Articles | Volume 16, issue 6
https://doi.org/10.5194/cp-16-2415-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-16-2415-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
02 Dec 2020
Technical note |
| 02 Dec 2020
| 02 Dec 2020
Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
Ross Marchant
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
present address: School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, Australia
Giuseppe Cortese
GNS Science, Lower Hutt, New Zealand
Yves Gally
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
Thibault de Garidel-Thoron
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
Luc Beaufort
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
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The coccoliths are major contributors to the particulate inorganic carbon in the ocean. They are extremely difficult to weigh because they are too small to be manipulated. We propose a universal method to measure thickness and weight of fine calcite using polarizing microscopy that does not require fine-tuning of the light or a calibration process. This method named "bidirectional circular polarization" uses two images taken with two directions of a circular polarizer.
Abhijith U. Venugopal, Nancy A. N. Bertler, Rebecca L. Pyne, Helle A. Kjær, V. Holly L. Winton, Paul A. Mayewski, and Giuseppe Cortese
Clim. Past Discuss., https://doi.org/10.5194/cp-2020-151, https://doi.org/10.5194/cp-2020-151, 2020
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We present a new and highly resolved glacial record of nitrate and calcium from a deep ice core obtained from Roosevelt Island, West Antarctica. Our data show a dependent association among nitrate and non-sea salt calcium (mineral dust) as observed previously in East Antarctica. The spatial pattern indicates that mineral dust is scavenging nitrate from the atmosphere and the westerlies are dispersing the dust-bound nitrate across Antarctica, making nitrate a potential paleo-westerly wind proxy.
Xinquan Zhou, Stéphanie Duchamp-Alphonse, Masa Kageyama, Franck Bassinot, Luc Beaufort, and Christophe Colin
Clim. Past, 16, 1969–1986, https://doi.org/10.5194/cp-16-1969-2020, https://doi.org/10.5194/cp-16-1969-2020, 2020
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We provide a high-resolution primary productivity (PP) record of the northeastern Bay of Bengal over the last 26 000 years. Combined with climate model outputs, we show that PP over the glacial period is controlled by river input nutrients under low sea level conditions and after the Last Glacial Maximum is controlled by upper seawater salinity stratification related to monsoon precipitation. During the deglaciation the Atlantic meridional overturning circulation is the main forcing factor.
Ross Marchant, Martin Tetard, Adnya Pratiwi, Michael Adebayo, and Thibault de Garidel-Thoron
J. Micropalaeontol., 39, 183–202, https://doi.org/10.5194/jm-39-183-2020, https://doi.org/10.5194/jm-39-183-2020, 2020
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Foraminifera are marine microorganisms with a calcium carbonate shell. Their fossil remains build up on the seafloor, forming kilometres of sediment over time. From analysis of the foraminiferal record we can estimate past climate conditions and the geological history of the Earth. We have developed an artificial intelligence system for automatically identifying foraminifera species, replacing the time-consuming manual approach and thus helping to make these analyses more efficient and accurate.
Cited articles
Abelmann, A:. Radiolarian taxa from Southern Ocean sediment traps (Atlantic sector), Polar Biol., 12, 373–385, 1992. a
Abelmann, A. and Nimmergut, A.: Radiolarians in the Sea of Okhotsk and their ecological implication for paleoenvironmental reconstructions, Deep-Sea Res. Pt. II, 52, 2302–2331, 2005. a
Abelmann, A., Brathauer, U., Gersonde, R., Siegier, R., and Zielinski, U.: Radiolarian-based transfer function for the estimation of sea surface temperatures in the Southern Ocean (Atlantic Sector), Paleoceanography, 14, 410–421, 1999. a
Apostol, L. A., Marquez, E., Gasmen, P., and Solano, G.: Radss: A radiolarian classifier using support vector machines, 7th International Conference on Information, Intelligence, Systems & Applications (IISA), 13–15 July 2016, Chalkidiki, Greece, 2016. a
Beaufort, L. and Dollfus, D.: Automatic recognition of coccoliths by dynamical neural networks, Mar. Micropaleontol., 51, 57–73, 2004. a
