Resultados de investigación

[Usage Notes] DHA meta-analysis data Percentage of DHA in total fatty acid were obtained from literatures. DHA contents in algae and consumers from marine and freshwater habitats were analyzed. DHA.csv, Colonization of new ecological niches has triggered large adaptive radiations. Although some lineages have made use of such opportunities, not all do so. The factors causing this variation among lineages are largely unknown. Here, we show that deficiency in docosahexaenoic acid (DHA), an essential ω-3 fatty acid, can constrain freshwater colonization by marine fishes. Our genomic analyses revealed multiple independent duplications of the fatty acid desaturase gene Fads2 in stickleback lineages that subsequently colonized and radiated in freshwater habitats, but not in close relatives that failed to colonize. Transgenic manipulation of Fads2 in marine stickleback increased their ability to synthesize DHA and survive on DHA-deficient diets. Multiple freshwater ray-finned fishes also show a convergent increase in Fads2 copies, indicating its key role in freshwater colonization., Peer reviewed

[Usage Notes] DATA_CNEORUM_seed_size_CONTROL_PLANT_ID_climate_vars This file contains the data on seed diameter of Cneorum tricoccon for all the study populations, considering only seeds collected directly from plants (i.e, undispersed seeds). Data on mean anual precipitation and temperature are given. We indicate the different species of disperser present in each population, with an extra-column showing if the disperser is native (in the case of lizards) or exotic (in the case of pine-martens). This is the file we have used to test for differences in seed size among populations and to assess whether this variable is affected by climatic variables. DATA_CNEORUM_seed_size_all_disp_climate_vars This file contains data on seed diameter of Cneorum tricoccon from the different treatments (controls vs defecated/dispersed by each of the three different species: pine martens or either of the two lizards, Podarcis lilfordi or Podarcis pityusensis). In another column we code the treatment as 0 or 1 (indicating control and defecated seeds, respectively). This is the data file used for the seed selection analyses. We also include data on the climatic variables obtained for each population (mean anual precipitation and temperature). DATA_CNEORUM_GERMINATION This file contains data on the germination of seeds of Cneorum tricoccon from the different treatments (control vs dispersed by any of the three species) in each population. Data are given for each separate seed, indicating whether the seed germinated (1) or not (0). Weight (g) of each seed is given in the last column., The disappearance of native seed dispersers due to anthropogenic activities is often accompanied by the introduction of alien species, which may to some extent replace the ecological service provided by the extinct ones. Yet, little empirical evidence exists demonstrating the evolutionary consequences of such alien ‘replacement’. Here, we document the conflicting selection exerted on seed size by two native lizards (Podarcis lilfordi and P. pityusensis) and an alien mammal species (Martes martes), all acting as legitimate seed dispersers of the Mediterranean relict Cneorum tricoccon. While lizards mostly exerted a negative directional selection on seed diameter, especially P. pityusensis, the much larger pine marten exerted positive selection on seed size. Our findings suggest that this among-disperser variation in the selection regimes, together with the occurrence of spatial variation in the presence of each of seed disperser, help to create the geographical variation observed for seed size of C. tricoccon. To our knowledge, this is the first empirical evidence showing opposing selective pressures between native and alien species in the seed dispersal process in an invaded ecosystem., Peer reviewed

[Usage Notes] This files contains all DNA and topoclimate data from the manuscript. The file README contains additional data for each individual., Geographic isolation substantially contributes to species endemism on oceanic islands when speciation involves the colonisation of a new island. However, less is understood about the drivers of speciation within islands. What is lacking is a general understanding of the geographic scale of gene flow limitation within islands, and thus the geographic scale and drivers of geographical speciation within insular contexts. Using a community of beetle species, we show that when dispersal ability and climate tolerance are restricted, microclimatic variation over distances of only a few kilometres can maintain strong geographic isolation and drive incipient speciation. Further to this, we demonstrate congruent diversification with gene flow across species, mediated by Quaternary climate oscillations that have facilitated a dynamic of isolation and secondary contact. The unprecedented scale of parallel species responses to a common environmental driver for evolutionary change has profound consequences for understanding past and future species responses to climate variation., Peer reviewed

