Landscape Ecology

, Volume 30, Issue 10, pp 1837–1850 | Cite as

Empirical validation of landscape resistance models: insights from the Greater Sage-Grouse (Centrocercus urophasianus)

  • Andrew J. Shirk
  • Michael A. Schroeder
  • Leslie A. Robb
  • Samuel A. Cushman
Research Article



The ability of landscapes to impede species’ movement or gene flow may be quantified by resistance models. Few studies have assessed the performance of resistance models parameterized by expert opinion. In addition, resistance models differ in terms of spatial and thematic resolution as well as their focus on the ecology of a particular species or more generally on the degree of human modification of the landscape (i.e. landscape integrity). The effect of these design decisions on model accuracy is poorly understood.


We sought to understand the influence of expert parameterization, resolution, and specificity (i.e. species-specific or landscape integrity) on the fit of resistance model predictions to empirical landscape patterns.


With genetic and observational data collected from Greater Sage-Grouse (Centrocercus urophasianus) in Washington State, USA, we used landscape genetic analysis and logistic regression to evaluate a range of resistance models in terms of their ability to predict empirical patterns of genetic differentiation and lek occupancy.


We found that species-specific, fine resolution resistance models generally had stronger relationships to empirical patterns than coarse resolution or landscape integrity models, and that the expert models were less predictive than alternative parameterizations.


Our study offers an empirical framework to validate expert resistance models, suggests the need to match the grain of the data to the scale at which the species responds to landscape heterogeneity, and underscores the limitations of landscape integrity models when the species under study does not meet their assumptions.


Centrocercus urophasianus Greater Sage-Grouse Landscape genetics Lek Resistance Validation 

Supplementary material

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Supplementary material 1 (XLSX 18 kb)
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Supplementary material 2 (XLSX 20 kb)
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Supplementary material 3 (DOCX 14 kb)


