Modeling Mortality of Loblolly Pine Plantations

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dc.contributor.authorThapa, Ramen
dc.contributor.committeechairBurkhart, Harold E.en
dc.contributor.committeememberReynolds, Marion R. Jr.en
dc.contributor.committeememberKim, Inyoungen
dc.contributor.committeememberRadtke, Philip J.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2014-03-20T08:00:49Zen
dc.date.available2014-03-20T08:00:49Zen
dc.date.issued2014-03-19en
dc.description.abstractAccurate prediction of mortality is an important component of forest growth and yield prediction systems, yet mortality remains one of the least understood components of the system. Whole-stand and individual-tree mortality models were developed for loblolly pine plantations throughout its geographic range in the United States. The model for predicting stand mortality were developed using stand characteristics and biophysical variables. The models were constructed using two modeling approaches. In the first approach, mortality functions for directly predicting tree number reduction were developed using algebraic difference equation method. In the second approach, a two-step modeling strategy was used where a model predicting the probability of tree death occurring over a period was developed in the first step and a function that estimates the reduction in tree number was developed in the second step. Individual-tree mortality models were developed using multilevel logistic regression and survival analysis techniques. Multilevel data structure inherent in permanent sample plots data i.e. measurement occasions nested within trees (e.g., repeated measurements) and trees nested within plots, is often ignored in modeling tree mortality in forestry applications. Multilevel mixed-effects logistic regression takes into account the full hierarchical structure of the data. Multilevel mixed-effects models gave better predictions than the fixed effects model; however, the model fits and predictions were further improved by taking into account the full hierarchical structure of the data. Semiparametric proportional hazards regression was also used to develop model for individual-tree mortality. Shared frailty model, mixed model extension of Cox proportional hazards model, was used to account for unobserved heterogeneity not explained by the observed covariates in the Cox model.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:2334en
dc.identifier.urihttp://hdl.handle.net/10919/46726en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectLobolly pine plantationsen
dc.subjectMortalityen
dc.subjectClimate and soilen
dc.subjectDifference mortality equationen
dc.subjectMultilevel logistic regressionen
dc.subjectCox proportional hazards modelen
dc.subjectShared frailtyen
dc.titleModeling Mortality of Loblolly Pine Plantationsen
dc.typeDissertationen
thesis.degree.disciplineForestryen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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