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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Fabrizio, Messina; Ashim, Pramanik; Alice, Sciortino;

    Carbon dots are carbon-based nanoparticles renowned for their intense light-emitting capabilities covering the whole visible light range. Here, we overcome these problems by solvothermally synthesizing carbon dots starting from Neutral Red, a common red-emitting dye, as a molecular precursor. The obtained nanoparticles are highly luminescent in the red region, with a quantum yield comparable to that of the starting dye. Most importantly, the nanoparticle carbogenic matrix protects the Neutral Red molecules from photobleaching under ultraviolet excitation while preventing aggregation-induced quenching, thus allowing solid-state emission inside PVA. Finally, the dye-based carbon dots demonstrate stable and efficient random lasing emission in the red region.

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    This record includes the two main datasets necessary to run the GSI tutorial presented here: GUESS The 10-member ensemble background files generated using the WRF-ARW numerical model for a regional domain centered in the center and northern Argentina. For more information about the model configuration see https://doi.org/10.1016/j.atmosres.2022.106456 The 00 subfolder includes a 10-member ensemble and the ensemble mean to run the GSI system using the ENKF version. The 01 to 10 subfolders include the background at the analysis time and files every 10 minutes inside the assimilation window to run the GSI system using the fGAT method. OBS Meteorological observations in bufr format. cimap.20181122.t12z.01h.prepbufr.nqc is derived from a prepbufr file available at https://rda.ucar.edu/datasets/ds337.0 plus observations from private automatic meteorological weather networks in Argentina. abig16.20181122.t12z.bufr_d was generated using GOES-16 data available at: The other radiance observations comes from the Global Data Assimilation System (GDAS) Model: https://www.nco.ncep.noaa.gov/pmb/products/gfs/ 1bamua.20181122.t12z.bufr_d ssmisu.20181122.t12z.bufr_d 1bhrs4.20181122.t12z.bufr_d airsev.20181122.t12z.bufr_d mtiasi.20181122.t12z.bufr_d 1bmhs.20181122.t12z.bufr_d atms.20181122.t12z.bufr_d satwnd.20181122.t12z.bufr_d

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Rovere, Alessio; Rubio Sandoval, Karla Zurisadai; Ryan, Deirdre D.; Richiano, Sebastian; +5 Authors

    This repository contains the supplementary information and raw data annexed to the manuscript "Quaternary and Pliocene sea-level changes at Camarones, central Patagonia, Argentina", authored by Karla Rubio-Sandoval et al. and submitted for consideration in the journal Quaternary Science Reviews. The folder contains the following items. 1. Raw_data.xlsxThis is an excel file that includes all survey and analytical data in several sheets, briefly described hereafter. - GNSS data. Data surveyed with differential GNSS in the field.- Sea level index points. Datapoints used as sea-level index points, and associated calculations of paleo Relative Sea Level.- AAR Summary. Table summarising the main results of the AAR analyses.- AAR complete sheet. The complete set of analytical data done for the Amino Acid Racemization dating.- Radiocarbon data. The analytical results of radiocarbon dating.- Literature ages. A compilation of the Electron Spin Resonance and U-series ages published for the Camarones site.- Transects. Topographical transects extracted from the TanDEM-X Digital Elevation model and referred to the GEOIDEAR 16 geoid.- Distance plot. Data for plotting Relative Sea Level vs distance along the coast of the sea-level index points described in the manuscript. 2. Holocene (folder)This folder contains two excel files ("Area_Camarones_Accepted.xlsx" and "Area_Camarones_Rejected.xlsx") that include the Holocene data described in the paper compiled following the standard HOLSEA template. 3. Runup_modellingThis folder contains three folders, each with a Jupyter notebook (.ipynb) and datasets to perform the runup calculations described in the manuscript.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Romano Muñoz, Cristo Omar; Garrido, Alberto C.; Barbeau Jr, David L.; Vera, Rocío B.; +18 Authors