The pellets were subjected to reciprocating rubbing under vacuum (10-7 mbar range) using an alumina sphere, 3 mm in diameter. A specially designed friction cell with nearly zero own gas emission was used. The motion frequency was 1 Hz. One motion cycle consisted of one forth and one back stroke, each of which lasted for about 40 ms. The indenter stood still for around 50 ms after the forth stroke and 870 ms after the back one. The stroke length was la = 7 mm, the mean sliding speed was Vs = 0.18 m s-1 and the normal load was in the range 0.22 – 0.88 N. The total and partial gas pressures were measured in the experimental vacuum chamber, where the pellets were rubbed, using an ionization Bayard-Alpert type vacuum gauge and a quadrupole mass-spectrometer, correspondingly. To quantify minute gas emission rates (<1 nmol/s) the experimental chamber was connected to a gas-expansion chamber through a diaphragm, which conductance under molecular gas flow was carefully measured. Before starting the experiments, the chamber was pumped out for at least 48 hours to achieve stable background pressure (the rate of change of low-pass filtered pressure signal ≤10-11 mbar s-1). The gas emission was evaluated from the pressure time series during the mechanical action and benchmarked against the stable background. The gas composition and the emission rates were determined using a previously developed statistical matrix method. X-ray diffraction (XRD) with Cu Kα (λ = 0.15418 nm) radiation was employed for the structural characterization of NaAlH4. To assess the thermal stability of NaAlH4 Temperature-Programmed Desorption Mass-Spectrometry (TPD-MS) was used. A portion of NaAlH4 powder was placed into an alumina crucible in a glove box under Ar atmosphere (MBraun, <1 ppm H2O, <10 ppm O2). Alumina crucible was used instead of a Pt one to avoid possible decomposition of NaAlH4 due to the catalytic effect of Pt. The crucible was transferred to the TPD-MS system, where it was set into another Pt crucible. During the transfer, the sample was briefly exposed to the atmospheric air (about 5 min). The test was carried out under an Ar flux of 50 ml/s and a heating rate of 5 ºC/min. The mechanically affected zones were characterized using FTIR spectrometry, Raman confocal spectroscopy (incident laser wavelength  = 532 nm and power 5.6 mW) and Scanning Electron Microscopy (Hitachi S 800) to contrast possible structural and chemical variations induced in the material by the mechanical action. All the results were benchmarked against the measurements on the pristine zones of the same pellets., Datasets of mass-spectrometry signals were obtained in the experiments with non-thermal dehydrogenation of sodium alanate through the application of mechanical energy. An application of mechanical energy was explored as a new non-thermal method to drive H2 emission from undoped sodium alanate at room temperature. Dehydrogenation reactions were studied on a micrometer scale using localized rubbing under ultrahigh vacuum. Mechanically Stimulated Gas Emission Mass-Spectrometry (MSGE-MS) including the Dynamic gas expansion method was used to determine the kinetic parameters of hydrogen emission as well as the composition and emission behaviour of trace gases. It was found that mild rubbing of NaAlH4 pellets under vacuum led to intensive and almost instantaneous gas emission. The dominating species in the emitted gases was H2 (>99%). Traces (~0.1%) of mono- and polyalanes, NaAlH4 vapours, CO2 and other no identified gases were registered. Several H2 emission modes, which characteristic time constants ranged widely from 0.6 to 465 s, were observed. None of the dehydrogenation reactions involved could be connected to either the thermal effect of friction or the direct coupling of mechanical forces to the energy landscape of chemical reactions. The study was complemented by structural, morphological, tribological, mechanical and surface analyses. It was suggested that the tribochemical reactions can be triggered by plastic deformation and shearing., This study was co-funded by Spanish Ministry for Science and Innovation (grants PID2019-111063RB-I00, PID2020-112770RB-C22 and RTI2018-099794-B-I00) and the Basque Government via the EMAITEK Plus 2020 programme., 1.Dataset of mass-spectrometry time series of mechanically stimulated gas emission from sodium alanate (NaAlH4) pellets under vacuum.-- 2. Dataset of Thermal Programmed Desorption – Mass-Spectrometry (TPD-MS) analysis of sodium alanate.-- 3. Dataset of X-ray diffraction of sodium alanate.-- 4. Dataset of micro-FTIR spectra of pristine and mechanically activated surfaces of pellets of sodium alanate.-- 5. Dataset of Raman spectra measured on the surfaces of pellets of sodium alanate., No

