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A conceptual framework for the spatial analysis of landscape genetic data

Conservation Genetics volume 14, pages 253–261 (2013)Cite this article

Abstract

Understanding how landscape heterogeneity constrains gene flow and the spread of adaptive genetic variation is important for biological conservation given current global change. However, the integration of population genetics, landscape ecology and spatial statistics remains an interdisciplinary challenge at the levels of concepts and methods. We present a conceptual framework to relate the spatial distribution of genetic variation to the processes of gene flow and adaptation as regulated by spatial heterogeneity of the environment, while explicitly considering the spatial and temporal dynamics of landscapes, organisms and their genes. When selecting the appropriate analytical methods, it is necessary to consider the effects of multiple processes and the nature of population genetic data. Our framework relates key landscape genetics questions to four levels of analysis: (i) node-based methods, which model the spatial distribution of alleles at sampling locations (nodes) from local site characteristics; these methods are suitable for modeling adaptive genetic variation while accounting for the presence of spatial autocorrelation. (ii) Link-based methods, which model the probability of gene flow between two patches (link) and relate neutral molecular marker data to landscape heterogeneity; these methods are suitable for modeling neutral genetic variation but are subject to inferential problems, which may be alleviated by reducing links based on a network model of the population. (iii) Neighborhood-based methods, which model the connectivity of a focal patch with all other patches in its local neighborhood; these methods provide a link to metapopulation theory and landscape connectivity modeling and may allow the integration of node- and link-based information, but applications in landscape genetics are still limited. (iv) Boundary-based methods, which delineate genetically homogeneous populations and infer the location of genetic boundaries; these methods are suitable for testing for barrier effects of landscape features in a hypothesis-testing framework. We conclude that the power to detect the effect of landscape heterogeneity on the spatial distribution of genetic variation can be increased by explicit consideration of underlying assumptions and choice of an appropriate analytical approach depending on the research question.

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References

  • Anderson CD, Epperson BK, Fortin MJ, Holderegger R, James PMA, Rosenber MS, Sribner KT, Spear SF (2010) Considering spatial and temporal scale in landscape-genetic studies of gene flow. Mol Ecol 19:3565–3575

    PubMed  Article  Google Scholar 

  • Angelone S, Kienast F, Holderegger R (2011) Where movement happens: scale-dependent landscape effects on genetic differentiation in the European tree frog. Ecography 34:714–722

    Article  Google Scholar 

  • Balkenhol N, Gugerli F, Cushman SA, Waits L, Coulon A, Arntzen J, Holderegger R, Wagner HH (2009a) Identifying future research needs in landscape genetics: where to from here? Landsc Ecol 24:455–463

    Article  Google Scholar 

  • Balkenhol N, Waits LP, Dezzani RJ (2009b) Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data. Ecography 32:818–830

    Article  Google Scholar 

  • Barbujani G, Sokal RR (1991) Genetic population structure of Italy. II. Physical and cultural barriers to gene flow. Am J Hum Genet 48:398–411

    PubMed  CAS  Google Scholar 

  • Barbujani G, Oden NL, Sokal RR (1989) Detecting regions of abrupt change in maps of biological variables. Syst Zool 38:376–389

    Article  Google Scholar 

  • Borcard D, Gillet F, Legendre P (2011) Numerical ecology with R. Springer, New York

    Book  Google Scholar 

  • Bulman CR, Wilson RJ, Holt AR, Bravo LG, Early RI, Warren MS, Thomas CD (2007) Minimum viable metapopulation size, extinction debt, and the conservation of a declining species. Ecol Appl 17:1460–1473

    PubMed  Article  Google Scholar 

  • Cushman SA, Landguth EL (2010) Spurious correlations and inference in landscape genetics. Mol Ecol 19:3592–3602

    PubMed  Article  Google Scholar 

  • Cushman S, McKelvey K, Hayden J, Schwartz M (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am Nat 168:486–499

    PubMed  Article  Google Scholar 

  • Dale MRT, Fortin MJ (2010) From graphs to spatial graphs. Annu Rev Ecol Evol Syst 41:21–38

    Article  Google Scholar 

  • DalGrande F, Widmer I, Wagner HH, Scheidegger C (2012) Vertical and horizontal photobiont transmission within populations of a lichen symbiosis. Mol Ecol. doi:10.1111/j.1365-294X.2012.05482.x

    Google Scholar 

  • Dawson KJ, Belkhir K (2001) A Bayesian approach to the identification of panmictic populations and the assignment of individuals. Genet Res 78:59–77

    PubMed  Article  CAS  Google Scholar 

  • Diniz-Filho JAF, Nabout JC, Telles MPdC, Soares TN, Rangel TFLVB (2009) A review of techniques for spatial modeling in geographical, conservation and landscape genetics. Genet Mol Biol 32:203–211

