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Supplementary Material 5: Complete dataset of wild lentil populations used and results, including Passport Information, Variables extraction per population and Predictive Characterization results. Explanations of each field can be found in the Lengend sheet., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary data to this article: Supplementary Document 1. Detailed description of the databases construction. Supplementary Figure 1. Processing and construction workflow of reference databases. The final databases used in the comparison are highlighted in red. Supplementary Figure 2. Determination of the optimal number of clusters for average abundance clustering analysis. The Elbow method was used to set the optimal number of clusters for the k-means algorithm at genus (A) and species (B) level. Supplementary Figure 3. Determination of the optimal number of clusters for z-scored average abundance patterns analysis. The Elbow method was used to set the optimal number of clusters for the k-means algorithm at genus (A) and species (B) level. Supplementary Figure 4. Bracken results comparison between the enriched filtered-MAG and original NT databases. From top to bottom, the performance of the databases was examined in terms of classification capacity for all samples as a whole, by host lifestyles and geographic origin at genus (A) and species (B) level. In the box plots, the black line within the box marks the median and the red triangle the mean, outliers are presented as red dots. Supplementary Figure 5. Relative abundances of top gut microbiota at the genus level. Only the 25 most abundant taxa in all samples were represented. Taxa are sorted by average relative abundances and their NCBI’s taxID is indicated in parentheses. In the box plots, the black line within the box marks the median and the red triangle the mean. Supplementary Figure 6. Universal core species. (A) Intersections between cores of interest. (B) Taxonomic relationships between universal core species. The number of core taxa assigned to a particular level is indicated inside the square brackets. (C) Prevalence-Abundance Heatmap. Taxa are sorted by average relative abundances and their NCBI’s taxID is indicated in parentheses. Supplementary Figure 7. Taxonomic relationships between different intersect core species in a prevalence gradient. Additional intersect cores were computed defining soft and medium prevalence species cores, which were compared to the corresponding universal species core, working in a prevalence range between 0.5 and 1. Supplementary Figure 8. Relative abundances of top gut microbiota at the species level. Only the 25 most abundant taxa in all samples were represented. Taxa are sorted by average relative abundances and their NCBI’s taxID is indicated in parentheses. In the box plots, the black line within the box marks the median and the red triangle the mean. Supplementary Figure 9. Relative abundances of individuals for the universal core taxa. Abundances are shown for the genus (A) and species (B) core taxa. Groups and taxa were clustered using the Complete method and Euclidean distances. In the case of the groups, this was done for every single group combination, and each taxon NCBI’s taxID is indicated in parentheses. Supplementary Figure 10. Abundance patterns of individuals for the universal core taxa. Z-scored abundances are shown for the genus (A) and species (B) core taxa. Groups and taxa were clustered using the Complete method and Euclidean distances. In the case of the groups, this was done for every single group combination, and each taxon NCBI’s taxID is indicated in parentheses. Supplementary Table 1. Summary information for the microbiome project genomes used in the construction of the enriched databases. Supplementary Table 2. Summary information for the metagenome studies used in this work. Supplementary Table 3. Summary of statistics for samples quality control. The different results generated by FastQC were aggregated with MultiQC for each study. Supplementary Table 4. Metadata for each human gut metagenome sample. Supplementary Table 5. Core genera membership for the different cores of interest. Presence-absence data is presented for the different cores of interest at 0.9, 0.7, and 0.5 prevalence thresholds. Prevalence values for the intersect core taxa at 1e−4 relative abundance threshold are also shown. NCBI’s taxIDs are indicated in parentheses. Supplementary Table 6. Core species membership for the different cores of interest. Presence-absence data is presented for the different cores of interest at 0.9, 0.7, and 0.5 prevalence thresholds. Prevalence values for the intersect core taxa at 1e−4 relative abundance threshold are also shown. NCBI’s taxIDs are indicated in parentheses. Supplementary Table 7. Universal core genera information and known characteristics., Peer reviewed

Supplementary S1: Yield and related traits in bread wheat. Table S1: Examples of genomic regions, candidate and cloned genes for yield and related traits in bread wheat. Supplementary S2: Drought tolerance. Table S2: Examples of genomic regions and candidate genes for drought tolerance. Supplementary S3: Heat tolerance. Table S3. Examples of genomic regions and candidate genes for heat tolerance. Supplementary S4: salinity tolerance in bread wheat. Table S4. Examples of genomic regions and candidate genes for salinity tolerance in bread wheat. Supplementary S5: Frost tolerance. Supplementary S6: Disease resistance. Table S5. Examples of genomic regions, candidate and cloned genes mapped for disease resistance in wheat species. Supplementary S7 insect and mite resistance. Table S6. Examples of genomic regions and candidate genes mapped for insect and mite resistance. Supplementary S8: Quality traits. Table S7. Examples of genomic regions, candidate and cloned genes for quality traits., Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence., Peer reviewed

