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Comparing Habitat Suitability and Connectivity Modeling Methods for Conserving Pronghorn Migrations

Poor, Erin E., Loucks, Colby, Jakes, Andrew, Urban, Dean L.·2012·PLoS ONE
DOI 10.1371/journal.pone.0049390PMC PMC3500376
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  • Circuit theory and least-cost path models have been shown to perform comparably in predicting seasonal ungulate migration routes.

    “For example, circuit theory and least-cost path models have been shown to perform comparably in predicting seasonal ungulate migration routes (Poor et al. 2012; McClure et al. 2016).”

    From Circuit‐theory applications to connectivity science and conservation (2019) · cites this paper as Poor et al. 2012

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Research works printed but not linked: 58

  1. J Berger, 2004, The last mile: how to sustain long-distance migration in mammals, Conserv Biol, 18, 320, 10.1111/j.1523-1739.2004.00548.x · DOI · Paper page
  2. DT Bolger, 2008, The need for integrative approaches to understand and conserve migratory ungulates, Ecol Lett, 11, 63, 10.1111/j.1461-0248.2007.01109.x · DOI · Paper page
  3. DS Wilcove, 2008, Going, going, gone: is animal migration disappearing?, PLoS Biol, 6, 1361, 10.1371/journal.pbio.0060188 · DOI
  4. MS Bowlin, 2010, Grand challenges in migration biology, Integr Comp Biol, 50, 261, 10.1093/icb/icq013 · DOI
  5. FJ Sanderson, 2006, Long-term population declines in Afro-Palearcic migrant birds, Biol Conserv, 131, 93, 10.1016/j.biocon.2006.02.008 · DOI
  6. CC Caudill, 2007, Slow dam passage in adult Columbia River salmonids associated with unsuccessful migration: delayed negative effects of passage obstacles or condition-dependent mortality?, Can J Fish Aquat Sci, 64, 979, 10.1139/f07-065 · DOI
  7. ML Keefer, 2005, Escapement, harvest, and unknown loss of radio-tagged adult salmonids in the Columbia River-Snake River hydrosystem, Can J Fish Aquat Sci, 62, 930, 10.1139/f04-246 · DOI
  8. H Dingle, 2007, What is Migration?, Bioscience, 57, 113, 10.1641/B570206 · DOI
  9. JM Fryxell, 1988, Causes and consequences of migration by large herbivores, Trends Ecol Evol, 3, 237, 10.1016/0169-5347(88)90166-8 · DOI
  10. M Hebblewhite, 2008, A multi-scale test of the forage maturation hypothesis in a partially migratory ungulate population, Ecol Monogr, 78, 141, 10.1890/06-1708.1 · DOI
  11. H Sawyer, 2009, Identifying and prioritizing ungulate migration routes for landscape level conservation, Ecol Appl, 19, 2016, 10.1890/08-2034.1 · DOI
  12. L Hannah, 1995, Human disturbance and natural habitat: a biome level analysis of a global data set, Biodivers and Conserv, 4, 128, 10.1007/BF00137781 · DOI
  13. Chape S, Blyth S, Fish L, Fax P, Spalding M (2003) United Nations List of Protected Areas. IUCN, Gland, Switzerland and Cambridge, UK and UNEO-WCMC, Cambridge, UK.
  14. CB Yackulic, 2011, Anthropogenic and environmental drivers of modern range loss in large mammals, Proc Natl Acad Sci USA, 108, 4024, 10.1073/pnas.1015097108 · DOI
  15. JH Brown, 1984, On the relationship between abundance and distribution of species, Am Nat, 124, 255, 10.1086/284267 · DOI
  16. R Channell, 2000, Trajectories to extinction: Spatial dynamics of the contraction of geographical ranges, J Biogeogr, 27, 169, 10.1046/j.1365-2699.2000.00382.x · DOI
  17. Smith DG, Hoppe TA (2000) Prairie Agricultural Landscapes: A Land Resource Review. Saskatchewan, Canada: Prairie Farm Rehabilitation Administration.
