Using bear rub data and spatial capture-recapture models to estimate trend in a brown bear population

Trends in population abundance can be challenging to quantify during range expansion and contraction, when there is spatial variation in trend, or the conservation area is large. We used genetic detection data from natural bear rubbing sites and spatial capture-recapture (SCR) modeling to estimate l...

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Bibliographic Details
Published in:Scientific reports 2019-11, Vol.9 (1), p.16804-11, Article 16804
Main Authors: Kendall, Katherine C., Graves, Tabitha A., Royle, J. Andrew, Macleod, Amy C., McKelvey, Kevin S., Boulanger, John, Waller, John S.
Format: Article
Language:English
Subjects:
631/114/2415
631/158/1745
631/601/18
Animal populations
Animals
Bears
Capture-recapture studies
Conservation areas
Conservation of Natural Resources
Contraction
Ecosystem
Gene mapping
Genotyping Techniques - veterinary
Grizzly bears
Growth rate
Hair - chemistry
Humanities and Social Sciences
Montana
multidisciplinary
Population Density
Population growth
Range extension
Science
Science (multidisciplinary)
Spatial Analysis
Spatial variations
Trends
Ursidae - classification
Ursidae - genetics
Ursidae - growth & development
Ursus arctos
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Summary:Trends in population abundance can be challenging to quantify during range expansion and contraction, when there is spatial variation in trend, or the conservation area is large. We used genetic detection data from natural bear rubbing sites and spatial capture-recapture (SCR) modeling to estimate local density and population growth rates in a grizzly bear population in northwestern Montana, USA. We visited bear rubs to collect hair in 2004, 2009—2012 (3,579—4,802 rubs) and detected 249—355 individual bears each year. We estimated the finite annual population rate of change 2004—2012 was 1.043 (95% CI = 1.017—1.069). Population density shifted from being concentrated in the north in 2004 to a more even distribution across the ecosystem by 2012. Our genetic detection sampling approach coupled with SCR modeling allowed us to estimate spatially variable growth rates of an expanding grizzly bear population and provided insight into how those patterns developed. The ability of SCR to utilize unstructured data and produce spatially explicit maps that indicate where population change is occurring promises to facilitate the monitoring of difficult-to-study species across large spatial areas.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-52783-5