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Dataset and code to explore the results of climate change and water management scenario runs [EN]., Conjunto de datos y código para la exploración y visualización de los resultados de las simulaciones bajo condiciones de cambio climático y gestión de los recursos hídricos [ES]., Este trabajo ha contado con financiación del Fondo Europeo de Desarrollo Regional (FEDER) a través del Programa Interreg V España-Francia-Andorra (POCTEFA 2014-2020) de la Unión Europea; de la Agencia Estatal de Investigación de España en el marco de la convocatoria cofinanciada ERA-NET WaterWorks 2015; y de la Fundación Biodiversidad del Ministerio para la Transición Ecológica de España. Los autores quieren también agradecer de forma especial a la Confederación Hidrográfica del Ebro (CHE) y a la Comunidad General de Riegos del Alto Aragón (CGRAA) por el apoyo recibido en la recopilación de datos, validación de los modelos, y creación de un foro de comunicación con los regantes y gestores de los recursos hídricos., No

The monitoring of lizards and geckos’ community in Doñana was initiated in 2005 as part of the Monitoring Program of Natural Resources and Processes. One of the aims of this project was to obtain a temporal and continuous series of data of the presence and abundance of these species to detect changes and trends in their wild populations within the protected area. The records have been collected during spring and autumn every year between 2005-2021 by members of the monitoring team in sampling transects in different habitats (dunes and Mediterranean vegetation) when reptile activity is higher. Dataset includes species name, number of individuals, sex, life stage, behaviour, coordinates, weather description (sky conditions, temperature, rain, or wind intensity), time of the day and other remarks., The aim of this project is to provide information about the evolution of the conservation status of Doñana. To do that, it has been designed a monitoring program of the dynamic of natural processes and the distribution and abundance of species and communities. This monitoring is generating time series of data which is being used to analyzed long-term trends.

Files list: 1. Murciano-Granadina-1040F.ped: Genotypic information of 50,514 SNPs genotyped in 1,040 Murciano-Granadina female goats obtained with the Goat SNP50 BeadChip (Illumina Inc., San Diego, CA). 2. Murciano-Granadina-1040F.map: Genomic positional information of the SNPs genotyped in 1,040 Murciano-Granadina female goats. 3. Pheno_file.txt: Milk production records from 820 Murciano-Granadina goats. Milk composition traits are normalized to a lactation of 210 days. Relationship between files: “.map” and “.ped” files are complementary, “.map” contains the genomic position and name of the genotypes displayed in “.ped” file. Files can be opened and manipulated with PLINK software (https://www.cog-genomics.org/plink/)., Genotypic information from 1,040 female Murciano-Granadina goats and the phenotypic records for milk traits of 820 female Murciano-Granadina goats. The dataset includes a README file with the information about each file., This research was funded by the European Regional Development Fund (FEDER)/Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación/Project Reference grant: PID2019-105805RB-I00 and by the CERCA Programme/Generalitat de Catalunya. We also acknowledge the support of the Spanish Ministerio de Ciencia e Innovación for the Center of Excellence Severo Ochoa 2020-2023 (CEX2019-000902-S) grant awarded to the Centre for Research in Agricultural Genomics (CRAG, Bellaterra, Spain). We also acknowledge the support of the CERCA programme of the Generalitat de Catalunya. Dailu Guan was funded by a PhD fellowship from the China Scholarship Council (CSC). Maria Luigi-Sierra was funded with a PhD fellowship Formación de Personal Investigador (BES-C-2017-079709) awarded by the Spanish Ministry of Economy and Competitivity., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000902-S), Peer reviewed

