Potential wild barley distributions in current (A), past (B) and future (C) climates.

<p>Distributions are based on ecological niche modelling using MaxEnt (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#s2" target="_blank">Materials and Methods</a>). D, differences between current and past modelled distributions, including areas lost and gained since the LGM. E, differences between future and current modelled distributions, including areas expected to be lost and gained by the 2080s. Note that past and future distribution maps take no account of rises or falls in sea levels or of other water bodies, and that these distributions are shown superimposed on current country boundaries.</p

STRUCTURE group richness (A and B, <i>K₁₀</i>) and ‘altitude richness’ (C, <i>Alt₁₀</i>) maps for wild barley.

<p>A and B, richness estimates for BOPA SNPs and nSSRs, respectively, <i>K</i> = 5 in STRUCTURE analysis. Both marker sets indicate the Eastern Mediterranean region as more diverse (highly diverse areas = dark brown) than Central Asia. C, ‘altitude richness’ of wild barley sample sites, based on five altitude categories (<200 m, 200 to 600 m, 600 to 1,000 m, 1,000 to 1,400 m, >1,400 m). Altitude data provide an indication of environmental heterogeneity and were downloaded from WorldClim (<a href="http://www.worldclim.org/" target="_blank">www.worldclim.org/</a>; values given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#pone.0086021.s001" target="_blank">Table S1</a>). Unlike the 19 bioclimatic variables used elsewhere in the current study, altitude data are actual values rather than interpolations from weather station records, so they are particularly appropriate for assessing real environmental heterogeneity <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#pone.0086021-Hijmans2" target="_blank">[22]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#pone.0086021-Farr1" target="_blank">[77]</a>. Altitude richness estimates indicate sample points in the Eastern Mediterranean region as more diverse than those in Central Asia. Not all of the original sample range could be included in analyses because of the required minimum sampling intensity to calculate standardised diversity values (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#s2" target="_blank">Materials and Methods</a>; compare the current figure with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#pone-0086021-g001" target="_blank">Figs. 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#pone-0086021-g003" target="_blank">3</a> [individual STRUCTURE <i>K</i> group assignments], see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#pone-0086021-g004" target="_blank">Fig. 4</a>). Accessions included in analyses in a particular geographic area are circumscribed by a dotted line. The analysis to generate ‘altitude richness’ was carried out in the same way as for STRUCTURE group richness, except ‘altitude category’ substituted for ‘STRUCTURE group’.</p

Comparison of mean linkage disequilibrium (LD) (<i>r²</i>) values across all wild barley chromosomes.

<p>CA = Central Asia, EM = Eastern Mediterranean sub-samples. Comparisons are for BOPA SNPs at five cM intervals (SNPs 0 to 5 cM apart, 5 to 10 cM apart, etc.). The dotted line indicates the average value of all plotted comparisons. The graph indicates that when compared to longer pairwise SNP distances, LD estimates for shorter pairwise SNP distances are relatively higher in the Central Asia sub-sample than in the Eastern Mediterranean sub-sample. The difference between sub-samples appears to be lost after about 15 cM.</p

Spatial autocorrelation analysis profiles for wild barley accessions based on BOPA SNPs, nSSRs and cpSSRs.

<p>Geographic distances on the <i>x</i>-axis are the mean values of distance classes. The symbols at the top of the figure mark observations significantly larger or smaller (<i>P</i>≤0.01) than the average for distance classes. Values for the <i>Sp</i> statistic, calculated from the regression slope of the graph and the kinship coefficient of the first distance class <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#pone.0086021-Vekemans1" target="_blank">[76]</a>, are also shown. Placing all three data sets on the same graph allows profiles to be compared. Increases in similarity at a distance class of around 1,000 km, and an earlier additional increase for cpSSRs at around 500 km, illustrate that a simple isolation-by-distance model is not sufficient to describe genetic variation in wild barley.</p

STRUCTURE group assignments for individual wild barley accessions.

<p>The results shown are based on <i>K</i> = 5 and for BOPA SNPs. The results for nSSRs (not shown) were similar. Results correspond with spatial autocorrelation analysis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086021#pone-0086021-g002" target="_blank">Fig. 2</a>) in describing a more complex genetic structure in wild barley than might be expected with a simple isolation-by-distance model.</p

Information on 24 tropical trees subjected to next-generation sequencing and screened for SSRs.

a<p>Based on the Agroforestree Database (<a href="http://www.worldagroforestry.org/resources/databases/agroforestree" target="_blank">www.worldagroforestry.org/resources/databases/agroforestree</a>), an open access resource of ICRAF that provides data on >650 trees.</p>b<p>The seed source of material for NGS varied and included natural stands, seed orchards and landraces. The numerical reference is the ICRAF accession number.</p>c<p>Current data from NGS; complete information is available at the tropiTree portal (<a href="http://bioinf.hutton.ac.uk/tropiTree" target="_blank">http://bioinf.hutton.ac.uk/tropiTree</a>). In () is the number of perfect SSRs identified. In [] is the percentage of the corresponding transcripts that have TAIR hits (for all SSRs).</p>d<p>Data from National Center for Biotechnology Information of the USA (NCBI) searches were included to illustrate previous sequencing work. Searches were undertaken on 14 April 2014 via the Entrez search system (<a href="http://www.ncbi.nlm.nih.gov/sites/gquery" target="_blank">www.ncbi.nlm.nih.gov/sites/gquery</a>). Species names for NCBI searches were checked as correct against current nomenclature using the Agroforestry Species Switchboard (<a href="http://www.worldagroforestry.org/products/switchboard/" target="_blank">www.worldagroforestry.org/products/switchboard/</a>), an open access resource of ICRAF that provides links to information on >20,000 plants. Current names were set as ‘organism’ in NCBI searches. In () is the number of ESTs listed in NCBI nucleotide citations (if any). In [] is the number of NGS studies cited in NCBI’s Sequence Read Archive (if any).</p>e<p>As well as being of importance to small-scale farmers, <i>Acacia mangium</i> and <i>Jatropha curcas</i> have wide commercial interests (see text), explaining the high NCBI citations.</p>f<p>Species were subject to primer validation (see text).</p

SSR repeats in 24 tree species subjected to next-generation sequencing.

<p>The proportion of di-, tri- and tetra-nucleotide repeats is shown. The species are ordered by the proportion of di-nucleotide repeats revealed.</p