University of Kentucky UKnowledge Theses and Dissertations--Biology Biology 2017 USING GENOMICS TO UNDERSTAND POPULATION DEMOGRAPHICS IN THE CONTEXT OF AMPHIBIAN CONSERVATION Schyler O. Nunziata University of Kentucky, [email protected] Digital Object Identifier: https://doi.org/10.13023/ETD.2017.390 Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Nunziata, Schyler O., "USING GENOMICS TO UNDERSTAND POPULATION DEMOGRAPHICS IN THE CONTEXT OF AMPHIBIAN CONSERVATION" (2017). Theses and Dissertations--Biology. 49. https://uknowledge.uky.edu/biology_etds/49 This Doctoral Dissertation is brought to you for free and open access by the Biology at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Biology by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known. I agree that the document mentioned above may be made available immediately for worldwide access unless an embargo applies. I retain all other ownership rights to the copyright of my work. I also retain the right to use in future works (such as articles or books) all or part of my work. I understand that I am free to register the copyright to my work. REVIEW, APPROVAL AND ACCEPTANCE The document mentioned above has been reviewed and accepted by the student’s advisor, on behalf of the advisory committee, and by the Director of Graduate Studies (DGS), on behalf of the program; we verify that this is the final, approved version of the student’s thesis including all changes required by the advisory committee. The undersigned agree to abide by the statements above. Schyler O. Nunziata, Student Dr. David W. Weisrock, Major Professor Dr. David F. Westneat, Director of Graduate Studies USING GENOMICS TO UNDERSTAND POPULATION DEMOGRAPHICS IN THE CONTEXT OF AMPHIBIAN CONSERVATION ________________________________________ DISSERTATION ________________________________________ A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Arts and Sciences at the University of Kentucky By Schyler Olivia Nunziata Lexington, Kentucky Director: Dr. David Weisrock, Professor of Biology Lexington, Kentucky 2017 Copyright © Schyler Olivia Nunziata 2017 ABSTRACT OF DISSERTATION USING GENOMICS TO UNDERSTAND POPULATION DEMOGRAPHICS IN THE CONTEXT OF AMPHIBIAN CONSERVATION Understanding the demography of species over recent history (e.g., < 100 years) is critical in studies of ecology and evolution, but records of population history are rarely available. Large single nucleotide polymorphism datasets generated with restriction-site associated DNA sequencing (RADseq), in combination with demographic inference methods, are improving our ability to gain insights into the population history of both model and non-model species. However, to assess the performance of genetic methods it is important to compare their estimates of population history to known demography, in both simulation and empirical settings. Here, I used a simulation approach to examine the potential for RADseq datasets to accurately estimate effective population size (Ne) in Wright-Fisher populations over the course of stable and declining population trends, and distinguish stable from steadily declining populations over a contemporary time scale (20 generations). Overall, my results reveal that demographic inference using genome-wide data can be successfully applied to estimate Ne, and the detection of population-size declines. Next, I assess these methods in an empirical study from a wetland with 37 years of amphibian mark-recapture data to study the utility of genetically-based demographic inference on salamander species with documented population declines (Ambystoma talpoideum) and expansions (A. opacum). For both species, demographic model inference supported population size changes that corroborated mark-recapture data. To further validate these findings, I used individual-based population models of the pond-breeding salamander, Ambystoma opacum, with life-history parameters estimated from a long-term dataset, over a 50 year projection. My results demonstrate that genetically estimated Ne is positively correlated with census size in isolated and subdivided A. opacum populations. Finally, I investigated metapopulation patterns of genomic diversity in A. opacum and A. talpoideum and how migration may impact Ne estimation. I found strong patterns of subpopulation structuring, signatures of migration between subpopulations, and differences in Ne at the subpopulation level in both species. Overall, my findings suggest the ability of genomic data to reconstruct recent demographic changes, which can have important applications to conservation biology, and ultimately can help us elucidate the effects of environmental disturbances in the demography of endangered or declining species. KEYWORDS: demographic inference, temporal samples, genetic monitoring, coalescent, Ambystoma Schyler Olivia Nunziata June 2, 2017 USING GENOMICS TO UNDERSTAND POPULATION DEMOGRAPHICS IN THE CONTEXT OF AMPHIBIAN CONSERVATION By Schyler Olivia Nunziata David W. Weisrock, Ph.D. Director of Dissertation David F. Westneat, Ph.D. Director of Graduate Studies June 2, 2017 ACKNOWLEDGEMENTS I thank all of my committee members, Drs. Catherine Linnen, Philip Crowley, Stacey Lance, and Steven Price for advice. I would especially like to thank my advisor, Dr. David Weisrock, for guidance throughout my graduate career. I thank David Scott for his expert advice on all things Ambystoma. I would like to thank the numerous people who have assisted with data collection, entry, and management of the Rainbow Bay study. I thank the University of Kentucky Center for Computational Sciences and the Lipscomb High Performance Computing Cluster for access to computing resources, and Vikram Gazula for help with script optimization. For help with lab and bioinformatic research, I want to thank Paul Hime, Scott Hotaling, and Robin Bagley. I thank all members of the combined Weisrock and Linnen Superlab. I would like to thank all of the organizations that provided funding, including: University of Kentucky (UKY) Department of Biology Mini-Ribble Grant, UKY College of Arts and Sciences Summer Research Fellowship, Kentucky NSF EPSCoR, SSAR Grants in Herpetology, Kentucky Academy of Sciences Marcia Athey Fund, and the National Science Foundation. iii TABLE OF CONTENTS Acknowledgement ......................................................................................................................... iii List of Tables ................................................................................................................................ vii List of Figures ................................................................................................................................ ix Chapter One: General Introduction Introduction ............................................................................................................................... 10 Estimation of effective size ....................................................................................................... 12 LD-based estimations of recent demographic history ............................................................... 12 Coalescent-based estimations of recent demographic history................................................... 14 Major differences between LD and coalescent Ne.................................................................... 16 Demographic inference in complex and dynamic systems ....................................................... 17 Case Studies with a focus on amphibians--Chapters 2 through 5 ............................................. 18 Chapter Two: Estimation of contemporary effective population size and population declines using RAD sequence data Abstract ..................................................................................................................................... 22 Introduction ............................................................................................................................... 23 Methods ..................................................................................................................................... 26 Data simulation ...................................................................................................................... 26 In silico RADseq mutations and data filtering ...................................................................... 27 Ne estimation and demographic inference ............................................................................. 29 Accuracy assessments ...........................................................................................................