    PubMed  Article  Google Scholar 

  • Dray S, Pélissier R, Couteron P, Fortin MJ, Legendre P, Peres-Neto PR, Bellier E, Bivand R, Blanchet FG, De Cáceres M, Dufour A, Heegard E, Jombart T, Munoz F, Oksanen J, Thioulouse J, Wagner HH. Community ecology in the age of multivariate multiscale spatial analysis. Ecol Monogr. doi:10.1890/11-1183.1

  • Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol Model 196:483–493

    Article  Google Scholar 

  • Durand E, Jay F, Gaggiotti OE, François O (2009) Spatial inference of admixture proportions and secondary contact zones. Mol Biol Evol 26:1963–1973

    PubMed  Article  CAS  Google Scholar 

  • Dyer RJ, Nason JD, Garrick RC (2010) Landscape modelling of gene flow: improved power using conditional genetic distance derived from the topology of population networks. Mol Ecol 19:3746–3759

    PubMed  Article  Google Scholar 

  • Epperson BK (2003) Geographical genetics. Princeton University Press, Princeton

    Google Scholar 

  • Folke C, Carpenter S, Walker B, Scheffer M, Elmqvist T, Gunderson L, Holling CS (2004) Regime shifts, resilience, and biodiversity in ecosystem management. Annu Rev Ecol Evol Syst 35:557–581

    Article  Google Scholar 

  • Foll M, Gaggiotti OE (2006) Identifying the environmental factors that determine the genetic structure of populations. Genetics 174:875–891

    PubMed  Article  CAS  Google Scholar 

  • Fortin MJ, Dale MRT (2005) Spatial analysis: a guide for ecologists. Cambridge University Press, New York

    Google Scholar 

  • Fortin MJ, Drapeau P, Jacquez GM (1996) Quantification of the spatial co-occurrences of ecological boundaries. Oikos 77:51–60

    Article  Google Scholar 

  • François O, Durand E (2010) Spatially explicit Bayesian clustering models in population genetics. Mol Ecol Resour 10:773–784

    PubMed  Article  Google Scholar 

  • Frankham R (2005) Genetics and extinction. Biol Conserv 126:131–140

    Article  Google Scholar 

  • Frankham R (2009) Introduction to conservation genetics. Cambridge University Press, New York

    Google Scholar 

  • Garant D, Kruuk LEB, McCleery RH, Sheldon BC (2007) The effects of environmental heterogeneity on multivariate selection on reproductive traits in female great tits. Evolution 61:1546–1559

    PubMed  Article  Google Scholar 

  • Garroway CJ, Bowman J, Wilson PJ (2011) Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti. Mol Ecol 20:3978–3988

    PubMed  Article  Google Scholar 

  • Greenwald KR (2010) Genetic data in population viability analysis: case studies with ambystomatid salamanders. Anim Conserv 13:115–122

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Holderegger R, Buehler D, Gugerli F, Manel S (2010) Landscape genetics of plants. Trends Plant Sci 15:675–683

    PubMed  Article  CAS  Google Scholar 

  • Holling CS (1992) Cross-scale morphology, geometry, and dynamics of ecosystems. Ecol Monogr 62:447–502

    Article  Google Scholar 

  • Jacquez GM, Maruca S, Fortin MJ (2000) From fields to objects: a review of geographic boundary analysis. J Geogr Syst 2:221–241

    Article  Google Scholar 

  • James PMA, Coltman DW, Murray BW, Hamelin RC, Sperling FAH (2011) Spatial genetic structure of a symbiotic beetle-fungal system: toward multi-taxa integrated landscape genetics. PLoS One 6:e25359

    PubMed  Article  CAS  Google Scholar 

  • Jay F (2011) PoPS: prediction of population genetic structure—program documentation and tutorial. University Joseph Fourier, Grenoble

    Google Scholar 

  • Keyghobadi N, Roland J, Matter SF, Strobeck C (2005) Among- and within-patch components of genetic diversity respond at different rates to habitat fragmentation: an empirical demonstration. Proc Biol Sci 272:553–560

    PubMed  Article  Google Scholar 

  • Lande R (1991) Isolation by distance in a quantitative trait. Genetics 128:443–452

    PubMed  CAS  Google Scholar 

  • Landguth EL, Cushman S, Murphy MA, Luikart G (2010) Relationships between migration rates and landscape resistance assessed using individual-based simulations. Mol Ecol Resour 10:854–862

    PubMed  Article  CAS  Google Scholar 

  • 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 10:831–844

    PubMed  Article  Google Scholar 

  • Legendre P, Legendre L (1998) Numerical ecology. Elsevier, New York

    Google Scholar 

  • Lichstein JW, Simons TR, Shriner SA, Franzreb KE (2002) Spatial autocorrelation and autoregressive models in ecology. Ecol Monogr 72:445–463

