Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-28T17:15:54.709Z Has data issue: false hasContentIssue false
  • English
  • Français

Gradual or pulsed evolution: when should punctuational explanations be preferred?

Published online by Cambridge University Press:  08 April 2016

Gene Hunt
Gene Hunt*
Affiliation:
Department of Paleobiology, National Museum of Natural History Smithsonian Institution, NHB, MRC 121, Post Office Box 37012, Washington D.C. 20013-7012. E-mail: hunte@si.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

The problem of gradual versus punctuated change within phyletic lineages can be understood in terms of the homogeneity of evolutionary dynamics. Hypotheses of punctuated change imply that the rules governing evolutionary change shift over time such that the normal dynamics of stasis are temporarily suspended, permitting a period of net evolutionary change. Such explanations are members of a larger class of models in which evolutionary dynamics are in some way heterogeneous over time. In this paper, I develop a likelihood-based statistical framework to evaluate the support for this kind of evolutionary model. This approach divides evolutionary sequences into nonoverlapping segments, each of which is fit to a separate evolutionary model. Models with heterogeneous dynamics are generally more complex—they require more parameters to specify—than uniform evolutionary models such as random walks and stasis. The Akaike Information Criterion can be used to judge whether the greater complexity of punctuational models is offset by a sufficient gain in log-likelihood for these models to be preferred.

I use this approach to analyze three case studies for which punctuational explanations have been proposed. In the first, a model of punctuated evolution best accounted for changes in pygidial morphology within a lineage of the trilobite Flexicalymene, but the uniform model of an unbiased random walk remains a plausible alternative. Body size evolution in the radiolarian Pseudocubus vema was neither purely gradual nor completely pulsed. Instead, the best-supported explanation posited a single, pulsed increase, followed later by a shift to an unbiased random walk. Finally, for the much-analyzed claim of “punctuated gradualism“ in the foraminifera Globorotalia, the best-supported model implied two periods of stasis separated by a period of elevated but not inherently directional evolution. Although the conclusions supported by these analyses generally refined rather than overturned previous views, the present approach differs from those prior in that all competing interpretations were formalized into explicit statistical models, allowing their relative support to be unambiguously compared.

