Landscape Ecology

, Volume 25, Issue 10, pp 1601–1612 | Cite as

Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho

  • Tzeidle N. Wasserman
  • Samuel A. Cushman
  • Michael K. Schwartz
  • David O. Wallin
Research Article


Individual-based analyses relating landscape structure to genetic distances across complex landscapes enable rigorous evaluation of multiple alternative hypotheses linking landscape structure to gene flow. We utilize two extensions to increase the rigor of the individual-based causal modeling approach to inferring relationships between landscape patterns and gene flow processes. First, we add a univariate scaling analysis to ensure that each landscape variable is represented in the functional form that represents the optimal scale of its association with gene flow. Second, we use a two-step form of the causal modeling approach to integrate model selection with null hypothesis testing in individual-based landscape genetic analysis. This series of causal modeling indicated that gene flow in American marten in northern Idaho was primarily related to elevation, and that alternative hypotheses involving isolation by distance, geographical barriers, effects of canopy closure, roads, tree size class and an empirical habitat model were not supported. Gene flow in the Northern Idaho American marten population is therefore driven by a gradient of landscape resistance that is a function of elevation, with minimum resistance to gene flow at 1500 m.


Landscape genetics Scale dependency Causal modeling American marten Population connectivity Gene flow 



This research was primarily supported by the U.S. Forest Service Rocky Mountain Research Station, the Idaho Department of Fish and Game, and Western Washington University, Huxley College of the Environment. We especially thank Jim Hayden of Idaho Fish and Game for his support and the RMRS Wildlife Genetics Lab in Missoula, MT. We also thank the two anonymous reviewers and Rolf Holderegger for their helpful insights and comments on earlier drafts of this manuscript.

Supplementary material

10980_2010_9525_MOESM1_ESM.doc (2 mb)
Supplementary material 1 (DOC 2075 kb)


