Skip to main content
SpringerLink
Log in

Scale dependent inference in landscape genetics

  • Research Article
  • Published:
Landscape Ecology Aims and scope Submit manuscript

Abstract

Ecological relationships between patterns and processes are highly scale dependent. This paper reports the first formal exploration of how changing scale of research away from the scale of the processes governing gene flow affects the results of landscape genetic analysis. We used an individual-based, spatially explicit simulation model to generate patterns of genetic similarity among organisms across a complex landscape that would result given a stipulated landscape resistance model. We then evaluated how changes to the grain, extent, and thematic resolution of that landscape model affect the nature and strength of observed landscape genetic pattern–process relationships. We evaluated three attributes of scale including thematic resolution, pixel size, and focal window size. We observed large effects of changing thematic resolution of analysis from the stipulated continuously scaled resistance process to a number of categorical reclassifications. Grain and window size have smaller but statistically significant effects on landscape genetic analyses. Importantly, power in landscape genetics increases as grain of analysis becomes finer. The analysis failed to identify the operative grain governing the process, with the general pattern of stronger apparent relationship with finer grain, even at grains finer than the governing process. The results suggest that correct specification of the thematic resolution of landscape resistance models dominates effects of grain and extent. This emphasizes the importance of evaluating a range of biologically realistic resistance hypotheses in studies to associate landscape patterns to gene flow processes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • 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 24:455–463

    Google Scholar 

  • Bowcock AM, Ruiz-Linares A, Tomfohrde J, Minch E, Kidd JR, Cavalli-Sforza LL (1994) High resolution of human evolutionary trees with polymorphic micorsatellites. Nature 368:455–457

    Article  CAS  PubMed  Google Scholar 

  • Corry RC, Nassauer JI (2005) Limitations of using landscape pattern indices to evaluate the ecological consequences of alternative plans and designs. Landscape Urban Plan 72:265–280

    Article  Google Scholar 

  • Coulon A, Cosson JF, Angibault JM, Cargnelutti B, Galan M, Morellet N, Petit E, Aulagnier S, Hewison AJM (2004) Landscape connectivity influences gene flow in a roe deer population inhabiting a fragmented landscape: an individual-based approach. Mol Ecol 13:2841–2850. doi:10.1111/j.1365-294X.2004.02253.x

    Article  CAS  PubMed  Google Scholar 

  • Cushman SA (2006) Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol Conserv 128:231–240. doi:10.1016/j.biocon.2005.09.031

    Article  Google Scholar 

  • Cushman SA, Landguth EL (in press) Spurious correlations and inference in landscape genetics. Mol Ecol

  • Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene-flow in complex landscapes: testing multiple models with causal modeling. Am Nat 168:486–499

    Article  PubMed  Google Scholar 

  • Cushman SA, McGarigal K, Neel M (2008a) Parsimony in landscape metrics: strength, universality, and consistency. Ecol Indic 8:691–703

    Article  Google Scholar 

  • Cushman SA, McKelvey K, Flather C, McGarigal K (2008b) Testing the use of forest communities to evaluate biological diversity. Frontiers Ecol Environ 6:13–17

    Article  Google Scholar 

  • Cushman SA, McGarial K, Gutzwiller K, Evans J (2009) The gradient paradigm: a conceptual and analytical framework for landscape ecology, chap 5. In: Cushman SA, Huettman F (eds) Spatial complexity, informatics and wildlife conservation. Springer, Tokyo

    Google Scholar 

  • Dupanloup I, Schneider S, Excoffier L (2001) A simulated annealing approach to define genetic structure of populations. Mol Ecol 58:2021–2036

    Google Scholar 

  • Epperson BK, McRae B, Scribner K, Cushman SA, Rosenberg MS, Fortin M-J, James PMA, Murphy M, Manel S, Legendre L, Dale MRT (in press) Utility of computer simulations in landscape genetics. Mol Ecol

  • ESRI (1999–2008) ArcGIS: Release 9.3. Environmental System Research Institute, Redlands, CA

  • Francois O, Ancelet S, Guillot G (2006) Bayesian clustering using hidden Markov random fields in spatial population genetics. Genetics 174:805–816

    Article  CAS  PubMed  Google Scholar 

  • Hargis CD, Bissonette JA, David JL (1998) The behavior of landscape metrics commonly used in the study of habitat fragmentation. Landscape Ecol 13:167–186

    Article  Google Scholar 

  • Hess G, Bay JM (1997) Generating confidence intervals for composition-based landscape indexes. Landscape Ecol 12:309–320

    Article  Google Scholar 

  • Holderegger R, Wagner HH (2008) Landscape genetics. Bioscience 58:199–207

    Article  Google Scholar 

  • Landguth EL, Cushman SA (2010) CDPOP: an individual-based, cost-distance spatial population genetics model. Mol Ecol Resour 10:156–161

