Skip to main content Accessibility help

Seven rules for simulations in paleobiology

  • Joëlle Barido-Sottani (a1), Erin E. Saupe (a2), Tara M. Smiley (a3), Laura C. Soul (a4), April M. Wright (a5) and Rachel C. M. Warnock (a6)...


Simulations are playing an increasingly important role in paleobiology. When designing a simulation study, many decisions have to be made and common challenges will be encountered along the way. Here, we outline seven rules for executing a good simulation study. We cover topics including the choice of study question, the empirical data used as a basis for the study, statistical and methodological concerns, how to validate the study, and how to ensure it can be reproduced and extended by others. We hope that these rules and the accompanying examples will guide paleobiologists when using simulation tools to address fundamental questions about the evolution of life.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Seven rules for simulations in paleobiology
      Available formats

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Seven rules for simulations in paleobiology
      Available formats

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Seven rules for simulations in paleobiology
      Available formats


This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hide All
Alroy, J. 2001. A multispecies overkill simulation of the end-Pleistocene megafaunal mass extinction. Science 292:18931896.
Arora, R. 2016. An introduction to big data, high performance computing, high-throughput computing, and hadoop. Pp. 1–12 in Conquering big data with high performance computing. Springer International Publishing, Cham, Switzerland.
Aze, T., Ezard, T. H. G., Purvis, A., Coxall, H. K., Stewart, D. R. M., Wade, B. S., and Pearson, P. N.. 2011. A phylogeny of cenozoic macroperforate planktonic foraminifera from fossil data. Biological Reviews 86:900927.
Bapst, D. W. 2012. paleotree: an R package for paleontological and phylogenetic analyses of evolution. Methods in Ecology and Evolution 3:803807.
Bapst, D. W. 2013. A stochastic rate-calibrated method for time-scaling phylogenies of fossil taxa. Methods in Ecology and Evolution 4:724733.
Bapst, D. W. 2014. Assessing the effect of time-scaling methods on phylogeny-based analyses in the fossil record. Paleobiology 40:331351.
Barido-Sottani, J., Aguirre-Fernández, G., Hopkins, M. H., Stadler, T., and Warnock, R. C. M.. 2019a. Ignoring stratigraphic age uncertainty leads to erroneous estimates of species divergence times under the fossilized birth-death process. Proceedings of the Royal Society of London B 286:20190685.
Barido-Sottani, J., Pett, W., O'Reilly, J. E., and Warnock, R. C. M.. 2019b. FossilSim: an R package for simulating fossil occurrence data under mechanistic models of preservation and recovery. Methods in Ecology and Evolution 10:835840.
Beaulieu, J. M., and O'Meara, B. C.. 2020. OUwie: analysis of evolutionary rates in an OU framework.
Brocklehurst, N., Dunne, E. M., Cashmore, D. D., and Fröbisch, J.. 2018. Physical and environmental drivers of Paleozoic tetrapod dispersal across Pangaea. Nature Communications 9:5216.
Brown, J. M. 2014. Predictive approaches to assessing the fit of evolutionary models. Systematic Biology 63:289292.
Darroch, S. A., and Saupe, E. E.. 2018. Reconstructing geographic range-size dynamics from fossil data. Paleobiology 44:2539.
Foote, M. 1996. Models of morphological diversification. Pp. 62–8 in Evolutionary paleobiology. Jablonski, D., Erwin, D. H. and Lipps, J. H., eds. University of Chicago Press, Chicago.
Foote, M. 1999. Morphological diversity in the evolutionary radiation of paleozoic and post-paleozoic crinoids. Paleobiology 25:1115.
Foote, M., and Raup, D. M.. 1996. Fossil preservation and the stratigraphic ranges of taxa. Paleobiology 22:121140.
Fraser, D. 2017. Can latitudinal richness gradients be measured in the terrestrial fossil record? Paleobiology 43:479494.
Garwood, R. J., Spencer, A. R., and Sutton, M. D.. 2019. Revosim: organism-level simulation of macro and microevolution. Palaeontology 62:339355.
Gibert, C., and Escarguel, G.. 2017. Evaluating the accuracy of biodiversity changes through geologic times: from simulation to solution. Paleobiology 43:667692.
Green, W. A., Hunt, G., Wing, S. L., and DiMichele, W. A.. 2011. Does extinction wield an axe or pruning shears? How interactions between phylogeny and ecology affect patterns of extinction. Paleobiology 37:7291.
Hawkins, A. D., Kowalewski, M., and Xiao, S.. 2018. Breaking down the lithification bias: the effect of preferential sampling of larger specimens on the estimate of species richness, evenness, and average specimen size. Paleobiology 44:326345.
Heath, T. A., Huelsenbeck, J. P., and Stadler, T.. 2014. The fossilized birth–death process for coherent calibration of divergence-time estimates. Proceedings of the National Academy of Sciences USA 111:E2957E2966.
Holland, S. M. 1995. The stratigraphic distribution of fossils. Paleobiology 21:92109.
Holland, S. M., and Patzkowsky, M. E.. 1999. Models for simulating the fossil record. Geology 27:491494.
Holland, S. M., and Patzkowsky, M. E.. 2015. The stratigraphy of mass extinction. Palaeontology 58:903924.
Kowalewski, M., and Novack-Gottshall, P.. 2010. Resampling methods in paleontology. Paleontological Society Papers 16:1954.