  1. Balkenhol N, Gugerli F, Cushman SA, Waits LP, Coulon A, Arntzen JW, Holderegger R, Wagner HH, Arens P, Campagne P, Dale VH, Nicieza AG, Smulders MJM, Tedesco E, Wang H, Wasserman TN (2009) Identifying future research needs in landscape genetics: where to from here? Landscape Ecol 24:455–463CrossRefGoogle Scholar
  2. Bissonette JA, Harrison DJ, Hargis CD, Chapin TG (1997) The influence of spatial scale and scale-sensitive properties on habitat selection by American marten. In: Bissonette JA (ed) Wildlife and landscape ecology. Springer, New York, pp 368–385Google Scholar
  3. Broquet T, Ray N, Petit E, Fryxell JM, Burel F (2006) Genetic isolation by distance and landscape connectivity in the American marten (Martes americana). Landscape Ecol 21:877–889CrossRefGoogle Scholar
  4. Buskirk SW, Powell RA (1994) Habitat ecology of fishers and American martens. In: Buskirk SW, Harestad AS, Raphael MG, Powell RA (eds) Martens, sables, and fishers. Cornell University Press, Ithaca, pp 283–296Google Scholar
  5. Castellano S, Balletto E (2002) Is the partial Mantel test inadequate? Evolution 56:1871–1873PubMedGoogle Scholar
  6. Chapin TG, Harrison DJ, Katnik DD (1998) Influence of landscape pattern on habitat use by American marten in an industrial forest. Conserv Biol 12:96–227CrossRefGoogle Scholar
  7. Corander J, Waldmann P, Sillanpaa MJ (2003) Bayesian analysis of genetic differentiation between populations. Genetics 163:367–374PubMedGoogle Scholar
  8. Coulon A, Cosson JF, Angibault JM, Cargnelutti B, Galan M, Morellet N, Petit E, Aulagnier S, Hewson AJM (2004) Landscape connectivity influences gene flow in a roe deer population inhabiting a fragmented landscape: an individual-based approach. Mol Ecol 13:2841–2850CrossRefPubMedGoogle Scholar
  9. Coulon A, Guillot G, Cosson GF, Angibault JMA, Aulagnier S, Cargnelutti B, Galan M, Hewison AJM (2006) Genetic structure is influenced by landscape features: empirical evidence from a roe deer population. Mol Ecol 15:1669–1679CrossRefPubMedGoogle Scholar
  10. Cushman SA, Landguth EL (2010a) Spurious correlations and inference in landscape genetics. Mol Ecol 19:3592–3602CrossRefPubMedGoogle Scholar
  11. Cushman SA, Landguth EL (2010b) Scale dependent inference in landscape genetics. Landscape Ecol 25:967–979CrossRefGoogle Scholar
  12. Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene-flow in complex landscapes: testing multiple models with causal modeling. Am Nat 168:486–499CrossRefPubMedGoogle Scholar
  13. Cushman SA, McKelvey KS, Schwartz MK (2008) Using empirically derived source-destination models to map regional conservation corridors. Conserv Biol 23:368–376CrossRefPubMedGoogle Scholar
  14. Dupanloup I, Schneider S, Excoffier L (2001) A simulated annealing approach to define genetic structure of populations. Mol Ecol 58:2021–2036Google Scholar
  15. ESRI (2003) ARCGIS. Environmental Systems Research Incorporated, RedlandsGoogle Scholar
  16. Evans JS, Cushman SA (2009) Gradient modeling of conifer species using random forests. Landscape Ecol 24:673–683CrossRefGoogle Scholar
  17. Evett IW, Weir BS (1998) Interpreting DNA evidence. Sinauer, SunderlandGoogle Scholar
  18. Francois O, Ancelet S, Guillot G (2006) Bayesian clustering using hidden Markov random fields in spatial population genetics. Genetics 174:805–816CrossRefPubMedGoogle Scholar
  19. Fry JA, Coan MJ, Homer CG, Meyer DK, Wickham JD (2008) Completion of the national land cover database (NLCD) 1992–2001 land cover change retrofit product. USGS OF 2008–1379Google Scholar
  20. Funk CW, Blouin MS, Corn PS, Maxell BA, Pilliod DS, Amish S, Allendorf FW (2005) Population structure of Columbia spotted frogs (Rana luteiventris) is strongly affected by the landscape. Mol Ecol 14:483–496CrossRefPubMedGoogle Scholar
  21. Goslee SC, Urban DL (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Stat Softw 22(7):1–19Google Scholar
  22. Goudet J (1995) FSTAT (version 1.2): a computer program to calculate F-statistics. J Hered 86:485–486Google Scholar
  23. Hargis CD (1996) The influence of forest fragmentation and landscape pattern on American marten and their prey. PhD dissertation, Utah State University, Logan, UtahGoogle Scholar
  24. Hargis CD, Bissonette JA, Turner DL (1999) The influence of forest fragmentation and landscape pattern on American martens. J Appl Ecol 36:157–172CrossRefGoogle Scholar
  25. Holderegger R, Wagner HH (2008) Landscape genetics. Bioscience 58:199–207CrossRefGoogle Scholar
  26. IPCC (2007) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  27. Krist FJ, Brown DG (1994) GIS modeling of paleo-indian period caribou migrations and viewsheds in northeastern lower Michigan. Photogramm Eng Remote Sensing 60:1129–1137Google Scholar
  28. Kyle CJ, Strobeck C (2003) Genetic homogeneity of Canadian mainland marten populations underscores the disctinctiveness of Newfoundland pine martens (Martes americana atrata). Can J Zool 81:57–66CrossRefGoogle Scholar
  29. Landguth EL, Cushman SA (2010) CDPOP: an individual-based, cost-distance spatial population genetics model. Mol Ecol Resour 10:156–161CrossRefGoogle Scholar
  30. Landguth EL, Cushman SA, Schwartz MK, McKelvey KS, Murphy M, Luikart G (in press) Quantifying the lag time to detect barriers in landscape genetics. Mol EcolGoogle Scholar
  31. Legendre P (1993) Spatial autocorrelation: trouble or new paradigm? Ecology 74:1659–1673CrossRefGoogle Scholar
  32. Legendre P, Fortin M-J (2010) Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Mol Ecol Resour 10:831–844CrossRefGoogle Scholar
