Contributors |
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xiii | |
Preface |
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xvii | |
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Chapter 1 Introduction: Global changes and sustainable ecosystem management |
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1 | (10) |
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1.1 Effects of Global Changes |
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1 | (1) |
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1.2 Sustainable Ecosystem Management |
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2 | (1) |
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3 | (8) |
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1.3.1 Review of Ecological Models |
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4 | (1) |
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1.3.2 Ecological Network Analysis and Structurally Dynamic Models |
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4 | (1) |
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1.3.3 Behavioral Monitoring and Species Distribution Models |
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5 | (1) |
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1.3.4 Ecological Risk Assessment |
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5 | (1) |
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1.3.5 Agriculture and Forest Ecosystems |
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6 | (1) |
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6 | (1) |
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1.3.7 Estuary and Marine Ecosystems |
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6 | (1) |
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7 | (4) |
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Chapter 2 Toward a new generation of ecological modelling techniques: Review and bibliometrics |
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11 | (34) |
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11 | (1) |
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2.2 Historical Development of Ecological Modelling |
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12 | (1) |
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2.3 Bibliometric Analysis of Modelling Approaches |
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13 | (9) |
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2.3.1 Data Sources and Analysis |
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13 | (2) |
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15 | (1) |
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2.3.3 Journal Distribution |
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16 | (1) |
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2.3.4 Country/Territory Distribution and International Collaboration |
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16 | (3) |
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19 | (3) |
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2.4 Brief Review of Modelling Techniques |
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22 | (13) |
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2.4.1 Structurally Dynamic Model |
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22 | (1) |
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2.4.2 Individual-Based Models |
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23 | (2) |
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2.4.3 Support Vector Machine |
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25 | (1) |
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2.4.4 Artificial Neural Networks |
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26 | (2) |
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28 | (3) |
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2.4.6 Evolutionary Computation |
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31 | (2) |
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2.4.7 Ordination and Classification Models |
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33 | (2) |
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2.4.8 k-Nearest Neighbors |
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35 | (1) |
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2.5 Future Perspectives of Ecological Modelling |
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35 | (10) |
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2.5.1 Big Data Age: Data-Intensive Modelling |
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35 | (1) |
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36 | (1) |
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2.5.3 Model Sensitivities and Uncertainties |
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36 | (1) |
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37 | (8) |
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Chapter 3 System-wide measures in ecological network analysis |
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45 | (24) |
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45 | (2) |
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3.2 Description of System-Wide Measures |
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47 | (7) |
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3.3 Ecosystem Models Used for Comparison |
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54 | (4) |
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58 | (3) |
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3.5 Observations and Discussion |
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61 | (8) |
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3.5.1 Clusters of Structure-Based Measures |
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61 | (1) |
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3.5.2 Clusters of Flow-Based Measures |
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61 | (3) |
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3.5.3 Clusters of Storage-Based Measures |
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64 | (1) |
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64 | (5) |
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Chapter 4 Application of structurally dynamic models (SDMs) to determine impacts of climate changes |
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69 | (18) |
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69 | (1) |
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70 | (11) |
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4.2.1 The Number of Feedbacks and Regulations Is Extremely High and Makes It Possible for the Living Organisms and Populations to Survive and Reproduce in Spite of Changes in External Conditions |
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71 | (1) |
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4.2.2 Ecosystems Show a High Degree of Heterogeneity in Space and in Time |
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71 | (1) |
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4.2.3 Ecosystems and Their Biological Components, the Species, Evolve Steadily and over the Long-Term Toward Higher Complexity |
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72 | (9) |
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4.3 Application of SDMs for the Assessment of Ecological Changes due to Climate Changes |
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81 | (2) |
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83 | (4) |
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85 | (2) |
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Chapter 5 Modelling animal behavior to monitor effects of stressors |
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87 | (28) |
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87 | (2) |
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5.2 Behavior Modelling: Dealing with Instantaneous or Whole Data Sets |
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89 | (3) |
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5.2.1 Parameter Extraction and State Identification |
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90 | (1) |
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5.2.2 Filtering and Intermittency |
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91 | (1) |
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5.2.3 Statistics and Informatics |
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91 | (1) |
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5.3 Higher Moments in Position Distribution |
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92 | (3) |
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5.4 Identifying Behavioral States |
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95 | (2) |
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5.5 Data Transformation and Filtering by Integration |
