FORECAST Modelling Workshop: Agendaweb.forestry.ubc.ca/ecomodels/book/FORECAST Workshop... ·...

FORECAST Modelling Workshop: FORECAST Modelling Workshop: AgendaAgenda

Day 1Day 1

•• Introduction to Introduction to

Modelling PhilosophyModelling Philosophy

•• Overview of FORECAST Overview of FORECAST

Structure and FunctionStructure and Function

•• Net Primary Production Net Primary Production

•• Nutrient Cycling / Nutrient Cycling /

DecompositionDecomposition

•• Boreal Mixedwood Boreal Mixedwood

ExampleExample

•• Intro to FORCEE and Intro to FORCEE and

HORIZONHORIZON

•• Intro to User Interface: Intro to User Interface:

FORECAST NavigatorFORECAST Navigator

Day 2Day 2

•• Site QualitySite Quality

•• Natural mortality & Natural mortality &

Individual stem rep.Individual stem rep.

•• Overview of Data Overview of Data

RequirementsRequirements

•• Developing Local Data Developing Local Data

SetsSets

•• Introduction to Data Introduction to Data

Editor Software and Editor Software and

Working with Data FilesWorking with Data Files

•• Using Output from Setup Using Output from Setup

Programs to Verify Programs to Verify

Integrity of Input DataIntegrity of Input Data

Day 3Day 3

•• Importance of Ecosystem Importance of Ecosystem

Starting Condition Starting Condition

•• Editing the Management Editing the Management

Options in Ecodata file Options in Ecodata file

•• Building an Ecostate fileBuilding an Ecostate file

•• Simulating alternative Simulating alternative

management strategies in management strategies in

FORECAST FORECAST

•• HandsHands--on Exampleson Examples

•• Future DevelopmentFuture Development

•• User SupportUser Support

•• Closing DiscussionClosing Discussion

FORECAST Workshop Day 2 FORECAST Workshop Day 2

MorningMorning

•• SimualtionSimualtion Algorithms: Site QualityAlgorithms: Site Quality

data requirementsdata requirements

site quality changesite quality change

•• Simulation Algorithms: Natural Mortality & Individual Tree RepreSimulation Algorithms: Natural Mortality & Individual Tree Representationsentation

•• Overview of Data RequirementsOverview of Data Requirements

general data categoriesgeneral data categories

example dataexample data

AfternoonAfternoon

•• Model Calibration: Developing Local Data SetsModel Calibration: Developing Local Data Sets

•• Introduction to Data Editor Software and Working with Data FilesIntroduction to Data Editor Software and Working with Data Files

•• Using Output from Setup Programs to Verify Integrity of Input DaUsing Output from Setup Programs to Verify Integrity of Input Datata

Simulation Algorithms: Simulation Algorithms: Site QualitySite Quality

Data RequirementsData Requirements

•• Data must be provided for a chronosequence of stands on at leastData must be provided for a chronosequence of stands on at least 2 2

sites of varying nutritional qualitysites of varying nutritional quality

biomass accumulation in various biomass componentsbiomass accumulation in various biomass components

height growthheight growth

stand density and natural mortality datastand density and natural mortality data

light profiles associated with foliage biomasslight profiles associated with foliage biomass

nutrient concentration data in plant tissuesnutrient concentration data in plant tissues

etc.etc.

•• Site Quality must be quantified consistently throughout data setSite Quality must be quantified consistently throughout data set

site index based on height growth, arbitrary index, othersite index based on height growth, arbitrary index, other

•• Site Quality is assumed to be primarily a function of nutrient sSite Quality is assumed to be primarily a function of nutrient status tatus

(nitrogen)(nitrogen)

should not be dominated by other soil or site factorsshould not be dominated by other soil or site factors


Estimation of relative nutrient abundance associated with site qEstimation of relative nutrient abundance associated with site qualityuality

