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3. Contamination of planktonic food webs in the Mediterranean Sea: Setting the frame for the MERITE-HIPPOCAMPE oceanographic cruise (spring 2019)

Author

Tedetti, Marc, Tronczynski, Jacek, Carlotti, François, Pagano, Marc, Ismail, Sana Ben, Sammari, Cherif, Hassen, Malika Bel, Desboeufs, Karine, Poindron, Charlotte, Chifflet, Sandrine, Zouari, Amel Bellaaj, Abdennadher, Moufida, Amri, Sirine, Bănaru, Daniela, Abdallah, Lotfi Ben, Bhairy, Nagib, Boudriga, Ismail, Bourin, Aude, Brach-Papa, Christophe, Briant, Nicolas, Cabrol, Léa, Chevalier, Cristele, Chouba, Lassaad, Coudray, Sylvain, Yahia, Mohamed Nejib Daly, de Garidel-Thoron, Thibault, Dufour, Aurélie, Dutay, Jean-Claude, Espinasse, Boris, Fierro-González, Pamela, Fornier, Michel, Garcia, Nicole, Giner, Franck, Guigue, Catherine, Guilloux, Loïc, Hamza, Asma, Heimbürger-Boavida, Lars-Eric, Jacquet, Stéphanie, Knoery, Joel, Lajnef, Rim, Belkahia, Nouha Makhlouf, Malengros, Deny, Martinot, Pauline L., Bosse, Anthony, Mazur, Jean-Charles, Meddeb, Marouan, Misson, Benjamin, Pringault, Olivier, Quéméneur, Marianne, Radakovitch, Olivier, Raimbault, Patrick, Ravel, Christophe, Rossi, Vincent, Rwawi, Chaimaa, Hlaili, Asma Sakka, Tesán-Onrubia, Javier Angel, Thomas, Bastien, Thyssen, Melilotus, Zaaboub, Noureddine, and Garnier, Cédric

Published
2023
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4. Size normalizing planktonic Foraminifera abundance in the water column.

Author

Chaabane, Sonia, de Garidel‐Thoron, Thibault, Giraud, Xavier, Meilland, Julie, Brummer, Geert‐Jan A., Jonkers, Lukas, Mortyn, P. Graham, Greco, Mattia, Casajus, Nicolas, Kucera, Michal, Sulpis, Olivier, Kuroyanagi, Azumi, Howa, Hélène, Beaugrand, Gregory, and Schiebel, Ralf

Subjects
DATABASES, FORAMINIFERA, PREDICTION models, DIATOMS, MULTIPLICATION
Abstract

Planktonic Foraminifera have been collected from the water column with different plankton sampling devices equipped with nets of various mesh sizes, which impedes direct comparison of observed quantifications. Here, we use data on the community size structure of planktonic Foraminifera to assess the impact of mesh size on the measured abundance (ind m−3) of planktonic Foraminifera. We use data from the FORCIS database (Chaabane et al., 2023, Scientific Data 10: 354) on the global ocean at different sampling depths over the past century. We find a global cumulative increase in abundance with size, which is best described using a Michaelis–Menten function. This function yields multiplication factors by which one size fraction can be normalized to any other size fraction equal to or larger than 100 μm. The resulting size normalization model is calibrated over a range of different depth intervals, and validated with an independent dataset from various depth ranges. The comparison to Berger's (1969, Deep. Res. Oceanogr. Abstr. 16: 1–24) equivalent catch approach shows a significant increase in the predictive skill of the model. The new size normalization scheme enables comparison of Foraminifera abundance data sampled with plankton nets of different mesh sizes, such as compiled in the FORCIS database. The correction methodology may be effectively employed for various other plankton groups such as diatoms and dinoflagellates. [ABSTRACT FROM AUTHOR]

Published
2024
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5. A New Method for the Detection of Siliceous Microfossils on Sediment Microscope Slides Using Convolutional Neural Networks.

Author

Godbillot, Camille, Marchant, Ross, Beaufort, Luc, Leblanc, Karine, Gally, Yves, Le, Thang D. Q., Chevalier, Cristele, and de Garidel‐Thoron, Thibault

Subjects
ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, MACHINE learning, OBJECT recognition (Computer vision), DIATOM frustules, FOSSIL microorganisms, FOSSIL diatoms
Abstract

Diatom communities preserved in sediment samples are valuable indicators for understanding the past and present dynamics of phytoplankton communities, and their response to environmental changes. These studies are traditionally achieved by counting methods using optical microscopy, a time‐consuming process that requires taxonomic expertise. With the advent of automated image acquisition workflows, large image data sets can now be acquired, but require efficient preprocessing methods. Detecting diatom frustules on microscope images is a challenge due to their low relief, diverse shapes, and tendency to aggregate, which prevent the use of traditional thresholding techniques. Deep learning algorithms have the potential to resolve these challenges, more particularly for the task of object detection. Here we explore the use of a Faster Region‐based Convolutional Neural Network model to detect siliceous biominerals, including diatoms, in microscope images of a sediment trap series from the Mediterranean Sea. Our workflow demonstrates promising results, achieving a precision score of 0.72 and a recall score of 0.74 when applied to a test set of Mediterranean diatom images. Our model performance decreases when used to detect fragments of these microfossils; it also decreases when particles are aggregated or when images are out of focus. Microfossil detection remains high when the model is used on a microscope image set of sediments from a different oceanic basin, demonstrating its potential for application in a wide range of contemporary and paleoenvironmental studies. This automated method provides a valuable tool for analyzing complex samples, particularly for rare species under‐represented in training data sets. Plain Language Summary: Microfossils preserved in ocean sediments are studied to explore the impact of climate change on planktonic communities. The usual way to count these microfossils is slow and requires an expert to identify them on microscope images. In this study, we explore how artificial intelligence can be used on microscope images to detect the microfossils produced by one particular group, diatoms. Our results show that models can be trained to identify these objects, including the ones that were not specifically shown to the model during the training phase. However, the quality of the microscope image, and of the sample preparation beforehand, can affect how well the model works. This new protocol has good potential to be used on diatom images differing in age and geographical origins. Adopting this method could make it possible to rapidly increase the temporal resolution and spatial extent of existing data on diatom diversity, which could thus improve our knowledge of plankton resilience to climate change. Key Points: Faster Region‐based Convolutional Neural Network models are efficient at detecting marine diatom frustules on microscope slide imagesThese models can be applied to detect the diatoms on images from a diverse array of environmental conditionsAdequate sample preparation and image quality enhance model results [ABSTRACT FROM AUTHOR]

