Snow Leopard Bibliography -- Query Results

Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., Ostrowski, S. (2023). Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection. Ecological Informatics, 77(102214), 1–14. 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. Keywords: Background subtraction, Deep learning, Hotspotter, Individual identification, PIE v2, Snow leopards https://snowleopardnetwork.org/b/show.php?record=1723
Henschel, P., & Ray, J. (2003). Leopards in African Rainforests: Survey and Monitoring Techniques (Wildlife Conservation Society, Ed.). Abstract: Monitoring Techniques Forest leopards have never been systematically surveyed in African forests, in spite of their potentially vital ecological role as the sole large mammalian predators in these systems. Because leopards are rarely seen in this habitat, and are difficult to survey using the most common techniques for assessing relative abundances of forest mammals, baseline knowledge of leopard ecology and responses to human disturbance in African forests remain largely unknown. This technical handbook sums up the experience gained during a two-year study of leopards by Philipp Henschel in the Lop‚ Reserve in Gabon, Central Africa, in 2001/2002, supplemented by additional experience from carnivore studies conducted by Justina Ray in southwestern Central African Republic and eastern Congo (Zaire) . The main focus of this effort has been to develop a protocol that can be used by fieldworkers across west and central Africa to estimate leopard densities in various forest types. In developing this manual, Henschel tested several indirect methods to assess leopard numbers in both logged and unlogged forests, with the main effort devoted to testing remote photography survey methods developed for tigers by Karanth (e.g., Karanth 1995, Karanth & Nichols 1998; 2000; 2002), and modifying them for the specific conditions characterizing African forest environments. This handbook summarizes the results of the field testing, and provides recommendations for techniques to assess leopard presence/absence, relative abundance, and densities in African forest sites. We briefly review the suitability of various methods for different study objectives and go into particular detail on remote photography survey methodology, adapting previously developed methods and sampling considerations specifically to the African forest environment. Finally, we briefly discuss how camera trapping may be used as a tool to survey other forest mammals. Developing a survey protocol for African leopards is a necessary first step towards a regional assessment and priority setting exercise targeted at forest leopards, similar to those carried out on large carnivores in Asian and South American forests. Keywords: forest leopards; african rainforests; survey; monitoring techniques; lope reserve; gabon; central africa; congo; zaire; field testing; populations; wild meat; relative abundance; density; live-trapping; presence and absense surveys; ad-hoc survey; bushmeat; systematic survey; monitoring; individual identification; tracks; Discriminant Function Analysis; genotyping; scat; Hair; Dna; remote photography; camera trapping; capture rates; Trailmaster; Camtrakker; bait; duikers; pigs; elephant; bongo; okapi; human hunters; 5300 https://snowleopardnetwork.org/b/show.php?record=382
Janecka, J. E., Jackson, R., Munkhtsog, B., Murphy, W. J. (2014). Characterization of 9 microsatellites and primers in snow leopards and a species-specific PCR assay for identifying noninvasive samples. Conservation Genetic Resource, 6(2), 369:373. Abstract: Molecular markers that can effectively identify noninvasively collected samples and provide genetic information are critical for understanding the distribution, status, and ecology of snow leopards (Panthera uncia). However, the low DNA quantity and quality in many noninvasive samples such as scats makes PCR amplification and genotyping challenging. We therefore designed primers for 9 microsatellites loci previously isolated in the domestic cat (Felis catus) specifically for snow leopard studies using noninvasive samples. The loci showed moderate levels of variation in two Mongolian snow leopard populations. Combined with seven other loci that we previously described, they have sufficient variation (He = 0.504, An = 3.6) for individual identification and population structure analysis. We designed a species species specific PCR assay using cytochrome b for identification of unknown snow leopard samples. These molecular markers facilitate in depth studies to assess distribution, abundance, population structure, and landscape connectivity of this endangered species. endangered species Keywords: Microsatellites,Cytochrome b, Snow Leopard, Noninvasive genetics, Individual identification https://snowleopardnetwork.org/b/show.php?record=1427
Janecka, J. E., Jackson, R., Munkhtsog, B., Murphy, W. J. (2014). Characterization of 9 microsatellites and primers in snow leopards and a species-specific PCR assay for identifying noninvasive samples. Conservation Genetic Resource, 6(2), 369:373. Abstract: Molecular markers that can effectively identify noninvasively collected samples and provide genetic information are critical for understanding the distribution, status, and ecology of snow leopards (Panthera uncia). However, the low DNA quantity and quality in many noninvasive samples such as scats makes PCR amplification and genotyping challenging. We therefore designed primers for 9 microsatellites loci previously isolated in the domestic cat (Felis catus) specifically for snow leopard studies using noninvasive samples. The loci showed moderate levels of variation in two Mongolian snow leopard populations. Combined with seven other loci that we previously described, they have sufficient variation (He = 0.504, An = 3.6) for individual identification and population structure analysis. We designed a species species specific PCR assay using cytochrome b for identification of unknown snow leopard samples. These molecular markers facilitate in depth studies to assess distribution, abundance, population structure, and landscape connectivity of this endangered species. Keywords: Microsatellites,Cytochrome b, Snow Leopard, Noninvasive genetics, Individual identification https://snowleopardnetwork.org/b/show.php?record=1428
Waits, L. P., Buckley-Beason, V. A., Johnson, W. E., Onorato, D., & McCarthy, T. (2006). A select panel of polymorphic microsatellite loci for individual identification of snow leopards (Panthera uncia) (Vol. 7). Abstract: Snow leopards (Panthera uncia) are elusive endangered carnivores found in remote mountain regions of Central Asia. New methods for identifying and counting snow leopards are needed for conservation and management efforts. To develop molecular genetic tools for individual identification of hair and faecal samples, we screened 50 microsatellite loci developed for the domestic cat (Felis catus) in 19 captive snow leopards. Forty-eight loci were polymorphic with numbers of alleles per locus ranging from two to 11. The probability of observing matching genotypes for unrelated individuals (2.1 x10-11) and siblings (7.5x10-5) using the 10 most polymorphic loci was low, suggesting that this panel would easily discriminate among individuals in the wild. Keywords: identification; leopard; leopards; microsatellites,noninvasive genetic sampling,Panthera uncia,snow leopard; panthera; Panthera-uncia; panthera uncia; snow; snow-leopard; snow-leopards; snow leopard; snow leopards; uncia; endangered; carnivores; carnivore; mountain; region; central; Central Asia; asia; methods; conservation; management; Molecular; individual identification; Hair; domestic; cat; felis; captive; number; probability; using; wild https://snowleopardnetwork.org/b/show.php?record=1001

Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., Ostrowski, S. (2023). Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection. Ecological Informatics, 77(102214), 1–14.

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.

Keywords: Background subtraction, Deep learning, Hotspotter, Individual identification, PIE v2, Snow leopards

https://snowleopardnetwork.org/b/show.php?record=1723

Henschel, P., & Ray, J. (2003). Leopards in African Rainforests: Survey and Monitoring Techniques (Wildlife Conservation Society, Ed.).

Abstract: Monitoring Techniques Forest leopards have never been systematically surveyed in African forests, in spite of their potentially vital ecological role as the sole large mammalian predators in these systems. Because leopards are rarely seen in this habitat, and are difficult to survey using the most common techniques for assessing relative abundances of forest mammals, baseline knowledge of leopard ecology and responses to human disturbance in African forests remain largely unknown. This technical handbook sums up the experience gained during a two-year study of leopards by Philipp Henschel in the Lop‚ Reserve in Gabon, Central Africa, in 2001/2002, supplemented by additional experience from carnivore studies conducted by Justina Ray in southwestern Central African Republic and eastern Congo (Zaire) . The main focus of this effort has been to develop a protocol that can be used by fieldworkers across west and central Africa to estimate leopard densities in various forest types. In developing this manual, Henschel tested several indirect methods to assess leopard numbers in both logged and unlogged forests, with the main effort devoted to testing remote photography survey methods developed for tigers by Karanth (e.g., Karanth 1995, Karanth & Nichols 1998; 2000; 2002), and modifying them for the specific conditions characterizing African forest environments. This handbook summarizes the results of the field testing, and provides recommendations for techniques to assess leopard presence/absence, relative abundance, and densities in African forest sites. We briefly review the suitability of various methods for different study objectives and go into particular detail on remote photography survey methodology, adapting previously developed methods and sampling considerations specifically to the African forest environment. Finally, we briefly discuss how camera trapping may be used as a tool to survey other forest mammals. Developing a survey protocol for African leopards is a necessary first step towards a regional assessment and priority setting exercise targeted at forest leopards, similar to those carried out on large carnivores in Asian and South American forests.

https://snowleopardnetwork.org/b/show.php?record=382

Janecka, J. E., Jackson, R., Munkhtsog, B., Murphy, W. J. (2014). Characterization of 9 microsatellites and primers in snow leopards and a species-specific PCR assay for identifying noninvasive samples. Conservation Genetic Resource, 6(2), 369:373.

Waits, L. P., Buckley-Beason, V. A., Johnson, W. E., Onorato, D., & McCarthy, T. (2006). A select panel of polymorphic microsatellite loci for individual identification of snow leopards (Panthera uncia) (Vol. 7).