Research progress on marine biological observation technology based on acoustic and optics

LI Rongmao

doi:10.14012/j.jfr.2024105

Journal of Fisheries Research > 2024 > 46(6): 685-694. > DOI: 10.14012/j.jfr.2024105

LI Rongmao. Research progress on marine biological observation technology based on acoustic and optics[J]. Journal of Fisheries Research, 2024, 46(6): 685-694. DOI: 10.14012/j.jfr.2024105

Citation:

LI Rongmao. Research progress on marine biological observation technology based on acoustic and optics[J]. Journal of Fisheries Research, 2024, 46(6): 685-694. DOI: 10.14012/j.jfr.2024105

Citation:

LI Rongmao. Research progress on marine biological observation technology based on acoustic and optics[J]. Journal of Fisheries Research, 2024, 46(6): 685-694. DOI: 10.14012/j.jfr.2024105

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Research progress on marine biological observation technology based on acoustic and optics

LI Rongmao

Fujian Fishery Resources Monitoring Center, Fuzhou 350003, China

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Received Date: August 12, 2024
Revised Date: October 21, 2024
Publication Date: December 24, 2024

Graphical Abstract

Abstract

Abstract

Background
The observation of marine biodiversity is fundamental for studying the structure and function of marine ecosystems. However, traditional observation methods are often inefficient, costly, and challenging for continuous monitoring.
Objective
This study aims to promote the application of new technologies and enhance the observation capabilities of marine organisms.
Progress
This article introduces innovative marine biodiversity observation technologies developed based on acoustic and optical principles. These new technologies include passive acoustic observation sensors, optical phytoplankton detectors utilizing spectral absorption, and plankton analyzers through optical imaging. The latter are further classified into flow imaging, silhouette imaging, dark field imaging, and holographic imaging based on differing imaging principles. Passive acoustic observation sensors are instrumental in revealing critical fish habitats. They offer advantages such as non-invasiveness, high spatiotemporal resolution, cost-effectiveness, and low operational costs. Optical phytoplankton detectors effectively identify bloom species within the water column and monitor phytoplankton communities. Flow imaging instruments collect fluorescence signals, such as scattering and chlorophyll, to observe nanoplankton and microplankton. Silhouette imaging employs backlighting to produce high-contrast images, characterized by low resolution but high observational capacity, making it suitable for studying planktonic communities. Dark field imaging relies on the scattering, reflection, and refraction of targets, enabling the observation of mesoplankton. Holographic imaging utilizes coherent light illumination to capture interferometric images of targets, followed by image reconstruction. This method boasts high resolution and a large depth of field, allowing for the observation of microplankton. By integrating multiple observation techniques, image data is classified using convolutional neural networks, achieving an accuracy rate exceeding 90%.
Prospect
With the rapid advancement of artificial intelligence technology, these new methodologies are expected to find broader applications, significantly enhancing the efficiency of marine biological observation.
- plankton,
- machine learning,
- acoustic observing,
- optical observing,
- image recognition

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References (78)

