On the nose: validating a novel, non-invasive method to identify individual koalas using unique nose patterns

Janine Duffy; Tori Stragliotto; Valentina S. A. Mella

doi:10.1071/WR24131

RESEARCH ARTICLE (Open Access)

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On the nose: validating a novel, non-invasive method to identify individual koalas using unique nose patterns

Janine Duffy ^A , Tori Stragliotto ^B and Valentina S. A. Mella

^B ^*

+ Author Affiliations

- Author Affiliations

^A Koala Clancy Foundation, PO Box 20, Bacchus Marsh, Vic 3340, Australia.

^B Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia.

^* Correspondence to: valentina.mella@sydney.edu.au

Handling Editor: Charlie Huveneers

Wildlife Research 51, WR24131 https://doi.org/10.1071/WR24131

Submitted: 12 January 2024 Accepted: 25 September 2024 Published: 21 October 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Non-invasive identification of individual animals in wild populations can occur in species with unique coat patterns (e.g. zebras, giraffes, jaguars). However, identification in other species often relies on capture-mark–recapture techniques, involving physical handling of animals. Identification of individual koalas (Phascolarctos cinereus) is difficult and has so far relied mostly on invasive methods such as ear tagging, microchipping and/or collaring, which require capture. The validation of a non-invasive method to identify koalas could improve monitoring of individuals in the wild, allowing targeting of specific koalas in disease and survival studies, reducing the need to capture individuals.

Aims

This study describes a novel effective method to identify koalas from their nose markings, specifically using the unpigmented pattern of the nose to determine unique features of individuals.

Methods

Photographs of koalas from different populations in Victoria and New South Wales (NSW), Australia, were examined in the study. Nose patterns were traced from photographs and matched through visual assessment if they were thought to belong to the same individual. Differences in identification success between datasets from different populations and the effect of sex on match success were evaluated statistically. For the NSW koalas, the effect of lighting conditions and photographic angle were also assessed.

Key results

Overall identification success was 89.7% (range 87.1–91.8%) and was not affected by any of the variables tested, demonstrating that nose patterns can be used reliably to identify individual koalas.

Conclusions

The proposed non-invasive method is simple, yet accurate and stable over time, hence it offers a vital tool for monitoring endangered koalas whilst minimising human interference.

Implications

Pattern-based recognition of koalas is cost-effective, reduces stress on the animals, has the potential to improve data collection and allows involvement of citizen scientists in monitoring of populations or individuals.

Keywords: biometric features, citizen science, individual identification, mark-recapture, nose markings, pattern-based identification, Phascolarctos cinereus, photographic identification.

References

Adobe Inc (2024) Adobe Illustrator. Available at https://adobe.com/products/illustrator

Anderson CJR, Da Vitoria Lobo N, Roth JD, Waterman JM (2010) Computer-aided photo-identification system with an application to polar bears based on whisker spot patterns. Journal of Mammalogy 91, 1350-1359.
| Crossref | Google Scholar |

Ardovini A, Cinque L, Sangineto E (2008) Identifying elephant photos by multi-curve matching. Pattern Recognition 41, 1867-1877.
| Crossref | Google Scholar |

Beausoleil NJ, Mellor DJ, Baker L, Baker SE, Bellio M, Clarke AS, Dale A, Garlick S, Jones B, Harvey A, Pitcher BJ, Sherwen S, Stockin KA, Zito S (2018) “Feelings and Fitness” not “Feelings or Fitness”–The Raison d’être of conservation welfare, which aligns conservation and animal welfare objectives. Frontiers in Veterinary Science 5, 296.
| Crossref | Google Scholar |

Bedetti A, Greyling C, Paul B, Blondeau J, Clark A, Malin H, Horne J, Makukule R, Wilmot J, Eggeling T, Kern J, Henley M (2020) System for Elephant Ear-pattern Knowledge (SEEK) to identify individual African elephants. Pachyderm 61, 63-77.
| Google Scholar |