S1 Text. Figs A-N and Tables A-G. The structural role of SARS-CoV-2 genetic background in the emergence and success of spike mutations: the case of the spike A222V mutation. S1 Movie. Structural changes from S:D614G to [S:A222V + S:D614G], starting from cryo-EM maps, in two different orientations. The subtle rearrangement of the RBDs and NTDs of subunits A and B of the spike is specifically highlighted. Position of residue 222 in each subunit is represented as a cyan sphere. S1 Table. Full Table of frequencies of sequences with A222V for the different PANGO lineages., Peer reviewed

The dataset contains measurements of Chlorophyll-a (mg m-3), nitrate (μM) and suspended matter (“SM”, g m-3) concentrations, temperature (ºC) and mesozooplankton biovolume (mL 100m-3), collected between 2002 and 2007, in a network of sampling stations (“Station_ID”) distributed across the northeast continental shelf of the Gulf of Cádiz. The year and Julian day (number of days after 1 January) in which each sample was obtained are indicated, as well as their geolocation with latitude and longitude coordinates in degrees. Chlorophyll-a, suspended matter (SM), nitrate and temperature data were taken from the surface layer (<5m) of the water column. Oblique plankton hauls were conducted up to 100 meters using a Bongo net with a 40-cm mouth diameter and 200 mm mesh size, for mesozooplankton sampling., [General Notes] The dataset is provided (“Dataset.txt”) within a compressed folder that also includes a single file in text format (“Readme.txt”) containing a detailed description of the data structure. Dataset.txt file has column titles as the first line. One column is written for each measured variable. Missing data are filled with NaN. Data files are in UTF8 encoding, plain text format with [space] used as the delimiter. The data are provided under an Attribution-ShareAlike 4.0 International license. However, if you use the data, so as to support the authors, please consider citing the above mentioned article where data collection and analytical techniques are given in detail. Here we only give a brief details and a guide to the contents of the data files., [Geographical coordinates of the sampling area] Coordinates.txt file provides the geographical coordinates of the sampling area., This data set includes physical and biogeochemical data collected between 2002 and 2007 on the northeast continental shelf of the Gulf of Cádiz., Data adquisition was supported by projects “Recursos pesqueros del golfo de Cádiz” and “Fluctuaciones y potencialidad de especies pesqueras de plataforma en la región atlántica andaluza” (Junta de Andalucía)., Peer reviewed

Supplementary Figure 1. Related to Figure 1. Characterization of the cholinergic system in bone. Supplementary Figure 2. Related to Figure 2. Interleukin-6 induces a cholinergic switch in sympathetic neurons. Supplementary Figure 3. Related to Figures 2 and 3. Interleukin-6 induces a cholinergic switch of sympathetic fibers in bone. Supplementary Figure 4. Related to Figure 4. Osteolineage cells contribute to the non-neuronal cholinergic system. Supplementary Figure 5. Related to Figure 5. GFRa2 loss causes reduced bone thickness and osteocyte degeneration. Supplementary Figure 6. Related to Figure 6. GFRa2 signaling maintains osteocyte connectivity and survival. Supplementary Figure 7. Related to Figure 7. Moderate exercise increases bone cholinergic innervation through sympathetic cholinergic fibers. Table S1. Oligonucleotide sequences used for mouse genotyping. Table S2. Oligonucleotide sequences used for quantitative real-time RT-PCR., Peer reviewed

Maps and diagrams of reproduction 1, 2 and 3., Peer reviewed

Table A.1: Most important crops (> 1000 ha) in the V (mid-west) administrative region of Navarra (130,000 ha) in 2007, 2019 and their change (in ha and %) along the survey period, broken down by each crop’s rain-fed and irrigated surface. Table A.2: Number of irrigated and non-irrigated points sampled each year during the survey period. Figure A.1: Species-level occurrence probabilities: a) before irrigation (excluding control sampling locations); and b) after irrigation was implemented. Species are organized by habitat classification: farmland, shrubland, (R) rocky habitat specialist species, (Fo) forest habitat specialist species, (W) wetland habitats specialist species, (U) urban habitat specialist species, and non-specialist. The short horizontal black lines show means, the boxes show 50% credible intervals (CI) and the vertical lines show the 95% CI. Figure A.2: Effect of arable land surface on species occurrence probabilities organized by habitat classification: farmland, shurbland, (R) rocky habitat specialist species, (Fo) forest habitat specialist species, (W) wetland habitats specialist species, (U) urban habitat specialist species, and non-specialist. The short horizontal black lines show the mean value (across MCMC iterations), the boxes show the 50% credible intervals (CI), and the vertical lines delineate the 95% CI. Light grey indicates no effect of arable land on occurrence probability, orange (negative effect) and blue (positive effect) indicate that the 50% CI does not overlap zero but the 95% CI does overlap zero, and red (negative effect) and dark blue (positive effect) indicate that the 95% CI does not overlap zero. Figure A.3: Detection probability by: a) hour; and b) date. The black lines show the mean values across all species analysed (with shaded 50% and 95% credible intervals). Light grey lines show the detection probabilities of each species included in the analysis. JAGS model code: Multi-species hierarchical occurrence model with a BACI design., Peer reviewed

