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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

21679463.zip contains: Cera et al. PED.R; Supplementary files_25112022.docx; data_Cera et al. Plant Ecology and Diversity.xlsx, Plants growing on extreme soils have mainly been described in relation to their adaptations to edaphic conditions, although herbivores may also be an important factor in these ecosystems. Gypsum soils occur in drylands often where livestock practices occur. However, it is unknown whether plant traits related to gypsum soil constraints are associated with resistance to herbivory. In order to assess whether gypsum specialist species might be favoured at higher grazing levels and to detect the traits involved, we evaluated the responses of gypsum specialists vs. generalists to three intensities of livestock pressure. We analysed the relative cover shifts of species along a livestock gradient, and variation in canopy height, canopy area, leaf carbon (C), nitrogen (N), and sulphur (S), specific leaf area (SLA) and leaf dry matter content (LDMC). We found that gypsum-specialists responded by increasing or maintaining their cover at medium and high grazing pressure, whereas most generalists responded by decreasing it. Gypsum-specialists showed higher leaf S than generalists, regardless of grazing intensity. All species showed similar patterns for traits linked to loss of above-ground biomass when grazing increased. Plant affinity to gypsum soils mediates vulnerability to grazing with foliar S possibly being a defence trait., This work was supported by Gobierno de España [MICINN, CGL2015-71360-P, CGL2016-80783-R and PID2019-111159GB-C31]; by European Union’s Horizon 2020 [H2020-MSCA-RISE-777803]; and by Consejo Superior de Investigaciones Científicas [COOPB20231]. AC and SP were funded by a FPI fellowship [MICINN, BES-2016-076455] and a Ramón y Cajal Fellowship [MICINN, RYC-2013-14164], respectively., Peer reviewed

[ES] MOPREDAS_Annual_Books_1916-1950 se compone de los datos digitalizados de los Libros Resúmenes Anuales editados por los sucesivos Servicios Meteorológicos de España en el periodo 1916-1950; [EN] MOPREDAS_Annual_Books_1916-1950 is a compilation of monthly precipitation data digitalized from Annual Books 1916-1950., [ES] Mopredas_Annual_Books_1916-1950 es el resultado de un proceso de digitalización de los datos publicados en los Libros Resúmenes Anuales editados por los servicios meteorológicos de España, periodo 1916-1950. Combinados con los datos incluidos en el BNDC han sido empleados para el desarrollo de las mallas de alta resolución denominadas MOPREDAS_1916-2015 y subsiguientes versiones, [EN from original) ] Mopredas_Annual_Books_1916-1950 is the result of a long digitalization procedure of Annual Books edited by Spanish Meteorological Services; the compilation includes hundreds of data not at present included in the Banco Nacional de Datos del Clima and increase the spatial and temporal density of precipitation records in pre-1950 period. Combined with BNDC after identification of digitalized data with BNDC observatories, it has been uses to develop the new high spatial density grid MOPREDAS_1916-2015 and versions, CGL2014-52135-C3-3-R Desarrollo de índices de sequía sectoriales: mejora de la monitorización y alerta temprana de las sequías en España DESEMON. Gobierno de España y UE, CGL2011-27574-C02-01 Impactos Hidrológicos del Calentamiento Global en España HIDROCAES Gobierno de España y UE, CGL2008-05112-C02-01/CLI. Proyecto Cambio climático: base de datos de precipitaciones, análisis de tendencias e impactos en los sistemas naturales Gobierno de España y UE, Three .csv files, No, Manual

