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 -- Contents -- Part I: Introduction -- Chapter 1: Ecological Informatics: An Introduction -- 1.1 Introduction -- 1.2 Data Management -- 1.3 Analysis and Synthesis -- 1.4 Communicating and Informing Decisions -- 1.5 Case Studies -- References -- Part II: Managing Ecological Data -- Chapter 2: Project Data Management Planning -- 2.1 Introduction -- 2.2 Components of a Data Management Plan -- 2.2.1 Context -- 2.2.2 Data Collection and Acquisition -- 2.2.3 Data Organization -- 2.2.4 Quality Assurance/Quality Control -- 2.2.5 Documentation -- 2.2.6 Storage and Preservation -- 2.2.7 Data Integration, Analysis, Modeling and Visualization -- 2.2.8 Data Policies -- Box 2.1 Recommended Data Citation Guidelines from Dryad Digital Repository (2016) -- 2.2.9 Communication and Dissemination of Research Outputs -- 2.2.10 Roles and Responsibilities -- 2.2.11 Budget -- 2.3 Developing and Using a Data Management Plan -- 2.3.1 Best Practices for Creating the Plan -- 2.3.2 Using the Plan -- 2.4 Conclusion -- References -- Chapter 3: Scientific Databases for Environmental Research -- 3.1 Introduction -- 3.2 Challenges for Scientific Databases -- 3.3 Examples of Scientific Databases -- 3.3.1 A Useful Analogy -- 3.3.2 Examples of Databases -- 3.4 Evolving a Database -- 3.4.1 A Strategy for Evolving a Database -- 3.4.2 Choosing Software -- 3.4.3 Database Management System (DBMS) Types -- 3.4.4 Data Models and Normalization -- 3.4.5 Advantages and Disadvantages of Using a DBMS -- 3.5 Interlinking Information Resources -- 3.5.1 A Database Related to the Human Genome Project -- 3.5.2 Environmental Databases for Sharing Data -- 3.5.3 Tools for Interlinking Information -- 3.6 Conclusions -- References -- Chapter 4: Quality Assurance and Quality Control (QA/QC) -- 4.1 Introduction -- 4.2 Quality Assurance -- 4.3 Quality Control -- 4.3.1 Data Filters. , 4.3.2 Graphical QC -- 4.3.3 Statistical QC -- 4.3.4 Treatment of Errors and Outliers -- 4.4 Implementing QA/QC -- 4.5 Conclusion -- References -- Chapter 5: Creating and Managing Metadata -- 5.1 Introduction -- 5.2 Metadata Descriptors -- 5.3 Metadata Standards -- 5.3.1 Dublin Core Metadata Initiative -- 5.3.2 Darwin Core -- 5.3.3 Ecological Metadata Language -- 5.3.4 GBIF Metadata Profile -- 5.3.5 FGDC CSDGM -- 5.3.6 ISO 19115 -- 5.4 Metadata Management -- 5.4.1 Metadata Tools -- 5.4.2 Best Practices for Creating and Managing Metadata -- 5.5 Conclusion -- References -- Chapter 6: Preserve: Protecting Data for Long-Term Use -- 6.1 Introduction -- 6.1.1 Preservation and Its Benefits -- 6.2 Practices for Preserving Ecological Data -- 6.2.1 Define the Contents of Your Data Files -- 6.2.2 Define the Parameters -- 6.2.3 Use Consistent Data Organization -- 6.2.4 Use Stable File Formats -- 6.2.5 Specify Spatial Information -- 6.2.6 Assign Descriptive File Names -- 6.2.7 Document Processing Information -- 6.2.8 Perform Quality Assurance -- 6.2.9 Provide Documentation -- 6.2.10 Protect Your Data -- 6.3 Prepare Your Data for Archival -- 6.4 What the Archive Does -- 6.4.1 Quality Assurance -- 6.4.2 Documentation and Metadata -- 6.4.3 Release of a Data Set -- 6.5 Data Users -- 6.6 Conclusions -- Appendix: Example R-Script for Processing Data -- References -- Chapter 7: Data Discovery -- 7.1 Introduction -- 7.2 Discovering Data Created by Others -- 7.2.1 Internet Search Engines -- 7.2.2 Data Repositories -- 7.2.3 Data Directories -- 7.2.4 Data Aggregators -- 7.3 Best Practices for Promoting Data Discovery and Reuse -- 7.3.1 Data Products -- Box 7.1 DataCite Recommendations for Data Citation -- Box 7.2 Dryad Digital Repository Data Citation Recommendations -- 7.3.2 Scientific Code -- References -- Chapter 8: Data Integration: Principles and Practice. , 8.1 Introduction -- 8.2 Essential Characteristics of All Data -- 8.3 Data as Records About Reality -- 8.4 Record-Keeping and Prose Documents as Data Integration Challenges -- 8.5 Formal Data Structures Facilitate Integration -- 8.5.1 Sets and Sequences -- 8.5.2 Matrices -- 8.5.3 Cross-classifications -- 8.5.4 Tables -- 8.5.5 Tables or Spreadsheets? -- 8.5.6 Tables or Cross-classifications? -- 8.5.7 Modeling True Tables -- 8.5.8 Need for Global Keys -- 8.6 Merging or JOINing Tables -- 8.6.1 APPENDING or Unioning -- 8.6.2 JOINs -- 8.7 The Datum Is the Atom -- 8.8 Conclusion -- References -- Part III: Analysis, Synthesis and Forecasting of Ecological Data -- Chapter 9: Inferential Modelling of Population Dynamics -- 9.1 Introduction -- 9.2 Inferential Modelling of Ecological Data by the Hybrid Evolutionary Algorithm -- 9.2.1 Population Dynamics of the Cyanobacterium Microcystis in Lake Müggelsee (Germany) -- 