CHAPTER 3 Ecological Informatics

CHAPTER 3

Ecological informatics: current scope and feature areas

F. Recknagel School of Earth and Environmental Sciences, University of Adelaide, Adelaide, Australia.

1 Introduction

Ecological informatics (ecoinformatics) is an interdisciplinary framework for the management, analysis and synthesis of ecological data by advanced computational technology [1]. Manage- ment of ecological data aims at facilitating data standardization, retrieval and sharing by means of metadata and object-oriented programming [2–6]. Analysis and synthesis of ecological data aim at elucidating principles of information processing, structuring and functioning of ecosystems, and forecasting of ecosystem behaviours by means of bio-inspired computation and hybrid models [1, 7–10]. Ecological informatics currently undergoes the process of consolidation as a discipline. It corresponds to and partially overlaps with the well-established disciplines of bioinformatics and ecological modelling, 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 [11, 12], protein structure and function [13, 14] as well as phenotypes of organisms utilizing DNA microarray, genomic, physiological and metabolic data [15] (Fig. 1a). In contrast, ecological informatics focuses on determining genotypes of populations by utilizing genomic, phenotypic and environmental data [16–18] as well as structure and functioning of ecosystems by utilizing community, environmental and climate data [19–23] (Fig. 1b). A comparison is made between ecological modelling and ecological informatics in Fig. 2. Even though both rely on similar ecological data, they adopt different approaches in utilizing the data. Whilst ecological modelling processes ecological data top down by ad hoc designed statistical or mathematical modelling methods [24, 25], ecological informatics infers multivariate equations and rule sets from ecological data patterns bottom up by computational techniques. The cross-sectional area between ecological modelling and ecological informatics reﬂ ects a new generation of hybrid models that enable to predict emergent ecosystem structures and behaviours, and ecosystem evolution [9, 26–30]. Typically hybrid models embody biologically-inspired computation in deterministic ecological models [32].

WIT Transactions on State of the Art in Science and Engineering, Vol 34, © 2009 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) doi:10.2495/978-1-84564-207-5/03 42 Handbook of Ecological Modelling and Informatics

a. DNA Microarray Data

ORGANISM

Gene Function and Interaction

Genomic Sequence Data ORGANS Protein Structure and Function

CELLS

Physiological Data Phenotypes of Organisms

GENES

Metabolic Data

b. Genomic Sequence Data AUTOTROPHIC PRODUCERS

Competition / Mutualism HETEROTROPHIC CONSUMERS Phenotypic Competition Data Competition Competition Predation / M > Genotypes of / Mutualis utua m Populations

Competition lism / Mutualism Decomposition Competition Community / Mutualism Data

> Predation > Structure and Functioning of Ecosystems Environmental DECOMPOSERS < Decomposition Data

on Competition / Mutualism

Competiti / Mutualism Competition Climate Data / Mutualism

Figure 1: Ecological informatics versus bioinformatics. (a) Scope of bioinformatics (modiﬁ ed from Oltavai and Barabasi [31]), (b) Scope of ecoinformatics.

Ecological Modelling Ecological Informatics

Differential Equations High Performance Computing Thermodynamics Biologically-Inspired Computation Multivariate Statistics Object-Oriented Data Warehousing Heuristics Metadata Concepts Semantic Webs GIS

Hybrid Approach

Top-Down Bottom-Up Mathematical Computational Approach Approach e.g. Statistical, e.g. Neural, Cellular, Process-based Evolutionary

Ecology

Biosphere Ecosystems Communities Populations Organisms Cells Genes

Figure 2: Ecological informatics vs. ecological modelling.

2 Feature areas

Current research in ecological informatics focuses on four major feature areas: understanding information processing and evolution in ecosystems, computational management of ecological data, computational analysis and synthesis of ecological data and hybrid modelling of ecological data. Great efforts are undertaken to address feature area (1) by studying both intraspeciﬁ c population adaptations to changing climate and habitat conditions [33–36] as well as interspeciﬁ c population relationships controlled by info-chemicals and allelopathy [37–41]. Feature area (2) aims at standardised archiving of highly complex and fragmented ecological data to allow ecological data sharing. The ecological metadata language EML (http://knb.

