| Records |
Author  |
Bohnett, E., Faryabi, S. P., Lewison, R., An, L., Bian, X., Rajabi, A. M., Jahed, N., Rooyesh, H., Mills, E., Ramos, S., Mesnildrey, N., Perez, C. M. S., Taylor, J., Terentyev, V., Ostrowski, S. |
| Title |
Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies |
Type |
Journal Article |
| Year |
2023 |
Publication |
Global Ecology & Conservation |
Abbreviated Journal |
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| Volume |
41 |
Issue |
e02350 |
Pages |
1-13 |
| Keywords |
Snow leopard, Artificial intelligence, Camera trap misclassification, individual ID, HotSpotter |
| Abstract |
Camera trapping is the most widely used data collection method for estimating snow leopard (Panthera uncia) abundance; however, the accuracy of this method is limited by human observer errors from misclassifying individuals in camera trap images. We evaluated the extent Whiskerbook (www.whiskerbook.org), an artificial intelligence (AI) software, could reduce this error rate and enhance the accuracy of capture-recapture abundance estimates. Using 439 images of 34 captive snow leopard individuals, classification was performed by five observers with prior experience in individual snow leopard ID (“experts”) and five observers with no such experience (“novices”). The “expert” observers classified 35 out of 34 snow leopard individuals, on average erroneously splitting one individual into two, thus resulting in a higher number than true individuals. The success rate of experts was 90 %, with less than a 3 % error in estimating the population size in capture-recapture modeling. However, the “novice” observers successfully matched 71 % of encounters, recognizing 25 out of 34 individuals, underestimating the population by 25 %. It was found that expert observers significantly outperformed novice observers, making statistically fewer errors (Mann Whitney U test P = 0.01) and finding the true number of individuals (P = 0.01). These differences were contrasted with a previous study by Johansson et al. 2020, using the same subset of 16 individuals from European zoos. With the help of AI and the Whiskerbook platform, “experts” were able to match 87 % of encounters and identify 15 out of 16 individuals, with modeled estimates of 16 ± 1 individuals. In contrast, “novices” were 63 % accurate in matching encounters and identified 12 out of 16 individuals, modeling 12 ± 1 individuals that underestimated the population size by 12 %. When comparing the performance of observers using AI and the Whiskerbook platform to observers performing the tasks manually, we found that observers using Whiskerbook made significantly fewer errors in splitting one individual into two (P = 0.04). However, there were also a significantly higher number of combination errors, where two individuals were combined into one (P = 0.01). Specifically, combination errors were found to be made by “novices” (P = 0.04). Although AI benefited both expert and novice observers, expert observers outperformed novices. Our results suggest that AI effectively reduced the misclassification of individual snow leopards in camera trap studies, improving abundance estimates. However, even with AI support, expert observers were needed to obtain the most accurate estimates. |
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SLN @ rakhee @ |
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1715 |
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Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., Ostrowski, S. |
| Title |
Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection |
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Journal Article |
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2023 |
Publication |
Ecological Informatics |
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77 |
Issue |
102214 |
Pages |
1-14 |
| Keywords |
Background subtraction, Deep learning, Hotspotter, Individual identification, PIE v2, Snow leopards |
| Abstract |
Photo-identification of individual snow leopards (Panthera uncia) is the primary data source for density estimation via capture-recapture statistical methods. To identify individual snow leopards in camera trap imagery, it is necessary to match individuals from a large number of images from multiple cameras and historical catalogues, which is both time-consuming and costly. The camouflaged snow leopards also make it difficult for machine learning to classify photos, as they blend in so well with the surrounding mountain environment, rendering applicable software solutions unavailable for the species. To potentially make snow leopard individual identification available via an artificial intelligence (AI) software interface, we first trained and evaluated image classification techniques for a convolutional neural network, pose invariant embeddings (PIE) (a triplet loss network), and compared the accuracy of PIE to that of the HotSpotter algorithm (a SIFT-based algorithm). Data were acquired from a curated library of free-ranging snow leopards taken in Afghanistan between 2012 and 2019 and from captive animals in zoos in Finland, Sweden, Germany, and the United States. We discovered several flaws in the initial PIE model, such as a small amount of background matching, that was addressed, albeit likely not fixed, using background subtraction (BGS) and left-right mirroring (LR) techniques which demonstrated reasonable accuracy (Rank 1: 74% Rank-5: 92%) comparable to the Hotspotter results (Rank 1: 74% Rank 2: 84%)The PIE BGS LR model, in conjunction with Hotspotter, yielded the following results: Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. In general, our findings indicate that PIE BGS LR, in conjunction with HotSpotter, can classify snow leopards more accurately than using either algorithm alone. |
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SLN @ rakhee @ |
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1723 |
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Bold, A.; Dorzhzunduy, S. |
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Report on Snow Leopards in the Southern Spurs of the Gobi Altai |
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Conference Article |
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1976 |
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11 |
