Philippa Kaur

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Effects of T. asperellum on salt ion content, sodium adsorption ration, and pH of maize seedlings under saline–alkaline stressAfter applying spore suspensions of T. asperellum at different concentrations, we observed significant increases in the soil contents of Ca2+, Mg2+, and K+ relative to those in the control, whereas, Na+, HCO3−, Cl−, and SO42− contents significantly decreased (Table 1). Thus, increasing T. asperellum spore densities in suspension effectively regulated the soil ion balance in the rhizosphere of maize seedlings, and all ions showed significant differences under treatment T3. Compared with those in the control, T3 significantly reduced the Na+ and HCO3− contents by 19.46% and 35.87% in XY335, and 20.02% and 36.29% in JY417, respectively, with an effect more pronounced than that with treatments T1 and T2. Although the Cl− and SO42− contents were low, their variation patterns were similar to that of HCO3− content. Overall, however, the composition of ions in the rhizosphere of maize seedlings was improved by the T. asperellum treatment.Table 1 Influence of T. asperellum on salt ion content, sodium adsorption ration (SAR), and pH value of maize seedlings rhizosphere soil (± SD).Full size tableAs shown in Table 1, compared with those in the control, T. asperellum treatment significantly reduced the soil pH and SAR values, although with no significant cultivar × treatment interaction effects (P  More

Whole genome resequencing of large yellow croaker populationsWe collected a total of 198 large yellow croaker individuals (Table S1). Of these, 50 individuals were captured in the Zhoushan Sea (the red dot in Fig. 1a) and 48 individuals had been farmed in Zhoushan (the orange dot in Fig. 1a). A further 38 individuals were captured in the Ningde Sea (the blue dot in Fig. 1a). and 62 individuals had been farmed in Ningde (the green dot in Fig. 1a). We performed whole-genome resequencing of these 198 large yellow croaker individuals. We obtained 1.42 Penta base-pairs of genomic DNA, representing about 11 × sequencing depth of the genome per individual. Raw reads were trimmed and aligned to the genome sequence. After variant calling and filtering, a total of 6,302,244 single nucleotide polymorphisms (SNPs) were identified. Using this SNP information, we performed the following population genomic analyses.Figure 1Population structure and relationship of large yellow croaker. (a) Geographic map indicating the sample origins of the large yellow croaker in this study. The gross appearance of a large yellow croaker individual is shown in the picture. The sampling area is highlighted by the red broken line. The dots of different color stand for different population. The number of individuals is given in parentheses after the population name. The geographical maps were generated by using R packages of maps v3.3.0 (https://cran.r-project.org/web/packages/maps) and mapdata v2.3.0 (https://cran.r-project.org/web/packages/mapdata). (b) PCA plot (PC1 and PC2) showing the genetic structure of the 198 large yellow croaker individuals. The degrees of explained variance is given in parentheses. Colors reflect the geographic regions in (a). (c) UMAP of the 198 large yellow croaker individuals. Colors reflect the geographic regions in (a).Full size imageGenetic population structure of the large yellow croaker individualsIn order to examine the genetic population structure of the large yellow croaker individuals, we performed principal component analysis (PCA). In the first component of the PCA, the Zhoushan farmed population separated from the other three populations (Fig. 1b). In the second component of the PCA, the Zhoushan sea-captured population formed a cluster. Also, the Ningde farmed population formed a cluster. The Ningde sea-captured population had a wider distribution than the other populations. Then, we performed uniform manifold approximation and projection (UMAP), a non-linear dimensionality method (Fig. 1c). The result of UMAP is similar to the result of PCA. UMAP showed that the Zhoushan farmed population formed a distinct cluster, and the Zhoushan sea-captured population and Ningde farmed population formed more scattered clusters. UMAP also showed that the Ningde sea-captured population had a wider distribution than the other populations.The evolutionary history of the individuals was inferred with the neighbour-joining (NJ) tree. The NJ tree