- Email: [email protected]
The geometric meaning of macroevolution
Update
TRENDS in Ecology and Evolution
Vol.18 No.6 June 2003
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| Research Focus
The geometric meaning of macroevolution Michael S.Y. Lee1 and Paul Doughty2 1
Department of Environmental Biology, University of Adelaide and Dept of Palaeontology, South Australian Museum, North Terrace, Adelaide, SA 5000, Australia 2 Department of Zoology & Entomology, University of Queensland, Brisbane, QLD 4072, Australia
Although the concept of bet-hedging has been useful in microevolutionary studies for over 25 years, a recent paper by Andrew Simons suggests that it is also applicable to macroevolutionary events, with the same fundamental process of selection working at all temporal scales. Whether macroevolution is merely microevolution writ long has been a prominent debate in biology, with protagonists often talking at cross-purposes. Some paleobiologists have stressed that long-term evolutionary phenomena, such as stasis, mass extinctions and adaptive radiations, cannot be predicted from population genetic theory, insisting that additional macroevolutionary laws applicable over vast timescales are needed [1 –3]. Population biologists have countered by stressing that these phenomena are nevertheless consistent with microevolutionary principles, rendering additional laws unnecessary or, at best, speculative and untestable [4,5]. A novel yet elementary new perspective could go a long way towards breaking this impasse. Simons [6] now proposes that the concepts of geometric mean fitness and bet-hedging can be applied at all timescales and all levels of biological organization, thus forming a single conceptual framework of natural selection working at both microevolutionary and macroevolutionary levels. Individuals, populations and higher taxa experience selective environments that fluctuate in time and space, although, traditionally, evolutionary models have assumed constancy for simplicity and tractability [7– 9]. In microevolutionary studies, the relative fitness of phenotypes varies over generations because of environmental fluctuations. As reproduction is a multiplicative process, the overall fitness of a genotype across several generations is proportional to the product (rather than the sum) of the generation-specific fitness values. Accordingly, the most appropriate measure of fitness in fluctuating environments is the geometric (rather than the arithmetic) mean fitness over all generations [8,10]. Unlike the arithmetic mean, the geometric mean is highly sensitive to single low values: the geometric mean across several generations will fall to zero if only one of those generations suffers total reproductive failure. Thus, in fluctuating environments, the fittest genotypes over multiple generations (those with the highest geometric mean) are sometimes not those with the highest arithmetic mean. Such ‘bet-hedging’ genotypes have the highest long-term Corresponding author: Michael S.Y. Lee ([email protected]). http://tree.trends.com
fitness in variable environments because they are least affected by catastrophic years, even though, in typical years, they have fewer offspring than do genotypes perched on short-term fitness peaks. The latter genotype would increase in frequency across consecutive ‘typical’ years, but this trend would be reversed (or ‘undone’) during the occasional bad year. The concepts of geometric mean fitness and bet-hedging have so far only been applied at microevolutionary timescales, such as those for seed dormancy [11], emergence time in weevils [12] and clutch size in birds [13]. Simons makes the novel point that there is no justification for restricting these concepts to short timeframes and individual organisms, and proposes applying it to macroevolutionary timescales and to all phylogenetic levels. Although similar approaches have already been applied in some macroevolutionary studies, they failed to identify explicitly the link to the microevolutionary realm. It is perhaps a testament to the micro – macro divide that studies at different scales have failed to recognize these common principles. Gilinsky [14], using families as proxies for species [15,16], confirmed that rates of speciation and extinction are highly correlated across a variety of taxa [17], probably because both processes are fostered by common underlying factors (e.g. low vagility, narrow niches or limited range [18,19]). Groups with high speciation– extinction rates experienced more rapid fluctuations in species diversity, and were termed ‘volatile’. Empirical data and simulations revealed that volatile groups went extinct early in geological time, their rapid diversity fluctuations making them likely to hit quickly the critical threshold of zero (Fig. 1). Only less volatile (here termed inert) groups persisted to the present. This stochastic process was proposed [14] to be responsible for the decline in extinction (and speciation) rates over geological time [20] (Fig. 1). This is a straightforward application of the bet-hedging perspective: for any given time period, temporal variability in fitness (volatility) lowers geometric mean fitness, leading to the prediction that volatile groups would be inexorably replaced by inert groups. Bet-hedging might also provide a novel perspective on mass extinctions. Taxonomic (and phenotypic) selectivity can differ between background and mass extinctions: the most successful groups (and traits) in ‘normal’ times often suffered the most during the great extinctions [2]. This has led to the proposal of a discrete new tier of evolutionary processes: global mass extinctions that are indifferent or even opposed to trends established during normal times
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Volatility of taxon
Mean global volatility
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Longevity (stratigraphic range) of taxon Geological time TRENDS in Ecology & Evolution
Fig. 1. Summary of Gilinsky’s [14] empirical data and simulations. Blue labels and curve show the inverse relationship between volatility and longevity of taxa: taxa with high speciation– extinction rates do not persist for long. Red labels and curve show the steady decline in the average volatility over geological time, caused primarily by the early extinction of volatile taxa.
