A deep-learning automated image recognition method for measuring pore patterns in closely related bolivinids and calibration for quantitative nitrate paleo-reconstructions

Eutrophication is accelerating the recent expansion of oxygen-depleted coastal marine environments. Several bolivinid foraminifera are abundant in these oxygen-depleted settings, and take up nitrate through the pores in their shells for denitrification. This makes their pore density a possible nitrate proxy. This study documents three aspects related to the porosity of bolivinids. 1. A new automated image analysis technique to determine the number of pores in bolivinids is tested. 2. The pore patterns of Bolivina spissa from five different ocean settings are analysed. The relationship between porosity, pore density and mean pore size significantly differs between the studied locations. Their porosity is mainly controlled by the size of the pores at the Gulf of Guayaquil (Peru), but by the number of pores at other studied locations. This might be related to the presence of a different cryptic Bolivina species in the Gulf of Guayaquil. 3. The pore densities of closely related bolivinids in core-top samples are calibrated as a bottom-water nitrate proxy. Bolivina spissa and Bolivina subadvena showed the same correlation between pore density and bottom-water nitrate concentrations, while the pore density of Bolivina argentea and Bolivina subadvena accumeata is much higher.

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Govindankutty Menon Anjaly, Davis Catherine V., Nürnberg Dirk, Nomaki Hidetaka, Salonen Iines, Schmiedl Gerhard, Glock Nicolaas (2023). A deep-learning automated image recognition method for measuring pore patterns in closely related bolivinids and calibration for quantitative nitrate paleo-reconstructions. Scientific Reports. 13 (1). 19628 (13p.). https://doi.org/10.1038/s41598-023-46605-y, https://archimer.ifremer.fr/doc/00860/97208/

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