The Resource The ecological detective : confronting models with data, Ray Hilborn and Marc Mangel
The ecological detective : confronting models with data, Ray Hilborn and Marc Mangel
Resource Information
The item The ecological detective : confronting models with data, Ray Hilborn and Marc Mangel represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of San Diego Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item The ecological detective : confronting models with data, Ray Hilborn and Marc Mangel represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of San Diego Libraries.
This item is available to borrow from 1 library branch.
 Summary
 The modern ecologist usually works in both the field and laboratory, uses statistics and computers, and often works with ecological concepts that are modelbased, if not modeldriven. 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
 Language
 eng
 Extent
 1 online resource (xvii, 315 pages)
 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 ByCatch
 A Monte Carlo Approach for Estimating the Chance of Success in an Observer ProgramImplications; 5. The Confrontation: Sum of Squares; The Basic Method; GoodnessofFit 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
 Isbn
 9781400847310
 Label
 The ecological detective : confronting models with data
 Title
 The ecological detective
 Title remainder
 confronting models with data
 Statement of responsibility
 Ray Hilborn and Marc Mangel
 Language
 eng
 Summary
 The modern ecologist usually works in both the field and laboratory, uses statistics and computers, and often works with ecological concepts that are modelbased, if not modeldriven. 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
 Cataloging source
 E7B
 http://library.link/vocab/creatorDate
 1947
 http://library.link/vocab/creatorName
 Hilborn, Ray
 Illustrations
 illustrations
 Index
 index present
 Language note
 English
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 http://library.link/vocab/relatedWorkOrContributorName
 Mangel, Marc
 Series statement
 Monographs in population biology
 Series volume
 28
 http://library.link/vocab/subjectName

 Ecology
 Écologie
 SCIENCE
 Ecology
 Modellen
 Ecologie
 Mathematisches Modell
 Ökologie
 Écologie
 Modèle mathématique
 Label
 The ecological detective : confronting models with data, Ray Hilborn and Marc Mangel
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 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 ByCatch
 A Monte Carlo Approach for Estimating the Chance of Success in an Observer ProgramImplications; 5. The Confrontation: Sum of Squares; The Basic Method; GoodnessofFit 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
 Control code
 ocn844328650
 Dimensions
 unknown
 Extent
 1 online resource (xvii, 315 pages)
 Form of item
 online
 Isbn
 9781400847310
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Note
 JSTOR
 Other physical details
 illustrations
 http://library.link/vocab/ext/overdrive/overdriveId
 22573/ctt22nbkx
 Specific material designation
 remote
 System control number
 (OCoLC)844328650
 Label
 The ecological detective : confronting models with data, Ray Hilborn and Marc Mangel
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 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 ByCatch
 A Monte Carlo Approach for Estimating the Chance of Success in an Observer ProgramImplications; 5. The Confrontation: Sum of Squares; The Basic Method; GoodnessofFit 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
 Control code
 ocn844328650
 Dimensions
 unknown
 Extent
 1 online resource (xvii, 315 pages)
 Form of item
 online
 Isbn
 9781400847310
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Note
 JSTOR
 Other physical details
 illustrations
 http://library.link/vocab/ext/overdrive/overdriveId
 22573/ctt22nbkx
 Specific material designation
 remote
 System control number
 (OCoLC)844328650
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.sandiego.edu/portal/Theecologicaldetectiveconfrontingmodels/97TjVxGA3co/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.sandiego.edu/portal/Theecologicaldetectiveconfrontingmodels/97TjVxGA3co/">The ecological detective : confronting models with data, Ray Hilborn and Marc Mangel</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.sandiego.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.sandiego.edu/">University of San Diego Libraries</a></span></span></span></span></div>