WIT Transactions on State of the Art in Science and Engineering, Vol 34, © 2009 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) 44 Handbook of Ecological Modelling and Informatics ecoinformatics.org/software/eml/) is an example for developing computational tools based on metadata concepts [2, 6] that will facilitate ecological data warehousing and sharing at global scale. Feature area (3) is being largely stimulated by both the availability of complex ecological data including genomic and phenotypic data and the development of bio-inspired computational techniques. The study of population genomics in their natural habitats without the need for isolation and laboratory cultivation of individual species has led to the new research area of ecogenomics [18] that promises to determine the impact of environmental and climate changes on biodiversity. Bio-inspired computational techniques prove to be superior in unraveling highly complex ecological data, coping with distinct nonlinearities and inducing predictive models by learning from temporal and spatial patterns. Recknagel and Cao [42] address the state of the art of ecological informatics by artiﬁ cial neural networks, evolutionary algorithms and object-oriented programming. Research into hybrid modelling in feature area (4) promises ecosystem models with improved accuracy and generality. Cao and Recknagel [32] provide a case study for multi-objective optimisation of process and parameter representations in process-based ecosystem models by the embodiment of evolutionary algorithms in ordinary differential equations for food web dynamics and nutrient cycles in lakes.

3 Future directions

Making informed decisions on preserving biodiversity and sustainable environments in spite of pollution, eutrophication and climate change is of vital importance for the habitat ‘earth’ in the 21st century. Ecological informatics is challenged to contribute ecological understanding and tools for integrating, analysing and synthesising the wealth of ecological knowledge and data for making informed decision at local, regional and global scales. It is anticipated that at the next stage, ecological informatics will distinctively focus on: (1) improved understanding of information processing in ecosystems [43]; (2) integrated analysis of genomic, phenotypic and ecological data to better understand biodiversity and ecosystem behaviour in response to habitat and climate changes; (3) integration and visualisation ecological data by GIS and remote sensing at regional and global scale [44]; (4) facilitating data sharing by www-based generic data warehouses tailored for ecosystem categories at the global scale [45], and (5) implementing hybrid model libraries generic for ecosystem categories at the global scale by object-oriented programming and interactive www-access.

References

[1] Recknagel, F. (ed.), Ecological Informatics. Understanding Ecology by Biologically-In- spired Computation, Springer-Verlag: Berlin, 2003. [2] Michener, W.K., Brunt, J.W., Helly, J.J., Kirchner, T.B. & Stanford, S.G., Nongeospatial metadata for the ecological sciences. Ecological Applications, 7(1), pp. 330–342, 1997. [3] Dolk, D.R., Integrated model management in the data warehouse area. European Journal of Operational Research, 1222, pp. 1999–1218, 2000. [4] Sen, A., Metadata management: past, present and future. Decision Support Systems, 1043, pp. 1–23, 2003. [5] Eleveld, M.A., Schrimpf, W.B.H. & Siegert, A.G., User requirements and information deﬁ - nition for the virtual coastal and marine data warehouse. Ocean & Coastal Management, 46, pp. 487–505, 2003.