[Methods] Using a sample of Atlantic rainforest avian frugivores we analyze fruit effectiveness as a nutritional food resource for seed dispersing birds, i.e., Resource-Provisioning Effectiveness (RPE), an overlooked aspect of seed dispersal effectiveness. We found ample variation in RPE among plant species, given not just variation in fruit design and nutrient content but also in fruit availability, accessibility and other features that determine how rewarding a fruit resource is for the frugivore. Yet fruit species show consistent RPE values despite highly variable outcomes of interactions with different avian frugivores. The rationale for our study includes four steps: 1) selecting a representative sample of frugivore species illustrating both the full range of body mass and phylogenetic diversity of the Brazilian Southeastern Atlantic forest frugivores; 2) compiling a literature dataset on fruit foraging and usage data for these frugivore species; 3) analyze variation in RPE across selected plant species by building effectiveness landscapes from the frugivore’s perspective; and 4) experimentally assess RPE subcomponents (i.e. fruit pulp digestibility) that refine the estimates of RPE variation among plant species. To estimate the RPE for fruit species consumed by each frugivore species we have defined its two components: the rate at which the fruits are ingested/handled by the animal (feeding rate) as the quantity component, and the energy gained from eating the fruit (fruit energy) as the quality component. The feeding rate is calculated by multiplying the bird visitation frequency to the plant by the number of fruits consumed in each visit; from a frugivore perspective these subcomponents of RPE-quantity illustrate potential encounter rates with the fruit resource (visitation) and potential handling/ingestion rates once the fruits are encountered (fruits/visit). Fruit-pulp energy yield is assessed using the specific energy (KJ/g) multiplied by the fruit pulp dry mass. Since the nutrient proportional contents of the fruit pulp are calculated based on its dry mass, we use this value as the profitable fruit mass per fruit (Herrera 1981). Specific energy was quantified using the following energy conversion factors for fruits (FAO, 2002): 14.1 KJ/g for proteins, 35 KJ/g for lipids and 15.1 KJ/g for carbohydrates. Ultimately, by multiplying the two components we obtained the total effect value for each specific interaction (RPE; Schupp, 1993; Schupp, Jordano, & Gómez, 2017): RPE[g energy assimilated] = [no fruits consumed/obs time (h)] × [g energy accumulated/fruit consumed] (1) Note that the RPE estimate for a given fruit species is specific for its interaction with a frugivore, so we used the average RPE values of a given fruit of all the frugivore species it provides food to, to characterize its overall or total effectiveness (RPET): RPET= sum(RPEi)/N (2) where, for a given focal fruit species, N is the number of pairwise interactions with RPE data available, and RPEi is the RPE value for a specific pairwise interaction i. Thus, RPET is fruit species-specific, while each pairwise interaction has a specific RPE value (RPEi) depending on the identity of the specific frugivore partner. Data collection RPE Quantity component Most of the data used to calculate the quantitative component have been obtained from available bibliographic sources. Data compilation comes from a total of 51 studies and four databases for frugivore-plant interactions from the Brazilian Atlantic Forest (see Data Sources section for a list of data sources used and Supp. Mat. Fig. S1 for a map of study locations). Variables collected from the bibliography were: number of visits, observation time and number of fruits consumed per visit. To reduce bias we divided the total number of visits to the plants by observation time to control for the different duration of each study. The fruit mass ingested per visit was positively correlated with frugivore body mass (Pearson’s correlation r = 0.588, p < 0.001, n = 541 distinct pairwise interactions; Supp. Mat. Fig. S2). Fruit mass-body mass correlation allowed the estimation of the number of fruits consumed per visit for avian species with no data on fruit consumption rates available; this was done for special cases when avian species had limited number of records in most studies (n = 7 fruit species for Aburria jacutinga, n = 2 fruit species for Penelope obscura, n = 3 fruit species for Procnias nudicollis). We have referred to a fruit as all the dispersing and consequently ingested units (i.e. diaspora). Diaspora and fruit are often the same thing; however, in some cases such as Cabralea canjerana or Virola spp., an aril (i.e. a fleshy covering in some seeds) acts as a diaspore, being smaller than the actual fruit. Therefore, when the diaspore was actually different from the fruit, the energy in the qualitative component has been measured accordingly. In other cases frugivores may peck pieces and ingest just a part of