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27-43 |
| Keywords |
gobi; Altai; Altay; status; distribution; browse; 2690; Russian |
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Estimates a population of 170-230 snow leopard within an area of 6600 km2 in Southern Gobi |
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In Russian
Title, Monographic: Proceedings of Institute of General and Experimental Biology
Place of Meeting: Ulaanbaator
Date of Copyright: 1976 |
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SLN @ rana @ 30 |
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186 |
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Bower, J.N. |
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Shy, elusive, struggling to survive: the snow leopard |
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1983 |
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The Explorer |
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9-11 |
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snow leopard |
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SLN @ rana @ 985 |
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187 |
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Bower, J.N. |
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For some endangered species, captive breeding programs are their last chance for life |
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Magazine Article |
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1980 |
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National Parks and Conservation Magazine |
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June |
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16-19 |
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snow leopard, captive breeding |
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English |
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1209 |
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Bowling, B. |
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The Legal Status of Snow Leopards in Afghanistan |
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Miscellaneous |
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2004 |
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legal; snow leopard; Afghanistan; Cities; Cbd; Convention on Biological Diversity; 5740 |
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United Nations Environment Programme |
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SLN @ rana @ 502 |
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188 |
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Braden, K. |
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The Geographical Distribution of the Snow Leopard in the USSR: Maps of Areas of Snow Leopard Habitation in the USSR |
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1982 |
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International Pedigree Book of Snow Leopards |
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3 |
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25-39 |
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Russia; Ussr; U.S.S.R.; Soviet-Union; status; distribution; maps; browse; soviet union; soviet; union; habitat; 2320 |
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Reviews published information from the USSR vs past status of the snow leopard in various parts of its range within that country. Maps provide locations in the USSR of evidence of snow leopard occurence from published records of the species over the last 100 yrs. |
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SLN @ rana @ 64 |
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189 |
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Braden, K. |
| Title |
Nature Preserves of the Soviet Union |
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1984 |
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11-14 |
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Russia; Soviet-Union; preserves; parks; conservation; Altay; reserves; Ussr; asia; browse; 4130 |
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Leif Blomqvist and Helsinki Zoo |
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Helsinki |
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L.Blomqvist |
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Full Text at URLTitle, Monographic: International Pedigree Book of Snow Leopards, Panthera Uncia, Volume 4Place of Meeting: Krefeld Zoo, GFRDate of Copyright: 1984 |
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SLN @ rana @ 71 |
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190 |
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Braden, K. |
| Title |
Snow leopard conservation in the USSR |
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Magazine Article |
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1988 |
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Snow Line |
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Fall |
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2-2 |
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conservation; snow leopard; Ussr |
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International Snow Leopard Trust |
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SLN @ rana @ 962 |
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191 |
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| Author |
Braden, K. |
| Title |
The Outlook for Snow Leopard Protection in Four New Countries of Central Asia |
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Miscellaneous |
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1992 |
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x |
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7 |
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Russia; Kazakhstan; Kyrgyzstan; Tadzhikstan; Uzbekistan; protected-areas; reserves; preserve; parks; conservation; economics; Culture; browse; 4700 |
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Islt |
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Seattle |
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Full Text at URLJournal Title: Snow Line |
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192 |
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