[6] Michener, W.K., Meta-information concepts for ecological data management. Ecological Infor- matics, 1, pp. 3–7, 2006. [7] Fielding, A., Machine Learning Methods for Ecological Applications, Kluwer: Dordrecht, 1999. [8] Lek, S. & Guegan, J.-F. (eds), Artifi cial Neuronal Networ ks. Application to Ecology and Evolution, Springer: Berlin, 2000. [9] Grimm, V. & Railsback, S.F., Individual-Based Modelling and Ecology, Princeton Univer- sity Press: Princeton, NJ, 2005. [10] Recknagel, F. (ed.), Ecological Informatics. Scope, Techniques and Applications, 2nd edn, Springer-Verlag: Berlin, 2006. [11] Overbeck, R., Fonstein, M., D’Souza, M., Pusch, G.D. & Maltsev, N., The use of gene clusters to infer functional coupling. Proceedings of the National Academy of Sciences USA, 96, pp. 2896–2901, 1999. [12] Wolf, Y.I., Rogozin, I.B., Kondrashov, A.S. & Koonin, E.V., Genome alignment, evolution of prokaryotic genome organization, and prediction of gene function using genomic con- text. Genome Research, 11, pp. 356–372, 2001. [13] Henikoff, S., Henikoff, J.G. & Pietrovski, S., Blocks+: a non-redundant database of protein alignment blocks derived from multiple compilations. Bioinformatics, 15, pp. 471–479, 1999. [14] Lupas, A., Van Dyke, M. & Stock, J., Predicting coiled coils from protein sequences. Science, 252, pp. 1162–1164, 1991. [15] Lockhardt, D. & Winzeler, E., Genomics, gene expression and DNA arrays. Nature, 405, pp. 827–836, 2000. [16] Doney, S.C., Abbott, M.R., Cullen, J.J., Karl, D.M. & Rothstein, L., From genes to ecosystems: the ocean’s new frontier. Front. Ecol. Environ., 2(9), pp. 457–466, 2004. [17] Ungerer, M.C., Johnson, L.C. & Herman, M.A., Ecological genomics: understanding gene and genome function in the natural environment. Heridity, 100, pp. 178-183. [18] Ouborg, N.J., & Vriezen, W.H., An ecologist’s guide to ecogenomics. Journal of Ecology, 95, pp. 8-16, 2007. [19] D’Angelo, D.J., Howard, L.M., Meyer, J.L., Gregory, S.V. & Ashkenas, L.R., Ecologi- cal uses of genetic algorithms: predicting fi sh distributions in complex physical habitats. Canadian Journal of Fisheries and Aquatic Science, 52, pp. 1893–1908, 1995. [20] Chon, T.-S., Park, Y.S., Kwak, I.-S. & Cha, E.Y., Non-linear approach to grouping, dynamics and organizational informatics of benthic macroinvertebrate communities in streams by artifi cial neural networks. Ecological Informatics. Scope, Techniques and Applications, 2nd edn, ed. F. Recknagel, Springer-Verlag: Berlin, pp. 187–238, 2006. [21] Park, Y.-S., Verdonschot, P.F.M., Chon, T.-S. & Lek, S., Patterning and predicting aquatic macro invertebrate diversities using artifi cial neural networks. Water Research, 37, pp. 1749–1758, 2003. [22] Jeong, K.-S., Recknagel, F. & Joo, G.-J., 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 artifi cial neural network. Ecological Informatics. Scope, Techniques and Applications, 2nd edn, ed. F. Recknagel, Springer-Verlag: Berlin, pp. 255–273, 2006. [23] Lek, S., Scardi, M., Verdonschot, P.F.M., Descy, J.-P. & Park, Y.-S. (eds), Modelling Com- munity Structure in Freshwater Ecosystems, Springer: Berlin, 2005. [24] Straskraba, M. & Gnauck, A., Freshwater Ecosystems: Modelling and Simulation, Elsevier: Amsterdam, 1985.

