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7646220.zip contains:, 1) TIP_data folder: INDICA_teinsertions_5_2_2_matrix_sorted_ID_MAF003.txt: TIP matrix used for TIP-eQTL mapping (binary form, where 0 = absence, 1= presence) // INDICA_teinsertions_5_2_2_matrix_sorted_ID_MAF003.info: TIP information (chrom,start,superfamily,family,maf) // JAPONICA_teinsertions_5_2_2_matrix_sorted_ID_MAF003.txt: TIP matrix used for TIP-eQTL mapping (binary form, where 0 = absence, 1= presence) // JAPONICA_teinsertions_5_2_2_matrix_sorted_ID_MAF003.info:TIP information (chrom,start,superfamily,family,maf), 2) SNP_data folder: SNP_matrix_final_IRGC_Indica_MAF003.lfmm: SNP binary matrix used for TIP-eQTL mapping // SNP_matrix_final_IRGC_Indica_MAF003.info: SNP information // SNP_matrix_final_IRGC_japonica_MAF003.lfmm: SNP binary matrix used for TIP-eQTL mapping // SNP_matrix_final_IRGC_japonica_MAF003.info:SNP information, 3) Expression data: Normalized expression data and accessory files used for TIP-eQTL analysis (as described in TIP-eQTL_matrix.R), Code: PopoolationTE2.sh: Bash script to reproduce PopoolationTE2 analysis (TIP detection) // TIP-eQTL_matrix.R: R code to reproduce eQTL analyses, Peer reviewed

1 file, Data used in the experimental study carried out in early life stages of the seahorse Hippocampus reidi to assess the influence of diet shift on changes and turnover rates and discrimination factors for carbon (δ13C) and nitrogen (δ15N) stable isotopes in juveniles. Newborn seahorses were fed for 60 days following two feeding schedules (A6 and A11) based initially on copepods Acartia tonsa and subsequently on Artemia nauplii (from days 6 and 11 onwards, respectively). Isotopic data correspond to raw data normalized for lipids, Spanish Government with Project Hippoeco (Ref. CGL2015-68110-R, Ministerio de Ciencia, Innovación y Universidades and Fondos FEDER), No

Data used in "Avian sibling cannibalism: Hoopoe mothers regularly use their last hatched nestlings to feed older siblings", Peer reviewed

Additional file 1: Table S1. Sample size of avian nests used to estimate prevalence of extra-pair paternity of the 60 bird species used in the study., Spanish Ministerio de Ciencia, Innovación y Universidades and European (FEDER), Peer reviewed

Figure S1 Map of the study area with distribution of sample locations, specifying year by color and number of samples by size of circle. Redrawn from Anderson et al. (2019).-- Figure S2 Distribution of observed heterozygosity values in the initial dataset prior to extracting high heterozygosity individuals (blue, ‘Initial’) and the final dataset (red, ‘Final’).-- Table S1 Number of loci retained through quality filtering process. NB: the order of filtering steps changed slightly between manuscripts; also the new dataset was filtered twice, first for extraction of low quality individuals (‘initial’ column) and then for the creation of a final dataset for genetic analysis (‘final’ column). To confirm robustness of results, an ‘alternative updated’ version of the final dataset is also reported here, which extracted the same individuals as the ‘updated final’ dataset prior to employing alternative thresholds in the final filtering steps. MAF = Minor allele frequency, HWE = Hardy-Weinberg expectation, LD = Linkage disequilibrium, LUPS = Loci under potential selection, FDR = False discovery rate.-- Table S2 Adjusted expected heterozygosity (Hnb), observed heterozygosity (Ho) and inbreeding coefficient (FIS) compared between results from original and updated analyses. Results from the new dataset using alternative filtering thresholds are provided in parentheses.-- Table S3 Fixation index (FST) values from the original data are below the diagonal and comparable values from the current study are above the diagonal. Results produced by the alternative new dataset are provided in parentheses above the diagonal. Unlike in the main document that reports p values adjusted by false discovery rate, p values here are adjusted using a Bonferroni correction (alpha value threshold of 0.00238). Significant values are bold.-- Figure S3 Discriminant Analyses of Principal Components produced by A) the original dataset using 62 principal components and 6 degrees of freedom, as recommended by alpha optimization analyses; B) the updated dataset using 60 principal components and 6 discriminant function, also as recommended by alpha optimization and cross validation; and C) the alternatively filtered updated dataset, also using 60 principal components and 6 discriminant function, as recommended by alpha optimization and cross validation. NB: the panel representing the original analysis is a recreation and, due to randomized initial seeding in some R coding, is very near but not an exact copy of the figure from Anderson et al 2019.-- Table S4 The k value representing the most likely number of independent genetic clusters present in a sample (from ADMIXTURE).-- Table S5 Relatedness (r) data summarized as number of dyads recommended as a particular type of relationship. NB: Anderson et al., (2019) only reported dyads that were highlighted by both software programs to increase robustness of observations, and also only focused on half- and full-sib dyads because there were sufficient novel observations without considering more distance degrees of relatedness. In the current study, we report COANCESTRY and RelatedAdmix results separately and incorporate more relationship degrees to better illustrate the change in results. Results from the alternatively filtered updated dataset are reported in parentheses., Peer reviewed

