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Resultados totales (Incluyendo duplicados): 36904
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DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397904
Set de datos (Dataset). 2025

BENDING THE SIERRA MADRE ORIENTAL: A MAASTRICHTIAN-PALEOCENE OROCLINE

  • Pastor-Galán, Daniel
  • Guerra Roel, Rafael
  • Chávez-Cabello, Gabriel
[Dataset for the manuscript] Bending the Sierra Madre Oriental: A Maastrichtian-Paleocene Orocline, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/397904
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397904
HANDLE: http://hdl.handle.net/10261/397904
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397904
PMID: http://hdl.handle.net/10261/397904
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397904
Ver en: http://hdl.handle.net/10261/397904
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397904

DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397930
Set de datos (Dataset). 2025

ADSORPTION OF RARE EARTH ELEMENTS (REES) ONTO SCHWERTMANNITE AND BASALUMINITE FROM ACID MINE DRAINAGE TO ESTUARY WATE

  • Carrero, Sergio
Data implement in the research article submited in ESPI., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/397930
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397930
HANDLE: http://hdl.handle.net/10261/397930
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397930
PMID: http://hdl.handle.net/10261/397930
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397930
Ver en: http://hdl.handle.net/10261/397930
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397930

DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397992
Set de datos (Dataset). 2025

DIFFERENTIAL CONTRIBUTION OF THREE LACTIC ACID BACTERIA STRAINS FROM A MICROBIAL CONSORTIUM TO MILK FERMENTATION BY UNTARGETED METABOLOMICS AND ANALYSIS OF VOLATILE COMPOUNDS

  • Rodríguez Álvarez, Javier
  • Suárez, Paula Rosa
  • Álvarez-Buylla, Jorge R.
  • Vázquez, Lucía
  • González-Iglesias, Héctor
  • Mayo Pérez, Baltasar
  • Flórez, Ana Belén
El dataset se puede consultar y descargar en el siguiente enlace https://doi.org/10.5281/zenodo.15187709, In this study, a leave-one-out inoculation strategy was followed for the fermentation of milk using as a starter the strains Lactococcus lactis LA1, Lactococcus cremoris LA10, and Lactiplantibacillus plantarum LA30), which had been found as components of a consortium in a naturally fermented milk (NFM). The dynamics of the individual strains in milk was assessed by culturing and strain-specific qPCR amplification. Metabolites and volatile compounds in the ensuing fermented milks were analysed, respectively, by solid-phase microextraction gas-chromatography (SPME-GC) and high-resolution mass spectrometry liquid chromatography (MS-LC). Metabolomic fingerprinting of fermented milks was performed by high-resolution mass spectrometry using a liquid chromatography (LC) system 1290 Infinity II (Agilent, Santa Clara, CA, USA) coupled to an Agilent 6560B ion mobility quadrupole-time-of-flight mass spectrometer (IM-QTOF MS) equipped with an Agilent G1607A dual Jetstream electrospray ionization (ESI) source. In this data dataset, mzml archives corresponding to positive and negative ionization modes analysis are uploaded., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/397992
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397992
HANDLE: http://hdl.handle.net/10261/397992
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397992
PMID: http://hdl.handle.net/10261/397992
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397992
Ver en: http://hdl.handle.net/10261/397992
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397992

DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397994
Set de datos (Dataset). 2025

WOMEN RESEARCHERS, ARTISTIC PRACTICES AND SCIENCE COMMUNICATION THROUGH ART

  • Azagra Caro, Joaquín
Survey data on over 6000 Spanish researchers, with information about gender, whether they engage into artistic practices and, if so, if they do it for science communication through art, political ideology, number of children and many control variables. The exploitation and variable description can be found here: Azagra Caro, J. (2025, February 17). Visibility without protection? Barriers to women researchers’ engagement in artistic practices and science communication through art. DIGITAL.CSIC. http://doi.org/10.20350/DIGITALCSIC/17090, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/397994
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397994
HANDLE: http://hdl.handle.net/10261/397994
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397994
PMID: http://hdl.handle.net/10261/397994
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397994
Ver en: http://hdl.handle.net/10261/397994
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397994

DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397997
Set de datos (Dataset). 2025

OCEAN WARMING EFFECTS ON CATCH AND REVENUE COMPOSITION IN THE NORTHWESTERN MEDITERRANEAN SEA. RCODE, RAW DATA, AND RESULTS [DATASET]

