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Evaluating population connectivity for species of conservation concern in the American Great Plains

Cushman, Samuel A., Landguth, Erin L., Flather, Curtis H.·2013·Biodiversity and Conservation
DOI 10.1007/s10531-013-0541-1
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Abstract

Habitat loss and fragmentation are widely recognized as among the most important threats to global biodiversity. New analytical approaches are providing an improved ability to predict the effects of landscape change on population connectivity at vast spatial extents. This paper presents an analysis of population connectivity for three species of conservation concern [swift fox (Vulpes velox); lesser prairie-chicken (Tympanuchus pallidicinctus); massasuaga (Sistrurus catenatus)] across the American Great Plains region. We used factorial least-cost path and resistant kernel analyses to predict effects of landscape conditions on corridor network connectivity. Our predictions of population connectivity provide testable hypotheses about the location of core habitats, corridors, and barriers to movement. The results indicate that connectivity is more sensitive to a species’ dispersal ability than variation in landscape resistance to movement. Thus, it may prove difficult to optimize conservation strategies to maintain population connectivity for multiple species with disparate dispersal abilities and independent distributions.

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  • Some current methods of mapping functional connectivity require independent, field-collected data, such as species distribution data, occurrence data or movement paths.

    “One concern with current methods of mapping functional connectivity is that some algorithms require independent, field-collected data, such as species distribution data, occurrence data or movement paths (Cushman, Landguth & Flather 2013;LaPoint et al. 2013).”

    From Landscape connectivity for wildlife: development and validation of multispecies linkage maps (2014) · cites this paper as Cushman, Landguth & Flather 2013

  • Several studies have found that corridors identified for one species are not necessarily used by other species.

    “Indeed, several studies have found that corridors identified for one species are not necessarily used by other species (Beier, Majka & Newell 2009;Cushman & Landguth 2012;Cushman, Landguth & Flather 2013;LaPoint et al. 2013).”

    From Landscape connectivity for wildlife: development and validation of multispecies linkage maps (2014) · cites this paper as Cushman, Landguth & Flather 2013

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Research works printed but not linked: 44

