adapting a mortality model for southeast interior british columbia
DESCRIPTION
Adapting a Mortality Model for Southeast Interior British Columbia. By - Temesgen H., V. LeMay, and P.L. Marshall University of British Columbia Forest Resources Management Vancouver, BC, V6T 1Z4 The 2001 Western Mensurationists' Meeting Klamath Falls, Oregon June 24-26/2001. - PowerPoint PPT PresentationTRANSCRIPT
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.