Criticality and Scaling in Evolutionary Ecology

PERSPECTIVES

Is there any particular situation where such complex structures emerge? The Criticality and scaling in answer is yes. In physics, fractal structures in space and time were known to evolutionary ecology emerge in the proximity of some types of phase transition10,11. The classic example is a magnetic material. A small piece of Ricard V. Solé iron can tug on a paper clip at room tem- Susanna C. Manrubia perature, but if we heat it to a high temperature T, no magnetic power is ob- Michael Benton served. We say the magnetization M is Stuart Kauffman zero. The atoms that form the iron are themselves like small magnets. Each atom Per Bak only interacts with its nearest neighbors and their natural tendency is to align Fluctuations in ecological systems are known to involve a wide range of spatial and spontaneously into small domains with temporal scales, often displaying self-similar (fractal) properties. Recent theoretical the same orientation. At high T the cou- approaches are trying to shed light on the nature of these complex dynamics. The pling between nearest atoms breaks results suggest that complexity in ecology and evolution comes from the network-like down because of thermal perturbations structure of multispecies communities that are close to instability. If true, these ideas and, therefore, the atoms can have any might change our understanding of how complexity emerges in the biosphere and how polarity (up or down) and M ϭ 0. But sud- macroevolutionary events could be decoupled from microevolutionary ones. denly, when the material is cooled down, order spontaneously shows up. There is a critical temperature at which global Ricard Solé is at the CSRG-Dept of Physics, FEN, Universitat Politècnica de Catalunya, MϾ Campus Nord Mòdul B4, 08034 Barcelona, Spain ([email protected]; magnetization appears ( 0) and both Susanna Manrubia is at the Fritz-Haber Institut der Max-Planck Gesellschaft, Faradeyweg 4–6, fractal-spatial and fractal-temporal fea- 14195 Berlin, Germany ([email protected]); Michael Benton is at the tures arise. These transitions are de- Dept of Geology, University of Bristol, Bristol, UK BS8 1RJ ([email protected]); scribed by an ‘order parameter’ (here M), Stuart Kauffman is at the Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA which is zero at the disordered phase ([email protected]); Per Bak is at the Bohr Institute, Biegdamsvej 17, DK-2100 Copenhagen, and positive otherwise. Denmark ([email protected]). Self-similarity is a defining characteristic of the critical state. The surprise came when physicists realized that very ractals are widespread in nature1,2 and ␶Ͼ0 is a given exponent, often called the different systems behaved exactly the Fhave features that look the same when critical exponent. The reason why these same when close to critical transition there is a change in scale: they are called laws are characteristic of fractal objects points6,10,11. Surprisingly, extremely sim- ‘self-similar’. In biology, self-similar pat- is that they are the only functions dis- ple models of these systems provided an terns are known to occur at many levels1–4 playing invariance under scale change. If exact description. This is a consequence (Box 1). But fractals are also present in we look at a larger or smaller scale – that of the so-called ‘universality’. Universal- time: the fluctuations of a given quantity is, if we take sЈ ϭ ␥s – it is not difficult to ity means that systems sharing a small can appear the same when observed at see that N(sЈ) ϭ CЈN(s) or, in other words, number of basic features behave identi- different temporal resolutions (Fig. 1a). a change of scale does not modify the cally at the critical point. Are there also This is the case for heartbeat intervals5, basic statistical behavior. universal principles behind apparently epidemics in small islands6, breeding bird The same argument can be employed different biological phenomena? populations7 or the fossil record8. for a time series (Fig. 1b). If self-similar Because fractals involve long-range behavior is present, then the time corre- From RNA viruses to epidemics correlations, they also reflect some key lations should decay in a power-law fash- Starting at the molecular level, an features of how living systems are or- ion9. This is something that has been example of a critical point in biological ganized and how they evolve in time. The widely observed and is known as ‘1/f- systems can be exemplified by the dy- implications for evolution are very im- noise’6,8,9. A 1/f-like signal looks like a namics of RNA viruses12. The under- portant, because cooperative effects mountain landscape in time, rather than standing of how these entities evolve and emerging from the interactions can lead to space. The self-similarity is described by adapt must take into account their ex- new, sometimes counterintuitive, results. the power spectrum P(f ), which measures treme variability, caused by the error- A consequence of this is that order could the contribution of each frequency to the prone RNA polymerase activity and the be generated through evolution by a syn- overall time series6. The 1/f noise is defined lack of proofreading mechanisms13. In- ergy between natural selection and self- as P(f ) Ϸ f-␤ being the exponent 0Ͻ␤Ͻ2. stead of a given single sequence, we have organizing processes.

