Resultados totales (Incluyendo duplicados): 34357
Encontrada(s) 3436 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/307926
Dataset. 2022

GENOMIC PATTERNS OF HOMOZYGOSITY AND INBREEDING DEPRESSION IN MURCIANO-GRANADINA GOATS - DATASET

  • Luigi-Sierra, Maria Gracia
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

DOI: http://hdl.handle.net/10261/307926
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/307926
HANDLE: http://hdl.handle.net/10261/307926
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/307926
PMID: http://hdl.handle.net/10261/307926
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/307926
Ver en: http://hdl.handle.net/10261/307926
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/307926

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/308912
Dataset. 2022

TRANSPOSABLE ELEMENT POLYMORPHISMS IMPROVE PREDICTION OF COMPLEX AGRONOMIC TRAITS IN RICE [DATASET]

  • Vourlaki, Ioanna-Theoni
  • Castanera, Raúl
  • Ramos-Onsins, Sebastian E.
  • Casacuberta, Josep M.
  • Pérez-Enciso, Miguel
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

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311232
Dataset. 2022

THERMAL PERFORMANCE OF SEAWEEDS AND SEAGRASSES ACROSS A REGIONAL CLIMATE GRADIENT [DATASET]

  • Bennett, Scott
  • Marbà, Núria
  • Vaquer-Sunyer, Raquel
  • Jordá, Gabriel
  • Forteza, Marina
  • Roca, Guillem
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

DOI: http://hdl.handle.net/10261/311232
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311232
HANDLE: http://hdl.handle.net/10261/311232
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311232
PMID: http://hdl.handle.net/10261/311232
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311232
Ver en: http://hdl.handle.net/10261/311232
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311232

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311636
Dataset. 2022

SUPPLEMENTARY DATA AND CODES FOR "CONFINEMENT-INDUCED ACCUMULATION ACCUMULATION AND DE-MIXING OF MICROSCOPIC ACTIVE-PASSIVE MIXTURES"

  • Williams, Stephen
  • Jeanneret, Raphaël
  • Tuval, Idan
  • Polin, Marco
The file includes the data used in all the figures and supplementary figures, as well as the codes used for simulations and escape rate estimation. The data files are in .mat format. The codes are Matlab codes with the exception of the analytical estimate of the escape rate which is a Mathematica worksheet., This file contains the data and codes used in the paper “Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures”, S. Williams et al, 2022., Grants: Agence Nationale de la Recherche: PSL - Paris Sciences et Lettres (ANR-10-IDEX-0001); ENS-ICFP - ENS- International Center for Fundamental Physics and its interfaces (ANR-10-LABX-0010), Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311636
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311636
HANDLE: http://hdl.handle.net/10261/311636
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311636
PMID: http://hdl.handle.net/10261/311636
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311636
Ver en: http://hdl.handle.net/10261/311636
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311636

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/312349
Dataset. 2022

AVIAN SEED DISPERSAL MAY BE INSUFFICIENT FOR PLANTS TO TRACK FUTURE TEMPERATURE CHANGE ON TROPICAL MOUNTAINS [DATASET]

