Sampling bias and the fossil record of planktonic foraminifera on land and in the deep sea

Journal article

Large-scale trends in planktonic foraminiferal diversity have so far been based on utilization of synoptic biostratigraphic range charts. Although this approach ensures the taxonomic consistency and quality of the data being used, it takes no formal account of any sampling biases that might exist in the fossil record. We demonstrate that the occurrence data of planktonic foraminifera, as recorded in the primary literature, are strongly biased by sampling. We do this by demonstrating that raw diversity curves derived from the land-based and deep-sea records are strikingly different, but that they each correlate with the intensity of sampling in their respective environments, and thus are ultimately controlled by the structure of the geological record in each setting. Because sampling of the Mesozoic record is best in our land record whereas sampling of the Cenozoic is best in our deep-sea record, we combine the two to generate the best-supported estimates of species and genus diversity over time from these data. We correct for sampling bias using shareholder quorum subsampling and a modeling approach. The data are then transformed to generate a range-through plot of species richness that is compared with two earlier estimates of the diversity history where comparable species-in-bin data can be recovered. No robust statistical correlation is found among the three estimates. Although differences in amplitude are to be expected, differences in the actual shape of the curve are surprising. We conclude that these differences stem from the nature of the data themselves, namely the taxonomic scheme adopted and the taxonomic coverage used. © 2012 The Paleontological Society. All rights reserved.

Authors: Lloyd, G; Pearson, P; Young, JR; Smith, AB

• Journal: Paleobiology
• Volume: 38
• Issue: 4
• Pages: 569-584
• Publication date: 2012-09-01
• DOI: 10.1666/11041.1
• EISSN: 1938-5331
• ISSN: 0094-8373
• Language: English