Note: Front Cover -- Advanced Modelling Techniques Studying Global Changes in Environmental Sciences -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Introduction: Global changes and sustainable ecosystem management -- 1.1. Effects of Global Changes -- 1.2. Sustainable Ecosystem Management -- 1.3. Outline of This Book -- 1.3.1. Review of ecological models -- 1.3.2. Ecological network analysis and structurally dynamic models -- 1.3.3. Behavioral monitoring and species distribution models -- 1.3.4. Ecological risk assessment -- 1.3.5. Agriculture and forest ecosystems -- 1.3.6. Urban ecosystems -- 1.3.7. Estuary and marine ecosystems -- References -- Chapter 2: Toward a new generation of ecological modelling techniques: Review and bibliometrics -- 2.1. Introduction -- 2.2. Historical Development of Ecological Modelling -- 2.3. Bibliometric Analysis of Modelling Approaches -- 2.3.1. Data Sources and Analysis -- 2.3.2. Publication Output -- 2.3.3. Journal Distribution -- 2.3.4. Country/Territory Distribution and International Collaboration -- 2.3.5. Keyword Analysis -- 2.4. Brief Review of Modelling Techniques -- 2.4.1. Structurally Dynamic Model -- 2.4.2. Individual-Based Models -- 2.4.3. Support Vector Machine -- 2.4.4. Artificial Neural Networks -- 2.4.5. Tree-Based Model -- 2.4.6. Evolutionary Computation -- 2.4.7. Ordination and Classification Models -- 2.4.8. k-Nearest Neighbors -- 2.5. Future Perspectives of Ecological Modelling -- 2.5.1. Big Data Age: Data-Intensive Modelling -- 2.5.2. Hybrid Models -- 2.5.3. Model Sensitivities and Uncertainties -- References -- Chapter 3: System-wide measures in ecological network analysis -- 3.1. Introduction -- 3.2. Description of system-wide Measures -- 3.3. Ecosystem Models Used for Comparison -- 3.4. Methods -- 3.5. Observations and Discussion -- 3.5.1. Clusters of Structure-Based Measures. , 3.5.2. Clusters of Flow-Based Measures -- 3.5.3. Clusters of Storage-Based Measures -- References -- Chapter 4: Application of structurally dynamic models (SDMs) to determine impacts of climate changes -- 4.1. Introduction -- 4.2. Development of SDM -- 4.2.1. The Number of Feedbacks and Regulations Is Extremely High and Makes It Possible for the Living Organisms and Populatio -- 4.2.2. Ecosystems Show a High Degree of Heterogeneity in Space and in Time -- 4.2.3. Ecosystems and Their Biological Components, the Species, Evolve Steadily and over the Long-Term Toward Higher Complexi -- 4.3. Application of SDMs for the Assessment of Ecological Changes due to Climate Changes -- 4.4. Conclusions -- References -- Chapter 5: Modelling animal behavior to monitor effects of stressors -- 5.1. Introduction -- 5.2. Behavior Modelling: Dealing with Instantaneous or Whole Data Sets -- 5.2.1. Parameter Extraction and State Identification -- 5.2.2. Filtering and Intermittency -- 5.2.3. Statistics and Informatics -- 5.3. Higher Moments in Position Distribution -- 5.4. Identifying Behavioral States -- 5.5. Data Transformation and Filtering by Integration -- 5.6. Intermittency -- 5.7. Discussion and Conclusion -- Acknowledgment -- References -- Chapter 6: Species distribution models for sustainable ecosystem management -- 6.1. Introduction -- 6.2. Model Development Procedure -- 6.3. Selected Models: Characteristics and Examples -- 6.3.1. Decision Trees -- 6.3.1.1. General characteristics -- 6.3.1.2. Examples -- 6.3.1.3. Additional remarks -- 6.3.2. Generalised Linear Models -- 6.3.2.1. General characteristics -- 6.3.2.2. Examples -- 6.3.2.3. Additional remarks -- 6.3.3. Artificial Neural Networks -- 6.3.3.1. General characteristics -- 6.3.3.2. Examples -- 6.3.3.3. Additional remarks -- 6.3.4. Fuzzy Logic -- 6.3.4.1. General characteristics -- 6.3.4.2. Examples. , 6.3.4.3. Additional remarks -- 6.3.5. Bayesian Belief Networks -- 6.3.5.1. General characteristics -- 6.3.5.2. Examples -- 6.3.5.3. Additional remarks -- 6.3.6. Summary of Advantages and Drawbacks -- 6.4. Future Perspectives -- References -- Chapter 7: Ecosystem risk assessment modelling method for emerging pollutants -- 7.1. Review of Ecological Risk Assessment Model Methods -- 7.2. The Selected Model Method -- 7.3. Case Study: Application of AQUATOX Models for Ecosystem Risk Assessment of Polycyclic Aromatic Hydrocarbons in Lake Ecos -- 7.3.1. Application of Models -- 7.3.2. Models -- 7.3.2.1. AQUATOX model -- 7.3.2.2. Parameterization -- 7.3.2.2.1. Biomass and physiological parameters of organisms -- 7.3.2.2.2. Characteristics of Baiyangdian Lake -- 7.3.2.2.3. PAHs model parameters -- 7.3.2.2.4. Determining PAHs water contamination -- 7.3.2.2.5. Sensitivity analysis -- 7.3.3. Results of Model Application -- 7.3.3.1. Model calibration -- 7.3.3.2. Sensitivity analysis -- 7.3.3.3. PAHs risk estimation -- 7.3.4. Discussion on the Model Application -- 7.3.4.1. Compare experiment-derived NOEC with model NOEC for PAHs -- 7.3.4.2. Compare traditional method with model method for ecological risk assessment for PAHs -- 7.4. Perspectives -- Acknowledgments -- References -- Chapter 8: Development of species sensitivity distribution (SSD) models for setting up the management priority with water qua -- 8.1. Introduction -- 8.2. Methods -- 8.2.1. BMC Platform Development for SSD Models -- 8.2.1.1. BMC structure -- 8.2.1.2. BMC functions -- 8.2.1.2.1. Fitting SSD models -- 8.2.1.2.2. Determining the best fitting model based on DIC -- 8.2.1.2.3. Uncertainty analysis -- 8.2.1.2.4. Calculating the eco-risk indicator: PAF and msPAF -- 8.2.2. Framework for Determination of WQC and Screening of PCCs -- 8.2.2.1. WQCs calculation -- 8.2.2.2. PCCs screening. , 8.2.3. Overview of BTB Areas, Occurrence of PTSs, and Ecotoxicity Data Preprocessing -- 8.3. Results and Discussion -- 8.3.1. Evaluation of the BMC Platform -- 8.3.1.1. Selection of the best SSD models -- 8.3.1.2. Priority and posterior distribution of SSDs parameters -- 8.3.1.3. CI for uncertainty analysis -- 8.3.1.4. Validation of SSD models -- 8.3.2. Eco-risks with Uncertainty -- 8.3.2.1. Generic eco-risks for a specific substance -- 8.3.2.2. Joint eco-risk for multiple substances based on response addition -- 8.3.3. Evaluation of Various WQC Strategies -- 8.3.3.1. Abundance of toxicity data -- 8.3.3.2. Limitation of toxicity data -- 8.3.3.3. Lack of toxicity data -- 8.3.3.4. Implication for improvement of the local WQC in BTB -- 8.3.4. Ranking and Screening Using Various PCC Strategies -- 8.3.4.1. PNEC -- 8.3.4.2. Eco-risk calculated by BMC -- 8.3.4.3. EEC/PNEC -- 8.3.4.4. PCC list in BTB area -- 8.3.4.5. Implication for update of the local PCC list in BTB -- 8.4. Conclusion -- Acknowledgments -- References -- Chapter 9: Modelling mixed forest stands: Methodological challenges