Using simulations to evaluate Mantel-based methods for assessing landscape resistance to gene flow

Ecology and Evolution

Volumn 6, Issue 12, 2016, Pages 4115-4128

  Zeller, Katherine A ^a   Creech, Tyler G ^b   Millette, Katie L ^c   Crowhurst, Rachel S ^b   Long, Robert A ^d   Wagner, Helene H ^e   Balkenhol, Niko ^f   Landguth, Erin L ^g  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b OREGON STATE UNIVERSITY   (United States)

^c MCGILL UNIVERSITY   (Canada)

^d Field Conservation Program   (United States)

^e UNIVERSITY OF TORONTO   (Canada)

^f UNIVERSITY OF GÖTTINGEN   (Germany)

^g UNIVERSITY OF MONTANA   (United States)

Author keywords

CDPOP; landscape fragmentation; landscape genetics; landscape resistance; simulations

Indexed keywords

EID: 84971265188     PISSN: None     EISSN: 20457758     Source Type: Journal    
DOI: 10.1002/ece3.2154     Document Type: Article

References (52)

1. Adriaensen, F., J. P. Chardon, G. De Blust, E. Swinnen, S. Villalba, H. Gulinck, et al. 2003. The application of ‘least-cost’ modelling as a functional landscape model. Land. Urban Plann. 64:233–247.
2. Balkenhol, N., L. P. Waits, and R. J. Dezzani. 2009. Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data. Ecography 32:818–830.
3. Bowcock, A. M., A. Ruiz-Linares, J. Tomfohrde, E. Minch, J. R. Kidd, and L. L. Cavalli-Sforza. 1994. High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368:455–457.
4. Box, G. E. P., and D. R. Cox. 1964. An analysis of transformations (with discussion). J. R. Stat. Soc. B 26:211–252.
5. Castillo, J. A., C. W. Epps, A. R. Davis, and S. A. Cushman. 2014. Landscape effects on gene flow for a climate-sensitive montane species, the American pika. Mol. Ecol. 23:843–856.
6. Cushman, S. A., and E. L. Landguth. 2010. Spurious correlations and inference in landscape genetics. Mol. Ecol. 19:3592–3602.
7. Cushman, S. A., K. S. McKelvey, J. Hayden, and M. K. Schwartz. 2006. Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am. Nat. 168:486–499.
8. Cushman, S. A., A. J. Shirk, and E. L. Landguth. 2013a. Landscape genetics and limiting factors. Conserv. Genet. 14:263–274.
9. Cushman, S. A., T. N. Wasserman, E. L. Landguth, and A. J. Shirk. 2013b. Re-evaluating causal modeling with mantel tests in landscape genetics. Diversity 5:51–72.
10. Dale, M. R. T., and M. J. Fortin. 2010. From graphs to spatial graphs. Annu. Rev. Ecol. Evol. Syst. 41:21–38.
11. Dijkstra, E. W. 1959. A note on two problems in connexion with graphs. Numer. Math. 1:269–271.
12. Diniz-Filho, J. A., T. N. Soares, J. S. Lima, R. Dobrovolski, V. L. Landeiro, M. Pires de Campos Telles, et al. 2013. Mantel test in population genetics. Genet. Mol. Biol. 36:475–485.
13. Dormann, C. F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G. Carré, et al. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46.
14. Dudaniec, R. Y., J. W. Wilmer, J. O. Hanson, M. Warren, S. Bell, and J. R. Rhodes. 2016. Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials. Mol. Ecol. 25:470–486.
15. Dyer, R. J., J. D. Nason, and R. Garrick. 2010. Landscape modelling of gene flow: improved power using conditional genetic distance derived from the topology of population networks. Mol. Ecol. 19:3746–3759.
16. Ferreras, P. 2001. Landscape structure and asymmetrical inter-patch connectivity in a metapopulation of the endangered Iberian lynx. Biol. Conserv. 100:125–136.
17. Garroway, C. J., J. Bowman, and P. J. Wilson. 2011. Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti. Mol. Ecol. 19:3978–3988.
18. Goslee, S. C., and D. L. Urban. 2007. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22:1–19.
19. Graves, T. A., P. Beier, and J. A. Royle. 2013. Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal. Mol. Ecol. 22:3888–3903.
20. Guillot, G., and F. Rousset. 2013. Dismantling the Mantel tests. Methods Ecol. Evol. 4:336–344.
21. Jaquiéry, J., T. Broquet, A. H. Hirzel, J. Yearsley, and N. Perrin. 2011. Inferring landscape effects on dispersal from genetic distances: how far can we go? Mol. Ecol. 20:692–705.
22. Kierepka, E. M., and E. K. Latch. 2014. Performance of partial statistics in individual-based landscape genetics. Mol. Ecol. Resour. 15:512–525.
23. Landguth, E. L., and S. A. Cushman. 2010. CDPOP: a spatially explicit cost distance population genetics program. Mol. Ecol. Resour. 10:156–161.