References

[1]	Appeltans W, Ahyong S T, Anderson G, et al. The magnitude of global marine species diversity[J]. Current Biology, 2012, 22(23): 2189 − 2202. DOI: 10.1016/j.cub.2012.09.036
[2]	Airoldi L, Balata D, Beck M W. The gray zone: relationships between habitat loss and marine diversity and their applications in conservation[J]. Journal of Experimental Marine Biology and Ecology, 2008, 366(1−2): 8 − 15. DOI: 10.1016/j.jembe.2008.07.034
[3]	Peterson G, Allen C R, Holling C S. Ecological resilience, biodiversity, and scale[J]. Ecosystems, 1998, 1(1): 6 − 18. DOI: 10.1007/s100219900002
[4]	孙军,刘东艳,钱树本. 一种海洋浮游植物定量研究分析方法——Utermöhl方法的介绍及其改进[J]. 黄渤海海洋,2002,20(2):105 − 112. Sun J, Liu D Y, Qian S B. A quantative research and analysis method for marine phytoplankton: an introduction to Utermöhl method and its modification[J]. Journal of Oceanography of Huanghai & Bohai Seas, 2002, 20(2): 105 − 112.
[5]	Wiebe P H, Benfield M C. From the Hensen net toward four-dimensional biological oceanography[J]. Progress in Oceanography, 2003, 56(1): 130 − 136. DOI: 10.1016/S0079-6611(02)00140-4
[6]	Batten S D, Clark R, Flinkman J, et al. CPR sampling: the technical background, materials and methods, consistency and comparability[J]. Progress in Oceanography, 2003, 58(2 − 4): 193 − 215. DOI: 10.1016/j.pocean.2003.08.004
[7]	Lo N C H, Jacobson L D, Squire J L. Indices of relative abundance from fish spotter data based on delta-Lognornial models[J]. Canadian Journal of Fisheries and Aquatic Sciences, 1992, 49(12): 2515 − 2526. DOI: 10.1139/f92-278
[8]	黄良敏,李军,张雅芝,等. 闽江口及附近海域渔业资源现存量评析[J]. 热带海洋学报,2010,29(5):142 − 148. DOI: 10.3969/j.issn.1009-5470.2010.05.022 Huang L M, Li J, Zhang Y Z, et al. Current fishery resource assessment in the Minjiang River Estuary and its neighboring waters[J]. Journal of Tropical Oceanography, 2010, 29(5): 142 − 148. DOI: 10.3969/j.issn.1009-5470.2010.05.022
[9]	徐开达,徐汉祥,王洋,等. 金属线码标记技术在渔业生物增殖放流中的应用[J]. 渔业现代化,2018,45(1):75 − 80. DOI: 10.3969/j.issn.1007-9580.2018.01.012 Xu K D, Xu H X, Wang Y, et al. The technology of coded wire tag and its application in fishery stock enhancement[J]. Fishery Modernization, 2018, 45(1): 75 − 80. DOI: 10.3969/j.issn.1007-9580.2018.01.012
[10]	Katsukawa T. Evaluation of current and alternative fisheries management scenarios based on spawning-per-recruit (SPR), revenue-per-recruit (RPR), and yield-per-recruit (YPR) diagrams[J]. ICES Journal of Marine Science, 2005, 62(5): 841 − 846. DOI: 10.1016/j.icesjms.2005.04.002
[11]	Sueur J, Farina A. Ecoacoustics: the ecological investigation and interpretation of environmental sound[J]. Biosemiotics, 2015, 8(3): 493 − 502. DOI: 10.1007/s12304-015-9248-x
[12]	Laiolo P. The emerging significance of bioacoustics in animal species conservation[J]. Biological Conservation, 2010, 143(7): 1635 − 1645. DOI: 10.1016/j.biocon.2010.03.025
[13]	Thomas J A, Kuechle V B. Quantitative analysis of Weddell seal (Leptonychotes weddelli) underwater vocalizations at McMurdo Sound, Antarctica[J]. The Journal of the Acoustical Society of America, 1982, 72(6): 1730 − 1738. DOI: 10.1121/1.388667
[14]	Tricas T C, Boyle K S. Acoustic behaviors in Hawaiian coral reef fish communities[J]. Marine Ecology Progress Series, 2014, 511: 1 − 16. DOI: 10.3354/meps10930
[15]	Henninger H P, Watson Ⅲ W H. Mechanisms underlying the production of carapace vibrations and associated waterborne sounds in the American lobster, Homarus americanus[J]. Journal of Experimental Biology, 2005, 208(17): 3421 − 3429. DOI: 10.1242/jeb.01771