and approaches -- 9.1. Introduction -- 9.2. Review Methodology -- 9.2.1. Literature Review on Modelling Mixed Forest Stands -- 9.2.2. Ranking of Forest Models -- 9.3. Results and Discussion -- 9.3.1. Patterns of Ecological Model Use in Mixed Forests -- 9.3.2. Model Ranking -- 9.3.2.1. FORMIX -- 9.3.2.2. FORMIND -- 9.3.2.3. SILVA -- 9.3.2.4. FORECAST -- 9.3.3. Comparison of the Top-Ranked Models -- 9.4. Conclusions -- Acknowledgments -- References -- Chapter 10: Decision in agroecosystems advanced modelling techniques studying global changes in environmental sciences -- 10.1. Introduction -- 10.2. Approaches Based on Management Strategy Simulation -- 10.2.1. Simulation of Discrete Events in Agroecosystem Dynamics -- 10.2.2. Simulation of Agroecosystem Control. , 10.3. Design of Agroecosystem Management Strategy -- 10.3.1. Hierarchical Planning -- 10.3.1.1. HTN planning concepts -- 10.3.1.2. Planning approach in HTNs -- 10.3.1.3. Illustration based on the problem of selecting an operating mode in agriculture -- 10.3.2. Planning as Weighted Constraint Satisfaction -- 10.3.2.1. Constraint satisfaction problem -- 10.3.2.2. Networks of weighted constraints -- 10.3.2.3. Illustration based on crop allocation -- 10.3.3. Planning Under Uncertainty with Markov Decision Processes -- 10.3.3.1. Markov decision processes -- 10.3.3.2. Illustration using a forest management problem -- 10.4. Strategy Design by Simulation and Learning -- 10.5. Illustrations -- 10.5.1. SAFIHR: Modelling a Farming Agent -- 10.5.1.1. Decision problem -- 10.5.1.2. SAFIHR: Continuous planning -- 10.5.1.3. Overview of the overall operation -- 10.6. Conclusion -- References -- Chapter 11: Ecosystem services in relation to carbon cycle of Asansol-Durgapur urban system, India -- 11.1. Introduction -- 11.2. Methods -- 11.2.1. Study Area -- 11.2.2. Urban Forest -- 11.2.3. Agriculture -- 11.2.4. Anthropogenic Activities -- 11.2.5. Cattle Production -- 11.3. Analysis and Discussion -- 11.3.1. Ecosystem Services and Disservices of Urban Forest -- 11.3.2. Ecosystem Services and Disservices of Agricultural Field -- 11.3.3. Ecosystem Services and Disservices Through Anthropogenic Activities -- 11.3.4. Ecosystem Services and Disservices Through Cattle Production -- 11.3.5. Impact on Biodiversity -- 11.3.6. Cultural Services and Disservices -- 11.3.7. Future Perspective of Ecosystem Services -- 11.4. Conclusions -- Acknowledgments -- References -- Chapter 12: Modelling the effects of climate change in estuarine ecosystems with coupled hydrodynamic and biogeochemical mode -- 12.1. Introduction -- 12.2. Coupled Hydrodynamic and Biogeochemical Models. , 12.3. Models as Effective Tools to Support Estuarine Climate Change Impacts Assessment.

Note: Cover -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- 1. Falling in Love: The Golden Age of Synthetic Pesticides -- 2. Trouble in Paradise: The USDA and the Rise of Critical Voices -- 3. Breakup? The Cultural Impact of Rachel Carson's Silent Spring -- 4. Foreign Affairs: How Pesticides Could Help Americans Feed the World and Win a War -- 5. The Twenty-Year Itch: Activists, Experts, and the Regulatory Era -- 6. Love Is Blind: Chemical Disasters at Home and Abroad -- 7. Recommitment: Endocrine Disruptors, GMOs, and Organic Food -- Conclusion -- Acknowledgments -- Notes -- Bibliography -- Index -- Back Cover.