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phrases in a song), drift (the decrease in singing rate with time) and maximum frequency. Their own experimental setup, however, rejects the first (always only four phrases were used) and therefore probably also the second (drift normally only becomes apparent towards the end of a song4). Furthermore, the sonagrams that they show do not suggest that the test repertoires differed systematically in maximum frequency. Maybe it is in an unseen part of the sonagrams that the systematic individual voice characteristics lie (for example, timbre or harmonics); the statistically significant effect of the actual tape recorder used may be an important clue here. An old concept in work on bird song is ‘habituation’, i.e. a stimulusspecific decrement in response to repeated exposure to particular songs’. One of the important hypotheses as to why birds sing repertoires is to counteract habituation. By switching to a new song type regularly, the listener reacts again more strongly.

Recent achievements of computer science provide unrivaled power for the advancement of ecology. This power is not merely computational: parallel computers, having hierarchical organization as their architectural principle, also provide metaphors for understanding complex systems. In this sense they might play for a science of ecological complexity a role like equilibrium-based metaphors had in the development of dynamic systems ecology. Parallel computers provide this opportunity through an informational view of ecological reality and multilevel modelling paradigms. Spatial and individual-oriented models allow application and full understanding of the new metaphors in the ecological context. Computers have always had a role in providing metaphors. To help our daily work, they use extended electronic incarnations of familiar objects such as desktops, paper and pencil, mailboxes. Some of these metaphors have affected the way we think. Word processors and electronic spreadsheets, for

Ferdinand0 Villa is at the Institute of Ecology, University of Panna, Viale delle Scienze, 43100 Parrna, Italy. 0

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In his review, Falls7 explicitly stated that individual voice characteristics should not be important because the function of repertoires is to avoid habituation. Similarly, the Beau Geste Hypothesis14, which stated that birds would change to a new song type and switch between singing perches simultaneously, so as to mislead potential settlers into believing the wood was more densely occupied than it really was, can only function if individual identity is not revealed. If what Weary and controlled Krebs found in carefully laboratory conditions is also true in the field, it will revolutionize our thinking about the function of song repertoires. Acknowledgements We thank H. Bluhme and Marcel Eens for providing some literature on human voices.

References 1 Nelson, D.A. and Croner,

Auk 108,42-52 2 Weary, D.M., Krebs, JR,

L.J. (1991) Eddyshaw,

R., McGregor,

P.K. and Horn, A. (1988)

Anim. Behav. 36, 1242-1244 3 Falls, J.B., Krebs, JR and McGregor, P.K. (1982) Anim. Behav. 30, 997-1009 4 Lambrechts, M. and Dhondt, A.A. (1986) Behav. Ecol. Sociobiol. 19, 57-63 5 Mundinger, P.C. (1982) in Acoustic Communication in Birds (Vol. 2) (Kroodsma, D.E. and Miller, E.H., eds), pp. 147-208, Academic Press 6 Dowsett-Lemaire, F. (1979) ibis 121,

453-468 7 Falls, J.B. (1982) in Acoustic Communication in Birds (Vol. 2) (Kroodsma, D.E. and Miller, E.H., eds), pp. 237-278, Academic Press 8 Van Elsacker, L., Pinxten, R. and Verheyen, RF. (1988) Behaviour 107,

122-130 9 Kersta, L.G. (1962) Nature 186,

1253-1257 IO McGregor, P.K., Krebs, J.R. and Perrins, CM. (1981) Am. Nat. 118, 149-159 11 Krebs, J.R., Ashcroft, R. and Webber, M. (1978) Nature 271, 539-542 12 Weary, D.M., Norris, K.J. and Falls, J.B. (1990) Auk 107, 623-625 13 Weary, D.M. and Krebs, J.R. (1992)

Anim. Behav. 43, 283-287 14 Krebs, J.R. (1977) Anim. Behav. 25, 475-478

New ComputerArchitecturesas Toolsfor EcologicalThought Ferdinand0 Villa example, have changed our ideas of writing and doing mathematics. In education, the computer-based metaphor of hypertext’ is changing information systems with a new concept of writing and the cooperative integration of different media. Metaphors are fundamental tools for scientific thought. Their role in stimulating hypotheses is well recognized2. By describing one thing in terms of another, metaphors can enable one to focus on important aspects of the realities under study, by using a known ‘reference’ concept as a guide to interpret the unknown. In this way, metaphors have served the development of theories in ecology and evolution, helping to delineate concepts such as equilibrium3 and adaptation4. My point here is to argue that new metaphors, coming from the most recent advances in computer science, can have a very important role in understanding complex ecological systems.