Published
2024
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6. A user‐friendly method to get automated pollen analysis from environmental samples

Author

Gimenez, Betty, primary, Joannin, Sébastien, additional, Pasquet, Jérôme, additional, Beaufort, Luc, additional, Gally, Yves, additional, de Garidel‐Thoron, Thibault, additional, Combourieu‐Nebout, Nathalie, additional, Bouby, Laurent, additional, Canal, Sandrine, additional, Ivorra, Sarah, additional, Limier, Bertrand, additional, Terral, Jean‐Frédéric, additional, Devaux, Céline, additional, and Peyron, Odile, additional

Published
2024
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8. The global genetic diversity of planktonic foraminifera reveals the structure of cryptic speciation in plankton.

Author

Morard, Raphaël, Darling, Kate F., Weiner, Agnes K. M., Hassenrück, Christiane, Vanni, Chiara, Cordier, Tristan, Henry, Nicolas, Greco, Mattia, Vollmar, Nele M., Milivojevic, Tamara, Rahman, Shirin Nurshan, Siccha, Michael, Meilland, Julie, Jonkers, Lukas, Quillévéré, Frédéric, Escarguel, Gilles, Douady, Christophe J., de Garidel‐Thoron, Thibault, de Vargas, Colomban, and Kucera, Michal

Subjects
GENETIC variation, BIOLOGICAL classification, FORAMINIFERA, PLANKTON, GENETIC speciation, RIBOSOMAL DNA
Abstract

The nature and extent of diversity in the plankton has fascinated scientists for over a century. Initially, the discovery of many new species in the remarkably uniform and unstructured pelagic environment appeared to challenge the concept of ecological niches. Later, it became obvious that only a fraction of plankton diversity had been formally described, because plankton assemblages are dominated by understudied eukaryotic lineages with small size that lack clearly distinguishable morphological features. The high diversity of the plankton has been confirmed by comprehensive metabarcoding surveys, but interpretation of the underlying molecular taxonomies is hindered by insufficient integration of genetic diversity with morphological taxonomy and ecological observations. Here we use planktonic foraminifera as a study model and reveal the full extent of their genetic diversity and investigate geographical and ecological patterns in their distribution. To this end, we assembled a global data set of ~7600 ribosomal DNA sequences obtained from morphologically characterised individual foraminifera, established a robust molecular taxonomic framework for the observed diversity, and used it to query a global metabarcoding data set covering ~1700 samples with ~2.48 billion reads. This allowed us to extract and assign 1 million reads, enabling characterisation of the structure of the genetic diversity of the group across ~1100 oceanic stations worldwide. Our sampling revealed the existence of, at most, 94 distinct molecular operational taxonomic units (MOTUs) at a level of divergence indicative of biological species. The genetic diversity only doubles the number of formally described species identified by morphological features. Furthermore, we observed that the allocation of genetic diversity to morphospecies is uneven. Only 16 morphospecies disguise evolutionarily significant genetic diversity, and the proportion of morphospecies that show genetic diversity increases poleward. Finally, we observe that MOTUs have a narrower geographic distribution than morphospecies and that in some cases the MOTUs belonging to the same morphospecies (cryptic species) have different environmental preferences. Overall, our analysis reveals that even in the light of global genetic sampling, planktonic foraminifera diversity is modest and finite. However, the extent and structure of the cryptic diversity reveals that genetic diversification is decoupled from morphological diversification, hinting at different mechanisms acting at different levels of divergence. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Advances in planktonic foraminifer research: New perspectives for paleoceanography

Author

Schiebel, Ralf, Smart, Sandi M., Jentzen, Anna, Jonkers, Lukas, Morard, Raphaël, Meilland, Julie, Michel, Elisabeth, Coxall, Helen K., Hull, Pincelli M., de Garidel-Thoron, Thibault, Aze, Tracy, Quillévéré, Frédéric, Ren, Haojia, Sigman, Daniel M., Vonhof, Hubert B., Martínez-García, Alfredo, Kučera, Michal, Bijma, Jelle, Spero, Howard J., and Haug, Gerald H.

Published
2018
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12. The FORCIS database: A global census of planktonic Foraminifera from ocean waters

Author

Chaabane, Sonia, primary, de Garidel-Thoron, Thibault, additional, Giraud, Xavier, additional, Schiebel, Ralf, additional, Beaugrand, Gregory, additional, Brummer, Geert-Jan, additional, Casajus, Nicolas, additional, Greco, Mattia, additional, Grigoratou, Maria, additional, Howa, Hélène, additional, Jonkers, Lukas, additional, Kucera, Michal, additional, Kuroyanagi, Azumi, additional, Meilland, Julie, additional, Monteiro, Fanny, additional, Mortyn, Graham, additional, Almogi-Labin, Ahuva, additional, Asahi, Hirofumi, additional, Avnaim-Katav, Simona, additional, Bassinot, Franck, additional, Davis, Catherine V., additional, Field, David B., additional, Hernández-Almeida, Iván, additional, Herut, Barak, additional, Hosie, Graham, additional, Howard, Will, additional, Jentzen, Anna, additional, Johns, David G., additional, Keigwin, Lloyd, additional, Kitchener, John, additional, Kohfeld, Karen E., additional, Lessa, Douglas V. O., additional, Manno, Clara, additional, Marchant, Margarita, additional, Ofstad, Siri, additional, Ortiz, Joseph D., additional, Post, Alexandra, additional, Rigual-Hernandez, Andres, additional, Rillo, Marina C., additional, Robinson, Karen, additional, Sagawa, Takuya, additional, Sierro, Francisco, additional, Takahashi, Kunio T., additional, Torfstein, Adi, additional, Venancio, Igor, additional, Yamasaki, Makoto, additional, and Ziveri, Patrizia, additional