[1,2]. However, the bet-hedging view [6] suggests an alternative interpretation. None of the hypothesized causes of mass extinctions is totally discrete in time or magnitude. Rather, they occur continuously, and in varying intensities from imperceptible (e.g. cosmic dust entering the atmosphere) to extreme (e.g. the Chicxulub crater). The large perturbations leading to mass extinctions might therefore be best viewed as extremes along a continuum of environmental disturbances that are outside the typical ‘experiences’ of most taxa. The ‘volatile’ groups (those with the highest speciation/extinction rates) would tend to suffer the most from any environmental fluctuations. These groups radiate most rapidly in amenable conditions but decline most rapidly in harsher times (epitomized by mass extinctions) [21], leading to differences in taxonomic and phenotypic success over these different timescales. Thus, the changes in selectivity observed during mass extinctions might represent an extreme response to a severe episode of continual environmental fluctuation [22], rather than a distinct evolutionary process. There is reason to expect nonlinearities in extinction effects along the entire continuum of disturbance intensities, not just at the extreme [6]. A similar shift of selection regimes occurs in the milder and more frequent climatic fluctuations caused by Milankovitch cycles, with sedentary specialists favoured during periods of climatic stability and vagile generalists favoured during briefer pulses of rapid change [23]. Traits favoured by infrequent large extinction events have been rejected as adaptations, their persistence being ascribed to good fortune (e.g. [1,24]). However, there is no single ‘correct’ timescale to measure fitness or adaptation [5,6,25]. Volatility, if it fosters adaptive radiation during an interval between large extinction events, would be an adaptation over this timescale. Equally though, if inertness has increased in prevalence during the Phanerozoic http://tree.trends.com
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through enhancing survivorship through repeated (if widely spaced) bouts of extreme environmental perturbations, it can legitimately considered an adaptation at this much longer timescale. The overall decline in volatility of the Earth’s biota has been interpreted as a byproduct of the superior survivorship of inert groups rather than ‘adaptation’ in the traditional sense [14]. A bet-hedging perspective would merge the two views: selective persistence of inert groups through repeated mass extinctions constitutes a true adaptation under virtually any definition of the term [5,25]. Thus, adaptations can occur at different hierarchical levels not only physically (organism, colony or species), but also temporally (ecological and evolutionary time). In both realms, selection at different levels can act in opposition, with traits advantageous at one level being neutral or deleterious at another. However, the macroevolutionary extrapolation of bethedging principle encounters complications. Although lineages with the best long-term success are (almost by definition) those with the highest geometric mean fitness, it remains to be established whether most traits can persist for long enough to affect such long-term fitness. As noted by Simons, the catastrophic fluctuations on macroevolutionary timescales might be so infrequent that organisms slipping through a mass extinction filter will undergo extensive evolution before the next filter comes, possibly losing the traits that conferred survival advantages. If so, the persistence of traits between these rare selection events is dubious, along with their status as adaptations built by natural selection for extinction resistance. Such evolutionary decay of the ‘fitness’ of organisms should be negligible on the shorter timescales that are the traditional domain of the bet-hedging perspective. Also, Simons observes that bet-hedging models for asexual organisms apply equally well for any branching hierarchical relationships, such as species-level phylogenies (Fig. 2; Box 1). However, the most well known organisms reproduce sexually. Microevolutionary (withinspecies) models for bet-hedging in sexual organisms will differ from macroevolutionary (across-species) models, because of gene flow between conspecific organisms but reproductive isolation between species (Fig. 2). This means that organisms passing through what constitutes an extreme event in the microevolutionary level can (potentially) interact with other survivors and transmit their characteristics horizontally, whereas species that pass through an analogous event at the macroevolutionary level cannot reproductively interact with other surviving species. Although the bet-hedging concept is, in principle, applicable to all temporal scales, the greatest challenge will be finding empirical support at higher levels. This will not be easy. Even at microevolutionary scales, support for bet-hedging in the evolution of life-history traits has been mixed [8]. For example, models of the evolution of reproductive effort predict that environmental variation will select for lower reproductive effort per reproductive bout, causing a shift away from semelparity and towards iteroparity [8]. However, the predicted difference between the arithmetic and geometric means is quantitatively
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Geological time
Ecological time
(b)
(a)
(c)
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Fig. 2. Organismal diversification through environmental fluctuations, illustrating self-similarity of selection in varying environments over all timescales (based on [6]). (a) A phylogeny of species through geological time, with the magnitude of environmental fluctuations indicated by intensity of colour. The rapidly speciating (top and bottom) clades are disproportionately affected by the brief extreme fluctuation shown in red. (b) shows in detail the population-level relationships of a short segment of the phylogeny (assuming the species is asexual). The topology is a fractal of the previous one, and smaller environmental fluctuations are still occurring constantly over this shorter timescale. (c) shows in detail the population-level relationships of a short segment of the phylogeny (assuming the species is sexual). Reticulate population relationships mean that (c) is not a fractal of (a).