contains two large groups (Fig. 2a). The first group was formed by the individuals of the Zhoushan farmed population plus several individuals of the Zhoushan sea-captured population. The other group was formed by the individuals in the other three groups. In this group, individuals of the Zhoushan sea-captured formed a distinct cluster from the individuals of the Ningde sea-captured population and those of the Ningde farmed population. The individuals of the Ningde sea-captured population and those of the Ningde farmed population together formed several small groups.Figure 2Neighbor-joining tree and admixture analysis using whole-genome SNP data. (a) Neighbor-joining tree of the 198 large yellow croaker individuals. The color scheme follows Fig. 1. The scale bar represents pairwise distances between different individuals. (b) Cross-validation error in the admixture analysis. The x-axis represents K values and the y-axis represents the corresponding cross-validation error. The cross-validation error was lowest at K = 3. (c) Admixture plot (K = 2, 3, 4) for the 198 large yellow croaker individuals. Each individual is shown as vertical bar divided into K colors. The color scheme follows Fig. 1. Individuals are arranged by population.Full size imageWe performed unsupervised clustering analysis with ADMIXTURE to evaluate the relatedness of the populations. Cross-validation error was lowest at K = 3, suggesting that the population genetic structure of our samples is best modelled by considering the admixture of the three genetic components (Fig. 2b). The individuals of the Zhoushan farmed population are composed of relatively uniform genetic components (Fig. 2c). The individuals of the Ningde farmed population are composed of genetic components that are also relatively uniform but different from those of the Zhoushan farmed population. Both the individuals of the Zhoushan sea-captured population and those of the Ningde sea-captured population were a mixture of the three genetic components.Trends of effective population sizeWe evaluated the extents of linkage disequilibrium for SNP pairs. The average r2 values of linkage disequilibrium decreased by increasing the marker distance between pairwise SNPs, with a rapidly declining trend observed over the first 500 kb (Fig. 3a). Using this information, we estimated the change of the effective population size over the past 1000 generations (Fig. 3b). All the four populations showed decreasing trends of effective population sizes, suggesting that their genetic diversities remain at a low level.Figure 3Trends of effective population sizes. (a) LD decay (r2) from 0 to 4000 kb for four populations. The x-axis represents marker distances between pairwise SNPs. The y-axis represents r2 values of linkage disequilibrium. (b) Effective population sizes of four populations over the past 1000 generations. All of the four populations showed decreasing trends.Full size imageDetection of putative genes associated with the adaptation to different sea environments of the Zhoushan Sea and Ningde SeaTo identify the genetic markers to differentiate individuals of the Zhoushan sea-captured and Ningde sea-captured, we calculated fixation index (Fst) values for each SNP. We identified total 819 SNPs as genetic markers (Table S2). To identify the genes associated with adaptation to the different living environments between these two regions, we calculated average Fst values in 40 kb windows with 10 kb steps (Fig. 4). We identified 47 regions with significant Fst values. The total size of these regions is 3.6 Mb. The sizes of the significant regions were between 40 kb to 0.31 Mb. These regions contained 88 genes (Table S3). We categorised the functions of these genes based on their gene ontology (GO) term annotations (Table S4). These genes include those involved in muscle structure development (GO:0061061) such as pdlim3a (pdz and lim domain 3). This gene is located in the region from 26,673,301 to 26,662,947 bp on chromosome 10, and is reported to be highly expressed in muscle and involved in the crosslinking of actin filaments15. We identified three upstream variants of this gene which are located at 26,675,034 bp, 26,675,134 bp, and 26,678,221 bp on chromosome 10 (Fig. 4). We also identified one downstream variant located at 26,660,973 bp on chromosome 10. Besides muscle structure development (GO:0061061), there are also some enriched GO terms such as regulation of response to external stimulus (GO:0032101) and