  18. JL Harrington, 2006, Characteristics of ungulate behavior and mortality associated with wire fences, Wildlife Soc B, 34, 1295, 10.2193/0091-7648(2006)34[1295:COUBAM]2.0.CO;2 · DOI
  19. E Vander Wal, 2009, Designating seasonality using rate of movement, J Wildlife Manage, 73, 1189, 10.2193/2008-137 · DOI
  20. JL Kolar, 2011, Migration patterns of pronghorn in southwestern North Dakota, J Wildlife Manage, 75, 198, 10.1002/jwmg.32 · DOI
  21. DM Beale, 1970, Forage use, water consumption and productivity of pronghorn antelope in western Utah, J Wildlife Manage, 34, 570, 10.2307/3798865 · DOI
  22. N Pettorelli, 2005, Using the satellite-derived NDVI to assess ecological responses to environmental change, Trends Ecol Evol, 20, 503, 10.1016/j.tree.2005.05.011 · DOI
  23. S Hamel, 2009, Spring Normalized Difference Vegetation Index (NDVI) predicts annual variation in timing of peak fæcal crude protein in mountain ungulates, J Appl Ecol, 46, 582, 10.1111/j.1365-2664.2009.01643.x · DOI · Paper page
  24. JJ Hervert, 2005, Home-range and habitat-use patterns of Sonoran pronghorn in Arizona, Wildlife Soc B, 33, 8, 10.2193/0091-7648(2005)33[8:HAHPOS]2.0.CO;2 · DOI
  25. CJ Martinka, 1967, Mortality of northern Montana pronghorns in a severe winter, J Wildlife Manage, 31, 159, 10.2307/3798371 · DOI
  26. MW Barrett, 1982, Distribution, behavior, and mortality of pronghorns during a severe winter in Alberta, J Wildlife Manage, 46, 991, 10.2307/3808232 · DOI
  27. WP Clary, 1983, Pronghorn reactions to winter sheep grazing, plant communities, and topography in the Great Basin, J Range Manage, 36, 749, 10.2307/3898201 · DOI
  28. JP Beckmann, 2012, Human-mediated shifts in animal habitat use: sequential changes in pronghorn use of a natural gas field in Greater Yellowstone, Biol Conserv, 147, 222, 10.1016/j.biocon.2012.01.003 · DOI
  29. SD Gavin, 2006, Do pronghorn (Antilocapra americana) perceive roads as predation risk?, Can J Zoolog, 84, 1775, 10.1139/z06-175 · DOI
  30. SJ Phillips, 2006, Maximum entropy modeling of species geographic distributions, Ecol Model, 190, 231, 10.1016/j.ecolmodel.2005.03.026 · DOI · Paper page
  31. M Austin, 2007, Species distribution models and ecological theory: a critical assessment and some possible new approaches, Ecol Model, 200, 1, 10.1016/j.ecolmodel.2006.07.005 · DOI
  32. E Wikramanayake, 2004, Designing a conservation landscape for tigers in human-dominated environments, Conserv Biol, 18, 839, 10.1111/j.1523-1739.2004.00145.x · DOI
  33. P Beier, 2008, Forks in the roads: choices in procedures for designing wild land linkages, Conserv Biol, 22, 836, 10.1111/j.1523-1739.2008.00942.x · DOI · Paper page
  34. XF Luan, 2011, Habitat evaluation of wild Amur tiger (Panthera tigris altaica) and conservation priority setting in north-east China, J Environ Manage, 92, 31, 10.1016/j.jenvman.2010.08.001 · DOI
  35. Phillips SJ, Dudik M, Schapire RE (2004) A maximum entropy approach to species distribution modeling. In Greiner R, Schuurmans D, technical coordinators. Proceedings of the International Machine Learning Conference. Alberta, Canada: ACM Press. pp 655–662. · DOI
  36. J Elith, 2006, Novel methods improve predictions of species' distributions from occurrence data, Ecography, 29, 129, 10.1111/j.2006.0906-7590.04596.x · DOI · Paper page
  37. MV Hurley, 2009, Utility of expert-based knowledge for predicting wildlife-vehicle collisions, J Wildlife Manage, 73, 278, 10.2193/2008-136 · DOI
  38. AP Clevenger, 2002, GIS-generated, expert based models for identifying wildlife habitat linkages and planning mitigation passages, Conserv Biol, 16, 503, 10.1046/j.1523-1739.2002.00328.x · DOI