Scaling and power laws The common feature of self-similar be- Box 1. Fractals everywhere havior is the presence of scaling laws4 The patterns displayed by many natural systems do not allow for a simple description using Euclidean (also known as power laws). Given the fre- geometry: they present scale-invariance; that is, no characteristic length measure can be obtained from them. Therefore, when observed at different resolutions, they display the same pattern. This is the case quency distribution N(s) of some quantity of river networks and mountains1, tree branching and blood vessels3 or forest spatial structures2,4. Even s (number of species, size, lifetime, etc.) at the molecular level, fractals can be observed: if we analyse the linear distribution of nucleotides in a it is said that it follows a power law if DNA chain, a self-similar pattern can also be detected5. These structures – the so-called fractals – share N(s) ϭ CsϪ␶ (Fig. 1a). Let us assume that s the presence of long-range correlations. stands for size. Here C is a constant and

(a) Box 2. Recipe for a catastrophe: the error catastrophe 4 10 A population of strings is simulated to explore how the ‘master’ sequence population behaves when the mutation rate is changed (a). Here, N ϭ 100 strings of size ␯ ϭ 15. Each string is defined by a sequence ⑀ S1S2…S␯ where Si {0,1}. At each time step, we choose a string and make a copy of it after removing 103 ϭ ϭ another randomly chosen string. This replication takes place with probability P 1 if Si 1 for all units (this is the master sequence) and with probability P ϭ 0.05 for any other string16. The copy process involves a mutation rate ␮, which means that the probability of error per unit and per replication cycle is 102 ␮ for each string. Starting from a random population, we repeat the same rules over 1ϫ104 steps and at Frequency the end we look into the system to see if there is any copy of the master sequence. Here the master sequence is the string 1111…11; that is, the one with the highest replication rate. This is averaged over 101 ϫ 2 1 10 trials and for each mutation rate we compute how many of these replicas (Pm) have at least one master sequence. Although our intuition would expect a smooth, continuous decay of this probability, 100 101 102 there is actually a sharp decay close to a given critical mutation rate (i.e. Pm acts as an order parameter Avalanche size for this transition). This phenomenon is known as the ‘error catastrophe’. It describes the breakdown of heredity and the transition from a localized set of mutant sequences (localized around the master sequence in sequence space) towards a random set of strings17. The inset graphs show the evolution (b) of relative population sizes of strings when (b) ␮ ϭ 0.05 and (c) ␮ ϭ 0.12. The master sequence is indicated by the unbroken line in these two graphs. 30 (a) 20 1.0

Extinction rate (c) 0 40 0 200 400 600 0.8 Geological time (million years) 30

20 0.6 10 (c) Frequency (b) 60 0 0 100 200 0.4 40 Time Order parameter

20 Frequency 0.2 0 0 100 200 Time

0.0 0.00 0.10 0.20 Mutation rate

(Online: Fig. I )