    This README file was generated on 2023-12-19 by Cristo O. Romano. \########################################################################################################### GENERAL INFORMATION 1\. Title of Dataset: Data from ‘Redefining the Huayquerian Stage (Upper Miocene to Lower Pliocene) of the South American chronostratigraphic scale based on biostratigraphical analyses and geochronological dating’ 2\. Author Information A. Corresponding Author Contact Information Name: Cristo O. Romano Institution: Instituto Argnetino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA) / Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Address: Mendoza, Mendoza Province, Argentina Email: romano.cristo@gmail.com B. Co-authors Information Name: Alberto C. Garrido Institution: Museo Provincial de Ciencias Naturles Prof. Dr Juan Olsacher (MOZ) / Centro de Investigación en Geociencias de la Patagonia (CIGPat) Address: Zapala, Neuquén Province, Argentina Name: David L. Barbeau Jr Institution: School of the Earth, Ocean & Environment, University of South Carolina Address: Columbia, South Carolina, USA Name: Rocío B. Vera Institution: Instituo de Estudios Andinos 'Don Pablo Groeber' (IDEAN), Estudios Paleobiológicos en Ambientes Contienteales, Universidad de Buenos Aires (UBA) / Facultad de Ciencias Exactas y Naturales, Departamento de Ciencis Geológicas, Laboratoio de Paleontología de Vertebrados (UBA) / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Ricardo Bonini Institution: Instituto de Investigacioñnes Arqueológicas y Paleontológicas del Cuaternario Pampeano (INCUAPA), Faculta de Ciencias Sociales, Universidad Nacional del Centro de la Provincia de Buenos Aires / CONICET Address: Olavarría, Buenos Aires Province, Argentina Name: Alberto Boscaini Institution: Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA), Departamento de Ecología, Genética y Evolución, Faculta de Ciencias Exactas y Naturales, UBA / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Esperanza Cerdeño Institution: IANIGLA / CONICET Address: Mendoza, Mendoza Province, Argentina Name: Laura E. Cruz Institution: División Paleontología Vertebrados, Museo Argentino de Ciencias Naturales Bernardino rivadavia (MACN) / Laboratroio de Anatomía y Biología Evolutiva de los Vertebrados (LABEV-UNLu), Departamento de Ciencias Básicas, Universidad Nacional de Luján (UNLu) / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Graciela I. Esteban Institution: Instituto Superior de Correlación Geológica (INSUGEO), Facultad de Ciencias Naturales e Instituto Miguel Lillo, Universidad Nacional de Tucumán Address: San Miguel de Tucumán, Tucumán Province, Argentina Name: Marcelo S. de la Fuente Institution: Instituto de Evolución, Ecología Histórica y Ambiente (IDEVEA) / CONICET Address: San Rafael, Mendoza Province, Argentina Name: Marcos Fernández-Monescillo Institution: Cátedra y Museo de Paelontología, Faculta de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba / CONICET Address: Córdoba, Córdoba Province, Argentina Name: Juan C. Fernicola Institution: División Paleontología Vertebrados, MACN / LABEV-UNLu / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Verónica Krapovickas Institution: IDEAN / UBA / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: M. Carolina Madozzo-Jaén Institution: INSUGEO / CONICET Address: San Miguel de Tucumán, Tucumán Province, Argentina Name: M. Encarnación Pérez Institution: Museo Paleontológico Egidio Feruglio (MEF) / CONICET Address: Trelew, Chubut Province, Argentina Name: François Pujos Institution: IANIGLA / CONICET Address: Mendoza, Mendoza Province, Argentina Name: Luciano Rasia Institution: División Paleontología Vertebrados, Museo de La Plata, Universidad Nacional de La Plata / CONICET Address: La Plata, Buenos Aires Province, Argentina Name: Guillermo Fm. Turazzini Institution: Laboratorio de Morfología Evolutiva y Paleobiología de Vertebrados, Departamento de Biodiversidad y Biología Experimental, Faculta de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA) / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Bárbara Vera Institution: Centro de Investigación Esquel de Montaña y Estepa Patagónica (CIEMEP) / CONICET Address: Esquel, Chubut Province, Argentina Name: Ross D. E. MacPhee Institution: Department of Mammalogy, American Museum of Natural History Address: New York, NY, USA Name: Analía M. Forasiepi Institution: IANIGLA / CONICET Address: Mendoza, Mendoza Province, Argentina Name: Francisco J. Prevosti Institution: Museo de Ciencias Antropológicas y Naturales, Universidad Nacional de La Rioja (UNLaR) / CONICET Address: La Rioja, La Rioja Province, Argentina 3\. Date of data colection (time range): 2013-2019 4\. Geographic location of data colecction: Las Huayquerías del Este, San Carlos department, Mendoza Province, Argentina. 