Lane, A., Janis, C. M., and Sepkoski, J. J.. 2005. Estimating paleodiversities: a test of the taxic and phylogenetic methods. Paleobiology 31:2134.
Lewitus, E., Bittner, L., Malviya, S., Bowler, C., and Morlon, H.. 2018. Clade-specific diversification dynamics of marine diatoms since the Jurassic. Nature Ecology and Evolution 2:1715.
Liow, L. H., Quental, T. B., and Marshall, C. R.. 2010. When can decreasing diversification rates be detected with molecular phylogenies and the fossil record? Systematic Biology 59:646659.
Nee, S. 2006. Birth-death models in macroevolution. Annual Review of Ecology, Evolution, and Systematics 37:117.
Novack-Gottshall, P. M. 2016. General models of ecological diversification. II. Simulations and empirical applications. Paleobiology 42:209239.
O'Connor, A., and Wills, M. A.. 2016. Measuring stratigraphic congruence across trees, higher taxa, and time. Systematic Biology 65:792811.
Orme, D., Freckleton, R. P., Thomas, G., Petzoldt, T., and Fritz, S. A.. 2018. caper: comparative analyses of phylogenetics and evolution in R, R package version 1.01.
Papadopoulou, A., Anastasiou, I., and Vogler, A. P.. 2010. Revisiting the insect mitochondrial molecular clock: the mid-Aegean trench calibration. Molecular Biology and Evolution 27:16591672.
Paradis, E., Claude, J., and Strimmer, . 2004. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289290.
Pennell, M. W., Eastman, J. M., Slater, G. J., Brown, J. W., Uyeda, J. C., FitzJohn, R. G., Alfaro, M. E., and Harmon, L. J.. 2014. geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics 30:2216–8.
Puttick, M. N., O'Reilly, J. E., Pisani, D., and Donoghue, P. C. J.. 2019. Probabilistic methods outperform parsimony in the phylogenetic analysis of data simulated without a probabilistic model. Palaeontology 62:117.
Raup, D. M. 1981. Extinction: bad genes or bad luck? Acta Geològica Hispànica 16:2533.
Raup, D. M. 1982. Biogeographic extinction: a feasibility test. Pp. 277281in Silver, L. T. and Schultz, P. H., eds. Geological implications of impacts of large asteroids and comets on the Earth. Geological Society of America, Boulder, Colo.
Raup, D. M., and Gould, S. J.. 1974. Stochastic simulation and evolution of morphology—towards a nomothetic paleontology. Systematic Biology 23:305322.
Revell, L. J. 2012. phytools: an R package for phylogenetic comparative biology (and other things). Methods in Ecology and Evolution 3:217223.
Robinson, D., and Foulds, L.. 1979. Comparison of weighted labelled trees. Pp. 119126in Horadam, A. F. and Wallis, W. D., eds. Combinatorial mathematics VI. Lecture Notes in Mathematics. Springer, Berlin.
Robinson, D., and Foulds, L.. 1981. Comparison of phylogenetic trees. Mathematical Biosciences 53:131147.
Saupe, E. E., Barve, N., Owens, H. L., Cooper, J. C., Hosner, P. A., and Peterson, A. T.. 2017. Reconstructing ecological niche evolution when niches are incompletely characterized. Systematic Biology 67:428438.
Saupe, E. E., Farnsworth, A., Lunt, D. J., Sagoo, N., Pham, K. V., and Field, D. J.. 2019a. Climatic shifts drove major contractions in avian latitudinal distributions throughout the Cenozoic. Proceedings of the National Academy of Sciences USA 116:1289512900.
Saupe, E. E., Myers, C. E., Peterson, A. T., Soberón, J., Singarayer, J., Valdes, P., and Qiao, H.. 2019b. Spatio-temporal climate change contributes to latitudinal diversity gradients. Nature Ecology and Evolution 3:14191429.
Siegfried, T. 2010. Odds are, it's wrong: science fails to face the shortcomings of statistics. Science News 177:2629.
Silvestro, D., Zizka, A., Bacon, C. D., Cascales-Minana, B., Salamin, N., and Antonelli, A.. 2016. Fossil biogeography: a new model to infer dispersal, extinction and sampling from palaeontological data. Philosophical Transactions of the Royal Society of London B 371:20150225.
Silvestro, D., Salamin, N., Antonelli, A., and Meyer, X.. 2018. Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework. Paleobiology 45:546570.
Smiley, T. M. 2018. Detecting diversification rates in relation to preservation and tectonic history from simulated fossil records. Paleobiology 44:124.
Soul, L. C., and Friedman, M.. 2017. Bias in phylogenetic measurements of extinction and a case study of end-Permian tetrapods. Palaeontology 60:169185.
Wang, S. C., Everson, P. J., Zhou, H. J., Park, D., and Chudzicki, D. J.. 2016. Adaptive credible intervals on stratigraphic ranges when recovery potential is unknown. Paleobiology 42:240256.
Warnock, R. C. M., Yang, Z., and Donoghue, P. C. J.. 2017. Testing the molecular clock using mechanistic models of fossil preservation and molecular evolution. Proceedings of the Royal Society of London B 284:20170227.
White, J. W., Rassweiler, A., Samhouri, J. F., Stier, A. C., and White, C.. 2014. Ecologists should not use statistical significance tests to interpret simulation model results. Oikos 123:385388.
Wright, A. M., and Hillis, D. M.. 2014. Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLoS ONE 9:e109210.
Wright, A. M., Lloyd, G. T., and Hillis, D. M.. 2016. Modeling character change heterogeneity in phylogenetic analyses of morphology through the use of priors. Systematic Biology 65:602611.

Seven rules for simulations in paleobiology

  • Joëlle Barido-Sottani (a1), Erin E. Saupe (a2), Tara M. Smiley (a3), Laura C. Soul (a4), April M. Wright (a5) and Rachel C. M. Warnock (a6)...


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.