Advanced modelling techniques studying global changes in environmental sciences /

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 crucia...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Park, Young-Seuk (Editor), Lek, Sovan (Editor), Baehr, Christophe (Editor), Jørgensen, Sven Erik, 1934- (Editor)
Format:	eBook
Language:	English
Language Notes:	English.
Published:	Amsterdam, Netherlands : Elsevier, 2015.
Edition:	First edition.
Series:	Developments in environmental modelling ; v. 27.
Subjects:	Ecology > Simulation methods. Ecosystem management > Simulation methods. Environmental sciences > Simulation methods. Global environmental change. Écologie > Méthodes de simulation. Écosystèmes > Gestion > Méthodes de simulation. Sciences de l'environnement > Méthodes de simulation. Changement global (Environnement) NATURE > Ecology. NATURE > Ecosystems & Habitats > Wilderness. SCIENCE > Environmental Science. SCIENCE > Life Sciences > Ecology. Ecology > Simulation methods Ecosystem management > Simulation methods Environmental sciences > Simulation methods Global environmental change Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.