Calcite production by Coccolithophores in the South East Pacific Ocean: from desert to jungle

International audienceBIOSOPE cruise achieved an oceanographic transect from the Marquise Islands to the Peru-Chili upwelling (PCU) via the centre of the South Pacific Gyre (SPG). Water samples from 6 depths in the euphotic zone were collected at 20 stations. The concentrations of suspended calcite particles, coccolithophores cells and detached coccoliths were estimated together with size and weight using an automatic polarizing microscope, a digital camera, and a collection of softwares performing morphometry and pattern recognition. Some of these softwares are new and described here for the first time. The coccolithophores standing stocks are usually low and reach maxima west of the PCU. The coccoliths of Emiliania huxleyi, Gephyrocapsa spp. and Crenalithus spp. (Order Isochrysidales) represent 50% of all the suspended calcite particles detected in the size range 0.1–46 µm (21% of PIC in term of the calcite weight). The latter species are found to grow preferentially in the Chlorophyll maximum zone. In the SPG their maximum concentrations was found to occur between 150 and 200 m, which is very deep for these taxa. The weight and size of coccoliths and coccospheres are correlated. Large and heavy coccoliths and coccospheres are found in the regions with relative higher fertility in the Marquises Island and in the PCU. Small and light coccoliths and coccospheres are found west of the PCU. This distribution may correspond to that of the concentration of calcium and carbonate ions

Coccolithophore community response to ocean acidification and warming in the Eastern Mediterranean Sea : results from a mesocosm experiment

Unidad de excelencia María de Maeztu CEX2019-000940-MMesocosm experiments have been fundamental to investigate the effects of elevated CO and ocean acidification (OA) on planktic communities. However, few of these experiments have been conducted using naturally nutrient-limited waters and/or considering the combined effects of OA and ocean warming (OW). Coccolithophores are a group of calcifying phytoplankton that can reach high abundances in the Mediterranean Sea, and whose responses to OA are modulated by temperature and nutrients. We present the results of the first land-based mesocosm experiment testing the effects of combined OA and OW on an oligotrophic Eastern Mediterranean coccolithophore community. Coccolithophore cell abundance drastically decreased under OW and combined OA and OW (greenhouse, GH) conditions. Emiliania huxleyi calcite mass decreased consistently only in the GH treatment; moreover, anomalous calcifications (i.e. coccolith malformations) were particularly common in the perturbed treatments, especially under OA. Overall, these data suggest that the projected increase in sea surface temperatures, including marine heatwaves, will cause rapid changes in Eastern Mediterranean coccolithophore communities, and that these effects will be exacerbated by OA

Emiliania huxleyi coccolith calcite mass modulation by morphological changes and ecology in the Mediterranean Sea

Unidad de excelencia María de Maeztu MdM-2015-0552To understand the response of marine calcifying organisms under high CO2 scenarios, it is critical to study their calcification patterns in the natural environment. This paper focuses on a major calcifying phytoplankton group, the coccolithophores, through the analysis of water samples collected along a W-E Mediterranean transect during two research cruises, in April 2011 (Meteor cruise M84/3) and May 2013 (MedSeA cruise 2013). The Mediterranean Sea is a marginal sea characterized by large biogeochemical gradients. Currently, it is undergoing both warming and ocean acidification, processes which are rapidly modifying species distribution and calcification. The species Emiliania huxleyi largely dominates the total coccolithophore production in present day oceans and marine basins, including the Mediterranean Sea. A series of morphometric measurements were performed on the coccoliths of this species to estimate their mass, length and calculate a calcification index (proxy for the size-normalized calcification degree). The most abundant morphotype of E. huxleyi in the Mediterranean Sea is Type A. Coccoliths of this morphotype were additionally analyzed based on scanning electron microscopy images: four calcification varieties were quantified, according to the relationship between slit length-tube width, and the state of the central area(open or closed). The average E. huxleyi coccolith mass along the Mediterranean oceanographic transect depended strongly on both the average coccolith length and calcification index. The variability in average coccolith length and calcification index across samples reflected oscillations in the relative abundance of the calcification varieties. We also demonstrated that the distribution of the calcification varieties followed the main environmental gradients (carbonate chemistry, salinity, temperature, nutrient concentrations). Hence, shifts in the distribution of the calcification varieties and of the average E. huxleyi coccolith mass are to be expected in the Mediterranean Sea under climate change. These physiological and ecological responses will modulate the net coccolithophore calcification and, ultimately, the regional carbonate export to the seafloor