Fig. S1: (a) Northern Hemisphere regions with mean annual temperature (MAT) ＜4.9℃ during the period of 1970-2000 (data from worldclim.org) and (b) Northern Hemisphere regions with CMIP5 projected MAT ＞4.9℃ during the period of 2061-2080.-- Fig. S2: Xylem growth rings of Norway spruce (Picea abies) in the middle of the growing season showing tracheids at different differentiation stages. Just below the cambium (CC) are cells in the enlargement stage (PC), followed by cells in the phase of secondary wall formation and wall-thickening (SW), and then mature cells (MT).-- Figure S3. Variable importance from the boosted regression tree (a) showing the marginal effect of the predictor on the cell-wall-thickening DOY probability. These predictors were ranked by their relative importance; (b-g) Partial dependence plots of cell-wall-thickening DOY (day of the year) on mean annual temperature (MAT), photoperiod, chilling, forcing, scPDSI, and spring temperature variation (Temp_variation).-- Figure S4: Variation in photoperiod (a), chilling (b), forcing (c), scPDSI (d), spring temperature variance (e), and frost frequency (f), according to the mean annual temperature (MAT) at the sites. Regression lines represent the predicted values estimated by natural cubic splines. Shadowed areas indicate 95% confidence interval. Biomes include boreal (B), temperate (T), Mediterranean (M), and subtropical (S).-- Figure S5: Boxplots, with jitter, displaying the distribution of selected tree species ranging across the mean annual temperature (MAT) gradients. Species are reported with the following acronyms and classified into early (JUPR, Juniperus przewalskii; JUTH, Juniperus thurifera; LADE, Larix decidua; PIHA, Pinus halepensis,; PIHE, Pinus heldreichii; PILE, Pinus leucodermis; PILO, Pinus longaeva; PIMA, Pinus massoniana; PIPE, Pinus peuce; PIPI, Pinus pinaster; PISY, Pinus sylvestris; PITA, Pinus tabulaeformis; and PIUN, Pinus uncina) and late (ABAL, Abies alba; ABBA, Abies balsamea; ABGE, Abies georgei; CELI, Cedrus libani; PCAB, Picea abies; PCMA, Picea mariana; PICE, Pinus cembra) successional species types (see Species classification Materials and Methods).-- Figure S6: Changes in the cell-wall-thickening DOY (day of the year) for Picea abies (PCAB) along the mean annual temperature (MAT) gradients of sites, as fitted by a linear mixed effect model with sites as the random effect.-- Figure S7: Changes in cell-wall-thickening DOY (day of the year) along the mean annual temperature (MAT) gradients of the sites (a) between the cold and warm sites classified by the transition temperature at MAT=4.9±1.1℃ across biomes: boreal sites excluded the few dots above 4.9 ℃ (intercept: 167.86, slope: -4.96), cold-temperate sites (MAT below 4.9 ℃; intercept: 178.68, slope: -5.24), warm-temperate sites (MAT above 4.9 ℃; intercept: 183.22, slope: -4.04); Mediterranean sites excluded the few dots below 4.9 ℃ (intercept: 147.60, slope: -0.515).-- Figure S8: Summary of the direction and magnitude of the effect of multiple predictors on cell-wall-thickening DOY (day of the year) in different biomes at cold or warm sites, as fitted by linear mixed effect models. The upper (a, b) and middle (c,d) panels were from cold and warm sites, respectively. Significant effects occur when no overlaps exist between the 95% error bars and zero; the blue and red colors denote the positive versus negative effects, respectively. Variance partitioning for each model indicating the relative importance of each predictor is also shown in e, and the sample sizes for each model are reported. B_Cold: boreal sites excluded the few dots above 4.9 ℃; T_Cold: temperate sites with MAT below 4.9 ℃; T_Warm: temperate sites with MAT above 4.9 ℃; M_Warm: Mediterranean sites excluded the few dots with MAT below 4.9 ℃.-- Figure S9: Variations (violin plots) and distributions (nested boxplots) of frost frequency and local spring temperature variation for early versus late successional species in the cold (a and c) and warm zones (b and d). Cold and warm sites were classified by the transition temperature at a mean annual temperature of MAT=4.9±1.1℃. E: early successional species; L: late successional species.-- Table S1: The sites, species, and years included in the analysis. The species were reported with the following acronyms and classified into early (E) and late (L) successional species type: ABAL, Abies alba, L; ABBA, Abies balsamea, L; ABGE, Abies georgei, L; CELI, Cedrus libani, L; JUPR, Juniperus przewalskii, E; JUTH, Juniperus thurifera, E; LADE, Larix decidua, E; PCAB, Picea abies, L; PCMA, Picea mariana, L; PICE, Pinus cembra, L; PIHA, Pinus halepensis, E; PIHE, Pinus heldreichii, E; PILE, Pinus leucodermis, E; PILO, Pinus longaeva, E; PIMA, Pinus massoniana, E; PIPE, Pinus peuce, E; PIPI, Pinus pinaster, E; PISY, Pinus sylvestris, E; PITA, Pinus tabulaeformis, E; and PIUN, Pinus uncinata, E. The entire study area was divided into subtropical (S), Mediterranean (M), temperate (T), and boreal (B) biomes. The site temperature for each site was computed as the average of the mean annual temperatures (MATs) across all years providing observations for the site. Sites with climate data obtained from nearby weather stations are indicated by *.-- Table S2. Descriptive summary statistics of cell-wall-thickening DOY (day of year) for all observations, for observations at cold sites, and for observations at warm sites.-- Table S3. Summary of linear mixed modeling of the effects of selected predictors on cell-wall-thickening DOY (day of year); CI: confidence interval.-- Table S4: Variance inflation factor (vif) of each predictor variable in the linear mixed models for all observations and subset modelings corresponding to the results shown in Fig. 3., Peer reviewed

