4.2 Article

Ecological investigations of grizzly bears in Canada using DNA from hair, 1995-2005: a review of methods and progress

期刊

URSUS
卷 21, 期 2, 页码 169-188

出版社

INT ASSOC BEAR RESEARCH & MANAGEMENT-IBA
DOI: 10.2192/1537-6176-21.2.169

关键词

Alberta; British Columbia; density estimation; DNA; fragmentation; grizzly bear; population distribution; trend monitoring; Ursus arctos

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资金

  1. Natural Sciences and Engineering Research Council of Canada
  2. Killam Foundation
  3. Alberta Ingenuity
  4. Wilburforce Foundation
  5. BC Ministry of Environment
  6. BC Ministry of Forests
  7. Parks Canada
  8. BC Parks
  9. Alberta Sustainable Resource Development
  10. Habitat Conservation Trust Fund
  11. Columbia Basin Fish and Wildlife Compensation Program
  12. Tembec Industries Inc.
  13. Forest Renewal BC
  14. BC Grizzly Bear Conservation Strategy
  15. Foothills Model Forest

向作者/读者索取更多资源

Grizzly bears (Ursus arctos) occur across British Columbia and in Alberta in mostly forested, mountainous, and boreal ecosystems. These dense forests make sighting bears from aircraft uncommon and aerial census impractical. Since 1995, we have used genetic sampling using DNA from bear hair collected with barbed wire hair traps to explore a suite of ecological questions of grizzly bears in western Canada. During 1995-2005, we conducted large-scale sampling (1,650 to 9,866 km(2) grids) in 26 areas (covering a combined 110,405 km(2)), where genetic identification of 1,412 grizzly bears was recorded. Abundance estimation was the primary goal of most surveys. We also used DNA from bear hair to examine population trend, distribution, and presence in areas where grizzly bears were rare, as well as population fragmentation in a region with a high human population. Combining spatial variation in detecting bears with that of human, landscape, and ecological features has allowed us to quantify factors that influence grizzly bear distribution, population fragmentation, and competition with black bears (U. americanus), and to map variation in bear densities. We summarize these studies and discuss lessons learned that are relevant to improving sampling efficiency, study designs, and resulting inference.

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