[16]	Coquereau L, Grall J, Chauvaud L, et al. Sound production and associated behaviours of benthic invertebrates from a coastal habitat in the north-east Atlantic[J]. Marine Biology, 2016, 163(5): 127. DOI: 10.1007/s00227-016-2902-2
[17]	Song Z, Ou W, Su Y, et al. Sounds of snapping shrimp (Alpheidae) as important input to the soundscape in the southeast China coastal sea[J]. Frontiers in Marine Science, 2023, 10: 1029003.
[18]	Mann D A, Grothues T M. Short-term upwelling events modulate fish sound production at a mid-Atlantic Ocean observatory[J]. Marine Ecology Progress Series, 2009, 375: 65 − 71. DOI: 10.3354/meps07720
[19]	Lillis A, Mooney T A. Snapping shrimp sound production patterns on Caribbean coral reefs: relationships with celestial cycles and environmental variables[J]. Coral Reefs, 2018, 37(2): 597 − 607. DOI: 10.1007/s00338-018-1684-z
[20]	Parsons M J G, Salgado-Kent C P, Marley S A, et al. Characterizing diversity and variation in fish choruses in Darwin Harbour[J]. ICES Journal of Marine Science, 2016, 73(8): 2058 − 2074. DOI: 10.1093/icesjms/fsw037
[21]	Au W W L, Mobley J, Burgess W C, et al. Seasonal and diurnal trends of chorusing humpback whales wintering in waters off western Maui[J]. Marine Mammal Science, 2000, 16(3): 530 − 544. DOI: 10.1111/j.1748-7692.2000.tb00949.x
[22]	Kaplan M B, Lammers M O, Zang E, et al. Acoustic and biological trends on coral reefs off Maui, Hawaii[J]. Coral Reefs, 2018, 37(1): 121 − 133. DOI: 10.1007/s00338-017-1638-x
[23]	Luczkovich J J, Pullinger R C, Johnson S E, et al. Identifying sciaenid critical spawning habitats by the use of passive acoustics[J]. Transactions of the American Fisheries Society, 2008, 137(2): 576 − 605. DOI: 10.1577/T05-290.1
[24]	Picciulin M, Kéver L, Parmentier E, et al. Listening to the unseen: passive acoustic monitoring reveals the presence of a cryptic fish species[J]. Aquatic Conservation: Marine and Freshwater Ecosystems, 2019, 29(2): 202 − 210. DOI: 10.1002/aqc.2973
[25]	Versluis M, Schmitz B, von der Heydt A, et al. How snapping shrimp snap: through cavitating bubbles[J]. Science, 2000, 289(5487): 2114 − 2117. DOI: 10.1126/science.289.5487.2114
[26]	Au W W L, Banks K. The acoustics of the snapping shrimp Synalpheus parneomeris in Kaneohe Bay[J]. The Journal of the Acoustical Society of America, 1998, 103(1): 41 − 47. DOI: 10.1121/1.423234
[27]	Kaplan M B, Mooney T A, Partan J, et al. Coral reef species assemblages are associated with ambient soundscapes[J]. Marine Ecology Progress Series, 2015, 533: 93 − 107. DOI: 10.3354/meps11382
[28]	Parsons M, Erbe C, McCauley R, et al. Long-term monitoring of soundscapes and deciphering a usable index: examples of fish choruses from Australia[J]. Proceedings of Meetings on Acoustics, 2016, 27(1): 010023.
[29]	Locascio J, Mann D, Wilcox K, et al. Incorporation of acoustic sensors on a coastal ocean monitoring platform for measurements of biological activity[J]. Marine Technology Society Journal, 2018, 52(3): 64 − 70. DOI: 10.4031/MTSJ.52.3.9
[30]	Sayigh L, Daher M A, Allen J, et al. The Watkins marine mammal sound database: an online, freely accessible resource[J]. Proceedings of Meetings on Acoustics, 2016, 27(1): 040013.
[31]	Kasten E P, Gage S H, Fox J, et al. The remote environmental assessment laboratory’s acoustic library: an archive for studying soundscape ecology[J]. Ecological Informatics, 2012, 12: 50 − 67. DOI: 10.1016/j.ecoinf.2012.08.001
