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Ecological Dynamics of a Terrestrial Orchid Symbiosis by Jeffrey M. Diez

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Ecological Dynamics of a Terrestrial Orchid Symbiosis by Jeffrey M. Diez Ecological dynamics of a terrestrial orchid symbiosis by Jeffrey M. Diez (Under the direction of H. Ronald Pulliam) Abstract My dissertation research tested elements of niche theory by determining how the popula- tion dynamics of a terrestrial orchid, Goodyera pubescens, respond to abiotic gradients, and how mycorrhizal fungi influence patterns of orchid recruitment. Long-term study plots were established along a gradient from the southeastern Piedmont to the southern Appalachian Mountains, encompassing a wide range of environmental conditions. I generated predictive models to show how basic vital rates such as survival and reproduction depend on soil mois- ture and understory light availability using a combination of approaches including abiotic and demographic monitoring, soil analyses, targeted ¯eld experiments, genetic analysis, and computer modelling. I then used these ¯ndings to compare predicted population growth rates across abiotic gradients from modelling to realized distributions and abundances from ¯eld data. I also conducted the ¯rst study to jointly characterize the spatial genetic struc- ture of a plant and its mycorrhizal fungi, in order to test hypotheses about how patterns of genetic diversity are shaped by demographic processes of a symbiotic plant-fungal system along environmental gradients. Through ¯eld experiments I found strong evidence for fungal limitations on patterns of plant recruitment that were context dependent (i.e. symbiotic recruitment increased with both soil organic content and moisture). A key to these studies has been the use of newly-available Bayesian statistical approaches to integrate multiple types of data from a range of spatial and temporal scales, in order to build predictive models with reasonable estimates of uncertainty. Index words: demography, Orchidaceae, hierarchical Bayes, predictive modeling, niche theory Ecological dynamics of a terrestrial orchid symbiosis by Jeffrey M. Diez B.A., University of Pennsylvania, 1997 M.A., University of Pennsylvania, 1997 A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial Ful¯llment of the Requirements for the Degree Doctor of Philosophy Athens, Georgia 2005 °c 2005 Je®rey M. Diez All Rights Reserved Ecological dynamics of a terrestrial orchid symbiosis by Jeffrey M. Diez Approved: Major Professor: H. Ronald Pulliam Committee: Mark D. Hunter David Porter James Hamrick David C. Coleman Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia December 2005 Acknowledgments Financial support for this work was provided by National Science Foundation Grant Number DEB-0235371 to H. Ronald Pulliam, a Sigma Xi grant to J. Diez, the Institute of Ecology via TA support, and the Coweeta Long-term Ecological Research (LTER) site. Lab support was generously provided by David Porter of UGA and by Dennis Whigham at the Smithsonian Environmental Research Center (SERC). I have been fortunate to have been a part of a spectacular lab group that has always been available for ¯eld work and discussion: most notably Itamar Giladi, Robert Harris, Robert Warren, Scott Eustis, Arlena Wartell, Brad Shankweiler, Michael Bokaemper, Cathy Rickets, and Mia White, and including Jamila Mohammed, JP Schmidt, Andrew Hunt, Shelley Masters, Sharon Bellou, Kyung-Ah Koo, Emily Franzen, Erin Mordecai, and Rachel Ge®en. The insights and feedback along the way have been invaluable. Scott Eustis must have worn out three pairs of shoes, and perhaps a knee or two, collecting the light and moisture data used in this work. I thank Tom Maddox and Molly Neely for much help with soil processing and gen- eral understanding of lab methodology. I appreciate the invaluable discussions with Melissa McCormick and Jay O'Neill on a range of topics related to orchid ecology and mycorrhizae, and their admirable patience with me in the genetics lab. Thelma Richardson and Jeremy Sanderling helped with computer issues, and I thank Janice Sand for everything she has done to make the Institute function e±ciently and enjoyably all these years. Valuable statistical advice and training was provided by Mike Conroy (and the Conroy lab group), Chris Fon- nesbeck, Jim Clark (via an NSF-funded Summer Institute on Ecological Forecasting), and Steve Rathbun. Further discussions with J.P. Schmidt and Seth Wenger were very helpful. iv v My committee members have been an inspiration and a pleasure to interact with over these years. Mark Hunter has been an all-around inspiration and invariably generous with his time. I appreciate David Porter's generosity with lab resources, kind patience, and tutelage on how curiosity, observation, and willingness to try things combine to make science fun. I thank Jim Hamrick for both inspiring and improving the genetics work in this study. And for an inspiring introduction to the complexity of soil ecosystems, and willingness to join the committee at late notice, I'm indebted to David Coleman. I reserve a special thanks for whatever wind blew me into the o±ce of Ron Pulliam to begin with. I could not have imagined a mentor with such intellectual capacity, genuine and contagious curiosity, and patience. I leave here enriched and inspired by the example of how science can be done. Finally, I am most fortunate to have found in Itamar Giladi an unforgettable colleague and friend. Thank you does not capture it. And of course to my family I owe the greatest of thanks for the unwavering support, love and inspiration throughout my life. Table of Contents Page Acknowledgments . iv List of Figures . vii List of Tables . xiii Chapter 1 Introduction and Literature Review . 