Beaufort, L., Chen, M. T., Chivas, A., and Manighetti, B.: Campagne IPHIS – IMAGES Ill/MD 106 du 23-05-97 au 28-06-97. Les Publications de l'Institut francais pour la recherche et la technologie polaires, Les Rapports des campagnes a la mer, 151 pp., available at: https://archimer.ifremer.fr/doc/00629/74140/ (last access: 17 November 2020), 1997. a
Beaufort, L., de Garidel-Thoron, T., Mix, A. C., and Pisias, N. G.: ENSO-like forcing on oceanic primary production during the Late Pleistocene, Science 293, 2440–2444, 2001. a
Beaufort, L., Barbarin, N., and Gally, Y.: Optical measurements to determine the thickness of calcite crystals and the mass of thin carbonate particles such as coccoliths, Nat. Protoc., 9, 633–642, 2014. a
Bjørklund, K. R. and Goll, R. M.: Internal skeletal structures of Collosphaera and Trisolenia: A case of repetitive evolution in the Collosphaeridae (Radiolaria), J. Paleontol., 53, 1293–1326, 1979. a
Boltovskoy, D. and Jankilevich, S. S.: Radiolarian distribution in east equatorial Pacific plankton, Oceanol. Acta, 8, 101–123, 1985. a
Boltovskoy, D., Kling, S. A., Takahashi, K., and Bjorklund, K.: World atlas of distribution of living radiolaria, Palaeontol. Electron., 13, 1–230, 2010. a
Boltovskoy, D., Anderson, O. R., and Correa, N. M.: Radiolaria and Phaeodaria, in: Handbook of the Protists, edited by: Archibald, J. M. and Simpson, A. G. B., Slamovits, C., Springer, 1–33, 2017. a
Bourel, B., Marchant, R., de Garidel-Thoron, T., Tetard, M., Barboni, D., Gally, Y., and Beaufort, L.: Automated recognition by multiple convolutional neural networks of modern, fossil, intact and damaged pollen grains, Comput. Geosci., 140, 104498, https://doi.org/10.1016/j.cageo.2020.104498, 2020. a
Budai, A., Riedel, W. R., and Westberg, M. J.: A general-purpose paleontologic information decide, J. Paleontol., 54, 259–262, 1980. a
Campbell, A. S.: Radiolaria, Part D: Protista 3, in: Treatise on Invertebrate Paleontology, edited by: Moore, R. C., Geological Society of America, University of Kansas Press, Lawrence, USA, DI-DI63, 1954. a
Caulet, J. P. and Nigrini, C.: The genus Pterocorys (Radiolaria) from the tropical Late Neogene of the Indian and Pacific Oceans, Micropaleontology, 34, 217–235, 1988. a
Caulet, J. P., Vénec-Peyré, M. T., Vergnaud-Grazzini, C., and Nigrini, C.: Variation of South Somalian upwelling during the last 160 ka: Radiolarian and foraminifera records in Core MD-85674, in: Upwelling Systems: Evolution Since the Early Miocene, edited by: Summerhayes, C. P., Prell,W. L., and Emeis, K. C., Geol. Soc. Spec. Publ., 64, Geological Society, London, UK, 379–389, 1992. a
Caulet, J. P., Sanfilippo, A., and Nigrini, C.: “Radworld”, a taxonomic relational database for radiolarians, in: InterRad II and Triassic Stratigraphy Symposium: a joint international conference hosted by the International Association of Radiolarian Paleontologists, IGCP 467 and the Subcommission of Triassic Stratigraphy, edited by: Lüer, V., Hollis, C., Campbell, H., and Simes, J., GNS Science, Lower Hutt, New Zealand, p. 47, 2006. a
Dieleman, S., De Fauw, J., and Kavukcuoglu, K.: Exploiting Cyclic Symmetry in Convolutional Neural Networks, arXiv [preprint], arXiv:1602.02660, 8 February 2016. a
Dollfus, D. and Beaufort, L.: Fat neural network for recognition of position-normalised objects, Neural Networks, 12, 553–560, 1999. a
Dolven, J. K. and Skjerpen, H. A.: An online micropaleontology database: Radiolaria.org, Eclogae Geol. Helv., Supplement 1, 63–66, 2006. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, arXiv [preprint], arXiv:1512.03385, 10 December 2015. a
Hernández-Almeida, I., Bjørklund, K. R., Sierro, F. J., Filippelli, G. M., Cacho, I., and Flores, J. A.: A high resolution opal and radiolarian record from the subpolar North Atlantic during the Mid-Pleistocene Transition (1069–779 ka): Palaeoceanographic implications, Palaeogeogr. Palaeocl., 391, 49–70, 2013. a
Itaki, T., Matsuoka, A., Yoshida, K., Machidori, S., Shinzawa, M., and Todo, T.: Late spring radiolarian fauna in the surface water off Tassha, Aikawa Town, Sado Island, central Japan, Sci. Rep. Niigata Univ. (Geol.), 17, 41–51, 2003. a
Keceli, A. S., Kaya, A., and Keceli, S.U.: Classification of radiolarian images with hand-crafted and deep features, Comput. Geosci., 109, 67–74, 2017. a
Lazarus, D.: A brief review of radiolarian research, Paläontol. Z., 79, 183–200, 2005.