Bodesheim P, Blunk J, Körschens M, Brust C-A, Käding C, Denzler J (2022) Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research – individual identification and attribute prediction with image features from deep neural networks and decoupled decision models applied to elephants and great apes. Mammalian Biology 102, 875-897.
| Crossref | Google Scholar |

Bretagnolle V, Thibault J-C, Dominici J-M (1994) Field identification of individual ospreys using head marking pattern. The Journal of Wildlife Management 58, 175-178.
| Crossref | Google Scholar |

Caro TM (1994) ‘Cheetahs of the Serengeti Plains: group living in an asocial species.’ (The University of Chicago Press: Chicago, Illinois)

Choi HI, Lee Y, Shin H, Lee S, Choi SS, Han CY, Kwon S-H (2021) The formation and invariance of canine nose pattern of beagle dogs from early puppy to young adult periods. Animals 11, 2664.
| Crossref | Google Scholar |

Chui SYS, Karczmarski L (2022) Everyone matters: identification with facial wrinkles allows more accurate inference of elephant social dynamics. Mammalian Biology 102, 645-666.
| Crossref | Google Scholar |

Clutton-Brock T, Sheldon BC (2010) Individuals and populations: the role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends in Ecology & Evolution 25, 562-573.
| Crossref | Google Scholar | PubMed |

Danaher M, Schlagloth R, Hewson M, Geddes C (2023) One person and a camera: a relatively non-intrusive approach to Koala citizen science. Australian Zoologist 43, 52-66.
| Crossref | Google Scholar |

Das A, Sowmya S, Sinha S, Chandru S (2021) Identification of a zebra based on its stripes through pattern recognition. In ‘2021 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C)’, Bangalore, India. pp. 120–122. (IEEE) doi:10.1109/ICDI3C53598.2021.00033

Department of Agriculture Water and the Environment (2022) National Recovery Plan for the Koala Phascolarctos cinereus (combined populations of Queensland, New South Wales and the Australian Capital Territory). Department of Agriculture Water and the Environment.

Ellis WAH, Melzer A, Carrick FN, Hasegawa M (2002) Tree use, diet and home range of the koala (Phascolarctos cinereus) at Blair Athol, central Queensland. Wildlife Research 29, 303-311.
| Crossref | Google Scholar |

Environment Protection And Biodiversity Conservation Act (1999) Commonwealth of Australia. Available at https://www.legislation.gov.au/Series/C2004A00485

Foster JB (1966) The giraffe of nairobi national park: home range, sex ratios, the herd, and food. African Journal of Ecology 4, 139-148.
| Crossref | Google Scholar |

Gilkinson AK, Pearson HC, Weltz F, Davis RW (2007) Photo-identification of sea otters using nose scars. The Journal of Wildlife Management 71, 2045-2051.
| Crossref | Google Scholar |

Gould J, Beranek C, Madani G (2024) Dragon detectives: citizen science confirms photo-ID as an effective tool for monitoring an endangered reptile. Wildlife Research 51, WR23036.
| Crossref | Google Scholar |

Gowans S, Whitehead H (2001) Photographic identification of northern bottlenose whales (Hyperoodon ampullatus): sources of heterogeneity from natural marks. Marine Mammal Science 17, 76-93.
| Crossref | Google Scholar |

Griffith JE (2010) Studies into the diagnosis, treatment and management of Chlamydiosis in Koalas. PhD Thesis, The University of Sydney.

Güthlin D, Storch I, Küchenhoff H (2014) Is it possible to individually identify red foxes from photographs? Wildlife Society Bulletin 38, 205-210.
| Crossref | Google Scholar |

Guy AJ, Curnoe D, Banks PB (2013) A survey of current mammal rehabilitation and release practices. Biodiversity and Conservation 22, 825-837.
| Crossref | Google Scholar |

Hiby L, Lovell P, Patil N, Kumar NS, Gopalaswamy AM, Karanth KU (2009) A tiger cannot change its stripes: using a three-dimensional model to match images of living tigers and tiger skins. Biology Letters 5, 383-386.
| Crossref | Google Scholar | PubMed |

