Table Of Contents:
Preface xiii
Model examples, software, homepages and contact person for the various model types xv

Introduction: sub-disciplines of ecology and the history of ecological modeling 1(8)

S.E. Jørgensen

History of the ecological sub-disciplines 1(4)

The development of ecological modeling 5(4)

Overview of the model types available for ecological modeling 9(32)

S.E. Jørgensen

T.-S. Chon

Issues in model development 9(10)

Presentation of spatial distribution 11(4)

Computational realization of biological properties 15(2)

Revealing environmental factors 17(1)

Data handling and model construction 18(1)

Increasing number of model types 19(2)

Characteristics of the model types available today 21(16)

Dynamic models: Chapters 5 and 11 22(1)

Static models: Chapters 5 and 11 23(1)

Population dynamic model: Chapter 12 24(1)

Structurally dynamic models: Chapter 13 25(1)

Fuzzy models: Chapter 8 26(1)

Models in ecological informatics: Chapter 9 27(1)

Individual-based models and cellular automata: Chapters 7 and 16 28(2)

Spatial models: Chapters 6 and 20 30(1)

Ecotoxicological models: Chapters 14 and 15 31(2)

Stochastic models: Chapter 12 33(1)

Rule-based models: Chapter 17 34(1)

Hybrid models: Chapter 10 35(1)

Mediated/institutionalized models: Chapters 4 and 19 35(2)

Network analyses and calculations: Chapter 18 37(1)

Applicability of the model types 37(4)

Ecological informatics: current scope and feature areas 41(8)

F. Recknagel

Introduction 41(2)

Feature areas 43(1)

Future directions 44(5)

Model making 49(6)

S.E. Jørgensen

Modelling procedure 49(2)

Institutionalized modeling 51(2)

The institutionalized modelling process 52(1)

When to apply IMM? 53(2)

Ecopath with Ecosim: linking fisheries and ecology 55(16)

V. Christensen

Why ecosystem modeling in fisheries? 55(1)

The Ecopath with Ecosim (EwE) modeling approach 56(4)

Model overview 56(1)

Mass-balance 56(2)

The foraging arena 58(1)

Ecosim 59(1)

EwE modules and applications 60(1)

EwE applications 61(1)

Getting hold of the EwE software 62(1)

Exercise: trawling cultivates the ocean bottom for squid 63(8)

Surface modelling of population distribution 71(28)

T.-X. Yue

Y.-A. Wang

Z.-M. Fan

Introduction 71(1)

YUE-SMPD 72(2)

Approaches to population distribution analyses 72(1)

YUE-SMPD formulation 73(1)

An application of YUE-SMPD 74(18)

Major data layers 74(12)

YUE-SMPD operation 86(1)

Change trend of population distribution in China 87(3)

Scenarios of population distribution in China 90(2)

Discussion 92(7)

Individual-based models 99(26)

T.-S. Chon

Sang Hee Lee

C. Jeaung

H.K. Cho

Seung Ho Lee

Y.-J. Chung

Introduction 99(1)

Properties of individuals 100(1)

Model construction 101(2)

Program outline and system environment 101(1)

Variables 102(1)

Model structure and interaction 102(1)

Parameters and input data 102(1)

Output and model results 102(1)

Flocking behavior 103(6)

Program outline and system environment 103(1)

Variables 103(2)

Model structure and interaction 105(2)

Parameters and input data 107(1)

Output and results 107(2)

Population dispersal 109(16)

Program outline and system environment 109(2)

Variables 111(2)

Model structure and interaction 113(1)

Parameters and input data 113(1)

Output and results 114(11)

A fuzzy approach to ecological modelling and data analysis 125(16)

A. Salski

B. Holsten

M. Trepel

Imprecision, uncertainty and heterogeneity of environmental data 125(1)

Fuzzy sets and fuzzy logic in ecological applications 126(1)

Fuzzy classification and spatial data analysis 126(1)

Fuzzy modelling, decision making and ecosystem management 127(1)

Hybrid approaches to data analysis and ecological modelling 127(1)

Fuzzy classification: a fuzzy clustering approach 127(5)

