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https://ir.library.oregonstate.edu/concern/articles/1z40kv57m

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  • Connectivity models using empirically-derived landscape resistance maps can predict potential linkages among fragmented animal and plant populations. However, such models have rarely been used to guide systematic decision-making, such as identifying the most important habitat patches and dispersal corridors to protect or restore in order to maximize regional connectivity. Combining resistance models with network theory offers one means of prioritizing management for connectivity, and we applied this approach to a metapopulation of desert bighorn sheep (Ovis canadensis nelsoni) in the Mojave Desert of the southwestern United States. We used a genetic-based landscape resistance model to construct network models of genetic connectivity (potential for gene flow) and demographic connectivity (potential for colonization of empty habitat patches), which may differ because of sex-biased dispersal in bighorn sheep. We identified high-priority habitat patches and corridors and found that the type of connectivity and the network metric used to quantify connectivity had substantial effects on prioritization results, although some features ranked highly across all combinations. Rankings were also sensitive to our empirically-derived estimates of maximum effective dispersal distance, highlighting the importance of this often-ignored parameter. Patch-based analogs of our network metrics predicted both neutral and mitochondrial genetic diversity of 25 populations within the study area. This study demonstrates that network theory can enhance the utility of landscape resistance models as tools for conservation, but it is critical to consider the implications of sex-biased dispersal, the biological relevance of network metrics, and the uncertainty associated with dispersal range and behavior when using this approach.
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  • description.provenance : Approved for entry into archive by Erin Clark(erin.clark@oregonstate.edu) on 2014-05-07T20:10:29Z (GMT) No. of bitstreams: 2 CreechTylerFisheriesWildlifeUsingNetworkTheory.pdf: 961627 bytes, checksum: 287fa786ebfaa37f5722fcee71122545 (MD5) CreechTylerFisheriesWildlifeUsingNetworkTheory_SupplementaryMaterial.pdf: 1304347 bytes, checksum: b1d1269d79037c9db3b3b2d0086537fd (MD5)
  • description.provenance : Submitted by Erin Clark (erin.clark@oregonstate.edu) on 2014-05-07T20:10:05Z No. of bitstreams: 2 CreechTylerFisheriesWildlifeUsingNetworkTheory.pdf: 961627 bytes, checksum: 287fa786ebfaa37f5722fcee71122545 (MD5) CreechTylerFisheriesWildlifeUsingNetworkTheory_SupplementaryMaterial.pdf: 1304347 bytes, checksum: b1d1269d79037c9db3b3b2d0086537fd (MD5)
  • description.provenance : Made available in DSpace on 2014-05-07T20:10:30Z (GMT). No. of bitstreams: 2 CreechTylerFisheriesWildlifeUsingNetworkTheory.pdf: 961627 bytes, checksum: 287fa786ebfaa37f5722fcee71122545 (MD5) CreechTylerFisheriesWildlifeUsingNetworkTheory_SupplementaryMaterial.pdf: 1304347 bytes, checksum: b1d1269d79037c9db3b3b2d0086537fd (MD5) Previous issue date: 2014-04

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