Variable preservation potential and richness in the fossil record of vertebrates

Fiona M Walker, Alexander Dunhill, Michael Benton

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Abstract

Variation in preservation and sampling probability clouds our estimates of past biodiversity. The most extreme examples are Lagerstätten faunas and floras. Although such deposits provide a wealth of information and represent true richness better than other deposits, they can create misleading diversity peaks because of their species richness. Here, we investigate how Lagerst€atten formations add to time series of vertebrate richness in the UK, Germany and China. The first two nations are associated with well-studied fossil records and the last is a country where palaeontology has a much shorter history; all three nations include noted Lagerstätten in their fossil records. Lagerstätten provide a larger proportion of China’s sampled richness than in Germany or the UK, despite comprising a smaller proportion of its fossiliferous deposits. The proportions of taxa that are unique to Lagerstätten vary through time and between countries. Further, in all regions, we find little overlap between the taxa occurring in Lagerstätten and in ‘ordinary’ formations within the same time bin, indicating that Lagerstätten preserve unusual faunas. As expected, fragile taxa make up a greater proportion of richness in Lagerstätten than the remainder of the fossil record. Surprisingly, we find that Lagerstätten account for a minority of peaks in the palaeodiversity curves of all vertebrates (18% in the UK; 36% in Germany and China), and Lagerst€atten count is gen- erally not a good overall predictor of the palaeodiversity sig- nal. Vastly different sampling probabilities through taxa, locations and time require serious consideration when analysing palaeodiversity curves.
Original languageEnglish
Pages (from-to)313-329
JournalPalaeontology
Volume63
Issue number2
DOIs
Publication statusPublished - 28 Feb 2020

Keywords

  • Lagerstätte
  • Biota
  • diversity
  • sampling bias
  • preservation potential
  • linear model

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