Metaphors and complexity in systems ecology Until the 1970s the predominant notion of ecological complexity was tied to the metaphor of a cybernetic system. This conceptualization yielded state-variable mathematical models such as the Lotka-Volterra equations. Much of current ecological theory is built around dynamic equilibrium3, with the notion of complexity implied in works on dynamic chaos5 and catastrophe theory6. More recent contributions have emphasized the hierarchical organization of biological systems and the complexity that results from the organization of low-level interacting system components. Generally, complex patterns in time and space are observed when processes act at a small scale with respect to the global system7,8. For example, Hassell et aL9 find nonrandom spatial pattern and chaos in a model hostparasitoid system when dispersal 179

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1. Twodifferent system interpretations of ecological relevance, leading to different notions of complexity Dynamic

System

conceptualization through

Suitable

metaphors

Specification of inner mechanism Principal

means of analysis Key behaviours

System

organization

Lumped

view

Cybernetic

systems

Differential equations

view

Lower-level processes/entities Parallel computers, cellular automata, learning systems

Centralized or difference

Distributed component

between entities

Computer

simulation

Equilibrium, dynamic complexity

Self-organization, structural complexity

Fixed, single-level

Variable,

between spatial patches is local. Complex behaviour does not show up within the conventional patch dynamics approach, where patchto-patch migration is nonlocal. The notion of complexity is very different in these views (Table I), as is the system specification. In general, the organizational view acknowledges the importance of scale in determining observed ecological phenomenalO. This view has been profitably applied to ecology within the framework of hierarchy theory1’,‘2. Nevertheless, the study of organized complexity has until now lacked ‘reference’ concepts of a power comparable to that of a cybernetic system. New generation computers can provide such concepts. The most recent parallel machines get their computational power from the organization of many processing elements. As such, they can be ‘reference systems’ that ecologists can use in formulating hypotheses. Modelling paradigms based on parallel architecture embody different interpretations of complexity, and have in this regard a great potential for ecology. Parallel computer architectures Parallel computers are systems of computers, with many processing units that work simultaneously. To date they exist in two basic variations. In the first, the units are a small number (usually two to eight) and execute different codes in parallel, breaking a program into independent instruction flows. This ‘instruction-level’ parallelism, dubbed MIMD (Multiple Instructions Multiple Data), characterizes many high-throughput mainframe computers, such as CRAY machines. Low-cost MIMD systems Itransputers) are used in ecological simulations13. 180

Organizational

state variables

multilevel

SIMD (Single Instruction Multiple Data) machines are characterized by the variable organization of processing elements, which are usually thousands in number and execute simultaneously the same instruction. Each processor operates on its own data memory and can access any other’s through programmable connections. Processing units can be virtually shaped in predefined geometries and grouped in various ways; specific hardware allows retrieval of information, such as the sum of local data, from groups of processors as a whole. This feature effectively defines higher levels of computational organization. If, for example, processors in a particular subset each represent a differently aged animal, the age distribution a population attribute - can be fetched in another by a single global operation. Programming SIMD machines proceeds by specifying the ‘behaviour’ of a single processing unit in each group; programming language instructions are automatically replicated to all units during execution. These locally defined computer systems allow conventional problems such as search or matrix computation to be solved very efficiently. Specific engineering and artificial intelligence applications include image processing, artificial vision, natural language processing and speech recognition14. SIMD machines are usually large and expensive systems, like the Connection MachineIs, although far less expensive machines are available for restricted application+. MIMD machines (transputers in particular) can also be used in SIMD mode, with each processor emulating many. The need for solving complex problems has resulted in the design

