Landscape Ecology

, Volume 25, Issue 10, pp 1613–1625 | Cite as

Movement behavior explains genetic differentiation in American black bears

Research Article


Individual-based landscape genetic analyses provide empirically based models of gene flow. It would be valuable to verify the predictions of these models using independent data of a different type. Analyses using different data sources that produce consistent results provide strong support for the generality of the findings. Mating and dispersal movements are the mechanisms through which gene flow operates in animal populations. The best means to verify landscape genetic predictions would be to use movement data to independently predict landscape resistance. We used path-level, conditional logistic regression to predict landscape resistance for American black bear (Ursus americanus) in a landscape in which previous work predicted population connectivity using individual-based landscape genetics. We found consistent landscape factors influence genetic differentiation and movement path selection, with strong similarities between the predicted landscape resistance surfaces. Genetic differentiation in American black bear is driven by spring movement (mating and dispersal) in relation to residential development, roads, elevation and forest cover. Given the limited periods of the year when gene flow events primarily occur, models of landscape connectivity should carefully consider temporal changes in functional landscape resistance.


Movement behavior Path-level analysis Connectivity Landscape genetics Black bear Ursus americanus 



Funding and support were provided by the Idaho Department of Transportation, Idaho Department of Fish and Game, US Fish and Wildlife Service, US Forest Service, and the University of Idaho. We greatly thank J. Rachlow, J. Hayden, W. Wakkinen, P. Zager, W. Kasworm, T. Radandt, M. Proctor, and T. Johnson for their invaluable guidance, support, and tremendous effort in the field. We also thank Michael Schwartz and Kevin McKelvey at the Rocky Mountain Research Station.

Supplementary material

10980_2010_9534_MOESM1_ESM.doc (476 kb)
Supplementary material 1 (DOC 17 kb)


