BUSCADOR RECOLECTA

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

Peer reviewed

Experimental Methods (preparation of triple cation perovskite precursor solution, perovskite solar cell fabrication, encapsulation, and indoor and outdoor photostability studies) and Algorithms and Data Processing Methods (algorithm testing strategy and results of different algorithms)., Peer reviewed

Supplementary Material for Frontiers in Environmental Science 10: 868527 (2022), 1. Riparian zones are vital areas of interaction between land and rivers and are often degraded by several pressures such as urbanisation, intensive agriculture and river engineering works. 2. This policy brief provides five key policy messages and recommendations to be considered by policy-makers, scientists, managers, and stakeholders to enhance riparian zone management. 3. Adopting an integrated socio-economic and environmentally dynamic view will ensure the sustainable management of riparian zones. 4. In light of climate change, it is critically important to conserve and/or restore the ecological integrity of riparian zones. 5. European Union Directives and national-scale legislation and regulations need updating to ensure coordinated implementation of riparian zone-related policies. 6. Stakeholder knowledge exchange, policy co-creation and adaptive management are key to enhancing riparian zone functions., Peer reviewed

Detailed synthesis and methods, DLS analysis, UV/vis spectra, FT-IR spectra, TGA, NMR spectra, DSC curves, MALDI-TOF spectra, FE-SEM and EDX data and images, CO2 adsorption–desorption isotherms, optical transmission and reflectance spectra, and PXRD diffractograms., Peer reviewed

-Materials, instrumentation, experimental procedures, spectroscopic and photophysical characterization of all compounds, and refs 22–36 (PDF). -Mono-di-benzyl-C70⊂4·(BArF)8 (reactive orientation 1) (MPG). -Mono-di-benzyl-C70⊂4·(BArF)8 (reactive orientation 2 - most abundant) (MPG). -Mono-di-benzyl-C70⊂[10]CPP⊂6·(BArF)8 (reactive orientation 1 - most abundant) (MPG). -Mono-di-benzyl-C70⊂[10]CPP⊂6·(BArF)8 (reactive orientation 2) (MPG). -MD simulation of bis-dibenzyl-C70⊂[10]CPP⊂6·(BArF)8 (2 o’clock regio-isomer) (MOV)., Peer reviewed