of computational systems that can be programmed to contain organization in more simultaneous levels. This feature makes parallel architecture relevant to life sciences not only for the computational power, but also for the expressive implications it contains. Parallel computer metaphors The parallel between ecological organization and computer systems has been noted and discussed in different interpretations17. The structure of parallel machines is itself a proper metaphor for many natural systems: ecological systems can be viewed as informational, with interacting units processing the information contained in their initial conditions and in the environment. System evolution and pattern formation over space and time may then be viewed as computation and formalized accordinglyIs. The potential of the informational view can be exploited through proper modelling paradigms, which define basic modes of interaction between components. Different formalisms give different interpretations of the organization of basic units. As such, they can serve as metaphors to ‘read’ the behaviour of locally specified systems in different ways. Cellular automata This old branch of computer science has recently gained new interest from ecologists19~20. Many identical components (cells) are arranged in space in a fixed structure. Each is in one of a small number of possible states. The state of each cell is calculated on the basis of its previous state and that of the neighbouring cells. Very simple rules define the behaviour of individual cells; nevertheless, the largescale system can show surprisingly complex behaviours in time and including self-organization, space, spatial chaos and reversibility. Cellular automata can simply model phenomena whose system-level analytical treatment is exceedingly complex: striking examples come from physicsIb. Cellular automata have been used in ecology as spatial modelling too1s9~20and, in more abstract conceptualizations, as models of genetic driftI and prebiotic evolution2’.

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Their strict definition makes them more suited to exploration of ideal system properties than to predictive study of real systems. They are maybe the simplest artificial systems with a multilevel definition, yet they are capable of all sorts of complex behaviours. In this sense they can be to a science of ecological organization what Lotka-Volterra models have been to dynamic systems ecology. Learning systems Development of pattern in space or time can be viewed as learning22 when structural organization reflects conditions experienced by the system. In this case the achieved structure can be thought of as the ‘memory’ of the system. This is explicit in the formulation of neural networks23, which are parallel computer models intended to simulate the workings of the brain. Learning in a neural network takes place by modification of the network’s internal structure. The network is composed of simple connected entities (the neurons) and learns to recognize partially specified input patterns after a ‘training’ in which the strength of connections is modified until stable. The basic model has many variations, some based on supervised learning and others on self-organization. Neural networks are applied in engineering as pattern recognition tools. They can also help in ecological simulations: as an example, they can mode1 animal cognitive structures in individual-oriented models (see below). But their ability to ‘learn by organization’ can have important conceptual implications for ecology. What is probably a mechanism at the level of the cerebral cortex becomes, in higherlevel contexts, a powerful tool to generate hypotheses. The ‘patternas-knowledge’ metaphor can be significant in evolutionary study, or, for instance, in interpreting community organization as the result of ‘learning the environment’. Ecologicalrealizations:spatialand individual-orientedmodelling The basic units in the discussed modelling paradigms are very simple and abstract. The usefulness of these system views depends on ecologically sound conceptualiz-

ations. Ecological realizations differ according to which basic units are implied. Spatial modelling is a suitable field for a two-dimensional cellular automaton representation20. The work of Hassell et a1.9 provides an example. Other applications use parallel computers in complex predictive models (Box I), drawing on the cellular automaton basic concept for a more realistic picture of ecological systems. Individual-oriented simulation modelling24*25is perhaps the most promising field to benefit from parallel computers. Individual organisms hold, as basic units of ecological systems, more historical and biological relevance than arbitrary space tessellations or more abstract entities. Modelling of individuals - self-conscious entities interacting in parallel - allows full realization of the ‘nature as a parallel processing systemI metaphor. Individual-oriented models of plants and animals, as recently reviewed25e26,have already made considerable contributions to ecology, although they have been limited by the processing power of conventional computers. They also constitute a move towards more robust and testable hypotheses, since the modelled entities are easier to observe and measure25,27. In individual-oriented models, the experimenter specifies the behaviour of individuals, and studies higher organizational levels by detecting spatial and temporal invariance across different scales. Populations and communities are studied by observing the behaviour of the simulated system at the appropriate scales. This implies that higher levels of organization are not defined a priori: in fact, these models lead to unification of conventionally separate ecological disciplines25, and can potentially help in understanding ‘intermediate’ levels (e.g. the metapopulation28). Ecological models of individuals differ from previously discussed parallel formalisms in at least two respects: ( 1) Considerable detail is needed in the individual specification to reach ecological realism. Observation of the simulated system can thus become difficult24; for this reason, extended formalisms allowing automatic detection of emerg-