Table S1. Results from DNA concentration and purity quantification using ND-1000-Spectrophotometer (mean ± sd). Large standard deviations for average concentration values are due to variation in tissue quantity among samples. All samples were however diluted to the concentration of 2.5 ng/µl before telomere length estimation. Three linear models (Concentration/[260/289]/[260/230] as dependent variable with Kenward-Roger approximation for degrees of freedom) were ran to test the differences among populations. Differences in DNA concentration were not statistically significant (F5, 531=1.02, p=0.40) while the differences in both 260/280 (F5, 531=2.45, p=0.03) and 260/230 (F5, 531=8.70, p<.0001) ratios reached statistical significance. Including the ratio-values as covariates in the statistical analyses presented in the main text with telomere length as dependent variable did not change the results or conclusion, thus these covariates were removed from the final models to reduce model parameters., Table S2. Population specific (Mean ± sd) efficiencies and Cq-values for control gene (SCG) and telomere (TELO) assays. Three linear models (SCG Cq/SCG Efficiency/TELO Efficiency as dependent variable with Kenward-Roger approximation for degrees of freedom) were ran to test the differences among populations. Differences in SCG Cq-values were not statistically significant (F5, 528=1.48, p=0.20) while the differences in both SCG (F5, 528=8.11, p<.0001) and TELO (F5, 528=12.66, p<.0001) efficiencies reached statistical significance. Including both assay efficiencies as covariates in the statistical analyses presented in the main text with telomere length as dependent variable did not change the results or conclusion, thus these covariates were removed from the final models to reduce model parameters., Table S3. Results of linear mixed models explaining the effects of Age class and Population on telomere length using subsets of the whole data including samples analyzed only with a) QuantStudio or b) MicPCR, Figure S1. Locations of the study sites; breeding area of the pied flycatcher in Eurasia shown in orange. Birds from all populations are expected to migrate through Iberian Peninsula and west coast of Africa to their Sub-Saharan non-breeding grounds described in Ouwehand et al. 2016 (black circle; Finnish and Estonian birds blue circle; English and Spanish birds red circle). Map modified from: BirdLife International. 2018. Ficedula hypoleuca. The IUCN Red List of Threatened Species 2018: e.T22709308A131952521. https://dx.doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22709308A131952521.en. Downloaded on 10 August 2021., Figure S2. Illustrating the telomere lengths (T/S ratios) of the same sample measured both with QuantStudio and MicPCR. Telomere length estimates are consistently somewhat higher for MicPCR (15 out of 20 samples) accounting for somewhat low agreement repeatability of 0.851 (95% Cl [0.66, 0.94], P<0.001) between the two machines., Figure S3. Individual raw telomere length values (T/S ratio) per population and age class. See sample sizes for Population: Nestling/Fledgling/Adult in the caption for Figure 1., Figure S4. Relative telomere length in six pied flycatcher populations across a north-south gradient in Europe, from the early nestling period (Nestling; 5 days after hatching), to fledging (Fledgling; 12 days after hatching) and adulthood (Adult; end of the rearing period) using subsets of data including rTL values obtained only with a) QuantStudio, or b) MicPCR. Values are estimated marginal means based on z-scored telomere length values ± s.e.m. Sample sizes [for Population: Nestling/Fledgling/Adult] are a) Oulu: 16/15/29; Turku: 15/13/32; Kilingi-Nõmme: 18/17/31; East Dartmoor: 17/17/31; La Hiruela: 23/12/33; Valsaín: 19/17/39, and b) Oulu: 3/4/12; Turku: 6/6/9; Kilingi-Nõmme: 4/3/12; East Dartmoor: 6/5/14; La Hiruela: 12/7/19; Valsaín: 5/6/10., Figure S5. Associations between migration distance (km) and relative telomere length (mean based on z-scored values) in the pied flycatcher fledglings (12 days after hatching; circles) and adults (averaged breeding pair; squares). Standard errors of the means (± sem) have been added to illustrate the population variation in telomere length. Fledgling values (circles) have been moved slightly to the right to clarify the error bars. Populations from the shortest migration distance to the longest: Spain (average of Valsaín and La Hiruela, red), England (East Dartmoor, yellow), Estonia (Kilingi-Nõmme, green), southern Finland (Turku, blue), and northern Finland (Oulu, purple)., Figure S6. Pied flycatcher chick body mass adjusted for clutch size at day 5 (A), day 12 (B) and growth rate (Δ mass between days 12 and 5; C) in six populations across a north-south gradient in Europe. Statistically significant differences after Tukey-Kramer adjustment for multiple comparisons are indicated with different letters. Values are estimated marginal means ± s.e.m. Sample sizes [for Population: Day5/Day12/Growth] are: Oulu, Finland: 19/19/17; Turku, Finland: 21/19/18; Kilingi-Nõmme, Estonia: 22/20/19; East Dartmoor, England: 20/22/18; La Hiruela, Spain: 33/19/18; Valsaín, Spain: 24/23/21., Figure S7. Change in relative telomere length during nestling period in the pied flycatcher (Δ telomere length between days 12 and 5) in six populations across a north-south gradient in Europe. The effect of population was marginally significant (p = 0.06) in explaining variation in early-life telomere change (see results for details). Values are estimated marginal means based on z-scored telomere length values ± s.e.m. Sample sizes [for Population] are: Oulu, Finland: 17; Turku, Finland: 18; Kilingi-Nõmme, Estonia: 19; East Dartmoor, England: 21; La Hiruela, Spain: 18; Valsaín, Spain: 21., Table of Contents: Table S1. Results from DNA concentration and purity quantification (p. 2).-- Table S2. Population specific efficiencies and Cq-values for control gene and telomere assays (p. 3).-- Table S3. Results of linear mixed models explaining the effects of Age class and Population on telomere length using subsets of the data (p. 4).-- Figure S1. Locations of the study sites (p. 5).-- Figure S2. Illustrating the telomere lengths of the same sample measured both with two qPCR machines (p. 6).-- Figure S3. Individual raw telomere length values (p. 6).-- Figure S4. Telomere length values in different populations using subsets of the data (p. 7).-- Figure S5. Associations between migration distance and telomere length (p. 8).-- Figure S6. Pied flycatcher chick body mass adjusted for clutch size (p. 9).-- Figure S7. Change in telomere length during nestling period (p. 10)., Peer reviewed