Published
2023
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14. A new combination of automated detection and classification of Mediterranean pollen grains from annual pollen traps

Author

Gimenez, Betty, Peyron, Odile, Devaux, Celine, Joannin, Sébastien, Barboni, Doris, Beaufort, Luc, Bouby, Laurent, Canal, Sandrine, Combourieu-Nebout, Nathalie, Gally, Yves, de Garidel-Thoron, Thibault, Ivorra, Sarah, Limier, Bertrand, Terral, Jean-Frédéric, Pasquet, Jerome, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Laboratoire Chrono-environnement (UMR 6249) (LCE), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut Français de Pondichéry (IFP), Ministère de l'Europe et des Affaires étrangères (MEAE)-Centre National de la Recherche Scientifique (CNRS), Centre de Bio-Archéologie et d'Ecologie (CBAE), Université Montpellier 2 - Sciences et Techniques (UM2)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Histoire naturelle de l'Homme préhistorique (HNHP), Muséum national d'Histoire naturelle (MNHN)-Université de Perpignan Via Domitia (UPVD)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université Paul-Valéry - Montpellier 3 (UPVM)

Subjects
[SDE]Environmental Sciences
Abstract

Pollen is a valuable proxy for reconstructing current and past vegetation. Automating identification and counting of pollen grains could greatly help palynologists, by increasing sample size, and thus their spatial and temporal resolutions, and standardizing methods and results. Several recent studies have already shown the potential of deep learning for automatic pollen recognition, especially for aeropalynology. Studies on pollen traps and fossil samples remain scarce, most probably because they contain many non-pollen particles and damaged pollen grains, increasing the difficulty of the task. Here, we test a new combination of last-generation deep-learning algorithms for automatic detection and classification of pollen from annual traps containing as many as 70 Mediterranean taxa, and many debris types. A total of 16 traps were collected each of three consecutive years in six locations in France. For each trap, one slide was mounted, and photographed partially with an automatic microscope. This operation produced 1,024 images of 204x204µm per slide, which contained a few pollen grains, that could be damaged, cut, or clumped, and many debris. We first trained YOLOv5 to detect the single category of pollen on 85% of 4,096 images (256 images per slide) containing 12,344 manually detected pollen grains. On the remaining 15% of the annotated images, the model left 0.7% pollen undetected, and falsely detected 12% of debris which are meant to be excluded by the subsequent classification. We then applied the model on the remaining 12,288 images and obtained 42,156 additional pollen grains. For the classification, we have trained so far ResNet50 on 85% of 8,000 manually identified pollen grains among 26 classes, made of a single or a few pollen taxa, and one extra class of debris. On the remaining 15% of the images, we obtained a class-mean accuracy of 0.73 with per-class accuracy ranging from 0.28 to 0.96. The best classification rates were obtained for taxa we had most images for (Pistacia sp., Quercus ilex, Lycopodium sp.). We are now improving the classification, e.g. by testing other algorithms, increasing the training dataset and/or through data augmentation.

Published
2023

15. The FORCIS database: A global census of planktonic Foraminifera from ocean waters

Author

Chaabane, Sonia, de Garidel-Thoron, Thibault, Giraud, Xavier, Schiebel, Ralf, Beaugrand, Gregory, Brummer, Geert-Jan, Casajus, Nicolas, Greco, Mattia, Grigoratou, Maria, Howa, Hélène, Jonkers, Lukas, Kucera, Michal, Kuroyanagi, Azumi, Meilland, Julie, Monteiro, Fanny, Mortyn, Graham, Almogi-Labin, Ahuva, Asahi, Hirofumi, Avnaim-Katav, Simona, Bassinot, Franck, Davis, Catherine V., Field, David B., Hernández-Almeida, Iván, Herut, Barak, Hosie, Graham, Howard, Will, Jentzen, Anna, Johns, David G., Keigwin, Lloyd, Kitchener, John, Kohfeld, Karen E., Lessa, Douglas V. O., Manno, Clara, Marchant, Margarita, Ofstad, Siri, Ortiz, Joseph D., Post, Alexandra, Rigual-Hernandez, Andres, Rillo, Marina C., Robinson, Karen, Sagawa, Takuya, Sierro, Francisco, Takahashi, Kunio T., Torfstein, Adi, Venancio, Igor, Yamasaki, Makoto, and Ziveri, Patrizia

Subjects
Climate change, Biodiversity
Abstract

The data currently described was generated within the EU/FP7 HeCaToS project (Hepatic and Cardiac Toxicity Systems modeling). The project aimed to develop an in silico prediction system to contribute to drug safety assessment for humans. For this purpose, multi-omics data of repeated dose toxicity were obtained for 10 hepatotoxic and 10 cardiotoxic compounds. Most data were gained from in vitro experiments in which 3D microtissues (either hepatic or cardiac) were exposed to a therapeutic (physiologically relevant concentrations calculated through PBPK-modeling) or a toxic dosing profile (IC20 after 7 days). Exposures lasted for 14 days and samples were obtained at 7 time points (therapeutic doses: 2-8-24-72-168-240-336 h; toxic doses 0-2-8-24-72-168-240 h). Transcriptomics (RNA sequencing & microRNA sequencing), proteomics (LC-MS), epigenomics (MeDIP sequencing) and metabolomics (LC-MS & NMR) data were obtained from these samples. Furthermore, functional endpoints (ATP content, Caspase3/7 and O2 consumption) were measured in exposed microtissues. Additionally, multi-omics data from human biopsies from patients are available. This data is now being released to the scientific community through the BioStudies data repository ( https://www.ebi.ac.uk/biostudies/ ).

