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Sexually dimorphic foraging in bees and robust measurement of biodiversity

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TitleInfo
Title
Sexually dimorphic foraging in bees and robust measurement of biodiversity
Name (type = personal)
NamePart (type = family)
Roswell
NamePart (type = given)
Michael
NamePart (type = date)
1988-
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Michael Roswell
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author
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Winfree
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Rachael
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Rachael Winfree
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Advisory Committee
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chair
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Pinsky
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Malin
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Malin Pinsky
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Advisory Committee
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internal member
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Jensen
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Olaf P
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Olaf P Jensen
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Advisory Committee
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internal member
Name (type = personal)
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Dushoff
NamePart (type = given)
Jonathan
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Jonathan Dushoff
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
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Text
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theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2020
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2020-01
Language
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English
Abstract (type = abstract)
The field of ecology relies on the concept of species diversity to describe the structure of biological communities and ecosystems. But do we measure and interpret species diversity correctly? In my dissertation, I address two problems with the current species diversity paradigm. First, in measuring species diversity, we imagine that the roles that individuals play in ecosystems are cast by their species identities. However, if variation in the traits and behaviors of individuals of the same species is large, relative to variation between different species, this paradigm may fail. Second, we imagine that we can estimate species diversity in comparable ways in different systems. Yet our tools measure diversity pertain to samples, which, even if collected the same way, may not be equally representative. Finally, diversity estimates contain uncertainty, but existing tools to describe that uncertainty make risky statistical assumptions.

In my dissertation research, I tested whether variation within bee species might be as large as variation between them. Then, I addressed methodological challenges of comparing biodiversity. To support this research, I collected an unusual bee-plant interaction dataset. I designed my study to reveal aspects of community diversity and variation within species that could be masked by undersampling in more typical datasets. My dataset is particularly large: ~ 20,000 records of bees visiting flowers, collected at only six meadows in a single summer. Each community is relatively well-sampled; the number of individuals and interactions per community is several times what pollination ecologists usually collect.

In my first chapter, on the degree of variation within species, I show how different sexes of the same bee species interact with different groups of plants. Pollination ecologists tend to lump all bees, regardless of sex, into groups by species. When lumping the sexes, ecologists assume that male bees are, from an ecological standpoint, the same as females, which, in fact, collect food for their offspring and thus forage at higher rates. I show that differences between sexes, within species, are comparable to differences between bee species. These differences arise from distinct activity periods for male and female bees, and also from flower choices made by the two sexes when they co-occur. My findings challenge lumping organisms simply by species, and may help land managers decide which mix of plants will best support bee populations.

In my second chapter, I use my dataset to illustrate best practices for measuring diversity. Rather than simply reveal patterns emerging from my own data, this chapter provides guidance on measuring the biodiversity of ecological communities more generally. Currently, conservation decisions and peer-reviewed papers rest on estimates of biodiversity that undershoot more in some places than others, often to the point of incorrectly identifying which communities are more diverse. Ecologists also use many metrics to compare biodiversity, and may lack conceptual justification for choosing one versus the other. This chapter clarifies the biases arising from traditional tools of standardizing samples to make them comparable, reviews newer methods to reduce these biases, and provides a novel conceptual overview of diversity metrics themselves.

In my third chapter, I showed that tools to quantify the uncertainty associated with different diversity estimates are not all reliable. In this chapter I define criteria for valid confidence intervals, and introduce two tools asses those criteria, “slugplots” and “checkplots.” These plots show that popular asymptotic biodiversity estimates lack robust uncertainty estimates, which makes using them even harder. I also show an under-reported bias/variance tradeoff within a popular family of diversity metrics.

This dissertation provides new insights into the natural history of bees, clarifies best practices for measuring diversity, and rigorously assesses the statistical tools that ecologists rely upon to compare biodiversity.
Subject (authority = RUETD)
Topic
Ecology and Evolution
Subject (authority = LCSH)
Topic
Bees -- Food
Subject (authority = LCSH)
Topic
Biodiversity
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Rutgers University Electronic Theses and Dissertations
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ETD_10563
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1 online resource (xiii, 157 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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Identifier (type = doi)
doi:10.7282/t3-b3jt-5z80
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
Roswell
GivenName
Michael
Role
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RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-01-16 00:25:36
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Name
Michael Roswell
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Affiliation
Rutgers University. School of Graduate Studies
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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.
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2020-01-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2022-01-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after January 30th, 2022.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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