the whole fruit or infructescence (e.g. catkins of Cecropia spp. or syconia from Ficus spp.). For those large infructescences that birds do not consume whole, we used the number of pecks and assessed the percentage of a single fruit actually consumed, corresponding to a given number of pecks and the beak size of the frugivore. Data on each species’ gape size and fruit length (obtained from Bello et al., 2017 and Galetti et al., 2013) allowed us to estimate the total number of pecks needed to consume an infructescence (Supp. Mat. Table S1). RPE Quality component All fruit pulp dry masses and nutrient proportions were obtained from available databases (Pizo, 2017 unpublished; Jordano 1995, 2007; Bello et al., 2017). In a few cases, pulp dry mass values were missing; these were estimated using additional species-specific data such as pulp fresh mass or water proportion (6 species records imputed in this way; see Supp. Mat. Table S2). However, when species-specific data were unavailable, we estimated the specific values by averaging the data available for congeneric species. This type of data imputation was done for a few cases and only when essential to retain a given species in the dataset (9 species records were imputed this way; see Supp. Mat. Table S3). When calculating specific energy, we preferably used the non-structural carbohydrate content for the carbohydrate value; however, when this information was not available total carbohydrate content or total sugar content was used (see Suppl. Mat. Table S4). Due to the diverse origin of nutritional data, variation in the techniques and devices used for their analyses is likely. This variation in methods together with the use of different carbohydrate content variables have limitations when comparing energetic values among fruits. We therefore want to highlight these possible limitations and advise to take results cautiously. While the quality component gives us an approximate idea of the nutritional value of each fruit it does not offer a detailed ranking. A total of 315 different fruit species were reported to be eaten by the frugivore species considered in this study, of which 36 fruits had enough data to calculate the quantity and quality component. [Usage Notes] Please visit the GitHub repository for this dataset to find updates and additional information: https://github.com/PJordano-Lab/MS_JEcol_Resource-provisioning-effectiveness., 1. Mutualistic interactions between frugivorous birds and fleshy fruited plants are key processes for both natural plant regeneration and the maintenance of birds relying on fruit resources. However, seed dispersal effectiveness (SDE) has been frequently studied only from the plant’s perspective, i.e. the contribution of animals to plant fitness. 2. Using a sample of Atlantic rainforest avian frugivores, this study presents a first comparative, empirical study of fruit effectiveness as a nutritional food resource for seed dispersing birds through the use of Resource-Provisioning Effectiveness (RPE) models. RPE is the product of the amount of fruits a frugivore can consume (quantity component) and the quality of the ingesta in terms of energy and nutrients obtained (quality component). 3. Our results show wide variation in Resource-Provisioning Effectiveness among fleshy-fruited plant species. Energy-rich fruits consistently show a smaller quantity component, while energetically-poor fruits are consistently the most consumed, with fruit species spanning a gradient from these two extremes. 4. The specific RPEi resulting from a pairwise fruit-frugivore interaction is positively correlated to the total RPE (RPET) that a given fruit species has for the whole frugivore assemblage. RPE therefore appears to be a characteristic feature of the fruit species, rather than of the specific frugivore partner. 5. Only the fruit’s specific energy content showed a significant phylogenetic signal, suggesting potential constraints for free covariation between RPE and SDE of fruits and frugivores. 6. Synthesis: We analyze variation in the effectiveness of fleshy fruit food provisioning to avian frugivores by explicitly redefining Resource-Provisioning Effectiveness within the Seed Dispersal Effectiveness framework. We found ample variation in RPE among plant species, showing differences in both quantity and quality components of fruit resources rewards for the frugivores. Our findings help unravel how seed-dispersing birds may discriminate among alternative fruit resources and to understand the configuration of mutual dependencies among mutualistic partners., Agencia Estatal de Investigación, Award: CGL2017-82847-P. Fundación La Caixa, Award: LCF/BQ/ES18/11670007. Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía, Award: RNM–5731., Peer reviewed