Supplemental Material contains a description of the on-site SOC, a MATLAB program to reproduce the bands of the 16-orbital model and to obtain the parameter $\eta$ from {L\"{o}wdin's} partition technique, and a TB electronic dispersion of slabs using the 16-orbital model., Peer reviewed

6 figures, 3 tables, Supplementary material for the article https://doi.org/10.1016/j.marenvres.2021.105315, Figure S1. Results of Vtg mRNA expression in females after normalization process with a different number of reference genes.-- Figure S2. Results of Vtg mRNA expression in males after normalization process with a different number of reference genes.-- Figure S3. Individual observation of RT-qPCR data for female and male different Vtg domains normalized with different number of reference genes.-- Figure S4. Bioanalizer profiles of three samples of RNA selected to assess RNA quality.-- Figure S5. Melt curve analysis of reference genes and vitellogenin primer pairs.-- Figure S6. Results of 1% agarose gel electrophoresis of PCR product using all primer pairs tested.-- Table S1. Equations of standard curves for primers pair efficiency.-- Table S2. Power analysis showing the effect size that could be confidently detected (% change in comparison with control values) in our RT-qPCR analyses results using a sample size of 3, and the averaged observed standard deviation (SD) in our samples.-- Table S3. Results of Two-Way ANOVA performed in females and males respectively to evaluate the effect of factor "time” (t4 and t24), factor “chemical” (C, SC and EE2) and the interaction of the two factors on Vtg mNRA normalized expression levels with different number of reference genes.-- Zip mmc2. Sequences.-- Zip mmc3. Alignments, Peer reviewed

