Optimizing dispersal and corridor models using landscape genetics

Authors

  • CLINTON W. EPPS,

    1. Department of Environmental Science, Policy and Management, University of California Berkeley, 137 Mulford Hall, Berkeley, California 94720–3114, USA;
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  • JOHN D. WEHAUSEN,

    1. White Mountain Research Station, University of California, 3000 E. Line Street, Bishop, California 93514, USA;
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  • VERNON C. BLEICH,

    1. California Department of Fish and Game, Sierra Nevada Bighorn Sheep Recovery Program, 407 West Line Street, Bishop, California 93514, USA; and
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  • STEVEN G. TORRES,

    1. California Department of Fish and Game, Wildlife Investigations Laboratory, 1701 Nimbus Road, Suite D, Room # 170, Rancho Cordova, California 95670, USA
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  • JUSTIN S. BRASHARES

    1. Department of Environmental Science, Policy and Management, University of California Berkeley, 137 Mulford Hall, Berkeley, California 94720–3114, USA;
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Clinton W. Epps, 137 Mulford Hall #3114, University of California, Berkeley, CA 94720–3114. E-mail: buzzard@nature.berkeley.edu, phone/Fax: 510-643-3918.

Summary

  • 1Better tools are needed to predict population connectivity in complex landscapes. ‘Least-cost modelling’ is one commonly employed approach in which dispersal costs are assigned to distinct habitat types and the least-costly dispersal paths among habitat patches are calculated using a geographical information system (GIS). Because adequate data on dispersal are usually lacking, dispersal costs are often assigned solely from expert opinion. Spatially explicit, high-resolution genetic data may be used to infer variation in animal movements. We employ such an approach to estimate habitat-specific migration rates and to develop least-cost connectivity models for desert bighorn sheep Ovis canadensis nelsoni.
  • 2Bighorn sheep dispersal is thought to be affected by distance and topography. We incorporated both factors into least-cost GIS models with different parameter values and estimated effective geographical distances among 26 populations. We assessed which model was correlated most strongly with gene flow estimates among those populations, while controlling for the effect of anthropogenic barriers. We used the best-fitting model to (i) determine whether migration rates are higher over sloped terrain than flat terrain; (ii) predict probable movement corridors; (iii) predict which populations are connected by migration; and (iv) investigate how anthropogenic barriers and translocated populations have affected landscape connectivity.
  • 3Migration models were correlated most strongly with migration when areas of at least 10% slope had 1/10th the cost of areas of lower slope; thus, gene flow occurred over longer distances when ‘escape terrain’ was available. Optimal parameter values were consistent across two measures of gene flow and three methods for defining population polygons.
  • 4Anthropogenic barriers disrupted numerous corridors predicted to be high-use dispersal routes, indicating priority areas for mitigation. However, population translocations have restored high-use dispersal routes in several other areas. Known intermountain movements of bighorn sheep were largely consistent with predicted corridors.
  • 5Synthesis and applications. Population genetic data provided sufficient resolution to infer how landscape features influenced the behaviour of dispersing desert bighorn sheep. Anthropogenic barriers that block high-use dispersal corridors should be mitigated, but population translocations may help maintain connectivity. We conclude that developing least-cost models from similar empirical data could significantly improve the utility of these tools.

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