Handbook Of Ecological Modelling And Informatics

Handbook of Ecological Modellingand InformaticsWITPRESSWIT Press publishes leading books in Science and Technology.Visit our website for the current list of titles.www.witpress.comWITeLibraryHome of the Transactions of the Wessex Institute, the WIT electronic-library provides the internationalscientific community with immediate and permanent access to individual papers presented at WITconferences. Visit the WIT eLibrary athttp://library.witpress.com

Handbook of Ecological Modellingand InformaticsEditors:S.E. JørgensenThe University of Pharmaceutical Science, DenmarkT-S. ChonPusan National University, KoreaF. RecknagelUniversity of Adelaide, Australia

Editors:S.E. JørgensenThe University of Pharmaceutical Science, DenmarkT-S. ChonPusan National University, KoreaF. RecknagelUniversity of Adelaide, AustraliaPublished byWIT PressAshurst Lodge, Ashurst, Southampton, SO40 7AA, UKTel: 44 (0) 238 029 3223; Fax: 44 (0) 238 029 2853E-Mail: witpress@witpress.comhttp://www.witpress.comFor USA, Canada and MexicoWIT Press25 Bridge Street, Billerica, MA 01821, USATel: 978 667 5841; Fax: 978 667 7582E-Mail: infousa@witpress.comhttp://www.witpress.comBritish Library Cataloguing-in-Publication DataA Catalogue record for this book is availablefrom the British LibraryISBN: 978-1-84564-207-5Library of Congress Catalog Card Number: 2008930449The texts of the papers in this volume were setindividually by the authors or under their supervision.No responsibility is assumed by the Publisher, the Editors and Authors for any injury and/or damage topersons or property as a matter of products liability, negligence or otherwise, or from any use or operationof any methods, products, instructions or ideas contained in the material herein. The Publisher does notnecessarily endorse the ideas held, or views expressed by the Editors or Authors of the material contained inits publications. WIT Press 2009Printed in Great Britain by Athenaeum Press Ltd.All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmittedin any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the priorwritten permission of the Publisher.

ContentsPreface .xiiiModel examples, software, homepages and contact person for the variousmodel types .xvChapter 1Introduction: sub-disciplines of ecology and the history of ecological modeling .S.E. Jørgensen11 History of the ecological sub-disciplines .2 The development of ecological modeling .15Chapter 2Overview of the model types available for ecological modeling .S.E. Jørgensen & T.-S. Chon91 Issues in model development .1.1 Presentation of spatial distribution .1.2 Computational realization of biological properties .1.3 Revealing environmental factors .1.4 Data handling and model construction .2 Increasing number of model types .3 Characteristics of the model types available today .3.1 Dynamic models: Chapters 5 and 11 .3.2 Static models: Chapters 5 and 11 .3.3 Population dynamic model: Chapter 12 .3.4 Structurally dynamic models: Chapter 13 .3.5 Fuzzy models: Chapter 8 .3.6 Models in ecological informatics: Chapter 9 .3.7 Individual-based models and cellular automata: Chapters 7 and 16 .3.8 Spatial models: Chapters 6 and 20 .3.9 Ecotoxicological models: Chapters 14 and 15 .3.10 Stochastic models: Chapter 12 .3.11 Rule-based models: Chapter 17 .3.12 Hybrid models: Chapter 10 .9111517181921222324252627283031333435

3.13 Mediated/institutionalized models: Chapters 4 and 19 .3.14 Network analyses and calculations: Chapter 18 .4 Applicability of the model types .353737Chapter 3Ecological informatics: current scope and feature areas .F. Recknagel411 Introduction .2 Feature areas .3 Future directions .414344Chapter 4Model making .S.E. Jørgensen491 Modelling procedure .2 Institutionalized modeling .2.1 The institutionalized modelling process .3 When to apply IMM? .49515253Chapter 5Ecopath with Ecosim: linking fisheries and ecology .V. Christensen551 Why ecosystem modeling in fisheries? .2 The Ecopath with Ecosim (EwE) modeling approach .2.1 Model overview.2.2 Mass-balance .2.3 The foraging arena .2.4 Ecosim .3 EwE modules and applications .5 EwE applications .6 Getting hold of the EwE software .7 Exercise: trawling cultivates the ocean bottom for squid .55565656585960616263Chapter 6Surface modelling of population distribution .T.-X. Yue, Y.-A. Wang & Z.-M. Fan711 Introduction .2 YUE-SMPD .2.1 Approaches to population distribution analyses .2.2 YUE-SMPD formulation .3 An application of YUE-SMPD .3.1 Major data layers .3.2 YUE-SMPD operation .3.3 Change trend of population distribution in China .3.4 Scenarios of population distribution in China .4 Discussion .71727273747486879092

Chapter 7Individual-based models .T.-S. Chon, Sang Hee Lee, C. Jeoung, H.K. Cho, Seung Ho Lee & Y.-J. Chung991 Introduction .2 Properties of individuals .3 Model construction .3.1 Program outline and system environment .3.2 Variables .3.3 Model structure and interaction .3.4 Parameters and input data .3.5 Output and model results .4 Case study 1: flocking behavior .4.1 Program outline and system environment .4.2 Variables .4.3 Model structure and interaction .4.4 Parameters and input data .4.5 Output and results .5 Case study 2: population dispersal .5.1 Program outline and system environment .5.2 Variables .5.3 Model structure and interaction .5.4 Parameters and input data .5.5 Output and results 1113113114Chapter 8A fuzzy approach to ecological modelling and data analysis . 125A. Salski, B. Holsten & M. Trepel1 Imprecision, uncertainty and heterogeneity of environmental data .2 Fuzzy sets and fuzzy logic in ecological applications .2.1 Fuzzy classification and spatial data analysis .2.2 Fuzzy modelling, decision making and ecosystem management .2.3 Hybrid approaches to data analysis and ecological modelling .3 Fuzzy classification: a fuzzy clustering approach .3.1 An application example: fuzzy classification of wetlands for determinationof water quality improvement potentials .4 Fuzzy modelling .4.1 An application example: a fuzzy and neuro-fuzzy approach to modellingcattle grazing in Western Europe .5 Final remarks .125126126127127127129132134138Chapter 9Ecological informatics by means of neural, evolutionary andobject-oriented computation . 141F. Recknagel & H. Cao1 Introduction .2 Artificial neural networks .2.1 Supervised feedforward ANN .2.2 Supervised feedback ANN .2.3 Non-supervised ANN .141141143144145

