Clusters resulting from the bivariate LISA analysis of the frequency of marker M86.

<p>The plot shows the distribution of the clusters obtained from the bivariate LISA analysis of the correlation of the frequency of marker M86 compared with the weighted values of the environmental variable “percentage of maximum possible sunshine in March”. The colors of the cluster correspond to different spatial autocorrelation regimes, as explained in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086668#pone-0086668-g005" target="_blank">Figure 5</a>.</p

List of the environmental variables used in Matsam association analyses: 118 total variables = altitude+9 climatic variables ×13 periods (12 months+yearly mean).

<p>List of the environmental variables used in Matsam association analyses: 118 total variables = altitude+9 climatic variables ×13 periods (12 months+yearly mean).</p

Logistic regression of marker M16 on four environmental variables.

<p>Results of the logistic regression of marker M16 on A) yearly mean of diurnal temperature range (DTR), B) yearly mean of the percentage of maximum possible sunshine (SUN), C) frequency of precipitation in November (RDO), and D) relative humidity in October (REH). Blue dots represent locations where the band is present (1) or absent (0). Grey lines show the upper and the lower limit of the confidence interval at 99.9%.</p

Clusters resulting from the bivariate LISA analysis of the frequency of marker M16.

<p>The plot shows the distribution of the clusters obtained from the bivariate LISA analysis of the correlation of the frequency of marker M16 with the weighted values of the environmental variable “number of days with more than 0.1 mm of rain in November”. The colors of the cluster correspond to different spatial autocorrelation regimes: red = high marker frequencies correlated with high mean of environmental variables values measured at the nearest 90 neighbouring farms (see the text for further details); blue = low marker frequency correlated with low environmental variable values; purple = low marker frequency correlated with high environmental variable values; pale red = high marker frequency correlated with low environmental variable values. Locations with frequencies showing no spatial dependence are displayed in white.</p

The four AFLP markers most significantly associated with environmental variables.

<p>From left to right: the average frequency of the marker over the whole study area; the Posterior Probability for the marker to be under selection provided by BayeScan; the F_ST value provided by Mcheza; the detail of monthly or yearly environmental variables associated with the corresponding marker.</p

Number of significant models identified by MATSAM for the most significant confidence levels.

<p>Number of models identified by Matsam, Bayescan and Mcheza (panel A) and by Matsam alone (panel B) at different levels of statistical significance (Wald test). The colour shades vary from dark green = large number of significant associations, to red = no significant associations.</p

List of goat breeds included in the study (ID numbers on the first column was used to identify the breeds on the maps in Figures 5, 6 and Figure S1).

<p>List of goat breeds included in the study (ID numbers on the first column was used to identify the breeds on the maps in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086668#pone-0086668-g005" target="_blank">Figures 5</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086668#pone-0086668-g006" target="_blank">6</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086668#pone.0086668.s001" target="_blank">Figure S1</a>).</p

Outlier loci identified by BayeScan.

<p>Axis X shows the posterior odds (PO), i.e. the ratio between the posterior probability (PP) of the model with selection and the PP of the neutral model. The Y axis shows the F_ST index values.</p

Results of the FCA analysis for AFLP and MS-AFLP markers.

<p>Percentage of inertia explained by the first 4 factors of the Factorial Correspondence Analysis for AFLP and MS-AFLP markers.</p><p>Results of the FCA analysis for AFLP and MS-AFLP markers.</p

Factorial Correspondence Analysis evidencing the relationships among AFLP genotypes and MS-AFLP epigenotypes of different saffron crocus accessions.

<p>1A) Factorial Correspondence Analysis showing multivariate relationships among AFLP genotypes of different accessions on the axes corresponding to first (x axis, 9.72% inertia) vs. second (y axis, 6.75% of inertia) main factors (1A I). The two populations Pop A and Pop B correspond to the main clusters identified by STRUCTURE analysis at K = 2 (1A II). Population A included just the S accessions and three different genotypes, while population B included both S and NS accessions and 9 different genotypes. Inside population B, samples included in the dashed line refer to accessions from Iran, India, Afghanistan and Turkey. 1B) Factorial Correspondence Analysis showing multivariate relationships among MS-AFLP epigenotypes of different accessions on the axes corresponding to first (x axis, 12.88% of inertia) vs. second (y axis, 10.15% of inertia) main factors (1B I). The two populations Pop C and Pop D correspond to the main clusters identified by STRUCTURE analysis at K = 2 (1B II). Population C included just Spanish accessions and 6 effective epigenotypes, while population D included both S and NS accessions and 22 effective epigenotypes.</p