[32]	Larsen O N, Radford C. Acoustic conditions affecting sound communication in air and underwater[M]//Slabbekoorn H, Dooling R J, Popper A N, et al. Effects of anthropogenic noise on animals. New York: Springer, 2018: 109 − 144.
[33]	Au W W L, Hastings M C. Principles of marine bioacoustics[M]. New York: Springer, 2008.
[34]	Hilty J, Merenlender A. Faunal indicator taxa selection for monitoring ecosystem health[J]. Biological Conservation, 2000, 92(2): 185 − 197. DOI: 10.1016/S0006-3207(99)00052-X
[35]	Kirkpatrick G J, Millie D F, Moline M A, et al. Optical discrimination of a phytoplankton species in natural mixed populations[J]. Limnology and Oceanography, 2000, 45(2): 467 − 471. DOI: 10.4319/lo.2000.45.2.0467
[36]	Millie D F, Schofield O M, Kirkpatrick G J, et al. Detection of harmful algal blooms using photopigments and absorption signatures: a case study of the Florida red tide dinoflagellate, Gymnodinium breve[J]. Limnology and Oceanography, 1997, 42(5part2): 1240 − 1251. DOI: 10.4319/lo.1997.42.5_part_2.1240
[37]	Shapiro J, Dixon L K, Schofield O M, et al. New sensors for ocean observing: the optical phytoplankton discriminator[M]//Liu Y G, Kerkering H, Weisberg R H. Coastal ocean observing systems. Amsterdam: Academic Press, 2015: 326 − 350.
[38]	Beckler J S, Arutunian E, Moore T, et al. Coastal harmful algae bloom monitoring via a sustainable, sail-powered mobile platform[J]. Frontiers in Marine Science, 2019, 6: 587. DOI: 10.3389/fmars.2019.00587
[39]	Fragoso G M, Johnsen G, Chauton M S, et al. Phytoplankton community succession and dynamics using optical approaches[J]. Continental Shelf Research, 2021, 213: 104322. DOI: 10.1016/j.csr.2020.104322
[40]	Dubelaar G B J, Groenewegen A C, Stokdijk W, et al. Optical plankton analyser: a flow cytometer for plankton analysis, Ⅱ: specifications[J]. Cytometry, 1989, 10(5): 529 − 539. DOI: 10.1002/cyto.990100508
[41]	Dubelaar G B J, Gerritzen P L, Beeker A E R, et al. Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters[J]. Cytometry, 1999, 37(4): 247 − 254. DOI: 10.1002/(SICI)1097-0320(19991201)37:4<247::AID-CYTO1>3.0.CO;2-9
[42]	Olson R J, Sosik H M. A submersible imaging-in-flow instrument to analyze nano-and microplankton: imaging FlowCytobot[J]. Limnology and Oceanography: Methods, 2007, 5(6): 195 − 203. DOI: 10.4319/lom.2007.5.195
[43]	Fragoso G M, Poulton A J, Pratt N J, et al. Trait-based analysis of subpolar North Atlantic phytoplankton and plastidic ciliate communities using automated flow cytometer[J]. Limnology and Oceanography, 2019, 64(4): 1763 − 1778. DOI: 10.1002/lno.11189
[44]	Campbell L, Olson R J, Sosik H M, et al. First harmful Dinophysis (Dinophyceae, Dinophysiales) bloom in the U.S. is revealed by automated imaging flow cytometry[J]. Journal of Phycology, 2010, 46(1): 66 − 75. DOI: 10.1111/j.1529-8817.2009.00791.x
[45]	Kraft K, Seppälä J, Hällfors H, et al. First application of IFCB high-frequency imaging-in-flow cytometry to investigate bloom-forming filamentous Cyanobacteria in the Baltic Sea[J]. Frontiers in Marine Science, 2021, 8: 594144. DOI: 10.3389/fmars.2021.594144
[46]	Guo J H, Ma Y Y, Lee J H W. Real-time automated identification of algal bloom species for fisheries management in subtropical coastal waters[J]. Journal of Hydro-Environment Research, 2021, 36: 1 − 32.
[47]	Agarwal V, Chávez-Casillas J, Mouw C B. Sub-monthly prediction of harmful algal blooms based on automated cell imaging[J]. Harmful Algae, 2023, 122: 102386. DOI: 10.1016/j.hal.2023.102386