small, making it difficult to distinguish which quantity selection is targeting. Moreover, long-term data of environmental variation will be difficult to obtain for even single populations in many cases. Testing bet-hedging at macroevolutionary scales runs into additional obstacles. Identifying the relevant environmental agents, their variation through time, their interaction with particular clade-level traits, and the heritability of such traits (Box 1) could prove to be very difficult. Furthermore, the effects under investigation might also be more subtle than at microevolutionary scales. The efficacy of selection on populations, species and clades is likely to be far less than selection of genes in a gene pool for the simple reason of sample size. In the latter case, the contribution to fitness of a gene is assessed across a plethora of individuals spanning numerous generations, before it is selectively maintained or eliminated. By contrast, higher entities are less numerous, and the deterministic action of selection at such levels is far more likely to be swamped by random drift (i.e. chance survival or extinction of species or entire clades [5]). In spite of these rather pessimistic caveats, however, the macroevolutionary studies discussed above [14,23], which have (implicitly) used bet hedging models, have found convincing empirical support for at least some of their predictions. This is an encouraging start, and should inspire more explicit studies. http://tree.trends.com
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Box 1. Heritability of clade-level traits The process of evolution by natural selection requires that there be nonrandom proliferation of varying hereditary entities. Williams [5] suggested a simple test to see if an entity could evolve adaptations by natural selection: if its history can be modeled as a dendrogram, then it usually indicates a continuity of information (i.e. heredity). Sexually reproducing individuals fail the dendrogram test because their genotypes are scrambled every generation. Genes, asexual organisms, species and clades all pass the dendrogram test and hence can be subject to evolution by natural selection (Fig. 2, main text). It follows that natural selection can shape adaptations at these levels. In some cases, organism-level traits can influence the ‘fitness’ of higher entities. The evolution of specialization and low vagility has been shown to be favoured during long stable periods, resulting in increased speciation and diversification [23]. When the environment changes, however, there is evidence that such organisms (and the higher entities containing them) are most vulnerable to extinction [18,23]. The heritability of such organism-level features is not controversial. The fitness of populations, species and clades, however, can be influenced by characteristics that manifest themselves beyond the traits of individual organisms, such as geographical range, population size and variation, and speciation rates. There is some evidence that such emergent features are also heritable. For example, in his study of late Cretaceous gastropod and bivalve mollusks [26], Jablonski found that sister taxa occurred over similar lengths of coastline indicating heritability for geographical range. Similarly, related lineages also tend to share similar speciation rates [19]. This is consistent with higher-level traits, such as geographical range, speciation rate and phenotypic variability, being ultimately determined by organism-level features with a strongly heritable component (e.g. locomotor ability, habitat specificity, mating systems, developmental plasticity or mutation rate).
Acknowledgements We thank A.M. Simons, A.F. Hugall and the M. Blows lab for helpful comments. M.S.Y.L. is supported by the Australian Research Council and P.D. by University of Queensland.
