Inferring landscape effects on gene flow: a new model selection framework
Version of Record online: 13 AUG 2010
© 2010 Blackwell Publishing Ltd
Special Issue: SPECIAL ISSUE ON LANDSCAPE GENETICS
Volume 19, Issue 17, pages 3603–3619, September 2010
How to Cite
SHIRK, A. J., WALLIN, D. O., CUSHMAN, S. A., RICE, C. G. and WARHEIT, K. I. (2010), Inferring landscape effects on gene flow: a new model selection framework. Molecular Ecology, 19: 3603–3619. doi: 10.1111/j.1365-294X.2010.04745.x
- Issue online: 25 AUG 2010
- Version of Record online: 13 AUG 2010
- Received 16 December 2009; revision received 19 May 2010; accepted 19 May 2010
Table S1 PCR Conditions and multiplex groups
Table S2 Causal modeling results based on proportion of shared alleles (DPS) genetic distance
Table S3 Causal modeling results based on Rousset’s a genetic distance
Fig. S1 A Gaussian function relating elevation to landscape resistance. This function has three parameters: Rmax (resistance ranges from 1 to Rmax), Eopt (the optimal elevation where resistance is lowest), and ES.D. (the standard deviation about the optimal elevation defining the shape of the response curve).
Fig. S2 Population structure based on STRUCTURE.
Fig. S3 STRUCTURE population assignments and fractional probability of subpopulation membership (Q).
Fig. S4 The second round of multivariate IBR model optimization for landcover and elevation following the road optimization (depicted in Fig. 3) is shown.
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|MEC_4745_sm_TableS1-3.doc||62K||Supporting info item|
Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.