한국수산과학회지 (Korean Journal of Fisheries and Aquatic Sciences)

  • 제57권5호
  • /
  • Pages.607-611
  • /
  • 2024
  • /
  • 0374-8111(pISSN)
  • /
  • 2287-8815(eISSN)
한국수산과학회 (The Korean Society of Fisheries and Aquatic Science)
권호 표지
DOI QR코드

DOI QR Code

AI기반 프로그램을 이용한 백령도 점박이물범(Phoca largha)의 사진개체 식별과 휴식지 선호도 연구

Application of an AI-based Program for Photo-identification of Spotted Seals Phoca largha, and Analysis of Site Preference on Baengnyeongdo Island

  • 어남규 (국립수산과학원 고래연구소) ;
  • 김소라 (국립수산과학원 고래연구소) ;
  • 박겸준 (국립수산과학원 고래연구소) ;
  • 이상헌 (부산대학교 해양학과) ;
  • 김현우 (국립수산과학원 연근해자원과)
  • Namgyu Uh (Cetacean Research Institute, National Institute of Fisheries Science) ;
  • So Ra Kim (Cetacean Research Institute, National Institute of Fisheries Science) ;
  • Kyum Joon Park (Cetacean Research Institute, National Institute of Fisheries Science) ;
  • Sang-Heon Lee (Department of Oceanography, Pusan National University) ;
  • Hyun Woo Kim (Coastal Water Fisheries Resources Research Division, National Institute of Fisheries Science)
  • 투고 : 2024.09.11
  • 심사 : 2024.10.07
  • 발행 : 2024.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this study, the AI-based photo-identification program IBEIS was used to efficiently analyze the photo-identification of spotted seals Phoca largha, around Baengnyeongdo Island, Korea. A total of 3,640 photographs collected during field surveys from 2016 to 2021 were analyzed, leading to the identification of 1,234 seals. The spotted seal population around Baengnyeongdo Island was estimated to be at least 1,200 individuals. The photo-identification method relies on the unique black spot patterns on the left cheek of the seals, which remain consistent over time. This method allows for long-term ecological studies and tracking of individual seals. In addition, site preferences of the spotted seals were analyzed, revealing that more than 70% of individuals were re-identified at the site where they were first observed and indicating site preference. The results suggest that environmental and ecological factors may influence habitat preferences. Future studies, including collaborative efforts with neighboring countries such as North Korea and China, will provide more comprehensive insights into the habitat usage patterns of the species. This study offers a foundation for estimating the population size of spotted seals and key data for habitat management and conservation strategies by using AI-based photo identification methods.

키워드

과제정보

본 연구는 국립수산과학원 2024년도 수산시험연구사업 중 고래류 평가 및 관리 연구(R2024004)의 지원을 받아 수행되었습니다. 조사에 참여한 모든 연구원께 감사합니다.

