John W. Murray (1936–2021) was a pioneering micropalaeontologist and became a globally celebrated foraminiferal ecologist and an authority on the smaller benthic foraminifera of the UK and neighbouring areas. This interest developed at an early stage in his career, when he was working on the benthic foraminifera of Christchurch Harbour (Dorset, UK). Despite several career moves (Marine Biological Association of the UK in Plymouth, Bristol University, Exeter University, Southampton University), benthic foraminifera remained his primary research focus, culminating in the authorship of a series of systematic and ecological textbooks that have proved to be invaluable to generations of students and the wider scientific community.
The biological pump (BP) constitutes a key component of the global carbon cycle by mediating the export of organic carbon from the surface ocean to the deep sea. However, long-term variability of BP strength in the northeastern Indian Ocean (NEIO), particularly within the strongly stratified Bay of Bengal (BoB), remains inadequately constrained due to the complex interplay between monsoon forcing and freshwater-induced stratification. Here, we present a high-resolution reconstruction of BP variability over the last ∼ 53 kyr based on primary productivity (PP) estimates derived from quantitative analyses of coccolithophore assemblages in a sediment core from the southern BoB. Coccoliths are abundant and well preserved throughout the record, with assemblages dominated by Florisphaera profunda, Gephyrocapsa oceanica, and Emiliania huxleyi. The reconstructed PP record exhibits pronounced glacial–interglacial (G–IG) variability, characterized by reduced productivity during the Last Glacial Maximum, followed by a progressive increase during the deglaciation and a peak in the Early Holocene. Principal component analysis of productivity records across the Indian Ocean (IO) identifies G–IG climate variability as the dominant mode of basin-scale productivity change while distinguishing secondary atmospheric and hydrological controls on regional productivity. Comparison with PP records from other sectors of the IO further highlights marked spatial heterogeneity in BP responses to basin-scale climatic forcing. In the NEIO, productivity was primarily controlled by G–IG changes in upper-ocean stratification and nutrient availability driven by sea level fluctuations and freshwater forcing, whereas secondary variability reflects the distinct atmospheric and hydrological expressions of the Indian Summer Monsoon. Our findings indicate that regional hydrographic processes governed the expression of basin-scale climatic forcing, distinguishing the freshwater-controlled NEIO from the wind-driven upwelling systems of the western tropical IO.
Reconstructing relative sea level (RSL) is essential for understanding coastal evolution and mitigating impacts of climate change. Foraminiferal assemblages are established indicators for past sea levels, requiring modern training sets that link assemblage composition to known tidal elevations. However, seasonal and spatial variability in assemblages can affect reconstruction reliability. Foraminiferal environmental DNA (eDNA) has recently been validated as a robust proxy for reconstructing past sea levels. However, eDNA assemblages may be subject to similar spatiotemporal variability along with additional factors affecting DNA transport and preservation. The spatiotemporal stability of intertidal eDNA assemblages – and their influence on RSL reconstruction – remains uncertain. We conducted a 2-year eDNA monitoring study at three stations of varying tidal elevation, sampling across dry and wet seasons to assess variability in mangrove and mudflat environments of Hong Kong. Mid-mangrove eDNA communities exhibited temporal and spatial stability, whereas mudflat and upper-mangrove assemblages, particularly among monothalamous foraminifera, showed pronounced seasonal shifts. Despite this variability in the upper mangrove, eDNA-based elevation estimates in mangrove environments consistently aligned with observed elevations (within 95 % credible intervals), demonstrating reliable calibration for paleoenvironmental applications. However, mudflat samples exhibited an overprediction bias, especially during the wet season, reflecting their heightened sensitivity to exogenous eDNA inputs. These findings highlight the need to account for seasonal and environmental variability in eDNA-based RSL reconstruction. Stable mangroves are optimal for establishing elevation–assemblage relationships, while transitional or mudflat zones require caution due to higher variability. Our study provides guidance for applying foraminiferal eDNA as a proxy in past sea-level reconstruction in dynamic coastal settings.
The Miogypsinidae comprise orbitoidal larger benthic foraminifera with generally eccentric position of the nepionic stage and a fan of equatorial chamberlets; the members of this family are widely used in biostratigraphy of upper Oligocene–middle Miocene deposits. Here, a taxonomic review of the genus Miogypsinella Hanzawa, 1940 (type species: M. borodinensis Hanzawa, 1940) and Miogypsinoides lateralis Hanzawa, 1940 is presented based on the reassessment of the type specimens. The palaeobiogeographical patterns of these taxa are discussed based on a compilation of the geographical and stratigraphical ranges of all their reported occurrences including illustrations. The computed tomography (CT)-scan analysis of the type material of M. borodinensis, from the Chattian of the central Indo-Pacific (Kitadaito Jima), indicates that it is a valid species different from Miogypsinoides complanatus. Two Miogypsinella species (M. borodinensis, M. ubaghsi) are widespread in the Chattian–Aquitanian of the Indo-Pacific region and one species (M. bermudezi) in the Aquitanian of the eastern Pacific (Mexico). Miogypsinella appeared in the Chattian in the central Indo-Pacific, reached Central America, and disappeared in the Aquitanian in both areas. The American Oligocene faunas of larger benthic foraminifera were, therefore, not distinctly isolated as previously proposed. Miogypsinoides lateralis is a clearly distinct species that is an exception to the presumed morphological trend within the Miogypsinoides species lineage. Miogypsinoides lateralis possesses morphometric values comparable to those of the Burdigalian–Langhian M. indica but occurring earlier, in the Aquitanian. The latest Aquitanian initiation of the Indonesian Seaway restriction and widespread drowning of former carbonate platforms likely brought about the extinction of Miogypsinella borodinensis, M. bermudezi, M. ubaghsi, and Miogypsinoides lateralis.
Living and fossil benthic foraminifera are widely used as bioindicators of ecosystem quality, but their analysis is time-consuming. With the help of artificial intelligence, models for the automatic identification of fossil planktonic foraminifera and, more recently, benthic foraminifera are emerging. Although large datasets of images can now be acquired automatically, efficient processing pipelines are still needed. Here we use convolutional neural networks (CNNs) to automatically identify living benthic foraminifera at the species level. Rose Bengal staining is widely used in ecological and biomonitoring studies to identify living foraminifera, which reflect present environmental conditions. We investigate both low-diversity assemblages from an intertidal mudflat on the French Atlantic coast (Bourgneuf Bay) and high-diversity assemblages from the French Mediterranean coast. Samples were imaged using a modified 3D printer equipped with a camera. The three trained CNN models are efficient in identifying species of the total community (i.e., living and dead foraminifera together) in both low- and high-diversity settings with an accuracy of 96.0 % and 82.2 %, respectively. The results compare well to published indices for ecosystem quality in high-diversity ecosystems. One CNN model can distinguish both species and vital status (i.e., living and dead specimens) for the low-diversity spot with an accuracy of 94.2 %. The results of this first application of machine learning for identifying living benthic foraminifera are very encouraging for improving the efficiency and applicability of these bio-indicators in biomonitoring studies. This approach highlights the potential of automated image-based identification to accelerate the use of benthic foraminifera as bioindicators in large-scale environmental monitoring programs.
