A fundamental question in evolutionary biology and paleobiology is how quickly populations and/or species evolve and under what circumstances. Because the fossil record affords us the most direct view of how species lineages have changed in the past, considerable effort has gone into developing methodological approaches for assessing rates of evolution as well as what has been termed “mode of evolution” which generally describes pattern of evolution, for example whether the morphological change captured in fossil time series are best characterized as static, punctuated, or trending (e.g., Sheets and Mitchell, 2001; Hunt, 2006, 2008; Voje et al., 2018). The rock record from which these samples are taken, however, is incomplete, due to spatially and temporally heterogenous sediment deposition and erosion. The resulting structure of the stratigraphic record may confound the direct application of evolutionary models to fossil time series, most of which come from single localities. This pressing issue is tackled in a new study entitled “Identification of the mode of evolution in incomplete carbonate successions” (Hohmann et al., 2024).

The “carbonate successions” part of the title is important. Previous similar work (Hannisdal, 2006) used models for siliciclastic depositional systems to simulate the rock record. Here, the authors simulate sediment deposition across a carbonate platform, a system that has been treated as fundamentally different from siliciclastic settings, from both the point of view of geology (see Wagoner et al., 1990; Schlager, 2005) and ecology (Hopkins et al., 2014). The authors do find that stratigraphic structure impacts the identification of mode of evolution but not necessarily in the way one might expect; specifically, it is less important how much time is represented compared to the size and distribution of gaps, regardless of where you are sampling along the platform. This result provides an important guiding principle for selecting fossil time series for future investigations. 

Another very useful result of this study is the impact of time series length, which in this case should be understood as the density of sampling over a particular time interval. Counterintuitively, the probability of selecting the data-generating model as the best model decreases with increased length. The authors propose several explanations for this, all of which should inspire further work. There are also many other variables that could be explored in the simulations of the carbonate models as well as the fossil time series. For example, the authors chose to minimize within-sample variation in order to avoid conflating variability with evolutionary trends. But greater variance also potentially impacts model selection results and underlies questions about how variation relates to evolvability and the potential for directional change. 

Lastly, readers of Hohmann et al. (2024) are encouraged to also peruse the reviews and author replies associated with the PCI Paleo peer review process. The discussion contained in these documents touch on several important topics, including model performance and model selection, the nature of nested model systems, and the potential of the forwarding modeling approach.  

References

Hannisdal, B. (2006). Phenotypic evolution in the fossil record: Numerical experiments. The Journal of Geology, 114(2), 133–153. https://doi.org/10.1086/499569

Hohmann, N., Koelewijn, J. R., Burgess, P., and Jarochowska, E. (2024). Identification of the mode of evolution in incomplete carbonate successions. bioRxiv, 572098, ver. 4 peer-reviewed by PCI Paleo. https://doi.org/10.1101/2023.12.18.572098

Hopkins, M. J., Simpson, C., and Kiessling, W. (2014). Differential niche dynamics among major marine invertebrate clades. Ecology Letters, 17(3), 314–323. https://doi.org/10.1111/ele.12232

Hunt, G. (2006). Fitting and comparing models of phyletic evolution: Random walks and beyond. Paleobiology, 32(4), 578–601. https://doi.org/10.1666/05070.1

Hunt, G. (2008). Gradual or pulsed evolution: When should punctuational explanations be preferred? Paleobiology, 34(3), 360–377. https://doi.org/10.1666/07073.1

Schlager, W. (Ed.). (2005). Carbonate Sedimentology and Sequence Stratigraphy. SEPM Concepts in Sedimentology and Paleontology No. 8. https://doi.org/10.2110/csp.05.08

Sheets, H. D., and Mitchell, C. E. (2001). Why the null matters: Statistical tests, random walks and evolution. Genetica, 112, 105–125. https://doi.org/10.1023/A:1013308409951

Voje, K. L., Starrfelt, J., and Liow, L. H. (2018). Model adequacy and microevolutionary explanations for stasis in the fossil record. The American Naturalist, 191(4), 509–523. https://doi.org/10.1086/696265

Wagoner, J. C. V., Mitchum, R. M., Campion, K. M., and Rahmanian, V. D. (1990). Siliciclastic Sequence Stratigraphy in Well Logs, Cores, and Outcrops: Concepts for High-Resolution Correlation of Time and Facies. AAPG Methods in Exploration Series No. 7. https://doi.org/10.1306/Mth7510