Download of the data available in the publisher platform., Transposon Insertion Polymorphisms (TIPs) are a significant source of genetic variation. Previous work (Castanera et al., 2021) has shown that TIPs can improve detection of causative loci on agronomic traits in rice. Here, we quantify the fraction of variance explained by Single Nucleotide Polymorphisms (SNPs) compared to TIPs, and we explore whether TIPs can improve prediction of phenotypes when compared to using only SNPs. We used eleven traits of agronomic relevance from by five different rice population groups (Aus, Indica, Aromatic, Japonica and Admixed), 738 varieties in total. We assess prediction by applying data split validation in two scenarios. In the within population scenario, we predicted performance of improved Indica varieties using the rest of Indica and additional samples. In the across population scenario, we predicted all Aromatic and Admixed samples using the rest of populations. In each scenario, Bayes C and a Bayesian reproducible kernel Hilbert space regression were compared. We find that TIPs can explain an important fraction of total genetic variance, often more than the fraction explained by SNPs, and that they also improve genomic prediction, especially in the across population prediction scenario, where TIPs outperformed SNPs in nine out of the eleven traits analyzed. In some phenotypes like leaf senescence or grain width, using TIPs increased predictive correlation by 40%. Our results evidence, for the first time, that TIPs genotyping can improve prediction on complex agronomic traits in rice, especially when samples to be predicted are less related to training samples., Dataset contains: Scripts: BayesC_PREDICTION.MODEL.R : An R script for genomic prediction within and across population analysis applying "BayesC"; RKHS_PREDICTION.MODEL.R : An R script for genomic prediction within and across population analysis applying "RKHS"; RKHS_GENETIC_VARIANCE_INFERENCE.R : An R script for genetic variance inference within and across population applying "RKHS". And Data: Accessions_Traits.csv : A csv file of the 11 traits and their corresponding phenotypic values for the 738 accessions. Data transformed as described in manuscript; snps. RData : SNPs matrix in R format; mitedtx_matrix.RData: Merged matrix of MITE and DTX TIPs in R format; rlxrix_matrix.RData: Merged matrix of RLX and RIX TIPs in R format; Additive_Matrix.RData : The three additive-relationship matrices for each marker (SNPs, MITE/DTX, RLX/RIX) to be used in RKHS method script., Peer reviewed

Data Files Description: NPC_XL_Identification_Inter_Crosslinked.csv: Inter-protein cross-links identified by pLink 2; NPC_XL_spectra.mgf: MS2 spectra data for the identified cross-links; NPC_XL_proteins.fasta : Protein sequences used for search., This repository contains chemical cross-linking mass spectrometry data of affinity-purified Yeast nuclear pore complexes., [Sample Processing] NPCs were immuno-purified from Mlp1 tagged S. cerevisiae strains (Kim et al., 2018). After native elution, 1.0 mM disuccinimidyl suberate (DSS) was added and the sample was incubated at 25ºC for 40 min with shaking (1,200 rpm). The reaction was quenched by adding a final concentration of 50 mM freshly prepared ammonium bicarbonate and incubating for 20 min with shaking (1,200 rpm) at 25ºC. The sample (50 µg) was then concentrated and denatured at 98ºC for 5 min in a solubilization buffer (10% solution of 1-dodecyl-3-methylimidazolium chloride (C12-mim-Cl) in 50 mM ammonium bicarbonate, pH 8.0, 100 mM DTT). After denaturation, the sample was centrifuged at 21,130 g for 10 min and the supernatant was transferred to a 100 kDa MWCO ultrafiltration unit (MRCF0R100, Microcon). The sample was quickly spun at 1,000 g for 2 min and washed twice with 50 mM ammonium bicarbonate. After alkylation (50 mM iodoacetamide), the cross-linked NPC in-filter was digested by trypsin and lysC O/N at 37ºC. After proteolysis, the sample was recovered by centrifugation and peptides were fractionated into 10-12 fractions by using a stage tip self-packed with basic C18 resins (Dr. Masch GmbH). Fractionated samples were pooled prior to LC/MS analysis. Desalted cross-link peptides were dissolved in the sample loading buffer (5% Methanol, 0.2% FA), separated with an automated nanoLC device (nLC1200, Thermo Fisher), and analyzed by an Orbitrap Q Exactive HFX (Pharma mode) mass spectrometer (Thermo Fisher) as previously described (Xiang et al., 2020; Xiang et al., 2021). Briefly, peptides were loaded onto an analytical column (C18, 1.6 μm particle size, 100 Å pore size, 75 μm × 25 cm; IonOpticks) and eluted using a 120-min liquid chromatography gradient. The flow rate was approximately 300 nl/min. The spray voltage was 1.7 kV. The QE HF-X instrument was operated in the data-dependent mode, where the top 10 most abundant ions (mass range 380 – 2,000, charge state 4 - 8) were