•• Calculated separately for each plant species in setup programs aCalculated separately for each plant species in setup programs and for nd for

each nutrient simulatedeach nutrient simulated

•• Accounts for changing degree of soil root occupancy over time peAccounts for changing degree of soil root occupancy over time period riod

of stand developmentof stand development

ExampleExample

If:If: Trees of species X (nutrient limited) are calculated to have Trees of species X (nutrient limited) are calculated to have

taken up 20 kg hataken up 20 kg ha --11 of nutrient 1 in time step t, but to have of nutrient 1 in time step t, but to have

only had 50% of the maximum observed only had 50% of the maximum observed FRBFRB in that yearin that year

•• Empirical approachEmpirical approach for estimating temporal pattern of nutrient for estimating temporal pattern of nutrient

availability that likely occurred on each of the varying site quavailability that likely occurred on each of the varying site qualities in alities in

order to have supported observed growthorder to have supported observed growth

Then:Then: It is assumed that total amount of nutrient 1 available on the It is assumed that total amount of nutrient 1 available on the

site that year must have been 40 kg hasite that year must have been 40 kg ha --11


Show LIVE Demo TREEGROW of nutrient abundance!!Show LIVE Demo TREEGROW of nutrient abundance!!

•• Plant growth response to changing soil nutrient availability wilPlant growth response to changing soil nutrient availability will normally be l normally be

much faster than changes in soil processesmuch faster than changes in soil processes

•• Plant site quality will move towards that indicated by nutrient Plant site quality will move towards that indicated by nutrient abundance abundance

associated with prevailing soil conditions at a damping rate defassociated with prevailing soil conditions at a damping rate defined by the ined by the

user in the user in the ECODATA FileECODATA File

•• Site quality for soil processes follows a plant site quality defSite quality for soil processes follows a plant site quality defined by a ined by a

weighted average of that for all trees and plants (weighted average of that for all trees and plants (SOILDATA fileSOILDATA file))


Site Quality Change: Site Quality Change: 2 types of site quality represented2 types of site quality represented

1. Nutritional site quality as perceived by individual plant spe1. Nutritional site quality as perceived by individual plant speciescies

2. Site quality for soil processes2. Site quality for soil processes

Nutritional site quality perceived by plantsNutritional site quality perceived by plants


Site Quality Change: Site Quality Change: 2 types of site quality represented2 types of site quality represented

1. Nutritional site quality as perceived by individual plant spe1. Nutritional site quality as perceived by individual plant speciescies

2. Site quality for soil processes2. Site quality for soil processes

•• Forest soils have considerable Forest soils have considerable ““nutritional inertianutritional inertia”” that reflects: large mass that reflects: large mass

of of high C/N ratio organic matterhigh C/N ratio organic matter, , soil fauna & florasoil fauna & flora, and influence of , and influence of

understory vegetationunderstory vegetation

•• Several years of sustained alterations of litterfall quality andSeveral years of sustained alterations of litterfall quality and nutrient nutrient

availability may cause changes in processes associated with soilavailability may cause changes in processes associated with soil site site

qualityquality

•• Inertia is represented by having soil site quality follow plant Inertia is represented by having soil site quality follow plant site quality but site quality but

with user defined damping function: with user defined damping function: (Tugboat and barge analogy)(Tugboat and barge analogy)

Site quality for soil processesSite quality for soil processes


Site Quality ChangeSite Quality Change

Impacts on Tree growthImpacts on Tree growth

Impacts on Soil processesImpacts on Soil processes

•• Shifting Annual Potential Growth (APG) curves Shifting Annual Potential Growth (APG) curves

•• Changes in nutrient uptake demands, internal cycling, etcChanges in nutrient uptake demands, internal cycling, etc

•• Fine root turnover ratesFine root turnover rates

•• Carbon Allocation patternsCarbon Allocation patterns

•• Evaluated every time step based on nutrient abundanceEvaluated every time step based on nutrient abundance

•• Linear interpolation between rates derived for each site qualityLinear interpolation between rates derived for each site quality in TREEGROWin TREEGROW

•• Uses site quality values as reference points for interpolatingUses site quality values as reference points for interpolating

