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A Generalized Mixed Logistic Model for Predicting Individual Tree Survival Probability with Unequal Measurement Intervals

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Based on a generalized linear mixed-model approach, three survival probability models were developed for lodgepole pine (Pinus contorta var. latifolia Engelm.) trees in boreal mixedwood stands in Alberta, Canada. Besides a traditional logistic model and a revised form of that model, a new logistic model was proposed to handle unequal measurement intervals typical of repeatedly measured growth-and-yield data. Various stand and tree characteristics were examined for their contribution to model improvement. Annual diameter increment, the ratio of basal area of larger trees to total stand basal area, and the basal area of larger deciduous trees were found to be significant predictors and were included in the final mixed models for predicting the probability of a tree surviving the coming growth period. Two random parameters were incorporated in the traditional and new models, one added to the intercept of the linear predictors and the other added to the parameter for annual diameter increment. One random parameter was added to the parameter for annual diameter increment in the revised traditional model. The new model provided improved stand density predictions at the population and plot levels over the two forms of the traditional model for a new data set.
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Keywords: empirical best linear unbiased predictor; generalized linear mixed model; individual tree mortality; subject-specific prediction; unequal measurement interval

Document Type: Research Article

Publication date: 2013-04-16

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
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    June 1, 2016 to Feb. 28, 2017

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