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Margaret Memanus

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Margaret Memanus POPULATION GENETICS, LARVAL DISPERSAL, AND DEMOGRAPIDC CONNECTIVITY IN MARINE SYSTEMS A THESIS SUBMII lED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI'I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN OCEANOGRAPHY DECEMBER 2007 By Kimberley A. Weersing Thesis Committee: Robert Toonen, Chairperson Craig Smith Margaret MeManus We certify that we have read this thesis and that, in our opinion, it is satisfactory in scope and quality as a thesis for the degree of Master of Science in Oceanography. THESIS COMMITtEE ii Acknowledgements My process of attending graduate school, from submitting my application to completing my thesis, was the product of many people's time, energy, and support. It is to these many friends, teachers, and colleagues that I wish to express my deep gratitude. In particular, I thank Rob Toonen for his mentorship and for providing me with the opportunity to study and conduct research in such a remarkable scientific community. I also thank Craig Smith and Margaret McManus, not only for their critical insights into the development of my project, but also for sharing hands-on research opportunities with me in New Zealand and California. I would like to extend my appreciation to Ben Bergen, Brian Bowen, Steve Karl, and Andy Taylor for their valuable statistics and experimental design advice. I give special thanks to my funding agencies, HIMB-NWlll Coral Reef Research Partnership and NSF, and to Eric DeCarlo and Chris Measures for awarding me with a teaching assistantship earlier this year. Finally, I am grateful to Shimi Rii for her incredible support and friendship during the long grind of data collection, and to Chris Neufeld for helping to make this entire journey possible. iii Abstract Population connectivity plays significant roles on both evolutionary and ecological time-scales, however efforts at constraining the magnitude and pattern of demographic exchange between populations of marine organisms has been encumbered by the difficulty of tracking the trajectory and fate of propaguIes. I survey 300 published studies to synthesize life-history and population genetic structure data from a broad array of marine taxa to determine how well pelagic larval duration (PLO) correlates with population genetic estimates of dispersal for benthic organisms. Expanding on earlier studies, I further explore other potential biophysical correlates of population substructure (genetic marker class, habitat type, and larval swimming ability) that have not been considered previously. In contrast to previous studies concluding that longer planktonic periods confer greater dispersal ability, average PLO was poorly correlated with population connectivity (FSf) except among species in intertidal ecosystems. For species in which minimum, maximum and mean PLO were available, both minimum and maximum PLO are better correlated with Fsr than is the mean estimate. Furthermore, even this weak correlation appears to be anchored by non-pelagic dispersal, because removal of species that lack a pelagic phase entirely (the zero PLO class) from the analysis resulted in a non-significant relationship between Fsr and mean estimated PLO. A 3-way ANCOVA instead reveals that differences among genetic marker classes (mtDNA, allozymes, and microsatellites) are responsible for most of the variation in FSf (F =7.113, df =2, P =0.001). while neither habitat nor swimming ability were significant factors. In contrast to the general expectation that microsatellite-based studies should provide the finest resolution of population structure, this survey finds that significantly iv higher values of FST are obtained with mtDNA than with either microsatellites or aIlozymes (which were not significantly different). Useful predictors of the pattern and scale of dispersal playa central role in both ecological and evolutionary studies, but as yet remain elusive; this study suggests that mean PLO is at best a weak predictor of population genetic structure and that estimates of dispersal in the sea will need to encompass both behavioral and physical transport processes. v TABLE OF CONTENTS Acknowledgements .....•..............•...........................••.•.•.••..........•.............iii Abstract ....•.•.•........•..............•...•.....•.................•..................•...•.•.•••......iv ~stof1Lables ..••.•...•..............................•••••••.•••••.................................. viii List of Figures .........................••.•.•.•..............................................•........ix Introduction ...•.•.•.•......•........................................................................... 1 Methods ...........................................................•......•.