cell–cell signalling (GO:0007267).Figure 4Genomic regions associated with regional differentiation of large yellow croaker between Zhoushan sea and Ningde sea. Manhattan plot for average Fst values in 40 kb windows with 10 kb steps between Zhoushan sea-captured population and Ningde sea-captured population. The x-axis represents chromosomal positions and the y-axis represents the average Fst values.Full size imageDetection of putative genes under selective sweep between the Zhoushan sea-captured population and farmed populationTo identify the genes under selective sweep in the domestication process, we analysed single Fst values for single SNPs and average Fst values in 40 kb windows with 10 kb steps separately both in the Zhoushan and Ningde regions. Between the Zhoushan sea-captured population and farmed population, we identified 23,862 SNPs with significant Fst values by single SNP analysis (Table S5). In the sliding window analysis, the number of significant regions was 317, and the total size of significant regions was 59 Mb (Fig. 5a). The sizes of significant regions were between 40 kb to 8.1 Mb. These regions contain 1709 genes (Table S6). We identified the strong peak of Fst signal on chromosome 11, which contains 423 genes such as hsp90ab1 (heat shock protein 90 alpha family class B member 1). GO analysis showed that genes involved in the regulation of fatty acid oxidation (GO:0031998), the steroid hormone mediated signalling pathway (GO:0043401), fatty acid metabolic processes (GO:0006631), membrane lipid metabolic processes (GO:0006643), regulation of fatty acid metabolic processes (GO:0019217), and long-chain fatty acid transport (GO:0015909). These GO terms include plenty of lipid metabolism-related genes such as ppara (peroxisome proliferator activated receptor alpha), pnpla2 (Patatin like phospholipase domain containing 2). It is worth mentioning that there were plenty of genes related to carbohydrate derivative metabolic processes (GO:1901135) with differences between the Zhoushan sea-captured population and farmed populations (Table S7). Additionally, a number of the growth relative genes include the developmental growth involved in morphogenesis (GO:0060560). Genes were found related to embryo development ending in birth or egg hatching (GO:0009792). Additionally, 47 genes related immune system development (GO:0002520) were obtained, such as taf3 (tata-box binding protein associated factor 3), irf7 (interferon regulatory factor 7) and rps7 (ribosomal protein s7) (Table S7).Figure 5Genomic regions associated with domestication of large yellow croaker between Zhoushan sea or Ningde sea. (a) Manhattan plot for average Fst values in 40 kb windows with 10 kb steps between Zhoushan sea-captured and Zhoushan farmed. (b) Manhattan plot for average Fst values in 40 kb windows with 10 kb steps between Ningde sea-captured and Ningde farmed. The x-axis represents chromosomal positions and the y-axis represents the average Fst values.Full size imageMoreover, we found that anxa2a (annexin a2a; from 16,718,332 bp to 16,713,531 bp on chromosome 21) have a splice donor site variant at 16,715,408 bp on chromosome 21. This mutation is located at the fifth intron of anxa2a, and is predicted to lead to a premature truncation. The anxa2a gene encodes a phospholipid-binding protein, and is involved in variety of intracellular processes including endocytosis, exocytosis, membrane domain organisation, actin remodelling, signal transduction, protein assembly16. This batch of samples came from breeding selection for a freeze-resistant population. We identified nine downstream mutations (16,713,395 bp, 16,713,442 bp, 16,713,443 bp, 16,713,593 bp, 16,715,408 bp, 16,715,741 bp, 16,716,027 bp, 16,716,216 bp and 16,717,363 bp on chromosome 21) of ice2 (interactor of little elongation complex ELL subunit 2) gene, which is located in the region from 16,727,361 to 16,718,192 bp on chromosome 21. This gene is involved in cold acclimation and determines freezing tolerance17.Detection of putative genes under selective sweep between the Ningde sea-captured and farmed populationFor the Ningde farmed population, we identified 660 SNPs with significant Fst values (Table S8). In the sliding window analysis, the number of significant regions was 42, and the total size of significant regions was 7.8 Mb (Fig. 5b). The sizes of significant regions were between 40 kb to 2.0 Mb. These regions contain 238 genes (Table S9). GO analysis showed