Ecological Informatics: Scope, Techniques and Applications

508 Pages·2006·8.406 MB·English

by  A. Salski (auth.), Associate Professor Friedrich Recknagel (eds.)

Friedrich Recknagel (Ed.)
Ecological Informatics
Scope, Techniques and Applications
Friedrich Recknagel (Ed.)

Ecological Informatics

Scope, Techniques and Applications

2nd Edition

With 174 Figures and a CD-ROM
EDITOR

ASSOCIATE PROFESSOR FRIEDRICH RECKNAGEL
SCHOOL OF EARTH AND ENVIRONMENTAL SCIENCES
THE UNIVERSITY OF ADELAIDE
5005 AUSTRALIA

E-mail: [e-mail]

ISBN 3-540-43455-0 Springer Berlin Heidelberg New York 1st edition 2003
ISBN 10 3-540-28383-8 Springer Berlin Heidelberg New York
ISBN 13 978-3540-28383-6 Springer Berlin Heidelberg New York

Library of Congress Control Number: 2005930717

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ToKarina,Melanie,NatalieandPhilipp
Preface 2nd Edition
Ecological informatics (ecoinformatics) is an interdisciplinary framework for the
processing, archival, analysis and synthesis of ecological data by advanced
computational technology (Recknagel 2003). Processing and archival of
ecological data aim at facilitating data standardization, retrieval and sharing by
means of metadata and object-oriented programming (e.g. Michener et al. 1997;
Dolk 2000; Sen 2003; Eleveld, Schrimpf and Siegert 2003). Analysis and
synthesis of ecological data aim at elucidating principles of information
processing, structuring and functioning of ecosystems, and forecasting of
ecosystems behaviours by means of bio-inspired computation (e.g. Fielding 1999;
Lek and Guegan 2000; Recknagel 2003).
Ecological informatics currently undergoes the process of consolidation as a
discipline. It corresponds and partially overlaps with the well-established
disciplines bioinformatics and ecological modeling but is taking its distinct shape
and scope. In Fig. 1 a comparison is made between ecological informatics and
bioinformatics. Even though both are based on the same computational technology
their focus is different. Bioinformatics focuses very much on determining gene
function and interaction (e.g. Overbeck et al. 1999; Wolf et al. 2001), protein
structure and function (e.g. Henikoff et al. 1999; Lupas, Van Dyke and Stock
1991) as well as phenotype of organisms utilizing DNA microarray, genomic,
physiological and metabolic data (e.g. Lockhardt and Winzeler 2000) (Fig. 1a). By
contrast ecological informatics focuses to determine population function and
interactions as well as ecosystem structure and functioning by utilizing genomic,
phenotypic, community, environmental and climate data (e.g. D’Angelo et al.
1995; Chon et al. 2003; Park et al. 2003, Jeong, Recknagel and Joo 2003) (Fig.
1b).
A comparison is made between ecological modeling and ecological informatics
in Fig. 2. Even though both rely on similar ecological data they adopt different
approaches in utilizing the data. Whilst ecological modeling processes ecological
data top down by ad hoc designed statistical or mathematical models (e.g.
Straskraba and Gnauck 1985; Jorgensen 1994), ecological informatics infers
ecological processes from ecological data patterns bottom up by computational
techniques. The cross-sectional area between ecological modeling and ecological
informatics reflects a new generation of hybrid models that enable to predict
emergent ecosystem structures and behaviours, and ecosystem evolution (e.g.
Booth 1997; Downing 1997; Hraber and Milne 1997; Huse, Strand and Giske
1999). Typically those models embody biologically-inspired computation in
deterministic ecological models.
VIII Preface
Figure 1. Ecological informatics versus bioinformatics, a) Scope of
bioinformatics (modified from Oltvai and Barabasi (2002)), b) Scope of
ecoinformatics
Preface IX
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DDiiffffeerreennttiiaall EEqquuaattiioonnss HHHiiiggghhh PPPeeerrrfffooorrrmmmaaannnccceee CCCooommmpppuuutttiiinnnggg
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Figure 2. Ecological informatics versus ecological modeling
The term ecological informatics was suggested at the International Conference
on Applications of Machine Learning to Ecological Modelling in 2000 (see
Ecological Modelling 2001, 195) when the International Society for Ecological
