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: Intro -- Preface -- Acknowledgements -- Contents -- Contributors -- An Overview of Conservation Paleobiology -- 1 Defining and Establishing Conservation Paleobiologyas a Discipline -- 2 Data in Conservation Paleobiology -- 3 Looking Forward -- References -- Should Conservation Paleobiologists Save the World on Their Own Time? -- 1 Always Academicize? -- 2 To Advocate, or Not to Advocate -- 3 Speaking Honestly to Power -- 4 From Pure Scientist to Honest Broker -- 5 Keeping It Real -- 6 Overcoming the Fear Factor -- 7 Later Is Too Late -- References -- Conceptions of Long-Term Data Among Marine Conservation Biologists and What Conservation Paleobiologists Need to Know -- 1 What is "Long Term"? -- 2 Survey Implementation -- 3 Survey Responses and What They Mean for Conservation Paleobiologists -- Conservation Goals -- Long-Term Data -- Environmental Stressors -- Baselines -- Challenges -- 4 Takeaways for Conservation Paleobiologists -- 5 Moving Forward -- Appendix 1: Survey Questions -- Appendix 2: Survey Population Selection -- Appendix 3: Categorization of Responses -- References -- Effectively Connecting Conservation Paleobiological Research to Environmental Management: Examples from Greater Everglades' Restoration of Southwest Florida -- 1 Introduction -- 2 Defining the Problem -- 3 Ensuring Success as a Conservation Paleobiologist -- Developing Partnerships and Collaborative Teams -- Becoming or Engaging a Liaison -- Participate in "Management Collaboratives" -- Compose Technical Reports in Addition to Peer-Reviewed Journal Articles -- Present Your Findings to Stake Holder Groups -- Attend and Present at Environmental Science and Restoration Conferences -- Train our Students -- Reward Faculty for Conducting Community-Engaged Scholarship -- Promote and Reward Community Service for Work with Environmental Agencies and NGOs. , 4 Case Studies from Greater Everglades' Restoration -- Case Study 1: Water Management of the Caloosahatchee River -- Case Study 2: Picayune Strand Restoration Project -- 5 Conclusions -- References -- Using the Fossil Record to Establish a Baseline and Recommendations for Oyster Mitigation in the Mid-Atlantic U.S. -- 1 Introduction -- 2 Methods -- Pleistocene Localities -- Field and Museum Sampling -- Oyster Size and Abundance Data -- Reconstructing Paleotemperature and Salinity -- Modern and Colonial Data -- 3 Results -- Paleoenvironmental Reconstruction of Holland Point -- Paleotemperature -- Paleosalinity -- Shell Height -- Growth Rate -- 4 Discussion -- Comparing Pleistocene to Modern Oysters -- Environmental Controls on Oyster Size -- Human Factors Influencing Oyster Size -- Implications for Restoration -- A Role for Conservation Paleobiology -- 5 Conclusion -- References -- Coral Reefs in Crisis: The Reliability of Deep-Time Food Web Reconstructions as Analogs for the Present -- 1 Introduction -- Preserving the Past -- Endangered Coral Reefs -- 2 Fossilizing a Coral Reef -- Dietary Breadth -- Trophic Chains and Levels -- Modularity -- 3 Guild Structure and Diversity -- Identifying Guilds in a Food Web -- 4 Reconstructing the Community -- Diversity and Evenness -- Simulated Food Webs -- 5 Summary -- Appendix 1 -- Hypergeometric Variance -- Appendix 2 -- References -- Exploring the Species -Area Relationship Within a Paleontological Context, and the Implications for Modern Conservation Biology -- 1 Introduction -- 2 Geological Setting -- 3 Methods -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Marine Refugia Past, Present, and Future: Lessons from Ancient Geologic Crises for Modern Marine Ecosystem Conservation -- 1 Introduction -- 2 Defining Refugium. , A Species Must Have a Range Contraction, Range Shift, or Migration in Order to Escape the Onset of Global Environmental Degradation That Would Otherwise Cause Extinction of That Species -- Range Shifts -- Habitat Shifts -- Isolated Geographic Refugia -- Life History Refugia -- Cryptic Refugia -- Harvest Refugia -- The Environmental Conditions of a Refugium Are Sufficiently Habitable Such That the Species' Population Remains Viable During Its Time in the Refugium -- A Species' Population Is Smaller in the Refugium Than Its Pre-environmental Perturbation Size -- The Species Remains in the Refugium for Many Generations -- After the Environmental Crisis Ends, the Species Recovers by Inhabiting Newly Re-opened Habitats, Either Through Population Expansion or Through Adaptive Radiation -- Otherwise, the Refugium Became a Trap -- 3 Identifying Ancient Refugia -- Fossil Data -- Phylogeographic Studies -- Species Distribution Models -- 4 Lessons from the Past for Identifying Future Refugia -- As the Marine Environment Continues to Change, Refugia May Need to Shift -- Refugial Size and Connectivity Can Enhance Survivorship, But Can Also Have Evolutionary Consequences -- Conditions Inside Refugia May Not Necessarily Remain Pristine, But Will Need to Be of Sufficiently Lower Magnitude of Total Stress to Maintain Viable Populations -- Beware the Refugial Trap -- 5 Future Directions for Investigating Ancient Refugia -- 6 Conclusions -- Appendix -- References -- Training Tomorrow's Conservation Paleobiologists -- 1 Business As Usual Is Not Enough -- 2 A Call to Action -- 3 Bridging the Gap -- Recommendation 1 -- Recommendation 2 -- Recommendation 3 -- Recommendation 4 -- Recommendation 5 -- Recommendation 6 -- 4 Okay, But… -- 5 In the Meantime… -- 6 A Bright Future -- References -- A Conceptual Map of Conservation Paleobiology: Visualizinga Discipline. , 1 Determining the Current State and Structure of Conservation Paleobiology -- 2 Mapping a Discipline -- Bibliographic Co-Authorship Visualizations -- Text Co-Occurrence Visualizations -- Bibliographic Co-Citation Visualizations -- Bibliographic Coupling Visualizations -- 3 Bibliometric Networks -- Bibliographic Co-Authorship Networks -- Text Co-Occurrence Networks -- Bibliographic Co-Citation Networks -- Bibliometric Coupling Networks -- 4 The Intellectual Landscape -- 5 Emerging Frontiers -- 6 Conclusions -- References -- Index.

Note: Cover -- Half Title -- Title -- Copyright -- Dedication -- Contents -- List of contributors -- Preface -- Foreword -- Acknowledgements -- List of journals -- 1 Environmental science on the move -- 2 The sustainability debate -- 3 Environmental politics and policy processes -- 4 Environmental and ecological economics -- 5 Biodiversity and ethics -- 6 Population, adaptation and resilience -- 7 Climate change -- 8 Managing the oceans -- 9 Coastal processes and management -- 10 GIS and environmental management -- 11 Soil erosion and land degradation -- 12 River processes and management -- 13 Groundwater pollution and protection -- 14 Marine and estuarine pollution -- 15 Urban air pollution and public health -- 16 Preventing disease -- 17 Environmental risk management -- 18 Waste management -- 19 Managing the global commons -- Index.