References 1 Gould, S.J. (1985) The paradox of the first tier: an agenda for paleobiology. Paleobiology 11, 1 – 12 2 Jablonski, D. (1986) Background and mass extinctions: the alternation of macroevolutionary regimes. Science 231, 129– 133 3 Gould, S.J. (1997) The paradox of the visibly irrelevant. Nat. Hist. 106 (11), 12 – 66 4 Charlesworth, B. et al. (1982) A Neo-Darwinian commentary on macroevolution. Evolution 36, 474 – 498 5 Williams, G.C. (1992) Natural Selection: Domains, Levels and Challenges, Oxford University Press 6 Simons, A.M. (2002) The continuity of microevolution and macroevolution. J. Evol. Biol. 15, 688 – 701 7 Levins, R. (1968) Evolution in Changing Environments, Princeton University Press 8 Roff, D.A. (2002) Life History Evolution, Sinauer 9 Meyers, L.A. and Bull, J.J. (2002) Fighting change with change: adaptive variation in an uncertain world. Trends Ecol. Evol. 17, 551 – 557 10 Seger, J. and Brockmann, J. (1987) What is bet-hedging? Oxf. Surv. Evol. Biol. 4, 182– 191 11 Ritland, K. (1983) The joint evolution of seed dormancy and flowering time in annual plants living in a variable environment. Theor. Pop. Biol. 24, 213– 243 12 Menu, F. (1993) Strategies of emergence in the chestnut weevil Curculio elephas (Coleoptera: Curculionidae). Oecologia 96, 383 – 390 13 Boyce, M.S. and Perrins, C.M. (1987) Optimizing Great Tit size in a fluctuating environment. Ecology 68, 142 – 153 14 Gilinsky, N.L. (1994) Volatility and the Phanerozoic decline of background extinction intensity. Paleobiology 20, 445– 458 15 Sepkoski, J.J. Jr and Kendrick, D.C. (1993) Numerical experiments
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with model monophyletic and paraphyletic taxa. Paleobiology 19, 168 – 194 Lee, M.S.Y. (1997) Documenting present and past biodiversity: conservation biology meets palaeontology. Trends Ecol. Evol. 12, 132–133 Sepkoski, J.J. Jr (1984) A kinetic model of Phanerozoic taxonomic diversification. III. Post-Paleozoic families and multiple equilibria. Paleobiology 10, 246– 267 McKinney, M.L. (1997) Extinction vulnerability and selectivity: Combining ecological and paleontological views. Annu. Rev. Ecol. Syst. 28, 495– 516 Savolainen, V. et al. (2002) Is cladogenesis heritable? Syst. Biol. 51, 835 – 843 Raup, D.M. and Sepkoski, J.J. Jr (1982) Mass extinctions and the marine fossil record. Science 215, 1501– 1503 Sepkoski, J.J. Jr (2001) Mass extinctions, concept of. Encycl. Biodiv. 4, 97 – 122
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22 Miller, A.I. (1998) Biotic transitions in global marine diversity. Science 281, 1157– 1160 23 Jannson, R. and Dynesius, M. (2002) The fate of clades in a world of recurrent climatic change: Milankovitch oscillations and evolution. Annu. Rev. Ecol. Syst. 33, 707 – 740 24 Jablonski, D. (1989) The biology of mass extinctions: a paleontological view. Philos. Trans. R. Soc. Lond. Ser. B 325, 357 – 368 25 Vermeij, G.J. (1996) Adaptations of clades: resistance and response. In Adaptation (Rose, M.R. and Lauder, G.V., eds) pp. 363 – 380, Academic Press 26 Jablonski, D. (1987) Heritability at the species level: analysis of geographic ranges of Cretaceous molluscs. Science 238, 360– 363
0169-5347/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0169-5347(03)00103-4
Catastrophes on Daisyworld David M. Wilkinson Biological and Earth Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AP, UK
For 20 years, a research tradition based on ‘Daisyworld’ models, which have strong coupling between life and the abiotic environment, has developed largely independently of mainstream theoretical ecology. A new paper in this tradition shows how small changes in external forcing can lead to catastrophic environmental change on this virtual planet. This has potential implications for the way that we view the Earth system, both in respect to the effects of human actions and for testing Lovelock’s Gaia hypothesis.
Lotka’s philosophy (for ecological examples, see [3– 6]). However, most of these ‘Daisyworld’ models have been published in nonecological journals, and, as such, they have had little impact on mainstream ecological thinking. For example, out of the 73 citations to one of the founding papers of this approach [7] listed by Web of Science, only eight were from ecological journals. A new paper developing these models, by Graeme Ackland, Michael Clark and Tim Lenton [8], offers much to think about for more traditionally minded ecologists.
Open a textbook on mathematical ecology and, along with the logistic growth curve, you will find that Lotka – Volterra models of interacting populations form a starting point for much of theoretical ecology. These models are typical of most mathematical ecology in treating the interactions between the organisms without considering feedbacks between the organisms and the ‘abiotic’ environment. Gould [1] described the conventional position well, writing ‘the environment proposes and natural selection disposes’. Although most modern ecologists probably only know the name of Alfred Lotka through his eponymous equation, it is not typical of his approach to ecology. He regarded the organic and ‘abiotic’ aspects of the world as a single system in which it was impossible to understand the working of any part of the system without understanding the whole [2]. This lead him to suggest that modelling the whole system (biotic and abiotic) would prove simpler than trying to model an unrealistically isolated fragment. However, mainstream theoretical ecology has usually followed the reductionist approach of considering organisms as isolated systems. Over the past 20 years, an alternative approach to ecological modelling has developed that is much closer to
Daisyworld The origin of the Daisyworld model lies in an attempt by James Lovelock to counter claims of teleology levelled against his GAIA hypothesis (see Glossary; Box 1). In classic Daisyworlds, a cloudless planet is the home of two kinds of daisy. One type is dark (ground covered by it reflects less light than does bare ground) and the other is light, reflecting more light than does bare ground. The daisies are conventionally referred to as ‘black’ and ‘white’, although the key point is the way in which their ALBEDO differs from unvegetated ground. The growth rates of these daisies are assumed to be a parabolic function of temperature. Black daisies absorb more solar energy than do white ones and so are warmer. The temperature
Corresponding author: David M. Wilkinson ([email protected]). http://tree.trends.com
Glossary Albedo : the reflectivity of a surface. High albedo means high reflectance (e.g. snow fields). Cellular automata : spatially explicit models in which the state of a given ‘cell’ is determined by the state of surrounding ‘cells’. Gaia : in its recent formulation, the Gaia hypothesis proposes that the Earth’s biota and its surface environment form a self-regulating system that keeps the planet in a habitable state. Phase transition : the transition between different phases of a substance (e.g. for water, the transitions between ice and liquid water or liquid water and steam).