Informatics ISEI (www.waite.Adelaide.edu.au/ISEI) was founded. Since then an
increasing number of researchers and research groups identify with this area, and
biennial international conferences are organized by the ISEI. Also the new journal
Ecological Informatics will be issued by Elsevier in October 2005
(www.elsevier.com/locate/ecolinf).
The contents of the 2nd edition of the book Ecological Informatics has been
revised and extended. Two new chapters have been added to Part I: Introduction.
Chapter 2 by Bredeweg et al. provides an introduction to the novel concept of
qualitative reasoning that emerges as an alternative approach to fuzzy logic for
automated processing and utilizing of heuristic ecological knowledge. Exemplary
applications to population and community dynamics illustrate the potential of the
approach. Chapter 7 by Tempesti et al. addresses the novel concept of self-
X Preface
replicating cellular automata inspired by the nature of the genome as the
hereditary information of an organism. The authors demonstrate how self-
replicating cellular automata can be explored for the design of nano-scale circuits
for computer hardware. The paper contributes to the fast growing research on bio-
inspired design of both computer software and hardware.
Three new chapters have been added to Part IV: Prediction and Elucidation of
Lake and Marine Ecosystems. Chapter 16 by Recknagel et al. presents an
integrated approach of super- and non-supervised artificial neural networks
(ANN) for understanding and forecasting of phytoplankton population dynamics
in limnological time series data. The authors complement qualitative ordination
and clustering by non-supervised ANN with sensitivity curves from supervised
ANN to reveal complex ecological relationships. They apply recurrent supervised
ANN for 7-days-ahead forecasting of algal species abundances and succession.
Chapter 17 by Cao et al. introduces hybrid evolutionary algorithms (HEA) as
powerful tools for the discovery of predictive rule sets. The underlying algorithms
optimize both the rule structures and multiple parameters. The authors
demonstrate that the rule sets discovered in complex limnological time series data
achieve not only highly accurate 7-days-ahead forecasting of algal species
abundances and succession but provide a high degree of explanation by means of
THEN- and ELSE-branch specific sensitivity analysis. A CD with a demo version
of HEA is attached and instructions for HEA can be found in the Appendix.
Chapter 20 by Atanasova et al. demonstrates computational assemblage of
ordinary differential equations (ODE) based on an ecological process function
library and measured ecological data. The authors document automatically
assembled ODE for chlorophyll a in a lake and related validation results that
indicate possibilities and limitations of the approach.
I want to thank all of the authors who contributed to the book with great
enthusiasm and delivered on time. Finally I express my thanks to Dr. Christian
Witschel and Agata Oelschlaeger of the Geosciences Editorial Team of the
Springer-Verlag for their close collaboration in producing the book
References:
Booth, G., 1997. Gecko: A continuous 2D world for ecological modeling. Artificial Life 3,
147-163.
Chon, T.-S., Park, Y.S., Kwak, I.-S. and E.Y. Cha, 2003. Non-linear approach to grouping,
dynamics and organizational informatics of benthic macroinvertebrate communities in
streams by artificial neural networks. In: Recknagel, F. (ed.), 2003. Ecological
Informatics. Understanding Ecology by Biologically-Inspired Computation. Springer-
Verlag, Berlin, Heidelberg, New York, 127-178.
D’Angelo, D.J., Howard, L.M., Meyer, J.L., Gregory, S.V. and L.R. Ashkenas, 1995.
Ecological uses of genetic algorithms: predicting fish distributions in complex physical
habitats. Can.J.Fish.Aquat.Sci. 52, 1893-1908.
Dolk, D.R., 2000. Integrated model management in the data warehouse area. European
Journal of Operational Research 1222, 1999-218.
Downing, K., 1997. EUZONE: Simulating the evolution of aquatic ecosystems. Artificial
Life 3, 307-333.
Preface XI
Eleveld, M.A., Schrimpf, W.B.H. and A.G. Siegert, 2003. User requirements and
information definition for the virtual coastal and marine data warehouse. Ocean &
