BUSCADOR RECOLECTA

Table S1. Plant species selected, localization, coordinates, and voucher numbers. Table S2. Chemical composition of the EOs from the most active species: L. luisieri (1 and 2) (Ll1 and Ll2), M. suaveolens (Ms); S. hybrid (Sh), S. montana (Sm), T. vulgaris (Tv), and T. zygis (Tz). RI, Retention index; RT, Retention time; HD, EOs obtained by hydrodistillation; SD, EOs obtained by steam distillation. Figure S1. Percentage of antiprotozoal activity of EOs obtained by hydrodistillation (HD) and steam distillation (SD) from L. x intermedia “Abrial” (Lab), L. x intermedia “Grosso” (Lg), L. x intermedia “Super” (Lsu), L. lanata (Ll), L. angustifolia (La), L. mallete (Lm), T. mastichina (Tm), O. virens (Ov), S. blancoana (Sb), S. officinalis (So), S. sclarea (Ss), R. officinalis (Ro), S. chamaecyparissus (Sc), T. vulgare (Tav), D. graveolens (Dg). A. Anti-Leishmania activity. B. Anti-Phytomonas activity of EOs. Figure S2. Chromatograms of the most active species EOs: L. luisieri (1 and 2) (Ll1 and Ll2), M. suaveolens (Ms); S. hybrid (Sh), S. montana (Sm), T. vulgaris (Tv), and T. zygis (Tz); HD, EOs obtained by hydrodistillation; SD, EOs obtained by steam distillation., Peer reviewed

pH values of different dilutions of the aqueous extracts (BC350AE and BC700AE) obtained from washing BC350 and BC700 (3% (w/v) with distilled water), respectively, for 24 h. Figure S1. SR-FTIR spectroscopy of grape pomace (GP) and biochars prepared at 350 °C and 700 °C (BC350, BC700, respectively). Full 2nd derivative spectra (wavenumber range between 3100 and 900 cm−1) along with the first two main principal components (PC-1 and PC-2) for different wavenumber ranges, which explain the largest contributions of signal shifts between samples. Numbers in red indicate the wavenumber values of the most representative peaks. Figure S2. SR-FTIR spectroscopy of grape pomace (GP), washed (BC350W) and unwashed (BC350) biochar prepared at 350 °C, and BC350 aqueous extract (BC350AE). Full 2nd derivative spectra (wavenumber range between 3100 and 900 cm−1) along with the first two main principal components (PC-1 and PC-2) for different wavenumber ranges, which explain the largest contributions of signal shifts between samples. Numbers in red indicate the wavenumber values of the most representative peaks. Figure S3. SR-FTIR spectroscopy of grape pomace (GP), washed (BC700W) and unwashed (BC700) biochar prepared at 700 °C, and BC700 aqueous extract (BC700AE). Full 2nd derivative spectra (wavenumber range between 3100 and 900 cm−1) and along with the first two main principal components (PC-1 and PC-2) for different wavenumber ranges, which explain the largest contributions of signal shifts between samples. Numbers in red indicate the wavenumber values of the most representative peaks. Figure S4. Effect of different concentrations (3%, 1.5% and 0.75%) of the unwashed biochar obtained at 700 °C (BC700) on 3-week old tomato plants 24 h and 6 days after its transplantation into biochar-amended sandy soils. Figure S5. Effect of different concentrations (3%, 1.5% and 0.75%) of the unwashed biochar obtained at 350 °C (BC350) on roots and shoots of tomato plants 3 weeks after its transplantation into biochar-amended sandy soils. Figure S6. Effect of different concentrations of BC350W on the percentage of seed germination (a), number of leaves (b), shoot length (c), relative water content (RCW) (d) and total fresh biomass (f) of 6-week-old tomato plants. Means ± standard errors (n ≥ 8). Significant differences with respect to the control according to Mann–Whitney U test. ***p < 0.001; **p < 0.01., Peer reviewed

Figures S1–S2: 1H-NMR and 13C-NMR of compound 1a; Figure S3: 13C-NMR of compound 2; Figures S4–S5: 13C-NMR of compounds 4–5; Figures S6–S7: 1H-NMR an 13C-NMR of compound 7a; Figures S8–S10: 13C-NMR of compounds 8–10; Figures S11–S20: 1H-NMR and 13C-NMR of compounds 11–14 and 15–16; Figure S21: 1H-NMR of compound 17; Figures S22–S27: 1H-NMR and 13C-NMR of compounds 18,18a and 19., Peer reviewed

