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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Princeton, N.J. :
Princeton University Press,
1997.
|
| Series: | Monographs in population biology ;
28. |
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | 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 experimentation? How do we integrate modeling and statistics? How do we confront multiple hypotheses with data and assign degrees of belief to different hypotheses? How do we deal with time series (in which data are linked from one measurement to the next) or put multiple sources of data into one. |
| Physical Description: | 1 online resource (xvii, 315 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781400847310 1400847311 |
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MARC
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| 100 | 1 | |a Hilborn, Ray, |d 1947- |e author. |1 https://id.oclc.org/worldcat/entity/E39PBJfcRKhVMFqVRbVDtgTWDq |0 http://id.loc.gov/authorities/names/n86805296 | |
| 245 | 1 | 4 | |a The ecological detective : |b confronting models with data / |c Ray Hilborn and Marc Mangel. |
| 260 | |a Princeton, N.J. : |b Princeton University Press, |c 1997. | ||
| 300 | |a 1 online resource (xvii, 315 pages) : |b illustrations | ||
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| 490 | 1 | |a Monographs in population biology ; |v 28 | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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 experimentation? How do we integrate modeling and statistics? How do we confront multiple hypotheses with data and assign degrees of belief to different hypotheses? How do we deal with time series (in which data are linked from one measurement to the next) or put multiple sources of data into one. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Ecology |x Mathematical models. | |
| 650 | 6 | |a Écologie |x Modèles mathématiques. | |
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| 650 | 7 | |a Ecology |x Mathematical models |2 fast | |
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| 776 | 0 | 8 | |i Print version: |a Hilborn, Ray. |t Ecological Detective : Confronting Models with Data (MPB-28). |d Princeton : Princeton University Press, ©2013 |z 9780691034973 |
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| author | Hilborn, Ray, 1947- Mangel, Marc |
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| contents | 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. |
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| id | ZDB-4-EBA-ocn844328650 |
| illustrated | Illustrated |
| indexdate | 2025-04-11T08:41:25Z |
| institution | BVB |
| isbn | 9781400847310 1400847311 |
| language | English |
| oclc_num | 844328650 |
| open_access_boolean | |
| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource (xvii, 315 pages) : illustrations |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 1997 |
| publishDateSearch | 1997 |
| publishDateSort | 1997 |
| publisher | Princeton University Press, |
| record_format | marc |
| series | Monographs in population biology ; |
| series2 | Monographs in population biology ; |
| spelling | Hilborn, Ray, 1947- author. https://id.oclc.org/worldcat/entity/E39PBJfcRKhVMFqVRbVDtgTWDq http://id.loc.gov/authorities/names/n86805296 The ecological detective : confronting models with data / Ray Hilborn and Marc Mangel. Princeton, N.J. : Princeton University Press, 1997. 1 online resource (xvii, 315 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Monographs in population biology ; 28 Includes bibliographical references and index. 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. 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 experimentation? How do we integrate modeling and statistics? How do we confront multiple hypotheses with data and assign degrees of belief to different hypotheses? How do we deal with time series (in which data are linked from one measurement to the next) or put multiple sources of data into one. English. Ecology Mathematical models. Écologie Modèles mathématiques. SCIENCE Life Sciences Ecology. bisacsh Ecology Mathematical models fast Mathematisches Modell gnd http://d-nb.info/gnd/4114528-8 Ökologie gnd Modellen. gtt Ecologie. gtt Écologie. rasuqam Modèle mathématique. rasuqam Mangel, Marc, author. http://id.loc.gov/authorities/names/n83158475 has work: The ecological detective (Text) https://id.oclc.org/worldcat/entity/E39PCH4Mp4dBdQ68tHpFkyVy8d https://id.oclc.org/worldcat/ontology/hasWork Print version: Hilborn, Ray. Ecological Detective : Confronting Models with Data (MPB-28). Princeton : Princeton University Press, ©2013 9780691034973 Monographs in population biology ; 28. http://id.loc.gov/authorities/names/n42017020 |
| spellingShingle | Hilborn, Ray, 1947- Mangel, Marc The ecological detective : confronting models with data / Monographs in population biology ; 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. Ecology Mathematical models. Écologie Modèles mathématiques. SCIENCE Life Sciences Ecology. bisacsh Ecology Mathematical models fast Mathematisches Modell gnd http://d-nb.info/gnd/4114528-8 Ökologie gnd Modellen. gtt Ecologie. gtt Écologie. rasuqam Modèle mathématique. rasuqam |
| subject_GND | http://d-nb.info/gnd/4114528-8 |
| title | The ecological detective : confronting models with data / |
| title_auth | The ecological detective : confronting models with data / |
| title_exact_search | The ecological detective : confronting models with data / |
| title_full | The ecological detective : confronting models with data / Ray Hilborn and Marc Mangel. |
| title_fullStr | The ecological detective : confronting models with data / Ray Hilborn and Marc Mangel. |
| title_full_unstemmed | The ecological detective : confronting models with data / Ray Hilborn and Marc Mangel. |
| title_short | The ecological detective : |
| title_sort | ecological detective confronting models with data |
| title_sub | confronting models with data / |
| topic | Ecology Mathematical models. Écologie Modèles mathématiques. SCIENCE Life Sciences Ecology. bisacsh Ecology Mathematical models fast Mathematisches Modell gnd http://d-nb.info/gnd/4114528-8 Ökologie gnd Modellen. gtt Ecologie. gtt Écologie. rasuqam Modèle mathématique. rasuqam |
| topic_facet | Ecology Mathematical models. Écologie Modèles mathématiques. SCIENCE Life Sciences Ecology. Ecology Mathematical models Mathematisches Modell Ökologie Modellen. Ecologie. Écologie. Modèle mathématique. |
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