We provide a biochronology of Jurassic planktonic foramininfera, using first order linkage to ammonite and nannofossil stratigraphy and geochronology. This enigmatic and understudied group of microfossils occurred from middle Toarcian through Tithonian time, from ~180 to ~143 Ma; its origin is unknown. There are three genera: Globuligerina, Conoglobigerina and Petaloglobigerina. The genus Globuligerina, with a smooth to pustulose test surface texture appeared in Toarcian (late Early Jurassic) and Conoglobigerina, with a rough reticulate test surface texture in Oxfordian (early Late Jurassic) time. The genus Petaloglobigerina, with a petaloid last whorl and one or more twisted and claviform chambers evolved in early Kimmeridgian time from Globuligerina balakhmatovae. We recognize stratigraphic events from eleven species across four evolutionary lineages, calibrated to Geologic Time Scale 2020. A dramatic faunal change over, which is not well documented led to the survival of only one taxon, most likely Gobuligerina oxfordiana in the Tithonian. During the Berriasian several new taxa appeared.

Foraminifera are single-celled marine organisms that construct shells that remain as fossils in the marine sediments. Classifying and counting these fossils are important in e.g. paleo-oceanographic and -climatological research. However, the identification and counting process has been performed manually since the 1800s and is laborious and time-consuming. In this work, we present a deep learning-based instance segmentation model for classifying, detecting, and segmenting microscopic foraminifera. Our model is based on the Mask R-CNN architecture, using model weight parameters that have learned on the COCO detection dataset. We use a fine-tuning approach to adapt the parameters on a novel object detection dataset of more than 7000 microscopic foraminifera and sediment grains. The model achieves a (COCO-style) average precision of 0.78±0.00 on the classification and detection task, and 0.80±0.00 on the segmentation task. When the model is evaluated without challenging sediment grain images, the average precision for both tasks increases to 0.84±0.00 and 0.86±0.00, respectively. Prediction results are analyzed both quantitatively and qualitatively and discussed. Based on our findings we propose several directions for future work, and conclude that our proposed model is an important step towards automating the identification and counting of microscopic foraminifera.

This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically Convolutional Neural Networks (CNNs), to analyze digital images of foraminifera taken at different illumination angles. The dataset is composed of 1437 groups of sixteen grayscale images, one group for each foraminifer, that are then converted to RGB images with various processing methods. These RGB images are fed into a set of CNNs, organized in an Ensemble Learning (EL) environment. The ensemble is built by training different networks using different approaches for creating the RGB images. The study finds that an ensemble of CNN models trained on different RGB images improves the system's performance compared to other state-of-the-art approaches. The proposed system was also found to outperform human experts in classification accuracy.

The present work focuses on fresh water signatures at the sediment-water interface (1 cm) using foraminiferal species in both austral winter and summer in eleven longitudinal transects on the Western South Atlantic continental margin between 27° and 37° S, at water depths of 11.7 to 250 m. Here we show that depth, salinity, temperature, oxygen, grain size (mud and sand percentage), suspended matter, organic matter, SiO₄, NO₂, and NO₃ in this order of importance are responsible for the distribution of foraminiferal species and thecamoebians. The presence of these microfossils indicate freshwater influx in four sectors over the continental shelf: Itajaí-Açu River, Laguna estuarine system, Patos Lagoon and RdlP (Rio de la Plata) will be explored further in detail. Our findings on freshwater signature on the continental shelf sediments through benthic species indicator are comparable to other continental systems worldwide, and a paleo record study would be useful for three South American countries (Brazil, Argentina and Uruguay). A freshwater signature in the continental shelf indicates deposition sites probably linked to anthropogenic impact since most of the pollutants and contaminants are dumped into water bodies that eventually reach and accumulate in the ocean. Therefore, the freshwater-related species on the continental shelf reflects exactly where the depositional sediment sites are, and where anthropogenic impacts accumulate. Foraminiferal microhabitat occupation within these zones is discussed in detail. And we conclude that together with the fauna, the abiotic parameters play an important role in determining the occurrence and degree of marine eutrophication induced by the input of polluted river waters, also showing possible anoxic environments on the shelf.

Planktonic foraminiferal biomineralization intensity, reflected by their shell calcite mass, affects global carbonate deposition and is known to follow the climate cycles by being increased during glacial stages and decreased during interglacial ones. Here we measure the dissolution state and the mass of the shells of the planktonic foraminifera species Globigerina bulloides from a Tropical Eastern North Atlantic site over the last two glacial-interglacial climatic transitions and we report no major changes in plankton calcite production with the atmospheric pCO2 variations. We attribute this consistency in foraminifera calcification to the climatic and hydrological stability of the tropical regions. We however recorded increased shell masses midway through the penultimate deglaciation (Termination II). In order to elucidate the cause of the increased shell weights we performed δ18O, Mg/Ca and μCT measurements on the same shells from a number of samples surrounding this event. We find that shells of increased mass are internally contaminated by sediment infilling and that shell weights are responding to local hydrographic changes.

Reef-dwelling calcifiers face numerous environmental stresses associated with anthropogenic carbon dioxide emissions, including ocean acidification and warming. Photosymbiont-bearing calcifiers, such as large benthic foraminifera, are particularly sensitive. To gain insight into their resistance and adaptive mechanisms to climate change, Amphistegina lobifera from the Gulf of Aqaba were cultured under elevated pCO2 (492, 963, and 3182 ppm) fully-crossed with elevated temperature (28°C and 31°C) for two months. Differential protein abundances in host and photosymbionts amongst treatments were investigated alongside physiological responses and microenvironmental pH variations. Over 1000 proteins were identified, of which one-third varied significantly between treatments. Thermal stress induced protein depletions, along with reduced holobiont growth. Elevated pCO2 caused only minor proteomic alterations and color changes. However, combined stressors reduced pore sizes and increased microenvironmental pH, indicating adaptive modifications to gas exchange. Notably, substantial proteomic variations at moderate-pCO2 and 31°C indicate cellular stress, while stable physiological performance at high-pCO2 and 31°C is scrutinized by putative decreases in test stability. Our experiment shows that the effects of climate change can be missed when stressors are assessed in isolation, and that physiological responses should be assessed across organismal levels to make more realistic predictions for the fate of reef calcifiers.