  1. Beier P, Brost B (2010) Use of land facets to plan for climate change: conserving the arenas, not the actors. Conserv Biol 24:701–710 · DOI
  2. Beier P, Loe S (1992) In my experience: a checklist for evaluating the impacts to wildlife movement corridors. Wildl Soc Bull 20:434–440
  3. Beier P, Penrod KL, Luke C, Spencer WD, Cabañero C (2006) South coast missing linkages: restoring connectivity to wildlands in the largest metropolitan area in the United States. In: Crooks KR, Sanjayan MA (eds) Connectivity and conservation. Cambridge University Press, Cambridge, pp 555–586 · DOI · Paper page
  4. Berger J, Cain SL, Berger KM (2006) Connecting the dots: an invariant migration corridor links the Holocene to the present. Biol Lett 2:528–531 · DOI
  5. Clark RW, Brown WS, Stechert R, Zamudio KR (2008) Integrating individual behaviour and landscape genetics: the population structure of timber rattlesnake hibernacula. Mol Ecol 17:719–730 · DOI
  6. Compton B, McGarigal K, Cushman SA, Gamble L (2007) A resistant kernel model of connectivity for vernal pool breeding amphibians. Conserv Biol 21:788–799 · DOI · Paper page
  7. Cushman SA (2006) Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol Conserv 128:231–240 · DOI
  8. Cushman SA, Landguth EL (2010) Scale dependent inference in landscape genetics. Landsc Ecol 25:967–979 · DOI · Paper page
  9. Cushman SA, Landguth EL (2012a) Multi-species connectivity in the northern Rocky Mountains. Ecol Model 231:101–112 · DOI · Paper page
  10. Cushman SA, Landguth EL (2012b) Ecological associations, dispersal ability, and landscape connectivity in the northern Rocky Mountains. Research Paper RMRS-RP-90. U.S. Department of Agriculture, Fort Collins · DOI
  11. Cushman SA, Lewis J (2010) Movement behavior explains genetic differentiation in American black bear. Landsc Ecol 25:1613–1625 · DOI
  12. Cushman SA, Schwartz MK, Hayden J, McKelvey KS (2006) Gene flow in complex landscapes: confronting models with data. Am Nat 168:486–499 · DOI · Paper page
  13. Cushman SA, McKelvey KS, Schwartz MK (2009) Using empirically derived source-destination models to map regional conservation corridors. Conserv Biol 23:368–376 · DOI
  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 349–368 · DOI
  15. Cushman SA, Compton BW, McGarigal K (2010b) Habitat fragmentation effects depend on complex interactions between population size and dispersal ability: modeling influences of roads, agriculture and residential development across a range of life-history characteristics. In: Cushman SA, Huettman F (eds) Spatial complexity, informatics, and wildlife conservation. Springer, Tokyo, pp 369–387 · DOI
  16. Cushman SA, Gutzwiller K, Evans JS, McGarigal K (2010c) The gradient paradigm: a conceptual and analytical framework for landscape ecology. In: Spatial complexity, informatics, and wildlife conservation. Springer, Japan, pp 83–108 · DOI · Paper page
  17. Cushman SA, Landguth EL, Flather CH (2012) Evaluating the sufficiency of protected lands for maintaining wildlife population connectivity in the U.S. northern Rocky Mountains. Divers Distrib 18:873–884 · DOI · Paper page
  18. Cushman SA, McRae B, Adriansen F, Beier P, Shirley M, Zeller K (2013) Biological corridors and connectivity. In: MacDonald D (ed) Conservation in theory and practice, Wiley, New York · DOI · Paper page
  19. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271 · DOI
  20. Fry JA, Coan MJ, Homer CG, Meyer DK, Wickham JD (2009) Completion of the National Land Cover Database (NLCD) 1992–2001 Land cover change retrofit product: U.S. geological survey open-file report 2008–1379 · DOI
  21. Haddad NM, Bowne DR, Cunningham A, Danielson BJ, Levey DJ, Sargent S, Spira T (2003) Corridor use by diverse taxa. Ecology 84:609–615 · DOI
  22. Harris LD, Gallagher PB (1989) New initiatives for wildlife conservation: the need for movement corridors. In: Mackintosh G (ed) Preserving communities and corridors. Defenders of Wildlife, Washington, DC, pp 11–34
  23. Harrison S, Bruna E (1999) Habitat fragmentation and large-scale conservation: what do we know for sure. Ecography 22:225–232 · DOI
  24. Hobbs RJ (1992) The role of corridors in conservation: a solution or bandwagon. Trends Ecol Evol 7:389–392 · DOI
  25. Kamler, JF, Ballard WB, Gese EM, Harrison RL, Karki SM (2004) Dispersal characteristics of swift foxes. USDA National Wildlife Research Center—Staff Publications. Paper 339 · DOI
  26. Landguth EL, Hand BK, Glassy J, Cushman SA (2012) UNICOR: a species connectivity and corridor network simulator. Ecography 35:9–14 · DOI
  27. McCarthy MA, Possingham HP (2007) Active adaptive management for conservation. Conserv Biol 21:956–963 · DOI
  28. McComb BC, Zuckerburg B, Vesely D et al (2010) Monitoring animal populations and their habitats a practitioner’s guide. CRC Press, Boca Raton · DOI
  29. McGarigal K, Cushman SA (2005) The gradient concept of landscape structure. In: Wiens J, Moss M (eds) Issues and perspectives in landscape ecology. Cambridge University Press, Cambridge, pp 112–119 · DOI
  30. Mercure A, Ralls K, Koepfli KP, Wayne RK (1993) Genetic subdivisions among small canids: mitochondrial DNA differentiation of swift, kit, and arctic foxes. Evolution 47:1313–1328 · DOI
  31. Neilson RP, Drapek RJ (1998) Potentially complex biosphere responses to transient global warming. Glob Chang Biol 4:505–521 · DOI
  32. Nichols JD, Williams BK (2006) Monitoring for conservation. Trends Ecol Evol 21:668–673 · DOI
  33. Reding DM, Cushman SA, Gosselink TE, Clark WR (2013) Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus). Landsc Ecol 28:471–486 · DOI
  34. Rosenberg DK, Noon BR, Meslow EC (1997) Biological corridors: form, function, and efficacy. Bioscience 47:677–687 · DOI
  35. Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22:25–33 · DOI
  36. Seoane J, Bustamante J, Diaz-Delgado RC (2005) Effect of expert opinion on the predictive ability of environmental models of bird distribution. Conserv Biol 19:512–522 · DOI
  37. 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 19:3603–3619 · DOI · Paper page
  38. Short Bull RA, Cushman SA, Mace R, Chilton T, Kendall KC, Landguth EL, Schwartz MK (2011) Why replication is important in landscape genetics: American black bear in the Rocky Mountains. Mol Ecol 20(6):1092–1107 · DOI
  39. Simberloff D, Farr JA, Cox J, Mehlman DW (1992) Movement corridors: conservation bargains or poor investments? Conserv Biol 6:493–504 · DOI
  40. Spear SF, Balkenhol N, Fortin MJ, McRae BH, Scribner K (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591 · DOI · Paper page
  41. Wasserman TN, Cushman SA, Schwartz MK, Wallin DO (2010) Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landsc Ecol 25:1601–1612 · DOI
  42. Wiens JA (2001) The landscape concept of dispersal. In: Clobert J, Danchin E, Dhondt AA, Nichols JD (eds) Dispersal. Oxford University Press, New York, pp 96–109 · DOI
  43. With KA, Gardner RH, Turner MG (1997) Landscape connectivity and population distributions in heterogeneous environments. Oikos 78:151–169 · DOI
  44. Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27:777–797 · DOI · Paper page

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Metadata

Authors
Cushman, S. A., Landguth, E. L., & Flather, C. H.
Venue
Biodiversity and Conservation
Publisher
Springer Science and Business Media LLC
Published
2013-08-09
Volume
22
Issue
11
Pages
2583-2605
ISSN
  • 0960-3115
  • 1572-9710
DOI
10.1007/s10531-013-0541-1
Cited by
110
References
0
Language
en
Licence
www.springer.com/tdm (tdm)

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