Published
2023

17. Environmental Controls of Size Distribution of Modern Planktonic Foraminifera in the Tropical Indian Ocean

Author

Adebayo, Michael B., Bolton, Clara T., Marchant, Ross, Bassinot, Franck, Conrod, Sandrine, De Garidel‐thoron, Thibault, Adebayo, Michael B., Bolton, Clara T., Marchant, Ross, Bassinot, Franck, Conrod, Sandrine, and De Garidel‐thoron, Thibault

Abstract

Paleoceanographic studies often rely on abundance changes in microfossil species, with little consideration for characteristics such as organism size, which may also be related to environmental changes. Using a tropical Indian Ocean (TIO) core-top data set, we test the Optimum size-hypothesis (OSH), investigating whether relative abundance or environmental variables are better descriptors of planktonic foraminifera species' optimum conditions. We also investigate the environmental drivers of whole-assemblage planktonic foraminiferal test size variation in the TIO. We use an automated imaging and sorting system (MiSo) to identify planktonic foraminiferal species, analyze their morphology, and quantify fragmentation rate using machine learning techniques. Machine model accuracy is confirmed by comparison with human classifiers (97% accuracy). Data for 33 environmental parameters were extracted from modern databases and, through exploratory factor analysis and regression models, we explore relationships between planktonic foraminiferal size and oceanographic parameters in the TIO. Results show that the size frequency distribution of most planktonic foraminifera species is unimodal, with some larger species showing multimodal distributions. Assemblage size95/5 (95th percentile size) increases with increasing species diversity, and this is attributed to vertical niche separation induced by thermal stratification. Our test for the OSH reveals that relative abundance is not a good predictor of species' optima and within-species size95/5 response to environmental parameters is species-specific, with parameters related to carbonate ion concentration, temperature, and salinity being primary drivers. At the species and assemblage levels, our analyses indicate that carbonate ion concentration and temperature play important roles in determining size trends in TIO planktonic foraminifera. Key Points Optimum size-hypothesis holds true in planktonic foraminifera if one considers th

Published
2023
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19. Size Distribution of Modern Planktonic Foraminifera in the tropical Indian Ocean: Environmental Controls and Paleo-reconstruction Potentials

Author

Adebayo, Michael, Bolton, Clara, Marchant, Ross, Bassinot, Franck, Conrod, Sandrine, de Garidel-Thoron, Thibault, Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Queensland University of Technology [Brisbane] (QUT), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDE]Environmental Sciences
Abstract

Présentation de congrès; Palaeoceanographic studies often rely on microfossil species abundance changes, with little consideration for traits like size that could also relate to environmental changes. We hypothesize that whole-assemblage and/or species-specific planktonic foraminiferal test size could be good predictors of environmental variables, and we test this using a tropical Indian Ocean core-top dataset. We use an automated imaging and sorting system (MiSo) and a convolutional neural network model (CNN) to identify species, analyze morphology, and quantify fragmentation using machine learning techniques. A total of 311380 images were acquired at an average of 3797 images per sample. Machine model accuracy is confirmed by comparison with human classifiers (98% accuracy achieved). Data for 32 environmental parameters are extracted from modern databases and, through Exploratory Factor Analysis and regression models, we investigate the potential of using planktonic foraminiferal size to reconstruct oceanographic parameters. The size frequency distribution of most planktonic foraminifera species is unimodal and larger species show polymodal distributions. Within our tropical dataset, we find that intraspecies size response to environmental parameters is species-specific with carbonate ion concentration, temperature, and salinity identified as primary drivers. At the assemblage level, our analyses suggest that internal biogenic processes (primary) and temperature (secondary) are key drivers of morphometric changes in planktonic foraminifera. Our assessment of the potential to utilize assemblage size in reconstructing sea surface temperature in the tropical Indian Ocean showed that the reconstructed SST of the test MD90-0963 downcore site, relatively followed the delta O18 signals from previous works for the same site.

Published
2022

22. Machine learning techniques to characterize functional traits of plankton from image data

Author

Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Erica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L.C., Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten Hvitfeldt, Kiørboe, Thomas, Lalonde, Jean-Francois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, and Martini, Séverine

Abstract

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms., Limnology and Oceanography, 67 (8), ISSN:0024-3590, ISSN:1939-5590

Published
2022

24. The Foraminiferal Response to Climate Stressors Project: Tracking the Community Response of Planktonic Foraminifera to Historical Climate Change

Author

De Garidel-thoron, Thibault, Chaabane, Sonia, Giraud, Xavier, Meilland, Julie, Jonkers, Lukas, Kucera, Michal, Brummer, Geert-jan A., Grigoratou, Maria, Monteiro, Fanny M., Greco, Mattia, Mortyn, P. Graham, Kuroyanagi, Azumi, Howa, Helene, Beaugrand, Gregory, Schiebel, Ralf, De Garidel-thoron, Thibault, Chaabane, Sonia, Giraud, Xavier, Meilland, Julie, Jonkers, Lukas, Kucera, Michal, Brummer, Geert-jan A., Grigoratou, Maria, Monteiro, Fanny M., Greco, Mattia, Mortyn, P. Graham, Kuroyanagi, Azumi, Howa, Helene, Beaugrand, Gregory, and Schiebel, Ralf

Abstract

Planktonic Foraminifera are ubiquitous marine protozoa inhabiting the upper ocean. During life, they secrete calcareous shells, which accumulate in marine sediments, providing a geological record of past spatial and temporal changes in their community structure. As a result, they provide the opportunity to analyze both current and historical patterns of species distribution and community turnover in this plankton group on a global scale. The FORCIS project aims to unlock this potential by synthesizing a comprehensive global database of abundance and diversity observations of living planktonic Foraminifera in the upper ocean over more than 100 years starting from 1910. The database will allow for unravelling the impact of multiple global-change stressors acting on planktonic Foraminifera in historical times, using an approach that combines statistical analysis of temporal diversity changes in response to environmental changes with numerical modeling of species response based on their ecological traits.

Published
2022
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25. Machine learning techniques to characterize functional traits of plankton from image data.