a cloud of mutants around the so-called theoretical19 work show that critical ‘master’ sequence. This cloud is known points could play a very important role in as a quasispecies, a term first coined by the evolution of quasispecies. Manfred Eigen in 1971 (Refs 14,15). These transitions are well known in Fig. 1. Scaling and self-similarity. (a) Power RNA viruses adapt to a changing the dynamics of infectious diseases20 and law: in a log–log plot, a power law distribution, 21 defined as N(s) Ϸ sϪ␣ gives a straight line. environment by making use of their in some habitat fragmentation models . Many small events are observed, but large variability. Selection pressures by the A simple, but important, illustration of events are also likely to occur. (b) Time fluc- immune system force the virus quasi- a critical phenomenon in epidemics is tuations in the fossil record: here the total species to evolve. The quasispecies provided by contact processes22,23 (CPs). ϫ extinction rate ( 100) for marine animal model is consistent with this obser- The rules in the simplest model are de- families (see Benton29 for definitions of extinction measures) during the Phanerozoic (570 vation, but something defeats our intui- fined as follows (Fig. 2): (1) active (in- million years ago to the present) is shown (this tion: there is a critical mutation rate be- fected) particles (A) die (becoming in- and other data are available from http://ibs. yond which heredity breaks down. This active or susceptible) with probability ␥; ucl.ac.uk/benton/foss2.htlm). A wide spec- is referred to as the error catastrophe, (2) if a given active particle survives, trum of extinction events is observed. (c) The sand pile is a simple example of how to get a and it is nothing but a phase transition each susceptible or inactive neighbor can critical point. By slowly adding grains of sand, point, which poses serious limitations to become infected or active, respectively, the system is driven to instability. After a criti- the virus complexity (Box 2). (In fact, the with probability ␤. For a given ␥, propa- cal slope is reached, the addition of a single ␤ quasispecies model behaves as a mag- gation occurs only if a given threshold c grain can generate an avalanche involving s netic system15.) Available molecular data is reached. grains of sand. Most times, only small avalanches are observed, but eventually very confirmed the theory: RNA viruses do Another instance of threshold phe- large avalanches will occur. The number of replicate close to the error catastro- nomena in ecology has been recently avalanches N(s) involving s grains will follow a phe16,17. This property can only be under- reported in relation to the avifauna of power law. stood under the framework of critical Hawaiian Islands24. After a gradual accu- transitions. Recent experimental18 and mulation of species, numerous extinction

TREE vol. 14, no. 4 April 1999 157 PERSPECTIVES

Evolution and extinction (a) The fossil record is almost entirely 1.0 formed of extinct groups28,29. If extinction is the fate of most lineages, one should ask whether an external or internal pro- (b) cess is the relevant aspect of extinction 0.8 dynamics. Many recent theories claim 200 that external stresses are the cause of extinction events28. However, we should (c) ask if these events are the causal agents 0.6 100 of extinction or the trigger points for a 100 80 complex biotic response. Time steps 60 Power laws are observed not only in the distribution of extinction events and

