Landscape Ecology

, Volume 30, Issue 8, pp 1405–1420 | Cite as

A comparative framework to infer landscape effects on population genetic structure: are habitat suitability models effective in explaining gene flow?

  • María C. Mateo-Sánchez
  • Niko Balkenhol
  • Samuel Cushman
  • Trinidad Pérez
  • Ana Domínguez
  • Santiago Saura
Research Article
First Online:
Received:
Accepted:

Abstract

Context

Most current methods to assess connectivity begin with landscape resistance maps. The prevailing resistance models are commonly based on expert opinion and, more recently, on a direct transformation of habitat suitability. However, habitat associations are not necessarily accurate indicators of dispersal, and thus may fail as a surrogate of resistance to movement. Genetic data can provide valuable insights in this respect.

Objectives

We aim at directly comparing the utility of habitat suitability models for estimating landscape resistance versus other approaches based on actual connectivity data.

Methods

We develop a framework to compare landscape resistance models based on (1) a genetic-based multi model optimization and (2) a direct conversion of habitat suitability into landscape resistance. We applied this framework to the endangered brown bear in the Cantabrian Range (NW Spain).

Results

We found that the genetic-based optimization produced a resistance model that was more related to species movement than were models produced by direct conversion of habitat suitability. Certain land cover types and transport infrastructures were restrictive factors for species occurrence, but did not appear to impede the brown bear movements that determined observed genetic structure.

Conclusions

In this study case, habitat suitability is not synonymous with permeability for dispersal, and does not seem to provide the best way to estimate actual landscape resistance. We highlight the general utility of this comparative approach to provide a comprehensive and practical assessment of factors involved in species movements, with the final aim of improving the initiatives to enhance landscape connectivity in conservation planning.

Keywords

Gene flow Habitat suitability Landscape resistance Species movement Landscape genetics Brown bear 
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Notes

Acknowledgments

Funding was provided by the Spanish Ministry of Science and Innovation research grant GEFOUR (AGL2012-31099) and Technical University of Madrid. The non-invasive genotyping of bears was funded by the government of “Principado de Asturias”, the government of Junta de Castilla y León and the Picos de Europa National Park along the years 2005 and 2010. We are also grateful to the Regional Administration involved in the brown bear management: Junta de Castilla y León, Gobierno de Cantabria, Principado de Asturias and Xunta de Galicia for providing data. Thanks also to the support provided by Fundación Oso Pardo.

Supplementary material

10980_2015_194_MOESM1_ESM.docx (12 kb)
Supplementary material 1 (DOCX 12 kb)