    Article  CAS  Google Scholar 

  • Lausch A, Herzog F (2002) Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability. Ecol Indic 2:3–15

    Article  Google Scholar 

  • Levin SA (1992) The problem of pattern and scale in ecology. Ecology 73:1943–1967

    Article  Google Scholar 

  • Li H, Wu J (2004) Use and misuse of landscape indices. Landscape Ecol 19:389–399

    Article  Google Scholar 

  • Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197

    Article  Google Scholar 

  • Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220

    CAS  PubMed  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • McRae BH (2006) Isolation by resistance. Evol Int J Org Evol 60:1551–1561

    Google Scholar 

  • McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci USA 104:19885–19890. doi:10.1073/pnas.0706568104

    Article  CAS  PubMed  Google Scholar 

  • Neel MC, Cushman SA, McGarigal K (2004) Behavior and stability of landscape metrics across controlled gradients of landscape structure. Landscape Ecol 19:435–455

    Article  Google Scholar 

  • Pritchard JK, Stephens M, Peter D (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    CAS  PubMed  Google Scholar 

  • R Development Core Team (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org

  • Riitters KH, O’Neill RV, Hunsaker CT, Wickham JD, Yankee DH, Timmins SP, Jones KB, Jackson BL (1995) A factor analysis of landscape pattern and structure metrics. Landscape Ecol 10:23–39

    Article  Google Scholar 

  • Saura S, Martinez-Millan J (2001) Sensitivity of landscape pattern metrics to map spatial extent. Photogramm Eng Remote Sens 67:1027–1036

    Google Scholar 

  • 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 90:3222–3232

    Article  PubMed  Google Scholar 

  • Segelbarcher G, Cushman SA, Epperson BK, Fortin M-J, Francois O, Hardy OJ, Holderegger R, Manel S (2010) Applications of landscape genetics in conservation biology: concepts and challenges. Conserv Genet. doi:10.1007/s10592-009-0044-5

  • Shao G, Liu D, Zhao G (2001) Relationships of image classification accuracy and variation of landscape statistics. Can J Remote Sens 27:33–43

    Google Scholar 

  • Shen W, Wu J, Ren H (2003) Effects of changing spatial extent on landscape pattern analysis. Acta Ecol Sin 23:2219–2231

    Google Scholar 

  • Smouse PE, Long JC, Sokal RR (1986) Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Syst Zool 35:627–632

    Article  Google Scholar 

  • 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 98:128–142

    Article  CAS  PubMed  Google Scholar 

  • 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 17:569–586

    Article  Google Scholar 

  • Tischendor L (2001) Can landscape indices predict ecological processes consistently? Landscape Ecol 16:235–254

    Article  Google Scholar 

  • Turner MG, O’Neill RV, Gardner RH, Milne BT (1989) Effects of changing spatial scale on the analysis of landscape pattern. Landscape Ecol 3:153–162

    Article  Google Scholar 

  • Wickham JD, Ritters KH (1995) Sensitivity of landscape metrics to pixel size. Int J Remote Sens 16:3585–3594

    Article  Google Scholar 

  • Wickham JD, O’Neill RV, Riitters KH, Wade T, Jones KB (1997) Sensitivity of selected landscape pattern metrics to land-cover misclassification and differences in land-cover composition. Photogram Eng Remote Sens 63:397–402

    Google Scholar 

  • Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397

    Article  Google Scholar 

  • Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecol 19:125–138

    Article  Google Scholar 

  • Wu J, Hobbs R (2002) Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landscape Ecol 17:355–365

    Article  Google Scholar 

  • Wu J, Loucks OL (1995) From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. Q Rev Biol 70:439–466

    Article  Google Scholar 

  • Wu J, Jelinski DE, Luck M, Tueller PT (2000) Multiscale analysis of landscape heterogeneity: scale variance and pattern metrics. Geogr Inf Syst 6:6–19

    Google Scholar 

  • Wu J, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landscape Ecol 17:761–782

    Article  Google Scholar 

  • Zhao W, Fu B, Chen L (2003) The effects of grain change on landscape indices. Quat Sci 23:326–333

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. US Forest Service, Rocky Mountain Research Station, 800 E Beckwith, Missoula, MT, 59801, USA

    Samuel A. Cushman

  2. Individualized Interdisciplinary Graduate Program, Mathematics Building, University of Montana, 800 E Beckwith, Missoula, MT, 59801, USA

    Erin L. Landguth

Authors
  1. Samuel A. Cushman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erin L. Landguth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samuel A. Cushman.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cushman, S.A., Landguth, E.L. Scale dependent inference in landscape genetics. Landscape Ecol 25, 967–979 (2010). https://doi.org/10.1007/s10980-010-9467-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-010-9467-0

Keywords

Search

Navigation