  33. Legendre P, Legendre L (1998) Numerical ecology. Elsevier, AmsterdamGoogle Scholar
  34. Legendre P, Troussellier M (1988) Aquatic heterotrophic bacteria: modeling in the presence of spatial autocorrelation. Limnol Oceanogr 33:1055–1067CrossRefGoogle Scholar
  35. Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197CrossRefGoogle Scholar
  36. Manni F, Guerard E, Heyer E (2004) Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by using Monmonier’s algorithm. Hum Biol 76:173–190CrossRefPubMedGoogle Scholar
  37. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220PubMedGoogle Scholar
  38. McRae BH (2006) Isolation by resistance. Evolution 60:1551–1561PubMedGoogle Scholar
  39. McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci USA 104:19885–19890CrossRefPubMedGoogle Scholar
  40. Michels E, Cottenie K, Neys L, DeGalas K, Coppin P, DeMeester L (2001) Geographical and genetic distances among zooplankton populations in a set of interconnected ponds: a plea for using GIS modeling of the effective geographical distance. Mol Ecol 10:1929–1938CrossRefPubMedGoogle Scholar
  41. Mills LS, Pilgrim K, Schwartz MK, McKelvey K (2001) Identifying lynx and other North American felids based on MtDNA analysis. Conserv Genet 1:285–289CrossRefGoogle Scholar
  42. Peakall R, Smouse PE (2005) GENALEX 6: genetic analysis in EXCEL: population genetic software for teaching and research. Mol Ecol Notes 6:288–295CrossRefGoogle Scholar
  43. Pérez-Espona S, Pérez-Barbería FJ, McLeod JE, Jiggins CD, Gordon IJ, Pemberton JM (2008) Landscape features affect gene flow of Scottish Highland red deer (Cervus elaphus). Mol Ecol 17:981–996CrossRefPubMedGoogle Scholar
  44. Phillips DM (1994) Social and spatial characteristics, and dispersal of marten in a forest preserve and industrial forest. M.S. thesis, University of Maine, Orono, USAGoogle Scholar
  45. Pritchard JK, Stephens M, Peter D (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedGoogle Scholar
  46. Proctor MF, McLellan BN, Strobeck C, Barclay RMR (2005) Genetic analysis reveals demographic fragmentation of grizzly bears yielding vulnerability by small populations. Proc R Soc B Biol Sci 272:2409–2416CrossRefGoogle Scholar
  47. R Development Core Team (2007) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna,
  48. Raufaste N, Rousset F (2001) Are partial Mantel tests adequate? Evolution 55:1703–1705PubMedGoogle Scholar
  49. Raymond M, Rousset F (1995) GENEPOP (version 1.2), population genetics software for exact tests and ecumenicism. J Hered 86:248–249Google Scholar
  50. Riddle A, Pilgrim KL, Mills LS, McKelvey KS, Ruggiero LF (2003) Identification of mustelids using mitochondrial DNA and non-invasive sampling. Conserv Genet 4:241–243CrossRefGoogle Scholar
  51. Ruggiero LF, Aubrey KB, Buskirk J, Lyona ND, Zielinski WJ (1994) The scientific basis for conserving forest carnivores: American marten, fisher lynx, and wolverine in the western United States. U.S. Forest Service General Technical Report RM-254Google Scholar
  52. Schwartz MK, McKelvey KS (2009) Why sampling scheme matters: the effect of sampling scheme on landscape genetic results. Conserv Genet 10:441–452CrossRefGoogle Scholar
  53. Schwartz MK, Copeland JP, Anderson NJ, Squires JR, Inman RM, McKelvey KS, Pilgrim KL, Waits LP, Cushman SA (2009) Wolverine gene flow across a narrow climatic niche. Ecology 90:3222–3232CrossRefPubMedGoogle Scholar
  54. Shirk A, Wallin DO, Cushman SA, Rice C, Warheit K (2010) Inferring landscape effects on gene flow: a new multi-scale model selection framework. Mol Ecol 19:3489–3495CrossRefGoogle Scholar
  55. 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
  56. Spear SF, Peterson CR, Matacq M, Storfer A (2005) Landscape genetics of the blotched tiger salamander (Ambystoma tigrinum melanostictum). Mol Ecol 14:2553–2564CrossRefPubMedGoogle Scholar
  57. Storfer A, Murphy MA, Evans JS, Goldberg CS, Robinson S, Spear SF, Dezzani R, Delmelle E, Vierling L, Waits LP (2007) Putting the ‘‘landscape’’ in landscape genetics. Heredity 98:128–142CrossRefPubMedGoogle Scholar
  58. Taylor PD, Fahrig L, Henein K, Merriam G (1993) Connectivity is a vital element of landscape structure. Oikos 68:571–573CrossRefGoogle Scholar
  59. Thompson CM, McGarigal K (2002) The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA). Landscape Ecol 17:569–586CrossRefGoogle Scholar
  60. Vitalis R, Couvet D (2001) Estimation of effective population size and migration rate from one- and two-locus identity measures. Genetics 157:911–925PubMedGoogle Scholar
  61. Walker W, Craighead FL (1997) Analyzing wildlife movement corridors in Montana using GIS. In: Proceedings of the 1997 ESRI user conferenceGoogle Scholar
  62. Wasserman TN (2008) Habitat relationships and landscape genetics of Martes americana in northern Idaho. M.S. thesis, Western Washington University, BellinghamGoogle Scholar
  63. Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397CrossRefGoogle Scholar
  64. Wright S (1943) Isolation by distance. Genetics 28:114–138PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Tzeidle N. Wasserman
    • 1
  • Samuel A. Cushman
    • 2
  • Michael K. Schwartz
    • 2
  • David O. Wallin
    • 3
  1. 1.School of ForestryNorthern Arizona UniversityFlagstaffUSA
  2. 2.USDA Forest ServiceRocky Mountain Research StationMissoulaUSA
  3. 3.Huxley College of the EnvironmentWestern Washington UniversityBellinghamUSA

Personalised recommendations