  • Espasandín Soneira, Lucía
  • Ramírez Benítez, Francisco
  • Ortega Cerdà, Miquel
  • Villarino, Ernesto
  • Chust, Guillem
  • Sbragaglia, Valerio
  • Coll, Marta
The authors acknowledge institutional support of the “Severo Ochoa Centre of Excellence” accreditation to ICM-CSIC (#CEX2019-000928-S). [...] V.S. is supported by a “Ramón y Cajal” research fellowship (RYC2021-033065-I) granted by the Spanish Ministry of Science and Innovation. This study is a contribution to the European Union's Horizon 2020 research and innovation programmes under grant agreement no. 869300 (FutureMARES project) and no. 101059877 (GES4SEAS). The authors also acknowledge the support from the Catalan government through the iMARES research group of quality from the Institute of Marine Sciences (ICM-CSIC) in Barcelona, - MTC & MTR/ # Rcode, raw data, and results of the Mean Temperature of the Catch (MTC) and the Mean Temperature of the Revenue (MTR) analysis. - RESULTS/ # Results of the MTC and MTR analysis: (1) TXTs summaries of the results of the linear regression models (i.e., MTC vs Year and MTR vs Year); (2) CSVs with MTC and MTR results and the strength of the underlying processes; and (3) CSVs with the temporal rate of change of species abundance and revenue. - Ta preference/ # Rcode of the species temperature of preference calculation and the final results. - MUMIN analysis/ # Rcode, input data, and results of the MUMIN model analysis. - Sea temperature analysis/ # Results obtained from the sea temperature analysis, Peer reviewed

DOI: http://hdl.handle.net/10261/397997
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397997
HANDLE: http://hdl.handle.net/10261/397997
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397997
PMID: http://hdl.handle.net/10261/397997
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397997
Ver en: http://hdl.handle.net/10261/397997
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397997

DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397999
Set de datos (Dataset). 2024

KOSMOS 2018 GRAN CANARIA MESOCOSM STUDY: PROKARYOTES [DATASET]

  • Gómez-Letona, Markel
  • Sebastián, Marta
  • Montero, María F.
  • Riebesell, Ulf
  • Arístegui, Javier
This dataset contains the results of the analysis of standing stocks and activity of prokaryotes during the KOSMOS mesocosm experiment carried out in the framework of the Ocean Artificial Upwelling project. The experiment was carried out in the autumn of 2018 in the oligotrophic waters off Gran Canaria. During the 39 days of experiment nutrient-rich deep water was added to the mesocosms in two modes (singular vs recurring additions), with four levels of intensity. Prokaryotic abundances and viability were quantified with a FACSCalibur flow cytometer (Becton-Dickinson). Prokaryotic activity (as protein synthesis) was assessed by means of BioOrthogonal Non-Canonical Amino acid Tagging (BONCAT), using a Zeiss Axio Imager.Z2m Epifluorescence Microscope (AxioCam MRm, Carl Zeiss MicroImaging). The aim of this dataset was to study the effect of different upwelling regimes on the prokaryotic communities and its potential implications for organic matter cycling, This study is a contribution to the Ocean Artificial Upwelling project (Ocean artUp), funded by an Advanced Grant of the European Research Council (No. 695094) awarded to UR. Additional support was provided through projects FLUXES (CTM2015-69392-C3-1-R) and e-IMPACT (PID2019-109084RB-C21) funded by the Spanish National Science Plan. MGL was supported by the Ministerio de Universidades, Gobierno de España (FPU17-01435) during their PhD. MS was supported by the Project MIAU (RTI2018-101025-B-I00), MICOLOR (PID2021-125469NB-C31), the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), and by a Viera y Clavijo contract funded by the ACIISI and the ULPGC. JA was supported by a Helmholtz International Fellow Award, 2015 (Helmholtz Association, Germany) and by the United States National Science Foundation grant OCE-1840868 to the Scientific Committee on Oceanic Research (SCOR, United States) WG 155, Peer reviewed

DOI: http://hdl.handle.net/10261/397999
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397999
HANDLE: http://hdl.handle.net/10261/397999
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397999
PMID: http://hdl.handle.net/10261/397999
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397999
Ver en: http://hdl.handle.net/10261/397999
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/397999

DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398002
Set de datos (Dataset). 2025

DATA AND CODE FROM: ACTIVE RESTORATION OF A LONG-LIVED OCTOCORAL DRIVES RAPID FUNCTIONAL RECOVERY IN A TEMPERATE REEF [DATASET]

