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Realizing the Niche's Breadth: Inferring Ecological Process with Species Generalism

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Realizing the Niche's Breadth: Inferring Ecological Process with Species Generalism Realizing the Niche's Breadth: Inferring Ecological Process with Species Generalism Jasper McChesney A thesis submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Masters of Science in Ecology. March 2010 Chapel Hill Advisory Committee Robert K. Peet Peter S. White Alan S. Weakley Abstract Jasper McChesney: Realizing the Niche’s Breadth: Inferring Ecological Process with Species Generalism (Under the direction of Robert K. Peet) Various ecological processes affect generalist and specialist species differently. By measuring niche breadth in communities, we can infer those processes. Simulation models provided proofs of concept for three analyses that were applied to Carolina Vegetation Survey data. (1) A refined method for estimating niche breadth using co- occurrence data is presented. (2) It is shown that sampling grain can affect the quality of such estimates and also illuminate the scale of the processes limiting species’ distributions. Tress showed surprising sensitivity to spatial variation at a small scale (10 cm2) while herbs responded more strongly at a larger one (1000 m2). (3) The relationship between assemblage richness and the generalism of its members is explored. There is evidence that in some community types, important factors affecting richness include an evolutionary generalism-competition trade-off and the availability of specialist competitors in the local pool. ii Table of Contents Abstract.......................................................................................................................................................................... ii List of Tables................................................................................................................................................................ iv List of Figures................................................................................................................................................................ v Acknowledgments.......................................................................................................................................................vii Preface.........................................................................................................................................................................viii 1. Variations in Theta.................................................................................................................................................... 1 2. The Multi-Grain Niche ........................................................................................................................................... 41 3. A Wealth of Niches................................................................................................................................................. 73 Postscript....................................................................................................................................................................100 Appendices ................................................................................................................................................................102 iii List of Tables Table 1-1. Simulation parameters were varied to create 72 distinct scenarios....................................................... 10 Table 1-2. Performance selected metrics.. ................................................................................................................ 22 Table. 1-3. Woody plants in the CVS database........................................................................................................ 25 Table 2-1. Simulation parameters.............................................................................................................................. 47 Table 3-1. Fixed simulation parameters.................................................................................................................... 80 Table 3-2. Parameters varying by scenario............................................................................................................... 80 Table 3-3. Relationships between four per-plot generalism statistics and plot richness ....................................... 84 Table 3-4. Dependence of plot generalism on richness in all of CVS. ................................................................... 86 Table 3-5. Communities in CVS matching each of the proposed models.............................................................. 89 iv List of Figures Fig. 1-1. Example niche curves and three possible focal species widths (a-c). ........................................................ 5 Fig. 1-2 Effect of niche width means and distribution on estimates (r2). ................................................................ 12 Fig. 1-3. Positioning of sites (abscissa) and niche optima (panels) on a gradient. ................................................. 13 Fig. 1-4. Increase in ranked metric performance by sub-sampling.......................................................................... 15 Fig. 1-5. Rank performance of three co-occurrer counting methods....................................................................... 16 Fig. 1-6. Interaction of niche sizes with co-occurrer counting methods. ................................................................ 18 Fig. 1-7. Effect of plots saturation on metric rank for four richness correction methods. ..................................... 20 Fig. 1-8. Performance of selected metrics with various mean widths ..................................................................... 23 Fig. 1-9. Scaled commonness and niche width estimates from metric #55n of CVS woody plants. .................... 24 Table 2-1. Simulation parameters.............................................................................................................................. 47 Fig. 2-2. Performance with different sampling grains.............................................................................................. 50 Fig. 2-3. Causes of plot heterogeneity ....................................................................................................................... 51 Fig. 2-4. Effect of plot heterogeneity on metric performance.................................................................................. 52 Fig. 2-5. Most suited grain for measuring niche widths at the given level of spatial heterogeneity ..................... 53 Fig. 2-6. Density plot showing the difference, in area, between the best performing grain in each scenario and the grain having the greatest variation in its species' niche width estimates.. ................................................ 55 Fig. 2-7. Change in mean generalism score as sampling grain varies..................................................................... 56 Fig. 2-8. Grain correction techniques.. ...................................................................................................................... 58 Fig. 2-9. Generalism scores for all sampled species in the Carolina Vegetation Survey data............................... 61 Fig. 2-10. Distribution of species’ most limiting scale............................................................................................. 62 Fig. 2-11. Selected species illustrating different relationships between sampling grain and estimated niche width. ................................................................................................................................................................... 63 Fig. 2-12 Differences in generalism across grains by growth habit and native-status ........................................... 64 v Fig. 3-1. Hypothesized richness-generalism patterns in four pure models.. ........................................................... 75 Fig. 3-2. Simulation model results for four pure scenarios...................................................................................... 83 Fig. 3-3. Generalism and richness patterns in plants of the Carolina Vegetation Survey...................................... 85 Fig. 3-4. Generalism-richness relationships in CVS communites.. ......................................................................... 87 Fig. 3-5. Loess-smoothed relationship between plot richness and mean plot generalism in the six most common community types in CVS. .................................................................................................................................. 88 Table 3-5. Communities in CVS matching each of the proposed models .............................................................. 89 Figure 3-6. Richness and generalism relationship in two human systems ............................................................. 91 vi Acknowledgments I must give thanks for the feedback provided me by my advisory committee, as well as Dr. Jack Weiss, regarding some statistical matters, and my colleague Nick Adams. vii Preface The niche concept has been with ecology for a long time (Grinnell, 1924), but its meaning has varied between fields and individual researchers; shifted over time; and has been debated, perhaps, ad nauseum. Some have abandoned the term as too amorphous and laden with baggage to be useful, while others have tried to reformulate it (e.g., Leibold, 1995). But not all ecological terms must be precise to be useful: “competition,” for instance, has had no less tangled a history, yet still refers
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