Description
TitleSexually dimorphic foraging in bees and robust measurement of biodiversity
Date Created2020
Other Date2020-01 (degree)
Extent1 online resource (xiii, 157 pages) : illustrations
DescriptionThe 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.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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