1 2 Multi-scale Controls of Soil Moisture: A hierarchical Bayes model of soil moisture . 12 3 Spatial Patterns of Symbiotic Germination in a Terrestrial Orchid . 32 4 Hierarchical Analysis of Terrestrial Orchid Distributions Across Environmental Gradients . 56 5 Demographic Responses of a Terrestrial Orchid to Abiotic Gra- dients . 78 6 Spatial Genetic Structure of a Terrestrial Orchid and its Sym- biotic Fungi . 106 7 Conclusions . 129 Literature Cited . 130 vi List of Figures 2.1 Hierarchical Study Design. A hierarchial sampling design was used to explicitly investigate scale-speci¯c controls on soil moisture. At the largest scale, three landscapes were sampled across a gradient stretching from the Piedmont of Georgia to the southern Appalachian mountains. Within each landscape 4 to 6 population-level sites (grids) were selected to represent a range of topographic conditions. Each site was then divided into 2 x 2 meter cells, sixteen of which were regularly monitored for soil moisture and charac- terized for soil properties. 26 2.2 Large-scale precipitation patterns. At both yearly (a) and monthly (b) time scales, there are consistent patterns of precipitation di®erences at the largest spatial scale of the study . 27 2.3 Spatial Patterns consistent across spatial scales Patterns of measured soil moisture remain consistent across spatial scales, from (a) the among pop- ulation soil moisture values in the Coweeta landscape, and (b) for example, within the 16 measured cells in one of the study grids at Coweeta . 28 2.4 Relationship between precipitation events and soil moisture His- tograms show the rainfall events throughout the year, with mm on the y-axis. Overlaid lines represent the biweekly grid-level average of 16 TDR (percent soil moisture) readings on each grid. Data is shown for the three di®erent land- scapes, from south to north: (a) Whitehall forest, Athens, GA; (b) Nancytown forest, Cornelia, GA; and (c) Coweeta forest, Otto, NC . 29 vii viii 2.5 Coe±cient Estimates: Lines represent 95% credible intervals of posterior distributions of regression coe±cients. Those not overlapping zero may be considered 'signi¯cant' . 30 3.1 Experiment 1, Basic seed packet design. Seed packets were placed at four distance classes from clusters of adult plants. 50 3.2 Experiment 2, Basic seed packet design. Eight seed packets were placed at each of 16 cells within all 16 study grids within .5 meters of the abiotic sampling. 50 3.3 Goodyera germination probabilities. Shown is a declining trend of fewer successful seed germinations at further distance classes from adult plants. 51 3.4 Predicted germination probabilities as a function of distance. Using estimated parameters of the most well-supported model (the mixed model), predictions are made within the MCMC statistical routine across the entire ¯rst meter from adult plants. Dotted lines indicate the 95% credible interval for the model predictions. 52 3.5 Coe±cient Estimates. Lines represent 95% posterior credible intervals for estimated e®ects of % organic content, soil moisture, and pH in the three land- scapes. Soil moisture values are averages across a year, representing relative soil moisture across the sites. 53 3.6 Predictive Recruitment Surfaces: Predicted germination success as a function of organic content of the soil and percent soil moisture. The sur- faces represent the mean of the posterior distributions when predictions are made at regular intervals across the abiotic space. The individual points are places with actual data; the height of these points represent model predictions at those data points. Di®erences in the response surfaces among landscapes arise from the landscape-level intercepts. 54 ix 3.7 Observed seedlings. The observed probability of seedling production is sig- ni¯cantly related to germination probability in seed packets at the population level. The y-axis represents the total number of seedlings observed in each of the 16 populations between 2001-2004, divided by the number of observed flowering stalks. The x-axis is the predicted population-level seed packet suc- cess rate (¯nding a protocorm in a packet). 55 4.1 Hierarchical parameter estimates: Shown are 95% credible intervals for estimated coe±cients in the hierarchical model. 74 4.2 Spatial e®ects: Given are the total number of cells within grids that had spatial e®ects signi¯cantly di®erent from zero for each response variable .
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