References

  1. Adriaensen F, Chardon J, De Blust G, Swinnen E, Villalba S, Gulinck H, Matthysen E (2003) The application of ‘least-cost’modelling as a functional landscape model. Landscape Urban Plan 64(4):233–247CrossRefGoogle Scholar
  2. Balkenhol N, Gugerli F, Cushman SA, Waits LP, Coulon A, Arntzen J, Holderegger R, Wagner HH (2009) Identifying future research needs in landscape genetics: where to from here? Landscape Ecol 24(4):455–463CrossRefGoogle Scholar
  3. Beier P, Majka DR, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22(4):836–851PubMedCrossRefGoogle Scholar
  4. Bowcock A, Ruiz-Linares A, Tomfohrde J, Minch E, Kidd J, Cavalli-Sforza LL (1994) High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368(6470):455–457PubMedCrossRefGoogle Scholar
  5. Bowne DR, Bowers MA (2004) Interpatch movements in spatially structured populations: a literature review. Landscape Ecol 19(1):1–20CrossRefGoogle Scholar
  6. 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(4):843–856PubMedCrossRefGoogle Scholar
  7. Chetkiewicz CLB, St. Clair CC, Boyce MS (2006) Corridors for conservation: integrating pattern and process. Annu Rev Ecol Evol Syst 37:317–342CrossRefGoogle Scholar
  8. Clevenger A, Purroy F, Pelton M (1992) Brown bear (Ursus arctos L.) habitat use in the Cantabrian Mountains Spain. Mammalia 56(2):203–214CrossRefGoogle Scholar
  9. Clevenger AP, Purroy FJ, Campos MA (1997) Habitat assessment of a relict brown bear Ursus arctos population in northern Spain. Biol Conserv 80(1):17–22CrossRefGoogle Scholar
  10. Crooks KR, Sanjayan MA (2006) Connectivity conservation. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  11. Cushman SA, Landguth EL (2010) Spurious correlations and inference in landscape genetics. Mol Ecol 19(17):3592–3602PubMedCrossRefGoogle Scholar
  12. Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am Nat 168(4):486–499PubMedCrossRefGoogle Scholar
  13. Cushman SA, McRae B, Adriansen F, Beier P, Shirley M, Zeller K (2013a) Biological corridors and connectivity. In: McDonald D (ed) Conservation in theory and practice. Wiley, New York, pp 284–404Google Scholar
  14. Cushman SA, Wasserman TN, Landguth EL, Shirk AJ (2013b) Re-evaluating causal modeling with Mantel tests in landscape genetics. Diversity 5(1):51–72CrossRefGoogle Scholar
  15. Dieringer D, Schlötterer C (2003) Microsatellite analyser (MSA): a platform independent analysis tool for large microsatellite data sets. Mol Ecol Notes 3(1):167–169CrossRefGoogle Scholar
  16. Dudaniec RY, Rhodes JR, Worthington Wilmer J, Lyons M, Lee KE, McAlpine CA, Carrick FN (2013) Using multilevel models to identify drivers of landscape-genetic structure among management areas. Mol Ecol 22:3752–3765PubMedCrossRefGoogle Scholar
  17. Goslee SC, Urban DL (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Stat Softw 22(7):1–19Google Scholar
  18. Graves TA, Beier P, Royle JA (2013) Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal. Mol Ecol 22:3888–3903PubMedCrossRefGoogle Scholar
  19. Guillot G, Rousset F (2013) Dismantling the Mantel tests. Methods Ecol Evol 4:336–344CrossRefGoogle Scholar
  20. Haddad NM, Tewksbury JJ (2005) Low-quality habitat corridors as movement conduits for two butterfly species. Ecol Appl 15(1):250–257CrossRefGoogle Scholar
  21. Holderegger R, Wagner HH (2008) Landscape genetics. Bioscience 58(3):199–207CrossRefGoogle Scholar
  22. Horskins K, Mather PB, Wilson JC (2006) Corridors and connectivity: when use and function do not equate. Landscape Ecol 21(5):641–655CrossRefGoogle Scholar
  23. Laiolo P, Tella JL (2006) Landscape bioacoustics allows detection of the effects of habitat patchiness on population structure. Ecology 87(5):1203–1214PubMedCrossRefGoogle Scholar
  24. Landguth EL, Cushman SA (2010) CDPOP: a spatially explicit cost distance population genetics program. Mol Ecol Resour 10(1):156–161PubMedCrossRefGoogle Scholar
  25. Landguth E, Cushman SA, Schwartz M, McKelvey K, Murphy M, Luikart G (2010) Quantifying the lag time to detect barriers in landscape genetics. Mol Ecol 19(19):4179–4191PubMedCrossRefGoogle Scholar
  26. Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18(4):189–197CrossRefGoogle Scholar
  27. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27(2 Part 1):209–220PubMedGoogle Scholar
  28. Mateo Sánchez MC, Cushman SA, Saura S (2014a) Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain). Int J Geogr Inf Sci 28(8):1531–1546CrossRefGoogle Scholar
  29. Mateo‐Sánchez MC, Cushman SA, Saura S (2014b) Connecting endangered brown bear subpopulations in the Cantabrian Range (north‐western Spain). Anim Conserv 17:430–440CrossRefGoogle Scholar
  30. McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci 104(50):19885–19890PubMedCrossRefPubMedCentralGoogle Scholar
  31. Naves J, Palomero G (1993) El oso pardo en España: (Ursus arctos). Icona, MadridGoogle Scholar
  32. Naves J, Wiegand T, Revilla E, Delibes M (2003) Endangered species constrained by natural and human factors: the case of brown bears in northern Spain. Conserv Biol 17(5):1276–1289CrossRefGoogle Scholar
  33. McGarigal K, Cushman S, Neel, M, Ene E (2002) FRAGSTATS: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, AmherstGoogle Scholar