ing system structure have been developed (Box 2); (2) In cellularautomataand neural networks, rules of evolution remain the same over time. Furthermore, the structure of interaction is fixed. In neural networks, the formalism allows only quantitative modification. In cellular automata, the considered neighbourhood is the same for all individual cells. Nature, of course, is much more complex: rules may change during system development, and the structure of interaction is highly variable. Individual-oriented models allow great freedom in specification, and can simulate adaptive systems where the rules themselves evolve. Parallel computers are necessary to stand the computational load these models require. In their simplicity, parallel metaphors such as cellular automata and learning systems provide basic interpretations for emerging organizational pattern. The complexity 181

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eses about ecological complexity through multilevel modelling paradigms and their ecological realization. Cellular automata teach us how to interpret complexity as the result of simple small-scale processes. Learning systems provide a powerful, causal conceptual framework in which to interpret ecological organization. Spatial and individualoriented models, with their multilevel specification, allow full exploitation of the potential of the new metaphors in ecological study. Local system specification can be thought of as a language extension, of the same kind used in programming parallel machines. The resulting notion of simplicity20~22, based on scaling the system description, is a most relevant new tool in the hands of ecologists. Inspired by multilevel metaphors and provided with multilevel computers, we can now explore the scale spectrum in searching for the simplest description of ecological phenomena, and use it as a basis for explanation. Research in this direction will probably affect the meaning of some words, such as the familiar ones that follow. Nonlinearity This is the source of complex behaviour in dynamic systems composed of a single organizational level. A broader view of ecological complexity, encompassing the views summarized in Table 1, will consider hierarchical organization along with nonlinearity. The availability of multilevel artificial systems will help our understanding of how these two concepts relate in the explanation of complex phenomena.

often observed

in spatial models24 and in ecological reality can be studied in terms of known concepts, in the same way that simple cybernetic systems have been used in dynamic systems ecology.

that

is

and

individual-oriented

Newwordsfor ecologicallanguage Metaphors emphasizing regulation and internal feedback have been, in the past, relevant to ecology. Parallel computer metaphors may promote new hypoth182

Causality Ulanowicz29Jo was first to point out the link between hierarchical organization and causality in ecosystem development. Multilevel formalisms will help in clarifying definitions and roles. A deeper knowledge of how causality relates to organization will reflect on the definition of the role of modelling in ecological

study3’.

Stockasticity Stochastic behaviour in cellular automata and individual-oriented models can arise from determinis-

7992

tic rules at lower levels20,24. The definition of stochasticity can thus depend on the scale chosen. In a language based on local system definition, words such as ‘noise’ will probably change or lose their present meaning. Conclusion Pattees pointed out the difference between simulation models of observable phenomena and original model systems expressing a particular idea of the world. We can say that ecology can benefit from simulations by means of original realizations, which, as cellular automata and learning systems do, can serve as metaphors to lead us in interpreting the outcome of simulation models. Parallel computers and parallel modelling paradigms are particularly interesting as metaphors to understand hierarchical organization and complexity; as conventional computers do in other fields, they give us new ways of thinking as well as a means to have our thinking realized. Their contribution to ecological science will probably become more and more important in the years to come, allowing the development of a ‘computational modelling’ that will be much more than simulation modelling. Acknowledgemen& I am indebted to many people for comment, discussion and constructive criticism on early drafts. Donald DeAngelis’ comment was particularly helpful. I also owe a debt of gratitude to Charles Puccia, Robert Ulanowicz, Robert Costanza, Antonio Bodini, Paolo Menozzi, Irene0 Ferrari, Franc0 Sartore, Orazio Rossi and an anonymous referee for their help in improving the manuscript and shaping the ideas expressed. Research financed by 40%grantsfrom the Italian Ministry of Public Education to Prof. 0. Rossi.

