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Joint estimation of sampling and turnover rates from fossil databases: capture-mark-recapture methods revisited

Published online by Cambridge University Press:  20 May 2016

Sean R. Connolly
Affiliation:
Department of Geosciences, University of Arizona, Tucson, Arizona 85721
Arnold I. Miller
Affiliation:
Department of Geology, Post Office Box 210013, University of Cincinnati, Cincinnati, Ohio 45221-0013

Abstract

The estimation and interpretation of temporal patterns in origination and extinction rates is a major goal of paleobiology. However, the possibility of coincident variation in the quality and completeness of the fossil record makes the identification of such patterns particularly difficult. Previously, Nichols and Pollock (1983) proposed that capture-mark-recapture (CMR) models be adapted to address this problem. These models can be used to estimate both sampling and turnover rates, reducing the risk of confounding the two quantities. Since that time, theoretical advances have made possible the application of these tools to a much broader range of problems. This paper reviews those advances likely to be of greatest relevance in paleobiological studies. They include (1) joint estimation of per-taxon origination and extinction rates, (2) modeling sampling or turnover rates as explicit functions of causal variables, (3) ranking of alternative models according to their fit to the data, and (4) estimation of parameter values using multiple models. These are illustrated by application to an Ordovician database of benthic marine genera from key higher taxa. Robustness of these methods to violation of assumptions likely to be suspect in paleobiological studies further suggests that these models can make an important contribution to the quantitative study of macroevolutionary dynamics.

