The intensity of the Oxygen minimum zone (OMZ) of the eastern North Pacific (ENP) experienced strong variations during the last glacial, mirroring changes in the balance between export production (O2 consumption) and water mass ventilation (O2 renewal). In this paper we present a new benthic foraminiferal assemblages record from Core MD02‐2508, recovered from the Pacific slope off Baja California, Mexico. The record reflects oxygen conditions at the northern limit of the OMZ during the last 80 kyr. We statistically identified three assemblages, characteristic of dysoxic, suboxic, and oxic conditions, which we used to produce the first semiquantitative reconstruction of [O2] for the northeastern Pacific Ocean. Our results show that the estimated [O2] covaries with δ18O records from the North Greenland Ice Core Project. Oxygen concentrations overall exhibit moderate values (~1 mL.L−1) during stadials, reaching ~ 2 mL.L−1 during stadials corresponding to Heinrich events in the Atlantic Ocean. Conversely, bottom waters at the core location were strongly depleted in oxygen (<0.5 mL.L−1) during interstadials. Benthic foraminiferal abundance increased with higher export production as recorded by geochemical tracers (Cd/Al ratio). This export production signal increases (decreases) with a fall (rise) in [O2] during interstadials (stadials), suggesting a relationship between both parameters during these intervals. The influence of ventilation on oxygenation is also a key player. O2 pulses suggested by the downcore records of serial/spiral test ratio and abundance of oxic species may be explained by enhanced ventilation during Heinrich stadials, in agreement with latest modeling‐based oceanic circulation reconstructions.
Abstract. Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large foraminifera image sets using convolutional neural networks. Construction of the classifier is demonstrated on the publicly available Endless Forams image set with a best accuracy of approximately 90 %. A complete automatic analysis is performed for benthic species dated to the last deglacial period for a sediment core from the north-eastern Pacific and for planktonic species dated from the present until 180 000 years ago in a core from the western Pacific warm pool. The relative abundances from automatic counting based on more than 500 000 images compare favourably with manual counting, showing the same signal dynamics. Our workflow opens the way to automated palaeoceanographic reconstruction based on computer image analysis and is freely available for use.
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