  34. Newby JR (2011) Puma dispersal ecology in the central Rocky Mountains. PhD thesis University of MontanaGoogle Scholar
  35. O’Brien D, Manseau M, Fall A, Fortin MJ (2006) Testing the importance of spatial configuration of winter habitat for woodland caribou: an application of graph theory. Biol Conserv 130(1):70–83CrossRefGoogle Scholar
  36. Palomero G, Ballesteros F, Herrero J, Quesada CN (2007) Demografía distribución genética y conservación del oso pardo cantábrico Parques Nacionales, MadridGoogle Scholar
  37. Pérez T, Vázquez F, Naves J, Fernández A, Corao A, Albornoz J, Domínguez A (2009) Non-invasive genetic study of the endangered Cantabrian brown bear (Ursus arctos). Conserv Genet 10(2):291–301CrossRefGoogle Scholar
  38. Pérez T, Naves J, Vázquez JF, Seijas J, Corao A, Albornoz J, Domínguez A (2010) Evidence for improved connectivity between Cantabrian brown bear subpopulations. Ursus 21(1):104–108CrossRefGoogle Scholar
  39. Pérez T, Naves J, Vázquez F, Fernández-Gil A, Albornoz J, Revilla E, Delibes M, Domínguez A (2014) Estimating the population size of the endangered brown bear through genetic sampling. Wildl Biol 20:300–309CrossRefGoogle Scholar
  40. Peterman WE, Connette GM, Semlitsch RD, Eggert LS (2014) Ecological resistance surfaces predict fine-scale genetic differentiation in a terrestrial woodland salamander. Mol Ecol 23:2402–2413PubMedCrossRefGoogle Scholar
  41. Reding DM, Cushman S, Gosselink TE, Clark WR (2013) Linking movement behavior and fin-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus). Land scape Ecol 28:471–486CrossRefGoogle Scholar
  42. Ruiz-González A, Gurrutxaga M, Cushman SA, Madeira MJ, Randi E, Gómez-Moliner BJ (2014) Landscape genetics for the empirical assessment of resistance surfaces: the European pine marten (Martes martes) as a target-species of a regional ecological network. PLoS One. doi: 10.1371/journal.pone.0110552 PubMedPubMedCentralGoogle Scholar
  43. Selkoe K, Watson JR, White C, Horin TB, Iacchei M, Mitarai S, Siegel DA, Gaines SD, Toonen RJ (2010) Taking the chaos out of genetic patchiness: seascape genetics reveals ecological and oceanographic drivers of genetic patterns in three temperate reef species. Mol Ecol 19:3708–3726PubMedCrossRefGoogle Scholar
  44. Seoane J, Carrascal LM, Alonso CL, Palomino D (2005) Species-specific traits associated to prediction errors in bird habitat suitability modeling. Ecol Model 185(2):299–308CrossRefGoogle Scholar
  45. Shirk A, Wallin D, Cushman SA, Rice C, Warheit K (2010) Inferring landscape effects on gene flow: a new model selection framework. Mol Ecol 19(17):3603–3619PubMedCrossRefGoogle Scholar
  46. Shirk A, Cushman S, Landguth E (2012) Simulating pattern-process relationships to validate landscape genetic models. Int J Ecol 2(539109):1–8CrossRefGoogle Scholar
  47. Singleton PH, Gaines WL, Lehmkuhl, JF (2002) Landscape permeability for large carnivores in Washington: a geographic information system weighted-distance and least-cost corridor assessment. US Department of Agriculture, Forest Service, Pacific Northwest Research Station, PortlandGoogle Scholar
  48. Smouse PE, Long JC, Sokal RR (1986) Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Syst Zool 35(4):627–632CrossRefGoogle Scholar
  49. Spear SF, Peterson CR, Matocq MD, Storfer A (2005) Landscape genetics of the blotched tiger salamander (Ambystoma tigrinum melanostictum). Mol Ecol 14(8):2553–2564PubMedCrossRefGoogle Scholar
  50. 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(17):3576–3591PubMedCrossRefGoogle Scholar
  51. Storfer A, Murphy M, Evans J, Goldberg C, Robinson S, Spear S, Dezzani R, Delmelle E, Vierling L, Waits L (2007) Putting the ‘landscape’ in landscape genetics. Heredity 98(3):128–142PubMedCrossRefGoogle Scholar
  52. Van Strien MJ, Keller D, Holderegger R (2012) A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models. Mol Ecol 21:4010–4023CrossRefGoogle Scholar
  53. Wagner HH, Fortin MJ (2013) A conceptual framework for the spatial analysis of landscape genetic data. Conserv Genet 14:253–261CrossRefGoogle Scholar
  54. Wang YH, Yang K, Bridgman CL, Lin LK (2008) Habitat suitability modelling to correlate gene flow with landscape connectivity. Landscape Ecol 23(8):989–1000Google Scholar
  55. Wasserman TN, Cushman SA, Schwartz MK, Wallin DO (2010) Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecol 25(10):1601–1612CrossRefGoogle Scholar
  56. Wiegand T, Naves J, Stephan T, Fernandez A (1998) Assessing the risk of extinction for the brown bear (Ursus arctos) in the Cordillera Cantabrica, Spain. Ecol Monogr 68(4):539–570CrossRefGoogle Scholar
  57. Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landscape Ecol 27(6):777–797CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • María C. Mateo-Sánchez
    • 1
  • Niko Balkenhol
    • 2
  • Samuel Cushman
    • 3
  • Trinidad Pérez
    • 4
  • Ana Domínguez
    • 4
  • Santiago Saura
    • 1
  1. 1.E.T.S.I Montes, Forestal y del Medio NaturalTechnical University of MadridMadridSpain
  2. 2.Department of Forest Zoology and Forest ConservationUniversity of GoettingenGoettingenGermany
  3. 3.Rocky Mountain Research StationU.S Forest ServiceFlagstaffUSA
  4. 4.Department of Biología FuncionalUniversity of OviedoOviedoSpain

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