References

I Fiderio, I. (1988) Byte 13, 237-244 2 Weinberg, G.M. (1975) An introduction to General Systems Thinking, Wiley 3 DeAngeiis, D.L. and Waterhouse, I.C. ( 1987) Ecol. Monogr. 57, l-2 I 4 Krimbas, C.B. (1984) Evol. No/. 17, l-57 5 May, R.M. (1974) Science 186, 645-647 6 Loehle. C. (1989) Ecol. Model. 40, 125-152 7 Kolasa, 1. and Pickett, S.T.A., eds

( I99 I)

Ecologica/ Heterogeneity, Springer-Verlag 8 Langton. C.G. (1990) in Artificial Life (Santa Fe Institute Studies in the Sciences of Complexity, Vol. 61 (Langton, CC., ed.1. pp. l-47, Addison-Wesley 9 Hassell. M.P., Comins, H.N. and May, R.M.

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(1991) Nature 353, 255-258 IO Menge, B.A. and Olson. A.M. ( 1990) Trends Ecol. Evoi. 5, 52-57 II Allen, T.F.H. and Starr, T.B. (1982) Hierarchy: Perspectives for Ecological Complexity, University of Chicago Press I2 O’Neill, R.V., DeAngelis, D.L., Waide, LB. and Allen, T.F.H. (1985) A Hierarchical Concept of Ecosystems, Princeton University Press 13 Costanza, R. and Maxwell, T. Ecol. Mode/. lin press) I4 Hord, R.M. (19901 Parallel Supercomputing in SIMD Architectures, CRC Press I5 Hillis, W.D. (1985) The Connection Machine, MIT Press 16 Toffoli, T. and Margulis, N. ( 1987) Cellular Automata Machines: A New Environment for Modelling, MIT Press I7 Huberman, B.A., ed. (1988) The Ecology of Computation, Elsevier I8 Wolfram, S. (1984) Nature 31 I, 419-434 I9 CtirBn, T. and Bartha, S. (1991) Trends Ecol. Evol. 7, 38-42 20 Hogeweg, P. ( 1988) Appl. Math. Comput. 27, 81-100 21 Tamayo, P. and Hartman, H. (19881 in

The marine benthic fauna and flora that inhabit the shallow arctic su6kttorul zone comprise a relatively young marine ussem61agechuructerized6g speciesof either Pacificor Atlantic affinity and notably few endemics. The young character of neurshore arctic communities, us well us their biogeographicalcomposition, is largely a product of the Pleistoceneglaciation. However, analysis of more recent collectionsand comparison Getween the origins of the benthic fauna and flora present some interesting paradoxes to biogeogruphers. One enigma is the low frequency of algal species with Pacificaffinities in the Arctic, especially in the Chuhchi, Beaufort and East Siberian Seas of the Eastern Arctic, which receive direct inputs of northwurdflowingPacific waters. In contrast, animal species with Pacific affinities are found throughout the nearshore regions of the Arctic, reaching their highest frequency in the marginal seas Getween the New SiGeriunIslands and the Canadian Archipelago. Organization of published and unpublished data, additional field collections,and the use of cludistics and molecular DNA techniques6g sgstematistsare a high priority forfuture research in reconstructing the evolutionof the arctic bioticassemblage.