•• Decomposition rates Decomposition rates

•• CEC and AECCEC and AEC

•• Ion specific leachingIon specific leaching

•• Humus chemistryHumus chemistry


Site Quality Change: Site Quality Change: SomeSome physical site attributes are unaffected by physical site attributes are unaffected by

site quality changesite quality change

Aspects of the Geochemical cycle:Aspects of the Geochemical cycle:

•• Precipitation inputs of nutrientsPrecipitation inputs of nutrients

•• UpUp--slope seepage input of nutrientsslope seepage input of nutrients

•• Nutrient inputs from mineral weatheringNutrient inputs from mineral weathering

Inherent soil moisture regime:Inherent soil moisture regime:

•• Typically a function of slope position or position on Typically a function of slope position or position on

moisture gradient moisture gradient

•• An Ecostate file contains a static upper limit on foliage An Ecostate file contains a static upper limit on foliage

biomass associated with starting site qualitybiomass associated with starting site quality

•• Represents moistureRepresents moisture--limited carrying capacity of site limited carrying capacity of site

regardless of nutrient statusregardless of nutrient status

Simulation Algorithms: Simulation Algorithms: Representation of Individual Stems Representation of Individual Stems

in FORECASTin FORECAST

1. Data Requirements1. Data Requirements

2. Calculations in TREEGROW program2. Calculations in TREEGROW program

3. Calculations in Ecosystem Simulation Module3. Calculations in Ecosystem Simulation Module

Representation of individual stems: Representation of individual stems: Data requirementsData requirements

•• Stem size class frequency Stem size class frequency

distribution data from stand table for distribution data from stand table for

up to10 size classes: up to10 size classes: Biomass,Biomass,

Volume, or DBHVolume, or DBH

TREEDATA FileTREEDATA File

•• Data from series of remeasurements Data from series of remeasurements

of a single stand(s) or a series of of a single stand(s) or a series of

stand ages from a chronosequencestand ages from a chronosequence

•• Data for up to 10 stand ages may Data for up to 10 stand ages may

entered but only last age requiredentered but only last age required

•• May provide unique data for each May provide unique data for each

site quality representedsite quality represented

Proportion of stems in a given class

Stem size class boundaries

0

0.1

0.2

0.3

0.4

0.5

55 yrsyrs

2 3 4 5 6 7 8 9 10 11 12

2100 stems/ha2100 stems/ha

0.0

0.1

0.2

0.3

0.4

0.5

15 15 yrsyrs

6 8 10 12 14 16 18 20 22 24 26


0

0.1

0.2

0.3

0.4

0.5

45 45 yrsyrs

50 53 56 59 62 65 68 71 74 77 80


0

0.1

0.2

0.3

0.4

0.5

95 95 yrsyrs

65 70 75 80 85 90 95 100 105 110 115


2 3 4 5 6 7 8 9 10 11 12

0

0.1

0.2

0.3

0.4

Representation of individual stems: Representation of individual stems: TREEGROW programTREEGROW program

1. A frequency distribution curve is 1. A frequency distribution curve is

generated from the size class boundaries generated from the size class boundaries

and associated proportions in the tree and associated proportions in the tree

data filedata file

Calculations in TREEGROW: Calculations in TREEGROW: Propensity for size differentiationPropensity for size differentiation

Age 5Age 5

Pro

port

ion

Class size boundaries

4. The 11 representative trees define the 4. The 11 representative trees define the

boundaries of 10 size classesboundaries of 10 size classes

5. Repeat process for each stand age5. Repeat process for each stand age

2. Integrate area under the curve to get 2. Integrate area under the curve to get

proportion of total area for each of the proportion of total area for each of the

size classes size classes

1 2 3 4 5 6 7 8 9 10 11

1 210 420 630 840 1050 1260 1470 1680 1890 2100

2 4.7 5.5 6 6.25 6.5 6.75 7 7.55 8.29 12

Tree #Tree #

SizeSize

3.3. Calculate the stem size and tree # for 11 Calculate the stem size and tree # for 11

representative trees distributed evenly in representative trees distributed evenly in

the populationthe population

5. Calculate growth rates for each of the 5. Calculate growth rates for each of the

11 representative trees for each stand 11 representative trees for each stand

age midpointage midpoint

Representation of individual stems: Representation of individual stems: TREEGROW TREEGROW (continued)(continued)