fragmented by high-energy collisional dissociation (HCD). The target resolution was 120,000 for MS and 15,000 for tandem MS (MS/MS) analyses. The quadrupole isolation window was 1.8 Th; the maximum injection time for MS/MS was set at 200 ms., [Data processing] The raw data were searched with pLink2 (Chen et al., 2019b). An initial MS1 search window of 5 Da was allowed to cover all isotopic peaks of the cross-linked peptides. The data were automatically filtered using a mass accuracy of MS1 ≤ 10 ppm (parts per million) and MS2 ≤ 20 ppm of the theoretical monoisotopic (A0) and other isotopic masses (A+1, A+2, A+3, and A+4) as specified in the software. Other search parameters included cysteine carbamidomethyl as a fixed modification and methionine oxidation as a variable modification. A maximum of two trypsin missed-cleavage sites was allowed. The initial search results were obtained using a default 5% false discovery rate (FDR) expected by the target-decoy search strategy. Spectra were manually verified to improve data quality (Kim et al., 2018; Shi et al., 2014). Cross-linking data were analyzed and plotted with CX-Circos (http://cx-circos.net)., No

Growth rates of Cystoseira compressa from Crete have been removed from the dataset and analysis due to poor condition of the plants., Comparative patterns in thermal performance between populations have fundamental implications for a species thermal sensitivity to warming and extreme events. Despite this, within-species variation in thermal performance is seldom measured. Here we compare thermal performance between-species variation within communities, for two species of seagrass (Posidonia oceanica and Cymodocea nodosa) and two species of seaweed (Padina pavonica and Cystoseira compressa). Experimental populations from four locations spanning approximately 75% of each species global distribution and a 6ºC gradient in summer temperatures were exposed to 10 temperature treatments (15ºC to 36ºC), reflecting median, maximum and future temperatures. Experimental thermal performance displayed the greatest variability between species, with optimal temperatures differing by over 10ºC within the same location. Within-species differences in thermal performance were also important for P. oceanica which displayed large thermal safety margins within cool and warm-edge populations and small safety margins within central populations. Our findings suggest patterns of thermal performance in Mediterranean seagrasses and seaweeds retain deep ‘pre-Mediterranean’ evolutionary legacies, suggesting marked differences in sensitivity to warming within and between benthic marine communities., [Field collections] Thermal tolerance experiments were conducted on two seagrass species (P. oceanica and Cymodocea nodosa) and two brown seaweed species (Cystoseira compressa and P. pavonica) from four locations spanning 8 degrees in latitude and 30 degrees in longitude across the Mediterranean (Fig. 1, Table S1). These four species were chosen as they are dominant foundation species and cosmopolitan across the Mediterranean Sea. Thermal performance experiments from Catalonia and Mallorca were conducted simultaneously in June 2016 for seaweeds (P. pavonica and C. compressa) and in August 2016 for seagrasses (P. oceanica and C. nodosa). Experiments for all four species were conducted in July 2017 for Crete and in September 2017 for Cyprus., [Sea temperature measurements and reconstruction] Sea surface temperature data for each collection site were based on daily SST maps with a spatial resolution of 1/4°, obtained from the National Center for Environmental Information (NCEI, https://www.ncdc.noaa.gov/oisst (Reynolds et al. 2007). These maps have been generated through the optimal interpolation of Advanced Very High Resolution Radiometer (AVHRR) data for the period 1981-2019. Underwater temperature loggers (ONSET Hobo pro v2 Data logger) were deployed at each site and recorded hourly temperatures throughout one year. In order to obtain an extended time series of temperature at each collection site, a calibration procedure was performed comparing logger data with sea surface temperature from the nearest point on SST maps. In particular, SST data were linearly fitted to logger data for the common period. Then, the calibration coefficients were applied to the whole SST time series to obtain corrected-SST data and reconstruct daily habitat temperatures from 1981-2019., [Species description and distribution] The species used