  • Zentner, Yanis
  • Garrabou, Joaquim
  • Margarit, Núria
  • Rovira, Graciel·la
  • Gómez-Gras, D.
  • Linares, Cristina
Whether restoration actions achieve full ecological recovery is still debated. This is particularly controversial in the marine realm, where the success of restoration is mostly evaluated in terms of the short-term survival of transplanted organisms. In view of this, we combined population and trait-based approaches to explore the long-term effectiveness of active restoration of a key Mediterranean octocoral. For this purpose, an assemblage with restored Corallium rubrum colonies was monitored over ten years and compared with a nearby reference site. Our results revealed growth of the transplanted colonies followed by a change in the functional structure (i.e., dominance and diversity of traits) of the restored assemblage. Interestingly, this change was related not only to the development of the coral but also to the arrival and/or increase of species with different traits. Overall, our findings provide an example of how active restoration of long-lived octocorals can be an effective tool for recovering highly high-diverse coralligenous assemblages at decadal timescales, This work was supported by grant RTI2018-095346-B-I00 HEATMED funded by MCIN/AEI/10.13039/501100011033 and by” ERDF/UE;” grant TED2021-131622B-I00 CORFUN funded by MCIN/AEI/10.13039/501100011033 and by “European Union NextGenerationEU”/PRTR;” the Public agreement PTOP- 2017-130 Seguiment de la Biodiversitat marina funded by Generalitat de Catalunya; the Public agreement PTOP- 2021-3 Seguiment de la Biodiversitat marina funded by Generalitat de Catalunya public agreement; the grant SEP-210597628 Futuremares funded by UE H2020; grant MED0200736 MPA4Change funded by Interreg EuroMED Euro and by UE Grant ICREA Academia 2019 funded by Institució Catalana de Recerca i Estudis Avançats (to C.L.); grant Severo Ochoa Centre of Excellence CEX2019-000928-S funded by MCIU/AEI/10.13039/501100011033 (to J.G.); grant FPU20/03574 funded by MCIU/AEI/10.13039/501100011033 (to Y.Z.); and Grant 2021 SGR 01073 funded by Departament de Recerca i Universitats, Generalitat de Catalunya, Peer reviewed

DOI: http://hdl.handle.net/10261/398002
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398002
HANDLE: http://hdl.handle.net/10261/398002
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398002
PMID: http://hdl.handle.net/10261/398002
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398002
Ver en: http://hdl.handle.net/10261/398002
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398002

DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398024
Set de datos (Dataset). 2025

ASSESSING THE SUCCESS OF MARINE ECOSYSTEM RESTORATION [DATASET]

  • Danovaro, Roberto
  • Aronson, James
  • Bianchelli, Silvia
  • Boström, Christoffer
  • Chen, W.
  • Cimino, R.
  • Cortina-Segarra, J.
  • D'Ambrosio, Paolo
  • Gambi, Cristina
  • Garrabou, Joaquim
  • Giorgetti, Alessandra
  • Grehan, Anthony
  • Hannachi, Amel Salhi
  • Mangialajo, Luisa
  • Morato, Telmo
  • Orfanidis, Sotiris
  • Papadopoulou, Nadia
  • Ramírez-Llodra, Eva
  • Smith, Christopher J.
  • Snelgrove, Paul V. R.
  • Koppel, J. van del
  • Tatenhove, Jan van
  • Fraschetti, Simonetta
This research was carried out within the framework of the Horizon Europe projects, REDRESS (N. 101135492; R.D., S.B., C.C., J.C., C.G., A.Gr., T.M., N.P., E.R., P.V.S., C.S., and J.V.T.); with the contribution from the projects CLIMAREST (N. 101093865; RD, C.C., J.C., C.G., and S.F.), FORESCUE (Biodiversa+2021-134; R.D., S.B, S.O., L.M., and S.F), MERCES (N. 689518; R.D., J.A., S.B., C.B., W.C., R.C., C.C., J.G., A.Gr., T.M., N.P., E.R., C.S., P.V.S., J.V.K., J.V.T., and S.F.), Generalitat Valenciana, Conselleria d'Educació PROMETEO (Project R2D, CIPROM/2021/001; J.C-S.) and by the “National Biodiversity Future Center—NBFC (NRRP; R.D., S.B., C.C., P.D., and S.F.; Decree no. 3175 of 18 December 2021 by the Italian Ministry of University and Research, funded by the European Union - NextGenerationEU, Award Number: project code CN_00000033, Concession Decree No. 1034, of 17 June 2022)”, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