Calculating rates of size differentiation for Calculating rates of size differentiation for TREETRNDTREETRND filefile

Age 1 2 3 4 5 6 7 8 9 10 11

1 to 5 (3) 0.4 0.9 1.1 1.2 1.3 1.3 1.4 1.4 1.5 1.7 2.4

5 to 15 (10) 0.4 0.5 0.6 0.6 0.8 0.9 0.9 0.9 1.0 1.0 1.4

15 to 45 (30) 1.5 1.6 1.6 1.7 1.6 1.6 1.7 1.7 1.7 1.8 1.8

45 to 95 (70) 1.0 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.4

Actual growth rates per yearActual growth rates per year

6. Determine if mortality shift is necessary6. Determine if mortality shift is necessary

8. Store relative growth rates (RGR) for 8. Store relative growth rates (RGR) for

each of the 11 representative trees at each of the 11 representative trees at

each age midpoint in the each age midpoint in the TREETRNDTREETRND file file

for use in ecosystem simulation modulefor use in ecosystem simulation module

9. Convert cumulative mortality to % dying 9. Convert cumulative mortality to % dying

in each size segment and save in in each size segment and save in

TREETRND file for each age midpointTREETRND file for each age midpoint

RGR values stored in RGR values stored in TREETRND TREETRND filefile

Age 1 2 3 4 5 6 7 8 9 10 11

3 0.31 0.72 0.85 0.92 0.96 1 1.04 1.08 1.16 1.28 1.85

10 0.47 0.55 0.66 0.71 0.97 1 1.03 1.06 1.12 1.19 1.65

30 0.89 0.99 1 1.01 0.99 1 1.01 1.03 1.06 1.09 1.09

70 0.86 0.96 0.97 1 0.99 1 1.01 1 1 1.01 1.21

7. Calculate growth rates for each of the 7. Calculate growth rates for each of the

11 representative trees relative to the 11 representative trees relative to the

median tree for a given agemedian tree for a given age

Largest treeLargest tree

Median treeMedian tree

Smallest live canopy treeSmallest live canopy tree

Stem SizeStem Size

Stand AgeStand Age

Stem Frequency

Stem Frequency

Representation of individual stems:Representation of individual stems: TREEGROW programTREEGROW program

An example of size class frequency distributions through timeAn example of size class frequency distributions through time

Representation of individual stems: Representation of individual stems: TREEGROW programTREEGROW program

0

20

40

60

80

100

Simulated stand tableSimulated stand table

Stand

Age

Maximum

Frequency

5 1698

15 736

45 456

95 332

Stand table data Stand table data vsvs. Simulation results. Simulation results

0

20

40

60

80

100

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

105

110

115

120

125

130

135

140

145

150

21002100 17501750 12001200 825825

Ste

m fre

quency a

s a

perc

enta

ge o

f th

e

maxim

um

fre

quency a

t a g

iven a

ge

Stand

Age

Maximum

Frequency

5 1848

15 1137

45 696

95 330

Actual stand table dataActual stand table data

Representation of individual stems: Representation of individual stems: Ecosystem moduleEcosystem module

Distribution of standDistribution of stand--level stemwood production among individual stemslevel stemwood production among individual stems

1. Stand1. Stand--level calculations to determine the annual production of stemwoolevel calculations to determine the annual production of stemwood d

biomassbiomass

2. Read 2. Read TREETRNDTREETRND file and interpolate over stored age midpoints to get current file and interpolate over stored age midpoints to get current

year RGR values for 11 representative trees adjusted for site quyear RGR values for 11 representative trees adjusted for site quality. ality.

3. Determine 3. Determine tree #tree # of 11 representative trees distributed evenly through the curreof 11 representative trees distributed evenly through the current nt

population of stems in a given cohort.population of stems in a given cohort.