2.4 Evolutionary algorithms . 1502.5 Object-oriented programming . 1533 Conclusions . 160Chapter 10Hybridisation of process-based ecosystem models with evolutionary algorithms:multi-objective optimisation of process and parameter representations ofthe lake simulation library SALMO-OO . 169H. Cao & F. Recknagel1 Introduction .2 Evolutionary algorithm for the optimisationof process representations and parameters .2.1 Encoding .2.2 Fitness evaluation .2.3 Genetic operators .3 Optimisation of process representations in SALMO-OO by means of EA .3.1 Experimental settings and measures .3.2 Results and discussion .4 Case study of parameter optimisation in SALMO-OO .4.1 Experimental settings and measures .4.2 Results and discussion .5 Conclusions and future work .169170171171172172172175176176178184Chapter 11Biogeochemical models . 187S.E. Jørgensen1234The characteristics of biogeochemical models .The application of biogeochemical models .Biogeochemical models .Model of sub-surface wetland.4.1 The state variables .4.2 Forcing functions .4.3 Process equations .4.4 Parameters .4.5 Differential equations .4.6 Model results .4.7 Additional results .4.8 Comparison of simulated and measured values .4.9 Practical information about forcing functions and parameters .187188190190191193194195195197197197198Chapter 12Stochastic population dynamic models as probability networks . 199M.E. Borsuk & D.C. Lee1 Introduction .1.1 Population dynamic models .1.2 Stochasticity .1.3 Stochastic models .1.4 Probability networks .2 Methods .199199199200201201

2.1 Model construction.2.2 Communicating results .2.3 Use of probability networks .3 Example models and their applications .3.1 BayVAM and westslope cutthroat troutin the Upper Missouri River Basin, USA .3.2 CATCH-Net and brown trout in the Rhine River Basin, Switzerland .4 Availability of models and software .4.1 BayVAM/Netica .4.2 CATCH-Net/Analytica .201202202203203210217217217Chapter 13Structurally dynamic models . 221S.E. Jørgensen12345Introduction: why structurally dynamic models? .Ecosystem characteristics .Structurally dynamic models .Development of SDM for Darwin’s finches.Model of the ectoparasite–bird interactions .221221226233234Chapter 14Ecotoxicological models . 241S.E. Jørgensen1 Introduction: characteristics of ecotoxicological models .2 Classification of ecotoxicological models .2.1 Food chain or food web dynamic models .2.2 Static models of the mass flows of toxic substances .2.3 A dynamic model of a toxic substance in one trophic level .2.4 Ecotoxicological models in population dynamics .2.5 Ecotoxicological models with effect components .3 The application of parameter estimation methods in ecotoxicological modelling .4 Biogeochemical and ecotoxicological models: tylosine .4.1 The equations .4.2 State variables .4.3 Differential equations and initial values: (process abbreviations, see below) .4.4 Site-specific parameter .4.5 Climatic forcing functions as graphs .4.6 Processes ter 15Behavioral methods in ecotoxicology . 255T.-S. Chon, C.W. Ji1, Y.-S. Park & S.E. Jørgensen1 Why behavioral methods in ecotoxicology?.1.1 Behavioral monitoring .1.2 Behaviors linked with genes and populations .2 Monitoring at the individual level .2.1 Monitoring processes .2.2 Data preparation .2.3 Statistical description .2.4 Analysis of data structure .255255256257257258259262

2.5 Pattern detection by learning method . 2643 Modeling the gene–individual–population relationships . 2703.1 Program outline and system environment . 271Chapter 16Cellular automata .Q. Chen1 Introduction to cellular automata .1.1 Definition of cellular automata .1.2 Neighbourhood schemes .1.3 Local evolution rules .1.4 Initial conditions .1.5 Boundary conditions .1.6 Development of cellular automata .2 Development and application of EcoCA .2.1 Development of EcoCA .2.2 Application of EcoCA .2.3 User guide for EcoCA .3 Development and application of LYC .3.1 Description of study area .3.2 Model development .3.3 Results and discussion .3.4 User guide for LYC .4 Discussion .Chapter 17Rule-based ecological model .Q. Chen & A. Mynett1 Introduction to rule-based technique .1.1 Feature reasoning .1.2 Case reasoning .1.3 Decision tree .2 Rule-based modelling of algal biomass in Dutch coastal waters .2.1 Description of study area .2.2 Model development .2.3 Model testing .3 Integrated numerical and rule-based technique .3.1 Description of study area .3.2 Model development .3.2 Results .4 Discussion .5 User guide for FuzzHab .5.1 Factor selection .5.2 Rule generation .5.3 Modelling 320320321

Chapter 18Network calculations II: a user’s manual for EcoNet .C. Kazanci1 Introduction .2 How to create an EcoNet model .2.1 EcoNet model structure .2.2 EcoNet model flexibility .2.3 A few rules about EcoNet models .3 How to run an EcoNet model .3.1 Fourth-order Runge–Kutta method .3.2 Numerical solution methods .3.3 Stochastic method .3.4 Fro

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