identified genes related to the reproduction process such as female gonad development (GO:0008585), i.e. esr1 (estrogen receptor 1), foxo3 (forkhead box O3); the development of primary female sexual characteristics (GO:0046545) and embryonic appendage morphogenesis (GO:0035113), such as mbnl1 (muscle blind like splicing regulator 1); as well as embryonic limb morphogenesis (GO:0030326) and the response to steroid hormones (GO:0048545). Additionally, genes related to digestive tract development (GO:0048565) were enriched, such as hnf1b (hnf1 homeobox b) (Table S10). As per the results of SNPs with the highest Fst analysis between the Ningde sea-captured and farmed population, we identified a downstream variant of esr1, which is located at 9,103,629 bp on chromosome 11. This gene is located in the region from 9,129,853 and 9,108,464 bp on chromosome 11 and encodes estrogen receptor 1, which plays a critical role in responding to steroid hormones (Fig. 5b). Genes involved in visual system development (GO:0150063) such as prox1 (prospero-related homeobox1), nr2e1 (nuclear receptor subfamily 2 group e member 1) and znf513a (zinc finger protein 513a) were also enriched. The znf513a gene is located in the region from 11,664,515 to 11,657,703 bp on chromosome 11 and has a downstream variant located at 11,652,743 bp on this chromosome (Fig. 5b). More

Study sitesTo obtain a cross-section of land-use types through the Eastern Alps (Fig. 2), rooting samples were taken from Tyrol (Austria) and from South Tyrol and northern Trentino (both in Italy), which include two climatic regions—the central European climatic region in the northern part and the sub-Mediterranean climatic region in the southern part of the research area47. The average annual precipitation at the 13 study sites ranges from 400 to 2000 mm, with maximum rainfall observed from June to July47. Mean annual temperature ranges from 0 °C to 9 °C. Additional climatic variability was added by sampling at elevations from 650 to 2680 m a.s.l. The bedrock in the research area is composed of calcareous sedimentary rock in the northern and southern regions and of crystalline rock in the main chain of the Alps, sometimes with superimposed calcareous isles: Stubai Valley (North Tyrol) is geologically dominated by silicate with transitions to limestone; Ötz Valley, Ziller Valley and Igls/Patsch (all North Tyrol), Passeier Valley, Mühlbach, Matsch, Ritten and Jenesien (South Tyrol) are geologically dominated by silicate; and Leutasch (North Tyrol), St. Vigil and Toblach (both South Tyrol) and Monte Bondone (near Trento) are geologically dominated by limestone. The pH of the topsoil (0–10 cm), which ranges from 3.7 to 7.832, is determined by bedrock and land use48. For more details on the study region, see Supplementary Appendix S1.Figure 2Site, sample number and analyzed land-use types in the Eastern Alps. Study sites: B = Monte Bondone; I = Igls/Patsch; J = Jenesien; L = Leutasch; M = Mühlbach; M2 = Matsch; O = Ötz Valley; P = Passeier Valley; R = Ritten; S = Stubai Valley; T = Toblach; V = St. Vigil; and Z = Ziller Valley. The map was created using ArcGIS 10.2.2 (ESRI Inc.) and edited in Microsoft PowerPoint 365 MSO (Map data: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community).Full size imageTo be representative, the most widespread vegetation communities in the 13 study sites for all land-use types (arable land, intensively used hay meadow, lightly managed hay meadow, pasture, agriculturally unused grasslands, and forest) were analyzed (Supplementary Appendix S2). Overall, a total of 171 soil samples were taken, with 15 samples from arable land, 56 samples from intensively used hay meadows, 15 samples from extensively managed hay meadows, 16 samples from lightly stocked pastures, 32 samples from agriculturally unused grasslands, and 37 samples from forests. Meadows that are mown and fertilized with slurry and/or manure at least twice a year were defined as intensively used hay meadows. An extensively managed hay meadow was not fertilized and mown only once a year. Pastures were extensively grazed by cattle and/or sheep (annual average stocking intensity: 0.15–0.4 livestock units (LU) ha−1 year−1) but not mown. As arable land, we defined different crops typical for the region, especially maize and bread cereal crops, as well as vegetables and potatoes. Agriculturally unused grasslands included all grassland areas that were abandoned for at least five years or have never been