4. Generate unique RGR values for each tree in tree list using l4. Generate unique RGR values for each tree in tree list using linear interpolation inear interpolation

between values for 11 representative trees. between values for 11 representative trees.

5. Annual production of stand5. Annual production of stand--level stemwood biomass is distributed to individual level stemwood biomass is distributed to individual

stems based upon assigned stems based upon assigned RGRRGR values:values:

production stemwood standNet treeRGR

treeRGR treeofGrowth

=

1

×

∑=

n

i

i

ii

Representation of individual stems: Representation of individual stems: Ecosystem moduleEcosystem module

Related Simulation Events (in order of occurrence)Related Simulation Events (in order of occurrence)

•• Simulation of Simulation of heightheight, , diameterdiameter, and , and volumevolume growth for individual stemsgrowth for individual stems

•• IndividualIndividual stems are divided into 10 stem biomass classes by adjusted so thstems are divided into 10 stem biomass classes by adjusted so that at

10% of the total stems are represented in each class10% of the total stems are represented in each class

•• Thinning / Partial harvest Thinning / Partial harvest -- Trees removed from specified size classesTrees removed from specified size classes

•• Natural Mortality simulatedNatural Mortality simulated -- smallest trees are assumed to die first in densitysmallest trees are assumed to die first in density--

dependent mortality. Density Independent mortality distributed edependent mortality. Density Independent mortality distributed evenly among all venly among all

size classes.size classes.

•• Mass of other biomass components associated with dying stems is Mass of other biomass components associated with dying stems is estimated by estimated by

multiplying the total mortality stemwood by ratios of standmultiplying the total mortality stemwood by ratios of stand--level stemwood level stemwood

biomass and each biomass componentbiomass and each biomass component

Simulation Algorithms: Simulation Algorithms: Natural MortalityNatural Mortality

1. Data Requirements1. Data Requirements

2. Calculations in TREEGROW program2. Calculations in TREEGROW program

•• DensityDensity--dependent mortalitydependent mortality

•• DensityDensity--independent mortalityindependent mortality

3. Calculations in Ecosystem Simulation Module3. Calculations in Ecosystem Simulation Module

Simulation of Natural Mortality: Simulation of Natural Mortality: Data RequirementsData Requirements

1. Stem density data for stand age 1. Stem density data for stand age

series: series: resampled plots or resampled plots or

chronosequencechronosequence

1. Stem Density

0

500

1000

1500

2000

2500

0 20 40 60 80 100

Stand Age

Stems/ ha

2. Mortality

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

Stand Age

Proportion

Dependent

Independent

2. Proportion of mortality that is 2. Proportion of mortality that is

density dependent and independentdensity dependent and independent

3. Canopy top height, height of 3. Canopy top height, height of

smallest live tree and height of smallest live tree and height of

canopy bottom canopy bottom

3. Canopy data

0

10

20

30

40

0 50 100 150 200

Stand age

height (m)

Top Ht

SLT

Bottom

Simulation of Natural Mortality: Simulation of Natural Mortality: TREEGROW programTREEGROW program

DensityDensity--independent mortality:independent mortality:

1. Mortality caused by factors other than shading: Insect kills,1. Mortality caused by factors other than shading: Insect kills, pathogens, pathogens,

wind damage etc. wind damage etc.

2. Example of rate calculations in simulation algorithm:2. Example of rate calculations in simulation algorithm:

AgeAge 1515 2525

DensityDensity 17001700 13001300

D.D.-- IndptIndpt MortMort.. 0.250.25

3. This 3. This speciesspecies--specificspecific densitydensity--independent mortality rate independent mortality rate

(10/(10/densitydensitytt) will be applied in ecosystem module for a specified ) will be applied in ecosystem module for a specified

stand age regardless of current stand density and light conditiostand age regardless of current stand density and light conditionn

17001700--130013002525--1515

= 40= 40

D. D. Indpt mort Indpt mort / / yryr = .25 * 40 = 10= .25 * 40 = 10

D. D. -- Dpt mort Dpt mort / / yryr = 40 = 40 --10 = 3010 = 30

Total mortality /Total mortality / yryr ==

Simulation of Natural Mortality: Simulation of Natural Mortality: TREEGROW programTREEGROW program

1. Density dependent mortality is assumed to occur primarily in 1. Density dependent mortality is assumed to occur primarily in

response to light competition.response to light competition.

