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The ecological detective : confronting models with data /
The modern ecologist usually works in both the field and laboratory, uses statistics and computers, and often works with ecological concepts that are model-based, if not model-driven. How do we make the field and laboratory coherent? How do we link models and data? How do we use statistics to help e...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Princeton, N.J. :
Princeton University Press,
1997.
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| Colección: | Monographs in population biology ;
28. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; MONOGRAPHS IN POPULATION BIOLOGY; Title; Copyright; Dedication; Contents; Preface: Beyond the Null Hypothesis; About the Title; The Audience and Assumed Background; Computer Programming; Realism and Professionalism; Acknowledgments; 1. An Ecological Scenario and the Tools of the Ecological Detective; An Ecological Scenario; The Tools for Ecological Detection; 2. Alternative Views of the Scientific Method and of Modeling; Alternative Views of the Scientific Method; Statistical Inference in Experimental Trees; Unique Aspects of Ecological Data.
- Distinguishing between Models and HypothesesTypes and Uses of Models; Nested Models; Model Complexity; 3. Probability and Probability Models: Know Your Data; Descriptions of Randomness; Always Plot Your Data; Experiments, Events, and Probability; Process and Observation Uncertainties; Some Useful Probability Distributions; The Monte Carlo Method; 4. Incidental Catch in Fisheries: Seabirds in the New Zealand Squid Trawl Fishery; Motivation; The Ecological Setting; Statistically Meaningful Data
- The Data; A Negative Binomial Model of By-Catch.
- A Monte Carlo Approach for Estimating the Chance of Success in an Observer ProgramImplications; 5. The Confrontation: Sum of Squares; The Basic Method; Goodness-of-Fit Profiles; Model Selection Using Sum of Squares; 6. The Evolutionary Ecology of Insect Oviposition Behavior; Motivation; The Ecological Setting; The Data; The Models; The Confrontation; Implications; 7. The Confrontation: Likelihood and Maximum Likelihood; Overview; Likelihood and Maximum Likelihood; Determining the Appropriate Likelihood; Model Selection Using Likelihoods; Robustness: Don't Let Outliers Ruin Your Life.
- Bounding the Estimated Parameter: Confidence IntervalsThe Bootstrap Method; Linear Regression, Analysis of Variance, and Maximum Likelihood; 8. Conservation Biology of Wildebeest in the Serengeti; Motivation; The Ecological Setting; The Data; The Models: What Happens When Rainfall Returns to Normal (the 1978 Question)?; The Models: What Is the Intensity of Poaching (the 1992 Question)?; The Confrontation: The Effects of Rainfall; The Confrontation: The Effects of Poaching; Implications; 9. The Confrontation: Bayesian Goodness of Fit; Why Bother with Bayesian Analysis?; Some Examples.
- More Technical ExamplesModel versus Model versus Model; 10. Management of Hake Fisheries in Namibia Motivation; The Impact of Environmental Change; The Ecological Setting; The Data; The Models; The Confrontation; Bayesian Analysis of the LRSG Parameters; Implications; 11. The Confrontation: Understanding How the Best Fit Is Found; Introduction; Direct Search and Graphics; Newton's Method and Gradient Search; Nongradient Methods: Avoiding the Derivative; The Art of Fitting; Hints for Special Problems; Appendix: The Method of Multiple Working Hypotheses
- References; Index.