Type
Articles
Information
Paleobiology , Volume 34 , Issue 3 , Summer 2008 , pp. 360 - 377
Copyright
Copyright © The Paleontological Society 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19:716–723.CrossRefGoogle Scholar
Anderson, D. R., Burnham, K. P., and Thompson, W. L. 2000. Null hypothesis testing: problems, prevalence, and an alternative. Journal of Wildlife Management 64:912–923.CrossRefGoogle Scholar
Bookstein, F. L. 1987. Random walk and the existence of evolutionary rates. Paleobiology 13:446–464.CrossRefGoogle Scholar
Bookstein, F. L., Gingerich, P. D., and Kluge, A. G. 1978. Hierarchical linear modeling of the tempo and mode of evolution. Paleobiology 4:120–134.CrossRefGoogle Scholar
Brett, C. E., and Baird, G. C. 2002. Revised stratigraphy of the Trenton Group in its type area, central New York State: sedimentology and tectonics of a Middle Ordovician shelf-to-basin succession. Physics and Chemistry of the Earth 27:231–263.CrossRefGoogle Scholar
Burnham, K. P., and Anderson, D. R. 2004. Multimodel inference. Understanding AIC and BIC in model selection. Sociological Methods and Research 33:261–304.CrossRefGoogle Scholar
Butler, M. A., and King, A. A. 2004. Phylogenetic comparative analysis: a modeling approach for adaptive evolution. American Naturalist 164:683–695.CrossRefGoogle ScholarPubMed
Charlesworth, B. 1984. Some quantitative methods for studying evolutionary patterns in single characters. Paleobiology 10:308–318.CrossRefGoogle Scholar
Cisne, J. L., and Rabe, B. D. 1978. Coenocorrelation: gradient analysis of fossil communities and its applications to stratigraphy. Lethaia 11:341–364.CrossRefGoogle Scholar
Cisne, J. L., Chandlee, G. O., Rabe, B. D., and Cohen, J. A. 1980. Geographic variation and episodic evolution in an Ordovician trilobite. Science 209:925–927.CrossRefGoogle Scholar
Eldredge, N., and Gould, S. J. 1972. Punctuated equilibria: an alternative to phyletic gradualism. Pp. 82–115 in Schopf, T. J. M., ed. Models in paleobiology. Freeman, Cooper, San Francisco.Google Scholar
Eldredge, N., Thompson, J. N., Brakefield, P. M., Gavrilets, S., Jablonski, D., Jackson, J. B. C., Lenski, R. E., Lieberman, B. S., McPeek, M. A., and Miller, W. I. 2005. The dynamics of evolutionary stasis. In Vrba, E. S. and Eldredge, N., eds. Macroevolution: diversity, disparity, contingency. Paleobiology 31(Suppl. to No. 2):133–145.CrossRefGoogle Scholar
Erwin, D. H., and Anstey, R. L. 1995. Speciation in the fossil record. Pp. 11–38 in Erwin, D. H. and Anstey, R. L., eds. New approaches to speciation in the fossil record. Columbia University Press, New York.Google Scholar
Estes, S., and Arnold, S. J. 2007. Resolving the paradox of stasis: models with stabilizing selection explain evolutionary divergence on all timescales. American Naturalist 169:227–244.CrossRefGoogle ScholarPubMed
Forster, M. 2001. The new science of simplicity. Pp. 83–119 in Zellner, A., Keuzenkamp, A., and McAleer, M., eds. Simplicity, inference and modelling. Cambridge University Press, Cambridge.Google Scholar
Forster, M., and Sober, E. 1994. How to tell when simpler, more unified or less ad hoc theories will provide more accurate predictions. British Journal of the Philosophy of Science 45:1–35.CrossRefGoogle Scholar
Fortey, R. A. 1985. Gradualism and punctuated equilibria as competing and complementary theories. Special Papers in Palaeontology 33:17–28.Google Scholar
Gingerich, P. D. 1985. Species in the fossil record: concepts, trends, and transitions. Paleobiology 11:27–41.CrossRefGoogle Scholar
Gingerich, P. D. 1993. Quantification and comparison of evolutionary rates. American Journal of Science 293-A:453–478.CrossRefGoogle Scholar
Gould, S. J. 2002. The structure of evolutionary theory. Belknap Press of Harvard University Press, Cambridge.Google Scholar
Gould, S. J., and Eldredge, N. 1977. Punctuated equilibria: the tempo and mode of evolution reconsidered. Paleobiology 3:115–151.CrossRefGoogle Scholar
Gradstein, F. M., Ogg, J. G., and Smith, A. G., eds. 2004. A geological time scale 2004. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Hannisdal, B. 2006. Phenotypic evolution in the fossil record: numerical experiments. Journal of Geology 114:133–153.CrossRefGoogle Scholar
Hannisdal, B. 2007. Inferring phenotypic evolution in the fossil record by Bayesian inversion. Paleobiology 33:98–115.CrossRefGoogle Scholar
Hansen, T. F. 1997. Stabilizing selection and the comparative analysis of adaptation. Evolution 51:1341–1351.CrossRefGoogle ScholarPubMed
Hansen, T. F., and Martins, E. P. 1996. Translating between microevolutionary process and macroevolutionary patterns: the correlation structure of interspecific data. Evolution 50:1404–1417.CrossRefGoogle ScholarPubMed
Hoffman, A. 1989. Arguments on evolution. Oxford University Press, New York.Google Scholar
Hunt, G. 2004. Phenotypic variation in fossil samples: modeling the consequences of time-averaging. Paleobiology 30:426–443.2.0.CO;2>CrossRefGoogle Scholar
Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology 32:578–601.CrossRefGoogle Scholar
Hunt, G. 2007. The relative importance of directional change, random walks, and stasis in the evolution of fossil lineages. Proceedings of the National Academy of Sciences USA 104:18404–18408.CrossRefGoogle ScholarPubMed
Hunt, G. 2008. PaleoTS: modeling evolution in paleontological time-series, Version 0.3–1.Google Scholar
Hunt, G., Bell, M. A., and Travis, M. P. 2008. Evolution toward a new adaptive optimum: phenotypic evolution in a fossil stickleback lineage. Evolution 62:700–710.CrossRefGoogle Scholar
Hurvich, C. M., and Tsai, C.-L. 1989. Regression and time series model selection in small samples. Biometrika 76:297–307.CrossRefGoogle Scholar
Jackson, J. B. C., and Cheetham, A. H. 1999. Tempo and mode of speciation in the sea. Trends in Ecology and Evolution 14:72–77.CrossRefGoogle ScholarPubMed
Kalinowski, S. T., and Taper, M. L. 2005. Likelihood-based confidence intervals of relative fitness for a common experimental design. Canadian Journal of Fisheries and Aquatic Science 62:693–699.CrossRefGoogle Scholar
Kellogg, D. E. 1975. The role of phyletic change in the evolution of Pseudocubus vema . Paleobiology 1:359–370.CrossRefGoogle Scholar
Kitchell, J. A., Estabrook, G., and MacLeod, N. 1987. Testing for equality of rates of evolution. Paleobiology 13:272–285.CrossRefGoogle Scholar
Levinton, J. S. 2001. Genetics, paleontology, and macroevolution. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Link, W. A., and Barker, R. J. 2006. Model weights and the foundations of multimodel inference. Ecology 87:2626–2635.CrossRefGoogle ScholarPubMed
Lovy, D. 1996. WinDig, Version 2. 5.Google Scholar
MacLeod, N. 1991. Punctuated anagenesis and the importance of stratigraphy to paleobiology. Paleobiology 17:167–188.CrossRefGoogle Scholar
Malmgren, B. A., Berggren, W. A., and Lohmann, G. P. 1983. Evidence for punctuated gradualism in the Late Neogene Globorotalia tumida lineage of planktonic foraminifera. Paleobiology 9:377–389.CrossRefGoogle Scholar
Martins, E. P., Diniz-Filho, J. A. F., and Housworth, E. A. 2002. Adaptive constraints and the phylogenetic comparative method: a computer simulation test. Evolution 56:1–13.Google ScholarPubMed
Meeker, W. Q., and Escobar, L. A. 1995. Teaching about approximate confidence regions based on maximum likelihood estimation. American Statistician 49:48–53.Google Scholar
R Development Core Team. 2007. R: a language and environment for statistical computing, Version 2.6.1. R Foundation for Statistical Computing, Vienna.Google Scholar
Roopnarine, P. D. 2001. The description and classification of evolutionary mode: a computational approach. Paleobiology 27:446–465.2.0.CO;2>CrossRefGoogle Scholar
Roopnarine, P. D., Byars, G., and Fitzgerald, P. 1999. Anagenetic evolution, stratophenetic patterns, and random walk models. Paleobiology 25:41–57.Google Scholar
Sheets, H. D., and Mitchell, C. E. 2001. Why the null matters: statistical tests, random walks and evolution. Genetica 112–113:105–125.CrossRefGoogle Scholar
Wagner, P. J. 2000. Likelihood tests of hypothesized durations: determining and accommodating biasing factors. Paleobiology 26:431–449.2.0.CO;2>CrossRefGoogle Scholar