2. TREEGROW simulates the development of a population of 2. TREEGROW simulates the development of a population of

stems according to the stem densities and mortality rates in stems according to the stem densities and mortality rates in

the setup data.the setup data.

3. In conjunction with the light submodel, and tree height data,3. In conjunction with the light submodel, and tree height data,

the light levels at the top of the largest live tree is determinthe light levels at the top of the largest live tree is determined ed

for each time step.for each time step.

4. This level becomes the 4. This level becomes the speciesspecies--specificspecific criterion for shade criterion for shade

related mortality in the ecosystem module.related mortality in the ecosystem module.

DensityDensity--dependent mortality:dependent mortality:

Simulation of shadeSimulation of shade--related mortality: related mortality: Ecosystem moduleEcosystem module

Canopy top height: Canopy top height:

derived from height derived from height

growth datagrowth data

Based on simulation rules derived in TREEGROWBased on simulation rules derived in TREEGROW

TimeTime

Smallest live tree height: determined Smallest live tree height: determined

by critical light intensity valueby critical light intensity value

Canopy bottom height: Canopy bottom height:

light intensity at which light intensity at which

branch death occursbranch death occurs

Model Calibration: Model Calibration: Calibration CycleCalibration Cycle

Data AcquisitionData Acquisition

Data Assessment:Data Assessment:

Review output of

setup programs

Expert OpinionExpert OpinionData Assessment:Data Assessment:

Review output of

ecosystem simulation

module

Scenario Scenario

AnalysisAnalysis

General Data Requirements for FORECASTGeneral Data Requirements for FORECAST

1. Growth and Yield1. Growth and Yield

2. Density and Mortality2. Density and Mortality

3. Light Model3. Light Model

4. Nutrient Cycling and Decomposition4. Nutrient Cycling and Decomposition

5. Understory representation5. Understory representation

6. Snag Submodel6. Snag Submodel

Species SpecificSpecies Specific


Data categoriesData categories Potential sourcesPotential sources

Growth and Yield (speciesGrowth and Yield (species--specific)specific)

•• Stemwood biomass / volume Stemwood biomass / volume

accumulationaccumulation•• Local G&Y models; Permanent Local G&Y models; Permanent

Sample Plot data, ChronosequenceSample Plot data, Chronosequence

•• Individual tree size variationIndividual tree size variation •• Local G&Y models, Stand table data, Local G&Y models, Stand table data,

ChronosequenceChronosequence

•• Height growth data (average top Height growth data (average top

height, smallest live tree, canopy height, smallest live tree, canopy

bottom height)bottom height)

•• Local G&Y models; Permanent Sample Local G&Y models; Permanent Sample

Plot data, ChronosequencePlot data, Chronosequence

•• Biomass accumulation for roots Biomass accumulation for roots

and other biomass components and other biomass components

(branches, bark, etc.)(branches, bark, etc.)

•• Literature sources, allometric biomass Literature sources, allometric biomass

equations, Chronosequenceequations, Chronosequence

•• Foliage biomass accumulationFoliage biomass accumulation •• Literature sources, estimate from LAI, Literature sources, estimate from LAI,

crown closure & crown ratio data, and crown closure & crown ratio data, and

allometric biomass equationsallometric biomass equations



Density and Mortality (speciesDensity and Mortality (species--specific)specific)

•• Stand density data for an age Stand density data for an age

seriesseries•• Local G&Y models; Permanent Local G&Y models; Permanent

Sample Plot data, ChronosequenceSample Plot data, Chronosequence

•• Density independent mortality for Density independent mortality for

an age series (natural mortality an age series (natural mortality

other than shade related)other than shade related)