0.4 MWM 40 lifetimes30, but also in the tree-like or- 0 20 31 Number of infected units 0 100 200 ganization of taxonomy . The presence 0 of these power laws soon led to the pro- X 103 104 105 106 0.2 posal that large-scale evolution would be Population size the result of an SOC-like phenomenon. This idea generated a number of models exhibiting criticality. Among them, the 0.0 following can be cited. 0.0 0.2 0.4 0.6 0.8 1.0 • Models based on tunable rugged fitness Probability of infection (β ) landscapes (Fig. 3): in these models, Fig. 2. Phase transitions in contact processes. A one-dimensional lattice of N ϭ 150 automata is used. species are evolving on landscapes that (a) The initial condition is random and the probability of decay is ␥ ϭ 0.5. After T ϭ 250 time steps, we deform because of the adaptive moves32 check if the propagation still persists. By repeating the same process, we calculate the probability of of other species. Genetic changes in dif- propagation (␤) for different infection rates. Insets: (b) at the critical point (x), very long transients are ferent species alter the ruggedness of observed, with fractal-like trees of propagating infected units. Available data consistently show that such breakpoints exist, such as in (c), where the number of months with measles (MWM) is plotted against their landscapes. If useful in the future the population size. Data compiled from Ref. 22. evolution of the progeny, the ruggedness itself evolves. Species can also invade one another’s niches. The losing species goes extinct, whereas the invaders com- events occurred once a critical number all the time. At the SOC state, there is prise a new sibling species created in the of introduced species was reached. The one complex system, with its own emer- niche. Species whose landscapes have a statistical features where shown to be gent dynamics. This new state cannot ruggedness that is useful tend to succeed power-law distributed, which suggests be anticipated from the properties of at invasion. The result is that landscape that the accumulation of new species drive individual units. In an ecological context, ruggedness evolves without group selec- ecosystems to criticality. However, this the addition of new species would place tion to reduce the probability of extinc- is different from the previous scenarios. ecosystems close to a critical state, where tion events and increase mean fitness. Now, there is no parameter tuning the dy- the collective and not the individual spe- The result also yields a power law distri- namics: as species number increases the cies would be the relevant object in the bution of extinction events (also called system becomes spontaneously unstable. long run. coevolutionary avalanches). Models of SOC systems are very sim- • Models with external dynamics: in the Criticality and self-organized ple and typically parameter-independent. Bak–Sneppen (BS) model26,30,33, species criticality In spite of their simplicity (and because are first assigned random fitness values. If fractal structures and self-similar of their universality), they can be used to At each time step, the species with the fluctuations are so common, perhaps model complex systems. In this respect, lowest fitness goes extinct, and is re- some universal dynamical processes are it has been shown that the dynamics of placed by, or mutates to, another spe- at work. A possible scenario was pro- measles in small islands show power law cies with a random fitness. This affects posed in 1987 (Refs 25–27) and is known behavior6. In this study, the basic rules the livelihood of interacting species, as self-organized criticality (SOC). Self- are precisely the ones involved in the which are also assigned new random fit- organized criticality is easily stated as ‘forest fire’ model, one of the simplest nesses. As this darwinian evolution con- follows: large, far from equilibrium, com- and best-known models displaying SOC tinues, extinction eventually takes place plex systems, formed by many interact- (Box 3)26,27. in the form of coevolutionary avalanches ing parts, spontaneously evolve towards the critical point. A simple metaphor of an SOC process Box 3. The forest fire cellular automaton is provided by a sandpile25,26 (Fig. 1c). This is a cellular-automata model, which has been shown to display complex spatial and temporal frac- We add sand slowly, one grain at a time. tal behavior26. Because of its simplicity and its relation with infectious dynamics, it has been used as a At the beginning, we have a flat pile, and model of epidemics on small islands6. The system is described by a two-dimensional lattice of L ϫ L the grains stay where they land. The nodes where ‘trees’ are scattered. Each site can take three allowed states: empty (E, no tree), green (G, tree) or burning (B, burning tree). The rules are defined by a set of transitions between states. Specifi- grains are basically independent and their cally, E→G with probability p and G→B with probability f. An additional rule is required for the propagation behavior described by gravity and fric- of the fire front: if one of the eight nearest trees of a green tree is burning, the tree starts to burn. Burning tion forces. But, as the slope increases, trees only burn for a single step (becoming empty afterwards). Fractal clusters of burning trees are we reach a regime where avalanches involving grains in interaction are occurring