5\. Information about funding sources that supported the collection of the data: This research was supported by ANPCYT (Projects PICT 2015-966, 2019-2874), Argentina \########################################################################################################### SHARING/ACCESS INFORMATION 6\. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain 7\. Links to publications that cite or use the data: Romano, C. O., Garrido, A. C., Barbeau, D. L., Vera, R. B., Bonini, R., Boscaini, A., Cerdeño, E., Cruz L. E., Esteban, G. I., de la Fuente, M. S., Fernández-Monescillo, M., Fernicola, J. C., Krapovickas, V., Madozzo-Jaén, M. C., Pérez, M. E., Pujos, F., Rasia, L., Turazzini, G. F., Vera, B., MacPhee, R. D. E., Forasiepi, A. M. & Prevosti, F. J. (in press). Redefining the Huayquerian Stage (Upper Miocene – Lower Pliocene) of the South American chronostratigraphic scale based on biostratigraphical analyses and geochronological dating. Papers in Palaeontology, DOI: 10.1002/spp2.1539 8\. Links to other publicly accessible locations of the data: None 9\. Links/relationships to ancillary data sets: None 10\. Was data derived from another source? No A. If yes, list source(s): NA 11\. Recommended citation for this dataset: Romano, C. O., Garrido, A. C., Barbeau, D. L., Vera, R. B., Bonini, R., Boscaini, A., Cerdeño, E., Cruz L. E., Esteban, G. I., de la Fuente, M. S., Fernández-Monescillo, M., Fernicola, J. C., Krapovickas, V., Madozzo-Jaén, M. C., Pérez, M. E., Pujos, F., Rasia, L., Turazzini, G. F., Vera, B., MacPhee, R. D. E., Forasiepi, A. M. & Prevosti, F. J. (in press). Data from: 'Redefining the Huayquerian Stage (Upper Miocene – Lower Pliocene) of the South American chronostratigraphic scale based on biostratigraphical analyses and geochronological dating.' Dryad Digital Repository, https://doi.org/10.5061/dryad.ngf1vhj0t \########################################################################################################### DATA & FILE OVERVIEW 12\. File List (description of the data and file structure) A- Data 1. Excel file with raw U-Pb zircon geochronology data of tuffaceous samples (except T3), from Huayquerías del Este, Mendoza Province, Argentina. B- Data 2. Excel file with raw U-Pb zircon geochronology data of T3 tuff sample, from Huayquerías del Este, Mendoza Province, Argentina. Including information about the methodology (provided by the laboratory). C- Data 3. Excel file with detailed information on the fossil specimens collected in the Huayquerías del Este, Argentina. D- Data 4. Excel file with presence-absence matrices for similarity analysis. \[Access this dataset on Dryad] 13\. Relationship between files, if important: The presence and absence matrices in Data 4 are based on the specimens listed in Data 3. 14\. Additional related data collected that was not included in the current data package: None 15\. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA \########################################################################################################### DATA-SPECIFIC INFORMATION FOR: 'Data 1.csv' 1\. Samples: Around 3 kg of rock from each of the 10 tuffaceous levels (T): T1, T2, T4, T5, T6, T10, T11, TM1, and TM2. 2\. Geographic location of data colecction: Las Huayquerías del Este, San Carlos department, Mendoza Province, Argentina. 3\. Formation, fossil site and coordinates (latitude; longitude) of the collected samples: T0: Huayquerías Formation, Río Seco de la Horqueta, 33°50'00.4"S, 68°28'59.6"W T1: Bajada Grande Formation, Río Seco de la Isla Grande, 33°58'50.6"S, 68°26'31.6"W T4: Bajada Grande Formation, Río Seco de la Isla Grande, 33°58'43.4"S, 68°26'15.8"W T5: Bajada Grande Formation, Cerro Parvitas, 33°44'30.2"S, 68°40'55.8"W T6: Huayquerías Formation, Río Seco de la Última Aguada, 33°54'23.8"S, 68°27'18.4"W T10: Huayquerías Formation, Río Seco de Los Pajaritos, 33°55'28.5"S, 68°26'51.0"W T11: Tunuyán Formation, Río Seco de Los Pajaritos, 33°44'30.2"S, 68°40'55.8"W TM1: Huayquerías Formation, Río Seco del Carrizalito, 33°54'58.1"S, 68°32'55.0"W TM2: Huayquerías Formation, Río Seco