Benthic foraminifera are one of the most commonly used indicators to infer paleodepth. The information on depth distribution of fossil benthic foraminifera is generally obtained from normal marine environments. However, a significant gap exists with respect to implications of benthic foraminiferal distributions in unique sedimentary successions, such as those deposited under upwelling regimes. In such settings, the paleobathymetric signal is somewhat obscured by the extreme food fluxes and oxygen depletion at the seafloor that cause changes in benthic foraminiferal assemblage composition. Nevertheless, the dynamics of upwelling systems, and as a result the sediment and organic matter accumulation, are known to be directly influenced by eustatic changes, making paleobathymetric reconstruction highly valuable for understanding these systems. The Upper Cretaceous high productivity marine succession of southern Israel, with its variable lithologies, provides a unique opportunity for addressing this issue. Through this succession, a significant turnover in the benthic foraminiferal assemblages is observed associated with a sharp change in lithology from phosphate (Phosphate Member) to organic rich carbonates (Oil Shale Member; OSM). Statistical nMDS analysis distinguished four groups of species indicative of distinct depth habitats: <200 m, 100–300 m, 300–500 m, and >500 m. Each one of these groups corresponds to different parts of the sequence. According to our analysis, the shift in the benthic foraminiferal assemblages is attributed to a distinct regional deepening from shelf environment (<200 m) in the Phosphate Member (upper Campanian) to upper bathyal (200–500 m) at the base of OSM (base Maastrichtian), and deeper to middle bathyal (>500 m) during the Maastrichtian. While taking into account other factors affecting benthic foraminiferal distribution, this study demonstrates that depth distribution models based on normal marine settings might also be applicable as proxies for paleobathymetry in high productivity environments.
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- Campanian-Maastrichtian interval
- Depth distribution model
- High productivity
- Statistical nMDS analysis
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