Coastal Management 46, 487-505.
Fielding, A., 1999. Machine Learning Methods for Ecological Applications. Kluwer, 1-262.
Henikoff, S., Henikoff, J.G. and S. Pietrovski, 1999. Blocks+: a non-redundant database of
protein alignment blocks derived from multiple compilations. Bioinformatics 15, 471-
479.
Hraber, P. and B.T. Milne, 1997. Community assembly in a model ecosystem. Ecological
Modelling 103, 267-285.
Huse, G., Strand, E. and J. Giske, 1999. Implementing behaviour in individual-based
models using neural networks and genetic algorithms. Evolutionary Ecology 13, 469-
483.
Jeong, K.-S., Recknagel, F. and G.-J. Joo, 2003. Prediction and elucidation of population
dynamics of the blue-green algae Microcystis aeruginosa and the diatom Stephanodiscus
hantzschii in the Nakdong River-Reservoir System (South Korea) by a recurrent artificial
neural network. In: Recknagel, F. (ed.), 2003. Ecological Informatics. Understanding
Ecology by Biologically-Inspired Computation. Springer-Verlag, Berlin, Heidelberg,
New York, 195-213.
Jorgensen, S.E., 1995. Fundamentals of Ecological Modelling. Elsevier, Amsterdam, 1-628.
Lek, S. and J-F. Guegan (eds.), 2000. Artificial Neuronal Networks. Application to Ecology
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Lockhardt, D. and E. Winzeler, 2000. Genomics, gene expression and DNA arrays. Nature
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Lupas, A., Van Dyke, M. and J. Stock, 1991. Predicting coiled coils from protein
sequences. Science 252, 1162-1164.
Michener, W.K., Brunt, J.W., Helly, J.J., Kirchner, T.B., and S.G.Stanford, 1997.
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Friedrich Recknagel
Adelaide, 15 May 2005
Preface XIII
Preface 1st Edition
In the 50s and 60s cross-sectional data of lake surveys were utilized for steady
state assessments of the eutrophication status of lakes by univariate nonlinear
regression. This statistical approach (see Table 1) became exemplary for river,
grassland and forest models and - because of simplicity - widespread for
classification of ecosystems.
In the 70s and 80s multivariate time series data were collected from ecosystems
such as lakes, rivers, forests and grasslands in order to improve understanding of
ecosystem dynamics. Process-based differential equations were used for the
computer simulation of food web dynamics and functional group succession. This
differential equation approach (see Table 1) is still widely used for scenario
analysis.
Table 1. Concepts for Ecosystems Analysis, Synthesis and Forecasting
SSttaattiissttiiccaall RReeggrreessssiioonn DDiiffffeerreennttiiaall EEqquuaattiioonnss CCoommppuuttaattiioonnaall
AApppprrooaacchh AApppprrooaacchh AApppprrooaacchh
EEccoossyysstteemm SStteeaaddyy SSttaatteess TTrraannssiittiioonnaall SSttaatteess EEvvoollvviinngg SSttaatteess
RReepprreesseennttaattiioonn
EEccoossyysstteemm UUnniivvaarriiaattee NNoonnlliinneeaarr // MMuullttiivvaarriiaattee NNoonnlliinneeaarr MMuullttiivvaarriiaattee NNoonnlliinneeaarr
AApppprrooxxiimmaattiioonn MMuullttiivvaarriiaattee LLiinneeaarr
EEccoossyysstteemm CCrroossss--SSeeccttiioonnaall NNuuttrriieenntt NNuuttrriieenntt CCyycclleess aanndd SSppeecciieess SSuucccceessssiioonn
CCoommpplleexxiittyy aanndd AAbbuunnddaannccee MMeeaannss FFoooodd WWeebb DDyynnaammiiccss aanndd EEccoossyysstteemm
EEvvoolluuttiioonn
AAqquuaattiicc EExxaammpplleess PPhhoosspphhoorruuss--CChhlloorroopphhyyllll AAQQUUAAMMOODD44;; NNoonnlliinneeaarr RReeggrreessssiioonn99;;
RReellaattiioonnsshhiipp11,,22;; MMSS--CCLLEEAANNEERR55;; NNoonnlliinneeaarr PPCCAA1100;;
EExxtteerrnnaall PP--LLooaaddiinngg BBiieerrmmaann66;; DDEELLAAQQUUAA1111;; AANNNNAA1122;;
CCoonncceepptt33 JJoorrggeennsseenn77;; EEvvoollvveedd RRuulleess1133;;
SSAALLMMOO88 EEvvoollvveedd EEqquuaattiioonnss1144,,1155;;
EECCHHOO1166;; GGEECCKKOO1177
PPootteennttiiaall EEccoossyysstteemm CCllaassssiiffiiccaattiioonn SScceennaarriioo AAnnaallyyssiiss EEccoossyysstteemm FFoorreeccaassttiinngg
AApppplliiccaattiioonnss
1Sakamoto M (1966) Primary production by phytoplankton community in some Japanese
lakes and its dependence on lake depth. Arch. Hydrobiol. 62, 1-28
2 Dillon P, Rigler F (1974) The phosphorus-chlorophyll relationship in lakes.
Limnol.Oceanogr. 19, 135-148