used for agriculture, such as alpine grasslands. Finally, all permanent deciduous, coniferous or mixed forests were combined into the forest land-use type (thus, no energy forests).Data collection and analysisVegetation and site variables depending on land-use types were used to explain the rooting parameters. As Fig. 1 shows, dependencies between explanatory variables and rooting parameters are not always strictly unidirectional. For example, vegetation composition influences rooting; however, rooting patterns can also influence vegetation composition. We considered as many different dependencies as possible in the applied methods and interpreted discovered statistically significant relationships as associations rather than causal (unidirectional) impacts.Rooting parameters: root mass, root length and rooting depthOverall, 171 rooting samples (Appendices S1 and S2) were taken between 1994 and 2017 in the field with core samplers of 6.8–7.7 cm diameter and a maximum core depth of 70 cm. Before coring, the vegetation was characterized with the standard phytosociological method of Braun-Blanquet49 to directly connect rooting and vegetation characteristics. The size of the vegetation survey areas was determined by the minimal area of a plant community as the area with 90% or more of all plant species within this ecosystem. The survey area ranged between 1 m × 1 m in homogenous meadows and 10 m × 10 m in forests. Even though we chose the rooting survey areas to be homogeneous regarding vegetation composition, it was possible that the rooting measured in the soil cores was affected by species other than those located above the core area due to large heterogeneity within plant communities50. Nevertheless, this error should be negligible.As the data for this analysis were derived from a collection of rooting analyses from different research projects in the past 20 years using the same methodological approach, the number of samples per land-use type and per site was unbalanced (Supplementary Appendix S2). For example, some land-use types were represented only at one site (e.g., all agriculturally unused areas were at site I), while others were represented at three or even more than 10 sites. In addition, the number of samples within each land-use type was also unbalanced: 15 samples for arable land, 32 for agriculturally unused grasslands or 56 samples for intensively used hay meadows. The original data collection included the most common and important plant communities in the project areas except for arable land. Thus, the rooting of the most common crops (maize: n = 3; barley: 3; oat: 3; wheat: 3; and vegetables: 3) was analyzed near Innsbruck in an area specially selected for this purpose.In the laboratory, the soil cores were split into the O-horizon (if present) and mineral soil layers of various thicknesses (0–3 cm, 3–8 cm, 8–13 cm, 13–23 cm, 23–38 cm, 38–53 cm, and  > 53 cm). Root extraction was performed manually with the roots cleared of soil in sieving cascades under running water51. Afterwards, the roots were sorted into three size categories18: (1) very fine roots (diameter between 0 and 1 mm); (2) fine roots (diameter between 1 and 5 mm); and (3) coarse roots (diameter between 5 and 20 mm). Roots of woody species with a diameter larger than 20 mm were not taken into account, as the distribution and diameter of coarse roots (especially trees) in the soil vary greatly spatially; therefore, a single survey cannot be representative of the rooting of an ecosystem50,52. The reason for this classification was due to the different functions of the classes. Very fine roots have a dominant role in the uptake of water and nutrients and may be the main source of stabilized carbon input to soil1. Fine roots are mainly responsible for the transport, anchoring and storage of carbohydrates and are also able to take up water. Coarse roots are important for water transfer and the stabilization of plants. To account for the different specific root lengths (SRLs) of very fine and coarse roots from herbaceous and woody species29, we classified the single samples according to the cover of herbaceous and woody species from the phytosociological surveys into pure grassland samples, mixed grassland samples (dominance of woody species:  50%)18. The conversion of root mass to rooting length was carried out using previously published Eqs. 19 (Table 1). Finally, the maximum depth (RD90%), above which 90% of the total root mass was found, was calculated for each root sample using the equation:$$RD_{90% } = RM_{tot} frac{{arctan left( {frac{{RM_{90% } }}{{RM_{tot} }}} right)}}{{m_{max } }},$$
