Using combined estimator approaches regular mortality models for white spruce and black spruce across the Boreal Forest of Canada are developed. For irregular time intervals between measures a modified logistic regression was used to model the annual probability of survival where permanent sample plot (PSPs) data from Alberta used. The PSP data were divided into two regions
to represent local scale populations, whereas the entire dataset represented the population at the larger scale. Monte Carlo simulations were used to: i) randomly sample from each region (10% and 30% sampling intensity); ii) fit the survival model by region using the sample data; iii) use each of the
combined estimators to weight the regional estimated parameters and obtain a larger scale model; and iv) calculate the bias and precision for each large scale model parameter and combined estimator. Therefore, in this research, several combined estimators were used to combine local scale models to
obtain a general model for the regional scale to improve model precision over naive approaches for the target species.