158 TREE vol. 14, no. 4 April 1999 PERSPECTIVES

Prospects Scaling behavior and critical points are commonplace in different biological systems. In some cases, such as in epi- demiology or the effects of habitat fragmentation, the available information is rather detailed. But many open problems remain to be solved, and new theoretical and field studies are needed, in particular: • The analysis of scaling in multispecies communities. New models involving ecological timescales that can incorporate individual-based interactions should be explored38. • Analysis of long-term field studies of species removal and development of new theoretical models able to test the presence of criticality and avalanches in response to perturbations, as well as the relevance of higher-order interactions35. • Studies on the spatiotemporal behavior of rainforest plots4, both in terms of species diversity and canopy structure. Calculation of fractal properties and Fig. 3. The fitness landscape: this was first introduced by Wright (see Kauffman32 and references cited temporal dynamics would greatly help, therein) as a useful tool for thinking about complex genetic systems. The horizontal axes (the scale is as a quantitative way to understand how arbitrary) usually stands for gene frequency, but they can even be drawn with phenotypic variables. Maxima diversity and nonequilibrium dynamics represent local optima and natural selection is interpreted in terms of a hill-climbing process, directing the 38 population up towards the current nearest peak. The real landscape is multidimensional and cannot be are related . visualized in such a simple way. Landscapes can be smooth and single peaked, rugged and multipeaked, or • Fine-scale analysis of fossil record entirely random, and evolution searches such landscapes using mutation, recombination and selection. time series, with comparisons between fluctuations in physical variables (such as in Newman39) and biotic fluctuations. Analysis of fractal patterns of diversifi- of all sizes. The largest ones can repre- network properties are important, a cation and taxonomy and their possible sent mass extinction events, which thus change in one species could propagate dynamical origin are also required. might take place without an external through the system in a highly nonlinear, There is a need for new theoretical stress. This model leads to SOC and unpredictable way. The consequences frameworks that allow us to understand power laws for any macroscopic quantity. of such propagation are unlikely to be how self-organization, selection and his- • Network-ecosystem models: here, the explained through the adaptation or se- torical accident find their natural places system is assumed to be defined at the lection processes that can be applied to with one another. ecological timescale by a network of con- single species or simple two-species sys- The great success of theoretical nections34. Ecological responses are the tems. The highly unpredictable network physics gives us confidence that simple relevant component of the dynamics, dynamics could provide the natural models can account for the macroscopic instead of coevolutionary avalanches. At source of decoupling between micro- and behavior of complex systems. This state- each step, random changes in the con- macroevolutionary dynamics34. ment is apparently unlikely to be ex- nectivity are allowed to occur. The re- It is worth mentioning that field data tended to biology, where details often sponse of the system to such changes is support the view of a patterned, network- matter. But population and evolutionary highly nonlinear and might generate dependent response of ecosystems to biologists have been successfully using large extinction events. Diversification is perturbations. In this context, the collec- oversimplified models of ecology and introduced and the correct statistical tive features of network interactions be- evolution – and certainly, at some scale, distribution of extinction events, life- come clear when the energy flow between many details do not matter. We should times and even a fractal taxonomy34 are species is analysed. Links representing not forget that physics and biology have obtained. a relatively small strength can have a often come to a common language where A particularly important consequence large impact on stability, whereas inter- challenging ideas have been born. We of these models is the decoupling be- actions involving an important flow of believe that the potential results of such tween micro- and macroevolutionary pro- energy can have a small impact36. In ad- a dialogue are well worth the effort. cesses34. Across some short timescale, dition, at a different scale, an extensive adaptation of species to a given abiotic analysis of available ecological time se- Acknowledgements and biotic environment takes place. We ries revealed that in most cases the ob- We thank D. Alonso, J. Bascompte, can clearly identify coevolution between served fluctuations were characterized E. Domingo, R. Engelhardt, T. Keitt, directly related species, but little attention by a very small Largest Lyapunov Expo- M. Newman, P. Schuster and J. Quer for has been paid to the indirect effects (i.e. nent37 (LLE). Roughly, the LE is negative useful comments and discussions. This 34,35 work has been supported by grants higher-order interactions ). Because for equilibrium points and positive for DGYCIT PB94-1195 (RVS, SCM), the Santa such interactions reflect network proper- chaotic dynamics, respectively. The SOC Fe Institute (RVS, PB), the Humboldt ties, which are not reducible to the two- theory typically predicts an LE close to Foundation (SCM), Leverhulme grant species pairs, we might ask how relevant zero, precisely at the point separating (MJB) and the Division of Materials are such indirect effects in the long run. If stable from unstable dynamics. Science (PB).