Lazarus, D., Spencer-Cervato, C., Pika-Biolzi, M., Beckmann, J. P., Von Salis, K., Hilbrecht, H., and Thierstein, H.: Revised chronology of Neogene DSDP Holes from the world ocean, Ocean Drilling Program Technical Note, 24, 1–301, 1985. a
Lazarus, D., Faust, K., and Popova-Goll, I.: New species of prunoid radiolarians from the Antarctic Neogene, J. Micropaleontology, 24, 97–121, 2005. a
Lazarus, D., Suzuki, N., Caulet, J. P., Nigrini, C., Goll, I., Goll, R., Dolven, J. K., Diver, P., and Sanfilippo, A.: An evaluated list of Cenozoic-Recent radiolarian species names (Polycystinea), based on those used in the DSDP, ODP and IODP deep-sea drilling programs, Zootaxa, 3999, 301–333, 2015. a, b
Ling, H. Y. and Anikouchine, W. A.: Some spumellarian Radiolarian from the Java, Philippine, and Mariana Trenches, J. Paleon. 41, 1481–1491, 1967. a
Matsuoka, A.: Catalogue of living polycystine radiolarians in surface waters in the East China Sea around Sesoko Island, Okinawa Prefecture, Japan, Sci. Rep. Niigata Univ. (Geol.), 32, 57–90, 2017. a
Matsuzaki, K. M., Suzuki, N., Nishi, H., Hayashi, H., Gyawali, B. R., Takashima, R., and Ikehara, M.: Early to middle Pleistocene paleoceanographic history of southern Japan based on radiolarian data from IODP Exp 314/315 Sites C0001 and C0002, Mar. Micropaleontol., 118, 17–33, 2015. a
Moore, T. C.: Method of randomly distributing grains for microscopic examination, J. Sediment. Petrol., 43, 904–906, 1973. a
Moore Jr., T. J.: Radiolarian stratigraphy, Leg 138, Proc. Ocean Drill. Prog. Sci. Results, 138, 191–232, 1995. a
Motoyama, I., Yamada, Y., Hoshiba, M., and Itaki, T.: Radiolarian Assemblages in Surface Sediments of the Japan Sea, Paleontol. Res., 20, 176–206, 2016. a
Nigrini, C.: Radiolarian zones in the Quaternary of the equatorial Pacific Ocean, in: The Micropalaeontology of Oceans, edited by: Funnell, B. M. and Riedel, W. R., Cambridge University Press, Cambridge, UK, 443–461, 1971. a
Nigrini, C. and Lombari, G.: A guide to Miocene Radiolaria, Cushman Foundation Foraminiferal Research, Sp. Pub., 22, S1–S102, N1–N206, 1984. a
Nigrini, C. and Moore, T. C.: A guide to modern Radiolaria – with taxonomic descriptions and illustrations of radiolarian species, Cushman Foundation for Foraminiferal Research, Sp. Pub., Washington, USA, 16, 1979. a
Nigrini, C. and Sanfilippo, A.: Cenozoic radiolarian stratigraphy for low and middle latitudes with descriptions of biomarkers and stratigraphically useful species, ODP Tech, Note 27, available at: http://www-odp.tamu.edu/publications/tnotes/tn27/index.html (last access: 17 November 2020), 2001. a, b
Nigrini, C., Sanfilippo, A., and Moore Jr., T. J.,: Cenozoic radiolarian biostratigraphy: a magnetobiostratigraphic chronology of Cenozoic sequences from ODP Sites 1218, 1219, and 1220, equatorial Pacific, in: Proc. ODP, Sci. Results 199, edited by: Wilson, P. A., Lyle, M., and Firth, J. V., Ocean Drilling Program, College Station, TX, USA, 1–76, 2005. a
Panitz, S., Cortese, G., Neil, H. L., and Diekmann, B.: A radiolarian-based palaeoclimate history of Core Y9 (Northeast of Campbell Plateau, New Zealand) for the last 160 kyr, Mar. Micropaleontol., 116, 1–14, 2015. a
Renaudie, J., Gray, R., and Lazarus, D. B.: Accuracy of a neural net classification of closely-related species of microfossils from a sparse dataset of unedited images, PeerJ Preprints, 6, e27328v1, https://doi.org/10.7287/peerj.preprints.27328v1, 2018. a
Riedel, W. R.: Subclass Radiolaria, in: The fossil record, edited by: Harland, W. B., Holland, C. H., House, M. R., Hughes, N. F., Reynolds, A. B., Rudwick, M. J. S., Satterthwaite, G. E., Tarlo, I. B. H., and Willey, E. C., Geol. Soc., London, UK, 291–298, 1967. a
Rosenthal, Y., Holbourn, A. E., Kulhanek, D. K., and the Expedition 363 Scientists: Western Pacific Warm Pool, Proceedings of the International Ocean Discovery Program, 363: College Station, TX (International Ocean Discovery Program), 2018. a