(1)
where RM90% is 90% of the total root mass (kg m-2) and mmax is the maximum slope of the saturation curve. In the same way, the depths above which 50% (RD50%) and 95% (RD95%) of the total root mass occurred were calculated. In forests and in dwarf shrub-rich communities, the rooting depths and distributions could be biased by the fact that the sampling depth was very shallow, which could lead to underestimating the 50%, 90% and 95% rooting depths53. In grassland ecosystems, croplands and in dwarf shrub rich communities, however, the 70 cm sampling depth is sufficient because most roots are within the top 30 cm18.Table 1 Linear functions to calculate the root length on the basis of root dry weight for different vegetation communities: grassland communities (G), mixed grassland communities (M), and dwarf shrub-rich or tree-rich vegetation communities (W). y = root length (mm m-2) and x = root dry mass (g m-2).Full size tableEnvironmental variablesFor every root sample, we collected 79 potential impact variables on rooting, including 19 site variables, six land-use variables and 53 vegetation variables (see Table 2 and Appendices S1, S3 and S4).Table 2 Groups of variables used to explain rooting parameters, including information on the type (V, vegetation variable; S, site variable; and LU, land-use variable), the number of variables of each group (no.) and examples (for details, see Appendices S1, S3 and S4).Full size tableVegetation variablesIn total, 53 vegetation variables were collected and divided a priori into four groups (Table 2, Supplementary Appendix S3). Variables included in the richness group were ‘number of plant species’, ‘number of taxonomic groups’ and ‘functional types’ (after38). All variables that displayed information on the mean species cover, plant cover variance or dominance of species, the Shannon–Wiener and Evenness indices (both after54) and the total vegetation cover were allocated to the community composition group. We calculated the Shannon–Wiener and Evenness indices54 for species composition, functional types and functional traits.The cover of functional types group included variables that provide information on the abundance, dominance and composition of single plant functional types (see Supplementary Appendix S3). Finally, the community-level trait group (see Supplementary Appendix S3) contained leaf, plant height and root traits (effect traits in sensu55) used to assess the relative effects of aboveground and root trait turnover at the community level. They were calculated for each sample using trait values taken from the literature and the measured abundance of each species within the single community (i.e., community weighted mean56). We used mean root density and main rooting depth for the single species57,58,59. The rooting density of the species was classified into sparse, medium dense, dense, and very dense roots59. The mean leaf size and plant height of the species (sources:60,61; http://www.floraweb.de/; own observations) were classified according to the following thresholds. Plant height was divided into small (mean plant height  90 cm) species. Leaf size was classified as small (mean leaf area  70 cm2) species. In accordance with other authors62,63, most plant species showed clear allometric allocation trends between leaves, stems and root biomass for different groups of plant species. In particular, a trend towards a decreased root mass fraction with plant size was detected.Site characteristicsImportant meteorological parameters were measured at eight study sites at a distance of  1%). We investigated whether all these species were summarized into PCA components, i.e., into species groups with similar habitat requirements. Species not included in any component were treated as their own component (however, in our study, all species were included in a component). The multiple correlation coefficient (R2) of each component with the vegetation and site components/variables was computed. A high R2 denotes that the information of the key species is covered by the vegetation and site variables.All technical details and further detailed descriptions of the methods can be found in Supplementary Appendix S13. Statistical analyses were conducted with Stata/MP 13.1 for Windows. More

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