A new method for identifying key fossil species in the Miocene Calcareous Nannofossil Zone: insights from deep convolutional neural networks

BackgroundCalcareous nannofossils are minute microfossils widely present in marine strata. Their identification holds significant value in studies related to stratigraphic dating, paleo-environmental evolution, and paleoclimate reconstruction. However, the process of identifying these fossils is time consuming, and the discrepancies between the results obtained from different manual identification methods are substantial, hindering quantification efforts. Therefore, it is necessary to explore automated assisted identification of fossil species. This study mainly focused on 18 key fossil species from the Miocene era. Five convolutional neural network (CNN) models and 10 data augmentation techniques were compared. These models and techniques were employed to analyze and collectively train two- and three-dimensional fossil morphologies and structures obtained from three different fossils observed under single-polarized light microscopy, orthogonal polarized light microscopy, and scanning electron microscopy. Finally, the model performance was evaluated based on the predictive outcomes on the test set, using metrics such as confusion matrix and top-k accuracy. ResultThe results indicate that, for the calcareous nannofossil images, the most effective data augmentation approach is a combination of four methods: random rotation, random mirroring, random brightness, and gamma correction. Among the CNN models, DenseNet121 exhibits the optimal performance, achieving an identification accuracy of 94.56%. Moreover, this model can distinguish other fossils beyond the 18 key fossil species and non-fossil debris. Based on the confusion matrix, the evaluation results reveal that the model has strong generalization capability and outputs highly credible identification results.ConclusionDrawing on the identification results from CNN, this study asserts a robust correlation among extinction photographs, planar images, and stereoscopic morphological images of fossil species. Collective training facilitates the joint extraction and analysis of fossil features under different imaging methods. CNN demonstrates many advantages in the identification of calcareous nannofossils, offering convenience to researchers in various fields, such as stratigraphy, paleo-ecology, paleoclimate, and paleo-environments of ancient oceans. It has great potential for advancing the development of marine surveys and stratigraphic recognition processes in the future

Vision-based techniques for automatic marine plankton classification

Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.Funding for open access charge: Universidad de Málaga / CBUA. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors wish to thank Alonso Hernández-Guerra for his frm support in the development of oceanographic technology. Special thanks to Laia Armengol for her help in the domain of plankton. This study has been funded by Feder of the UE through the RES-COAST Mac-Interreg pro ject (MAC2/3.5b/314). We also acknowledge the European Union projects SUMMER (Grant Agreement 817806) and TRIATLAS (Grant Agreement 817578) from the Horizon 2020 Research and Innovation Programme and the Ministry of Science from the Spanish Government through the Project DESAFÍO (PID2020-118118RB-I00)

Fat neural network for recognition of position-normalised objects

International audienceThe design of a recognition system for natural objects is difficult, mainly because such objects are subject to a strong variability that cannot be easily modelled: planktonic species possess such highly variable forms. Existing plankton recognition systems usually comprise feature extraction processing upstream of a classifier. Drawbacks of such an approach are that the design of relevant feature extraction processes may be very difficult, especially if classes are numerous and if intra-class variability is high, so that the system becomes specific to the problem for which features have been tuned. The opposite course that we take is based on a structured multi-layer neural network with no shared weights, which generates its own features during training. Such a large parameterised - fat - network exhibits good generalisation capabilities for pattern recognition problems dealing with position-normalised objects, even with as many as one thousand weights as training examples. The advantage of such large networks, in terms of generalisation efficiency, adaptability and classification rime, is demonstrated by applying the network to three plankton recognition and face recognition problems. Its ability to perform good generalisation with few training examples, but many weights, is an open theoretical problem. (C) 1999 Published by Elsevier Science Ltd. All rights reserved