Fig. S1. Conceptual model of avoidance. A primary repellent evokes reflexive withdrawal immediately after exposure. A secondary repellent is avoided because an animal associates a conditional stimulus (sound, sight, taste) to an aversive experience (e.g., illness, pain) with a sensory stimulus. A generalized conditional aversion may occur against the food that previously contained the chemical and it is avoided by only the type of food as the cue to refusing it. Fig. S2. Diagram of test of avoidance to treated sorghum seeds. Fig. S3. Diagram of experiment of avoidance to soybean seeds treated with imidacloprid. Fig. S4. Diagram of experiment of avoidance to soybean cotyledons treated with neonicotinoids. Fig. S5. Frequency of signs of intoxication observed over the days in each treatment during the Exposure 1 (A) and Exposure 2 (B) periods of the sorghum experiment. Signs of intoxication were measured as present or absent at each moment of observation, consisting of from lethargy to motionless. Letters indicate significant differences among treatments. IMI: red bars, CLO: green bars, THI: blue bars. Fig. S6. Body weights of survivors and dead in the sorghum experiment. The deaths only occurred in the sorghum experiment, during the Exposure 1 period, in birds treated with imidacloprid (3 dead) and clothianidin (1 dead). In the dead, the BWs were measured at the time of death. In survivors, BWs were measured at the end of the Exposure 1 period. Table S1. Records of maximum and minimum temperature and humidity in each period, and average daily thermal amplitude. Table S2. Doves that consumed soybeans (12 of the 40). Consumption was measured from the fourth day of the "Pre-Exposure 1" period. The number of seeds consumed was calculated based on the average daily weight of soybeans used to measure the humidity factor. Table S3. Average mass in grams of seeds and cotyledons. "Ad Sur" sorghum is the AD75STA commercial hybrid sorghum of "adSur, Agrosemillas del Sur SA", used in COM, IMI and THI treatments of the sorghum test. “DK” sorghum is the “Dekalb” commercial hybrid sorghum seeds of Syngenta®, used in CLO treatment of the sorghum test. Table S4. Average consumption in grams (mean ± SE) of test food and the total consumption (grams of test food + grams of maintenance food) during periods of exposure to neonicotinoids. In the sorghum seeds, CON is the untreated sorghum control and COM is the commercial sorghum control. Table S5. Video records of the behavior of some doves during the first 15 minutes of exposure to food in the different treatments and periods. “1rt peck” indicates the time in seconds when the bird first pecked at the food inside the feeder after it was offered; “n peck food” is the number of times the bird pecked at the feeder food during the first 15 minutes of its exposure to the food; “n grit peck” is the number of times the bird pecked at the grit; “n drink” is the number of times the bird drank water from the drinking fountain; “n grooming” is the number of times the bird groomed itself; “n shaking” is the number of times the bird shook; “on perch” is the number of times the bird climbed on the perch. In the sorghum experiment, CON is the untreated sorghum control and COM is the commercial sorghum control. Table S6. Body condition index (SMI) and the p-value of the paired Wilcoxon test used to compare the initial and final body conditions of each treatment in the three experiments. In the sorghum experiment, CON is the untreated sorghum control and COM is the commercial sorghum control., Peer reviewed

[EN] It contains a netCDF file which needs specific data analysis software. [ES] Contiene un fichero netCDF que necesita software de análisis de datos específico., [EN] This dataset includes series of the Standardized Evapotranspiration Deficit at 0.25º spatial resolution and monthly time resolution at global scale from 1980., [ES] Esta base de datos proporciona el Standardized Evapotranspiration Deficit Index a escala global con una resolución espacial de 0.25 grados y una resolución temporal mensual desde 1980., This work was supported by the research projects PCIN-2015-220 and CGL2014-52135-C03-01 financed by the Spanish Commission of Science and Technology and FEDER. IMDROFLOOD financed by the Water Works 2014 co-funded all of the European Commission and INDECIS, which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS(Sweden), DLR(Germany),BMWFW(Austria), IFD (Denmark), MINECO (Spain), and ANR (France), with co-funding by the European Union (Grant 690462)., Peer reviewed