ecosystems, gene networks, database presentation formats, ontologies

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1 300 Part 6 Chapter # THE ONTOLOGY OF ECOSYSTEMS Suslov V.V. *1, Sergeev M.G. 2, Yurlova N.I. 3, Miginsky D.S. 1, 2 1 Institute of Cytology and Genetics, SB RAS, Novosibirsk, , Russia * Corresponding author: valya@bionet.nsc.ru Key words: ecosystems, gene networks, database presentation formats, ontologies SUMMARY Motivation: The ontology of a knowledge domain is a set of statements that are true, no matter what circumstances may arise in that knowledge domain. The aim of this work is to develop a formal ontological description of the structural and functional organization of ecosystems (which all together comprise the knowledge domain) with due regard to their possible dynamics/development/evolution. Since ecology is an essentially synthetic science (Begon et al., 1989; Margalef, 1992), multidisciplinary research is a necessity. However, what happens is that individuals working in different fields stick to their respective paradigms and terminology, which brings up a terminology mess and hampers comparison (Sergeev et al., 2006). Providing a uniform format for description and comparison, this formalization was originally developed in response to a need of developing a strategy for the creation of a database for an all-round description of biosystems (Miginsky et al., 2006). Now that a major portion of this task has been completed, the ontology can be used for navigation in this database. Results: The definition of an econetwork is provided; the ontology of their micro- and macrodynamics is described. The ontology provides eco- and gene networks linkage. INTRODUCTION There is a wealth of empirical data on the organization of ecosystems and a lot of attempts at making generalizations about the available material (Begon et al., 1989; Allen, Hoekstra, 1992). The main factors which keep scientists trying are as follows: a true complexity of organism communities, inert and biologically inert components (Begon et al., 1989); deficient integrity of the ecosystems due to the lack of a data storage like the genome (Dajoz, 1976; Margalef, 1992), which is why the community structure and characteristics are subject to variation; a conflict of different ecosystem description formats, due to which the knowledge domain is fuzzily defined and the ontology may not be developed by definition. There are three approaches in use to describe ecosystems: 1) functional, which was pioneered by Tansley (1935) and later formalized in the form of trophic networks (Luczkovich, 2003); 2) geoecological, extensively formalized in the works of Sochava (1978). Here a biogeocenosis is treated as an elementary dynamic coevolving system of live, inert and biologically inert elements. Unlike trophic networks, this system has a certain dimensionality (Sukachev, 1972); 3) paragenetic, based on correlations of the creatures ontogeny programs in a community (Razumovsky, 1981). This was first developed to be used in parasitology (Pavlovsky, 1934; Beklemishev, 1970а). Common to these three approaches is that they share the concept of an elementary object (EO) which is a minimal fundamental object which cannot be broken down into anything smaller without loss of properties. EOs can form groups of a certain