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| Research Focus
The geometric meaning of macroevolution Michael S.Y. Lee1 and Paul Doughty2 1
Department of Environmental Biology, University of Adelaide and Dept of Palaeontology, South Australian Museum, North Terrace, Adelaide, SA 5000, Australia 2 Department of Zoology & Entomology, University of Queensland, Brisbane, QLD 4072, Australia
Although the concept of bet-hedging has been useful in microevolutionary studies for over 25 years, a recent paper by Andrew Simons suggests that it is also applicable to macroevolutionary events, with the same fundamental process of selection working at all temporal scales. Whether macroevolution is merely microevolution writ long has been a prominent debate in biology, with protagonists often talking at cross-purposes. Some paleobiologists have stressed that long-term evolutionary phenomena, such as stasis, mass extinctions and adaptive radiations, cannot be predicted from population genetic theory, insisting that additional macroevolutionary laws applicable over vast timescales are needed [1 –3]. Population biologists have countered by stressing that these phenomena are nevertheless consistent with microevolutionary principles, rendering additional laws unnecessary or, at best, speculative and untestable [4,5]. A novel yet elementary new perspective could go a long way towards breaking this impasse. Simons [6] now proposes that the concepts of geometric mean fitness and bet-hedging can be applied at all timescales and all levels of biological organization, thus forming a single conceptual framework of natural selection working at both microevolutionary and macroevolutionary levels. Individuals, populations and higher taxa experience selective environments that fluctuate in time and space, although, traditionally, evolutionary models have assumed constancy for simplicity and tractability [7– 9]. In microevolutionary studies, the relative fitness of phenotypes varies over generations because of environmental fluctuations. As reproduction is a multiplicative process, the overall fitness of a genotype across several generations is proportional to the product (rather than the sum) of the generation-specific fitness values. Accordingly, the most appropriate measure of fitness in fluctuating environments is the geometric (rather than the arithmetic) mean fitness over all generations [8,10]. Unlike the arithmetic mean, the geometric mean is highly sensitive to single low values: the geometric mean across several generations will fall to zero if only one of those generations suffers total reproductive failure. Thus, in fluctuating environments, the fittest genotypes over multiple generations (those with the highest geometric mean) are sometimes not those with the highest arithmetic mean. Such ‘bet-hedging’ genotypes have the highest long-term Corresponding author: Michael S.Y. Lee ([email protected]). http://tree.trends.com
fitness in variable environments because they are least affected by catastrophic years, even though, in typical years, they have fewer offspring than do genotypes perched on short-term fitness peaks. The latter genotype would increase in frequency across consecutive ‘typical’ years, but this trend would be reversed (or ‘undone’) during the occasional bad year. The concepts of geometric mean fitness and bet-hedging have so far only been applied at microevolutionary timescales, such as those for seed dormancy [11], emergence time in weevils [12] and clutch size in birds [13]. Simons makes the novel point that there is no justification for restricting these concepts to short timeframes and individual organisms, and proposes applying it to macroevolutionary timescales and to all phylogenetic levels. Although similar approaches have already been applied in some macroevolutionary studies, they failed to identify explicitly the link to the microevolutionary realm. It is perhaps a testament to the micro – macro divide that studies at different scales have failed to recognize these common principles. Gilinsky [14], using families as proxies for species [15,16], confirmed that rates of speciation and extinction are highly correlated across a variety of taxa [17], probably because both processes are fostered by common underlying factors (e.g. low vagility, narrow niches or limited range [18,19]). Groups with high speciation– extinction rates experienced more rapid fluctuations in species diversity, and were termed ‘volatile’. Empirical data and simulations revealed that volatile groups went extinct early in geological time, their rapid diversity fluctuations making them likely to hit quickly the critical threshold of zero (Fig. 1). Only less volatile (here termed inert) groups persisted to the present. This stochastic process was proposed [14] to be responsible for the decline in extinction (and speciation) rates over geological time [20] (Fig. 1). This is a straightforward application of the bet-hedging perspective: for any given time period, temporal variability in fitness (volatility) lowers geometric mean fitness, leading to the prediction that volatile groups would be inexorably replaced by inert groups. Bet-hedging might also provide a novel perspective on mass extinctions. Taxonomic (and phenotypic) selectivity can differ between background and mass extinctions: the most successful groups (and traits) in ‘normal’ times often suffered the most during the great extinctions [2]. This has led to the proposal of a discrete new tier of evolutionary processes: global mass extinctions that are indifferent or even opposed to trends established during normal times
Update
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Volatility of taxon
Mean global volatility
264
Longevity (stratigraphic range) of taxon Geological time TRENDS in Ecology & Evolution
Fig. 1. Summary of Gilinsky’s [14] empirical data and simulations. Blue labels and curve show the inverse relationship between volatility and longevity of taxa: taxa with high speciation– extinction rates do not persist for long. Red labels and curve show the steady decline in the average volatility over geological time, caused primarily by the early extinction of volatile taxa.
