Ecological Model Types: Theories and Applications, Second Edition presents an understanding of how to quantitatively analyze complex and dynamic ecosystems with the tools available today. Recently, besides process-based models, data driven models such as machine learning methods are popularly applied in ecological and environmental models. This second edition covers both process-based models and data-driven models which are fundamental and popular in ecological modeling studies with theories and applications. It also explains how ecological modeling can assist the implementation of sustainable development as well as how mathematical models and systems analysis can describe ecological processes, which can support sustainable management of resources.
1. Chapter 1. Introduction: an overview - p. 1 2. Section 1: Process-based models - p. S17 3. Chapter 2. Biogeochemical models - p. 19 4. Chapter 3. Dynamic population models - p. 47 5. Chapter 4. Ecotoxicological models - p. 75 6. Chapter 5. Structurally dynamic models - p. 117 7. Chapter 6. Steady state models - p. 139 8. Chapter 7. Spatially explicit models - p. 169 9. Chapter 8. Fugacity models - p. 201 10. Chapter 9. Network models: EcoNet - p. 239 11. Chapter 10. Process-based modeling in ecology - p. 251 12. Section 2: Data-driven models - p. S295 13. Chapter 11. Artificial neural networks (multilayer perceptron) for ecological modeling - p. 297 14. Chapter 12. Decision tree models - p. 323 15. Chapter 13. Ensemble models - p. 343 16. Chapter 14. Support vector machines for ecological modeling - p. 369 17. Chapter 15. Deep learning: long short-term memory networks - p. 395 18. Chapter 16. Automated machine learning - p. 415 19. Index - p. 429
Ecological Model Types: Theories and Applications, Second Edition presents an understanding of how to quantitatively analyze complex and dynamic ecosystems with the tools available today. Recently, besides process-based models, data driven models such as machine learning methods are popularly applied in ecological and environmental models. This second edition covers both process-based models and data-driven models which are fundamental and popular in ecological modeling studies with theories and applications. It also explains how ecological modeling can assist the implementation of sustainable development as well as how mathematical models and systems analysis can describe ecological processes, which can support sustainable management of resources.
1. Chapter 1. Introduction: an overview - p. 1 2. Section 1: Process-based models - p. S17 3. Chapter 2. Biogeochemical models - p. 19 4. Chapter 3. Dynamic population models - p. 47 5. Chapter 4. Ecotoxicological models - p. 75 6. Chapter 5. Structurally dynamic models - p. 117 7. Chapter 6. Steady state models - p. 139 8. Chapter 7. Spatially explicit models - p. 169 9. Chapter 8. Fugacity models - p. 201 10. Chapter 9. Network models: EcoNet - p. 239 11. Chapter 10. Process-based modeling in ecology - p. 251 12. Section 2: Data-driven models - p. S295 13. Chapter 11. Artificial neural networks (multilayer perceptron) for ecological modeling - p. 297 14. Chapter 12. Decision tree models - p. 323 15. Chapter 13. Ensemble models - p. 343 16. Chapter 14. Support vector machines for ecological modeling - p. 369 17. Chapter 15. Deep learning: long short-term memory networks - p. 395 18. Chapter 16. Automated machine learning - p. 415 19. Index - p. 429