[Usage Notes] Dataset consists of genomic data generated in vcf format., [Aim] Differentiation of island lineages from mainland relatives and radiation after initial colonization are two important processes generating island diversity. Both of these processes are influenced by colonization dynamics and contemporary connections between island species and their source populations. The classic island progression rule model, i.e., dispersal from mainland to older islands and subsequently to younger islands, can be considered a null hypothesis, particularly for islands that are geographically aligned perpendicular to the mainland coast with ages inversely proportional to distance from the mainland. Alterations to this pattern have been reported, particularly in archipelagoes that are geographically closely adjacent to mainland source populations. Here we aim to integrate genomic and environmental niche data to infer the colonization history of a Canary endemic species and to understand its current diversity patterns., [Location] Canary Islands., [Taxon] Lavatera acerifolia (Malvaceae)., [Methods] We used high-throughput genotyping-by-sequencing (GBS) combined with species distribution modelling (SDM) projected onto past conditions. Genetic structure (clustering methods), relatedness (coalescent and ML trees), nucleotide diversity and differentiation (population genetics) were assessed based on SNPs obtained from three alternative bioinformatics pipelines. The influence of environmental variables over time was assessed with a GLM in which the response variable was amount of heterozygous sites per individual., [Results] Four genetic groups were identified arranged along a longitudinal gradient, and the earliest diverging coincides with the older, and easternmost, islands (Lanzarote and Fuerteventura). Genetic diversity is reduced in the westernmost islands, which are more distant from the mainland, host few populations and yet apparently offer more suitable habitats., [Main conclusions] The inferred colonization scenario generally fits the progression rule model, but suggests a more complex pattern for the central islands. For the westernmost islands, the contrast between high availability of suitable habitats and reduced genetic diversity and number of populations suggests a colonization front moving at a slow pace, rather than local extinctions, as an explanation for the scarcity of populations in those islands. Historical projections of SDM lend support to this interpretation., Spanish Ministry of Science, Technology and Innovation, Award: CGL2010-16138. Spanish Ministry of Economy and Competitivity, Award: CGL2013-49097-C2-1-P, CGL2017-88500-P (AEI/FEDER, UE). FPI fellowship, Award: BES-2011-049852., Peer reviewed

Soil acidification as a global change factor can devastatingly affect plant growth and productivity. In frequently disturbed ecosystems, plant resprouting ability strongly determines biomass reconstruction and resilience after aboveground damage. However, how plant regrowth responds to soil acidification remains largely unknown, especially regarding the role of the rhizosphere in mediating this response. We manipulated a soil-acidification gradient via adding purified elemental sulfur powder at various rates (0-50 g S m−2 year−1) in a frequently mown meadow. Shoot regrowth of functional groups were measured after clipping and supporting roles of rhizosphere versus bulk soils were disentangled using isotope labelling along the acidification gradient. Regrowth of grasses and sedges increased while forbs decreased along the acidification gradient. The results suggest that grasses were competitors capable of taking up nutrients from both rhizosphere and bulk soils, while sedges were acid-tolerators with lower sensitivity to decreased nitrogen-mineralization rates. Forbs, as typical ruderals, were vulnerable to N competition with microbes, particularly in the rhizosphere soil. Therefore, biomass regrowth of forbs was explained more by physicochemical and biological parameters from the rhizosphere than bulk soil Synthesis. Divergent interplay between plant functional groups and rhizosphere soils was the prominent driver for biomass regrowth responding to soil acidification., Strategic Priority Research Program of the Chinese Academy of Sciences, Award: XDA23080400. National Natural Science Foundation of China, Award: 32071563. National Natural Science Foundation of China, Award: 31870441. National Natural Science Foundation of China, Award: 32101320., Peer reviewed

[Methods used for collection/generation of data] Standardized behavioral tests were continuously recorded by a camera attached to each arena controlled by a Raspberry Pi 3 system. All the behavioral tests were analyzed using a trained deep learning algorithm., [Methods for processing the data] Deep learning algorithm and R-Studio., The tests started with wild individuals on March 11th, 2019 and ended on April 23rd, 2019. Reared individuals were tested starting on July 19th, 2019 and ending on August 22nd, 2019., Project funded by the research project FISHOBES (grant no. CTM2017-91490-EXP) funded by the Spanish Ministry of Science and Innovation (MICINN). Marco Signaroli was supported by a “Ayudas para contratos predoctorales” (grant no. PRE2020-095580) funded by MCIN/AEI /10.13039/501100011033 and the FSE “invierte en tu futuro”. Aina Pons was supported by an FPI predoctoral fellowship (ref. FPI/2269/2019) from the Balearic Islands Government General Direction of Innovation and Research. Josep Alós received funding from the CLOCKS I+D+I project (grant no. PID2019-104940GA-I00) and the JSATS PIE project (grant no. PIE202030E002) funded by MCIN/AEI/10.13039/501100011033 and the FSE “invierte en tu futuro”., Peer reviewed