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97 | (8) |
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105 | (2) |
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5.7 Discussion and Conclusion |
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107 | (8) |
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110 | (1) |
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111 | (4) |
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Chapter 6 Species distribution models for sustainable ecosystem management |
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115 | (20) |
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115 | (1) |
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6.2 Model Development Procedure |
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116 | (1) |
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6.3 Selected Models: Characteristics and Examples |
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117 | (11) |
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118 | (1) |
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6.3.2 Generalised Linear Models |
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119 | (2) |
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6.3.3 Artificial Neural Networks |
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121 | (2) |
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123 | (2) |
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6.3.5 Bayesian Belief Networks |
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125 | (2) |
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6.3.6 Summary of Advantages and Drawbacks |
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127 | (1) |
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128 | (7) |
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129 | (6) |
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Chapter 7 Ecosystem risk assessment modelling method for emerging pollutants |
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135 | (28) |
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7.1 Review of Ecological Risk Assessment Model Methods |
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135 | (1) |
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7.2 The Selected Model Method |
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136 | (2) |
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7.3 Case Study: Application of AQUATOX Models for Ecosystem Risk Assessment of Polycyclic Aromatic Hydrocarbons in Lake Ecosystems |
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138 | (19) |
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7.3.1 Application of Models |
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138 | (1) |
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139 | (7) |
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7.3.3 Results of Model Application |
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146 | (7) |
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7.3.4 Discussion on the Model Application |
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153 | (4) |
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157 | (6) |
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158 | (1) |
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158 | (5) |
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Chapter 8 Development of species sensitivity distribution (SSD) models for setting up the management priority with water quality criteria of toxic chemicals |
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163 | (26) |
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163 | (1) |
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164 | (8) |
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8.2.1 BMC Platform Development for SSD Models |
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164 | (5) |
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8.2.2 Framework for Determination of WQC and Screening of PCCs |
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169 | (2) |
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8.2.3 Overview of BTB Areas, Occurrence of PTSs, and Ecotoxicity Data Preprocessing |
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171 | (1) |
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8.3 Results and Discussion |
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172 | (11) |
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8.3.1 Evaluation of the BMC Platform |
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172 | (4) |
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8.3.2 Eco-risks with Uncertainty |
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176 | (1) |
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8.3.3 Evaluation of Various WQC Strategies |
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177 | (3) |
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8.3.4 Ranking and Screening Using Various PCC Strategies |
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180 | (3) |
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183 | (6) |
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183 | (1) |
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183 | (6) |
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Chapter 9 Modelling mixed forest stands: Methodological challenges and approaches |
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189 | (28) |
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189 | (1) |
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190 | (2) |
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9.2.1 Literature Review on Modelling Mixed Forest Stands |
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190 | (1) |
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9.2.2 Ranking of Forest Models |
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191 | (1) |
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9.3 Results and Discussion |
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192 | (17) |
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9.3.1 Patterns of Ecological Model Use in Mixed Forests |
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192 | (3) |
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195 | (12) |
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9.3.3 Comparison of the Top-Ranked Models |
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207 | (2) |
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209 | (8) |
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210 | (1) |
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211 | (6) |
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Chapter 10 Decision in agroecosystems advanced modelling techniques studying global changes in environmental sciences |
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217 | (30) |
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217 | (2) |
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10.2 Approaches Based on Management Strategy Simulation |
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219 | (6) |
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10.2.1 Simulation of Discrete Events in Agroecosystem Dynamics |
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219 | (4) |
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10.2.2 Simulation of Agroecosystem Control |
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223 | (2) |
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10.3 Design of Agroecosystem Management Strategy |
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225 | (10) |
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10.3.1 Hierarchical Planning |
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226 | (3) |
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10.3.2 Planning as Weighted Constraint Satisfaction |
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229 | (3) |
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10.3.3 Planning Under Uncertainty with Markov Decision Processes |
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232 | (3) |
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10.4 Strategy Design by Simulation and Learning |
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235 | (1) |
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236 | (5) |
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10.5.1 SAFIHR: Modelling a Farming Agent |
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236 | (5) |
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241 | (6) |
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242 | (5) |
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Chapter 11 Ecosystem services in relation to carbon cycle of Asansol-Durgapur urban system, India |
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247 | (24) |
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247 | (3) |
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250 | (3) |