    Article  Google Scholar 

  • Manel S, Joost S, Epperson BK, Holderegger R, Storfer A, Rosenberg MS, Scribner KT, Bonin A, Fortin MJ (2010) Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field. Mol Ecol 19:3760–3772

    PubMed  Article  CAS  Google Scholar 

  • McRae BH, Dickson BG, Keitt TH, Shah VB (2008) Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89:2712–2724

    PubMed  Article  Google Scholar 

  • Méndez M, Tella JL, Godoy JA (2011) Restricted gene flow and genetic drift in recently fragmented populations of an endangered steppe bird. Biol Conserv 144:2615–2622

    Article  Google Scholar 

  • Monmonier MS (1973) Maximum-difference barriers: an alternative numerical regionalization method. Geogr Anal 5:245–261

    Article  Google Scholar 

  • Murphy MA, Dezzani RJ, Pilliod DS, Storfer A (2010) Landscape genetics of high mountain frog metapopulations. Mol Ecol 19:3634–3649

    PubMed  Article  Google Scholar 

  • Oden NL, Sokal R, Fortin MJ, Goebl H (1993) Categorical wombling: detecting regions of significant change in spatially located categorical variables. Geogr Anal 25:315–336

    Article  Google Scholar 

  • Safner T, Miller MP, McRae BH, Fortin MJ, Manel S (2011) Comparison of bayesian clustering and edge detection methods for inferring boundaries in landscape genetics. Int J Mol Sci 12:865–889

    PubMed  Article  CAS  Google Scholar 

  • Slatkin M, Arter HE (1991) Spatial autocorrelation methods in population genetics. Am Nat 138:499–517

    Article  Google Scholar 

  • Sokal RR (1979) Testing statistical significance of geographic variation patterns. Syst Zool 28:227–232

    Article  Google Scholar 

  • Spear SF, Balkenhol N, Fortin MJ, McRae BH, Scribner KT (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591

    PubMed  Article  Google Scholar 

  • Spielman D, Brook BW, Frankham R, Schaal BA (2004) Most species are not driven to extinction before genetic factors impact them. Proc Natl Acad Sci USA 101:15261–15264

    PubMed  Article  CAS  Google Scholar 

  • St-Louis V, Fortin MJ, Desrochers A (2004) Spatial association between forest heterogeneity and breeding territory boundaries of two forest songbirds. Landsc Ecol 19:591–601

    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

    PubMed  Article  CAS  Google Scholar 

  • Storfer A, Murphy MA, Spear SF, Holderegger R, Waits LP (2010) Landscape genetics: where are we now? Mol Ecol 19:3496–3514

    PubMed  Article  Google Scholar 

  • Wagner HH, Fortin MJ (2005) Spatial analysis of landscapes: concepts and statistics. Ecology 86:1975–1987

    Article  Google Scholar 

  • Ward EJ (2008) A review and comparison of four commonly used Bayesian and maximum likelihood model selection tools. Ecol Model 211:1–10

    Article  CAS  Google Scholar 

  • Wright S (1943) Isolation by distance. Genetics 28:114–138

    PubMed  CAS  Google Scholar 

  • Wright S (1948) On the roles of directed and random changes in gene frequency in the genetics of populations. Evolution 2:279–294

    PubMed  Article  CAS  Google Scholar 

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Acknowledgments

This work resulted from a Distributed Graduate Seminar (Developing Best Practices for Testing Landscape Effects on Gene Flow), conducted through the National Center for Ecological Analysis and Synthesis, a center funded by the National Science Foundation grant #EF-0553768, the University of California, Santa Barbara, and the State of California. It was also supported by NSERC discovery grants to HHW and MJF. We thank Michelle DiLeo and Yessica Rico for their thoughtful comments.

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  1. Department of Ecology and Evolutionary Biology, University of Toronto, 3359 Mississauga Road, Mississauga, ON, L5L 1C6, Canada

    Helene H. Wagner

  2. Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord St, Toronto, ON, M5S 3G5, Canada

    Marie-Josée Fortin

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  1. Helene H. Wagner
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  2. Marie-Josée Fortin
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Correspondence to Helene H. Wagner.

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Wagner, H.H., Fortin, MJ. A conceptual framework for the spatial analysis of landscape genetic data. Conserv Genet 14, 253–261 (2013). https://doi.org/10.1007/s10592-012-0391-5

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Keywords

  • Spatial statistics
  • Neutral genetic variation
  • Adaptive genetic variation
  • Node analyses
  • Link analyses
  • Neighborhood analyses
  • Boundary analyses