Author

Orenstein, Eric C, Orenstein, Eric C, Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C, Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S, Ferrario, Filippo, Giering, Sarah LC, Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten Hvitfeldt, Kiørboe, Thomas, Lalonde, Jean-François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark D, Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon-Martin, Sonnet, Virginie, Sosik, Heidi M, Stemmann, Lars S, Stock, Michiel, Terbiyik-Kurt, Tuba, Valcárcel-Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M, Irisson, Jean-Olivier, Orenstein, Eric C, Orenstein, Eric C, Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C, Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S, Ferrario, Filippo, Giering, Sarah LC, Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten Hvitfeldt, Kiørboe, Thomas, Lalonde, Jean-François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark D, Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon-Martin, Sonnet, Virginie, Sosik, Heidi M, Stemmann, Lars S, Stock, Michiel, Terbiyik-Kurt, Tuba, Valcárcel-Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M, and Irisson, Jean-Olivier

Abstract

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Published
2022

26. Machine learning techniques to characterise functional traits of plankton image data

Author

Orenstein, Eric, Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica, Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey, Ferrario, Filippo, Giering, Sarah, Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten, Kiorboe, Thomas, Lalonde, Jean-Francois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark, Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon-Martin, Sonnet, Virginie, Sosik, Heidi, Stemmann, Lars, Stock, Michiel, Terbiyik-Kurt, Tuba, Valcárcel-Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya, Irisson, Jean-Olivier, Orenstein, Eric, Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica, Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey, Ferrario, Filippo, Giering, Sarah, Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten, Kiorboe, Thomas, Lalonde, Jean-Francois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark, Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon-Martin, Sonnet, Virginie, Sosik, Heidi, Stemmann, Lars, Stock, Michiel, Terbiyik-Kurt, Tuba, Valcárcel-Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya, and Irisson, Jean-Olivier

Abstract

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Published
2022

27. Supplemental Information: Machine learning techniques to characterize functional traits of plankton from image data

Author

Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., Irisson, Jean-Olivier, Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., and Irisson, Jean-Olivier

Published
2022

28. Machine learning techniques to characterize functional traits of plankton from image data

Author

Centre National de la Recherche Scientifique (France), Belmont Forum, Université Laval, Natural Sciences and Engineering Research Council of Canada, Research Foundation - Flanders, ETH Zurich, Gordon and Betty Moore Foundation, National Science Foundation (US), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brasil), Agence Nationale de la Recherche (France), Ministerio de Economía y Competitividad (España), Institut Universitaire de France, Simons Foundation, Sorbonne Université, Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., Irisson, Jean-Olivier, Centre National de la Recherche Scientifique (France), Belmont Forum, Université Laval, Natural Sciences and Engineering Research Council of Canada, Research Foundation - Flanders, ETH Zurich, Gordon and Betty Moore Foundation, National Science Foundation (US), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brasil), Agence Nationale de la Recherche (France), Ministerio de Economía y Competitividad (España), Institut Universitaire de France, Simons Foundation, Sorbonne Université, Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., and Irisson, Jean-Olivier

Abstract

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Published
2022

29. The Foraminiferal Response to Climate Stressors Project: Tracking the Community Response of Planktonic Foraminifera to Historical Climate Change

Author

de Garidel-Thoron, Thibault, primary, Chaabane, Sonia, additional, Giraud, Xavier, additional, Meilland, Julie, additional, Jonkers, Lukas, additional, Kucera, Michal, additional, Brummer, Geert-Jan A., additional, Grigoratou, Maria, additional, Monteiro, Fanny M., additional, Greco, Mattia, additional, Mortyn, P. Graham, additional, Kuroyanagi, Azumi, additional, Howa, Hélène, additional, Beaugrand, Gregory, additional, and Schiebel, Ralf, additional

Published
2022
Full Text
View/download PDF

30. Automatic detection and classification of Mediterranean pollen grains: application to the wild and domesticated grapevine

Author

Gimenez, Betty, Peyron, Odile, Devaux, Celine, Joannin, Sébastien, Barboni, Doris, Beaufort, Luc, Bouby, Laurent, Canal, Sandrine, Combourieu-Nebout, Nathalie, de Garidel-Thoron, Thibault, Gally, Yves, Ivorra, Sarah, Jeanty, Angèle, Limier, Bertrand, Pasquet, Jerome, Joannin, Sebastien, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Histoire naturelle de l'Homme préhistorique (HNHP), and Muséum national d'Histoire naturelle (MNHN)-Université de Perpignan Via Domitia (UPVD)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDE.MCG] Environmental Sciences/Global Changes, [SDE.MCG]Environmental Sciences/Global Changes, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, [SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces, environment
Abstract

International audience

Published
2022

31. Planktonic foraminifera response to climatic changes

Author

Sonia, Chaabane, DE GARIDEL-THORON, Thibault, Forcis Working Group, WG, Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Fondation pour la recherche sur la Biodiversité (FRB), and de Garidel-Thoron, Thibault

Subjects
[SDE.BE] Environmental Sciences/Biodiversity and Ecology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

32. Automatic recognition of microfossils using convolutionnal neural networks : applications of a high-throughput workflow for paleoceanographic reconstructions and biostratigraphy

Author

de Garidel-Thoron, Thibault, Marchant, Ross, Tetard, Martin, Adebayo, Michael, Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Queensland University of Technology [Brisbane] (QUT), SGF, CNRS, Laboratoire de Géologie de Lyon ou l’étude de la Terre, des planètes et de l’environnement, and Sciencesconf.org, CCSD

Subjects
[SDU] Sciences of the Universe [physics], image recognition, neural network, [SDU]Sciences of the Universe [physics], paleoclimatology, microfossils, Foraminifera, biostratigraphy, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

33. Environmental controls of size distribution of modern planktonic foraminifera in the equatorial Indian ocean: A calibration study

Author

Adebayo, Michael, de Garidel-Thoron, Thibault, Bolton, C. T., Marchant, Ross, Bassinot, Franck, Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), SGF, CNRS, Laboratoire de Géologie de Lyon ou l’étude de la Terre, des planètes et de l’environnement, Queensland University of Technology [Brisbane] (QUT), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)

Subjects
planktonic foraminifera, Holocene, [SDU]Sciences of the Universe [physics], [SDE]Environmental Sciences, paleoceanographic reconstructions, equatorial Indian ocean, Automated foraminiferal analysis, ComputingMilieux_MISCELLANEOUS
Abstract

