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

Adapting a GY model The Northern Idaho prognosis variant (NI) has been adapted to the southeast interior of BC, Prognosis BC The Northern Idaho prognosis variant (NI) has been adapted to the southeast interior of BC, Prognosis BC US Habitat Types BC Biogeoclimatic Ecosystem Ecosystem Classification units

Adapting a GY model (cont’d) Different measurement units (metric), basic functions (e.g., volume and taper) and standards Different measurement units (metric), basic functions (e.g., volume and taper) and standards Classification of US habitat type to BEC can be subjective Classification of US habitat type to BEC can be subjective Sub-models coefficients and model form may not fit BC data Sub-models coefficients and model form may not fit BC data Insufficient ground data for some types of stands Insufficient ground data for some types of stands

Sub-model components: Sub-model components: large tree diameter and height growth large tree diameter and height growth small tree diameter and height growth small tree diameter and height growth small and large tree crown ratio small and large tree crown ratio mortality and regeneration mortality and regeneration others others Adapting a GY model

BACKGROUND Mortality is: Mortality is:  an essential attribute of any stand growth projection system  frequently expressed as a function of tree size, stand density, individual tree competition, and tree vigor In Prognosis BC, periodic mortality rate is predicted using tree (R a ) and stand based (R b ) mortality functions In Prognosis BC, periodic mortality rate is predicted using tree (R a ) and stand based (R b ) mortality functions

BACKGROUND (cont’d) R a is a logistic function of tree size taken in context of stand structure. R a is a logistic function of tree size taken in context of stand structure. R b operates as a convergence on normal basal area stocking and maximum basal area (BAMAX) R b operates as a convergence on normal basal area stocking and maximum basal area (BAMAX) R b is based on the concept that: R b is based on the concept that:  for each stand, there is a normal stocking density  there is a BAMAX that a site can sustain and this maximum varies by site quality by site quality

Objectives to adapt a mortality model for southeast interior BC to adapt a mortality model for southeast interior BC to evaluate selected mortality models for conifers and hardwoods in southeast interior BC to evaluate selected mortality models for conifers and hardwoods in southeast interior BC

METHODS 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

METHODS(cont’d) METHODS (cont’d) For each PSP, individual tree records were coded, as either live or dead at each measurement period, and variables listed in the mortality models were extracted For each PSP, individual tree records were coded, as either live or dead at each measurement period, and variables listed in the mortality models were extracted

METHODS (cont’d) Only species/zone combinations with more than 30 dead trees were selected. Only species/zone combinations with more than 30 dead trees were selected. To handle the unequal re- measurement periods in the PSP data sets, each model was weighted by the number of years between remeasurement periods. To handle the unequal re- measurement periods in the PSP data sets, each model was weighted by the number of years between remeasurement periods. The PSP data set was divided into model (70%) and test data (30%) sets The PSP data set was divided into model (70%) and test data (30%) sets Observed and predicted number of live and dead trees by species/zone were compared and then a model was selected Observed and predicted number of live and dead trees by species/zone were compared and then a model was selected

RESULTS Noticeable differences were found in the % of correctly classified trees among the five models and the species/zone combinations considered in this study Noticeable differences were found in the % of correctly classified trees among the five models and the species/zone combinations considered in this study Model 5 had lower Akaike Information Criterion (AIC) and Schwartz Criterion (SC) for most species/zone combinations Model 5 had lower Akaike Information Criterion (AIC) and Schwartz Criterion (SC) for most species/zone combinations

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

Number of observed (N_OBS) and predicted (N_Exp) dead trees by species in the ICH zone, using Model 5 on test data

Number of observed (N_obs) and predicted (N_Exp) dead trees by diameter class in the ICH zone, using Model 5 on test data

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

Number of observed (N_OBS) and predicted (N_Exp) dead trees by species in the IDF zone, using Model 5 on test data

Number of observed (N_obs) and predicted (N_Exp) dead trees by diameter class in the IDF zone, using Model 5 on test data

For species/zone combination with little or no data substitution by similar species or BEC zone is suggested. substitution by similar species or BEC zone is suggested. FORUSE FORUSE Bl in IDFICH Bl in IDFICH Cw in IDF ICH Cw in IDF ICH E in MSICH E in MSICH Fd in PP IDF Fd in PP IDF

Summary Model 5 predicts mortality of both conifers and hardwoods reasonably well Model 5 predicts mortality of both conifers and hardwoods reasonably well BC based BAMAX values improved the predictive ability of the model BC based BAMAX values improved the predictive ability of the model Inclusion of eco-physical factors such as slope, aspect, and elevation into the mortality model might increase the predictive ability of the model. Inclusion of eco-physical factors such as slope, aspect, and elevation into the mortality model might increase the predictive ability of the model.