DataSheet_1_A multi-proxy framework to detect insect defoliations in tree rings: a case study on pine processionary.docx contains Supplementary Figures 1-10., Assessing and reconstructing the impacts of defoliation caused by insect herbivores on tree growth, carbon budget and water use, and differentiating these impacts from other stresses and disturbances such as droughts requires multi-proxy approaches. Here we present a methodological framework to pinpoint the impacts of pine processionary moth (Thaumetopoea pityocampa), a major winter-feeding defoliator, on tree cover (remote-sensing indices), radial growth and wood features (anatomy, density, lignin/carbohydrate ratio of cell walls, δ13C and δ18O of wood cellulose) of drought-prone pine (Pinus nigra) forests in north-eastern Spain. We compared host defoliated (D) and coexisting non-defoliated (ND) pines along with non-host oaks (Quercus faginea) following a strong insect outbreak occurring in 2016 at two climatically contrasting sites (cool-wet Huesca and warm-dry Teruel). Changes in tree-ring width and wood density were analyzed and their responses to climate variables (including a drought index) were compared between D and ND trees. The Normalized Difference Infrared Index showed reductions due to the outbreak of –47.3% and –55.6% in Huesca and Teruel, respectively. The D pines showed: a strong drop in growth (–96.3% on average), a reduction in tracheid lumen diameter (–35.0%) and lower lignin/carbohydrate ratios of tracheid cell-walls. Both pines and oaks showed synchronous growth reductions during dry years. In the wet Huesca site, lower wood δ13C values and a stronger coupling between δ13C and δ18O were observed in D as compared with ND pines. In the dry Teruel site, the minimum wood density of ND pines responded more negatively to spring drought than that of D pines. We argue that multi-proxy assessments that combine several variables have the potential to improve our ability to pinpoint and reconstruct insect outbreaks using tree-ring data., Peer reviewed

