Fine-grained adaptive divergence in an amphibian: genetic basis of phenotypic divergence and the role of nonrandom gene flow in restricting effective migration among wetlands
Version of Record online: 7 JAN 2013
© 2013 Blackwell Publishing Ltd
Volume 22, Issue 5, pages 1322–1340, March 2013
How to Cite
Richter-Boix, A., Quintela, M., Kierczak, M., Franch, M. and Laurila, A. (2013), Fine-grained adaptive divergence in an amphibian: genetic basis of phenotypic divergence and the role of nonrandom gene flow in restricting effective migration among wetlands. Molecular Ecology, 22: 1322–1340. doi: 10.1111/mec.12181
- Issue online: 25 FEB 2013
- Version of Record online: 7 JAN 2013
- Manuscript Accepted: 21 NOV 2012
- Manuscript Revised: 10 NOV 2012
- Manuscript Received: 7 JUN 2012
- Royal Swedish Academy of Sciences
- Swedish Research Council
- Spanish Ministry of Education and Culture postdoctoral grant. Grant Number: MEC2007-0944
- Beatriu de Pinós postdoctoral fellowship. Grant Number: 2008 BP A 00032
- Spanish Ministry of Science
- José Castillejo. Grant Number: JC2008-00321
Section 1 Predator abundance estimation details and pcr information.
Table S1 Environmental description of localities.
Fig. S1 Map and localization of the study sites.
Section 2 Component analyses of environmental variables and spatial distribution.
Table S2 Component analysis for mixtures of quantitative and qualitative variables.
Table S3 Correlogram results from habitat parameters (CS1 and CS2).
Fig. S2 Visualization of environmental characters in CS1 and CS2.
Fig. S3 Correlograms results from habitat parameters (CS1 and CS2).
Section 3 Landscape resistance models: testing isolation by resistance.
Table S4 Conductance values for each land-cover type.
Table S5 Results and correlation coefficients of the IBR models.
Fig. S4 Map with the study area showing the cover types, landscapes and sampling sites.
Fig. S5 Resistance map for the best IBR model.
Fig. S6 Mean ± 2 SE of (a) mass at metamorphosis, (b) larval period, and (c) growth rate.
Section 4 Description of TRβ Haplotypes and genotypic information per population.
Table S6 Base composition of TRβ haplotypes detected and their frequencies.
Table S7 Summary statistics for TRβ gene variation in the 17 R. Arvalis populations.
Table S8 FSTS for TRβ between population pairs.
Table S9 Results from the machine-learning approach for phenotype-TRβ gene relationship.
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