Ecological Forecasting

 
 
Princeton University Press
  • 1. Auflage
  • |
  • erschienen am 22. Mai 2017
  • |
  • 288 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-4008-8545-9 (ISBN)
 

An authoritative and accessible introduction to the concepts and tools needed to make ecology a more predictive science

Ecologists are being asked to respond to unprecedented environmental challenges. How can they provide the best available scientific information about what will happen in the future? Ecological Forecasting is the first book to bring together the concepts and tools needed to make ecology a more predictive science.

Ecological Forecasting presents a new way of doing ecology. A closer connection between data and models can help us to project our current understanding of ecological processes into new places and times. This accessible and comprehensive book covers a wealth of topics, including Bayesian calibration and the complexities of real-world data; uncertainty quantification, partitioning, propagation, and analysis; feedbacks from models to measurements; state-space models and data fusion; iterative forecasting and the forecast cycle; and decision support.

  • Features case studies that highlight the advances and opportunities in forecasting across a range of ecological subdisciplines, such as epidemiology, fisheries, endangered species, biodiversity, and the carbon cycle
  • Presents a probabilistic approach to prediction and iteratively updating forecasts based on new data
  • Describes statistical and informatics tools for bringing models and data together, with emphasis on:

  • Quantifying and partitioning uncertainties

  • Dealing with the complexities of real-world data

  • Feedbacks to identifying data needs, improving models, and decision support
  • Numerous hands-on activities in R available online
  • Englisch
  • Princeton
  • |
  • USA
  • Für Beruf und Forschung
  • Digitale Ausgabe
  • 1 halftone. 81 line illus. 6 tables.
  • |
  • 1 halftone. 81 line illus. 6 tables.
  • 7,11 MB
978-1-4008-8545-9 (9781400885459)
weitere Ausgaben werden ermittelt
Michael C. Dietze
Preface ix
Acknowledgments xi
1. Introduction 1
1.1 Why Forecast? 1
1.2 The Informatics Challenge in Forecasting 3
1.3 The Model-Data Loop 4
1.4 Why Bayes? 6
1.5 Models as Scaffolds 7
1.6 Case Studies and Decision Support 8
1.7 Key Concepts 10
1.8 Hands-on Activities 10
2. From Models to Forecasts 11
2.1 The Traditional Modeler's Toolbox 11
2.2 Example: The Logistic Growth Model 12
2.3 Adding Sources of Uncertainty 14
2.4 Thinking Probabilistically 23
2.5 Predictability 25
2.6 Key Concepts 33
2.7 Hands-on Activities 33
3. Data, Large and Small 34
3.1 The Data Cycle and Best Practices 34
3.2 Data Standards and Metadata 38
3.3 Handling Big Data 40
3.4 Key Concepts 43
3.5 Hands-on Activities 43
4. Scientific Workflows and the Informatics of Model-Data Fusion 44
4.1 Transparency, Accountability, and Repeatability 44
4.2 Workflows and Automation 45
4.3 Best Practices for Scientific Computing 48
4.4 Key Concepts 51
4.5 Hands-on Activities 52
5. Introduction to Bayes 53
5.1 Confronting Models with Data 53
5.2 Probability 101 54
5.3 The Likelihood 56
5.4 Bayes' Theorem 61
5.5 Prior Information 65
5.6 Numerical Methods for Bayes 68
5.7 Evaluating MCMC Output 71
5.8 Key Concepts 74
5.9 Hands-on Activities 75
6. Characterizing Uncertainty 76
6.1 Non-Gaussian Error 76
6.2 Heteroskedasticity 82
6.3 Observation Error 83
6.4 Missing Data and Inverse Modeling 87
6.5 Hierarchical Models and Process Error 90
6.6 Autocorrelation 94
6.7 Key Concepts 96
6.8 Hands-on Activities 97
7. Case Study: Biodiversity, Populations, and Endangered Species 98
7.1 Endangered Species 98
7.2 Biodiversity 104
7.3 Key Concepts 106
7.4 Hands-on Activities 107
8. Latent Variables and State-Space Models 108
8.1 Latent Variables 108
8.2 State Space 110
8.3 Hidden Markov Time-Series Model 111
8.4 Beyond Time 114
8.5 Key Concepts 116
8.6 Hands-on Activities 117
9. Fusing Data Sources 118
9.1 Meta-analysis 120
9.2 Combining Data: Practice, Pitfalls, and Opportunities 123
9.3 Combining Data and Models across Space and Time 127
9.4 Key Concepts 130
9.5 Hands-on Activities 130
10. Case Study: Natural Resources 131
10.1 Fisheries 131
10.2 Case Study: Baltic Salmon 133
10.3 Key Concepts 137
11. Propagating, Analyzing, and Reducing Uncertainty 138
11.1 Sensitivity Analysis 138
11.2 Uncertainty Propagation 145
11.3 Uncertainty Analysis 155
11.4 Tools for Model-Data Feedbacks 158
11.5 Key Concepts 162
11.6 Hands-on Activities 163
Appendix A Properties of Means and Variances 163
Appendix B Common Variance Approximations 164
12. Case Study: Carbon Cycle 165
12.1 Carbon Cycle Uncertainties 165
12.2 State of the Science 166
12.3 Case Study: Model-Data Feedbacks 171
12.4 Key Concepts 174
12.5 Hands-on Activities 174
13. Data Assimilation 1: Analytical Methods 175
13.1 The Forecast Cycle 175
13.2 Kalman Filter 178
13.3 Extended Kalman Filter 183
13.4 Key Concepts 185
13.5 Hands-on Activities 186
14. Data Assimilation 2: Monte Carlo Methods 187
14.1 Ensemble Filters 187
14.2 Particle Filter 190
14.3 Model Averaging and Reversible Jump MCMC 194
14.4 Generalizing the Forecast Cycle 195
14.5 Key Concepts 197
14.6 Hands-on Activities 198
15. Epidemiology 199
15.1 Theory 200
15.2 Ecological Forecasting 201
15.3 Examples of Epidemiological Forecasting 202
15.4 Case Study: Influenza 205
15.5 Key Concepts 207
16. Assessing Model Performance 208
16.1 Visualization 208
16.2 Basic Model Diagnostics 211
16.3 Model Benchmarks 215
16.4 Data Mining the Residuals 217
16.5 Comparing Model Performance to Simple Statistics 217
16.6 Key Concepts 219
16.7 Hands-on Activities 219
17. Projection and Decision Support 221
17.1 Projections, Predictions, and Forecasting 222
17.2 Decision Support 223
17.3 Key Concepts 235
17.4 Hands-on Activities 236
18. Final Thoughts 237
18.1 Lessons Learned 237
18.2 Future Directions 240
References 245
Index 261

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