This PDF file includes: Figs. S1 to S13., Peer reviewed

Table S1: Test statistic and p-values of Kruskall–Wallis tests comparing differences in oviposition on four different oviposition substrates (cabbage, rape, pea, and aluminium foil) for the strains DBM-W, DBM-NOQA, DBM-C, and DBM-P of P. xylostella. The significance values have been adjusted by the Bonferroni correction for multiple tests. Significant p-values (p ≤ 0.05) are shown in bold type; Table S2: Comparison of the preference between abaxial and adaxial leaf surfaces among the three P. xylostella strains reared on cabbage (DBM-C), artificial diet (DBM-G88), and pea (DBM-P). Oviposition preference data were analyzed using a one-tailed, two-sample test of proportions (p ≤ 0.05) comparing the percentages of the total number of eggs laid on the abaxial side of leaves (n = 3–96, except in the case of C. papaya for DBM-C, where n = 2).; and Table S3: Total glucosinolate content (TOT) and content of aliphatic glucosinolates with sulfur-containing side chains (AS), other aliphatic glucosinolates (AO), benzenic glucosinolates (BEN), and indolic glucosinolates (IN) for each of the plant types tested (A). Glucosinolate richness (S), Shannon’s diversity index for the four glucosinolate classes (HA), Shannon’s diversity index for the relative concentrations of all individual glucosinolates (HB), and chemical complexity index for glucosinolates (CCI) for each of the plant types tested (B). Values based on means across replicates taken from Badenes-Pérez et al. 2020 [10]., Peer reviewed

Table S1: Pairwise comparisons in OPI between plant species after conducting Kruskal–Wallis tests; Table S2: Comparison between P. rapae and P. xylostella for the percentage of eggs laid on plant species tested compared to A. thaliana (n = 3). Significant differences are shown in bold type; Table S3: Significance of correlations between oviposition preference index in two-choice tests (OPI), total oviposition in no-choice tests (TO), and larval survival (LS) for P. rapae and P. xylostella in the plants tested; Table S4: Mean ± SE glucosinolate content (µmol g-1 plant dry weight) in the plants tested [37]; Table S5: Total glucosinolate content (TOT) and content of aliphatic glucosinolates with sulfur-containing side chains (AS), other aliphatic glucosinolates (AO), benzenic glucosinolates (BEN), and indolic glucosinolates (IN) for each of the plant types tested (A). Glucosinolate richness (S), Shannon’s diversity index for the four glucosinolate classes (HA), Shannon’s diversity index for the relative concentrations of all individual glucosinolates (HB), and chemical complexity index for glucosinolates (CCI) for each of the plant types tested (B) [37]; Table S6: Correlations between oviposition preference index (OPI), total oviposition (TO), and larval survival (LS) and glucosinolate richness (S), Shannon’s diversity index for the four glucosinolate classes (HA), Shannon’s diversity index for the relative concentrations of all individual glucosinolates (HB), glucosinolate complexity index (GCI), total glucosinolate content (TOT), aliphatic glucosinolates with sulfur-containing side chains (AS), other aliphatic glucosinolates (AO), benzenic (BEN), and indolic glucosinolates (IN) as shown by CATPCA analysis; Table S7: Pairwise comparisons in total oviposition in no-choice tests (TO) between plant species after conducting Kruskal–Wallis tests; Table S8: Comparison between P. rapae and P. xylostella for the percentage of eggs laid on the abaxial side of the leaves in the plant species tested (n = 3–96, except in the case of A. argenteum, C. bursa-pastoris, and T. majus for P. rapae and in the case of C. papaya and M. oleifera for P. xylostella, in which n = 2); Table S9: Pairwise comparisons in total oviposition in no-choice tests (TO) between plant species after conducting Kruskal–Wallis tests; Table S8: Pairwise comparisons in larval survival (LS) between plant species after conducting Kruskal–Wallis tests; Table S10: Origin of the seeds of the plant species tested; Figure S1: CATPCA plots showing the relationship between oviposition preference index (OPI) (A), total oviposition (TO) (B), and larval survival (LS) (C) and total glucosinolate content (TOT) and content of aliphatic glucosinolates with sulfur-containing side chains (AS), other aliphatic glucosinolates (AO), benzenic glucosinolates (BEN), and indolic glucosinolates (IN) in the plant species tested. Reference [37] is cited in the Supplementary Materials., Peer reviewed