24. Landguth, E., S. Cushman, M. Schwartz, K. McKelvey, M. Murphy, and G. Luikart. 2010. Quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19:4179–4191.
25. Landguth, E. L., B. C. Fedy, S. J. Oyler-McCance, A. L. Garey, S. L. Emel, M. Mumma, et al. 2012a. Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern. Mol. Ecol. Resour. 12:276–284.
26. Landguth, E., B. Hand, J. Glassy, S. Cushman, and M. Sawaya. 2012b. UNICOR: a species connectivity and corridor network simulator. Ecography 35:9–14.
27. Legendre, P., and M.-J. Fortin. 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.
28. Legendre, P., and L. Legendre. 2012. Numerical ecology. 3rd English ed. Elsevier, Amsterdam.
29. Legendre, P., M.-J. Fortin, and D. Borcard. 2015. Should the Mantel test be used in spatial analysis? Methods Ecol. Evol. 6:1239–1247.
30. Manel, S., M. K. Schwartz, G. Luikart, and P. Taberlet. 2003. Landscape genetics: combining landscape ecology and population genetics. Trends Ecol. Evol. 18:189–197.
31. Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Res. 27:209–220.
32. McGarigal, K., S. A. Cushman, and E. Ene. 2012. FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available from: http://www.umass.edu/landeco/research/fragstats/fragstats.html
33. McRae, B. H. 2006. Isolation by resistance. Evolution 60:1661–1561.
34. Moran, P. A. 1950. Notes on continuous stochastic phenomena. Biometrika 37:17–23.
35. Nei, M. 1973. Analysis of gene diversity in subdivided populations. Proc. Natl Acad. Sci. USA 70:3321–3323.
36. Oden, N. L., and R. R. Sokal. 1992. An investigation of three-matrix permutation tests. J. Classif. 9:275–290.
37. Oyler-McCance, S. J., B. C. Fedy, and E. L. Landguth. 2012. Sample design effects in landscape genetics. Conserv. Genet. 14:275–285.
38. Peterman, W. E., G. M. Connette, R. D. Semlitsch, and L. S. Eggert. 2014. Ecological resistance surfaces predict fine-scale genetic differentiation in a terrestrial woodland salamander. Mol. Ecol. 23:2402–2413.
39. R Development Core Team. 2011. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
40. Reding, D. M., S. A. Cushman, T. E. Gosselink, and W. R. Clark. 2013. Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus). Landscape Ecol. 28:471–486.
41. Richardson, J. L., S. P. Brady, I. J. Wang, and S. F. Spear. 2016. Navigating the pitfalls and promise of landscape genetics. Mol. Ecol. 25:849–863.
42. Ruiz-Lopez, M. J., C. Barelli, F. Rovero, K. Hodges, C. Roos, W. E. Peterman, et al. 2016. A novel landscape genetic approach demonstrates the effects of human disturbance on the Udzungwa red colobus monkey (Procolobus gordonorum). Heredity 116:167–176.
43. Schumaker, N. H. 1996. Using landscape indices to predict habitat connectivity. Ecology 7:1210–1225.
44. Shirk, A. J., D. O. Wallin, S. A. Cushman, C. G. Rice, and K. I. Warheit. 2010. Inferring landscape effects on gene flow: a new model selection framework. Mol. Ecol. 19:3603–3619.
45. Smouse, P. E., J. C. Long, and R. R. Sokal. 1986. Multiple regression and correlation extensions of the mantel test of matrix correspondence. Syst. Zool. 35:627–632.
46. Sokal, R. R. 1979. Testing statistical significance of geographic variation patterns. Syst. Zool. 28:227–232.
47. Spear, S. F., N. Balkenhol, M.-J. Fortin, B. H. McRae, and K. Scribner. 2010. Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol. Ecol. 19:3576–3591.
48. Storfer, A., M. A. Murphy, S. F. Spear, R. Holderegger, and L. P. Waits. 2010. Landscape genetics: where are we now? Mol. Ecol. 19:3496–3514.
49. Wagner, H. H., and M. J. Fortin. 2013. A conceptual framework for the spatial analysis of landscape genetic data. Conserv. Genet. 14:253–261.
50. Wagner, H. H., and M.-J. Fortin. 2015. Basics of spatial data analysis: linking landscape and genetic data for landscape genetic studies. Pp. 77–98 in N. Balkenhol, S. A. Cushman, A. T. Storfer, L. P. Waits, eds. Landscape genetics: concepts, methods, applications. John Wiley and Sons, Chichester, UK.
51. Wang, I. J. 2013. Examining the full effects of landscape heterogeneity on spatial genetic variation: a multiple matrix regression approach for quantifying geographic and ecological isolation. Evolution 67–12:3403–3411.
52. Wasserman, T., S. Cushman, M. Schwartz, and D. Wallin. 2010. Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecol. 25:1601–1612.