An application example: fuzzy classification of wetlands for determination of water quality improvement potentials 129(3)

Fuzzy modelling 132(6)

An application example: a fuzzy and neuro-fuzzy approach to modelling cattle grazing in Western Europe 134(4)

Final remarks 138(3)

Ecological informatics by means of neural, evolutionary and object-oriented computation 141(28)

F. Recknagel

H. Cao

Introduction 141(1)

Artificial neural networks 141(19)

Supervised feedforward ANN 143(1)

Supervised feedback ANN 144(1)

Non-supervised ANN 145(5)

Evolutionary algorithms 150(3)

Object-oriented programming 153(7)

Conclusions 160(9)

Hybridisation of process-based ecosystem models with evolutionary algorithms: multi-objective optimisation of process and parameter representations of the lake simulation library SALMO-OO 169(18)

H. Cao

F. Recknagel

Introduction 169(1)

Evolutionary algorithm for the optimisation of process representations and parameters 170(2)

Encoding 171(1)

Fitness evaluation 171(1)

Genetic operators 172(1)

Optimisation of process representations in SALMO-OO by means of EA 172(4)

Experimental settings and measures 172(3)

Results and discussion 175(1)

Case study of parameter optimisation in SALMO-OO 176(8)

Experimental settings and measures 176(2)

Results and discussion 178(6)

Conclusions and future work 184(3)

Biogeochemical models 187(12)

S.E. Jørgensen

The characteristics of biogeochemical models 187(1)

The application of biogeochemical models 188(2)

Biogeochemical models 190(1)

Model of sub-surface wetland 190(9)

The state variables 191(2)

Forcing functions 193(1)

Process equations 194(1)

Parameters 195(1)

Differential equations 195(2)

Model results 197(1)

Additional results 197(1)

Comparison of simulated and measured values 197(1)

Practical information about forcing functions and parameters 198(1)

Stochastic population dynamic models as probability networks 199(22)

M.E. Borsuk

D.C. Lee

Introduction 199(2)

Population dynamic models 199(1)

Stochasticity 199(1)

Stochastic models 200(1)

Probability networks 201(1)

Methods 201(2)

Model construction 201(1)

Communicating results 202(1)

Use of probability networks 202(1)

Example models and their applications 203(14)

Bay VAM and westslope cutthroat trout in the Upper Missouri River Basin, USA 203(7)

CATCH-Net and brown trout in the Rhine River Basin, Switzerland 210(7)

Availability of models and software 217(4)

Bay VAM/Netica 217(1)

CATCH-Net/Analytica 217(4)

Structurally dynamic models 221(20)

S.E. Jørgensen

Introduction: why structurally dynamic models? 221(1)

Ecosystem characteristics 221(5)

Structurally dynamic models 226(7)

Development of SDM for Darwin's finches 233(1)

Model of the ectoparasite-bird interactions 234(7)

Ecotoxicological models 241(14)

S.E. Jørgensen

Introduction: characteristics of ecotoxicological models 241(1)

Classification of ecotoxicological models 242(5)

Food chain or food web dynamic models 242(1)

Static models of the mass flows of toxic substances 242(1)

A dynamic model of a toxic substance in one trophic level 243(2)

Ecotoxicological models in population dynamics 245(1)

Ecotoxicological models with effect components 245(2)

The application of parameter estimation methods in ecotoxicological modelling 247(3)

Biogeochemical and ecotoxicological models: tylosine 250(5)

The equations 251(1)

State variables 251(1)

Differential equations and initial values: (process abbreviations, see below) 251(1)

Site-specific parameter 251(1)

Climatic forcing functions as graphs 251(1)

Processes 251(4)

Behavioral methods in ecotoxicology 255(28)

T.-S. Chon

C. W. Jil

Y.-S. Park

S.E. Jørgensen

Why behavioral methods in ecotoxicology? 255(2)

Behavioral monitoring 255(1)

Behaviors linked with genes and populations 256(1)

Monitoring at the individual level 257(13)

Monitoring processes 257(1)

Data preparation 258(1)

Statistical description 259(3)

Analysis of data structure 262(2)

Pattern detection by learning method 264(6)

