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Three approaches to automating taxonomy, with emphasis on the Odonata (dragonflies and damselflies)

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Title
Three approaches to automating taxonomy, with emphasis on the Odonata (dragonflies and damselflies)
Name (type = personal)
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Kuhn
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William Robert
NamePart (type = date)
1984-
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William Kuhn
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author
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Ware
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Jessica L
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Jessica L Ware
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Advisory Committee
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chair
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Russell
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Gareth J
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Gareth J Russell
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Advisory Committee
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internal member
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Flammang
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Brooke
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Brooke Flammang
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Advisory Committee
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internal member
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May
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Michael
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Michael May
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Advisory Committee
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outside member
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Polly
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P David
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P David Polly
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Advisory Committee
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outside member
Name (type = corporate)
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Rutgers University
Role
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degree grantor
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Graduate School - Newark
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Text
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theses
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2016
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2016-10
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2016
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xx
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eng
Abstract (type = abstract)
Taxonomy-the field charged with naming and classifying organisms-forms a foundation for biological research. An understanding of the species on Earth is needed for informing biodiversity research, conservation efforts, management strategies, and global policy. In recent decades, a "taxonomic impediment" has arisen: there is an urgent need to know the millions of yet-undiscovered species, while funding for the science charged with this task, taxonomy, and the number of trained taxonomists are declining. This work aims to provide three software tools for taxonomists that allow them to work more efficiently and effectively, reducing this impediment. First, a system for automatically landmarking images of specimens for geometric morphometric studies was introduced, which could greatly reduce the time required to manually landmark images for these studies while also increasing the possible sample size of such studies. The system's landmarking error, however, was extremely variable on test images of the wings of dragonflies and damselflies (Odonata), and was ultimately too large (300-500 px) to compete with manual landmarking at this time. Second, a method was presented for automatically standardizing and extracting descriptive features from images of insect wings in order to quantify the appearance of the wings. The standardization method was successful in converting scans of odonate wings into consistently-formatted square images, automatically. Then, features describing the color, texture, and shape of the wings were able to be extracted, producing a small set of 663 coefficients that were able to distinguish between species. Finally, a system called Odomatic was presented and tested for automatically identifying Odonata to species from images of their wings, using the feature extraction method combined with machine learning techniques. Odomatic was able to make classifications between 32 species with expert-level (up to 92%) accuracy, making it useful for quickly identifying specimens. The tools presented here will be deployed for use by odonate researchers through the website OdonataCentral.org, but will also be released as open-sourced Python scripts so that they can be customized to be implemented on other taxonomic groups. This work will enable taxonomists and other interested parties to make easier morphological comparisons and faster identifications.
Subject (authority = RUETD)
Topic
Biology
Subject (authority = ETD-LCSH)
Topic
Odonata
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_7461
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xi, 177 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by William Robert Kuhn
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TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
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rucore10002600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3SQ92Q4
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Kuhn
GivenName
William
MiddleName
Robert
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-08-01 07:22:31
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Name
William Kuhn
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Affiliation
Rutgers University. Graduate School - Newark
AssociatedObject
Type
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2017-10-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2017.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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