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| Author | Jinguo, Z. | ||||
| Title | Case report on vesicular calculus of snow leopard | Type | Conference Article | ||
| Year | 1994 | Publication  |
Abbreviated Journal | ||
| Volume | Issue | Pages | 209-212 | ||
| Keywords | veterinary; medicine; calculus; urinary-tract; treatment; browse; urinary tract; urinary; tract; 3840 | ||||
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| Publisher | Islt | Place of Publication | Usa | Editor | J.L.Fox; D.Jizeng |
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| Notes | Full Text at URLTitle, Monographic: Seventh International Snow Leopard TrustPlace of Meeting: ChinaDate of Copyright: 1994 | Approved | no | ||
| Call Number | SLN @ rana @ 235 | Serial | 496 | ||
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| Author | 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 | Type | Journal Article | ||
| Year | 2023 | Publication |
Ecological Informatics | Abbreviated Journal | |
| Volume | 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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| Notes | Approved | no | |||
| Call Number | SLN @ rakhee @ | Serial | 1723 | ||
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| Author | Ale, S.; Brown, J. | ||||
| Title | The contingencies of group size and vigilance | Type | Miscellaneous | ||
| Year | 2007 | Publication |
Evolutionary Ecology Research, | Abbreviated Journal | |
| Volume | 9 | Issue | Pages | 1263-1276 | |
| Keywords | attraction effect,contingency,dilution effect,fitness,group-size effect,many-eyes effect,predation risk,vigilance behaviour; predation; decline; potential; predators; predator; feeding; Animals; Animal; use; food; effects; Relationship; behaviour; methods; game; Interactions; interaction; factor; value; Energy | ||||
| Abstract | Background: Predation risk declines non-linearly with one's own vigilance and the vigilance of others in the group (the 'many-eyes' effect). Furthermore, as group size increases, the individual's risk of predation may decline through dilution with more potential victims, but may increase if larger groups attract more predators. These are known, respectively, as the dilution effect and the attraction effect. Assumptions: Feeding animals use vigilance to trade-off food and safety. Net feeding rate declines linearly with vigilance. Question: How do the many-eyes, dilution, and attraction effects interact to influence the relationship between group size and vigilance behaviour? Mathematical methods: We use game theory and the fitness-generating function to determine the ESS level of vigilance of an individual within a group. Predictions: Vigilance decreases with group size as a consequence of the many-eyes and dilution effects but increases with group size as a consequence of the attraction effect, when they act independent of each other. Their synergetic effects on vigilance depend upon the relative strengths of each and their interactions. Regardless, the influence of other factors on vigilance – such as encounter rate with predators, predator lethality, marginal value of energy, and value of vigilance – decline with group size. |
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| Language | English | Summary Language | Original Title | ||
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| Notes | Approved | no | |||
| Call Number | SLN @ rana @ 886 | Serial | 53 | ||
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