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Decreasing Phanerozoic extinction intensity as a consequence of Earth surface oxygenation and metazoan ecophysiology.

Richard G StockeyAlexandre PohlAndrew RidgwellSeth FinneganErik A Sperling
Published in: Proceedings of the National Academy of Sciences of the United States of America (2021)
The decline in background extinction rates of marine animals through geologic time is an established but unexplained feature of the Phanerozoic fossil record. There is also growing consensus that the ocean and atmosphere did not become oxygenated to near-modern levels until the mid-Paleozoic, coinciding with the onset of generally lower extinction rates. Physiological theory provides us with a possible causal link between these two observations-predicting that the synergistic impacts of oxygen and temperature on aerobic respiration would have made marine animals more vulnerable to ocean warming events during periods of limited surface oxygenation. Here, we evaluate the hypothesis that changes in surface oxygenation exerted a first-order control on extinction rates through the Phanerozoic using a combined Earth system and ecophysiological modeling approach. We find that although continental configuration, the efficiency of the biological carbon pump in the ocean, and initial climate state all impact the magnitude of modeled biodiversity loss across simulated warming events, atmospheric oxygen is the dominant predictor of extinction vulnerability, with metabolic habitat viability and global ecophysiotype extinction exhibiting inflection points around 40% of present atmospheric oxygen. Given this is the broad upper limit for estimates of early Paleozoic oxygen levels, our results are consistent with the relative frequency of high-magnitude extinction events (particularly those not included in the canonical big five mass extinctions) early in the Phanerozoic being a direct consequence of limited early Paleozoic oxygenation and temperature-dependent hypoxia responses.
Keyphrases
  • climate change
  • blood flow
  • particulate matter
  • machine learning
  • deep learning
  • clinical practice