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Testing the use of remote cameras to index body condition in brown and black bears Mowat, Garth; Smit, Laura
Description
Abstract: Body condition of large mammals is a highly labile parameter that is influenced by recent nutrition, the seasonal hormonal cycle, intrinsic traits such as age and reproductive status, and extrinsic stress factors such as predation risk, social dominance, or human disturbance. Body condition thus integrates many population and individual level factors into a single measure of performance, making it useful in long-term population monitoring. We measured body condition of brown (grizzly) and black bears (Ursus arctos and U. americanus) using remote cameras during 2016-2024 in southwest Canada and compared these data with measures derived from live-captured brown bears from the same study area. We found that on average, photograph-derived measures of body condition predicted late-summer and autumn weight gain similar to weight-per-length and body fat metrics measured from live-captured bears. The photo-derived metric predicted more modest declines in condition in spring than body fat whereas the weight-per-length metric did not indicate declines in condition through spring. We tested the repeatability of the photo measurements using untrained observers and the methods were generally repeatable but can be improved with training. Photographs are much less invasive than live capture–based condition metrics and they allow the comparison of body condition among individuals, seasons, and populations through time, while also considering the variation within the sample. Collection of many samples should enable the examination of novel aspects of the dynamics of population trend and individual performance of bears, and perhaps other species.
Note: These data and R scripts were used for the analysis the paper "Testing the use of remote cameras to index body condition in brown and black bears" which was published in the journal URSUS early in 2026.
Item Metadata
| Title |
Testing the use of remote cameras to index body condition in brown and black bears
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| Creator | |
| Contributor | |
| Date Issued |
2026-01-29
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| Description |
Abstract: Body condition of large mammals is a highly labile parameter that is influenced by recent nutrition, the seasonal hormonal cycle, intrinsic traits such as age and reproductive status, and extrinsic stress factors such as predation risk, social dominance, or human disturbance. Body condition thus integrates many population and individual level factors into a single measure of performance, making it useful in long-term population monitoring. We measured body condition of brown (grizzly) and black bears (Ursus arctos and U. americanus) using remote cameras during 2016-2024 in southwest Canada and compared these data with measures derived from live-captured brown bears from the same study area. We found that on average, photograph-derived measures of body condition predicted late-summer and autumn weight gain similar to weight-per-length and body fat metrics measured from live-captured bears. The photo-derived metric predicted more modest declines in condition in spring than body fat whereas the weight-per-length metric did not indicate declines in condition through spring. We tested the repeatability of the photo measurements using untrained observers and the methods were generally repeatable but can be improved with training. Photographs are much less invasive than live capture–based condition metrics and they allow the comparison of body condition among individuals, seasons, and populations through time, while also considering the variation within the sample. Collection of many samples should enable the examination of novel aspects of the dynamics of population trend and individual performance of bears, and perhaps other species.
Note: These data and R scripts were used for the analysis the paper "Testing the use of remote cameras to index body condition in brown and black bears" which was published in the journal URSUS early in 2026.
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| Notes |
This paper is now a galley proof, URL will be provided.
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| Date Available |
2025-08-14
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| Provider |
University of British Columbia Library
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| License |
CC BY-ND 4.0
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| DOI |
10.14288/1.0451392
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| URI | |
| Publisher DOI | |
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| Country |
Canada
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| Aggregated Source Repository |
Dataverse
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License
CC BY-ND 4.0