  1. Arthur SM, Manly BFJ, McDonald LL, Garner GW (1996) Assessing habitat selection when availability changes. Ecology 77:215–227CrossRefGoogle Scholar
  2. Beringer JJ, Seibert SG, Pelton MR (1990) Incidence of road crossing by black bears on Pisgah National Forest, North Carolina. Int Conf Bear Res Manag 8:85–92Google Scholar
  3. Brody AJ, Pelton MR (1989) Effects of roads on black bear movements in western North Carolina. Wildl Soc Bull 17:5–10Google Scholar
  4. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
  5. Clevenger AP, Pelton MR (1990) Pre and post breakup movements and space use of black bear family groups in Cherokee national forest, Tennessee. Int Conf Bear Res Manag 8:289–295Google Scholar
  6. Compton B, McGarigal K, Cushman SA, Gamble L (2007) A resistant kernel model of connectivity for vernal pool breeding amphibians. Conserv Biol 21:788–799CrossRefPubMedGoogle Scholar
  7. Coulon A, Cosson JF, Angibault JM, Cargnelutti B, Galan M, Morellet N, Petit E, Aulagnier S, Hewison AJM (2004) Landscape connectivity influences gene flow in a roe deer population inhabiting a fragmented landscape: an individual-based approach. Mol Ecol 13:2841–2850CrossRefPubMedGoogle Scholar
  8. Coulon A, Guillot G, Cosson JF, Angibault JMA, Aulagnier S, Cargnelutti B, Galan M, Hewison AJM (2006) Genetic structure is influenced by landscape features: empirical evidence from a roe deer population. Mol Ecol 15:1669–1679CrossRefPubMedGoogle Scholar
  9. Coulon A, Morellet N, Goulard M, Cargnelutti B, Angibault J-M, Hewston AJM (2008) Inferring the effects of landscape structure on roe deer (Capreolus capreolus) movements using a step selection function. Landscape Ecol 23:603–614CrossRefGoogle Scholar
  10. Cushman SA (2010) Animal movement data: GPS telemetry, autocorrelation and the need for path-level analysis. In: Cushman SA, Huettman F (eds) Spatial complexity, informatics and wildlife conservation. Springer, Tokyo, pp 131–150CrossRefGoogle Scholar
  11. Cushman SA, Landguth EL (2010a) Scale dependent inference in landscape genetics. Landscape Ecol 25:967–979CrossRefGoogle Scholar
  12. Cushman SA, Landguth EL (2010b) Spurious correlations and inference in landscape genetics. Mol Ecol 19:3592–3602CrossRefPubMedGoogle Scholar
  13. Cushman SA, Schwartz MK, Hayden J, McKelvey KS (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am Nat 168:486–499CrossRefPubMedGoogle Scholar
  14. Cushman SA, Chase MJ, Griffin C (2010a) Mapping landscape resistance to identify corridors and barriers for elephant movement in southern Africa. In: Cushman SA, Huettman F (eds) Spatial complexity, informatics and wildlife conservation. Springer, Tokyo, pp 349–368CrossRefGoogle Scholar
  15. Cushman SA, McGarial K, Gutzwiller K, Evans J (2010b) The gradient paradigm: a conceptual and analytical framework for landscape ecology. In: Cushman SA, Huettman F (eds) Spatial complexity, informatics and wildlife conservation. Springer, Tokyo, pp 83–110CrossRefGoogle Scholar
  16. ESRI (2005) ARCGIS. Environmental Systems Research Incorporated, Redlands, CAGoogle Scholar
  17. Fortin D, Beyer HL, Boyce MS, smith DW, Duchesne T, Mao JS (2005) Wolves influence elk movements: behavior shapes a trophic cascade in Yellowstone National Park. Ecology 86:1320–1330CrossRefGoogle Scholar
  18. Harris RB, Fancy SG, Douglas DC et al (1990) Tracking wildlife by satellite: current systems and performance. (Tech. Report 30). US Fish and Wildlife ServiceGoogle Scholar
  19. Hegel TM, Cushman SA, Huettmann F (2009) Current state of the art for statistical modelling of species distributions. In: Cushman SA, Huettman F (eds) Spatial complexity informatics and wildlife conservation. Springer, Tokyo, pp 273–312Google Scholar
  20. Johnson KG, Pelton MR (1980) Prebaiting and snaring techniques for black bears. Wildl Soc Bull 8:46–54Google Scholar
  21. Johnson CJ, Seip DR, Boyce MS (2004) A quantitative approach to conservation planning: using resource selection functions to map the distribution of mountain caribou at multiple scales. J Appl Ecol 41:238–251CrossRefGoogle Scholar
  22. Lee DJ, Vaughan MR (2003) Dispersal movements by subadult American black bears in Virginia. Ursus 14:162–170Google Scholar
  23. Lewis JS (2007) The effects of human influences on black bear habitat selection and movement patterns within a highway corridor. Thesis, University of Idaho, MoscowGoogle Scholar
  24. Lewis JS, Rachlow JL, Garton EO, Vierling LA (2007) Effects of habitat on GPS collar performance: using data screening to reduce location error. J Appl Ecol 44:663–671CrossRefGoogle Scholar
  25. Litvaitis JA, Titus K, Anderson EM (1994) Measuring vertebrate use of terrestrial habits and foods. In Bookhout TA (ed) Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, MD, pp 254–270Google Scholar
  26. Manly BFJ, McDonald LL, Thomas D (2002) Resource selection by animals: statistical design and analysis for field studies. Kluwer, BostonGoogle Scholar
  27. Osborn FV, Parker GE (2003) Towards an integrated approach for reducing the conflict between elephants and people: a review of current research. Oryx 3:1–5Google Scholar
  28. Reynolds DG, Beecham J (1980) Home range activities and reproduction of black bears in west-central Idaho. Int Conf Bear Res Manag 4:181–190Google Scholar
  29. Rogers LL (1977) Social relationships, movements, and population dynamics of black bears in northeastern Minnesota. PhD thesis, University of Minnesota, MinneapolisGoogle Scholar
  30. Schwartz CC, Franzmann AW (1992) Dispersal and survival of subadult black bears from the Kenai Peninsula, Alaska. J Wildl Manag 56:426–431CrossRefGoogle Scholar
  31. Shanahan DF, Possingham HP, Riginos C (2010) Models based on individual level movement predict spatial patterns of genetic relatedness for two Australian forest birds. Landscape Ecol. doi:10.1007/s10980-010-9542-6
  32. Shirk A, Wallin DO, Cushman SA, Rice RC, Warheit C (2010) Inferring landscape effects on gene flow: a new multi-scale model selection framework. Mol Ecol 19:3603–3619CrossRefPubMedGoogle Scholar
  33. Thompson CM, McGarigal K (2002) The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA). Landscape Ecol 17:569–586CrossRefGoogle Scholar
  34. Wasserman TN, Cushman SA, Schwartz MK, Wallin DO (2010) Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecol. doi:10.1007/s10980-010-9525-7
  35. Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397CrossRefGoogle Scholar

Copyright information

© US Government 2010

Authors and Affiliations

  1. 1.U.S. Forest Service, Rocky Mountain Research StationFlagstaffUSA
  2. 2.Graduate Degree Program in Ecology, Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsUSA

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