Assessing the quality of the fossil record: insights from vertebrates

Abstract

AbstractAssessing the quality of the fossil record is notoriously hard, and many recent attempts have used sampling proxies that can be questioned. For example, counts of geological formations and estimated outcrop areas might not be defensible as reliable sampling proxies: geological formations are units of enormously variable dimensions that depend on rock heterogeneity and fossil content (and so are not independent of the fossil record), and outcrop areas are not always proportional to rock exposure, probably a closer indicator of rock availability. It is shown that in many cases formation counts will always correlate with fossil counts, whatever the degree of sampling. It is not clear, in any case, that these proxies provide a good estimate of what is missing in the gap between the known fossil record and reality; rather they largely explore the gap between known and potential fossil records. Further, using simple, single numerical metrics to correct global-scale raw data, or to model sampling-driven patterns may be premature. There are perhaps four approaches to exploring the incompleteness of the fossil record, (1) regional-scale studies of geological completeness; (2) regional- or clade-scale studies of sampling completeness using comprehensive measures of sampling, such as numbers of localities or specimens or fossil quality; (3) phylogenetic and gap-counting methods; and (4) model-based approaches that compare sampling as one of several explanatory variables with measures of environmental change, singly and in combination. We suggest that palaeontologists, like other scientists, should accept that their data are patchy and incomplete, and use appropriate methods to deal with this issue in each analysis. All that matters is whether the data are adequate for a designated study or not. A single answer to the question of whether the fossil record is driven by macroevolution or megabias is unlikely ever to emerge because of temporal, geographical, and taxonomic variance in the data.