참고문헌

  1. Anderson CJR, Lobo NDV, Roth JD and Waterman JM. 2010. Computer-aided photo-identification system with an application to polar bears based on whisker spot patterns. J Mammal 91, 1350-1359. https://doi.org/10.1644/09-MAMM-A-425.1.
  2. Beaumont ES and Goold JC. 2007. Cheap and accessible method to aid individual photo-identification of grey seals, Halichoerus grypus. J Mar Biol Assoc U K 87, 1337-1343. https://doi.org/10.1017/S0025315407055907.
  3. Bolger DT, Morrison TA, Vance B, Lee D and Farid H. 2012. A computer-assisted system for photographic mark-recapture analysis. Methods Ecol Evol 3, 813-822. https://doi.org/10.1111/j.2041-210X.2012.00212.x.
  4. Chehrsimin T, Eerola T, Koivuniemi M, Auttila M, Levanen R, Niemi M, Kunnasranta M and Kalviainen H. 2017. Automatic individual identification of Saimaa ringed seals. IET Comput Vis 12, 146-152. https://doi.org/10.1049/ietcvi.2017.0082.
  5. Cordes LS and Thompson PM. 2015. Mark-resight estimates of seasonal variation in harbor seal abundance and site fidelity. Popul Ecol 57, 467-472. https://doi.org/10.1007/S10144-015-0496-Z.
  6. Crall JP, Stewart CV, Berger-Wolf TY, Rubenstein DI and Sundaresan SR. 2013. HotSpotter-Patterned species instance recognition. 2013 IEEE Int Workshop on Applications of Computer Vision (WACV) , Clearwater Beach, FL, U.S.A., 230-237. https://doi.org/10.1109/WACV.2013.6475023.
  7. Foster JB. 1966. The giraffe of Nairobi National Park: Home range, sex ratios, the herd, and food. Afr J Ecol 4, 139-148. https://doi.org/10.1111/j.1365-2028.1966.tb00889.x.
  8. Frost KJ and Burns JJ. 2018. Spotted seal: Phoca largha. Encyclopedia of Marine Mammals, Thirded Edition. Academic Press, Cambridge, MA, U.S.A., 928-931. https://doi.org/10.1016/B978-0-12-804327-1.00244-2.
  9. Harkonen T and Harding KC. 2001. Spatial structure of harbour seal populations and the implications thereof. Can J Zool 79, 2115-2127. https://doi.org/10.1139/z01-172.
  10. Hillman GR, Wursig B, Gailey GA, Kehtarnavaz N, Drobyshevsky A, Araabi BN, Tagare HD and Weller DW. 2003. Computer-assisted photo-identification of individual marine vertebrates: A multi-species system. Aquat Mamm 29, 117-123. https://doi.org/10.1578/016754203101023960.
  11. Islam SB, Valles D, Hibbitts TJ, Ryberg WA, Walkup DK and Forstner MRJ. 2023. Animal species recognition with deep convolutional neural networks from ecological camera trap images. Animals 13, 1526. https://doi.org/10.3390/ani13091526.
  12. Jefferson TA, Webber MA and Pitman RL. 2015. 6-Pinnipeds. In: Marine Mammals of the World (2nd ed.). Academic Press, Cambridge, MA, U.S.A., 358-522. https://doi.org/10.1016/B978-0-12-409542-7.50005-6.
  13. Kelly MJ. 2001. Computer-aided photograph matching in studies using individual identification: An example from Serengeti cheetahs. J Mammal 82, 440-449. https://doi.org/10.1644/1545-1542(2001)082<0440:CAPMIS>2.0.CO;2.
  14. Kim HW, An YR, Park TG, Kim ZG, Moon DY and Choi SG. 2010. Validity of photo-identification Method for Spotted Seals on Baekryongdo, Korea. Korean J Fish Aquatic Sci 43, 340-344. https://doi.org/10.5657/kfas.2010.43.4.340.
  15. Loughlin T, Cunningham L, Gales NJ, Wells R and Boyd L. 2010. Marking and capturing. In: Marine Mammal Ecology and Conservation. Boyd IL, Bowen WD and Iverson S, eds. Oxford University Press, Oxford, U.K., 16-41.
  16. Lowry LF, Frost KJ, Davis R, DeMaster DP and Suydam RS. 1998. Movements and behavior of satellite-tagged spotted seals (Phoca largha) in the Bering and Chukchi Seas. Polar Biol 19, 221-230. https://doi.org/10.1007/s003000050238.