2 Other topics related with bioinformatics 301 dimensionality, or scale, which establish various hierarchical relationships depending on the physical properties of the EOs and the approach used (Zherikhin, 1994). There are elementary interactions (EIs) between the EOs and the groups they are in. The EIs provide the exchange of matter, energy and information. The EI classification is poorly developed compared to that of EO. The most detailed classification by Beklemishev (1970b) is poorly formalized. The author argues that the same EI can be described by different classes or switched to a different class when the dimensionality/size changes. Given the settings, the development of the ontology is equivalent to EO formalization, no matter what descriptive approach or EI classification quality. METHODS AND ALGORITHMS The ontology was tested for applicability on two different ecosystems: parasitic, exemplified by the lifecycle of the trematode Echinoparyphium aconiatum, and steppen, by nongregarious grasshoppers (Sergeev et al., 2006). Data on the structure of network interactions in the course of the complex life cycle of the trematode Echinoparyphium aconiatum had previously been made available (Vodyanitskii et al., 2002) to the GeneNet database (Ananko et al., 2002). Data on the ecosystems comprising grasshoppers were collected by annotation of scientific publications (guided by Sergeev). RESULTS AND DISCUSSION An ecosystem can formally be rendered as a graph with EOs (no matter what properties) or EO groups as the nodes connected by EIs as the edges. Call this representation an econetwork. Two sorts of change can occur in econetworks: 1) microdynamics, changes in the strength of edges and nodes due to change in the values of variables characterizing EOs and EIs; 2) macrodynamics, qualitative changes to the graph due to the emergence or extinction of EOs and EIs. An example of microdynamics is population dynamics; a striking example of macrodynamics is seasonal communities rearrangements 3. As a microdynamic event, the ontology (Fig. 1a) distinguishes between microdynamic EOs (m1) and microdynamic EIs (m2). We classify EOs into the following subentries: live (m1.1), organisms and their factions: populations, guilds, synusia, and so on; inert (m1.2), landscape elements, chemicals, physical factors); biologically inert (m1.3), soils, silts, dead matter and biologically active compounds released by organisms to the environment (pheromones, vitamins, metabolites). Microdynamic EIs 4 (m2) can affect an EO by way of affecting its characteristics or location (m2.2, reaction) or can influence another EI (m2.1, regulatory effect 5 ). We classify microdynamic EIs into mass/energy- 3 Rank dynamics, which is the common lowermost level, should be mentioned (see Sergeev et al., 2006). The rank is a numerical characteristic of an EO such that its change may not affect the EO, but what it can do is strongly affect the interaction between other EOs in the eco- or gene network. For example, the parasite burden on an infected mollusk can be described by the following ranks: Мoll[s,re,m], where s is the sporocysts number, re is the rediae number, m is the metacercariae number. In ecosystems, ranks can be used to describe, for example, age-specific change in body size, biocenosis area change; in gene networks, to describe modification of the protein phosphorylation degree, DNA methylation degree, etc. 4 Micro- and macrodynamic EIs can be immediate (without intermediate stages) or mediate. For simplicity, this level of division is not present in Fig. 1a, b. 5 The agreed classification for the micro- and macrodynamic regulatory effects is in part identical to that used in GeneNet (Ananko et al., 2002): increase, decrease, switch-on, switch-off; but there is a difference too. Microdynamic regulatory effects are divided into common ones (М.2.1.1), which are

