東華大學圖書館 |

Exploring Model-Based Approaches for Simulating and Analyzing Cophylogenetic Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Exploring Model-Based Approaches for Simulating and Analyzing Cophylogenetic Data./
作者:	Dismukes, Wade Thomas.
面頁冊數:	1 online resource (154 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
標題:	Evolution & development. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29262003click for full text (PQDT)
ISBN:	9798351411651

Exploring Model-Based Approaches for Simulating and Analyzing Cophylogenetic Data.
Dismukes, Wade Thomas.

Exploring Model-Based Approaches for Simulating and Analyzing Cophylogenetic Data. - 1 online resource (154 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (Ph.D.)--Iowa State University, 2022.

Includes bibliographical references

When two lineages with an intimate ecological association have a repeated history of cospeciation (i.e., both speciate simultaneously), their evolutionary histories can become correlated through time. This correlated diversification can result in perfect concordance between pairs of phylogenies, a pattern often evoked in hosts and obligate symbionts. Yet, many of these presumed cases of cophylogeny do not show a perfect correspondence between host and symbiont phylogenies despite observations of obligate relationships between hosts and symbiont in nature. Previous researchers have primarily used model-free approaches to investigate cophylogenetics. My thesis focuses on moving cophylogenetics closer towards a model-based framework which will allow for more statistically rigorous quantification of cophylogenetic events and further understanding of how these assemblages of hosts and symbionts evolve.Review contrasting the assumptions of and outcomes of cophylogenetic methodsSince previous reviews on this topic, new methods have been introduced to test hypotheses of codiversification, and existing methods have been applied in novel ways to understand the parallel histories of associated lineages. Moreover, researchers are now addressing these problems using larger phylogenetic datasets while also applying phylogenetic comparative methods to integrate ecological interactions into macroevolution. I outline different types of ecological interactions that can potentially leave signatures in the branching histories of host-symbiont systems. I contrast how different datasets may violate the assumptions of cophylogenetic methods and how this may limit what conclusions can be drawn about the processes driving species interactions.Developing a generative model for cophylogenetic dataSimulation is a necessary step when testing and examining phylogenetic methods. Despite this, there was no available simulation software for cophylogenetic data. I developed the cophylogenetic birth-death model and software to simulate under this model to address this gap. The cophylogenetic birth-death model simulates a pair of phylogenies, the host and the symbiont phylogenies, and their ecological interactions. This model uses independent speciation and extinction for the host and the symbiont and parameters that affect both phylogenies, including cospeciation and host-switching.Using biogeographic models for examining cophylogenetic dataRecently probabilistic models such as the dispersal, extinction, and cladogenesis (DEC) have begun to be used in a phylogenetic context to estimate biogeographic parameters such as dispersal and extirpation. These models have a natural analogy to cophylogenetic data where the host phylogeny becomes the area cladogram, the symbiont phylogeny becomes the species tree, and the host-symbiont interaction matrix becomes the matrix representing the host ranges. As there is currently no method for estimating under the cophylogenetic birth-death model, I tested a Bayesian implementation of the DEC model to estimate symbiont dispersal to new hosts. To do this, I simulated data using treeducken to examine the DEC model's ability to estimate host dispersal.Using machine learning to estimate cophylogenetic parametersWithout a straightforward means to calculate a likelihood for the cophylogenetic birth-death model, I pursued alternative means to estimate the parameters of this model. In particular, I used deep learning to estimate cophylogenetic events. I trained this deep neural network using data simulated under my cophylogenetic birth-death model and then tested the performance of this method. I provide a case study of using this deep neural network on empirical data of figs and fig wasps.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798351411651Subjects--Topical Terms:

3172418
Evolution & development.
Subjects--Index Terms:

CoevolutionIndex Terms--Genre/Form:

542853
Electronic books.

Exploring Model-Based Approaches for Simulating and Analyzing Cophylogenetic Data.
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