•• Literature sources, Expert opinion, Literature sources, Expert opinion,

Chronosequence, Field studiesChronosequence, Field studies

•• Density dependent mortality for Density dependent mortality for

an age series (stand self thinning)an age series (stand self thinning)

•• Local G&Y models; Permanent Sample Local G&Y models; Permanent Sample

Plot data, Chronosequence, Literature Plot data, Chronosequence, Literature

sources, Expert opinionsources, Expert opinion



Light submodel (speciesLight submodel (species--specific)specific)

•• Light extinction curves (associated Light extinction curves (associated

with foliage biomass)with foliage biomass)•• Local light models; calibrated light Local light models; calibrated light

extinction equations, field extinction equations, field

measurements measurements

•• Crown development, timing of Crown development, timing of

canopy closure, max single tree canopy closure, max single tree

foliage biomassfoliage biomass

•• Permanent sample plots, stand table Permanent sample plots, stand table

data, literature sourcesdata, literature sources

•• Photosynthetic light saturation Photosynthetic light saturation

curvescurves•• Literature sources, field studiesLiterature sources, field studies



Nutrient Cycling and Decomposition (speciesNutrient Cycling and Decomposition (species--specific)specific)

•• Nutrient Concentrations in tree & Nutrient Concentrations in tree &

plant biomass componentsplant biomass components•• Literature sources, field Literature sources, field

measurements measurements

•• Mass loss data for litter types, Mass loss data for litter types,

CWD and humusCWD and humus•• Literature sources, local soil models, field Literature sources, local soil models, field

studiesstudies

•• Nutrient concentrations in litter Nutrient concentrations in litter

types undergoing decompositiontypes undergoing decomposition•• Literature sources, field studiesLiterature sources, field studies

•• Soil Physical attributes (Soil CEC Soil Physical attributes (Soil CEC

and AEC)and AEC)•• Local soil models, literature sources, Local soil models, literature sources,

CEC model developed at UBC, field CEC model developed at UBC, field

studiesstudies



Understory representation (speciesUnderstory representation (species--specific)specific)

•• Biomass accumulation, nutrient Biomass accumulation, nutrient

concentrations, height growth, etc.concentrations, height growth, etc.•• Literature sources, field studies, expert Literature sources, field studies, expert

opinion opinion

•• Estimates of percent cover by Estimates of percent cover by

forest typeforest type•• Literature sources, Terrestrial Ecosystem Literature sources, Terrestrial Ecosystem

Maps, Site associationsMaps, Site associations

•• Photosynthetic light saturation Photosynthetic light saturation

curvescurves•• Literature sources, field studiesLiterature sources, field studies



Snag Submodel (speciesSnag Submodel (species--specific)specific)

•• DensityDensity--independent mortality independent mortality

rates (associated with specific rates (associated with specific

natural disturbance events)natural disturbance events)

•• Literature sources, field studies, expert Literature sources, field studies, expert

opinion opinion

•• Snag persistence as a function of Snag persistence as a function of

DBH and DBH and hieghthieght•• Literature sources, field studies, expert Literature sources, field studies, expert

opinionopinion

Model Calibration: Model Calibration: Developing local data setsDeveloping local data sets

1. Calibration Cycle1. Calibration Cycle

2. Data Acquisition2. Data Acquisition

3. Entering and editing calibration data3. Entering and editing calibration data

3. Data Assessment: Setup programs3. Data Assessment: Setup programs

4. Data Assessment: Ecosystem Simulation Module4. Data Assessment: Ecosystem Simulation Module

HandsHands--on modelling taskson modelling tasks

1. Create Ecostate file using 80 1. Create Ecostate file using 80 yr yr fire cycle with Douglasfire cycle with Douglas--firfir

2. Set up 2x2 factorial experiment to compare management strateg2. Set up 2x2 factorial experiment to compare management strategiesies

Rotation lengthRotation length

ThinningThinning

FertilizerFertilizer

Mixed species Mixed species vsvs. monoculture. monoculture

3. Assess impact on a series of biophysical indicators of susta3. Assess impact on a series of biophysical indicators of sustainabilityinability

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