[48]	Samson S, Hopkins T, Remsen A, et al. A system for high-resolution zooplankton imaging[J]. IEEE Journal of Oceanic Engineering, 2001, 26(4): 671 − 676. DOI: 10.1109/48.972110
[49]	Cowen R K, Guigand C M. In situ ichthyoplankton imaging system (ISIIS): system design and preliminary results[J]. Limnology and Oceanography: Methods, 2008, 6(2): 126 − 132. DOI: 10.4319/lom.2008.6.126
[50]	Benfield M C, Schwehm C J, Fredericks R G, et al. Measurement of zooplankton distributions with a high-resolution digital camera system[M]//Seuront L, Strutton P G. Handbook of scaling methods in aquatic ecology: measurement, analysis, simulation. Boca Raton: CRC Press, 2003: 17 − 30.
[51]	Bi H S, Song J T, Zhao J, et al. Temporal characteristics of plankton indicators in coastal waters: high-frequency data from PlanktonScope[J]. Journal of Sea Research, 2022, 189: 102283. DOI: 10.1016/j.seares.2022.102283
[52]	Luo J Y, Grassian B, Tang D, et al. Environmental drivers of the fine-scale distribution of a gelatinous zooplankton community across a mesoscale front[J]. Marine Ecology Progress Series, 2014, 510: 129 − 149. DOI: 10.3354/meps10908
[53]	Song J T, Bi H S, Cai Z H, et al. Early warning of Noctiluca scintillans blooms using in-situ plankton imaging system: an example from Dapeng Bay, P.R. China[J]. Ecological Indicators, 2020, 112: 106123. DOI: 10.1016/j.ecolind.2020.106123
[54]	陈涛. 海洋浮游生物原位光学成像系统研究[D]. 深圳:中国科学院深圳先进技术研究院,2023. Chen T. The study of marine plankton in situ intelligent optical imaging system[D]. Shenzhen: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 2023.
[55]	Davis C S, Thwaites F T, Gallager S M, et al. A three-axis fast-tow digital video plankton recorder for rapid surveys of plankton taxa and hydrography[J]. Limnology and Oceanography: Methods, 2005, 3(2): 59 − 74. DOI: 10.4319/lom.2005.3.59
[56]	Picheral M, Guidi L, Stemmann L, et al. The underwater vision profiler 5: an advanced instrument for high spatial resolution studies of particle size spectra and zooplankton[J]. Limnology and Oceanography: Methods, 2010, 8(9): 462 − 473. DOI: 10.4319/lom.2010.8.462
[57]	Schulz J, Barz K, Ayon P, et al. Imaging of plankton specimens with the lightframe on-sight keyspecies investigation (LOKI) system[J]. Journal of the European Optical Society-Rapid Publications, 2010, 5: 10017s. DOI: 10.2971/jeos.2010.10017s
[58]	Orenstein E C, Ratelle D, Briseño-Avena C, et al. The scripps plankton camera system: a framework and platform for in situ microscopy[J]. Limnology and Oceanography: Methods, 2020, 18(11): 681 − 695. DOI: 10.1002/lom3.10394
[59]	Chen T, Li J P, Ma W Q, et al. Deep focus-extended darkfield imaging for in situ observation of marine plankton[J]. Frontiers in Marine Science, 2023, 10: 1074428. DOI: 10.3389/fmars.2023.1074428
[60]	Tang X O, Stewart W K, Huang H, et al. Automatic plankton image recognition[J]. Artificial Intelligence Review, 1998, 12(1): 177 − 199.
[61]	Kiko R, Picheral M, Antoine D, et al. A global marine particle size distribution dataset obtained with the underwater vision profiler 5[J]. Earth System Science Data, 2022, 14(9): 4315 − 4337. DOI: 10.5194/essd-14-4315-2022
[62]	Pesant S, Not F, Picheral M, et al. Open science resources for the discovery and analysis of Tara Oceans data[J]. Scientific Data, 2015, 2: 150023. DOI: 10.1038/sdata.2015.23
