Adapting a Mortality Model Adapting a Mortality Model for Southeast Interior British for Southeast Interior British

ColumbiaColumbia

By - Temesgen H., V. LeMay, and P.L. By - Temesgen H., V. LeMay, and P.L. MarshallMarshall

University of British ColumbiaUniversity of British Columbia

Forest Resources ManagementForest Resources Management

Vancouver, BC, V6T 1Z4Vancouver, BC, V6T 1Z4

The 2001 Western Mensurationists' The 2001 Western Mensurationists' MeetingMeeting

Klamath Falls, OregonKlamath Falls, Oregon

June 24-26/2001June 24-26/2001

Adapting a GY modelAdapting a GY model

• The Northern Idaho prognosis The Northern Idaho prognosis variant (NI) has been adapted to variant (NI) has been adapted to the southeast interior of BC, the southeast interior of BC, PrognosisPrognosisBCBC

US Habitat TypesUS Habitat Types

BC BiogeoclimaticBC Biogeoclimatic Ecosystem Ecosystem Classification unitsClassification units

Adapting a GY model Adapting a GY model (cont’d)(cont’d)

• Different measurement units Different measurement units (metric), basic functions (e.g., (metric), basic functions (e.g., volume and taper) and volume and taper) and standardsstandards

• Classification of US habitat Classification of US habitat type to BEC can be subjectivetype to BEC can be subjective

• Sub-models coefficients and Sub-models coefficients and model form may not fit BC model form may not fit BC datadata

• Insufficient ground data for Insufficient ground data for some types of standssome types of stands

Sub-model components:Sub-model components:

• large tree diameter and large tree diameter and height growthheight growth

• small tree diameter and small tree diameter and height growthheight growth

• small and large tree crown small and large tree crown ratioratio

• mortality and regenerationmortality and regeneration

• othersothers

Adapting a GY modelAdapting a GY model

BACKGROUNDBACKGROUND

• Mortality is:Mortality is:an essential attribute of any an essential attribute of any

stand growth projection stand growth projection system system

frequently expressed as a frequently expressed as a function of tree size, stand function of tree size, stand density, individual tree density, individual tree competition, and tree vigorcompetition, and tree vigor

• In PrognosisIn PrognosisBCBC, periodic , periodic mortality rate is predicted mortality rate is predicted using tree (Rusing tree (Raa) and stand based ) and stand based (R(Rbb) mortality functions) mortality functions

BACKGROUND (cont’d)BACKGROUND (cont’d)

• RRaa is a logistic function of tree is a logistic function of tree size taken in context of stand size taken in context of stand structure.structure.

• RRbb operates as a convergence on operates as a convergence on normal basal area stocking and normal basal area stocking and maximum basal area (BAMAX)maximum basal area (BAMAX)

• RRb b isis based on the concept that: based on the concept that: for each stand, there is a for each stand, there is a

normal stocking densitynormal stocking densitythere is a BAMAX that a site there is a BAMAX that a site

can sustain and this maximum can sustain and this maximum varies varies

by site qualityby site quality

ObjectivesObjectives

• to adapt a mortality to adapt a mortality model for southeast model for southeast interior BCinterior BC

• to evaluate selected to evaluate selected mortality models for mortality models for conifers and hardwoods in conifers and hardwoods in southeast interior BCsoutheast interior BC

METHODSMETHODS

• Three approaches of adapting mortality model were assessed, using BC based PSPs:

1. a multiplier function (Model 1)2. re-fit the same model form by

species/zone combination (Model 2)3. changing variables (Models 3, 4,

and 5)

• PSPs that were re-measured at 5 to 12 years interval and that consistently included all trees > 2.0 cm were included 

METHODSMETHODS (cont’d)(cont’d)

• For each PSP, individual tree For each PSP, individual tree records were coded, as either records were coded, as either live or dead at each live or dead at each measurement period, and measurement period, and variables listed in the mortality variables listed in the mortality models were extractedmodels were extracted

Annual

ZONE # of PSPs live dead Mort. (%)

ESSF 8 508 36 0.71

ICH 243 44991 5162 1.15

IDF 274 40497 3909 0.97

MS 137 13456 859 0.64

Total 662 99452 9966 1.00

# of trees

METHODS (cont’d)METHODS (cont’d)

• Only species/zone combinations with Only species/zone combinations with more than 30 dead trees were more than 30 dead trees were selected. selected.

