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Poriferal Vision: Classifying Benthic Sponge Spicules to Assess Historical Impacts of Marine Climate Change

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Foundations of Intelligent Systems (ISMIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12117))

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Abstract

Sponges and corals are ecologically important members of the marine community. Climate change, while harmful to corals, has historically been favorable to sponges. Sponge population dynamics are studied by analyzing core samples of marine sediment. To date this analysis has been performed by microscopic visual inspection of core cross sections to distinguish spicules (the rigid silica components of sponge skeletons) from the residue of other silica-using organisms. Since this analysis is both slow and error prone, complete analysis of multiple cross sections is impossible.

FlowCam® technology can produce tens of thousands of microphotographs of individual core sample particles in a few minutes. Individual photos must then be classified in silico. We have developed a Deep Learning classifier, called Poriferal Vision, that distinguishes sponge spicules from non-spicule particles. Small training sets were enhanced using image augmentation to achieve accuracy of at least 95%. A Support Vector Machine trained on the same data achieved accuracy of at most 86%. Our results demonstrate the efficacy of Deep Learning for analyzing core samples, and show that our classifier will be an effective tool for large-scale analysis.

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References

  1. Conway, K.W., Barrie, J.V., Krautter, M.: Geomorphology of unique reefs on the western Canadian shelf: sponge reefs mapped by multibeam bathymetry. Geo-Mar. Lett. 25(4), 205–213 (2005)

    Article  Google Scholar 

  2. Yahel, G., Whitney, F., Reiswig, H.M., Eerkes-Medrano, D.I., Leys, S.P.: In situ feeding and metabolism of glass sponges (Hexactinellida, Porifera) studied in a deep temperate fjord with a remotely operated submersible. Limnol. Oceanogr. 52(1), 428–440 (2007)

    Article  Google Scholar 

  3. Kahn, A.S., Yahel, G., Chu, J.W.F., Tunnicliffe, V., Leys, S.P.: Benthic grazing and carbon sequestration by deep-water glass sponge reefs. Limnol. Oceanogr. 60(1), 78–88 (2015)

    Article  Google Scholar 

  4. West, R.R.: Temporal changes in Carboniferous reef mound communities. PALAIOS 3(2), 152 (1988)

    Article  Google Scholar 

  5. Brunton, F.R., Dixon, O.A.: Siliceous sponge-microbe biotic associations and their recurrence through the phanerozoic as reef mound constructors. PALAIOS 9(4), 370 (1994)

    Article  Google Scholar 

  6. Kiessling, W., Simpson, C.: On the potential for ocean acidification to be a general cause of ancient reef crises: ancient reef crises. Glob. Change Biol. 17(1), 56–67 (2011)

    Article  Google Scholar 

  7. Aiello, I.W., Ravelo, A.C.: Evolution of marine sedimentation in the Bering Sea since the Pliocene. Geosphere 8(6), 1231–1253 (2012)

    Article  Google Scholar 

  8. Birkeland, C. (ed.): Life and Death of Coral Reefs. Chapman and Hall, New York (1997)

    Google Scholar 

  9. Reaka-Kudla, M.L., Wilson, D.E., Wilson, E.O.: Biodiversity II. Joseph Henry Press, Washington, D.C. (1997)

    Google Scholar 

  10. Glynn, P.W.: Coral reef bleaching: ecological perspectives. Coral Reefs 12(1), 1–17 (1993). https://doi.org/10.1007/BF00303779

    Article  Google Scholar 

  11. Hughes, T.P.: Climate change, human impacts, and the resilience of coral reefs. Science 301(5635), 929–933 (2003)

    Article  Google Scholar 

  12. De’ath, G., Fabricius, K.E., Sweatman, H., Puotinen, M.: The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl. Acad. Sci. 109(44), 17995–17999 (2012)

    Article  Google Scholar 

  13. Hoegh-Guldberg, O., et al.: Coral reefs under rapid climate change and ocean acidification. Science 318(5857), 1737–1742 (2007)

    Article  Google Scholar 

  14. Wisshak, M., Schönberg, C.H.L., Form, A., Freiwald, A.: Ocean acidification accelerates reef bioerosion. PLoS ONE 7(9), e45124 (2012)

    Article  Google Scholar 

  15. Albright, R.: Reviewing the effects of ocean acidification on sexual reproduction and early life history stages of reef-building corals. J. Mar. Biol. 2011, 1–14 (2011)

    Article  Google Scholar 

  16. Goreau, T., McClanahan, T., Hayes, R., Strong, A.: Conservation of coral reefs after the 1998 global bleaching event. Conserv. Biol. 14(1), 5–15 (2000)