The database and its accompanying files are as follows: GEoREST_database_Readme (.txt): provides an overview of the database goals and content, sharing/accessing information and methodologies used to develop the database. GEoREST_induced_seismicity_database_v20.11.2022 (.xlsx): database in a single Microsoft Excel spreadsheet. GEoREST_induced_seismicity_database_v20.11.2022 (.csv): database in ‎.csv format, considered as a standard machine-readable format for direct implementation of data in model developments. GEoREST_database_bibliography_v20.11.2022 (.docx): provides the full list of references used to collect data included in the database. GEoREST_database_dictionary_v20.11.2022 (.docx): presents unified, self-explanatory acronyms together with concise definitions for all variables included in the database., We present a comprehensive, publicly accessible database of injection-induced seismicity. The database is sourced from a comprehensive review of more than 500 published documents and contains information for 158 cases of induced earthquakes from around the world. The collected earthquakes are associated with a variety of geoenergy applications, which can be broadly categorized into geologic gas storage, geothermal energy development, shale gas fracturing, research projects and wastewater disposal. The compilation comprises more than 70 variables, including general project information, host rock properties, ‎in situ site characteristics, fault attributes, operational parameters, and recorded seismicity data (both overall seismic activities and the largest event sequence). The developed database opens up opportunities for improved understanding of the causative mechanisms of injection-induced seismicity and advancing on seismic hazard forecasting and mitigation., This research is funded by (1) the European Research Council (ERC) under ‎the ‎‎European Union’s Horizon ‎‎2020 Research and Innovation Program through the ‎Starting Grant ‎‎GEoREST (www.georest.eu) under Grant ‎agreement No. 801809, (2) MCIN/AEI/‎‎10.13039/501100011033 and the ‎European ‎Union ‎NextGenerationEU/PRTR through the international collaboration project EASYGEOCARBON ‎‎‎‎(www.easygeocarbon.com) under Grant ‎agreement No. PCI2021-122077-2B, (3) the European Union’s Horizon 2020 Research and Innovation ‎Programme through the Marie Sklodowska-Curie Action ARMISTICE (www.armistice-energy.eu) under grant â €Žagreement No. 882733. (4) the Secretariat for Universities and ‎Research of the Ministry of Business and Knowledge of the Government of Catalonia (AGAUR) and the European ‎Social Fund (FI-2019), (5) MCIN/AEI/ ‎‎10.13039/501100011033‎ through the Excellence Servero Ochoa (IDAEA-CSIC) under Grant agreement No. ‎CEX2018-000794-S, Peer reviewed

[ES] PACIS es un panel para realizar encuestas cuyo titular es el IESA-CSIC, un centro público de investigación científica perteneciente al Consejo Superior de Investigaciones Científicas. La información obtenida tanto en el momento de la inscripción como en los sucesivos cuestionarios que respondan sus integrantes está protegida por la Ley 12/89 que regula el secreto estadístico para las administraciones públicas, no pudiéndose tratar ni difundir más que de forma numérica y agregada, para garantizar el anonimato de los entrevistados. Las personas inscritas en PACIS se incluyen en un fichero automatizado que comprende los datos de contacto y variables de clasificación básicas, cuya finalidad es la realización de encuestas de opinión de carácter científico. Este fichero, que es propiedad del IESA-CSIC, no se comparte en ningún caso con terceros, y el IESA-CSIC aplica las medidas adecuadas de seguridad para cumplir con la Ley Orgánica 3/2018, de 5 de diciembre, de Protección de Datos Personales y garantía de los derechos digitales. Toda persona inscrita en PACIS puede ejercitar sus derechos de acceso, rectificación, cancelación y oposición dirigiéndose por email a agpd@pacis.es En las encuestas realizadas se recoge sólo la información personal necesaria para garantizar la calidad de la investigación. Finalizada la recogida de datos, se realiza un proceso de anonimización de la matriz de resultados desvinculándose los datos de personas inscritas en el fichero PACIS de sus respuestas a los cuestionarios. Los cuestionarios se ajustan a los distintos estándares de calidad específicos en la investigación mediante