are; 1 853, 1 472, 2801, 2 173 and 1 252, with FdHw-Salal significantly greater605040,14 3020100Figure 45 Estimated volume (m3/ha),cm at reference age 10 yr. MeansL to R are; 21, 21, 22, 25 and 28,with Cw-Foamflower significantlydifferent from Fd-Salal, ( a <.05)and FdHw-Salal at ( a < .10).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, 8.2and 7.8, with no significantFigure 46 Estimated Curtis' relative density,cm at reference age 10 yr.Means L to R are; 2.7, 2.4, 3.2, 3.0and 2.6, with no significantdifferences detected at ( a <.10).263stems/ha than Fd-Salal ( a <.05)and Cw-Foamflower (^<.05),and HwFd-Kindbergia issignificantly greater than Cw-Foamflower at ( a < .05).differences detected at ( a <.10).0s-,e^_A 2L1.S;1•13ovetr.r ,c1,15%, tisbe^typ-111:04-` clicmsc'SASSSASSOa 1020150Foxl Ave v3.Vov^. oat 1.0.er1#1"1108i0e,It'15aiyrIllustration Of Selected Variables FromPearsons CorrelationAppendix G26430 40 50 60 70 BO-10-10 0 10 20Percent (%) Slopei302010Figure 1(a f) Scatterplots of several thought-to-be important soil physical variables against % BasalArea 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 % coarsefragment content.10 ^0 20^40^60^80^100Soil Coarse Fragment Content (XVol, 10-30cm)Figure 1(a)Figure 1(c)Figure 1(e)Figure 1 (b)Figure 1(d)Figure 1(f)50403 3°2010605027! 40I 302010020^40^60^80^100^120Rooting Depth (cm)6050.2 4010^.. ^30^40^50^60^70^BO^90Percent (2) Soil Porosity1.0100 0^02^0.4^0.6^0.8Soil Percent (%) lbtal Nitrogen805040302010010 ^0 5^10^15^20^25Soil Exchangeable Bases--Calcium (meq)805040OO0^300aq^20 .^•• •10j010 ^0 1^2^3^4^5^6^7^8Soil Percent (2) Total Carbon10 0^20^40^60^80^100Soil Mineralizable Nitrogen (ppm)265Figure 2(a)Figure 2(c)Figure 2(e)Figure 2(b)Figure 2(d)Figure 2(a-e)^Scatterplots of several thought-to-be important soil chemistry variables against % BasalArea Reduction-BARS; (a) MSPH, (b) MSC, (c) MSN, (d) MSMN and (e) MEQCA. Notethe lack of correlation or trend, and outliers for virtually all variables.


Citation Scheme:


Citations by CSL (citeproc-js)

Usage Statistics



Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            async >
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:


Related Items