ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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In Chapter 1, I summarize relevant past work on food and nest recruitment. Then I describe T. rugatulus and its recruitment behavior, tandem running, and I explain its suitability for these questions. In Chapter 2, I investigate whether food and nest recruiters behave differently. I report two novel behaviors used by recruiters during their interaction with nestmates. Food recruiters perform these behaviors more often than nest recruiters, suggesting that they convey information about target type. In Chapter 3, I investigate whether colonies respond to a tradeoff between foraging and emigration by allocating their workforce adaptively. I describe how colonies responded when I posed a tradeoff by manipulating colony need for food and shelter and presenting both resources simultaneously. Recruitment and visitation to each target partially matched the predictions of the tradeoff hypothesis. In Chapter 4, I address the tuned error hypothesis, which states that the error rate in recruitment is adaptively tuned to the patch area of the target. Food tandem leaders lost followers at a higher rate than nest tandem leaders. This supports the tuned error hypothesis, because food targets generally have larger patch areas than nest targets with small entrances.
This work shows that animal groups face tradeoffs as individual animals do. It also suggests that colonies spatially allocate their workforce according to resource type. Investigating recruitment for multiple resource types gives a better understanding of exploitation of each resource type, how colonies make collective decisions under conflicting goals, as well as how colonies manage the exploitation of multiple types of resources differently. This has implications for managing the health of economically important social insects such as honeybees or invasive fire ants.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.