Ken Dunton is at the Universityof Texasat Austin, Marine Science Institute, Port Aransas,TX 78373, USA. 0

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Artificial Life (Santa Fe Institute Studies in the Sciences of Complexity, Vol. 6) (Langton, C.G., ed.), pp. 105-124, Addison-Wesley 22 Hogeweg, P. and Hesper, B. (1989) in Modeliing and Simulation Methodology (Elzas, MS., Oren, T.I. and Zeigler, B.P., eds), pp. 77-92, Elsevier 23 Wasserman, P.D. ( 19891 Neural Computing: Theory and Practice, Chapman & Hall 24 Hogeweg, P. and Hesper, B. II9901 Math. Comput. Model. I3,83-90 25 Huston, M., DeAngelis, D. and Post, W. ( 1988) Bioscience 38, 682-690 26 Hara. T. ( 1988) Trends Ecol. Evol. 3, 129-133 27 DeAngelis, D.L. (19881 Ecol. Model. 43, 57-73 28 Olivieri, I., Couvet, D. and Gouyon, P-H. (1990) Trends Ecol. Evol. 5, 207-210 29 Ulanowicz, R.E. (19861 Growth and Development - Ecosystem Phenomenology. Springer-Verlag 30 Ulanowicz, R.E. (1990) Oikos 57, 42-48 31 Ulanowicz, R.E. ( 1988) Ecol. Model. 43, 45-56 32 Pattee, H.H. (1988) in Artificial Life

(Santa Fe Institute Studies in the Sciences of Complexity, Vol. 6) (Langton, CC., ed.), pp. 63-77, Addison-Wesley 33 Sklar, F.H. and Costanza, R. (1990) in Quantitative Methods in Landscape Ecology (Ecological Studies, Vol. 85) (Turner, M.G. and Gardner, R.H., edsl, pp. 239-288, Springer-Verlag 34 Costanza, R., Sklar, F.H. and Day, J.W. (1990) Bioscience 40,91-I07 35 Haslett, j.H. ( 19901 Trends Ecol. Evol. 5. 214-218 36 Coulson, R.N. et a/. ( 19901 in Quantitative Methods in Landscape Ecology (Ecological Studies, Vol. 85) (Turner, M.G. and Gardner, R.H., eds), pp. 153-l 72, Springer-Verlag 37 Taylor, C.E., jefferson, D.R., Turner, S.R. and Goldman, SE. (1988) in Artificial Life (Santa Fe Institute Studies in the Sciences of Complexity, Vol. 6) (Langton, CC., ed.), pp. 275-295, Addison-Wesley 38 Hogeweg, P. ( 1988) in Artificial Life (Santa Fe Institute Studies in the Sciences of Complexity, Vol. 6) (Langton, C.G., ed.1, pp. 297-3 16, Addison-Wesley 39 Hogeweg, P. and Hesper, B. 11985) 1. Theor. Biol. 113, 31 l-330

ArcticBiogeography:The Paradox of the Marine BenthicFaunaand Flora Ken Dunton Arctic Ocean marine benthic communities are distinctive in that they are composed of a relatively young fauna comprising species of Pacific and/or Atlantic affinity and few endemics’m2. These characteristics have been cited repeatedly in zoogeographic studies of various taxonomicgroups, including polychaete9, seastars and bivalve molluscs5. The marine benthic vegetation of the Arctic also contains few endemics, yet it appears to be characterized by species of predominantly Atlantic affinity in both the Amerasia& and Eurasian7J sectors of the Arctic. The low number of endemics and the variable predominance of species with Atlantic or Pacific affinities have often been cited as evidence that the geographic distribution of the arctic biota remains in a highly dynamic state and is not in equilibrium6,9J0. Even more interesting but not previously noted is the apparent difference in the

biogeographic origins of the marine benthic fauna and flora throughout the Arctic. Previous faunistic and floristic analyses of the arctic biota were based on available checklists of animals and seaweeds from various disparate geographic regions. More extensive surveys of the arctic sublittoral biota have been completed in recent years, but much of this data remains unpublished or is located in relatively inaccessible Russian literature. Consequently, the absence of data from these collections hampers biogeographic investigations. As part of this review, and in collaboration with several systematists, I gathered information from these databases and the Russian literature (Tables I and 2) and compiled regional checklists for the seaweeds and benthic invertebrates. Here, I review these ‘new’ data, discuss the factors that have influenced the spread and establishment of marine benthic biota

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