• To handle the unequal re-To handle the unequal re-measurement periods in the PSP measurement periods in the PSP data sets, each model was weighted data sets, each model was weighted by the number of years between by the number of years between remeasurement periods.remeasurement periods.

• The PSP data set was divided into The PSP data set was divided into model (70%) and test data (30%) model (70%) and test data (30%) sets  sets  

• Observed and predicted number of Observed and predicted number of live and dead trees by species/zone live and dead trees by species/zone were compared and then a model were compared and then a model was selectedwas selected

RESULTSRESULTS

• Noticeable differences were Noticeable differences were found in the % of correctly found in the % of correctly classified trees among the classified trees among the five models and the five models and the species/zone combinations species/zone combinations considered in this studyconsidered in this study

• Model 5 had lower Akaike Model 5 had lower Akaike Information Criterion (AIC) Information Criterion (AIC) and Schwartz Criterion (SC) and Schwartz Criterion (SC) for most species/zone for most species/zone combinations combinations

Percent of correctly classified Percent of correctly classified trees in the ICH zone, using test trees in the ICH zone, using test

datadata

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

At Bl Cw E Fd Hw Lw Pl Pw Sx

Tree species

Perc

ent o

f cor

rect

ly c

lass

ified

tree

s

Model 1 Model 2 Model 3Model 4 Model 5

Number of observed (N_OBS) and Number of observed (N_OBS) and predicted (N_Exp) dead trees by predicted (N_Exp) dead trees by

species in the ICH zone, using Model species in the ICH zone, using Model 5 on test data5 on test data

0

100

200

300

400

500

600

700

At Bl Cw Ep Fd Hw Lw Pl Pw Sx

Tree Species

Numb

er of

dead

tree

s

N_OBSN_EXP

Number of observed (N_obs) and Number of observed (N_obs) and predicted (N_Exp) dead trees by predicted (N_Exp) dead trees by diameter class in the ICH zone, diameter class in the ICH zone,

using Model 5 on test datausing Model 5 on test data

Species=Douglas-fir

0

20

40

60

80

100

120

140

160

5 10 15 20 25 30 35 40 45 50 55 60

Diameter class (cm)

Num

ber

of

tree

s

N_obsN_EXP

Percent of correctly classified Percent of correctly classified trees in the IDF zone, using test trees in the IDF zone, using test

datadata

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

At E Fd Lw Pl Py Sx

Tree species

% o

f cor

rect

ly c

lassif

ied tr

ees

Model 1 Model 2 Model 3Model 4 Model 5

Number of observed (N_OBS) and Number of observed (N_OBS) and predicted (N_Exp) dead trees by predicted (N_Exp) dead trees by

species in the IDF zone, using Model species in the IDF zone, using Model 5 on test data5 on test data

0

100

200

300

400

500

600

700

At Ep Fd Lw Pl Py Sx

Tree Species

Numb

er o

f dea

d tre

es

N_OBSN_EXP

Number of observed (N_obs) and Number of observed (N_obs) and predicted (N_Exp) dead trees by predicted (N_Exp) dead trees by diameter class in the IDF zone, diameter class in the IDF zone,

using Model 5 on test datausing Model 5 on test data

Species=Douglas-fir

0

50

100

150

200

250

300

5 10 15 20 25 30 35 40 45 50 55 60

Diameter class (cm)

Num

ber o

f tre

es

N_obs

N_exp

For species/zone For species/zone combination with little or combination with little or

no datano data

• substitution by similar substitution by similar species or BEC zone is species or BEC zone is suggested.suggested.

FORFOR USEUSE•Bl in IDFBl in IDF ICHICH•Cw in IDF Cw in IDF ICHICH•E in MSE in MS ICHICH•Fd in PP Fd in PP IDFIDF

SummarySummary

• Model 5 predicts mortality of Model 5 predicts mortality of both conifers and hardwoods both conifers and hardwoods reasonably wellreasonably well

• BC based BAMAX values BC based BAMAX values improved the predictive ability improved the predictive ability of the modelof the model

• Inclusion of eco-physical Inclusion of eco-physical factors such as slope, aspect, factors such as slope, aspect, and elevation into the mortality and elevation into the mortality model might increase the model might increase the predictive ability of the model. predictive ability of the model.