International audience; Palaeoceanographic studies often rely on microfossil species abundance changes, with littleconsideration for species traits (e.g. size) that could be related to environmental changes. Wehypothesize that whole-assemblage and species-specific planktonic foraminifera (PF) testsize could be good predictors of environmental variables, and we test this using an EquatorialIndian Ocean (EIO) core-top sample set (62 viable samples). We use an automated imagingand sorting system (MiSo) to identify PF species, analyze morphology and quantifyfragmentation using machine learning techniques. Machine accuracy was confirmed bycomparisons with human classifiers. Data for 25 mean annual environmental parameterswere extracted from modern databases and, through Exploratory Factor Analysis andregression models, we investigate the potential of PF size, at the assemblage and specieslevel, for reconstructing oceanographic parameters in the Indian Ocean. Within our tropicaldataset, we find that SST is not a significant driver of assemblage size, although thermoclinedwelling species Globorotalia inflata and Globorotalia truncatulinoides show a significantrelationship with temperature. Our analyses indicate that deep carbonate ion concentrationand core depth may be important factors influencing PF size, especially in species that arelarge-sized or bear calcite crusts such as Globigerinoides conglobatus, Globorotaliamenardii, and Neogloboquadrina dutertrei. We propose that PF population size couldpotentially be useful to reconstruct bottom water carbonate concentrations and sea surfacetemperature. This approach will be tested on a new downcore record from the Arabian sea(ODP Site 722) during key Pleistocene glacial-interglacial transitions, where existing seasurface temperature and other paleo-reconstructions will allow meaningful comparisons.

Published
2021

34. The FORCIS project (Foraminifera Response to Climatic Stress: evaluating biodiversity changes of calcifying zooplankton in response to multiple stressors), and Mediterranean context for the response of planktonic foraminifera

Author

Mortyn, P. Graham, de Garidel-Thoron, Thibault, Forcis Working Group, Wg, Institut de Ciencia i Tecnologia Ambientals (ICTA), Universitat Autònoma de Barcelona (UAB), Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Fondation pour la recherche sur la Biodiversité (FRB), and de Garidel-Thoron, Thibault

Subjects
[SDE.BE] Environmental Sciences/Biodiversity and Ecology, [SDE.MCG] Environmental Sciences/Global Changes, [SDE.MCG]Environmental Sciences/Global Changes, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

35. Assessing the response of planktonic foraminifera biodiversity to multiple climatic stressors (FORCIS database)

Author

Sonia, Chaabane, de Garidel-Thoron, Thibault, Forcis Working Group, Wg, de Garidel-Thoron, Thibault, Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), and Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

39. Respuestas morfológicas de foraminíferos calcáreos a la variabilidad de la zona de mínimo de frente a Perú central desde el siglo XIX

Author

Romero, Dennis, Gutiérrez, Dimitri, Scholten, Jan, Salvatteci, Renato, de Garidel-Thoron, Thibault, Cardich, Jorge, and Sifeddine, Abdelfettah

Subjects
Posgrado, Cambio Climático, Ecología y Ambiente
Abstract

El Ecosistema de Humboldt frente a Perú se caracteriza por combinar condiciones de déficit de oxígeno y bajo pH. La presencia de una Zona de Mínimo de Oxígeno permite la preservación de los carbonatos en los sedimentos. Los foraminíferos calcáreos componen una importante fracción en este ambiente sedimentario exhibiendo atributos morfológicos como la cantidad de calcita (masa) producida por la testa o la cantidad de poros, que resultan ser útiles para el estudio de temáticas asociadas al cambio climático en los océanos. Por ello, se estudió la variabilidad de estos dos parámetros en un testigo sedimentario colectado en el talud continental frente a Pisco (300 m) para evaluar las condiciones de oxigenación y acidez en el fondo desde el siglo XIX. Para la estimación de la masa, las testas dos especies de foraminíferos, Globigerina bulloides y Bolivina seminuda, fueron pesadas en grupos obteniendo el peso promedio (μg). Como parámetro adicional, se normalizó la masa se normalizó por la longitud máxima. Para la determinación de poros, se contabilizaron manualmente en la especie B. seminuda, usando imágenes obtenidas con Microscopio Electrónico de Barrido, obteniendo la porosidad (%). El registro fue dividido en 3 períodos relacionados con las condiciones geoquímicas desde finales del siglo XIX. La testa de B. seminuda presentó una masa promedio significativamente diferente entre periodos, con testas más ligeras en el periodo más reciente (50 años). Esta especie también mostró estas diferencias en su masa normalizada, con una disminución progresiva de este parámetro desde finales del siglo XIX hasta el presente. Estas respuestas no fueron observadas en G. bulloides. Asimismo, la porosidad en las testas de B. seminuda fue significativamente mayor en el periodo más reciente. Estos resultados señalan una tendencia hacia condiciones más ácidas y más pobres en nitrato en la interfase agua-sedimento en los últimos 50 años. 2021-09-14

Published
2021

40. Bioactive trace metals and their isotopes as paleoproductivity proxies: An assessment using GEOTRACES-era data

Author

Horner, Tristan, primary, Little, Susan, additional, Conway, Tim, additional, Farmer, Jesse, additional, Hertzberg, Jennifer, additional, Janssen, David, additional, Lough, Alastair, additional, McKay, Jennifer, additional, Tessin, Allyson, additional, Galer, Stephen, additional, Jaccard, Sam, additional, Lacan, Francois, additional, Paytan, Adina, additional, Wuttig, Kathrin, additional, Bolton, Clara, additional, Calvo, Eva, additional, Cardinal, Damien, additional, de Garidel-Thoron, Thibault, additional, Fietz, Susanne, additional, Hendry, Katharine, additional, Marcantonio, Franco, additional, Rafter, Patrick, additional, Ren, Haojia, additional, Somes, Christopher, additional, Sutton, Jill, additional, Torfstein, Adi, additional, and Winckler, Gisela, additional

Published
2021
Full Text
View/download PDF

41. Automatic calcareous nannofossil biostratigraphy using the latest version of SYRACO

Author

Beaufort, Luc, Gally, Y., de Garidel-Thoron, Thibault, Marchant, Ross, Tetard, Martin, Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), and Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

42. Utilisation d’images microscopiques artificielles en complément d’images réelles pour la détection automatique de diatomées par apprentissage profond