9.2.2 Meta-Analysis of Population Dynamics of the Cyanobacterium -- 9.3 Inferential Modelling of Ecological Data by Regression Trees -- 9.3.1 Induction Algorithm of Regression Trees -- 9.3.2 Pruning of Regression Trees -- 9.3.3 Diatom Populations in Lake Prespa (Mazedonia) -- 9.3.4 Vegetation Status of Selected Land Sites in Victoria (Australia) -- 9.4 Conclusions -- References -- Chapter 10: Process-Based Modeling of Nutrient Cycles and Food-Web Dynamics -- 10.1 Introduction -- 10.2 Zero- and One-Dimensional Lake Models -- 10.2.1 Zero-Dimensional Model for the Phosphorus Cycle in a Hypereutrophic Wetland -- 10.2.2 One-Dimensional Model for Nutrient Cycles and Plankton Dynamics in Lakes and Reservoirs -- 10.3 Multi-dimensional Lake Models -- 10.3.1 Horizontal and Vertical Transport of Nutrients and Organisms -- 10.3.2 Multi-segment Lake Model for Studying Dreissenids and Macrophytes -- 10.4 Concluding Remarks -- References. , Chapter 11: Uncertainty Analysis by Bayesian Inference -- 11.1 Does Uncertainty Really Matter? -- 11.2 Hamilton Harbour -- 11.2.1 Introduction -- 11.2.2 Eutrophication Modeling to Elucidate the Role of Lower Food Web -- 11.2.3 Nutrient Export Modeling for the Hamilton Harbour Watershed -- 11.3 Bay of Quinte -- 11.3.1 Introduction -- 11.3.2 Modeling the Relationship Among Watershed Physiography, Land Use Patterns, and Phosphorus Loading -- 11.3.3 Eutrophication Risk Assessment with Process-Based Modeling and Determination of Water Quality Criteria -- 11.4 Concluding Remarks -- References -- Chapter 12: Multivariate Data Analysis by Means of Self-Organizing Maps -- 12.1 Introduction -- 12.2 Properties of a Self-Organizing Map -- 12.3 Data Preparation -- 12.3.1 Missing Values and Outliers -- 12.3.2 Data Transformation -- 12.3.3 Distance Measure -- 12.4 Self-Organizing Maps -- 12.4.1 Architecture -- 12.4.2 Learning Algorithm -- Box 12.1 Sequential Learning Algorithm of an SOM -- 12.4.3 Evaluation of Trained Map Quality -- 12.4.4 Optimum Map Size -- 12.4.5 Clustering SOM Units -- 12.4.6 Evaluation of Input Variables -- 12.4.7 Relations Between Biological and Environmental Variables -- 12.5 Application in Ecological Modelling -- 12.6 SOM Tools -- 12.7 Example of SOM Application -- 12.8 Advantages and Disadvantages -- 12.8.1 Utility for Training and Information Extraction -- 12.8.2 Visualization and Recognition -- 12.8.3 Architecture Flexibility -- 12.8.4 Flexibility in Combining with Other Models -- 12.8.5 Constraints on Measure Consistency and Output Variability -- 12.8.6 Necessity of Sufficient Data -- 12.9 Future Development -- 12.10 Conclusions -- References -- Chapter 13: GIS-Based Data Synthesis and Visualization -- 13.1 Introduction -- 13.2 Synthesizing Species Distributions by Virtual Species. , 13.3 Cartograms to Synthesize and Visualize Sampling Effort Bias -- 13.4 Fuzzy Methods to Synthesize Species Distribution Uncertainty -- 13.5 Synthesis of Remote Sensing Data -- 13.5.1 Exploratory Data Analysis -- 13.5.1.1 Correlation of Remotely Sensed Bands by Hexagon Binning -- 13.5.1.2 Correlation Among Several Layers by Texture Measures -- 13.5.2 Fourier Transformations -- 13.6 Synthesizing Diversity Measurements from Space: The Case of Generalized Entropy -- 13.7 Neutral Landscapes -- 13.8 Conclusions -- References -- Part IV: Communicating and Informing Decisions -- Chapter 14: Communicating and Disseminating Research Findings -- 14.1 Introduction -- 14.2 Publishing Research Findings -- 14.2.1 Scholarly Publications -- 14.2.1.1 Journal Articles -- 14.2.1.2 Abstracts -- 14.2.1.3 Technical Reports -- 14.2.1.4 Books and Book Chapters -- 14.2.2 Newspaper and Magazine Articles for General Audiences -- 14.2.3 Designing Effective Figures -- 14.3 Communicating Research Findings Outside of Publications -- 14.3.1 Simple Steps for Giving an Effective Presentation -- 14.3.2 Best Practices for Slides -- 14.3.2.1 Slide Design -- 14.3.2.2 Text Slides -- 14.3.2.3 Graphics -- 14.3.3 Handouts -- 14.3.4 Posters -- 14.4 Communication in a Virtual Environment -- 14.4.1 Websites -- 14.4.2 Types and Uses of Different Social Media -- 14.4.3 Simple Steps for Effective Use of Social Media -- 14.4.4 Understanding Your Social Media Impact -- 14.5 Metrics and Altmetrics -- 14.6 Conclusion -- References -- Chapter 15: Operational Forecasting in Ecology by Inferential Models and Remote Sensing -- 15.1 Introduction -- 15.2 Early Warning of HABs Based on Inferential Modelling -- 15.2.1 Cyanobacterium Cylindrospermopsis in Lake Wivenhoe (Australia) -- 15.2.2 Cyanotoxin Microcystins in Lake Vaal (South Africa) -- 15.3 Early Warning of HABs Based on Remotely-Sensed Data. , 15.3.1 Earth Observation of Water Quality Parameters.