del Carrizalito, 33°54'53.9”S, 68°33'02.1"W 4\. Number of variables: 23 5\. Tipe of case: spots on zircon crystals through laser ablation 6\. Number of cases/rows by sample (T): T0: 69 T1: 36 T4: 33 T5: 22 T6: 26 T10: 39 (big crystals) and 24 (small crystals) T11: 51 TM1: 38 TM2: 22 7\. Variable List: \* analysis: spot on zircon crystal identifier \* 207Pb/235U [ISOTOPIC RATIOS]: The relative abundance of 207Pb with respect to 235U measured in the zircon crystal. \* prop. 2s no sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 207Pb / 235U ratio in the zircon crystal expressed as a percent, including analytical uncertainties and propagated uncertainties but not including systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction \* prop. 2s w sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 207Pb / 235U ratio in the zircon crystal expressed as a percent, including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 206b/238Pb [ISOTOPIC RATIOS]: The relative abundance of 206Pb with respect to 238U measured in the zircon crystal \* prop. 2s no sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 206Pb / 238U ratio in the zircon crystal expressed as a percent, including analytical uncertainties and propagated uncertainties but not including systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction \* prop. 2s w sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 206Pb / 238U ratio in the zircon crystal expressed as a percent, including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 206/238 vs 207/235 error correlation [ISOTOPIC RATIOS]: A measurement of the covariance between the measured 206Pb/238U and 207Pb/235U ratios \* 238U/206Pb [ISOTOPIC RATIOS]: The relative abundance of 238U with respect to 206Pb measured in the zircon crystal \* prop. 2s no sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 238U / 206Pb ratio in the zircon crystal expressed as a percent, including analytical uncertainties and propagated uncertainties but not including systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction \* prop. 2s w sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 238U / 206Pb ratio in the zircon crystal expressed as a percent, including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 207Pb/206Pb [ISOTOPIC RATIOS]: The relative abundance of 207Pb with respect to 206Pb measured in the zircon crystal \* prop. 2s no sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 207Pb / 206Pb ratio in the zircon crystal expressed as a percent, including analytical uncertainties and propagated uncertainties but not including systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction \* prop. 2s w sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 207Pb / 235Pb ratio in the zircon crystal expressed as a percent, including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 238/206 vs 207/206 error correlation [ISOTOPIC RATIOS]: A measurement of the covariance between the measured 238U / 206Pb and 207Pb / 206Pb ratios \* [U] (ppm) [ELEMENTAL CONCENTRATIONS]: The concentration of uranium measured in the zircon crystal \* U/Th [ELEMENTAL CONCENTRATIONS]: The relative concentrations of uranium with respect to Th measured in the zircon crystal \* 207Pb/235U age (Ma) [APPARENT AGES]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 207Pb / 235U ratio measured from the zircon crystal \* prop. 2s w sys (Myr) [APPARENT AGES]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 207Pb / 235U ratio measured from the zircon crystal \* 206Pb/238U age (Ma) [APPARENT AGES]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 206Pb / 238U ratio measured from the zircon crystal. For crystals younger than ca. 1000 Ma, this apparent age is the most reliable \* prop. 2s w sys (Myr) [APPARENT AGES]: The 2s uncertainty of the age calculated from the measured 206Pb / 238U ratio in the zircon crystal expressed in millions of years (Myr), including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 207Pb/206Pb age (Ma) [APPARENT AGES]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 207Pb / 206Pb ratio measured from the zircon crystal. For crystals older than ca. 1000 Ma, this apparent age is the most reliable \* prop. 2s w sys (Myr) [APPARENT AGES]: The 2s uncertainty of the age calculated from the measured 207Pb / 206Pb ratio in the zircon crystal expressed in millions of years (Myr), including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* conc. (%) [APPARENT AGES]: The degree of concordance between apparent ages calculated from the 206Pb / 238U ratio relative to those calculated from the 207Pb / 206Pb ratio, expressed as a percentage of the former divided by the latter. Concordance cannot be reliably determined for young crystals (i.e., <500-800 Ma) because of the low concentration of 207Pb in young crystals 8\. Missing data codes: None 9\. Specialized formats or other abbreviations used: None 10\. Methodology for data acquisition: Zircon geochronology We sampled nine tuffaceous levels for dating (T), collecting c. 3 kg of rock, across the Huayquerías, Tunuyán, and Bajada Grande formations. The tuff levels were georeferenced using Global Positioning System (GPS). Zircon aliquots were acquired through conventional density and magnetic separation procedures conducted in the School of the Earth, Ocean and Environment at the University of South Carolina (USA). U-Pb zircon geochronology was achieved by laser-ablation inductively coupled plasma mass-spectrometry (LA-ICP-MS) at the University of South Carolina’s Center for Elemental Mass Spectrometry. Sample preparation, analysis, reduction, and filtering methods are detailed in Appendix S1 (1.1. Zircon geochronology) from Romano et al. (2023). Sample preparation methods Crystals were picked and mounted and then imaged using cathodoluminescence scanning electron microscopy at the Southeastern North Carolina Regional Microanalytical and Imaging Consortium at Fayetteville State University (USA). U-Pb zircon geochronology was achieved by laser-ablation inductively coupled plasma mass-spectrometry (LA-ICP-MS) at the University of South Carolina’s Center for Elemental Mass Spectrometry using a high-resolution single-collector Thermo (now Thermo Fisher) Element2 mass-spectrometer attached to a PhotonMachines (now Teledyne) G2 Analyte 193 nm ArF exciplex laser using a 25 µm circular spot. For each aliquot, sample (aka ‘unknown’) zircons were analyzed in batches of four to five analyses each, separated by the analysis of two natural reference zircons of known and well-constrained U-Pb isotope-dilution thermal ionization mass-spectrometry (ID-TIMS) ages. In order to maximize the age data acquired from each sample, in some cases sufficiently large sample crystals were ablated by multiple laser spots. Indurated samples were progressively disaggregated to sub-500 µm particle sizes using a Bico Braun WD Chipmunk jaw crusher and Bico Braun UA Pulverizer disc mill. Particularly friable samples were disaggregated by gentle treatment in a large mortar and pestle. At the end of each crushing or milling step, all material finer than 500 µm was removed from further disaggregation via separation with a US-35 mesh stainless steel sieve. Resulting disaggregated grains were separated by hydrodynamic characteristics (mainly density) using a MD Mineral Technologies MK-2 Gemeni shaking water table, operating at approximately 6 liters/minute water flow and 6 Hz shaking. Grain splits entering the most distant three of seven receptacles (those containing the densest grains) were combined and proceeded to further processing (below); splits entering the upper four receptacles were examined to confirm the absence of zircon, and archived. The aforementioned dense grains were further separated by their hydrodynamic characteristics using a 25 cm diameter ABS plastic Garrett-type gold pan with tap water, and a portable wash basin to retain the discarded grains. Grains retained within the gold pan proceeded to further processing; splits exiting the gold pan were examined to confirm the absence of zircon, and archived. Retained grains were then progressively separated by their effective magnetic susceptibility first with a hand-magnet, and then with an L-1 Frantz isodynamic magnetic separator operating at left-right and front-back angles of 15° and 20°, respectively. Paramagnetic grain fractions were progressively removed in ~0.2 ampere increments up to ~1.0 amperes, and archived. Nonmagnetic fractions at ~1.0 amperes proceeded to further processing. Resulting nonmagnetic dense mineral fractions were combined with lithium metatungstate (~2.89 g/cm3) in 15 mL centrifuge tubes, agitated and left to settle for at least four hours until grains separated into fully floating and sunken fractions with an intervening clear heavy liquid