in this study are all common species throughout the Mediterranean Sea, although differ in their biological traits, evolutionary histories and thermo-geographic affinities (Fig. S1). P. oceanica is endemic to the Mediterranean Sea with the all other Posidonia species found in temperate Australia (Aires et al. 2011). The distribution of P. oceanica is restricted to the Mediterranean, spanning from Gibraltar in the west to Cyprus in the east and north into the Aegean and Adriatic seas (Telesca et al. 2015) (Fig. S1A). C. nodosa distribution extends across the Mediterranean Sea and eastern Atlantic Ocean, where it is found from south west Portugal, down the African coast to Mauritania and west to Macaronesia (Alberto et al. 2008) (Fig. S1B). Congeneric species of C. nodosa are found in tropical waters of the Red Sea and Indo-Pacific, suggesting origins in the region at least prior to the closure of the Suez Isthmus, approximately 10Mya. Like C. nodosa, Cystoseira compressa has a distribution that extends across the Mediterranean and into the eastern Atlantic, where it is found west to Macaronesia and south to northwest Africa (Fig. S1C). The genus Cystoseira has recently been reclassified to include just four species with all congeneric Cystoseira spp. having warm-temperate distributions from the Mediterranean to the eastern Atlantic (Orellana et al. 2019). The distribution of Padina pavonica is conservatively considered to resemble C. nodosa and C. compressa, spanning throughout the Mediterranean and into the eastern Atlantic. We considered the poleward distribution limit of P. pavonica to be the British Isles 50ºN (Herbert et al. 2016). P. pavonica was previously thought to have a global distribution, but molecular analysis of the genus has found no evidence to support this (Silberfeld et al. 2013). Instead it has been suggested that P. pavonica was potentially misclassified outside of the Mediterranean, due to morphological similarity with congeneric species (Silberfeld et al. 2013). Padina is a monophyletic genus with a worldwide distribution from tropical to cold temperate waters (Silberfeld et al. 2013). Most species have a regional distribution, with few confirmed examples of species spanning beyond a single marine realm (sensu Spalding et al. 2007)., [Sample collection] Sample collections were conducted at two sites, separated by approximately 1 km, within each location. Collections were conducted at the same depth (1-3 m) at each location and were spaced across the reef or meadow to try and minimise relatedness between shoots or fragments. Upon collection, fragments were placed into a mesh bag and transported back to holding tanks in cool, damp, dark conditions (following Bennett et al. 2021). Fragments were kept in aerated holding tanks in the collection sites at ambient seawater temperature and maintained under a 14:10 light-dark cycle until transport back to Mallorca, where experiments were performed. Prior to transport, P. oceanica shoots were clipped to 25 cm length (from meristem to tip), to standardise initial conditions and remove old tissue for transport. For transport back to Mallorca, fragments were packed in layers within cool-boxes. Cool-packs were wrapped in damp tea towels (rinsed in seawater) and placed between layers of samples. Samples from Catalonia, Crete and Cyprus experienced approximately 12hrs of transit time. On arrival at the destination, samples were returned to holding tanks with aerated seawater and a 14:10 light-dark cycle., [Experimental design: thermal performance experiments] All experiments were run in climate-controlled incubation facilities of the Institut Mediterrani d’Estudis Avançats (Mallorca, Spain). Following 48 hrs under ambient (collection site) conditions, samples were transferred to individual experimental aquaria, which consisted of a double layered transparent plastic bag filled with 2 L of filtered seawater (60 μm) (following Savva et al. 2018). 