  1. Baldwin RF, Reed SE, McRae BH, Theobald DM, Sutherland RW (2012) Connectivity restoration in large landscapes: modeling landscape condition and ecological flows. Ecol Restor 30:274–279CrossRefGoogle Scholar
  2. Balkenhol N, Waits LP, Dezzani RJ (2009) Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data. Ecography 32(5):818–830Google Scholar
  3. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
  4. Castillo JA, Epps CW, Davis AR, Cushman SA (2014) Landscape effects on gene flow for a climate-sensitive montane species, the American pika. Mol Ecol 23:843–856CrossRefPubMedGoogle Scholar
  5. Connelly J, Knick S, Braun C, Baker W, Beever E, Christiansen T, Doherty K, Garton E, Hanser S, Johnson D (2011) Conservation of Greater Sage-Grouse. Stud Avian Biol 38:549–646Google Scholar
  6. Cushman SA, McGarigal K (2002) Hierarchical, multi-scale decomposition of species-environment relationships. Landscape Ecol 17(7):637–646CrossRefGoogle Scholar
  7. Cushman SA, McGarigal K (2004) Patterns in the species—environment relationship depend on both scale and choice of response variables. Oikos 105:117–124CrossRefGoogle Scholar
  8. Cushman SA, Landguth EL (2010a) Scale dependent inference in landscape genetics. Landscape Ecol 25:967–979CrossRefGoogle Scholar
  9. Cushman SA, Landguth EL (2010b) Spurious correlations and inference in landscape genetics. Mol Ecol 19:3592–3602CrossRefPubMedGoogle Scholar
  10. Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am Nat 168:486–499CrossRefPubMedGoogle Scholar
  11. Cushman SA, Raphael MG, Ruggiero LF, Shirk AJ, Wasserman TN, O’Doherty EC (2011) Limiting factors and landscape connectivity: the American marten in the Rocky Mountains. Landscape Ecol 26:1137–1149CrossRefGoogle Scholar
  12. Cushman SA, Shirk AJ, Landguth EL (2013a) Landscape genetics and limiting factors. Conserv Genet 14(2):263–274CrossRefGoogle Scholar
  13. Cushman SA, Wasserman TN, Landguth EL, Shirk AJ (2013b) Re-evaluating causal modeling with mantel tests in landscape genetics. Diversity 5:51–72CrossRefGoogle Scholar
  14. Diniz-Filho JAF, Soares TN, Lima JS, Dobrovolski R, Landeiro VL, Telles MPDC, Bini LM (2013) Mantel test in population genetics. Genet mol biol 36(4):475–485Google Scholar
  15. Dobzhansky T (1940) Speciation as a stage in evolutionary divergence. Am Nat 74:312–321CrossRefGoogle Scholar
  16. Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS (2007) Optimizing dispersal and corridor models using landscape genetics. J Appl Ecol 44:714–724CrossRefGoogle Scholar
  17. ESRI (2008) ArcGIS Desktop: release 10.0. Environmental Systems Research Institute, Redlands, CAGoogle Scholar
  18. Frankham R, Briscoe DA, Ballou JD (2002) Introduction to conservation genetics. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  19. Galpern P, Manseau M (2013) Finding the functional grain: comparing methods for scaling resistance surfaces. Landscape Ecol 28:1269–1281CrossRefGoogle Scholar
  20. Graves TA, Beier P, Royle JA (2013) Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal. Mol ecol 22(15):3888–3903Google Scholar
  21. Guillot G, Rousset F (2013) Dismantling the Mantel tests. Methods Ecol Evol 4(4):336–344Google Scholar
  22. Jombart T, Devillard S, Dufour AB, Pontier D (2008) Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101:92–103CrossRefPubMedGoogle Scholar
  23. Legendre P, Fortin MJ (2010) Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Mol Ecol Resour 10(5):831–844Google Scholar
  24. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220PubMedGoogle Scholar
  25. McRae BH (2006) Isolation by resistance. Evolution 60:1551–1561CrossRefPubMedGoogle Scholar
  26. R Development Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing. R Development Core Team, ViennaGoogle Scholar
  27. Schroeder MA, Hays DW, Livingston MF, Stream LE, Jacobson JE, Pierce DJ (2000) Changes in the distribution and abundance of sage-grouse in Washington. Northwest Nat 81:104–112CrossRefGoogle Scholar
  28. Shirk AJ, Wallin DO, Cushman SA, Rice CG, Warheit KI (2010) Inferring landscape effects on gene flow: a new model selection framework. Mol Ecol 19:3603–3619CrossRefPubMedGoogle Scholar
  29. Shirk AJ, Wasserman TN, Cushman SA, Raphael MG (2012) Scale dependency of American marten (Martes americana) habitat relations. In: Aubry KB, Zielinski WJ, Raphael MG, Proulx G, Buskirk SW (eds) Biology and conservation of martens, sables, and new synthesis. Cornell University Press, IthacaGoogle Scholar
  30. Short Bull RAS, Cushman SA, Mace R, Chilton T, Kendall KC, Landguth EL, Schwartz MK, McKelvey K, Allendorf FW, Luikart G (2011) Why replication is important in landscape genetics: American black bear in the Rocky Mountains. Mol Ecol 20:1092–1107CrossRefGoogle Scholar
  31. Smouse PE, Long JC, Sokal RR (1986) Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Syst Zool 35:627–632CrossRefGoogle Scholar
  32. 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 19:3576–3591CrossRefPubMedGoogle Scholar
  33. WHCWG (2010) Washington connected landscapes project: analysis of the Columbia Plateau ecoregion. Washington Department of Fish and Wildlife and Washington Department of Transportation, OlympiaGoogle Scholar
  34. WHCWG (2012) Washington connected landscapes project: analysis of the Columbia Plateau ecoregion. Washington Department of Fish and Wildlife and Washington Department of Transportation, OlympiaGoogle Scholar
  35. Whiteman K, Vaccaro J, Gonthier J, Bauer H (1994) The hydrogeologic framework and geochemistry of the Columbia Plateau aquifer system. US Government Printing Office, WashingtonGoogle Scholar
  36. Wright S (1943) Isolation by distance. Genetics 28:114–138PubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Andrew J. Shirk
    • 1
  • Michael A. Schroeder
    • 2
  • Leslie A. Robb
    • 3
  • Samuel A. Cushman
    • 4
  1. 1.University of Washington Climate Impacts GroupSeattleUSA
  2. 2.Washington Department of Fish and WildlifeBridgeportUSA
  3. 3.BridgeportUSA
  4. 4.USDA Forest ServiceRocky Mountain Research StationFlagstaffUSA

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