•.••.•......•...............4 Literature Survey ..................................................•......•.••.•.•.•.•...... .4 Definitions and data categorization .......................................................5 Effects of study scale and PLD on Fsr .•..•••••.••••.•••.••••••..••••••.•••••••••.•.• .•.•. 7 Effects of additional factors on Fsr ••.•.•.•.••••••.•.••••...••••...••.•.••••••.•.•.•.•.•.•.7 Results ..........••...................................•.•...............•................................8 Effects of study scale and PLD on Fsr ........•...•.••.•.••••.•.••••.••••••.•.•.•.•.•..••• 8 Effects of additional factors on FST •••••••••••••••••••••••••••••.•••..•.••.•.......•.••••• 11 Discussion ..........•.•............................................................................... 15 Effect ofPLD on Fsr ••••••.••••••••.••••••••••.••.••••.••••••.•.••••.•.••.•.•.•.•.•.....•.• 15 Effects of genetic marker class on Fsr ..••••••.••..••••.•.••••.••••.•.••••.•.•.•.•.•.••• 19 PLD and larval development ...............•.•...........................................20 Coupling larval behavior and physical processes •.....................................22 Conclusion ...•..•.•.•.•..............................................................................24 Literature Cited ........................................................••.•.••••.••••••••............26 Appendix A: Raw data .......•..••.•.•. 0 •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 34 Appendix B: Frequency statistics .............................•.•..•.•.•.••.•......•..............43 vi Appendix C: Full ANCOV A output ......................................•.......................44 Appendix D: Database sources (column 2 of Appendix A) ...................................45 Appendix E: Pelagic larval duration sources ••••••••.....•...................................•..52 vii LIST OF TABLES 1. Swimming velocities for various taxa ..............................7 2. Various predictors of FST••••••••••••••••••••••••••••••••••••••••••••• 9 3. Summary of analysis of covariance results ........................ 11 4. Bonferroni pairwise comparisons of mean FST values by genetic marker class ...................•.•..•.•............ 12 s. Bonferroni pairwise comparisons of mean FST values by habitat type •••••••••••••••••••••••••••••••••••••••••••••••.• 13 viii LIST OF FIGURES Figure 1. Global FST versus average, minimum, and maximum pelagic larval duration •.•............... " ........................... 10 2. Global FST versus mean pelagic larval duration plotted by habitat type ••••••••••••••••.••••••••••••••••••••••••••••••••••••••• 14 ix Introduction Dispersal plays a fundamental role in structuring populations and the wide range of developmental modes among marine species are presumed to have broad reaching micro-and macro-evolutionary ramifications (reviewed by Strathman 1985). Patterns and consequences of larval dispersal remain poorly understood, yet their importance in shaping ecological processes and conservation and management policies is unquestioned (Cowen et al. 2006). For example, a common thread woven throughout the rapidly expanding literature on marine reserves is that reserve configuration needs to reflect the dispersal of individuals between populations, however there is less agreement about how dispersal distance and the magnitude of exchange actually should be estimated (Halpern and Warner 2003, Palumbi 2003, Lockwood et al. 2002). The relative homogeneity of the marine environment, coupled with a perceived lack of conspicuous geographic barriers, has led to the expectation that most species should be well-mixed, with comparatively little structure among populations (Gaines and Lafferty 1995, Caley et al. 1996). A recent survey of dispersal in marine and terrestrial environments concludes that this conventional wisdom is supported by available data (Killian and Gaines 2003). However, the wealth of marine biodiversity in such regions as the Indo-West Pacific speaks of speciation that is driven by forces other than geographic isolation alone (e.g., Carlon and Budd 2002; reviewed by Briggs 2007). Indeed, a growing body of evidence reveals that non-random spatial distributions of dispersers can limit the potential for demographic exchange among marine organisms and that populations rarely constitute fully open or closed systems (Sponaugle et al. 2002). 1 Variation among species in patterns of connectivity complicates our ability to define discrete management units. This complexity is due in part to the challenge of studying dispersal of individuals directly, as most macroscopic marine species have a bipartite life cycle in which sessile or sedentary adults produce tiny planktonic propagules that are difficult or impossible to track (Bradbury and Snelgrove
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