  39. J Elith, 2011, A statistical explanation of MaxEnt for ecologists, Divers Distrib, 17, 43, 10.1111/j.1472-4642.2010.00725.x · DOI · Paper page
  40. SJ Phillips, 2009, Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data, Ecol Appl, 19, 181, 10.1890/07-2153.1 · DOI · Paper page
  41. AH Fielding, 1997, A review of methods for the assessment of prediction errors in conservation presence/absence models, Environ Conserv, 24, 38, 10.1017/S0376892997000088 · DOI · Paper page
  42. A Guisan, 2000, Predictive habitat distribution models in ecology, Ecol Model, 135, 147, 10.1016/S0304-3800(00)00354-9 · DOI · Paper page
  43. SJ Phillips, 2008, Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation, Ecography, 31, 161, 10.1111/j.0906-7590.2008.5203.x · DOI · Paper page
  44. TL Saaty, 1977, A scaling method for priorities in hierarchical structures, J Math Psychol, 15, 234, 10.1016/0022-2496(77)90033-5 · DOI
  45. D Urban, 2001, Landscape connectivity: a graph-theoretic perspective, Ecology, 82, 1205, 10.1890/0012-9658(2001)082[1205:LCAGTP]2.0.CO;2 · DOI
  46. DL Urban, 2009, Graph models of habitat mosaics, Ecol Lett, 12, 260, 10.1111/j.1461-0248.2008.01271.x · DOI · Paper page
  47. F Adriaensen, 2003, The application of ‘least-cost’ modeling as a functional landscape model, Landscape Urban Plan, 64, 233, 10.1016/S0169-2046(02)00242-6 · DOI · Paper page
  48. Theobald DM (2006) Exploring the functional connectivity of landscapes using landscape networks. In Crooks KR, Sanjayan M, editors. Connectivity Conservation. Cambridge, UK: Cambridge University Press. pp 416–443 · DOI
  49. BH McRae, 2007, Circuit theory predicts gene flow in plant and animal populations, Proc Natl Acad Sci USA, 104, 19885, 10.1073/pnas.0706568104 · DOI · Paper page
  50. SA Cushman, 2008, Use of empirically derived source-destination models to map regional conservation corridors, Conserv Biol, 23, 368, 10.1111/j.1523-1739.2008.01111.x · DOI
  51. N Owen-Smith, 2010, Foraging theory upscaled: the behavioural ecology of herbivore movement, Philos T Soc B, 365, 2267, 10.1098/rstb.2010.0095 · DOI
  52. SJ Boccadori, 2008, Yellowstone pronghorn alter resource selection after sagebrush decline, J Mammal, 89, 1031, 10.1644/07-MAMM-A-173.1 · DOI
  53. P Kaczensky, 2011, Connectivity of the Asiatic wild ass population in the Mongolian Gobi, Biol Conserv, 144, 920, 10.1016/j.biocon.2010.12.013 · DOI
  54. S Benhamou, 2011, Dynamic approach to space and habitat use based on biased random bridges, PLoS ONE, 6, 14592, 10.1371/journal.pone.0014592 · DOI
  55. JS Horne, 2007, Analyzing animal movements using Brownian bridges, Ecology, 88, 2354, 10.1890/06-0957.1 · DOI
  56. DJ Prosser, 2011, Wild bird migration across the Qinghai-Tibetan Plateau: A transmission route for highly pathogenic H5N1, PLoS ONE, 6, 17622, 10.1371/journal.pone.0017622 · DOI
  57. MS Webster, 2002, Links between worlds: unraveling migratory connectivity, Trends Ecol Evol, 17, 76, 10.1016/S0169-5347(01)02380-1 · DOI
  58. JA Hilty, 2006, Forecasting the effect of land-use change on native and non-native mammalian predator distributions, Biodivers Conserv, 15, 2853, 10.1007/s10531-005-1534-5 · DOI

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Metadata

Authors
Poor, E. E., Loucks, C., Jakes, A., & Urban, D. L.
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2012-11-16
Volume
7
Issue
11
Pages
e49390
ISSN
1932-6203
DOI
10.1371/journal.pone.0049390
PMC
PMC3500376
Cited by
120
References
5
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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