  • Nowak, Larissa
  • Schleuning, Matthias
  • Bender, Irene M. A.
  • Böhning-Gaese, Katrin
  • Dehling, D. Matthias
  • Fritz, Susanne A.
  • Kissling, W. Daniel
  • Mueller, Thomas
  • Neuschulz, Eike Lena
  • Pigot, Alex L.
  • Sorensen, Marjorie C.
  • Donoso, Isabel
These data are part of a publication on avian seed dispersal and climate change accepted by Global Ecology and Biogeograpy. -- The file includes 3 tables. -- Methods: S1 Plant species: Minimum and maximum elevation (m a.s.l.) were compiled from Brako, L. & Zarucchi, J.L. (1993). Catalogue of the flowering plants and Gymnosperms of Peru. Catálogo de las Angiospermas y Gimnospermas del Perú. Monogr. Syst. Bot. from Missouri Bot. Gard. 45; Growth form has been defined during field surveys; Fruit width (mm) was measured on fruits collected during the field surveys on 20 fruits per plant species; given are species mean values; Plant height (m) was measured during the field surveys on all individuals in the plots; given are species' mean values; Source indicates if trait values are species mean values based on data from our own field surveys, species mean values based on data from Ecuador or genus mean values. -- S2 Bird species: Minimum and maximum elevation (m a.s.l.) were compiled from Merkord, C.L. (2010). Seasonality and elevational migration in an Andean bird community. University of Missouri-Columbia; Walker, B., Stotz, D.F., Pequeño, T. & Fitzpatrick, J.W. (2006). Birds of the Manu Biosphere Reserve. Fieldiana Zool., 23–49 and complemented by Dehling, D.M., Sevillano, C.S. & Morales, L.V. (2013). Upper and lower elevational extremes of Andean birds from south-east Peru. Boletín Inf., 8, 32–38; Body mass (g) was compiled from Dunning, J.B. (2007). CRC Handbook of Avian Body Masses. CRC Press, Boca Raton; Bill width (mm) was measured on museum specimens (at least two adult males and females) according to measurement protocols from Eck, S., Töpfer, T., Fiebig, J., Heynen, I., Fiedler, W., Nicolai, B., et al. (2011). Measuring birds. Christ Media Natur, Minden; Kipp's index equals the Kipp's distance (mm) divided by the wing length (mm), which were both measured on museum specimens (at least two adult males and females) according to measurement protocols from Eck, S., Töpfer, T., Fiebig, J., Heynen, I., Fiedler, W., Nicolai, B., et al. (2011). Measuring birds. Christ Media Natur, Minden. -- S3 Projections: LDD (m) was simulated based on three trait-matching parameter values representing a low, an intermediate and a high degree of trait matching (s = 0.5, 1.5, 5.0, respectively), and estimated as the 95th and the 99th percentile of the simulated 10000 dispersal distances per plant species (95% and 99% LDD ability, respectively); given are mean, standard deviation and coefficient of variation across 10 independent iterations of the simulations; MAX (m) was simulated based on three trait-matching parameter values representing a low, an intermediate and a high degree of trait matching (s = 0.5, 1.5, 5.0, respectively), and was estimated as the maximum of the simulated 10000 dispersal distances per plant species; given are the mean and standard deviation across 10 independent iterations of the simulations; Vertical temperature shift (m) by 2070 was projected according to three future greenhouse gas emission scenarios (RCP 2.6, 4.5 and 8.5) and five general circulation models (GCMs; cc = CCSM4, he = HadGEM2-ES, mc = MIROC 5, mg = MRI-CGCM and no = NorESM1-M). Data on current and projected mean annual temperature along the Manú gradient was derived from Worldclim (Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol., 25, 1965–1978). Data on tropospheric lapse rate for our study regions was compiled from Mokhov, I.I. & Akperov, M.G. (2006). Tropospheric lapse rate and its relation to surface temperature from reanalysis data. Izv. Atmos. Ocean. Phys., 42, 430–438. Given are mean and standard deviation of the projected vertical distances across species' elevational range as well as mean and standard deviation across general circulation models; Required horizontal dispersal distance (m) considering an average slope of 11.45° is computed as vertical temperature shift (m) divided by the sine of the mean slope (°). This approximates the distance by which a plant species would have to disperse horizontally to shift its range upslope by a given vertical distance considering a mean slope of 11.45 °; Number of required LDD events by 2070 equals the projected number of LDD events plant species' would require to fully track projected vertical temperature shifts until 2070. This was computed as LDD ability (m)/ Required horizontal dispersal distance (m) for each plant species., [Aim] Climate change causes species’ range shifts globally. Terrestrial plant species often lag behind temperature shifts, and it is unclear to what extent animal-dispersed plants can track climate change. Here, we estimate the ability of bird-dispersed plant species to track future temperature change on a tropical mountain., [Location] Tropical elevational gradient (500–3500 m a.s.l.) in the Manú biosphere reserve, Peru, [Time period] 1960–1990 to 2061–2080, [Taxa] Fleshy-fruited plants, avian frugivores., [Methods] Using simulations based on the functional traits of avian frugivores and fruiting plants, we quantified the number of long-distance dispersal (LDD) events that woody plant species would require to track projected temperature shifts on a tropical mountain by the year 2070 under different greenhouse gas emission scenarios (RCP 2.6, 4.5 and 8.5). We applied this approach to 343 bird-dispersed woody plant species., [Results] Our simulations reveal that bird-dispersed plants differ in their climate-tracking ability, with large-fruited and canopy plants exhibiting a higher climate-tracking ability. Our simulations also suggest that even under scenarios of strong and intermediate mitigation of greenhouse gas emissions (RCP 2.6 and 4.5), sufficient upslope dispersal would require several LDD events by 2070, which is unlikely for the majority of woody plant species. Furthermore, the ability of plant species to track future temperature changes increased in simulations with a low degree of trait matching between plants and birds, suggesting that plants in generalised seed-dispersal systems may be more resilient to climate change., [Main conclusion] Our study illustrates how plant and animal functional traits can inform predictive models of species dispersal and range shifts under climate change and suggests that the biodiversity of tropical mountain ecosystems is highly vulnerable to future warming. The increasing availability of functional trait data for plants and animals globally will allow parameterisation of similar models for many other seed-dispersal systems., S1_Plant_traits_elevation, S2_Bird_traits_elevation, S3_Projections, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/312349
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/312349
HANDLE: http://hdl.handle.net/10261/312349
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/312349
PMID: http://hdl.handle.net/10261/312349
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/312349
Ver en: http://hdl.handle.net/10261/312349
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/312349