Sandoval, M. I.: Miocene to recent radiolarians from southern pacific coast of Costa Rica, Rev. Geol. Amér. Central, 58, 115–169, 2018. a
Sanfilippo, A. and Nigrini, C.: Code numbers for Cenozoic low latitude radiolarian biostratigraphic zones and GPTS conversion tables, Mar. Micropaleontol., 33, 109–156, 1998. a
Sanfilippo, A., Westberg-Smith, M. J., and Riedel, W. R.: Cenozoic Radiolaria, in: Plankton Stratigraphy (Vol. 2): Radiolaria, Diatoms, Silicoflagellates, Dinoflagellates, and Ichthyoliths, edited by: Bolli, H. M., Saunders, J. B., and Perch-Nielsen, K., Cambridge Univ. Press, Cambridge, UK, 631–712, 1985. a, b, c, d, e
Schneider, C. A., Rasband, W. S., and Eliceiri, K. W.: NIH Image to ImageJ: 25 years of image analysis, Nat. Methods, 9, 671–675, 2012. a
Schrock, R. R. and Twenhofel, W. H.: Principles of Invertebrate Palaeontology, New second edition, McGraw Hill, New York, USA, London, UK, 816 pp., 1953. a
Sharma, V., Singh, S., and Rawal, N.: Early Middle Miocene Radiolaria from Nicobar Islands, Northeast Indian Ocean, Micropaleontology, 45, 251–277, 1999. a
Suzuki, N. and Not, F.: Biology and Ecology of Radiolaria, in: Marine Protists, edited by: Ohtsuka, S., Suzaki, T., Horiguchi, T., Suzuki, N., and Not, F., Springer, Tokyo, Japan, 2015. a
Takahashi, K.: Radiolaria: flux, ecology, and taxonomy in the Pacific and Atlantic, Woods Hole Oceanogr. Inst., Ocean Biocoenosis Ser., 3, 1–303, 1991. a
Takahashi, K. and Honjo, S.: Radiolarian skeletons: size, weight, sinking speed, and residence time in tropical pelagic oceans, Deep-Sea Res., 30, 543–568, 1983. a
Tetard, M. and Marchant, R.: AutoRadio_Segmenter, a free ImageJ plugin for image segmentation, available at: https://github.com/microfossil/ImageJ-LabView-Scripts, last access: 17 November 2020. a
Tetard, M., Marchant, R., Cortese, G., Gally, Y., de Garidel-Thoron, T., and Beaufort, L.: The AutoRadio Database, available at: http://microautomate.cerege.fr/dat, last access: 17 November 2020. a
Vigour, R. and Lazarus, D.: Biostratigraphy of late Miocene–early Pliocene radiolarians from ODP Leg 183 Site 1138, in: Proc. ODP, Sci. Results, 183, edited by: Frey, F. A., Coffin, M. F., Wallace, P. J., and Quilty, P. G., 1–17, available at: http://www-odp.tamu.edu/publications/183_SR/007/007.htm (last access: 17 November 2020), 2002. a
Welling, L. A., Pisias, N. G., and Roelofs, A. K.: Radiolarian microfauna in the northern California Current System: indicators of multiple processes controlling productivity, in: Upwelling Systems. Evolution since the Early Miocene, edited by: Summerhayes, C. P., Prell, W. L. and Emeis, K. C., London Geological Society: Geological Society Special Publication, 64, 177–195, 1992. a, b
Zhang, L. L. and Suzuki, N.: Taxonomy and species diversity of Holocene pylonioid radiolarians from surface sediments of the northeastern Indian Ocean, Palaeontol. Electron., 20.3.48A, 1–68, 2017. a
Zhang, L. L., Chen, M. H., Xiang, R., Zhang, J. L., Liu, C. J., Huang, L. M., and Lu, J.: Distribution of polycystine radiolarians in the northern South China Sea in September 2005, Mar. Micropaleontol., 70, 20–38, 2009. a
Short summary
Radiolarians are marine micro-organisms that produce a siliceous shell that is preserved in the fossil record and can be used to reconstruct past climate variability. However, their study is only possible after a time-consuming manual selection of their shells from the sediment followed by their individual identification. Thus, we develop a new fully automated workflow consisting of microscopic radiolarian image acquisition, image processing and identification using artificial intelligence.
Radiolarians are marine micro-organisms that produce a siliceous shell that is preserved in the...
