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An examination of beta diversity indices and their predictors in two large-scale systems

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Date Issued:
2018
Abstract/Description:
Biodiversity is what conservation biology was developed to conserve. It is the physical manifestation of life as a concept and, be it for practical or idealistic reasons, all conservationists seek to protect or, in some cases, enhance it. Because of its monolithic importance to the field, much effort has been expended trying to better measure and understand it. Recently, greater attention has been paid to the partition of diversity; the observation that the total diversity of a system (?) can be broken down into within-site diversity (?) and between-site diversity (?). In particular, it has been noticed that the ? component of diversity is not as well studied or understood as the ? component. In this study I attempt to address this shortfall, by examining two questions: (1) how is ? is best measured and (2) what drives ?? To answer the first question, I look to find the measure of ? that is most robust to sampling error. While many ? indices have been proposed, few have considered how our methods of data gathering might affect those indices. Datasets collected from the real world will all likely have some sort of error within them as a result of the way they were sampled. Those errors will affect some indices more than others, and the indices that are least affected will be the most reliable for actual data. Once robust indices were identified, I used them to identify possible predictors of ? in two large, national datasets. The first dataset was the National Lakes Assessment created by the USEPA, in which diatoms were sampled from over 1000 lakes across the country. The second was the eBird dataset from the Cornell Lab of Ornithology, which used citizen science to generate a continuous dataset spanning both the last decade and the boundaries of the conterminous United States. ? calculated from these sources was regressed against relevant environmental variables to create a clearer understanding of the effects of the environment on the ? of two very different ecological systems.
Title: An examination of beta diversity indices and their predictors in two large-scale systems.
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Name(s): Schroeder, Philip, Author
Jenkins, David, Committee Chair
King, Joshua, Committee Member
Fedorka, Kenneth, Committee Member
Myers, Jonathan, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2018
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Biodiversity is what conservation biology was developed to conserve. It is the physical manifestation of life as a concept and, be it for practical or idealistic reasons, all conservationists seek to protect or, in some cases, enhance it. Because of its monolithic importance to the field, much effort has been expended trying to better measure and understand it. Recently, greater attention has been paid to the partition of diversity; the observation that the total diversity of a system (?) can be broken down into within-site diversity (?) and between-site diversity (?). In particular, it has been noticed that the ? component of diversity is not as well studied or understood as the ? component. In this study I attempt to address this shortfall, by examining two questions: (1) how is ? is best measured and (2) what drives ?? To answer the first question, I look to find the measure of ? that is most robust to sampling error. While many ? indices have been proposed, few have considered how our methods of data gathering might affect those indices. Datasets collected from the real world will all likely have some sort of error within them as a result of the way they were sampled. Those errors will affect some indices more than others, and the indices that are least affected will be the most reliable for actual data. Once robust indices were identified, I used them to identify possible predictors of ? in two large, national datasets. The first dataset was the National Lakes Assessment created by the USEPA, in which diatoms were sampled from over 1000 lakes across the country. The second was the eBird dataset from the Cornell Lab of Ornithology, which used citizen science to generate a continuous dataset spanning both the last decade and the boundaries of the conterminous United States. ? calculated from these sources was regressed against relevant environmental variables to create a clearer understanding of the effects of the environment on the ? of two very different ecological systems.
Identifier: CFE0007367 (IID), ucf:52088 (fedora)
Note(s): 2018-12-01
Ph.D.
Sciences, Biology
Doctoral
This record was generated from author submitted information.
Subject(s): Beta diversity -- birds -- diatoms -- error rates -- simulations
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007367
Restrictions on Access: public 2018-12-15
Host Institution: UCF

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