Author

Faure-Giovagnoli, Pierre, Venkataramanan, Aishwarya, Heudre, David, DE GARIDEL-THORON, Thibault, Usseglio-Polatera, Philippe, Pradalier, Cedric, Laviale, Martin, Laviale, Martin, Georgia Institute of Technology [Lorraine, France], Laboratoire Interdisciplinaire des Environnements Continentaux (LIEC), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Terre et Environnement de Lorraine (OTELo), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Zone Atelier du Bassin de la Moselle [LTSER France] (ZAM), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Direction Régionale de l'Environnement, de l'Aménagement et du Logement - Grand Est (DREAL Grand Est), Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Terre et Environnement de Lorraine (OTELo), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut Ecologie et Environnement (INEE), and Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDE.BE] Environmental Sciences/Biodiversity and Ecology, MESH: qualité de l’eau, bioindication, diatomées, analyse d’images, reconnaissance automatique, apprentissage machine, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDV.EE.ECO] Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Abstract

International audience; Les diatomées sont des microalgues présentes dans tous les milieux aquatiques. Ces organismes sont utilisés en routine comme bioindicateur de la qualité écologique des eaux douces dans le cadre de la mise en œuvre de la Directive Cadre européenne sur l'Eau (DCE). Les indices biologiques actuels basés sur les diatomées reposent sur des critères morphologiques (forme et ornementation de l’exosquellette siliceux, le frustule) pas toujours faciles à caractériser en routine (i.e. à l’aide de méthodes optiques conventionnelles). L’identification est donc chronophage, souvent sujette à de multiples biais (expérience de l'opérateur, qualité de l'image) et nécessite un niveau élevé d’expertise.Ceci justifie le développement d'un outil plus robuste, basé sur une classification automatique des diatomées. Cet objectif est toujours un défi d’actualité, depuis les premières tentatives datant des années 90. Dans ce contexte, le développement récent des approches d’apprentissage profond pour identifier et quantifier les traits des organismes à partir d’images semble prometteur pour résoudre les problèmes rencontrés jusqu’à présent. Notre objectif est donc de proposer un nouvel outil d’identification des diatomées basé sur des algorithmes de reconnaissance automatique de formes à partir d’images individuelles. Par rapport à l’approche classique d’identification des diatomées, cet outil se veut notamment plus robuste car indépendant de l’opérateur et permettra d’améliorer (gain de temps, coût) ceux actuellement disponibles dans le cadre du suivi réglementaire de l’état écologique des cours d’eau.Le développement de cet outil implique 1/l’acquisition d’une base de données représentative du milieu naturel (banques d’images de diatomées) qui permette 2) le développement des algorithmes d’identification des diatomées. En première approche, une banque simplifiée d’images représentatives de 209 espèces de diatomées (environ 9 000 images au total) a été créée à partir de guides d’identification en libre accès. Cette base d’images a permis de générer environ 30 000 images composites simulant des images d’échantillons naturels. Ceci a permis le développement de premiers algorithmes de détection (diatomées vs. débris) et de classification (espèces présentes). Les premiers résultats obtenus montrent que l’outil de reconnaissance permet de distinguer les diatomées présentes sur un échantillon (précision de 90%) ainsi que les débris (précision de 99%). L’outil a également été testé sur quelques images réelles avec une précision pouvant atteindre 86% pour les débris et 73% pour les diatomées présentes. Ces résultats encourageants démontrent la faisabilité de notre approche (preuve de concept). La banque d’images sera donc consolidée afin de la rendre plus représentative de l’ensemble des espèces indicatrices de diatomées retrouvées dans une zone géographique donnée (e.g. bassin de la Moselle, bassin Rhin-Meuse) et ainsi d’améliorer la performance des algorithmes de reconnaissance.

Published
2020

43. Classification automatique des diatomées par apprentissage profond pour l’amélioration du diagnostic écologique des milieux aquatiques

Author

Faure-Giovagnoli, Pierre, Venkataramanan, Aishwarya, Figus, Cécile, Heudre, David, DE GARIDEL-THORON, Thibault, Noûs, Camille, Usseglio-Polatera, Philippe, Pradalier, C., Laviale, Martin, Laviale, Martin, Lorraine Artificicial Intelligence - - LOR-AI2020 - ANR-20-THIA-0010 - PNIA - VALID, Georgia Tech Lorraine [Metz], Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Georgia Institute of Technology [Atlanta]-CentraleSupélec-Ecole Nationale Supérieure des Arts et Metiers Metz-Centre National de la Recherche Scientifique (CNRS), Laboratoire Interdisciplinaire des Environnements Continentaux (LIEC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Terre et Environnement de Lorraine (OTELo), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Zone Atelier du Bassin de la Moselle [LTSER France] (ZAM), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Direction Régionale de l'Environnement, de l'Aménagement et du Logement - Grand Est (DREAL Grand Est), Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Collège de France (CdF (institution))-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Laboratoire Cogitamus, ANR-20-THIA-0010,LOR-AI,Lorraine Artificicial Intelligence(2020), Ecole Nationale Supérieure des Arts et Metiers Metz-Georgia Institute of Technology [Atlanta]-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Terre et Environnement de Lorraine (OTELo), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDE.BE] Environmental Sciences/Biodiversity and Ecology, qualité de l’eau, diatomées, analyse d’images, apprentissage machine, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, bioindication, reconnaissance automatique
Abstract