3 302 Part 6 related (m2.2.1), topic (m2.2.2), fabric (m2.2.3), phoric (m2.2.4) 6 and developmental (m2.2.5). If the effect is directly proportional to the amount of substance (energy) consumed, the EI is trophic (m ); if the effect is strongly non-linear, the EI is information-related (m ) (Fig. 1a), among which we place tactile, visual, immune and the like interactions. Topic EIs (m2.2.2) are limited to competition for room within habitats (for example, competition for room by helminthes in the limited space of the host s body cavities), conditioning of biotope characteristics by various elements of the biogeocenosis 7 and physical limitations on the accessibility of the elements of biogeocenosis. Phoric EIs (m2.2.4) are EO relocations from structure to structure without much change (m ): active or passive relocations of live EOs 8 ; transport EIs (m ): essentially the same as phoric but relate to inert EOs. Fabric EIs (m2.2.3): the activity of organisms leading to a physical rearrangement of the biotope or biocenosis (for example, hole digging by soil and silt dwellers 9 ). Developmental (m2.2.5), which are the rearrangements that occur to EOs for intrinsic reasons: ontogenetic EIs for live EOs (m ); genesis-related EIs for inert and biologically inert EOs 10 (m ). Biochemical ontogenetic EIs (m ) provide a link between ecosystems and gene networks: they open access to genes related to ontogenesis. Linkage with gene networks can be envisaged for more EIs of live EOs as more data accrue. Macrodynamic EOs are the stages (М1) in the history of ecosystems (Fig. 1b). At each stage, the ecosystem parameters display stability (the same number of species, the biotope elements are all there, the same temperature, humidity and so on). It is this stability that accounts for there being stages, hence a stable graph of connections. We classify the stages into exogenous (М1.1), which can be primary (М1.1.1) or secondary (М1.1.2) 11, and endogenous (М1.2). Why is the history of ecosystems staged? The underlying mechanisms of being staged are the property of the ecosystem itself (a misbalance of biogeochemical cycles, the gene networks of edifying species). A change to the econetwok graph due to the addition or elimination of a community member describes the cenotic stage (М1.2.1); A change to the econetwok graph without change in the econetwok community describes the clinal stage (М1.2.2). Changes to the econetwok graph due to the ontogenesis of live microdynamic EOs or the genesis of inert microdynamic EOs describe developmental stages (М1.2.3): ontogenetic (М ) and genesis-related (М ), respectively. Macrodynamic EIs (М2) include regulatory effects (М2.1) and reactions (М2.2), which are essentially the transitions between stages and can be either exogenous (М2.2.1) 12 or endogenous (М2.2.2). The endogenous ones include cenotic (М ), clinal (М ) and developmental transitions (М ) which are ontogenetic (М ) and genesis-related (М ) (Fig. 1b). classified as per GeneNet, and those possessing an optimum (М2.1.2), which depend on the strength, or the amount, of the affecting object in the econetwork. The optimum number of parasites enhances the homeostasis of the ecosystem, while any number up or down disrupts it (Fig. 1a, b). 6 These terms were borrowed from Beklemishev (1970b), but our interpretation differs from the original. 7 The example of conditioning is a shading. Poikilothermic, grasshoppers often travel between open, warmed microstations and microstations covered with vegetation where they feed (Sergeev et al., 2006). 8 The encysted metacercariae E. aconiatum present in mollusks are liable to a peculiar sort of passive migration: being swallowed by birds who feed on mollusks (Sergeev et al., 2006). 9 If this activity brings up a new biotope, the ontology of macrodynamic events should be applied to. 10 Examples of ontogenetic EIs are growth, ontogeny, lifecycle, behavioural instinct. Examples of genesis-related EIs are soil genesis, sedimentogenesis, dead matter decomposition. 11 The primary stages are classified based on the astronomic events they are associated with (Dajoz, 1976). For simplicity, only circadian (М ) and seasonal stages (М ) are shown, while the others are in the astronomic group (М ) (Fig. 1b). The secondary stages are of extremely heterogenic origin and so their classification is beyond the scope of the present ontology. 12 For simplicity, the division into primary and secondary EIs is not shown (Fig. 1b).

4 Other topics related with bioinformatics 303 Figure 1. Components of the microdynamics (a) and macrodynamics (b) of econetworks. ACKNOWLEDGEMENTS The authors are grateful to N.A. Kolchanov for a fruitful discussion. The work is supported by RFBR grants Nos а, ; integration SB RAS projects Nos 119, 142, 145, 148; RAS projects of Biodiversity (12.4), Physics and Biology (10.4); project from RAS Presidium Origin and Evolution of the Biosphere. REFERENCES Allen T.F.H., Hoekstra T.W. Toward a unified ecology. New York a.o.: Columbia Univ. Press, p. Ananko E.A., Podkolodny N.L., Stepanenko I.L. et al. (2002) GeneNet: a database on structure and functional organisation of gene networks. Nucl. Acids Res., 30, Beklemishev N.V. (1970a) Parasite and Nidicole Populations and Micropopulations. Biotechnological fundamentals of Comparative Parasitology. Мoscow: Nauka, (in Russ.). Beklemishev N.V. (1970b) On Classification of Biocenotic Interactions, Ibid (in Russ.). Begon M., Harper J., Townsend C. (1989) Ecology. In 2 volumes Мoscow: Mir. (in Russ). Dajoz R. (1976) Précis d'écologie. Мoscow: Progress, 450 p. (in Russ.). Luczkovich J.J., Borgatti S.P., Johnson J.C. et al. (2003) Defining and measuring trophic role similarity in food webs using regular equivalence. J. Theor. Biol., 220(3), Margalef R. (1992) The Looks of the Biosphere. Мoscow: Nauka, 214 p. (in Russ.).

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