Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-23T17:37:03.956Z Has data issue: false hasContentIssue false
  • English
  • Français

An unknown Phanerozoic driver of brachiopod extinction rates unveiled by multivariate linear stochastic differential equations

Published online by Cambridge University Press:  28 June 2017

Trond Reitan  and
Lee Hsiang Liow
Trond Reitan
Affiliation:
Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Post Office Box 1066, Blindern, N-3016, Oslo, Norway. E-mail: [email protected]
Lee Hsiang Liow
Affiliation:
Natural History Museum, University of Oslo, P.O. Box 1172, Blindern, N-3018, Oslo, Norway, and Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Post Office Box 1066 Blindern, N-3016, Oslo, Norway. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Whether the evolutionary dynamics of one group of organisms influence that of another group of organisms over the vast timescale of the geological record is a difficult question to tackle. This is not least because multiple factors can influence or mask the effects of potential driving forces on evolutionary dynamics of the focal group. Here, we show how an approach amenable to causality inference for time series, linear stochastic differential equations (SDEs), can be used in a multivariate fashion to shed light on driving forces of diversification dynamics across the Phanerozoic. Using a new, enhanced stepwise search algorithm, we searched through hundreds of models to converge on a model that best describes the dynamic relationships that drove brachiopod and bivalve diversification rates. Using this multivariate framework, we characterized a slow process (half-life of c. 42 Myr) that drove brachiopod extinction. This slow process has yet to be identified from the geological record. Using our new framework for analyzing multiple linear SDEs, we also corroborate our previous findings that bivalve extinction drove brachiopod origination in the sense that brachiopods tended to diversify at a greater rate when bivalves were removed from the system. It is also very likely that bivalves “self-regulate” in the sense that bivalve extinctions also paved the way for higher bivalve origination rates. Multivariate linear SDEs as we presented them here are likely useful for studying other dynamic systems whose signatures are preserved in the paleontological record.

Type
Articles
Information
Paleobiology , Volume 43 , Issue 4 , November 2017 , pp. 537 - 549
Copyright
Copyright © 2017 The Paleontological Society. All rights reserved 