[25] Jorgensen, S.E., Fundamentals of Ecological Modelling, Elsevier: Amsterdam, 1995. [26] Booth, G., Gecko: a continuous 2D world for ecological modeling. Artifi cial Life, 3, pp. 147–163, 1997. [27] Downing, K., EUZONE: simulating the evolution of aquatic ecosystems. Artifi cial Life, 3, pp. 307–333, 1997. [28] Hraber, P. & Milne, B.T., Community assembly in a model ecosystem. Ecological Model- ling, 103, pp. 267–285, 1997. [29] Huse, G., Strand, E. & Giske, J., Implementing behaviour in individual-based models using neural networks and genetic algorithms. Evolutionary Ecology, 13, pp. 469–483, 1999. [30] Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W.M., Railsback, S.F., Thulke, H.-H., Weiner, J., Wiegand, T. & DeAngelis, D.L., Pattern-oriented modeling of agent based complex systems: lessons from ecology. Science, 310, pp. 987–991, 2005. [31] Oltavai, Z.N. & Barabasi, A.-L., Life’s complexity pyramid. Science, 298, pp. 763–764, 2002. [32] Cao, H. & Recknagel, F., Hybridisation of process-based ecosystem models with evolutionary algorithms: multi-objective optimisation of process representations and parameters of the lake simulation library SALMO-OO (Chapter 10). Handbook of Ecological Modelling and Informatics, eds S.E. Jorgensen, F. Recknagel & T.-S. Chon., WIT Press: Southampton, pp. 169–185, 2008. [33] West, K., Cohen, A. & Baron, M., Morphology and behaviour of crabs and gastropods from Lake Tanganyika, Africa: implications for lacustrine predator-prey coevolution. Evo- lution, 45(3), pp. 589–607, 1991. [34] Hairston, N.G., Lampert, W., Caceres, C.E., Holtmeier, C.L., Weider, L.J., Gaedke, U., Fischer, J.M., Fox, J.A. & Post, D.M., Rapid evolution revealed by dormant egg. Nature, 401, pp. 446, 1999. [35] Fischer, J.M., Klug, J.L., Ives, A.R. & Frost, T.M., Ecological history affects zooplankton community responses to acidifi cation. Ecology, 82(11), pp. 2984–3000, 2001. [36] Gyllström, M., Hansson, L.-A., Jeppesen, E., Garcia-Criado, F., Gross, E., Irvine, K., Kairesalo, R., Kornijow, M.R., Miracle, M., Nykänen, T., Nõges, T., Romo, S., Stephen, D., Moss, B. & van Donk, E., The role of climate in shaping zooplankton communities of shallow lakes. Limnology and Oceanography, 50, pp. 2008–2021, 2005. [37] Van Donk, E., Food web interactions in lakes: what is the impact of chemical information conveyance? Chemical Ecology: From Gene to Ecosystem, Springer: Berlin, pp. 145–160, 2006. [38] Mulderij, G., Smolders, A.J.P. & van Donk, E., Allelopathic effect of the aquatic macro- phyte, Stratiotes aloides, on natural phytoplankton. Freshwater Biology, 51, pp. 554–561, 2006. [39] Voss, M., et al., Infochemicals structure marine, terrestrial and freshwater food webs: Implications for ecological informatics. Ecological Informatics, 1, pp. 23–32, 2006. [40] Vijverberg, J. Doksæter, A. & van Donk, E., Contrasting life history responses to fi sh released infochemicals of two co-occuring Daphnia species that show different migration behaviour. Archive of Hydrobiology, 167, pp. 89–100, 2006. [41] Van Donk, E., Chemical information transfer in freshwater plankton. Ecological Informat- ics, 2(2), pp. 112–120, 2007. [42] Recknagel, F. & Cao, H., Ecological informatics by means of neural, evolutionary and object- oriented computation (Chapter 9). Handbook of Ecological Modelling and Informatics,

eds S.E. Jorgensen, F. Recknagel & T.S. Chon, WIT Press: Southampton, pp. 141–168, 2008. [43] Hogeweg, P., From population dynamics to ecoinformatics: ecosystems as multilevel information processing systems. Ecological Informatics, 2(2), pp. 103–111, 2007. [44] Foody, G.M. Ecological applications of remote sensing and GIS. Ecological Informatics, 2(2), pp. 71–72, 2007. [45] Jones, M., Meta-information systems and ontologies. Ecological Informatics, 2(3), pp. 193–194, 2007.