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Biostratigraphy: Challenges and Solutions in Correlating Rock Units with Time Units

Explore the complexities of biostratigraphy - from comparing rock and time units to challenges like preservation issues and the Signor-Lipps effect. Discover why biostrat correlations may not always align with true time correlations and the significance of a good index fossil. Learn about quantitative biostratigraphy and how it utilizes abundance peaks, species ratios, and statistical analysis for correlation.

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Biostratigraphy: Challenges and Solutions in Correlating Rock Units with Time Units

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  1. Biostratigraphy

  2. Comparing Rock and Time units

  3. Larger units are built from smaller ones • Eg, stages are defined by the zones in them. • We define bottoms only • If you define bottoms AND tops, one boundary has two definitions that may not coincide.

  4. Why aren’t biostrat correlations true time correlations? • Are you looking at last appearance or unconformity? • Facies dependence: facies are time-transgressive • Regional speciation & extinction • Shifting climate zones/biogeographic provinces

  5. Other challenges • Preservation problems • Poorly preserved organisms and less abundant organisms are unlikely to be found • Signor-Lipps effect: poorly preserved and less abundant species appear to go extinct earlier than they actually do. • Lazarus species – apparently come back from the dead because they weren’t preserved in between two occurrences • Zombie species - appear above their extinction because they were exposed by erosion and reworked, then deposited in younger sediment

  6. What makes a good index fossil? • Abundant • Facies independent (planktonic, nektonic) • Easily preserved and collected • Widely distributed (global if possible) • Short species life (rapidly evolving) • Easily identified • Best organisms: forams, rads, ammonites, graptolites, pollen, nannofossils • But zones are defined for less-than-ideal organisms, e.g., dinosaurs, clams, conodonts, trilobies

  7. Quantitative Biostratigraphy • Uses a wider range of data than appearance/disappearance: • Abundance peaks • Ratios of species • Based in sophisticated statistics • Correlation analysis (matches patterns of peaks) • Cluster analysis – makes groups for assemblage zones

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