TREE vol. 14, no. 4 April 1999 159 BOOK REVIEWS

References 15 Eigen, M., McCaskill, J. and Schuster, P. (1988) 26 Bak, P. (1996) How Nature Works, 1 Mandelbrot, B.B. (1990) The Fractal Geometry Molecular quasi-species, J. Phys. Chem. 92, Springer-Verlag of Nature, W.H. Freeman 6881–6891 27 Jensen, H.J. (1998) Self-organized Criticality, 2 Sugihara, G. and May, R.M. (1990) Applications 16 Swetina, J. and Schuster, P. (1982) Cambridge University Press of fractals in ecology, Trends Ecol. Evol. 5, Self-replication with errors: a model for 28 Raup, D.M. (1993) Extinction: Bad Genes or Bad 79–86 polynucleotide replication, Biophys. Chem. 16, Luck? Oxford University Press 3 West, G.B., Brown, J.H. and Enquist, B.J. (1997) 329–345 29 Benton, M.J. (1995) Diversification and A general model for the origin of allometric 17 Schuster, P. (1994) How RNA molecules and extinction in the history of life, Science 268, scaling laws in biology, Science 276, 122–126 viruses explore their worlds? in Complexity: 52–58 4 Solé, R.V. and Manrubia, S.C. (1995) Are Metaphors, Models and Reality (SFI Series) 30 Sneppen, K. et al. (1995) Evolution as a self- rainforests self-organized in a critical state? (Cowan, G., Pines, D. and Meltzer, D., eds), organized critical phenomenon, Proc. Natl. J. Theor. Biol. 173, 31–40 pp. 383–418, Addison Wesley Acad. Sci. U. S. A. 92, 5209–5213 5 Stanley, H.H. et al. (1996) Scaling and 18 Quer, J. et al. (1996) Reproducible nonlinear 31 Burlando, B. (1993) The fractal geometry of universality in animate and inanimate population dynamics and critical points evolution, J. Theor. Biol. 163, 161–172 systems, Physica A 231, 20–48 during replicative competitions of RNA virus 32 Kauffman, S. (1992) At Home in the Universe, 6 Rhodes, B. and Anderson, R.M. (1996) Power quasispecies, J. Mol. Biol. 264, 465–471 Oxford University Press laws governing epidemics in isolated 19 Solé, R.V. et al. (1997) Red Queen Dynamics, 33 Bak, P. and Sneppen, K. (1993) Punctuated populations, Nature 381, 600–602 Competition and Critical Points in a Model of equilibrium and criticality in a simple model 7 Keitt, T. and Stanley, H.E. (1998) Dynamics of RNA Virus Quasispecies (SFI working paper of evolution, Phys. Rev. Lett. 71, 4083–4086 North-American breeding bird populations, 97-11–085), Santa Fe Institute 34 Solé, R.V., Bascompte, J. and Manrubia, S.C. Nature 393, 257–260 20 Anderson, R.M. and May, R.M. (1991) Infectious (1996) Extinctions: bad genes or weak chaos? 8 Solé, R.V. et al. (1996) Self-similarity of Diseases and Control, Oxford University Press Proc. R. Soc. London Ser. B 263, 1407–1413 extinction statistics in the fossil record, 21 Bascompte, J. and Solé, R.V. (1996) Habitat 35 Brown, J.H. (1994) Complex ecological Nature 388, 764–767 fragmentation and extinction thresholds in systems, in Complexity: Metaphors, Models and 9 Halley, J.M. (1996) Ecology, evolution and 1/f spatially explicit models, J. Anim. Ecol. 64, Reality (SFI Series) (Cowan, G., Pines, D. and noise, Trends Ecol. Evol. 11, 33–37 465–473 Meltzer, D., eds), pp. 419–449, Addison Wesley 10 Binney, J.J. et al. (1993) The Theory of Critical 22 Bartlett, M.S. (1960) Stochastic Population 36 de Ruiter, P.C., Neutel, A. and Moore, J.C. Phenomena, Clarendon Press Models, John Wiley & Sons (1995) Energetics, patterns of interaction 11 Solé, R.V. et al. (1996) Phase transitions and 23 Durrett, R. and Levin, S.A. (1994) Stochastic strengths and stability in real ecosystems, complex systems, Complexity 1, 13–26 spatial models: a user’s guide to ecological Science 269, 1257–1260 12 Domingo, E. and Holland, J.J. (1994) Mutation applications, Philos. Trans. R. Soc. London 37 Ellner, S. and Turchin, P. (1995) Chaos in a rates and rapid evolution of RNA viruses, in Ser. B 343, 329–350 noisy world: new methods and evidence from The Evolutionary Biology of RNA Viruses 24 Keitt, T.M. and Marquet, P.A. (1996) The time-series analysis, Am. Nat. 145, 343–375 (Morse, S., ed.), pp. 161–183, Raven Press introduced Hawaiian avifauna reconsidered: 38 Solé, R.V. and Alonso, D. Random walks, 13 Nowak, M. (1992) What is a quasispecies? evidence for self-organized criticality? fractals and the origins of rainforest Trends Ecol. Evol. 7, 118–121 J. Theor. Biol. 182, 161–167 diversity, Adv. Complex Syst. (in press) 14 Eigen, M. (1971) Self-organization of matter 25 Bak, P., Tang, C. and Wiesenfeld, K. (1987) Self- 39 Newman, M.E.J. (1996) Selforganized and evolution of biological macromolecules, organized criticality: an explanation for 1/f criticality, evolution and the fossil record, Naturwissenschaften 58, 465–526 noise, Phys. Rev. Lett. 59, 381–384 Proc. R. Soc. London Ser. B 263, 1605–1610