[1,2]. However, the bet-hedging view [6] suggests an alternative interpretation. None of the hypothesized causes of mass extinctions is totally discrete in time or magnitude. Rather, they occur continuously, and in varying intensities from imperceptible (e.g. cosmic dust entering the atmosphere) to extreme (e.g. the Chicxulub crater). The large perturbations leading to mass extinctions might therefore be best viewed as extremes along a continuum of environmental disturbances that are outside the typical ‘experiences’ of most taxa. The ‘volatile’ groups (those with the highest speciation/extinction rates) would tend to suffer the most from any environmental fluctuations. These groups radiate most rapidly in amenable conditions but decline most rapidly in harsher times (epitomized by mass extinctions) [21], leading to differences in taxonomic and phenotypic success over these different timescales. Thus, the changes in selectivity observed during mass extinctions might represent an extreme response to a severe episode of continual environmental fluctuation [22], rather than a distinct evolutionary process. There is reason to expect nonlinearities in extinction effects along the entire continuum of disturbance intensities, not just at the extreme [6]. A similar shift of selection regimes occurs in the milder and more frequent climatic fluctuations caused by Milankovitch cycles, with sedentary specialists favoured during periods of climatic stability and vagile generalists favoured during briefer pulses of rapid change [23]. Traits favoured by infrequent large extinction events have been rejected as adaptations, their persistence being ascribed to good fortune (e.g. [1,24]). However, there is no single ‘correct’ timescale to measure fitness or adaptation [5,6,25]. Volatility, if it fosters adaptive radiation during an interval between large extinction events, would be an adaptation over this timescale. Equally though, if inertness has increased in prevalence during the Phanerozoic http://tree.trends.com
Vol.18 No.6 June 2003
through enhancing survivorship through repeated (if widely spaced) bouts of extreme environmental perturbations, it can legitimately considered an adaptation at this much longer timescale. The overall decline in volatility of the Earth’s biota has been interpreted as a byproduct of the superior survivorship of inert groups rather than ‘adaptation’ in the traditional sense [14]. A bet-hedging perspective would merge the two views: selective persistence of inert groups through repeated mass extinctions constitutes a true adaptation under virtually any definition of the term [5,25]. Thus, adaptations can occur at different hierarchical levels not only physically (organism, colony or species), but also temporally (ecological and evolutionary time). In both realms, selection at different levels can act in opposition, with traits advantageous at one level being neutral or deleterious at another. However, the macroevolutionary extrapolation of bethedging principle encounters complications. Although lineages with the best long-term success are (almost by definition) those with the highest geometric mean fitness, it remains to be established whether most traits can persist for long enough to affect such long-term fitness. As noted by Simons, the catastrophic fluctuations on macroevolutionary timescales might be so infrequent that organisms slipping through a mass extinction filter will undergo extensive evolution before the next filter comes, possibly losing the traits that conferred survival advantages. If so, the persistence of traits between these rare selection events is dubious, along with their status as adaptations built by natural selection for extinction resistance. Such evolutionary decay of the ‘fitness’ of organisms should be negligible on the shorter timescales that are the traditional domain of the bet-hedging perspective. Also, Simons observes that bet-hedging models for asexual organisms apply equally well for any branching hierarchical relationships, such as species-level phylogenies (Fig. 2; Box 1). However, the most well known organisms reproduce sexually. Microevolutionary (withinspecies) models for bet-hedging in sexual organisms will differ from macroevolutionary (across-species) models, because of gene flow between conspecific organisms but reproductive isolation between species (Fig. 2). This means that organisms passing through what constitutes an extreme event in the microevolutionary level can (potentially) interact with other survivors and transmit their characteristics horizontally, whereas species that pass through an analogous event at the macroevolutionary level cannot reproductively interact with other surviving species. Although the bet-hedging concept is, in principle, applicable to all temporal scales, the greatest challenge will be finding empirical support at higher levels. This will not be easy. Even at microevolutionary scales, support for bet-hedging in the evolution of life-history traits has been mixed [8]. For example, models of the evolution of reproductive effort predict that environmental variation will select for lower reproductive effort per reproductive bout, causing a shift away from semelparity and towards iteroparity [8]. However, the predicted difference between the arithmetic and geometric means is quantitatively