Modeling the gene-individual-population relationships 270(13)

Program outline and system environment 271(12)

Cellular automata 283(24)

Q. Chen

Introduction to cellular automata 283(3)

Definition of cellular automata 283(1)

Neighbourhood schemes 284(1)

Local evolution rules 284(1)

Initial conditions 285(1)

Boundary conditions 285(1)

Development of cellular automata 286(1)

Development and application of EcoCA 286(9)

Development of EcoCA 286(2)

Application of EcoCA 288(5)

User guide for EcoCA 293(2)

Development and application of LYC 295(8)

Description of study area 296(1)

Model development 297(2)

Results and discussion 299(3)

User guide for LYC 302(1)

Discussion 303(4)

Rule-based ecological model 307(18)

Q. Chen

A. Mynett

Introduction to rule-based technique 307(4)

Feature reasoning 307(2)

Case reasoning 309(1)

Decision tree 309(2)

Rule-based modelling of algal biomass in Dutch coastal waters 311(2)

Description of study area 311(1)

Model development 312(1)

Model testing 313(1)

Integrated numerical and rule-based technique 313(6)

Description of study area 314(1)

Model development 314(4)

Results 318(1)

Discussion 319(1)

User guide for FuzzHab 320(5)

Factor selection 320(1)

Rule generation 320(1)

Modelling 321(4)

Network calculations II: a user's manual for EcoNet 325(26)

C. Kazanci

Introduction 325(2)

How to create an EcoNet model 327(3)

EcoNet model structure 327(1)

EcoNet model flexibility 328(1)

A few rules about EcoNet models 329(1)

How to run an EcoNet model 330(5)

Fourth-order Runge-Kutta method 331(1)

Numerical solution methods 332(1)

Stochastic method 333(1)

From model to differential equation 334(1)

Simulation and analysis results 335(10)

Network diagram 335(1)

Time-course plot and data of compartment storage values 336(2)

Model information 338(1)

Adjacency matrix 338(1)

Flow coefficient matrix 338(2)

Flow matrix 340(1)

Network analysis 341(1)

Storage analysis 341(1)

Throughflow analysis 342(2)

Utility analysis 344(1)

Study of an EcoNet model 345(6)

Model description 345(1)

Flow analysis 346(2)

Storage and utility analysis 348(1)

Further analysis 349(2)

Mediating conceptual knowledge using qualitative reasoning 351(48)

B. Bredeweg

P. Salles

Introduction 351(1)

Background and principles 352(7)

How does it work? 352(1)

Model ingredients 353(1)

Qualitativeness 354(1)

Causality 355(1)

Inequality reasoning 356(1)

Correspondences 357(1)

Model fragments: reusing partial models 357(1)

Generating a state-graph 358(1)

Garp3: QR workbench 359(14)

Build environment 361(4)

Simulate environment 365(3)

Special features and support 368(5)

Support and getting started 373(1)

Examples of QR models 373(17)

Binary population interactions 373(8)

The Ants' Garden 381(5)

Towards the Millennium Development Goals 386(4)

Assignments 390(2)

Two and three populations 390(1)

Deforestation and vegetation growth 390(2)

Challenges with the population models 392(1)

Evaluating QR models 392(2)

Conclusion and discussion 394(5)

Models of flow pattern and mass distribution 399(10)

R. Miranda

R. Neves

Introduction 399(1)

Mohid overview 399(1)

Finite volumes 400(1)

Boundaries 400(1)

Hydrodynamic model 400(1)

Turbulence 401(1)

Transport models 401(2)

Lagrangian transport model 402(1)

Eulerian transport model 403(1)

Numerical modelling of water properties 403(1)

Water quality model 404(1)

Mohid's results 404(1)

Tagus operational model 404(1)

Sediment transport 405(1)

Conclusions 405(4)

Applications of data mining in ecological modelling 409(16)

M. Debeljak

S. Dzeroski

Introduction 409(1)

Data mining 410(5)

Data 410(1)

Patterns 411(1)

Algorithms 412(3)

Applications 415(6)

Equations 415(2)

Decision trees 417(3)

Predictive rules for regression 420(1)

Conclusions 421(4)
Index 425