    Article  Google Scholar 

  17. Nakićenović, N., Intergovernmental Panel on Climate Change (eds.): Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, New York (2000)

    Google Scholar 

  18. Bell, J.J., Davy, S.K., Jones, T., Taylor, M.W., Webster, N.S.: Could some coral reefs become sponge reefs as our climate changes? Glob. Change Biol. 19(9), 2613–2624 (2013)

    Article  Google Scholar 

  19. Chu, J., Leys, S.: High resolution mapping of community structure in three glass sponge reefs (Porifera, Hexactinellida). Mar. Ecol. Prog. Ser. 417, 97–113 (2010)

    Article  Google Scholar 

  20. Beaulieu, S.E.: Life on glass houses: sponge stalk communities in the deep sea. Mar. Biol. 138(4), 803–817 (2001)

    Article  Google Scholar 

  21. Marliave, J.B., Conway, K.W., Gibbs, D.M., Lamb, A., Gibbs, C.: Biodiversity and rockfish recruitment in sponge gardens and bioherms of southern British Columbia, Canada. Mar. Biol. 156(11), 2247–2254 (2009)

    Article  Google Scholar 

  22. Guillas, K.C., Kahn, A.S., Grant, N., Archer, S.K., Dunham, A., Leys, S.P.: Settlement of juvenile glass sponges and other invertebrate cryptofauna on the Hecate Strait glass sponge reefs. Invertebr. Biol. 138(4), e12266 (2019)

    Article  Google Scholar 

  23. Graham, M.D., et al.: High-resolution imaging particle analysis of freshwater cyanobacterial blooms: FlowCam analysis of cyanobacteria. Limnol. Oceanogr. Methods 16(10), 669–679 (2018)

    Article  Google Scholar 

  24. Widrow, B., Rumelhart, D.E., Lehr, M.A.: Neural networks: applications in industry, business and science. Commun. ACM 37, 93–106 (1994)

    Article  Google Scholar 

  25. Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29(9), 2352–2449 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  26. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  27. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019). https://doi.org/10.1186/s40537-019-0197-0

    Article  Google Scholar 

  28. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  29. Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10(5), 1055–1064 (1999)

    Article  Google Scholar 

  30. Schlung, S.A., et al.: Millennial-scale climate change and intermediate water circulation in the Bering Sea from 90 ka: a high-resolution record from IODP Site U1340: Bering Sea climate change since 90 KA. Paleoceanography 28(1), 54–67 (2013)

    Article  Google Scholar 

  31. Bradski, G.R., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, 1st edn. O’Reilly, Beijing (2011)

    Google Scholar 

  32. Ramer, U.: An iterative procedure for the polygonal approximation of plane curves. Comput. Graph. Image Process. 1(3), 244–256 (1972)

    Article  Google Scholar 

  33. Deng, G., Cahill, L.W.: An adaptive Gaussian filter for noise reduction and edge detection. In: 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference (1993)

    Google Scholar 

  34. Abadi, M., et al.: TensorFlow: a system for large-scale machine learning, p. 21 (2016)

    Google Scholar 

  35. Hahnloser, R.H.R., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., Seung, H.S.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405, 947–951 (2000)

    Article  Google Scholar 

  36. Kilian, J., Siegelmann, H.T.: The dynamic universality of sigmoidal neural networks. Inf. Comput. 128(1), 48–56 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  37. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  38. Sahak, R., Mansor, W., Lee, Y.K., Yassin, A.I.M., Zabidi, A.: Performance of combined support vector machine and principal component analysis in recognizing infant cry with asphyxia. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. San Jose State University, San Jose, CA, 95192, USA

    Saketh Saxena & Philip Heller

  2. Moss Landing Marine Laboratories, Moss Landing, CA, 95039, USA

    Amanda S. Kahn & Ivano Aiello

Authors
  1. Saketh Saxena
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  2. Philip Heller
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  3. Amanda S. Kahn
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  4. Ivano Aiello
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Corresponding author

Correspondence to Philip Heller .

Editor information

Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Denis Helic

  2. University of Klagenfurt, Klagenfurt, Austria

    Gerhard Leitner

  3. Graz University of Technology, Graz, Austria

    Martin Stettinger

  4. Graz University of Technology, Graz, Austria

    Alexander Felfernig

  5. University of North Carolina at Charlotte, Charlotte, NC, USA

    Zbigniew W. Raś

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Saxena, S., Heller, P., Kahn, A.S., Aiello, I. (2020). Poriferal Vision: Classifying Benthic Sponge Spicules to Assess Historical Impacts of Marine Climate Change. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_19

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  • DOI: https://doi.org/10.1007/978-3-030-59491-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59490-9

  • Online ISBN: 978-3-030-59491-6

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