Table S1. Soil samples collected per plant species, forest (Jaén JA; Segura SE), dominant mycorrhizal type (Myc) of plants (arbuscular mycorrhizal AM; ectomycorrhizal ECM) and season (Autumn; Spring). Enzymatic activities (pmol mg-1 h-1 SOM-1) related to carbon (C), nitrogen (N) and phosphorous (P) cycles in the soils collected under the different plant species are shown. The enzymes corresponding to each cycle are: Labile C = β-glucosidase + cellobiohydrolase; Non-labile C = β-xylosidase + β-glucuronidase + laccase; N = chitinase + leucine-aminopeptidase; P = acid + alkaline phosphatases. Table S2. Linear Mixed-Effect Models testing the effect of season, forest and plant species on soil properties and C and nutrient cycling. Season and forest were fixed factors, while the site nested in forest and the plant species nested in site and forest were random factors. Significant interactions among factors were not detected. Labile C = β-glucosidase + cellobiohydrolase; Non-labile C = β-glucuronidase + β-xylosidase + laccase; N cycling = chitinase + leucine-aminopeptidase; P cycling = acid + alkaline phosphatase. Soil properties: pH, SOM = soil organic matter, soil moisture. χ12 values for fixed and random factors and Bonferroni corrected p-values ‘***’ p < 0.001, ‘**’ p < 0.01, ‘*’ p < 0.05, ‘.’ p < 0.10, are given; significant effects are noted in bold. The coefficient of determination (pseudo-R2, i.e., variance explained) is shown for both pools of fixed and random factors. Table S3. Plant phylogenetic composition and morpho-functional traits predictors of carbon and nutrient cycling in soils, analysed by Redundancy Analysis. A Redundancy Analysis was carried out per forest, a) Jaén and b) Segura, to determine the non-redundant phylogenetic PCoA axes and morpho-functional traits that explained responses of carbon and nutrient cycling. ANOVA results (F statistic and p-value) were extracted from each Redundancy Analysis and those significant (p < 0.05) compiled in this table (see also Figure 4). Fig. S1. Relationship between N cycling-related enzymatic activities with C cycling-related ones. Pearson correlations were carried out to relate N-related enzymes (chitinase + leucine amino-peptidase) with hydrolytic (β-glucosidase + cellobiohydrolase + β-glucuronidase + β-xylosidase) and oxidative (laccase) C-related enzymes. Each plot shows the correlation coefficient (r) with its p-value. Fig. S2. Local Moran’s Index (LMI) calculated for carbon and nutrient cycling and cycling ratios and mapped onto the plant phylogeny per forest, a) Jaén and b) Segura. Positive/negative LMI values indicate that closely relatives tend to show similar/dissimilar values for a given variable. Values marked in red indicate significant and marginal autocorrelations, i.e., p < 0.05 and 0.05 > p < 0.10, respectively. Hotspots of positive (i.e., phylogenetic conservatism) and negative (i.e., phylogenetic divergence) autocorrelation are differentiated with triangles and all are represented by LMI with p < 0.05. See correspondence between acronyms and plant names in Table S1. Labile C = β-glucosidase and cellobiohydrolase; Non-labile C = β-xylosidase, β-glucuronidase and laccase; N = chitinase and leucine-aminopeptidase; P = acid and alkaline phosphatases. Figure S3. Phylogenetic composition of plant communities. Principal Coordinates Analysis (PCoA) was run over the phylogenetic distance matrix of plant species studied per forest, Jaén and Segura. Eigenvector values (PCoA-n) that significantly explained responses of carbon and nutrient cycling were used in RDA analyses. Scores of PCoA axis were plotted by plant species at each forest, a) Jaén and b) Segura, and may locate taxa across the phylogeny on the PCoA axes. The total percentage of variance explained by significant PCoA axes was 3.4 % for Jaén and 0.2 % for Segura., Peer reviewed

Table S1. Location, altitude and dominant vegetation at each study site. Table S2. Samples distribution by species, season, region and site. Figure S3. Sequencing rarefaction curves and sampling completeness. Methods S4. Root processing, DNA extraction, sequencing and bioinformatic analyses. Table S5. A recruitment network represented by its recruitment matrix. Table S6. Description of main network metrics and indices used throughout the work. Methods S7. General metrics describing the plant-AMF networks studied. Methods S8. Null models for modularity. Table S9. Maximum likelihood tree of the sequences defining the AMF virtual taxa sampled in this study. Table S10. PERMANOVA exploring the effect of region, season, site and plant species on AMF community composition. Table S11. Bayesian glmm testing the influence of the degree of canopy plant species on the dissimilarity in AMF communities between canopy and recruit species., Peer reviewed