[63]	Garcia-Sucerquia J, Xu W B, Jericho S K, et al. Digital in-line holographic microscopy[J]. Applied Optics, 2006, 45(5): 836 − 850. DOI: 10.1364/AO.45.000836
[64]	Stewart G L, Beers J R, Knox C. Application of holographic techniques to the study of marine plankton in the field and in the laboratory[C]//Ralph F, Wuerker E A. Proceedings of SPIE 0041, developments in laser technology Ⅱ. San Diego: SPIE, 1974: 183 − 188.
[65]	Heflinger L O, Stewart G L, Booth C R. Holographic motion pictures of microscopic plankton[J]. Applied Optics, 1978, 17(6): 951 − 954. DOI: 10.1364/AO.17.000951
[66]	Katz J, Donaghay P L, Zhang J, et al. Submersible holocamera for detection of particle characteristics and motions in the ocean[J]. Deep Sea Research Part Ⅰ: Oceanographic Research Papers, 1999, 46(8): 1455 − 1481. DOI: 10.1016/S0967-0637(99)00011-4
[67]	Owen R B, Zozulya A A. In-line digital holographic sensor for monitoring and characterizing marine particulates[J]. Optical Engineering, 2000, 39(8): 2187 − 2197. DOI: 10.1117/1.1305542
[68]	Rotermund L M, Samson J, Kreuzer H J. A submersible holographic microscope for 4-D in-situ studies of micro-organisms in the ocean with intensity and quantitative phase imaging[J]. Journal of Marine Science: Research & Development, 2016, 6(1): 1000181.
[69]	MacNeil L, Missan S, Luo J L, et al. Plankton classification with high-throughput submersible holographic microscopy and transfer learning[J]. BMC Ecology and Evolution, 2021, 21(1): 123. DOI: 10.1186/s12862-021-01839-0
[70]	MacNeil L, Desai D K, Costa M, et al. Combining multi-marker metabarcoding and digital holography to describe eukaryotic plankton across the Newfoundland Shelf[J]. Scientific Reports, 2022, 12(1): 13078. DOI: 10.1038/s41598-022-17313-w
[71]	MacNeil L, Costa M, Laroche J. Glimpsing the 2020 spring bloom in the Strait of Georgia (Canada) with autonomous ferry-based sensors[J]. Marine Ecology Progress Series, 2024, 736: 181 − 187. DOI: 10.3354/meps14574
[72]	Grosjean P, Picheral M, Warembourg C, et al. Enumeration, measurement, and identification of net zooplankton samples using the ZOOSCAN digital imaging system[J]. ICES Journal of Marine Science, 2004, 61(4): 518 − 525. DOI: 10.1016/j.icesjms.2004.03.012
[73]	Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273 − 297.
[74]	Ho T K. Random decision forests[C]//Raju K S, Govardhan A, Rani B P, et al. Proceedings of 3rd international conference on document analysis and recognition. Montreal: IEEE, 1995: 278 − 282.
[75]	Orenstein E C, Beijbom O. Transfer learning and deep feature extraction for planktonic image data sets[C]//Mercier J P, Trottier T, Giguere P, et al. Proceedings of 2017 IEEE winter conference on applications of computer vision. Santa Rosa: IEEE, 2017: 1082 − 1088.
[76]	Luo J Y, Irisson J O, Graham B, et al. Automated plankton image analysis using convolutional neural networks[J]. Limnology and Oceanography: Methods, 2018, 16(12): 814 − 827. DOI: 10.1002/lom3.10285
[77]	Yang Z Y, Li J P, Chen T, et al. Contrastive learning-based image retrieval for automatic recognition of in situ marine plankton images[J]. ICES Journal of Marine Science, 2022, 79(10): 2643 − 2655. DOI: 10.1093/icesjms/fsac198
[78]	Eerola T, Batrakhanov D, Barazandeh N V, et al. Survey of automatic plankton image recognition: challenges, existing solutions and future perspectives[J]. Artificial Intelligence Review, 2024, 57(5): 114. DOI: 10.1007/s10462-024-10745-y

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