Hundloe TJ, Hamilton C (1997) ‘Koalas and tourism: an economic evaluation.’ (Australia Institute: Lyneham, ACT)

Karanth KU, Nichols JD, Kumar NS, Hines JE (2006) Assessing tiger population dynamics using photographic capture–recapture sampling. Ecology 87, 2925-2937.
| Crossref | Google Scholar | PubMed |

Karczmarski L, Chan SCY, Rubenstein DI, Chui SYS, Cameron EZ (2022) Individual identification and photographic techniques in mammalian ecological and behavioural research—Part 1: methods and concepts. Mammalian Biology 102, 545-549.
| Crossref | Google Scholar |

Kelly MJ (2001) Computer-aided photograph matching in studies using individual identification: an example from Serengeti cheetahs. Journal of Mammalogy 82, 440-449.
| Crossref | Google Scholar |

Knox CD, Cree A, Seddon PJ (2013) Accurate identification of individual geckos (Naultinus gemmeus) through dorsal pattern differentiation. New Zealand Journal of Ecology 37, 60-66.
| Google Scholar |

Kratzing JE (1984) The anatomy and histology of the nasal cavity of the koala (Phascolarctos cinereus). Journal of Anatomy 138, 55-65.
| Google Scholar | PubMed |

Kumar S, Singh SK (2017) Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm. Multimedia Tools and Applications 76, 26551-26580.
| Crossref | Google Scholar |

Kumar S, Pandey A, Sai Ram Satwik K, Kumar S, Singh SK, Singh AK, Mohan A (2018) Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement 116, 1-17.
| Crossref | Google Scholar |

Lama FD, Rocha MD, Andrade MÂ, Nascimento LB (2011) The use of photography to identify individual tree frogs by their natural marks. South American Journal of Herpetology 6, 198-204.
| Crossref | Google Scholar |

Lassnig N, Guasch-Martínez S, Pinya S (2024) The individual color pattern on the back of Bufotes viridis balearicus (Boettger, 1880) allows individual photo-identification recognition for population studies. Canadian Journal of Zoology 102, 393-402.
| Crossref | Google Scholar |

Madani GF, Ashman KR, Mella VSA, Whisson DA (2020) A review of the ‘noose and flag’ method to capture free-ranging koalas. Australian Mammalogy 42, 341-348.
| Crossref | Google Scholar |

Möcklinghoff L, Schuchmann K-L, Marques MI (2018) New non-invasive photo-identification technique for free-ranging giant anteaters (Myrmecophaga tridactyla) facilitates urgently needed field studies. Journal of Natural History 52, 2397-2411.
| Crossref | Google Scholar |

Ness IFG, Jung TS, Schmiegelow FKA (2022) Evaluating likelihood-based photogrammetry for individual recognition of four species of northern ungulates. Mammalian Biology 102, 701-718.
| Crossref | Google Scholar |

Nipko RB, Holcombe BE, Kelly MJ (2020) Identifying individual jaguars and ocelots via pattern-recognition software: comparing HotSpotter and Wild-Id. Wildlife Society Bulletin 44, 424-433.
| Crossref | Google Scholar |

Norouzzadeh MS, Nguyen A, Kosmala M, Swanson A, Palmer MS, Packer C, Clune J (2018) Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences 115, E5716-E5725.
| Crossref | Google Scholar |

Patton FJ, Campbell PE (2011) Using eye and profile wrinkles to identify individual white rhinos. Pachyderm 50, 84-86.
| Google Scholar |

Petersen JCB (1972) An identification system for zebra (Equus burchelli, Gray. African Journal of Ecology 10, 59-63.
| Crossref | Google Scholar |

Prinsloo ND, Postma M, de Bruyn PJN (2021) How unique is unique? Quantifying geometric differences in stripe patterns of Cape mountain zebra, Equus zebra zebra (Perissodactyla: Equidae). Zoological Journal of the Linnean Society 191, 612-625.
| Crossref | Google Scholar |

