‘I know the tiger by his paw’: A non-invasive footprint identification technique for monitoring individual Amur tigers (Panthera tigris altaica) in snow
Apex predator populations are in decline around the world. Many exist at low density and are elusive, making the acquisition of reliable data on their numbers and distribution a considerable challenge. The Amur tiger (Panthera tigris altaica) is the largest of the five extant sub-species of tiger. T...
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Published in | Ecological informatics Vol. 73; p. 101947 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.03.2023
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Abstract | Apex predator populations are in decline around the world. Many exist at low density and are elusive, making the acquisition of reliable data on their numbers and distribution a considerable challenge. The Amur tiger (Panthera tigris altaica) is the largest of the five extant sub-species of tiger. The single most significant, contiguous population, an estimated 550 animals, exists in the Russian Far East, with smaller populations on the far eastern Sino-Russian border. For the last few decades, active efforts on the part of Chinese authorities have encouraged the recolonization of these populations back to their former ranges in Northeast China. Reliable data on Amur tiger numbers and distribution are required to assess population recovery at the landscape scale. Footprints, ubiquitous in the snow over range areas, could inform on these baseline data. This paper describes a statistically robust, cost-effective and non-invasive footprint identification technique (FIT) to identify individual tigers from footprints in snow. It is based on a rigorous data collection and data-processing protocol, combined with a cross-validated discriminant analysis method. A Ward's clustering technique provides a visual output of individual classification. The analytical tools are packaged in a user-friendly analytical interface. Between December 2011 and December 2012, we collected a series of 605 footprint images from 44 captive individual Amur tigers for a reference database from which to derive a classification algorithm. The 23 females and 21 males ranged in age from 3 to 13 years (female mean age 7.95 +/− 0.18; male mean age 8.08 +/− 0.19). 128 measurements (areas, lengths and angles) were taken from each print and analyzed with the FIT add-in to JMP software. The derived classification algorithm was then applied to 21 footprint trails collected from an unknown number of free-ranging Amur tigers during 2012 and 2015/2016. The algorithm predicted 7 Amur tigers at the site surveyed in 2012, and 4 tigers surveyed at two sites in 2015/16. We demonstrate that the footprint identification technique translates traditional tracking methodologies into a statistically robust and objective analytical tool that can be deployed by both scientists and local communities to monitor the recovery of big cat populations.
•Amur tigers can be identified as individuals from simple digital images of their footprints (pug marks)•This footprint identification technique (FIT) is a robust, statistically valid, cost-effective and non-invasive technique•It can be used within existing ecological modeling approaches, and/or as a complement to other non-invasive technologies such as camera traps•Engaging traditional ecological knowledge (TEK) as part of any conservation strategy is key to sustainability•For sustainable monitoring, werecommend the deployment of a toolbox of complementary non-invasive techniques adapted to local resources |
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AbstractList | Apex predator populations are in decline around the world. Many exist at low density and are elusive, making the acquisition of reliable data on their numbers and distribution a considerable challenge. The Amur tiger (Panthera tigris altaica) is the largest of the five extant sub-species of tiger. The single most significant, contiguous population, an estimated 550 animals, exists in the Russian Far East, with smaller populations on the far eastern Sino-Russian border. For the last few decades, active efforts on the part of Chinese authorities have encouraged the recolonization of these populations back to their former ranges in Northeast China. Reliable data on Amur tiger numbers and distribution are required to assess population recovery at the landscape scale. Footprints, ubiquitous in the snow over range areas, could inform on these baseline data. This paper describes a statistically robust, cost-effective and non-invasive footprint identification technique (FIT) to identify individual tigers from footprints in snow. It is based on a rigorous data collection and data-processing protocol, combined with a cross-validated discriminant analysis method. A Ward's clustering technique provides a visual output of individual classification. The analytical tools are packaged in a user-friendly analytical interface. Between December 2011 and December 2012, we collected a series of 605 footprint images from 44 captive individual Amur tigers for a reference database from which to derive a classification algorithm. The 23 females and 21 males ranged in age from 3 to 13 years (female mean age 7.95 +/− 0.18; male mean age 8.08 +/− 0.19). 128 measurements (areas, lengths and angles) were taken from each print and analyzed with the FIT add-in to JMP software. The derived classification algorithm was then applied to 21 footprint trails collected from an unknown number of free-ranging Amur tigers during 2012 and 2015/2016. The algorithm predicted 7 Amur tigers at the site surveyed in 2012, and 4 tigers surveyed at two sites in 2015/16. We demonstrate that the footprint identification technique translates traditional tracking methodologies into a statistically robust and objective analytical tool that can be deployed by both scientists and local communities to monitor the recovery of big cat populations.
•Amur tigers can be identified as individuals from simple digital images of their footprints (pug marks)•This footprint identification technique (FIT) is a robust, statistically valid, cost-effective and non-invasive technique•It can be used within existing ecological modeling approaches, and/or as a complement to other non-invasive technologies such as camera traps•Engaging traditional ecological knowledge (TEK) as part of any conservation strategy is key to sustainability•For sustainable monitoring, werecommend the deployment of a toolbox of complementary non-invasive techniques adapted to local resources |
ArticleNumber | 101947 |
Author | Jewell, Zoe C. Gu, Jiayin Morgan, Joseph Alibhai, Sky K. Jiang, Guangshun Liu, Dan |
Author_xml | – sequence: 1 givenname: Sky K. surname: Alibhai fullname: Alibhai, Sky K. organization: Nicholas School of the Environment, Duke University, Box 90329, Durham, NC 27708, USA – sequence: 2 givenname: Jiayin surname: Gu fullname: Gu, Jiayin organization: Feline Research Center of National Forestry and Grassland Administration, College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, PR China – sequence: 3 givenname: Zoe C. surname: Jewell fullname: Jewell, Zoe C. email: info@wildtrack.org organization: Nicholas School of the Environment, Duke University, Box 90329, Durham, NC 27708, USA – sequence: 4 givenname: Joseph surname: Morgan fullname: Morgan, Joseph organization: SAS Institute Inc., JMP Division, SAS Campus Drive, Cary, NC 27513, United States of America – sequence: 5 givenname: Dan surname: Liu fullname: Liu, Dan organization: Heilongjiang Siberia Tiger Park, Harbin, Heilongjiang, 150028, China – sequence: 6 givenname: Guangshun surname: Jiang fullname: Jiang, Guangshun email: jgshun@126.com organization: Feline Research Center of National Forestry and Grassland Administration, College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, PR China |
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Keywords | Wildlife biometrics Northeast China Non-invasive Population estimate Footprint morphometrics |
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Snippet | Apex predator populations are in decline around the world. Many exist at low density and are elusive, making the acquisition of reliable data on their numbers... |
SourceID | crossref elsevier |
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StartPage | 101947 |
SubjectTerms | Footprint morphometrics Non-invasive Northeast China Population estimate Wildlife biometrics |
Title | ‘I know the tiger by his paw’: A non-invasive footprint identification technique for monitoring individual Amur tigers (Panthera tigris altaica) in snow |
URI | https://dx.doi.org/10.1016/j.ecoinf.2022.101947 |
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