DOI: http://hdl.handle.net/10261/398024
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398024
HANDLE: http://hdl.handle.net/10261/398024
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398024
PMID: http://hdl.handle.net/10261/398024
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398024
Ver en: http://hdl.handle.net/10261/398024
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398024

DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398044
Set de datos (Dataset). 2025

DRONE-DERIVED DATASETS IN THE ASSESSMENT OF THE 2024 CUT-OFF LOW EVENT IN VALENCIA

  • Román, Alejandro
  • Tovar-Sánchez, Antonio
  • Larrad, Marcos
  • Rubiano, Francisco
  • Zafra González, José Manuel
  • Piñeiro Martínez de Lecea, Rafael
  • Castillo Talavera, Ángel
  • López Gómez, Félix Antonio
  • Lucía Vela, Ana
  • Allende, Ana
  • Sánchez, Gloria
  • Martínez-Alonso, Alberto
  • Samper, Daniel
  • García López-Davalillo, Juan Carlos
  • Galindo Jiménez, Inés
  • Navarro, Gabriel
[Description of methods used for collection/generation of data] Three different UAV platforms were used for data acquisition, each chosen based on their payload capacity and compatibility with various sensors:: 1) DJI Matrice 300 RTK (M300). This quadcopter supports a maximum payload of 2.5 kg and has a maximum take-off weight (MTOW) of 9 kg. It was equipped with several sensors to capture multispectral imagery, RGB data, and gas concentration measurements: - DJI Zenmuse H20T. A multi-sensor payload featuring a 20 MP RGB zoom camera (1/2.7” CMOS) and a 12 MP wide-angle RGB camera (1/2.3” CMOS). It includes a detachable gimbal and offers a maximum shutter speed of 1/8000 s. Under controlled conditions, the manufacturer specifies an accuracy of 0.2 m plus 0.15% of the target distance. - MicaSense RedEdge-MX. A multispectral dual sensor that captures 10 bands across the visible and near-infrared (NIR) spectrum, including blue (444, 475 nm), green (531, 560 nm), red (650, 668 nm), red edge (705, 717, 740 nm), and NIR (842 nm). The system integrates a Downwelling Light Sensor (DLS) with GPS to account for changes in solar irradiance, along with a radiometric calibration panel (RP04-1924106-0B) to ensure radiometric accuracy. - Sniffer4D V2 RS (Soarability). A multi-gas sensing and mapping system capable of simultaneously detecting total volatile organic compounds (TVOC, ppm), sulfur dioxide (SO₂, μg/m³), ozone and nitrogen dioxide (O₃ + NO₂, μg/m³), particulate matter (PM1.0, PM2.5, PM10, μg/m³), flammable gases (CxHy, %), and hydrogen sulfide (H₂S, μg/m³). It provides real-time visualization in both 2D and 3D. In addition, it was integrated with the Sniffer4D Multi-pass TDLAS Hyper-local Methane Module, enabling quantitative methane detection with a resolution of 1 ppm. 2) DJI Mavic 3E (M3E). A lightweight quadcopter equipped with a 20 MP wide-angle RGB camera (4/3” CMOS) and a 12 MP telephoto RGB camera with 56× hybrid zoom. It has an MTOW of 1,050 g and a maximum flight endurance of 45 minutes. 3) DJI Mavic 2 Enterprise Advanced (M2EA). This UAV features a 48 MP RGB optical sensor with a 1/2” CMOS sensor. It has an MTOW of 1,100 g and a maximum flight duration of 31 minutes., [Methods for processing the data] 1) Structure from Motion (SfM) photogrammetry: Aerial images acquired along pre-programmed flight paths with sufficient overlap were processed using Pix4D Mapper (Pix4D SA, Lausanne, Switzerland, v.4.8.3) to generate high-resolution topographic products. The workflow consisted of image alignment, point cloud generation, digital surface model (DSM) reconstruction, orthomosaic and reflectance maps generation. 2) Gas Sniffer data monitoring: The gas detection sensors were directly connected to the PSDK port of the DJI M300, which provided power and allowed real-time data transmission to the UAV’s remote controller. Measurements were logged at one-second intervals and simultaneously stored on the sensor’s internal SD card. To ensure stable sampling, the device incorporated a rear-mounted active air intake fan, drawing ambient air at a flow rate of roughly 10 L min⁻¹., [Environmental/experimental conditions] UAV flights were conducted in strict accordance with Spanish civil aviation regulations for emergency operations, under the supervision of the Spanish Aviation Safety Agency (AESA). Moreover, missions carried out following the Valencia cut-off low were undertaken within the framework of the Scientific-Technical Advisory Group for Emergency Crises (GADE) of the Spanish National Research Council (CSIC). For field operations, flight plans were pre-programmed using the DJI Pilot application (Shenzhen Dà-Jiang Innovations Sciences and Technologies Ltd., Guangdong, China) installed on the UAV controller. These plans were configured with fixed parameters, including flight altitude, speed, duration, ground sampling distance (GSD), and image overlap., This repository contains the original raw data, along with some of the topographic products generated after the initial data processing, captured immediately after the 2024 Valencia cut-off low event by a drone equipped with RGB, thermal, multispectral, and gas-sniffer sensors. These datasets were used to obtain the results published in Román et al. 2025 (DOI: available soon). The data, together with their rapid and preliminary processing, were also employed in real time during the emergency to support decision-makers in tasks related to prevention, evacuation, and reconstruction. Furthermore, access to these data makes the methodologies described in the manuscript reproducible, thereby contributing to the development of new tools that can enable faster, more efficient, and more accurate management during future emergencies., This work was financially supported by funds from PIE202430E238 and PIE202460E236. Sensors and UAV were funded by Spanish Government Infrastructure Project EQC2018-004275-P and EQC2019-005721. The Accreditation of IATA as a Center of Excellence, Severo Ochoa CEX2021-001189-S funded by MCIU/AEI/10.13039/501100011033 is fully acknowledged. A. Román, staff hired under the Generation D initiative, promoted by Red.es, an organisation attached to the Ministry for Digital Transformation and the Civil Service, for the attraction and retention of talent through grants and training contracts, financed by the Recovery, Transformation and Resilience Plan through the European Union's Next Generation funds. This study also represents a contribution to the CSIC Scientific-Technical Advisory Group for Emergency Crises (GADE-CSIC, Spain)., With funding form the Spanish government through the "Severa Ochoa Centre of Excellence" accreditation (CEX2021-001189-S)., File List: 6 folders: Dataset 1 - Object Detection Vehicle Yard; Dataset 2 - Object Detection Trash Items; Dataset 3 - Gas Sensors Paiporta; Dataset 4 - Damage Assessment Picanya; Dataset 5 - Damage Assessment Pont Nou; Dataset 6 - Sludge Characterization, Peer reviewed

