Hostname: page-component-7bb8b95d7b-qxsvm Total loading time: 0 Render date: 2024-09-24T05:28:55.331Z Has data issue: false hasContentIssue false

The evolutionary bootstrap: a new approach to the study of taxonomic diversity

Published online by Cambridge University Press:  08 April 2016

Norman L. Gilinsky
Affiliation:
Department of Geological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061
Richard K. Bambach
Affiliation:
Department of Geological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061

Abstract

The evolutionary bootstrap is a new approach to the analysis of patterns of taxonomic diversity. In general, the evolutionary bootstrap works by surveying the diversity history of a taxon, learning its dynamic properties, and then generating randomly large numbers of artificial diversity histories based upon what was learned. The distribution of artificial—or bootstrapped—diversity histories approximates the distribution of diversity histories that were possible for taxa with the dynamic properties of the real taxon, and serves as a paleontological null hypothesis for studying statistically the diversity history of the real taxon.

Two null hypotheses were established, the additive and the multiplicative. The additive null hypothesis assumes that the amount of diversity change that occurs in a higher taxon during an interval of time is independent of the number of member subtaxa present at the beginning of the interval. The multiplicative null hypothesis, in contrast, assumes that the amount of diversity change that occurs is dependent upon the number of member subtaxa present at the start. Thus the two null hypotheses represent end members of a diversity-independent/diversity-dependent continuum of possibilities.

Detailed analyses using the evolutionary bootstrap, in conjunction with the clade statistics of Gould et al. (1977), show that several of the 17 higher taxa studied have diversity histories that are statistically significantly different from the random expectation under one or both null hypotheses. Analyses of multiple taxa in aggregate also reveal several properties of diversity histories that are statistically significantly different from random. Real taxa tend to have higher uniformities and lower maximum diversities than expected under the multiplicative null hypothesis. They have lower uniformities, higher maximum diversities, and longer durations than expected under the additive null hypothesis. And, they have lower centers of gravity than expected under either null hypothesis. Overall, the results provide a possible statistical verification of the process of taxonomic (traditionally, adaptive) radiation and suggest the need to consider deterministic explanations for observed diversity patterns.

Type
Articles
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

Bambach, R. K. and Gilinsky, N. L. 1984. Mass extinctions considered as artifacts, not events. Geol. Soc. Am. Abstr. Progr. 16:436.Google Scholar
Diaconis, P. and Efron, B. 1984. Computer-intensive methods in statistics. Sci. Am. 248(5):116130.Google Scholar
Efron, B. 1979. Computers and the theory of statistics: thinking the unthinkable. SIAM Rev. 21:460480.Google Scholar
Efron, B. 1981. Nonparametric standard errors and confidence intervals. Can. J. Statis. 9:139172.Google Scholar
Efron, B. and Gong, G. 1983. A leisurely look at the bootstrap, the jackknife, and cross-validation. Am. Statist. 37:3648.Google Scholar
Fisher, R. A. 1954. Statistical Methods for Research Workers. 12th ed.Oliver & Boyd; Edinburgh.Google Scholar
Gilinsky, N. L. and Bambach, R. K. 1984. A new look at patterns of taxonomic diversity: evidence for extrinsic biotic control. Geol. Soc. Am. Abstr. Progr. 16:519.Google Scholar
Gilinsky, N. L. and Bambach, R. K. 1985. The roots beneath patterns of taxonomic diversity: implications for extinction. Geol. Soc. Am. Abstr. Progr. 17:592.Google Scholar
Gould, S. J. and Raup, D. M. 1975. The shape of evolution: a comparison of real and random clades. Geol. Soc. Am. Abstr. Progr. 7:1088.Google Scholar
Gould, S. J., Raup, D. M., Sepkoski, J. J. Jr., Schopf, T. J. M., and Simberloff, D. S. 1977. The shape of evolution: a comparison of real and random clades. Paleobiology. 3:2340.CrossRefGoogle Scholar
Harper, C. W. Jr. 1975. Standing diversity of fossil groups in successive intervals of geologic time: a new measure. J. Paleontol. 49:752757.Google Scholar
Kolata, G. 1984. The art of learning from experience. Science. 225:156158.Google Scholar
Raup, D. M. 1977. Stochastic models in evolutionary paleontology. Pp. 5978. In: Hallam, A., ed. Patterns of Evolution as Illustrated by the Fossil Record. Elsevier; Amsterdam.CrossRefGoogle Scholar
Raup, D. M. and Gould, S. J. 1974. Stochastic simulation and the evolution of morphology—towards a nomothetic paleontology. Syst. Zool. 23:305322.Google Scholar
Raup, D. M., Gould, S. J., Schopf, T. J. M., and Simberloff, D. S. 1973. Stochastic models of phylogeny and the evolution of diversity. J. Geol. 81:525542.Google Scholar
Raup, D. M. and Sepkoski, J. J. Jr. 1984. Periodicity of extinctions in the geologic past. Proc. Nat. Acad. Sci. U.S.A. 81:801805.CrossRefGoogle ScholarPubMed
Schopf, T. J. M., Raup, D. M., Gould, S. J., and Simberloff, D. S. 1975. Genomic versus morphologic rates of evolution: influence of morphologic complexity. Paleobiology. 1:6370.Google Scholar
Sepkoski, J. J. Jr. 1978. A kinetic model of Phanerozoic taxonomic diversity. I. Analysis of marine orders. Paleobiology. 4:223251.Google Scholar
Sepkoski, J. J. Jr. 1979. A kinetic model of Phanerozoic taxonomic diversity. II. Early Phanerozoic families and multiple equilibria. Paleobiology. 5:222251.Google Scholar
Sepkoski, J. J. Jr. 1982. A compendium of fossil marine families. Milwaukee Pub. Mus. Contr. Biol. Geol. no. 51. 125 pp.Google Scholar
Sepkoski, J. J. Jr. 1984. A kinetic model of Phanerozoic taxonomic diversity. III. Post-Paleozoic families and mass extinctions. Paleobiology. 10:246267.Google Scholar
Sepkoski, J. J. Jr. and Raup, D. M. 1985. Periodicity in marine mass extinction. Pp. 336. In: Elliot, D., ed. Dynamics of Extinction. Wiley; New York.Google Scholar
Sokal, R. and Rohlf, F. J. 1981. Biometry. 2d ed.W. H. Freeman; San Francisco.Google Scholar
Stanley, S. M., Signor, P. W. III, Lidgard, S., and Karr, A. F. 1981. Natural clades differ from “random” clades: simulations and analyses. Paleobiology. 7:115127.CrossRefGoogle Scholar