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dc.contributor.authorYoung, Brian D.
dc.date.accessioned2018-08-07T22:45:47Z
dc.date.available2018-08-07T22:45:47Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/11122/9155
dc.descriptionThesis (Ph.D.) University of Alaska Fairbanks, 2012
dc.description.abstractWithin the forest management community, diversity is often considered as simply a list of species present at a location. In this study, diversity refers to species richness and evenness and takes into account vegetation structure (i.e. size, density, and complexity) that characterize a given forest ecosystem and can typically be measured using existing forest inventories. Within interior Alaska the largest forest inventories are the Cooperative Alaska Forest Inventory and the Wainwright Forest Inventory. The limited distribution of these inventories constrains the predictions that can be made. In this thesis, I examine forest diversity in three distinct frameworks; Recruitment, Patterns, and Production. In Chapter 1, I explore forest management decisions that may shape forest diversity and its role and impacts in the boreal forest. In Chapter 2, I evaluate and map the relationships between recruitment and species and tree size diversity using a geospatial approach. My results show a consistent positive relationship between recruitment and species diversity and a general negative relationship between recruitment and tree size diversity, indicating a tradeoff between species diversity and tree size diversity in their effects on recruitment. In Chapter 3, I modeled and mapped current and possible future forest diversity patterns within the boreal forest of Alaska using machine learning. The results indicate that the geographic patterns of the two diversity measures differ greatly for both current conditions and future scenarios and that these are more strongly influenced by human impacts than by ecological factors. In Chapter 4, I developed a method for mapping and predicting forest biomass for the boreal forest of interior Alaska using three different machine-learning techniques. I developed first time high resolution prediction maps at a 1 km2 pixel size for aboveground woody biomass. My results indicate that the geographic patterns of biomass are strongly influenced by the tree size class diversity of a given stand. Finally, in Chapter 5, I argue that the methods and results developed for this dissertation can aid in our understanding of forest ecology and forest management decisions within the boreal region.
dc.subjectForestry
dc.subjectEcology
dc.subjectBiostatistics
dc.titleDiversity In The Boreal Forest Of Alaska: Distribution And Impacts On Ecosystem Services
dc.typeThesis
dc.type.degreephd
dc.identifier.departmentDepartment of Forest Sciences
dc.contributor.chairYarie, John
dc.contributor.committeeChapin, F. Stuart
dc.contributor.committeeGreenburg, Josh
dc.contributor.committeeHuettmann, Falk
dc.contributor.committeeVerbyla, David
refterms.dateFOA2020-03-06T01:12:38Z


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