Advanced Modelling Techniques Studying Global Changes in Environmental Sciences

 
 
Elsevier (Verlag)
  • 1. Auflage
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  • erschienen am 8. Oktober 2015
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  • 380 Seiten
 
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978-0-444-63543-3 (ISBN)
 

Advanced Modelling Techniques Studying Global Changes in Environmental Sciences discusses the need for immediate and effective action, guided by a scientific understanding of ecosystem function, to alleviate current pressures on the environment.

Research, especially in Ecological Modeling, is crucial to support the sustainable development paradigm, in which the economy, society, and the environment are integrated and positively reinforce each other.

Content from this book is drawn from the 2013 conference of the International Society for Ecological Modeling (ISEM), an important and active research community contributing to this arena.

Some progress towards gaining a better understanding of the processes of global change has been achieved, but much more is needed. This conference provides a forum to present current research using models to investigate actions towards mitigating and adapting to change.


  • Presents state-of-the-art modeling techniques
  • Drawn from the 2013 conference of the International Society for Ecological Modeling (ISEM), an important and active research community contributing to this arena
  • Integrates knowledge of advanced modeling techniques in ecological and environmental sciences
  • Describes new applications for sustainability
0167-8892
  • Englisch
  • Niederlande
Elsevier Science
  • 13,09 MB
978-0-444-63543-3 (9780444635433)
0444635432 (0444635432)
weitere Ausgaben werden ermittelt
  • 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
  • 12.4. Case Study: Effects of Climate Change in the Lower Trophic Levels Dynamics in the Aveiro Lagoon
  • 12.4.1. Study Area
  • 12.4.2. ECO-SELFE Model
  • 12.4.3. Climate Change Scenarios Simulation: Model Application
  • 12.4.4. Anticipating Changes in the Water Quality and Ecological Dynamics
  • 12.5. Conclusions
  • Acknowledgments
  • References
  • Chapter 13: Modelling nitrogen and carbon cycles in Hooghly estuary along with adjacent mangrove ecosystem
  • 13.1. Introduction
  • 13.2. Study Area and Experimental Works
  • 13.3. Model of Nitrogen Cycle
  • 13.4. Model of Carbon Cycle
  • 13.5. Sensitivity Analysis of the Model
  • 13.6. Model Calibration and Validation
  • 13.7. Results
  • 13.8. Discussion
  • 13.9. Conclusion
  • References
  • Chapter 14: Hydrodynamic and ecosystem coupled model and its application to the eutrophication problem
  • 14.1. Introduction
  • 14.2. Hydrodynamics and Ecosystem Coupled Model
  • 14.2.1. Structure of the Coupled Model
  • 14.2.2. Hydrodynamic Model
  • 14.2.3. Ecosystem Model
  • 14.2.4. Boundary Conditions
  • 14.2.5. Finite Difference Scheme
  • 14.2.6. Grid Generation
  • 14.2.7. Data Collection for Boundary Conditions
  • 14.3. Example of Application
  • 14.3.1. Eutrophication Problems in Tokyo Bay
  • 14.3.2. Application of Numerical Simulation
  • 14.3.3. Grid Generation
  • 14.3.4. Boundary Condition
  • 14.3.5. Initial Condition
  • 14.3.6. Computational Condition
  • 14.4. Example Results of Numerical Simulation
  • 14.4.1. Water Current
  • 14.4.2. Water Quality
  • 14.4.3. Effects of External Loading
  • 14.4.4. Effects of Reduction in External Loading
  • 14.5. Summary and Perspective
  • References
  • Chapter 15: Functioning of the phytoplankton in seas and estimates of primary production for aquatic ecosystems
  • 15.1. Introduction
  • 15.2. Methods and Materials
  • 15.2.1. Model of Biomass Dynamics
  • 15.2.2. Model Based on a Fitness Function
  • 15.3. Results and Discussion
  • References
  • Index
  • Back Cover

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