National audience; Les diatomées sont des microalgues présentes dans tous les milieux aquatiques. Ces organismes sont utilisés en routine comme bioindicateur de la qualité écologique des eaux douces dans le cadre de la mise en œuvre de la Directive Cadre européenne sur l'Eau (DCE). Les indices biologiques actuels basés sur les diatomées reposent sur des critères morphologiques (forme et ornementation de l’exosquellette siliceux, le frustule) pas toujours faciles à caractériser en routine (i.e. à l’aide de méthodes optiques conventionnelles). L’identification est donc chronophage, souvent sujette à de multiples biais (expérience de l'opérateur, qualité de l'image) et nécessite un niveau élevé d’expertise.Ceci justifie le développement d'un outil plus robuste, basé sur une classification automatique des diatomées. Cet objectif est toujours un défi d’actualité, depuis les premières tentatives datant des années 90. Dans ce contexte, le développement récent des approches d’apprentissage profond pour identifier et quantifier les traits des organismes à partir d’images semble prometteur pour résoudre les problèmes rencontrés jusqu’à présent. Notre objectif est donc de proposer un nouvel outil d’identification des diatomées basé sur des algorithmes de reconnaissance automatique de formes à partir d’images individuelles. Par rapport à l’approche classique d’identification des diatomées, cet outil se veut notamment plus robuste car indépendant de l’opérateur et permettra d’améliorer (gain de temps, coût) ceux actuellement disponibles dans le cadre du suivi réglementaire de l’état écologique des cours d’eau.Le développement de cet outil implique 1/l’acquisition d’une base de données représentative du milieu naturel (banques d’images de diatomées) qui permette 2) le développement des algorithmes d’identification des diatomées. En première approche, une banque simplifiée d’images représentatives de 209 espèces de diatomées (environ 9 000 images au total) a été créée à partir de guides d’identification en libre accès. Cette base d’images a permis de générer environ 30 000 images composites simulant des images d’échantillons naturels. Ceci a permis le développement de premiers algorithmes de détection (diatomées vs. débris) et de classification (espèces présentes). Les premiers résultats obtenus montrent que l’outil de reconnaissance permet de distinguer les diatomées présentes sur un échantillon (précision de 90%) ainsi que les débris (précision de 99%). L’outil a également été testé sur quelques images réelles avec une précision pouvant atteindre 86% pour les débris et 73% pour les diatomées présentes. Ces résultats encourageants démontrent la faisabilité de notre approche (preuve de concept). La banque d’images sera donc consolidée afin de la rendre plus représentative de l’ensemble des espèces indicatrices de diatomées retrouvées dans une zone géographique donnée (e.g. bassin de la Moselle, bassin Rhin-Meuse) et ainsi d’améliorer la performance des algorithmes de reconnaissance.

Published
2020

44. Automated analysis of foraminifera fossil records by image classification using a convolutional neural network

Author

Marchant, Ross, Tetard, Martin, Pratiwi, Adnya, Adebayo, Michael, De Garidel-thoron, Thibault, Marchant, Ross, Tetard, Martin, Pratiwi, Adnya, Adebayo, Michael, and De Garidel-thoron, Thibault

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.

Published
2020
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45. Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow

Author

Tetard, Martin, Marchant, Ross, Cortese, Giuseppe, Gally, Yves, de Garidel-Thoron, Thibault, Beaufort, Luc, Tetard, Martin, Marchant, Ross, Cortese, Giuseppe, Gally, Yves, de Garidel-Thoron, Thibault, and Beaufort, Luc

Abstract

Identification of microfossils is usually done by expert taxonomists and requires time and a significant amount of systematic knowledge developed over many years. These studies require manual identification of numerous specimens in many samples under a microscope, which is very tedious and time-consuming. Furthermore, identification may differ between operators, biasing reproducibility. Recent technological advances in image acquisition, processing and recognition now enable automated procedures for this process, from microscope image acquisition to taxonomic identification. A new workflow has been developed for automated radiolarian image acquisition, stacking, processing, segmentation and identification. The protocol includes a newly proposed methodology for preparing radiolarian microscopic slides. We mount eight samples per slide, using a recently developed 3D-printed decanter that enables the random and uniform settling of particles and minimizes the loss of material. Once ready, slides are automatically imaged using a transmitted light microscope. About 4000 specimens per slide (500 per sample) are captured in digital images that include stacking techniques to improve their focus and sharpness. Automated image processing and segmentation is then performed using a custom plug-in developed for the ImageJ software. Each individual radiolarian image is automatically classified by a convolutional neural network (CNN) trained on a Neogene to Quaternary radiolarian database (currently 21 746 images, corresponding to 132 classes) using the ParticleTrieur software. The trained CNN has an overall accuracy of about 90 %. The whole procedure, including the image acquisition, stacking, processing, segmentation and recognition, is entirely automated via a LabVIEW interface, and it takes approximately 1 h per sample. Census data count and classified radiolarian images are then automatically exported and saved. This new workflow paves the way for the analysi

Published
2020

48. Stable sea surface temperatures in the western Pacific warm pool over the past 1.75 million years

Author

de Garidel-Thoron, Thibault, Rosenthal, Yair, Bassinot, Franck, and Beaufort, Luc

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Thibault de Garidel-Thoron (corresponding author) [1]; Yair Rosenthal [1, 2]; Franck Bassinot [3]; Luc Beaufort [4] About 850,000 years ago, the period of the glacial cycles changed from 41,000 [...]

Published
2005
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49. Linking zooplankton time series to the fossil record.

Author

Jonkers, Lukas, Meilland, Julie, Rillo, Marina C, de Garidel-Thoron, Thibault, Kitchener, John A, and Kucera, Michal

Subjects
TIME series analysis, FOSSILS, FOSSIL foraminifera, ZOOPLANKTON, MARINE zooplankton, FOSSIL collection, GEOLOGY, PALEOECOLOGY
Abstract

Marine zooplankton time series are crucial to understand the dynamics of pelagic ecosystems. However, most observational time series are only a few decades long, which limits our understanding of long-term zooplankton dynamics, renders attribution of observed trends to global change ambiguous, and hampers prediction of future response to environmental change. Planktonic foraminifera are calcifying marine zooplankton that have the unique potential to substantially extend our view on plankton dynamics because their skeletal remains are preserved for millions of years in deep-sea sediments. Thus, linking sedimentary and modern time series offers great potential to study zooplankton dynamics across time scales not accessible by direct observations. However, this link is rarely made and the potential of planktonic foraminifera for advancing our understanding of zooplankton dynamics remains underexploited. This underutilization of this potential to bridge time scales is mainly because of the lack of collaboration between biologists, who have mostly focused on other (zoo)plankton, and micropalaeontologists, who have focussed too narrowly on fossil foraminifera. With this food for thought article, we aim to highlight the unique potential of planktonic foraminifera to bridge the gap between biology and geology. We strongly believe that such collaboration has large benefits to both scientific communities. [ABSTRACT FROM AUTHOR]

Published
2022
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