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

Cardenas, A. L., and Harries, P. J.. 2010. Effect of nutrient availability on marine origination rates throughout the Phanerozoic eon. Nature Geoscience 3:430–434.Google Scholar
Connolly, S. R., and Miller, A. I.. 2001a. Global Ordovician faunal transitions in the marine benthos: proximate causes. Paleobiology 27:779–795.Google Scholar
Connolly, S. R., and Miller, A. I.. 2001b. Joint estimation of sampling and turnover rates from fossil databases: capture–mark–recapture methods revisited. Paleobiology 27:751–767.2.0.CO;2>CrossRefGoogle Scholar
Connolly, S. R., and Miller, A. I.. 2002. Global Ordovician faunal transitions in the marine benthos: ultimate causes. Paleobiology 28:26–40.2.0.CO;2>CrossRefGoogle Scholar
Eichler, M. 2013. Causal inference with multiple time series: principles and problems. Philosophical Transactions of the Royal Society of London A 371:20110613.Google Scholar
Erwin, D. H. 2009. Climate as a driver of evolutionary change. Current Biology 19:R575–R583.Google Scholar
Finarelli, J. A., and Liow, L. H.. 2016. Diversification histories for North American and Eurasian carnivorans. Biological Journal of the Linnean Society 118:26–38.Google Scholar
Granger, C. W. J. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–238.Google Scholar
Hannisdal, B. 2011. Non-parametric inference of causual interactions from geological records. American Journal of Science 311:315–334.Google Scholar
Hlaváčková-Schindler, K., Paluš, M., Vejmelka, M., and Bhattacharya, J.. 2007. Causality detection based on information-theoretic approaches in time series analysis. Physics Reports 441:1–46.Google 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.Google Scholar
Jablonski, D. 2008. Biotic interactions and macroevolution: extensions and mismatches across scales and levels. Evolution 62:715–739.CrossRefGoogle ScholarPubMed
Lande, R. 1976. Natural-selection and random genetic drift in phenotypic evolution. Evolution 30:314–334.Google Scholar
Liow, L. H. 2013. Simultaneous estimation of occupancy and detection probabilities: an illustration using Cincinnatian brachiopods. Paleobiology 39:193–213.Google Scholar
Liow, L. H., and Finarelli, J. A.. 2014. A dynamic global equilibrium in carnivoran diversification over 20 million years. Proceedings of the Royal Society of London B 281:20132312.Google Scholar
Liow, L. H., Reitan, T., and Harnik, P. G.. 2015. Ecological interactions on macroevolutionary time scales: clams and brachiopods are more than ships that pass in the night. Ecology Letters 18:1030–1039.Google Scholar
Losos, J. B. 2010. Adaptive radiation, ecological opportunity, and evolutionary determinism. American Naturalist 175:623–639.CrossRefGoogle ScholarPubMed
Mayhew, P. J., Bell, M. A., Benton, T. G., and McGowan, A. J.. 2012. Biodiversity tracks temperature over time. Proceedings of the National Academy of Sciences USA 109:15141–15145.CrossRefGoogle ScholarPubMed
Miller, A. I., and Mao, S.. 1995. Association of orogenic activity with the Ordovician radiation of marine life. Geology 23:305–308.Google Scholar
Øksendal, B. 2010. Stochastic differential equations: an introduction with applications. Springer, Heidelberg.Google Scholar
Pradel, R. 1996. Utilization of capture–mark–recapture for the study of recruitment and population growth rate. Biometrics 52:703–709.Google Scholar
Prokoph, A., Shields, G. A., and Veizer, J.. 2008. Compilation and time-series analysis of a marine carbonate δ18O, δ13C, 87Sr/86Sr and δ34S database through Earth history. Earth-Science Reviews 87:113–133.Google Scholar
Raup, D. M. 1977. Stochastic models in evolutionary paleontology. Pp 123–158 in A. Hallam, ed. Patterns of evolution, as illustrated by the fossil record. Elsevier, New York.Google Scholar
Reitan, T. 2016. Multivariate linear SDE analysis of diversification (and sampling) rates for bivalves and brachiopods. http://folk.uio.no/trondr/multidiversity.Google Scholar
Reitan, T., Schweder, T., and Henderiks, J.. 2012. Phenotypic evolution studied by layered stochastic differential equations. Annals of Applied Statistics 6:1531–1551.Google Scholar
Särkkä, S., Vehtari, A., and Lampinen, J.. 2004). Time series prediction by Kalman smoother with cross-validated noise density. Pp. 1615–1619 in IEEE International Joint Conference on Neural Networks, Budapest.Google Scholar
Schweder, T. 1970. Decomposable Markov processes. Journal of Applied Probability 7:400–410.Google Scholar
Sepkoski, J. J. 1996. Competition in macroevolution: the double wedge revisited. Pp 211–255 in D. Jablonski, D. H. Erwin, and J. H. Lipps, eds. Evolutionary paleobiology. University of Chicago Press, Chicago, Ill.Google Scholar
Sugihara, G., May, R., Ye, H., Hsieh, C.-h., Deyle, E., Fogarty, M., and Munch, S.. 2012. Detecting causality in complex ecosystems. Science 338:496–500.Google Scholar
van der Niet, T., and Johnson, S. D.. 2012. Phylogenetic evidence for pollinator-driven diversification of angiosperms. Trends in Ecology and Evolution 27:353–361.Google Scholar
Ye, H., Deyle, E. R., Gilarranz, L. J., and Sugihara, G.. 2015. Distinguishing time-delayed causal interactions using convergent cross mapping. Scientific Reports 5:14750.Google Scholar