16 experimental bags were suspended within 80L temperature-controlled baths. In total, ten baths were used, one for each experimental temperature treatment. Bath temperatures were initially set to the acclimatization temperature (i.e. in situ temperatures) and were subsequently increased or decreased by 1 °C every 24 hours until the desired experimental temperature was achieved. Experimental temperatures were: 15, 18, 21, 24, 26, 28, 30, 32, 34 and 36°C (Table S2). For each species, four replicate aquarium bags were used for each temperature treatment with three individually marked seagrass shoots or three algal fragments placed into each bag. For P. oceanica, each marked plant was a single shoot including leaves, vertical rhizome and roots. For C. nodosa, each marked individual consisted of a 10 cm fragment of horizontal rhizome containing three vertical shoots. Individually marked seaweeds contained the holdfast, and 4-5 fronds of P. pavonica (0.98 ± 0.06 g FW; mean ± SE) or a standardised 5-8 cm fragment with meristematic tip for C. compressa (3.67 ± 0.1 g FW; mean ± SE). Experimental plants were cleaned of conspicuous epiphytes. Once the targeted temperatures were reached in all of the baths, experiments ran for 14 days for the algal species and 21 days for seagrasses to allow for measurable growth in all species at the end of the experiment. Experiments were conducted inside a temperature-controlled chamber at constant humidity and air temperature (15 °C). Bags were arranged in a 4x4 grid within each bath, enabling four species/population treatments to be run simultaneously. Bags were mixed within each bath so that one replicate bag was in each row and column of the grid, to minimise any potential within bath effects of bag position. Replicate bags were suspended with their surface kept open to allow gas exchange and were illuminated with a 14h light:10h dark photoperiod through fluorescent aquarium growth lamps. The water within the bags were mixed with aquaria pumps. The light intensity within each bag was measured via a photometric bulb sensor (LI-COR) and ranged between 180-258 μmol m-2 s-1. Light intensity was constant between experiments and did not significantly differ between experimental treatments (p > 0.05). The temperature in the baths was controlled and recorded with an IKS-AQUASTAR system, which was connected to heaters and thermometers. The seawater within the bags was renewed every 72 hrs and salinity was monitored daily with an YSI multi-parameter meter. Distilled water was added when necessary to ensure salinity levels remained within the range of 36-39 PSU, typical of the study region. Carbon and Nitrogen concentrations in the leaf tissue were measured at the end of the experiment for triplicates of the 24ºC treatment for each species and location (Fig. S2) at Unidade de Técnicas Instrumentais de Análise (University of Coruña, Spain) with an elemental analyser FlashEA112 (ThermoFinnigan)., [Growth measurements and statistical analyses] Net growth rate of seagrass shoots was measured using leaf piercing-technique (Short & Duarte 2001). At the beginning of the experiment seagrass shoots were pierced just below the ligule with a syringe needle and shoot growth rate was estimated as the elongation of leaf tissue in between the ligule and the mark position of all leaves in a shoot at the end of the experiment, divided by the experimental duration. Net growth rate of macroalgae individuals was measured as the difference in wet weight at the end of the experiment from the beginning of the experiment divided by the duration of the experiment. Moisture on macroalgae specimens was carefully removed before weighing them. Patterns of growth in response to temperature were examined for each experimental population using a gaussian function: g = ke[-0.5(TMA-μ)2/σ2], where k = amplitude, μ = mean and σ = standard deviation of the curve. Best fit values for each parameter were determined using a non-linear least squares regression using the ‘nlstools’ package (Baty et al. 2015) in R (Team 2020). 95% CI for each of the parameters were calculated using non-parametric bootstrapping of the mean centred residuals. The relationship between growth metrics and the best-fit model was determined by comparing the sum of squared deviations (SS) of the observed data from the model, to the SS of 104 randomly resampled datasets. Growth metrics were considered to display a significant relationship to the best-fit model if the observed SS was smaller than the 5th percentile of randomised SS. Upper thermal limits were defined as the optimal temperature + 2 standard deviations (95th percentile of curve) or where net growth = 0. Samples that had lost all pigment or structural integrity by the end of the experiment were considered dead and any positive growth was treated as zero., [Metabolic rates] Net production (NP), gross primary production (GPP) and respiration (R) were measured for all species from the four sites for five different experimental temperatures containing the