Type
Articles
Copyright
Copyright © The Paleontological Society 

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References

Literature Cited

Akaike, H. 1973. Information theory as an extension of the maximum likelihood principle. Pp. 267281in Petrov, B. N.Csaki, F., eds. Second international symposium on information theory. Akademiai Kiado, Budapest.Google Scholar
Akaike, H. 1983. Information measures and model selection. International Statistical Institute 44:277291.Google Scholar
Alroy, J. 1998. Equilibrial diversity dynamics in North American mammals. Pp. 232287in Mc, M. L.Kinney and Drake, J. A., eds. Biodiversity dynamics: turnover of populations, taxa, and communities. Columbia University Press, New York.Google Scholar
Anderson, D. R.Burnham, K. P. 1994. AIC model selection in overdispersed capture-recapture data. Ecology 75:17801793.Google Scholar
Arnason, A. N.Schwarz, C. J.Boyer, G. 1998. POPAN 5: a data maintenance and analysis system, Release 5.0. Department of Computer Science, University of Manitoba, Winnipeg.Google Scholar
Benton, M. J. 1995. Diversification and extinction in the history of life. Science 268:5258.Google Scholar
Bowring, S.Erwin, D. H. 1998. A new look at evolutionary rates in deep time: uniting paleontology and high-precision geochronology. GSA Today 8:18.Google Scholar
Buckland, S. T.Burnham, K. P.Augustin, N. H. 1997. Model selection: an integral part of inference. Biometrics 53:603618.Google Scholar
Burnham, K. P.Anderson, D. R. 1998. Model selection and inference: an information-theoretic approach. Springer, New York.Google Scholar
Carothers, A. D. 1973. The effects of unequal catchability on Jolly-Seber estimates. Biometrics 29:79100.Google Scholar
Caswell, H. 2001. Matrix population models. Sinauer, Sunderland, Mass.Google Scholar
Connolly, S. R.Miller, A. I. 2001. Global Ordovician faunal transitions in the marine benthos: proximate causes. Paleobiology 27:779795 (this volume).Google Scholar
Connolly, S. R.Miller, A. I. 2002. Global Ordovician faunal transitions in the marine benthos: ultimate causes. Paleobiology 28 (in press).Google Scholar
Conroy, M. J.Nichols, J. D. 1984. Testing for variation in taxonomic extinction probabilities: a suggested methodology and some results. Paleobiology 10:328337.Google Scholar
Cooch, E.White, G. C. 2000. Program MARK: a gentle introduction.Google Scholar
Cormack, R. M. 1972. The logic of capture-recapture estimates. Biometrics 28:337343.Google Scholar
Foote, M. 1994. Temporal variation in extinction risk and temporal scaling of extinction metrics. Paleobiology 20:424444.CrossRefGoogle Scholar
Foote, M. 1997. Estimating taxonomic durations and preservation probability. Paleobiology 23:278300.Google Scholar
Foote, M. 2000. Origination and extinction components of taxonomic diversity: general problems. Paleobiology 26:S74S102.Google Scholar
Franklin, A. B.Burnham, K. P.White, G. C.Anthony, R. G.Forsman, E. D.Schwarz, C.Nichols, J. D.Hines, J. 1999. Range-wide status and trends in Northern Spotted Owl populations. Colorado Cooperative Fish and Wildlife Research Unit and Oregon Cooperative Fish and Wildlife Research Unit, United States Geological Survey, Department of Fishery and Wildlife Biology, Fort Collins and Corvallis.Google Scholar
Garthwaite, P. H.Jolliffe, I. T.Jones, B. 1995. Statistical inference. Prentice-Hall, London.Google Scholar
Gilinsky, N. L.Good, I. J. 1991. Probabilities of origination, persistence, and extinction of families of marine invertebrate life. Paleobiology 17:145166.Google Scholar
Hargrove, J. W.Borland, C. H. 1994. Pooled population parameter estimates from mark-recapture data. Biometrics 50:11291141.Google Scholar
Harland, W. B.Armstrong, R. L.Cox, A. V.Craig, L. E.Smith, A. G.Smith, D. G. 1990. A geologic time scale 1989. Cambridge University Press, Cambridge.Google Scholar
Hurvich, C. M.Tsai, C.-L. 1989. Regression and time series model selection in small samples. Biometrika 76:297307.Google Scholar
Kitchell, J. A.Carr, T. R. 1985. Nonequilibrium model of diversification: faunal turnover dynamics. Pp. 277310in Valentine, J. W., ed. Phanerozoic diversity patterns: profiles in macroevolution. Princeton University Press, Princeton, N.J.Google Scholar
Lebreton, J.-D.Burnham, K. P.Clobert, J.Anderson, D. R. 1992. Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecological Monographs 62:67118.Google Scholar
Miller, A. I. 1997a. Comparative diversification dynamics among paleocontinents during the Ordovician Radiation. Géobios 20:397406.CrossRefGoogle Scholar
Miller, A. I. 1997b. A new look at age and area: the geographic and environmental expansion of genera during the Ordovician Radiation. Paleobiology 23:410419.CrossRefGoogle Scholar
Miller, A. I. 1998. Biotic transitions in global marine diversity. Science 281:11571160.Google Scholar
Miller, A. I.Foote, M. 1996. Calibrating the Ordovician Radiation of marine life: implications for Phanerozoic diversity trends. Paleobiology 22:304309.Google Scholar
Miller, A. I.Mao, S. 1998. Scales of diversification and the Ordovician Radiation. Pp. 288310in McKinney, M. L.Drake, J. A., eds. Biodiversity dynamics: turnover of populations, taxa, and communities. Columbia University Press, New York.Google Scholar
Nichols, J. D.Pollock, K. H. 1983. Estimating taxonomic diversity, extinction rates, and speciation rates from fossil data using capture-recapture models. Paleobiology 9:150163.Google Scholar
Nichols, J. D.Morris, R. W.Brownie, C.Pollock, K. H. 1986. Sources of variation in extinction rates, turnover, and diversity of marine invertebrate families during the Paleozoic. Paleobiology 12:421432.Google Scholar
Nichols, J. D.Hines, J. E.Lebreton, J.-D.Pradel, R. 2000. Estimation of contributions to population growth: a reversetime capture-recapture approach. Ecology 81:33623376.Google Scholar
Pitman, J. 1993. Probability. Springer, New York.Google Scholar
Pollock, K. H.Nichols, J. D.Brownie, C.Hines, J. E. 1990. Statistical inference for capture-recapture experiments. Wildlife Monographs 107:197.Google Scholar
Pradel, R. 1996. Utilization of capture-mark-recapture for the study of recruitment and population growth rate. Biometrics 52:703709.Google Scholar
Raup, D. M. 1975. Taxonomic diversity estimation using rarefaction. Paleobiology 1:333342.Google Scholar
Raup, D. M. 1997. Cohort analysis of generic survivorship. Paleobiology 4:115.Google Scholar
Sepkoski, J. J. Jr. 1979. A kinetic model of Phanerozoic taxonomic diversity. II. Early Phanerozoic families and multiple equilibria. Paleobiology 5:222251.CrossRefGoogle Scholar
Smith, S. G.Skalski, J. R.Schlechte, J. W.Hoffmann, A.Cassen, V. 1994. Statistical survival analysis of fish and wildlife tagging studies: SURPH. 1. Center for Quantitative Science, School of Fisheries, University of Washington, Seattle.Google Scholar
Stanley, S. M. 1979. Macroevolution: pattern and process. W. H. Freeman, San Francisco.Google Scholar
Tucker, R. D.McKerrow, W. S. 1995. Early Paleozoic chronology: a review in light of new U-Pb zircon ages from Newfoundland and Britain. Canadian Journal of Earth Sciences 32:368379.Google Scholar
White, G. C. 1983. Numerical estimation of survival rates from band-recovery and biotelemetry data. Journal of Wildlife Management 47:716728.Google Scholar
White, G. C. 2000. MARK: mark and recapture survival rate estimation, Version 1.7.Google Scholar
Williams, B.Conroy, M.Nichols, J. 2001. The analysis and management of animal populations. Academic Press, San Diego (in press).Google Scholar