Ecological Modeling: A Common-Sense Approach to Theory and Practice

Ecological Modeling:A Commonsense Approach to Theory and Practice explores how simulation modeling and its new ecological applications can offer solutions to complex natural resource management problems. This is a practical guide for students, teachers, and professional ecologists. Examines four phases of the modeling process: conceptual model formulation, quantitative model specification, model evaluation, and model use Provides useful building blocks for constructing systems simulation models Includes a format for reporting the development and use of simulation models Offers an integrated systems perspective for students, faculty, and professionals Features helpful insights from the author, gained over 30 years of university teaching "I can strongly recommend the book as textbook for all courses in population dynamic modeling particularly when the course is planned for the second or third year of a bachelor study in ecology, environmental science or ecological engineering. It uncovers very clearly for the readers the scientific idea and thinking behind modeling and all the necessary steps in the development of models." Ecological Modeling Journal, 2009

Table of Contents:
Preface xi Acknowledgments xiii 1 Introduction 1 1.1 Common-sense solutions: three exercises 1 1.2 Modeling theory 2 1.3 Modeling practice 2 1.4 Theory, practice, and common sense 3 1.5 Intended use of this book 3 Part 1 Common-sense solutions: three exercises 2 Common-sense solutions 5 2.1 Three problems 6 2.1.1 Harvesting food for the winter 6 2.1.2 Estimating the probability of population extinction 12 2.1.3 Managing the Commons 20 2.2 The systems approach to problem solving 49 2.2.1 The conceptual model (Phase I) 50 2.2.2 The quantitative model (Phase II) 51 2.2.3 Model evaluation (Phase III) 51 2.2.4 Model application (Phase IV) 51 2.3 The three problems revisited: the systems approach in theory and practice 51 Part 2 Modeling theory 3 Theory I: the conceptual model 53 3.1 State the model objectives (Ia) 54 3.2 Bound the system-of-interest (Ib) 55 3.3 Categorize the components within the system-of-interest (Ic) 57 3.3.1 State variables 57 3.3.2 Material transfers 59 3.3.3 Sources and sinks 61 3.3.4 Information transfers 61 3.3.5 Driving variables 62 3.3.6 Constants 62 3.3.7 Auxiliary variables 62 3.4 Identify the relationships among the components that are of interest (Id) 63 3.4.1 Submodels 63 3.5 Represent the conceptual model (Ie) 65 3.5.1 Conceptual-model diagrams 65 3.6 Describe the expected patterns of model behavior (If) 66 4 Theory II: the quantitative model 67 4.1 Select the general quantitative structure for the model (IIa) 68 4.2 Choose the basic time unit for the simulations (IIb) 72 4.3 Identify the functional forms of the model equations (IIc) 72 4.3.1 Information on which to base the choice of functional forms 73 4.3.2 Selecting types of equations to represent the chosen functional forms 73 4.4 Estimate the parameters of the model equations (IId) 75 4.4.1 Statistical analyses within the context of simulation model parameterization 75 4.4.2 Quantifying qualitative information 76 4.4.3 Deterministic- versus stochastic-model parameterization 76 4.5 Execute the baseline simulation (IIe) 77 4.5.1 Baseline simulations for stochastic models 78 5 Theory III: model evaluation 79 5.1 Assess the reasonableness of the model structure and the interpretability of functional relationships within the model (IIIa) 81 5.2 Evaluate the correspondence between model behavior and the expected patterns of model behavior (IIIb) 82 5.3 Examine the correspondence between model projections and the data from the real system (IIIc) 84 5.3.1 Quantitative versus qualitative model evaluation 86 5.4 Determine the sensitivity of model projections to changes in the values of important parameters (IIId) 86 5.4.1 Interpreting sensitivity analysis within a model evaluation framework 87 6 Theory IV: model application 89 6.1 Develop and execute the experimental design for the simulations (IVa) 89 6.2 Analyze and interpret the simulation results (IVb) 91 6.3 Communicate the simulation results (IVc) 91 Part 3 Modeling practice 7 Some common pitfalls 93 7.1 Phase I pitfalls: the conceptual model 93 7.2 Phase II pitfalls: the quantitative model 97 7.3 Phase III pitfalls: model evaluation 100 7.4 Phase IV pitfalls: model application 102 8 The modeling process in practice 105 8.1 Preliminary conceptual model (CM) 106 8.1.1 How to begin 106 8.1.2 Adding new components to the model 108 8.1.3 Describing expected patterns 108 8.1.4 Describing the plan of attack 108 8.2 Intermediate developmental models (IDMi) 109 8.2.1 Evaluate–adjust cycle for each developmental model 110 8.2.2 Sensitivity analysis of the last developmental model 112 8.3 Final model (FM) 112 Part 4 Theory, practice, and common sense 9 The common-sense problems revisted 115 9.1 Harvesting food for the winter 115 9.1.1 The preliminary conceptual model (CM) 115 9.1.2 The last (only) intermediate development model (IDMlast) 116 9.1.3 The final model (FM) 117 9.2 Estimating the probability of population extinction 117 9.2.1 The preliminary conceptual model (CM) 117 9.2.2 The intermediate development models (IDMi) 118 9.2.3 The final model (FM) 118 9.3 Managing the Commons 118 9.3.1 The preliminary conceptual model (CM) 118 9.3.2 The intermediate development models (IDMi) 120 9.3.3 The final model (FM) 121 10 Reflections 123 10.1 The systems approach as a complement to other methods of problem solving 123 10.2 Ecological modeling as a problem-solving process 126 10.3 Expectations for ecological models 127 10.4 A final thought 129 References 131 Appendix A: Introduction to the ecological modeling literature 133 Appendix B: Scientific reports for the examples in Chapter 2 139 B.1 Effect of deforestation on rate of food harvest 139 B.2 Effect of hurricane frequency on probability of population extinction 141 B.3 Effect of stocking rate on forage and animal production 143 Index 149