DOI: http://hdl.handle.net/10261/398044
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398044
HANDLE: http://hdl.handle.net/10261/398044
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398044
PMID: http://hdl.handle.net/10261/398044
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398044
Ver en: http://hdl.handle.net/10261/398044
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398044

DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398118
Set de datos (Dataset). 2025

RAW DISEASE-RELATED TRAITS FOR MANUSCRIPT "THE PACE OF ECOLOGICAL SUCCESSION AMONG HOST GENOTYPES DETERMINES THE VIRULENCE AND THE DYNAMICS OF GENOME EVOLUTION IN A PLANT RNA VIRUS"

  • Iglesia, Francisca de la
  • Ambrós, Silvia
  • Olmo-Uceda, Maria J.
  • Elena, Santiago F.
We have investigated how the dynamics of host replacement affects the evolution of a plant RNA virus. Specifically, we have examined how sudden vs. gradual transitions from more susceptible to less susceptible Arabidopsis thaliana genotypes influenced turnip mosaic virus’ virulence and population genomic diversity. Our results show that the evolution of virulence and the complexity of the mutant swarm is linked to both the type of host succession and the genetic basis of the host resistance. Faster changes in virulence are observed after sudden transitions, with an evolutionary trend towards more severe symptoms that appear latter during infection. Gradual transitions resulted in greater population mutational load and higher genetic polymorphism compared to sudden transitions. In contrast, beneficial mutations associated with sudden transitions had stronger fitness effects than those linked to gradual transitions. This research highlights the importance of considering the rate of environmental changes in virus evolution and provide insights into predicting how viruses adapt and evolve in temporally changing environments, with implications for agriculture and public health., Ministerio de Ciencia, Innovación y Universidades PID2022-136912NB-I00 Conselleria de Cultura, Educación y Ciencia, Generalitat Valenciana CIPROM/2022/59, Peer reviewed

DOI: http://hdl.handle.net/10261/398118
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398118
HANDLE: http://hdl.handle.net/10261/398118
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398118
PMID: http://hdl.handle.net/10261/398118
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398118
Ver en: http://hdl.handle.net/10261/398118
DIGITAL.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/398118

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