  17. Macleod K, Du Fresne S, Mackey B, Faustino C and Boyd I. 2010. Approaches to Marine Mammal Monitoring at Marine Renewable Energy Developments. Report by Sea Mammal Research Unit, St Andrews, U.K.
  18. Matthe M, Schonbrodt T and Berger G. 2008. Computergestutzte Bildanalyse von auchfleckenmustern des Kammmolchs Triturus cristatus. Z Feldherpetol 15, 89-94.
  19. McGinley E, Cogley A, Palmer L, McCaul P, Longo L, Silvennoinen J, Martin A, Gomez J, Bachmaier S, Mackey M, Kao C, Eastman S and Eastman C. 2023. Marina observation of sea turtles: Establishing a database of intracoastal waterway green sea turtles in Northeast Florida. Animals 13, 279. https://doi.org/10.3390/ani13020279.
  20. Moya O, Mansilla PL, Madrazo S, Igual JM, Rotger A, Romano A and Tavecchia G. 2015. APHIS: A new software for photo-matching in ecological studies. Ecol Inform 27, 64-70. https://doi.org/10.1016/j.ecoinf.2015.03.003.
  21. Nichols JD. 1992. Capture-recapture models: Using marked animals to study population dynamics. BioScience 42, 94-102. https://doi.org/10.2307/1311650.
  22. Oddone A. 2016. A mobile application for the image based ecological information system. M.D. Thesis, University of Chicago, Chicago, IL, U.S.A.
  23. Parham J, Crall J, Stewart C, Berger-Wolf T and Rubenstein D. 2017. Animal Population Censusing at Scale with Citizen Science and Photographic Identification. AAAI Spring Symposium on Artificial Intelligence for the Social Good Technical Report, 37-44.
  24. Parham J and Stewart C. 2016. Detecting Plains and Grevy's Zebras in the Real World. 2016 IEEE Winter Appl of Computer Vision Workshops (WACVW), Lake Placid, NY, U.S.A., 1-9. https://doi.org/10.1109/WACVW.2016.7470122.
  25. Park TG, An YR, Moon DY, Choi SG and Kim ZG. 2010. Distribution of the spotted seal, Phoca largha, along the Coast of Baekryongdo. Korean J Fish Aqua Sci 43, 659-664. https://doi.org/10.5657/kfas.2010.43.6.659.
  26. Pennycuick CJ. 1978. Identification using natural markings. In: Animal Marking: Stonehouse B, ed. Palgrave, London, U.K., 147-159. https://doi.org/10.1007/978-1-349-03711-7_16.
  27. Pennycuick CJ and Rudnai J. 1970. A method of identifying individual lions Panthera leo, with an analysis of the reliability of identification. J Zool 160, 497-508. https://doi.org/10.1111/j.1469-7998.1970.tb03093.x.
  28. Petersen JCB. 1972. An identification system for zebra (Equus burchelli, Gravy's zebra). Afr J Ecol 10, 59-63. https://doi.org/10.1111/j.1365-2028.1972.tb00858.x.
  29. Trukhin AM, Permyakov PA, Ryazanov SD, Lobanov V, Kim HW, Choi YM and Sohn H. 2021. Migrations of young spotted seals (Phoca largha) from Peter the Great Bay, Sea of Japan/East Sea, and the pattern of their use of seasonal habitats. PLoS One 16, e0244232. https://doi.org/10.1371/JOURNAL.PONE.0244232.
  30. Van Tienhoven A, Den Hartog J, Reijns R and Peddemors V. 2007. A computer-aided program for pattern-matching of-natural marks on the spotted raggedtooth shark Carcharias taurus. J Appl Ecol 44, 273-280. https://doi.org/10.1111/j.1365-2664.2006.01273.x.
  31. Vidya TNC, Prasad D and Ghosh A. 2014. Individual identification in Asian elephants. Gajah 40, 3-17.
  32. Wursig B and Jefferson TA. 1990. Methods of photo-identification for small cetaceans. Rep Int Whal Commn 12, 43-52.
  33. Zhuang H, Shao F, Zhang C, Xia W, Wang S, Qu F, Wang Z, Lu Z, Zhao L and Zhang Z. 2024. Spatial-temporal shifting patterns and in situ conservation of spotted seal (Phoca largha) populations in the Yellow Sea ecoregion. Integr Zool 19, 307-318. https://doi.org/10.1111/1749-4877.12731.