277 pages, Figs, tabs
Plant community ecology has traditionally taken a taxonomical approach based on population dynamics. This book contrasts such an approach with a trait-based approach.
After reviewing these two approaches, it then explains how models based on the Maximum Entropy Formalism can be used to predict the relative abundance of different species from a potential species pool. Following this it shows how the trait constraints, upon which the model is based, are necessary consequences of natural selection and population dynamics. The final sections of the book extend the discussion to macroecological patterns of species abundance and concludes with some outstanding unresolved questions.
Written for advanced undergraduates, graduates and researchers in plant ecology, this book demonstrates how a trait-based approach, can explain how the principle of natural selection and quantitative genetics can be combined with maximum entropy methods to explain and predict the structure of plant communities.
'... quantitatively strong and pleasantly readable ... The time spent reading From Plant Traits to Vegetation Structure will be very well spent in preparing for the next generation of models of community assembly.' Plant Science Bulletin
Preface; 1. Playing with loaded dice; 2. Population-based models of community assembly; 3. Trait-based community ecology; 4. Modeling trait-based environmental filters: Bayesian statistics, information theory and the maximum entropy formalism; 5. Community dynamics, natural selection and the origin of community-aggregated traits; 6. Community assembly during a Mediterranean succession; 7. The statistical mechanics of species abundance distributions; 8. Epilogue: traits are not enough.
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Bill Shipley obtained his PhD in plant ecology from the University of Ottawa in 1987 and now teaches plant ecology and statistics at the Universite de Sherbrooke (Qc) Canada. He is the author of over 70 peer-reviewed papers in ecology and statistics, and Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations, and Causal Inference (Cambridge University Press, 2000).