in-situ temperature during sampling up to a 6ºC warming (see SM Table S3 for details). Individuals of the different species were moved to methacrylate cylinders containing seawater treated with UV radiation to remove bacteria and phytoplankton, in incubation tanks at the 5 selected temperatures. Cylinders were closed using gas-tight lids that prevent gas exchange with the atmosphere, containing an optical dissolved oxygen sensor (ODOS® IKS), with a measuring range from 0-200 % saturation and accuracy at 25ºC of 1% saturation, and magnetic stirrers inserted to ensure mixing along the height of the core. Triplicates were measured for each species and location, along with controls consisting in cylinders filled with the UV-treated seawater, in order to account for any residual production or respiration derived from microorganisms (changes in oxygen in controls was subtracted from treatments). Oxygen was measured continuously and recorded every 15 minutes for 24 hours. Changes in the dissolved oxygen (DO) were assumed to result from the biological metabolic processes and represent NP. During the night, changes in DO are assumed to be driven by R, as in the absence of light, no photosynthetic production can occur. R was calculated from the rate of change in oxygen at night, from half an hour after lights went off to half an hour before light went on (NP in darkness equalled R). NP was calculated from the rate of change in DO, at 15 min intervals, accumulated over each 24 h period. Assuming that daytime R equals that during the night, GPP was estimated as the sum of NP and R. To derive daily metabolic rates, we accumulated individual estimates of GPP, NP, and R resolved at 15 min intervals over each 24 h period during experiments and reported them in mmol O2 m−3 day−1. A detailed description of calculation of metabolic rates can be found at Vaquer-Sunyer et al. (Vaquer-Sunyer et al. 2015)., [Thermal distribution and thermal safety margins] We estimated the realised thermal distribution for the four experimental species by downloading occurrence records from the Global Biodiversity Information Facility (GBIF.org (11/03/2020) GBIF Occurrence Download). Occurrence records from GBIF were screened for outliers and distributions were verified from the primary literature (Alberto et al. 2008, Draisma et al. 2010, Ni-Ni-Win et al. 2010, Silberfeld et al. 2013, Telesca et al. 2015, Orellana et al. 2019) and Enrique Ballesteros (pers. comms) (Fig. S1). Mean, 1st and 99th percentiles of daily SST’s were downloaded for each occurrence site for the period between 1981-2019 using the SST products described above (Table S4). Thermal range position of species at each experimental site were standardised by their global distribution using a Range Index (RI; Sagarin & Gaines 2002). Median SST at the experimental collection sites were standardized relative to the thermal range observed across a species realized distribution, using the equation: RI = 2(SM- DM)/DB where SM = the median temperature at the experimental collection site, Dm = the thermal midpoint of the species global thermal distribution and DB = range of median temperatures (ºC) that a species experiences across its distribution. The RI scales from -1 to 1, whereby ‘-1’ represents the cool, leading edge of a species distribution, ‘0’ represents the thermal midpoint of a species distribution and ‘1’ represents the warm, trailing edge of a species distribution (Sagarin & Gaines 2002). Thermal safety margins for each population were calculated as the difference between empirically derived upper thermal limits for each population and the maximum long term habitat temperatures recorded at collection sites. Each population’s thermal safety margin was plotted against its range position to examine patterns in thermal sensitivity across a species distribution., Horizon 2020 Framework Programme, Award: 659246; Juan de la Cierva Formacion, Award: FJCI-2016-30728; Spanish Ministry of Economy, Industry and Competitiveness, Award: MedShift, CGL2015-71809-P; Spanish Ministry of Science, Innovation and Universities, Award: SUMAECO, RTI2018-095441-B-C21, Trans_Mediterranean_metabolic_rates_upload; Trans_Mediterranean_seagrass_growth_rates_upload; Trans_Mediterranean_seaweed_growth_rates_upload; TransMediterranean_seaweed_growth_rates_upload, Peer reviewed

Zip file including raw trait, environmental, and community data, code for conducting analyses, and a spreadsheet summarizing model selection., Peer reviewed