Radford SL, Mckee J, Goldingay RL, Kavanagh RP (2006) The protocols for koala research using radio-collars: a review based on its application in a tall coastal forest in New South Wales and the implications for future research projects. Australian Mammalogy 28, 187-200.
| Crossref | Google Scholar |

R Development Core Team (2023) ‘R: a language and environment for statistical computing. Version 4.3.1.’ (R Foundation for Statistical Computing: Vienna, Austria) Available at https://www.R-project.org/

Robbins A, Loader J, Timms P, Hanger J (2018) Optimising the short and long-term clinical outcomes for koalas (Phascolarctos cinereus) during treatment for chlamydial infection and disease. PLoS ONE 13, e0209679.
| Crossref | Google Scholar | PubMed |

Searle SR, Speed FM, Milliken GA (1980) Population marginal means in the linear model: an alternative to least squares means. The American Statistician 34, 216-221.
| Crossref | Google Scholar |

Sherley RB, Burghardt T, Barham PJ, Campbell N, Cuthill IC (2010) Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus. Endangered Species Research 11, 101-111.
| Crossref | Google Scholar |

Shorrocks B, Croft DP (2009) Necks and networks: a preliminary study of population structure in the reticulated giraffe (Giraffa camelopardalis reticulata de Winston). African Journal of Ecology 47, 374-381.
| Crossref | Google Scholar |

Simpson SJ, Higgins DP, Timms P, Mella VSA, Crowther MS, Fernandez CM, McArthur C, Phillips S, Krockenberger MB (2023) Efficacy of a synthetic peptide Chlamydia pecorum major outer membrane protein vaccine in a wild koala (Phascolarctos cinereus) population. Scientific Reports 13, 15087.
| Crossref | Google Scholar | PubMed |

Sripian P, Teintipsiri T, Ngoprasert D (2015) Asiatic black bear identification based on chest marks photo classification. In ‘The 10th Asian Forum on Graphic Science’, 4-7 August 2015, Bangkok, Thailand.

Stein AB, Erckie B, Fuller TK, Marker L (2010) Camera-trapping as a method for monitoring rhino populations within the Waterberg Plateau Park, Namibia. Pachyderm 48, 67-70.
| Google Scholar |

Theimer TC, Ray DT, Bergman DL (2017) Camera angle and photographic identification of individual striped skunks. Wildlife Society Bulletin 41, 146-150.
| Crossref | Google Scholar |

Tye CA, Greene DU, Giuliano WM, Mccleery RA (2015) Using camera-trap photographs to identify individual fox squirrels (Sciurus niger) in the Southeastern United States. Wildlife Society Bulletin 39, 645-650.
| Crossref | Google Scholar |

Usher KM, Groom C, Saunders DA (2016) Identification of individual Carnaby’s Cockatoos Calyptorhynchus latirostris from distinctive plumage markings. Australian Zoologist 38, 62-82.
| Crossref | Google Scholar |

Van Tienhoven AM, Den Hartog JE, Reijns RA, Peddemors VM (2007) A computer-aided program for pattern-matching of natural marks on the spotted raggedtooth shark Carcharias taurus. Journal of Applied Ecology 44, 273-280.
| Crossref | Google Scholar |

Walker KA, Trites AW, Haulena M, Weary DM (2012) A review of the effects of different marking and tagging techniques on marine mammals. Wildlife Research 39, 15-30.
| Crossref | Google Scholar |

Wilson RP, McMahon CR (2006) Measuring devices on wild animals: what constitutes acceptable practice? Frontiers in Ecology and the Environment 4, 147-154.
| Crossref | Google Scholar |

Wuersig B, Jefferson TA (1990) Methods of photo-identification for small cetaceans. International Whaling Commission.

Yamamoto T, Nakamura T, Toda M (2022) Verification of natural marking for individual identification using a duplex marking approach in Ijima’s sea snakes, Emydocephalus ijimae (Reptilia: Elapidae). Zoological Studies 61, e75.
| Crossref | Google Scholar |