Derivation of the dispersion relation in high-q approximation considering the TM modes only; description of the method of the calculation of the isofrequency curves by minimizing the losses; derivation of the expressions for the directions of the group velocity and the imaginary part of the wavevector; comparison of the IFC obtained with the different approaches; analytical expression for the field distribution; conditions for the surface or volume type of the mode; and derivation of the dispersion relation of the TE modes in the ultrathin-slab limit., Peer reviewed

[EN] WETYDAS v.2.0.0. (Weather Types Data set of Spain) contains 143 TXT archives localized by their coordinates in 20-century reanalyses grid (from 36º-44 lat North and 350º-5º longitude) covering 1836 to 2015; information include Year, Month, Day, Direction (in deg), WT.dir (8 classes, only dependent on direction), Hyb (separate between directional type, Pure A or C types, or Hybrid type), WT (final classification with 26 classes), WT.num (same as WT column but the names of the 26 WT classes are converted to numbers from 1 to 26; classes are ordered from NE = 1), WT10 (idem as WT but reclassified to 10 classes only: 8 directional types + C and A), WT10.num (idem as WT10 but converted in numbers from 1 to 10), SF (geostrophic southerly flow index), WF (geostrophic westerly flow index), F (total flow index: square root of SF^2+WF2^2), ZS (southerly shear vorticity), ZW (easterly shear vorticity) and Z (shear vorticity: ZS + ZW). For details of the computation see Trigo and DaCamara (2000) Int. Jr. Clim. https://doi.org/10.1002/1097-0088(20001115)20:13<1559:AID-JOC555>3.0.CO;2-5; [ES] WETYDAS v.2.0.0. (Weather Types Data set of Spain), contiene 143 archivos TXT con la clasificación de tipos de tiempo en los puntos de la malla del reanálisis 20-century (36º-44 lat North y 350º-5º longitude) period 1836- 2015; la información se incluye en columnas con el año, mes y día, la dirección (en grados), WT.dir (8 clasificación en ocho clases solamente acorde la direción del flujo), Hyb (identificación entre tipos direccionales, A Puro, C Puro o tipo Híbrido), WT (clasificación final en 26 tipos), WT.num (igual que WT pero numeradas las clases con NE = 1), WT10 (idem a WT con clases reclasificadas a 10 tipos, 8 direcionlaes + C y A), WT10.num (idem a WT10 convirtiendo a número de 1 a 10), SF (índice del flujo geostrófico sur), WF (índice del flujo geostrófico oeste), F (iínidce flujo total: raíz cuadrada de SF^2+WF2^2), ZS (shear vorticity meridional), ZW (shear vorticity del este) y Z (shear vorticity: ZS + ZW). Detalles de cálculo según Trigo and DaCamara (2000) Int. Jr. Clim. https://doi.org/10.1002/1097-0088(20001115)20:13<1559:AID-JOC555>3.0.CO;2-5. Validación del método aplicado a la malla del reanálisis en Fernández-Granja, J.A., Brands, S., Bedia, J. et al. Exploring the limits of the Jenkinson–Collison weather types classification scheme: a global assessment based on various reanalyses. Clim Dyn 61, 1829–1845 (2023). https://doi.org/10.1007/s00382-022-06658-7, [ES] Hemos calculado la nueva base de datos de alta resolución espacial empleando la clasificación de Jenkinson & Collison clasificación de tipos de tiempo a la Península Ibérica en la malla de presiones diaria del reanálisis del siglo (Enero-1836 a Diciembre 2015). Dada su resolución, 1ºx1º lat/long, WETYDAS v.2.0.0. contiene 143 archivos acorde las coordenadas de la ventana analizada (36º-44 lat Norte/ 350º-5º longitud). Los tipos de tiempo incluyen 8 direccionales puros, el tipo Anticiclónico y Ciclónico puros más 8 Ciclónicos híbridos y 8 Anticiclónicos Híbridos (total 26 tipos). Se recalculan reclasificaciones para evitar los tipos híbridos. Los casos no determinados fueron diseminados entre la clasificación., [EN] It has been applied the Jenkinson & Collison classification of Weather Types to Iberian Peninsula and Balearic Island by using the daily NOAA/CIRES/DOE 20th Century Reanalysis (V3) dataset (January-1836/December-2015). WETYDAS v.2.0.0. grid resolution/1.0ºx1.0 lat/long produces 143 series. Weather Types classification includes 8 directional pure, Anticyclonic and Cyclonic pure types, and combination of previous ones in the hybrid types. Non determines cases were spread homogeneously., No