Update
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Geological time
Ecological time
(b)
(a)
(c)
TRENDS in Ecology & Evolution
Fig. 2. Organismal diversification through environmental fluctuations, illustrating self-similarity of selection in varying environments over all timescales (based on [6]). (a) A phylogeny of species through geological time, with the magnitude of environmental fluctuations indicated by intensity of colour. The rapidly speciating (top and bottom) clades are disproportionately affected by the brief extreme fluctuation shown in red. (b) shows in detail the population-level relationships of a short segment of the phylogeny (assuming the species is asexual). The topology is a fractal of the previous one, and smaller environmental fluctuations are still occurring constantly over this shorter timescale. (c) shows in detail the population-level relationships of a short segment of the phylogeny (assuming the species is sexual). Reticulate population relationships mean that (c) is not a fractal of (a).
small, making it difficult to distinguish which quantity selection is targeting. Moreover, long-term data of environmental variation will be difficult to obtain for even single populations in many cases. Testing bet-hedging at macroevolutionary scales runs into additional obstacles. Identifying the relevant environmental agents, their variation through time, their interaction with particular clade-level traits, and the heritability of such traits (Box 1) could prove to be very difficult. Furthermore, the effects under investigation might also be more subtle than at microevolutionary scales. The efficacy of selection on populations, species and clades is likely to be far less than selection of genes in a gene pool for the simple reason of sample size. In the latter case, the contribution to fitness of a gene is assessed across a plethora of individuals spanning numerous generations, before it is selectively maintained or eliminated. By contrast, higher entities are less numerous, and the deterministic action of selection at such levels is far more likely to be swamped by random drift (i.e. chance survival or extinction of species or entire clades [5]). In spite of these rather pessimistic caveats, however, the macroevolutionary studies discussed above [14,23], which have (implicitly) used bet hedging models, have found convincing empirical support for at least some of their predictions. This is an encouraging start, and should inspire more explicit studies. http://tree.trends.com
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Box 1. Heritability of clade-level traits The process of evolution by natural selection requires that there be nonrandom proliferation of varying hereditary entities. Williams [5] suggested a simple test to see if an entity could evolve adaptations by natural selection: if its history can be modeled as a dendrogram, then it usually indicates a continuity of information (i.e. heredity). Sexually reproducing individuals fail the dendrogram test because their genotypes are scrambled every generation. Genes, asexual organisms, species and clades all pass the dendrogram test and hence can be subject to evolution by natural selection (Fig. 2, main text). It follows that natural selection can shape adaptations at these levels. In some cases, organism-level traits can influence the ‘fitness’ of higher entities. The evolution of specialization and low vagility has been shown to be favoured during long stable periods, resulting in increased speciation and diversification [23]. When the environment changes, however, there is evidence that such organisms (and the higher entities containing them) are most vulnerable to extinction [18,23]. The heritability of such organism-level features is not controversial. The fitness of populations, species and clades, however, can be influenced by characteristics that manifest themselves beyond the traits of individual organisms, such as geographical range, population size and variation, and speciation rates. There is some evidence that such emergent features are also heritable. For example, in his study of late Cretaceous gastropod and bivalve mollusks [26], Jablonski found that sister taxa occurred over similar lengths of coastline indicating heritability for geographical range. Similarly, related lineages also tend to share similar speciation rates [19]. This is consistent with higher-level traits, such as geographical range, speciation rate and phenotypic variability, being ultimately determined by organism-level features with a strongly heritable component (e.g. locomotor ability, habitat specificity, mating systems, developmental plasticity or mutation rate).
Acknowledgements We thank A.M. Simons, A.F. Hugall and the M. Blows lab for helpful comments. M.S.Y.L. is supported by the Australian Research Council and P.D. by University of Queensland.
