DOI: 10.32469/10355/91518
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Preservation and bias in the Cambrian fossil record

Abstract: Paleontology views deep time ecosystems through a taphonomic lens, biased and restricted by the controls enacted on each fossil deposit by various environmental factors, and including chemical and biological variables. Human-induced bias has recently been acknowledged as an additional layer of complexity, contributing further inconsistencies of our understanding of metazoan diversity in deep time. The Cambrian Haiyan Lagerstatte of Yunnan, China, examined in Chapter 2, preserves an exceptionally diverse biota,… Show more

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(2 citation statements)
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“…Preservational and anthropogenic biases in the mineralogical record have received relatively minimal attention. However, important concepts and approaches have been devised by the paleobiological community, notably in their research on ancient ecosystems [18][19][20][21][22][23]. A brief survey of this paleobiological literature is, thus, informative.…”
Section: On the Nature Of Biases In Deep-time Datamentioning
confidence: 99%
See 1 more Smart Citation
“…Preservational and anthropogenic biases in the mineralogical record have received relatively minimal attention. However, important concepts and approaches have been devised by the paleobiological community, notably in their research on ancient ecosystems [18][19][20][21][22][23]. A brief survey of this paleobiological literature is, thus, informative.…”
Section: On the Nature Of Biases In Deep-time Datamentioning
confidence: 99%
“…In addition to these inevitable physically and chemically induced losses in the fossil record, a number of potentially avoidable anthropogenic biases exist (e.g., [22,45], and the references therein). Among the many pitfalls that may contribute to non-random collections in the field are the tendency to avoid large and bulky specimens, the difficulty in extracting specimens from the hardest lithologies, and the challenge in identifying and collecting microscopic species [34,37,[46][47][48].…”
Section: On the Nature Of Biases In Deep-time Datamentioning
confidence: 99%