Extreme warming at the end-Permian induced profound changes in marine biogeochemical cycling and animal habitability, leading to the largest metazoan extinction in Earth’s history. However, a causal mechanism for the extinction that is consistent with various proxy records of geochemical conditions through the interval has yet to be determined. Here we combine an Earth system model with global and local redox interpretations from the Permian/Triassic in an attempt to identify this causal mechanism. Our results show that a temperature-driven increase in microbial respiration can reconcile reconstructions of the spatial distribution of euxinia and seafloor anoxia spanning the Permian–Triassic transition. We illustrate how enhanced metabolic rates would have strengthened upper-ocean nutrient (phosphate) recycling, and thus shoaled and intensified the oxygen minimum zones, eventually causing euxinic waters to expand onto continental shelves and poison benthic habitats. Taken together, our findings demonstrate the sensitive interconnections between temperature, microbial metabolism, ocean redox state and carbon cycling during the end-Permian mass extinction. As enhanced microbial activity in the ocean interior also lowers subsurface dissolved inorganic carbon isotopic values, the carbon release as inferred from isotope changes in shallow subsurface carbonates is likely overestimated, not only for this event, but perhaps for many other carbon cycle and climate perturbations through Earth’s history.
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RBINS Staff Publications 2021
Subtidal natural hard substrates (SNHS) promote occupancy by rich benthic communities that provide irreplaceable and fundamental ecosystem functions, representing a global priority target for nature conservation and recognised in most European environmental legislation. However, scientifically validated methodologies for their quantitative spatial demarcation, including information on species occupancy and fine-scale environmental drivers (e.g., the effect of stone size on colonisation) are rare. This is, however, crucial information for sound ecological management. In this investigation, high-resolution (1 m) multibeam echosounder (MBES) depth and backscatter data and derivates, underwater imagery (UI) by video drop-frame, and grab sediment samples, all acquired within 32 km2 of seafloor in offshore Belgian waters, were integrated to produce a random forest (RF) spatial model, predicting the continuous distribution of the seafloor areal cover/m2 of the stones’ grain sizes promoting colonisation by sessile epilithic organisms. A semi-automated UI acquisition, processing, and analytical workflow was set up to quantitatively study the colonisation proportion of different grain sizes, identifying the colonisation potential to begin at stones with grain sizes Ø ≥ 2 cm. This parameter (i.e., % areal cover of stones Ø ≥ 2 cm/m2) was selected as the response variable for spatial predictive modelling. The model output is presented along with a protocol of error and uncertainty estimation. RF is confirmed as an accurate, versatile, and transferable mapping methodology, applicable to area-wide mapping of SNHS. UI is confirmed as an essential aid to acoustic seafloor classification, providing spatially representative numerical observations needed to carry out quantitative seafloor modelling of ecologically relevant parameters. This contribution sheds innovative insights into the ecologically relevant delineation of subtidal natural reef habitat, exploiting state-of-the-art underwater remote sensing and acoustic seafloor classification approaches.
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RBINS Staff Publications 2021