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250 | (1) |
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251 | (1) |
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252 | (1) |
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11.2.4 Anthropogenic Activities |
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252 | (1) |
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252 | (1) |
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11.3 Analysis and Discussion |
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253 | (10) |
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11.3.1 Ecosystem Services and Disservices of Urban Forest |
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253 | (1) |
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11.3.2 Ecosystem Services and Disservices of Agricultural Field |
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254 | (2) |
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11.3.3 Ecosystem Services and Disservices Through Anthropogenic Activities |
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256 | (3) |
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11.3.4 Ecosystem Services and Disservices Through Cattle Production |
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259 | (1) |
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11.3.5 Impact on Biodiversity |
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260 | (1) |
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11.3.6 Cultural Services and Disservices |
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260 | (1) |
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11.3.7 Future Perspective of Ecosystem Services |
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261 | (2) |
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263 | (8) |
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263 | (1) |
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264 | (7) |
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Chapter 12 Modelling the effects of climate change in estuarine ecosystems with coupled hydrodynamic and biogeochemical models |
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271 | (18) |
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271 | (1) |
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12.2 Coupled Hydrodynamic and Biogeochemical Models |
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272 | (1) |
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12.3 Models as Effective Tools to Support Estuarine Climate Change Impacts Assessment |
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273 | (2) |
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12.4 Case Study: Effects of Climate Change in the Lower Trophic Levels Dynamics in the Aveiro Lagoon |
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275 | (10) |
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275 | (2) |
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277 | (2) |
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12.4.3 Climate Change Scenarios Simulation: Model Application |
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279 | (2) |
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12.4.4 Anticipating Changes in the Water Quality and Ecological Dynamics |
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281 | (4) |
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285 | (4) |
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285 | (1) |
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285 | (4) |
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Chapter 13 Modelling nitrogen and carbon cycles in Hooghly estuary along with adjacent mangrove ecosystem |
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289 | (32) |
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289 | (2) |
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13.2 Study Area and Experimental Works |
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291 | (1) |
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13.3 Model of Nitrogen Cycle |
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292 | (8) |
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13.4 Model of Carbon Cycle |
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300 | (6) |
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13.5 Sensitivity Analysis of the Model |
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306 | (1) |
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13.6 Model Calibration and Validation |
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307 | (1) |
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307 | (7) |
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314 | (3) |
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317 | (4) |
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317 | (4) |
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Chapter 14 Hydrodynamic and ecosystem coupled model and its application to the eutrophication problem |
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321 | (18) |
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321 | (1) |
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14.2 Hydrodynamics and Ecosystem Coupled Model |
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322 | (6) |
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14.2.1 Structure of the Coupled Model |
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322 | (1) |
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14.2.2 Hydrodynamic Model |
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323 | (1) |
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324 | (2) |
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14.2.4 Boundary Conditions |
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326 | (1) |
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14.2.5 Finite Difference Scheme |
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327 | (1) |
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327 | (1) |
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14.2.7 Data Collection for Boundary Conditions |
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327 | (1) |
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14.3 Example of Application |
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328 | (4) |
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14.3.1 Eutrophication Problems in Tokyo Bay |
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328 | (1) |
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14.3.2 Application of Numerical Simulation |
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329 | (1) |
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329 | (1) |
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14.3.4 Boundary Condition |
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329 | (2) |
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331 | (1) |
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14.3.6 Computational Condition |
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331 | (1) |
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14.4 Example Results of Numerical Simulation |
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332 | (4) |
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332 | (1) |
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332 | (2) |
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14.4.3 Effects of External Loading |
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334 | (1) |
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14.4.4 Effects of Reduction in External Loading |
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335 | (1) |
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14.5 Summary and Perspective |
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336 | (3) |
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337 | (2) |
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Chapter 15 Functioning of the phytoplankton in seas and estimates of primary production for aquatic ecosystems |
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339 | (12) |
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339 | (1) |
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15.2 Methods and Materials |
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340 | (2) |
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15.2.1 Model of Biomass Dynamics |
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340 | (2) |
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15.2.2 Model Based on a Fitness Function |
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342 | (1) |
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15.3 Results and Discussion |
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342 | (9) |
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348 | (3) |
Index |
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351 | |