The following Supporting Information is available for this article: Fig. S1 Yeast two-hybrid screening of a cDNA normalized tomato library against the CP of PepMV and identification of SlGSTU38 as a possible CP-interacting protein. Fig. S2 Phylogenetic relationship of SlGSTU38 and its homologs. Fig. S3 Mock-inoculated and PepMV-infected WT and gstu38 Micro-Tom plants at 14-d postinoculation. Fig. S4 Subcellular localization and colocalization of CP and SlGSTU38. Fig. S5 RNA-seq data validation. Fig. S6 PepMV or SlGSTU38 knockout do not induce lipid peroxidation in Micro-Tom plants. Methods S1 Tomato cDNA library and yeast two-hybrid screening. Table S1 Primer sequences. Table S2 Construct summary. Table S3 Blastp output using the SlGSTU38 protein sequence as query in the Sol Genomics Network database. Table S4 RNA-seq results: number of expressed genes per treatment and in all treatments together. Table S5 RNA-seq results: genes exclusively expressed in mock-inoculated gstu38 (gstu38_M), mock-inoculated wild-type (WT_M), and PepMV-inoculated wild-type plants (WT_PepMV). Table S6 RNA-seq results: number of differentially expressed genes (DEGs) for all the possible pairwise comparisons between treatments. Table S7 Differentially expressed genes (DEGs) comparing mock-inoculated gstu38 with mock-inoculated wild-type (WT) plants., Peer reviewed

Appendix A. Supplementary data: Supplementary Figures S1-S23 and Table S1., The Vapor Pressure Deficit (VPD) is one of the most relevant surface meteorological variables; with important implications in ecology, hydrology, and atmosphere. By understanding the processes involved in the variability and trend of the VPD, it is possible to assess the possible impacts and implications related to both physical and human environments, like plant function, water use efficiency, net ecosystem production, atmospheric CO2 growth rate, etc. This study analysed recent temporal variability and trends in VPD in Spain between 1980 and 2020 using a recently developed high-quality dataset. Also, the connection between VPD and soil moisture and other key climate variables (e.g. air temperature, precipitation, and relative humidity) was assessed on different time scales varying from weekly to annual. The objective was to determine if changes in land-atmosphere feedbacks connected with soil moisture and evapotranspiration anomalies have been relevant to assess the interannual variability and trends in VPD. Results demonstrate that VPD exhibited a clear seasonality and dominant positive trends on both the seasonal (mainly spring and summer) and annual scales. Rather, trends were statistically non-significant (p > 0.05) during winter and autumn. Spatially, VPD positive trends were more pronounced in southern and eastern of Spain. Also, results suggest that recent trends of VPD shows low contribution of variables that drive land-atmosphere feedbacks (e.g. evapotranspiration, and soil moisture) in comparison to the role of global warming processes. Notably, the variability of VPD seems to be less coupled with soil moisture variability during summertime, while it is better interrelated during winter, indicating that VPD variability would be mostly related to climate variability mechanisms that control temperature and relative humidity than to land-atmosohere feedbacks. Overall, our findings highlight the importance of assessing driving forces and physical mechanisms that control VPD variability using high-quality climate datasets, especially, in semiarid and sub-humid regions of the world., This work was supported by projects PCI2019-103631, and PID2019-108589RA-I00 financed by the Spanish Commission of Science and Technology and FEDER; LINKB20080 of the programme i-LINK 2021 of CSIC, CROSSDRO financed by AXIS (Assessment of Cross(X) - sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and CSIC Interdisciplinary Thematic Platform (PTI) clima y servicios climáticos (PTI-CSC)., Peer reviewed

1 file.-- The file contains all raw data on Date, Season, Site, Sex, Maturity stage, Lenght, Weigth and Isotopic Values, The population of the pipefish Syngnathus acus inhabiting Cíes Archipelago (NW Spain) was monitored in 2017-2018 for spatial and temporal changes in abundances, reproduction traits, trophic niche occupancy and dietary regimes across reproduction states through an isotopic (δ13C and δ15N) approach, Proyecto Hippoparques (1541S/2015; Organismo Autónomo de Parques Nacionales de España, Ministerio para la Transición Ecológica, Spain), No