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/335447
Dataset. 2023

LIST OF GENE ONTOLOGY (GO) TERMS RELATED TO DEGS ACCORDING TO N-6/N-3 PUFA RATIO

  • Manaig, Yron Joseph Yabut
  • Criado-Mesas, Lourdes
  • Esteve-Codina, Anna
  • Mármol-Sánchez, Emilio
  • Castelló, Anna
  • Sánchez, Armand
  • Folch, Josep María
1 table., List of Gene Ontology (GO) terms related to DEGs according to n-6/n-3 PUFA ratio., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/335447
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/335447
HANDLE: http://hdl.handle.net/10261/335447
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/335447
PMID: http://hdl.handle.net/10261/335447
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/335447
Ver en: http://hdl.handle.net/10261/335447
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/335447

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338811
Dataset. 2022

REGIONAL SEA-LEVEL BUDGET FROM 1993-2016 [DATASET]

  • Camargo, Carolina M. L.
  • Riva, Riccardo
  • Hermans, Tim H. J.
  • Schütt, Eike M.
  • Marcos, Marta
  • Hernández Carrasco, Ismael
  • Slangen, Aimée B. A.
Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. -- This repository contains the following files: budget_components_ENS.nc Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. If required the individual data sets used for the ensemble, please contact the author. -- masks.nc netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired. -- dmaps_trend.pkl (and .xlsx) Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl). -- som_trend.pkl (and .xlsx). Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl), This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022), https://egusphere.copernicus.org/preprints/2022/egusphere-2022-876/., budget_components_ENS.nc, dmaps_trends.pkl, dmaps_trends.xlsx, masks.nc, SOM_trends.pkl, SOM_trends.xlsx, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/338811
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338811
HANDLE: http://hdl.handle.net/10261/338811
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338811
PMID: http://hdl.handle.net/10261/338811
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338811
Ver en: http://hdl.handle.net/10261/338811
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338811

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338820
Dataset. 2022

REGIONAL SEA-LEVEL BUDGET FROM 1993-2016 [DATASET]

  • Camargo, Carolina M. L.
  • Riva, Riccardo
  • Hermans, Tim H. J.
  • Schütt, Eike M.
  • Marcos, Marta
  • Hernández Carrasco, Ismael
  • Slangen, Aimée B. A.
This repository contains the following files: budget_components_ENS.nc. Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). If required the individual data sets used for the ensemble, please contact the author. -- masks.nc: netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired. -- dmaps_trend.pkl (and .xlsx): Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl). -- som_trend.pkl (and .xlsx): Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl), This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022, submited)., budget_components_ENS.nc, dmaps_trends.pkl, dmaps_trends.xlsx, masks.nc, SOM_trends.pkl, SOM_trends.xlsx, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/338820
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338820
HANDLE: http://hdl.handle.net/10261/338820
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338820
PMID: http://hdl.handle.net/10261/338820
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338820
Ver en: http://hdl.handle.net/10261/338820
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/338820

RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/96559
Dataset. 2024

ANTI ABORTION CONVERSATION DATASET

  • Morales-i-Gras, Jordi
  • Iranzo-Cabrera, María
  • Tarullo, Raquel
  • Calvo, Dafne
Dataset of tweets posted between September 1 and December 31, 2022, employing the Spanish hashtags #noalaborto, #provida, #salvemoslasdosvidas, and #sialavida. A total of 120,094 tweets were gathered using the Twitter Academic API.

Proyecto: //
DOI: https://hdl.handle.net/10550/96559
RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/96559
HANDLE: https://hdl.handle.net/10550/96559
RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/96559
PMID: https://hdl.handle.net/10550/96559
RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/96559
Ver en: https://hdl.handle.net/10550/96559
RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/96559

RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/75457
Dataset. 2020

CENTRAL IBERIAN MEDITERRANEAN AS REFUGIUM (MIS 5–3). DATA AND SCRIPT

  • Real Margalef, Cristina
  • Martínez Varea, Carmen María
  • Carrión Marco, Yolanda
  • Badal, Ernestina
  • Sanchis Serra, Alfred
  • Martínez Valle, Rafael
  • Guillem Calatayud, Pere Miquel
Se aporta por un lado los datos base de los restos arqueobotánicos y arqueozoológicos identificados en yacimientos del Paleolítico medio y superior de la región central del Mediterráneo ibérico (Cova Negra, Abrigo de la Quebrada, Cova de les Malladetes y Cova de les Cendres). Y por otro, el script generado en el software R (con el paquete "Vegan") para calcular diferentes índices de diversidad. Todo ello utilizado en el artículo "Could the central Iberian Mediterranean region be defined as a refugium? Fauna and flora in MIS 5–3 and their implications for Palaeolithic human behaviour".

Proyecto: //
DOI: https://hdl.handle.net/10550/75457
RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/75457
HANDLE: https://hdl.handle.net/10550/75457
RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/75457
PMID: https://hdl.handle.net/10550/75457
RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/75457
Ver en: https://hdl.handle.net/10550/75457
RODERIC. Repositorio Institucional de la Universitat de Valéncia
oai:roderic.uv.es:10550/75457

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