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with model monophyletic and paraphyletic taxa. Paleobiology 19, 168 – 194 Lee, M.S.Y. (1997) Documenting present and past biodiversity: conservation biology meets palaeontology. Trends Ecol. Evol. 12, 132–133 Sepkoski, J.J. Jr (1984) A kinetic model of Phanerozoic taxonomic diversification. III. Post-Paleozoic families and multiple equilibria. Paleobiology 10, 246– 267 McKinney, M.L. (1997) Extinction vulnerability and selectivity: Combining ecological and paleontological views. Annu. Rev. Ecol. Syst. 28, 495– 516 Savolainen, V. et al. (2002) Is cladogenesis heritable? Syst. Biol. 51, 835 – 843 Raup, D.M. and Sepkoski, J.J. Jr (1982) Mass extinctions and the marine fossil record. Science 215, 1501– 1503 Sepkoski, J.J. Jr (2001) Mass extinctions, concept of. Encycl. Biodiv. 4, 97 – 122
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22 Miller, A.I. (1998) Biotic transitions in global marine diversity. Science 281, 1157– 1160 23 Jannson, R. and Dynesius, M. (2002) The fate of clades in a world of recurrent climatic change: Milankovitch oscillations and evolution. Annu. Rev. Ecol. Syst. 33, 707 – 740 24 Jablonski, D. (1989) The biology of mass extinctions: a paleontological view. Philos. Trans. R. Soc. Lond. Ser. B 325, 357 – 368 25 Vermeij, G.J. (1996) Adaptations of clades: resistance and response. In Adaptation (Rose, M.R. and Lauder, G.V., eds) pp. 363 – 380, Academic Press 26 Jablonski, D. (1987) Heritability at the species level: analysis of geographic ranges of Cretaceous molluscs. Science 238, 360– 363
0169-5347/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0169-5347(03)00103-4
Catastrophes on Daisyworld David M. Wilkinson Biological and Earth Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AP, UK
For 20 years, a research tradition based on ‘Daisyworld’ models, which have strong coupling between life and the abiotic environment, has developed largely independently of mainstream theoretical ecology. A new paper in this tradition shows how small changes in external forcing can lead to catastrophic environmental change on this virtual planet. This has potential implications for the way that we view the Earth system, both in respect to the effects of human actions and for testing Lovelock’s Gaia hypothesis.
Lotka’s philosophy (for ecological examples, see [3– 6]). However, most of these ‘Daisyworld’ models have been published in nonecological journals, and, as such, they have had little impact on mainstream ecological thinking. For example, out of the 73 citations to one of the founding papers of this approach [7] listed by Web of Science, only eight were from ecological journals. A new paper developing these models, by Graeme Ackland, Michael Clark and Tim Lenton [8], offers much to think about for more traditionally minded ecologists.
Open a textbook on mathematical ecology and, along with the logistic growth curve, you will find that Lotka – Volterra models of interacting populations form a starting point for much of theoretical ecology. These models are typical of most mathematical ecology in treating the interactions between the organisms without considering feedbacks between the organisms and the ‘abiotic’ environment. Gould [1] described the conventional position well, writing ‘the environment proposes and natural selection disposes’. Although most modern ecologists probably only know the name of Alfred Lotka through his eponymous equation, it is not typical of his approach to ecology. He regarded the organic and ‘abiotic’ aspects of the world as a single system in which it was impossible to understand the working of any part of the system without understanding the whole [2]. This lead him to suggest that modelling the whole system (biotic and abiotic) would prove simpler than trying to model an unrealistically isolated fragment. However, mainstream theoretical ecology has usually followed the reductionist approach of considering organisms as isolated systems. Over the past 20 years, an alternative approach to ecological modelling has developed that is much closer to
Daisyworld The origin of the Daisyworld model lies in an attempt by James Lovelock to counter claims of teleology levelled against his GAIA hypothesis (see Glossary; Box 1). In classic Daisyworlds, a cloudless planet is the home of two kinds of daisy. One type is dark (ground covered by it reflects less light than does bare ground) and the other is light, reflecting more light than does bare ground. The daisies are conventionally referred to as ‘black’ and ‘white’, although the key point is the way in which their ALBEDO differs from unvegetated ground. The growth rates of these daisies are assumed to be a parabolic function of temperature. Black daisies absorb more solar energy than do white ones and so are warmer. The temperature
Corresponding author: David M. Wilkinson ([email protected]). http://tree.trends.com
Glossary Albedo : the reflectivity of a surface. High albedo means high reflectance (e.g. snow fields). Cellular automata : spatially explicit models in which the state of a given ‘cell’ is determined by the state of surrounding ‘cells’. Gaia : in its recent formulation, the Gaia hypothesis proposes that the Earth’s biota and its surface environment form a self-regulating system that keeps the planet in a habitable state. Phase transition : the transition between different phases of a substance (e.g. for water, the transitions between ice and liquid water or liquid water and steam).