influence or are used as models or sensitive snow), periglacial terrestrial, inland water, Poles apart barometers for processes that take place at marine benthos, sea ice and open seas. The lower latitudes much closer to home. In tak- text is peppered with references to the ing an holistic approach across marine, importance of polar habitats in the global The Biology of Polar Habitats freshwater, terrestrial and ice-driven bi- system but does not overlook some of the by G.E. Fogg omes, Fogg has created a volume that fills remarkable features that allow polar organ- an important and vacant niche. isms to survive and even flourish in situ. Oxford University Press, It quickly becomes apparent that de- On the one hand, we learn that rivers drain- Biology of Habitats, 1998. £45.00 hbk, £19.95 pbk (x ϩ 263 pages) spite the many similarities to be found in ing 14% of the Earth’s land area drain into ISBN 0 19 854954 7 / 0 19 854953 9 comparisons of the two polar regions they the Arctic Ocean, yet the longest river in are by no means two simple alternatives. Antarctica is only 40 km and spends most Many of the differences that exist are of the year completely frozen. On the other he Biology of Polar Habitats is one of a driven by geography. The Arctic consists hand, approximately 15 ϫ 106 km2 of sea- T series of texts giving overviews of dif- of northern continental regions fringing a ice form and melt over the Southern Ocean ferent habitats. The series’ remit is to give largely enclosed, relatively shallow and each year, influencing the biota of seas, an overview of design, physiology, ecol- cool ocean, whereas the Antarctic is a which are thought to be an important car- ogy and behaviour of organisms in the spe- large continent (greater in area than Europe bon sink, possibly buffering increasing cific habitats, pitched at a level appropriate or Australia) surrounded by a very cold atmospheric CO2 levels, while also account- for the biological or environmental stu- ocean and isolated from the rest of the ing for c. 25% of biogenic silica deposition dent, new field worker or knowledgeable world by oceanic and atmospheric cur- worldwide. naturalist. rents. The book highlights the scale of the Productive areas of research are likely Within this remit, it is plainly simplistic polar regions’ role as an energy sink, by to benefit from synergies of disciplines to attempt to recognize a ‘polar’ habitat. which they exert an important controlling across major biomes. Thus, fundamental This volume aims to demonstrate the range influence on global climates and circulation differences in patterns of environmental of habitats that exist towards both poles, patterns. variability acting on different timescales in the various important ways in which these Fogg introduces the reader to the ma- marine and terrestrial habitats are prob- two regions differ, and how the regions jor types of polar habitat: glacial (ice and ably behind the differences in physiology,