encuestas como UNE ISO-20252, ICC/ESOMAR, y específicos de Accesibilidad y Usabilidad en formatos digitales como UNE 139803 y WCAG 1.0. UNIVERSO: Personas con 18 años o más residentes en Andalucía. MARCO: Panel ciudadano PACIS. TAMAÑO DE LA MUESTRA: 774 entrevistas. TOMA DE DATOS: La muestra ha sido seleccionada entre las personas pertenecientes al PACIS. Se ha contactado por correo electrónico, mediante SMS y por teléfono y han rellenado el cuestionario por Internet (en una página web) o mediante entrevista telefónica. Algunos integrantes han sido contactados por varios canales. Para conseguir la muestra de 774 entrevistas se seleccionaron 1.600 personas integrantes del PACIS. TIPO DE MUESTREO: Selección estratificada por grupos de edad y sexo de los integrantes del PACIS, siendo los estratos proporcionales a la población andaluza mayor de 18 años. TIEMPO MEDIO DE LA ENTREVISTA: 23 minutos. ERROR MUESTRAL MÁXIMO: +/- 3,5%, [EN] PACIS is a survey panel. The owner of this panel is IESA-CSIC, a public scientific research center belonging to the Spanish National Research Council. The information obtained both at the time of registration and in the successive questionnaires answered by its members is protected by Law 12/89, which regulates statistical secret for public administrations, and can only be processed and disseminated in numerical and aggregate form, in order to guarantee the anonymity of the interviewees. The persons registered in PACIS are included in an automated file comprising contact data and basic classification variables, the purpose of which is to carry out scientific opinion surveys. This file, which is owned by IESA-CSIC, is not shared in any case with third parties, and IESA-CSIC applies appropriate security measures to comply with the Organic Law 3/2018, of December 5, on Personal Data Protection and guarantee of digital rights. Any person registered in PACIS can exercise their rights of access, rectification, cancellation and opposition by sending an email to agpd@pacis.es. In the surveys carried out, only the personal information necessary to guarantee the quality of the research is collected. Once the data collection has been completed, the results matrix is anonymized and the data of the persons registered in the PACIS file is unlinked to their answers to the questionnaires. The questionnaires comply with the different specific quality standards in survey research such as UNE ISO-20252, ICC/ESOMAR, and specific Accessibility and Usability standards in digital formats such as UNE 139803 and WCAG 1.0. UNIVERSE: People aged 18 years or older living in Andalusia. FRAMEWORK: PACIS citizen panel. SAMPLE SIZE: 774 interviews. DATA COLLECTION: The sample has been selected among people belonging to PACIS. They have been contacted by e-mail, by SMS and by telephone and have filled in the questionnaire by Internet (on a web page) or by telephone interview. Some members were contacted through several channels. To achieve the sample of 774 interviews, 1,600 PACIS members were selected. SAMPLE TYPE: Stratified selection by age and sex of PACIS members, with the strata being proportional to the Andalusian population over 18 years of age. AVERAGE INTERVIEW TIME: 23 minutes. MAXIMUM SAMPLING ERROR: +/- 3.5%., [ES] La encuesta CARE se realizó en Andalucía en febrero de 2018 en el marco de un proyecto de investigación más amplio sobre la toma de decisiones en el cuidado de las personas mayores del Instituto de Estudios Sociales Avanzados del Consejo Superior de Investigaciones Científicas (IESA-CSIC). La encuesta (N=744) aporta información sobre las múltiples percepciones y disposiciones de la población autóctona respecto a la toma de decisiones en el cuidado de las personas mayores, así como una amplia gama de factores explicativos. La recogida de datos se realizó principalmente mediante cuestionarios web autoadministrados, que se complementaron con entrevistas telefónicas para cubrir perfiles sociodemográficos menos proclives al uso de Internet., [EN] The CARE survey was conducted in Andalusia in February of 2018 in the framework of a broader research project on decision making in the care of the older people run by the Spanish Research Council’s Institute for Advanced Social Studies (IESA-CSIC). The survey (N=744) provides information on the multiple perceptions and dispositions of the native population regarding decision-making in the care of the older people, as well as a wide range of explanatory factors. Data were collected mainly by means of self-administered web questionnaires, which were complemented by telephone interviews to cover sociodemographic profiles less inclined to use the Internet., EP-1801 Ola 6ª Panel ciudadano para la investigación social en Andalucía, autofinanciado por el IESA/CSIC en el marco del programa de proyectos INTRAMURALES (ref. 201710E018)., CARE_Data file.csvCARE_Data file.savCARE_Codebook_EN.pdfCARE_Codebook_SP.pdfCARE_Readme_EN.pdfCARE_Readme_SP.pdf, Peer reviewed