Supplementary Figure 1 | Root diversity in example individuals of Papaver rhoeas from the experiment. From left to right: roots with decreasing numbers of secondary roots. The scale bar represents 50 mm., Supplementary Figure 2 | Temperature, potential evapotranspiration and precipitation at the study site. (A) Average daily temperature per month for each of the four experimental years. Colors and dot symbols correspond to the different experimental years and dotted lines to second order polynomial regressions. (B) Average potential evapotranspiration (PET) per month at the field site (Atlas Climático Digital de Aragón). The dotted line corresponds to a second order polynomial regression. (C) Difference between monthly precipitation (P) and potential evapotranspiration (PET) at the field site (red dots and red dotted line) and including the irrigated amount of water (yellow dots and yellow dotted line). Dotted lines correspond to second order polynomial regressions., Supplementary Figure 3 | Selection acting on root traits of ancestors. Model predictions of selection gradients are shown for number of secondary roots (A) and maximum rooting depth (B). Since no significant interactions with treatments existed (see ‘Results’), only significant linear (A) and quadratic (B) predictions are shown., Supplementary Figure 4 | Selection acting on root traits indicating root allocation strategies of ancestors. Selection gradients are shown for root weight ratio (RWR) (A), relative root branching (B), and relative rooting depth (C). Since no significant interactions with treatment existed (see ‘Results’), model predictions of significant quadratic (A, C) and linear (B) relationships are shown., Supplementary Table 1 | Means and coefficients of variation of measured root traits depending on maternal predictability treatments. The means of all root traits are shown for each of the descendant treatments depending maternal treatment, and also for each descendant treatment independent of the maternal treatment. The coefficient of variation (the ratio of the standard deviation to the mean, based on means, CVm) among treatments in descendants for each maternal treatment is also shown as well as the overall CV of ancestors (CVa) and the overall CV of descendants (CVd)., Supplementary Table 2 | Sample size per treatment, year and generation. The sample size per treatment and year is presented for the ancestral plants, and the sample size per treatment and generation is presented for the descendants that were subjected to the same treatment for four generations (referred to as ‘descendants – pure lines’) and for the descendants from all treatment combinations over generations used for the analysis on transgenerational plasticity. The hypothesis (H) tested for each group of data is shown., Climate forecasts show that in many regions the temporal distribution of precipitation events will become less predictable. Root traits may play key roles in dealing with changes in precipitation predictability, but their functional plastic responses, including transgenerational processes, are scarcely known. We investigated root trait plasticity of Papaver rhoeas with respect to higher versus lower intra-seasonal and inter-seasonal precipitation predictability (i.e., the degree of temporal autocorrelation among precipitation events) during a four-year outdoor multi-generation experiment. We first tested how the simulated predictability regimes affected intra-generational plasticity of root traits and allocation strategies of the ancestors, and investigated the selective forces acting on them. Second, we exposed three descendant generations to the same predictability regime experienced by their mothers or to a different one. We then investigated whether high inter-generational predictability causes root trait differentiation, whether transgenerational root plasticity existed and whether it was affected by the different predictability treatments. We found that the number of secondary roots, root biomass and root allocation strategies of ancestors were affected by changes in precipitation predictability, in line with intra-generational plasticity. Lower predictability induced a root response, possibly reflecting a fast-acquisitive strategy that increases water absorbance from shallow soil layers. Ancestors’ root traits were generally under selection, and the predictability treatments did neither affect the strength nor the direction of selection. Transgenerational effects were detected in root biomass and root weight ratio (RWR). In presence of lower predictability, descendants significantly reduced RWR compared to ancestors, leading to an increase in performance. This points to a change in root allocation in order to maintain or increase the descendants’ fitness. Moreover, transgenerational plasticity existed in maximum rooting depth and root biomass, and the less predictable treatment promoted the lowest coefficient of variation among descendants’ treatments in five out of six root traits. This shows that the level of maternal predictability determines the variation in the descendants’ responses, and suggests that lower phenotypic plasticity evolves in less predictable environments. Overall, our findings show that roots are functional plastic traits that rapidly respond to differences in precipitation predictability, and that the plasticity and adaptation of root traits may crucially determine how climate change will affect plants., Peer reviewed

Semi-structured interview questions: Adaptability (1-12); Practices related to the river (13-14); Milestones for the relation between society and the river (15-18)., The relations between preparedness and psycho-social attributes of people and communities exposed to river floods in a nearly pristine socio-hydrological system were investigated, applying a hydrological-hydraulic analysis of flood risk in combination with results from a survey, social cartography, semi-structured non-participant observation, and semi-structured interviews. Results show that preparedness in nearly pristine systems is noticeably different to that reported for altered systems. People adopt innovative and simple but efficient measures against floods, conditioned by (1) damage suffered during past floods, (2) perceived exposure to floods, and (3) the number of dependent people in the household. The studied system proved to be well adapted to floods but not resilient. Studying attributes that explain preparedness as part of flood risk management plans would contribute towards uncertainty reduction in risk calculations and increase the safety of goods and people from floods., This work was supported by the ARAUCO SA [PREGA Nr. 4503152513]., Peer reviewed