Carbon stocks in aboveground biomass.

<p>(a) Aboveground carbon stocks increased with tree species richness. (b) Structural equation model (SEM) linking C stocks to tree species diversity, stand age, and tree density. Variable abbreviations: S = species richness, PD = phylogenetic diversity, FD = functional diversity, DIV = diversity (latent variable related to previous three), AGE = stand age, DENS = tree density, C stock = C stored in the aboveground biomass. See legend of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167771#pone.0167771.g001" target="_blank">Fig 1</a> for details.</p

Stand basal area, vertical structure and leaf area as a function of tree species richness, stand age and tree density.

<p>Stand basal area (a), vertical structure, quantified as standard deviation of tree height (c), and leaf area index (e) as a function of tree species richness and stand age. Lines represent the linear regression between richness and the response variable (ignoring stand age). Structural equation models (SEM) for stand basal area (b), vertical structure (d) and leaf area (f) in dependence of stand age, tree diversity and tree stem density. Species richness enhanced wood production (SBA) and vertical structure but did not affect stand leaf area. Path diagrams indicate effects of tree species diversity on SBA, either directly or indirectly via increases in tree density. The diagrams show standardized path coefficients (red: positive; blue: negative) and associated statistical significances (*** P<0.001; ** P<0.01; *P<0.05; (*) P<0.1). Grey arrows indicate error terms of response variables. Thin black arrows indicate the loadings of indicator variables and their errors. Variable abbreviations: S = species richness, PD = phylogenetic diversity, FD = functional diversity, DIV = diversity (latent variable related to previous three), AGE = stand age, DENS = tree density, SBA = total stem basal area, SD_height = height variation, LAI_summer = LAI in summer. The goodness of fit of each model was assessed with chi-square tests. A P-value >0.05 indicate a good fit between the SEM model and the observed data (i.e. the observed and expected covariance matrices are not different).</p

Structural equation models fitting total stem basal area in 2008 (a, c) and increment of total stem basal area from 2008 to 2010 (b, d) in dependence of successional age, tree diversity, and tree stem density.

<p>Path diagrams indicate effects of tree species diversity on the two dependent variables, either directly or indirectly via increases in tree density. The diagrams show standardized path coefficients (red: positive; blue: negative) and associated statistical significances (*** P<0.001; ** P<0.01; * P<0.05; (*) P<0.1). Variable abbreviations: S = species richness, PD = phylogenetic diversity, FD = functional diversity, DIV = diversity (latent variable related to previous three), AGE = successional age, DENS = tree density, BA = total stem basal area, ∆BA = increment of total stem basal area.</p

Total stem basal area in 2008 (a, c) and increment of total stem basal area from 2008 to 2010 (b, d) as functions of tree species richness and successional age of the study plots.

<p>Growth was assessed separately for canopy trees with a diameter at breast height (d) of 10 cm or larger (a, b) and for understory trees with d between 3 and 10 cm (c, d).</p

Relative growth rate of individual stem basal area (RGR, 2008–2010 period) in dependence of successional age, tree species richness, and tree stem density.

<p>In the canopy tree (d>10 cm) cohort, RGR declines with diversity (a) due to its correlation with successional age (b; path from DIV via AGE to RGR); in the understory (3 cm</p

DataSheet_1_Genome-wide analysis of UDP-glycosyltransferases family and identification of UGT genes involved in drought stress of Platycodon grandiflorus.docx

IntroductionThe uridine diphosphate (UDP)-glycosyltransferase (UGT) family is the largest glycosyltransferase family, which is involved in the biosynthesis of natural plant products and response to abiotic stress. UGT has been studied in many medicinal plants, but there are few reports on Platycodon grandiflorus. This study is devoted to genome-wide analysis of UGT family and identification of UGT genes involved in drought stress of Platycodon grandiflorus (PgUGTs).MethodsThe genome data of Platycodon grandiflorus was used for genome-wide identification of PgUGTs, online website and bioinformatics analysis software was used to conduct bioinformatics analysis of PgUGT genes and the genes highly responsive to drought stress were screened out by qRT-PCR, these genes were cloned and conducted bioinformatics analysis.ResultsA total of 75 PgUGT genes were identified in P.grandiflorus genome and clustered into 14 subgroups. The PgUGTs were distributed on nine chromosomes, containing multiple cis-acting elements and 22 pairs of duplicate genes were identified. Protein-protein interaction analysis was performed to predict the interaction between PgUGT proteins. Additionally, six genes were upregulated after 3d under drought stress and three genes (PGrchr09G0563, PGrchr06G0523, PGrchr06G1266) responded significantly to drought stress, as confirmed by qRT-PCR. This was especially true for PGrchr06G1266, the expression of which increased 16.21-fold after 3d of treatment. We cloned and conducted bioinformatics analysis of three candidate genes, both of which contained conserved motifs and several cis-acting elements related to stress response, PGrchr06G1266 contained the most elements.DiscussionPgGT1 was confirmed to catalyze the C-3 position of platycodin D and only eight amino acids showed differences between gene PGr008G1527 and PgGT1, which means PGr008G1527 may be able to catalyze the C-3 position of platycodin D in the same manner as PgGT1. Seven genes were highly expressed in the roots, stems, and leaves, these genes may play important roles in the development of the roots, stems, and leaves of P. grandiflorus. Three genes were highly responsive to drought stress, among which the expression of PGrchr06G1266 was increased 16.21-fold after 3d of drought stress treatment, indicating that PGrchr06G1266 plays an important role in drought stress tolerance. To summarize, this study laied the foundation to better understand the molecular bases of responses to drought stress and the biosynthesis of platycodin.</p