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Bayesian Population Dynamics Modeling to Guide Population Restoration and Recovery of Endangered Mussels in the Clinch River, Tennessee and Virginia by Man Tang

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Bayesian Population Dynamics Modeling to Guide Population Restoration and Recovery of Endangered Mussels in the Clinch River, Tennessee and Virginia by Man Tang Bayesian population dynamics modeling to guide population restoration and recovery of endangered mussels in the Clinch River, Tennessee and Virginia By Man Tang Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science In Fisheries and Wildlife Yan Jiao, Chair Jess W. Jones, Co-chair Paul Angermeier Feng Guo December 9th, 2012 Blacksburg, Virginia Keywords: Bayesian analysis, risk assessment, population dynamics, freshwater mussels, and PIT tag. Copyright© 2012, Man Tang ! ! Bayesian population dynamics modeling to guide population restoration and recovery of endangered mussels in the Clinch River, Tennessee and Virginia Man Tang ABSTRACT Freshwater mussels have played an important role in the history of human culture and also in ecosystem functioning. But during the past several decades, the abundance and diversity of mussel species has declined all over the world. To address the urgent need to maintain and restore populations of endangered freshwater mussels, quantitative population dynamics modeling is needed to evaluate population status and guide the management of endangered freshwater mussels. One endangered mussel species, the oyster mussel (Epioblasma capsaeformis), was selected to study its population dynamics for my research. The analysis was based on two datasets, length frequency data from annual surveys conducted at three sites in Clinch River: Wallen Bend (Clinch River Mile 192) from 2004-2010, Frost Ford (CRM 182) from 2005 to 2010 and Swan Island (CRM 172) from 2005 to 2010, and age-length data based on shell thin-sections. Three hypothetical scenarios were assumed in model estimations: (1) constant natural mortality; (2) one constant natural mortality rate for young mussels and another one for adult mussels; (3) age-specific natural mortality. A Bayesian approach was used to analyze the age-structured models and a Bayesian model averaging approach was applied to average the results by weighting each model using the deviance information criterion (DIC). A risk assessment was conducted to evaluate alternative restoration strategies for E. capsaeformis. The results indicated that releasing adult mussels was the quickest way to increase mussel population size and increasing survival and fertility of young mussels was a suitable way to restore mussel populations in the long term. The population of E. capsaeformis at Frost Ford had a lower risk of decline compared with the populations at Wallen Bend and Swan Island. Passive integrated transponder (PIT) tags were applied in my fieldwork to monitor the translocation efficiency of E. capsaeformis and Actinonaias pectorosa at Cleveland Islands (CRM 270.8). Hierarchical Bayesian models were developed to address the individual variability and sex-related differences in growth. In model selection, the model considering individual variability and sex-related differences (if a species has sexual dimorphism) yielded the lowest DIC value. The results from the best model showed that the mean asymptotic length and mean growth rate of female E. capsaeformis were 45.34 mm and 0.279, which were higher than values estimated for males (42.09 mm and 0.216). The mean asymptotic length and mean growth rate for A. pectorosa were 104.2 mm and 0.063, respectively. To test for the existence of individual and sex-related variability in survival and recapture rates, Bayesian models were developed to address the variability in the analysis of the mark-recapture data of E. capsaeformis and A. pectorosa. DIC was used to compare different models. The median survival rates of male E. capsaeformis, female E. capsaeformis and A. pectorosa were high (>87%, >74% and >91%), indicating that the habitat at Cleveland Islands was suitable for these two mussel species within this survey duration. In addition, the median recapture rates for E. capsaeformis and A. pectorosa were >93% and >96%, indicating that the PIT tag technique provided an efficient monitoring approach. According to model comparison results, the non-hierarchical model or the model with sex–related differences (if a species is sexually dimorphic) in survival rate was suggested for analyzing mark-recapture data when sample sizes are small. ! iii! ACKNOWLEDGEMENTS This thesis would not have been completed without the help of many people. First, I would like to acknowledge my deep and sincere gratitude to my major advisor Dr. Yan Jiao. She provided me with the opportunity to conduct research in the field of fisheries management. Thanks for her guidance, support and encouragement in my study, research and graduate life at Virginia Tech. She has helped encourage me to think independently and creatively and do research as a scientist. I learned a lot and become a better person under her guidance. I am deeply indebted to my co-advisor Dr. Jess W. Jones for his generous help in guiding my field work, providing valuable suggestions for my thesis, revising my paper, and improving my writing skill. I would also like to extend thanks to Dr. Paul Angermeier and Dr. Feng Guo for their detailed and thoughtful comments, and for their important support throughout this work. I also wish to thank my teammates Yan Li, Dan Hua, Joshua Hatch, Irene Ballesta, Matt Vincent and Yingbin Wang, for their generous help during my study and research at Virginia Tech. I wish to extend my warmest thanks to the staff at the Mussel Lab who have helped with my fieldwork and to the other students in the Department of Fish and Wildlife Conservation who helped me with my study. Funding for my project was received from the U.S. Department of Agriculture’s Cooperative State Research, Education and Extension Service and for the U.S. Geological Survey’s Science Support Program is gratefully acknowledged. ! iv! TABLE OF CONTENTS Acknowledgements ........................................................................................................... iv List of Tables .................................................................................................................... vii List of Figures .................................................................................................................. viii Chapter 1 ............................................................................................................................. 1 General introduction ........................................................................................................... 1 1.1 Biological and ecological characteristics of freshwater mussels ......................... 1 1.2 The stock recruitment of freshwater mussels ....................................................... 1 1.3 Status of freshwater mussels in the North America ............................................. 3 1.4 Research and conservation of endangered freshwater mussels ............................ 4 1.5 Risk assessment and restoration strategies ........................................................... 4 1.6 Characteristics of example species ....................................................................... 5 1.7 Bayesian analysis ................................................................................................. 7 1.8 Goals and objectives ............................................................................................. 9 1.9 References .......................................................................................................... 10 Chapter 2 ........................................................................................................................... 17 Population dynamics modeling and risk assessment to restore populations of endangered freshwater mussels: a case study with the oyster mussel (Epioblasma capsaeformis)….. 17 2.1 Abstract ................................................................................................................. 17 2.2 Introduction ........................................................................................................... 18 2.3 Material and methods ............................................................................................ 20 2.3.1 Data sources ..................................................................................................... 20 2.3.2 Statistical age-structured model to estimate natural mortality rate and recruitment ................................................................................................................. 20 2.3.3 Alternative scenarios for natural mortality rate .............................................. 23 2.3.4 Bayesian approach and priors ........................................................................ 25 2.3.5 Bayesian model averaging (BMA) ................................................................. 25 2.3.6 Demographic model and risk assessment ....................................................... 27 2.4 Results…………………………………………………………………………...30 2.5 Discussion ............................................................................................................ 32 2.6 Acknowledgements .............................................................................................. 36 v! ! 2.7 References ............................................................................................................ 37 Chapter 3 ........................................................................................................................... 55 A hierarchical Bayesian approach for estimating
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