University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 12-2006 The Tangled Web of Community Ecology: Making Sense of Complex Data Monica Lynn Beals University of Tennessee - Knoxville Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Ecology and Evolutionary Biology Commons Recommended Citation Beals, Monica Lynn, "The Tangled Web of Community Ecology: Making Sense of Complex Data. " PhD diss., University of Tennessee, 2006. https://trace.tennessee.edu/utk_graddiss/1923 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Monica Lynn Beals entitled "The Tangled Web of Community Ecology: Making Sense of Complex Data." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Ecology and Evolutionary Biology. Susan Riechert, Major Professor We have read this dissertation and recommend its acceptance: Daniel Simberloff, James Drake, James Fordyce, Hamparsum Bozdogan Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) To the Graduate Council: I am submitting herewith a dissertation written by Monica Lynn Beals entitled “The tangled web of community ecology: making sense of complex data.” I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Ecology and Evolutionary Biology. Susan Riechert Major Professor We have read this dissertation and recommend its acceptance: Daniel Simberloff James Drake James Fordyce Hamparsum Bozdogan Accepted for the Council: Linda Painter Interim Dean of Graduate Studies (Original signatures are on file with official student records) THE TANGLED WEB OF COMMUNITY ECOLOGY: MAKING SENSE OF COMPLEX DATA A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Monica Lynn Beals December 2006 ACKNOWLEDGEMENTS I would like to thank my major professor, Susan Riechert, for her guidance and assistance in developing and carrying out this research. I would also like to thank my committee (Dan Simberloff, Jim Drake, Jim Fordyce and Hamparsum Bozdogan) for their assistance and feedback, and especially thanks to Jim D. and Jim F. for providing me with additional lab space. The Department of Ecology and Evolutionary Biology provided teaching assistantships for financial support. Thanks to all my friends and fellow graduate students for intellectually stimulating conversations and moral support. You are too many to list, but you know who you are. I owe special thanks to Sean McMahon and Justin Walguarnery, who went through the Statistics M.S. with me—I couldn’t have done it without them. Finally, I want to thank my parents (all of them: Ed, Chris, Jim, Helen and Frank) for their love and support throughout my life. And, of course, thanks to Dignan and Dora for their furry companionship. ii ABSTRACT Ecological communities are governed by complicated processes that give rise to observable patterns. Making sense of these patterns, much less inferring the underlying processes, has proved challenging for several reasons. Manipulative experiments in natural communities may not be feasible due to large numbers of variables, lack of adequate replication, or the risk of undesirable consequences (e.g., introducing an invasive species). The multivariate nature of ecological datasets presents analytical problems as well; many statistical techniques familiar to ecologists have difficulty handling large numbers of potentially collinear variables. I present results from three studies of spider communities in which I employ a combination of familiar and less familiar statistical approaches to elucidate the factors influencing community structure in spiders. These approaches include null model analyses, nonmetric multidimensional scaling (NMS) for variable reduction of predictor and response data matrices, multiple regression, and observed variable structural equation modeling (SEM). While NMS has been employed as a multivariate descriptive analysis, examples of its use in further analyses are rare. SEM is a technique widely applied in other fields, but has only recently been used in ecological studies. General results from analyses of these three studies suggest that: 1) significant patterns of spider species co-occurrence based on null model analyses are consistent with a hypothesis of shared habitat preferences rather than one of species interactions, 2) in multiple regressions using NMS axes as predictor and response variables to compare the roles of plant species composition and habitat architecture in influencing spider species composition, the plants explained as much or iii more variation as the architecture, and 3) based on SEM analyses using NMS axes for spider species, plant species, arthropod orders and habitat architecture as variables, plant species composition acts both indirectly (through its effect on arthropods and architecture) and directly. The combination in these analyses of a traditionally descriptive multivariate approach (NMS) with null models, a classic regression approach, and SEM permits the analysis of otherwise statistically intractable datasets (the original data matrices). This suite of approaches provides new insights into spider community structure, and can be applied by ecologists working in other systems as well. iv TABLE OF CONTENTS Chapter Page INTRODUCTION......................................................................................................... 1 Problem statement ............................................................................................... 1 Mechanisms of animal community organization .................................................. 3 The role of plant communities.................................................................. 3 Species interactions.................................................................................. 6 Studies of community organization in spiders ...................................................... 9 Dissertation overview ........................................................................................ 13 CHAPTER 1: Species co-occurrence and habitat partitioning in heterogeneous environments................................................................................................................. 19 Introduction....................................................................................................... 19 Methods............................................................................................................. 23 Study areas............................................................................................. 23 Co-occurrence........................................................................................ 24 Habitat partitioning ................................................................................ 28 Results .............................................................................................................. 29 Co-occurrence........................................................................................ 30 Habitat partitioning ................................................................................ 31 Discussion ......................................................................................................... 32 CHAPTER 2: Understanding community structure: a data-driven multivariate approach ....................................................................................................................... 39 Introduction....................................................................................................... 39 Methods............................................................................................................. 41 Study area .............................................................................................. 41 Data collection ....................................................................................... 43 Data analysis: variable reduction and regression..................................... 45 Results .............................................................................................................. 48 Variable reduction.................................................................................. 48 Regression analyses ............................................................................... 49 Discussion ......................................................................................................... 51 CHAPTER 3: Indirect and direct effects on habitat selection in spider communities: the roles of arthropods, vegetation architecture and plant species ........................................ 59 Introduction....................................................................................................... 59 Methods............................................................................................................