window. Centrifuge tubes were then placed vertically into liquid nitrogen to a sufficient depth to submerge one-half of the heavy liquid window. Upon complete freezing of the submerged liquid and sunken fraction, the remaining liquid and floating fraction were poured off into a funnel lined with a 20 µm pore-diameter filter paper cone (Fisherbrand Grade P8), rinsed repeatedly with de-ionized water, transferred to a second filter paper, re-rinsed, dried, and archived. The frozen sunken separate and remaining liquid were thawed in a 30°C oven, and poured off into a separate funnel lined with a 20 µm pore-diameter filter paper cone, and rinsed repeatedly with de-ionized water, transferred to a second filter paper, re-rinsed,and then dried in a ~30°C oven. Zircon crystals were picked from the resulting high-density nonmagnetic grains and placed onto double-sided tape attached to a 6” square glossy ceramic tile, and within the bounds of a 1” Buehler circular ring form, cut to ~1 cm depth. These grains were then bound in place using Buehler Epo-Thin epoxy resin, poured to a depth of approximately 5 mm within the ring form. Upon at least 48 hours of curing, the resulting mounts were pried from their tiles, and gently ground using wet sandpaper of 600 grit as needed in order to expose the grain cores, followed by polishing using 1 µm Buehler Micropolish Alumina polishing powder suspended in de-ionized water deployed upon Buehler TexMet C polishing cloths, and a Buehler MINIMET 1000 auto-polisher operating at 20-40 cycles per minute and 2–4 pounds of downward force. Such procedures were repeated in 5–20 minute increments until a reflected light microscope revealed an absence of scratches in target zircons. Mounts were then sonicated in a de-ionized water bath for ~15 minutes and then dried in a 30°C oven. Mounts were imaged using cathodoluminence scanning electron microscopy with a JEOL JXA-8530F Hyperprobe Electron Probe Microanalyzer. Analytical methods In this work, we used natural zircon 91500 (1062.4 ± 1.9 Ma 206Pb/238U ID-TIMS age, [U]= 43–114 ppm: Wiedenbeck et al. 1995, 2004) as our primary reference material (‘standard’) for all analyses. We used either Fish Canyon Tuff (28.61 ± 0.08 Ma 206Pb/238U CA-ID-TIMS age, [U]= 209–459 ppm: Bachmann et al. 2007) or SL2 (563.5 ± 3.2 Ma 206Pb/238U ID-TIMS age, [U]= ~518 ppm: Gehrels et al. 2008) as our secondary reference material to monitor the accuracy and precision of correction using the 91500 reference material. In most samples, we analyzed either Plešovice (337.1 ± 0.2 Ma 206Pb/238U ID-TIMS age, [U]= ~755 ppm: Sláma et al. 2008; Horstwood et al. 2016) or 94–35 (55.5 ± 1.5 Ma 206Pb/238U ID-TIMS age, [U]= 64–228 ppm: Klepeis et al. 1998) as an additional monitor ‘standard’ after every ~fourteenth ‘unknown’ analysis in order to further assess the quality of standard-unknown bracketing corrections. Analysis involved grain-ablation with a PhotonMachines/Teledyne Analyte G2 193 nm (deep ultraviolet) ArF exciplex laser with a (circular) spot diameter of 25 µm, aimed at the centers of the individual sample (‘unknown’) and reference (aka ‘standard’) zircon grains, mounted in 1” polished epoxy resin pucks contained within a nine-hole stage nested within a two-volume HelEx sample cell. For each aliquot, unknown zircons were analyzed in 4 to 15 batches (depending on the sample) of four to five grains each, separated by the analysis of two natural reference zircons of known and well-constrained U-Pb isotope-dilution thermal ionization mass-spectrometry (ID-TIMS) ages. For most samples, a third reference material was analyzed as a known monitor ‘standard’ after every ~fourteenth ‘unknown’ analysis in order to compare the quality of standard-unknown bracketing corrections for each of the two used reference materials. This standard-unknown bracketing approach is required to correct for instrumental age-offset and drift, and down-hole inter-elemental fractionation, carrying with it modest uncertainties in age accuracy and precision that are sacrificed for the efficiency of collecting large datasets. In this study, reference material 91500 (1065.4 ± 0.3 Ma 207Pb/206Pb ID-TIMS weighted-mean age: Wiedenbeck et al. 1995; [U]= 43–114 ppm: Wiedenbeck et al. 2004) was analyzed after every four to five ‘unknown’ analyses. Reference material Fish Canyon (28.48 ± 0.02 Ma 206Pb/238U ID-TIMS weighted-mean age; [U]= 200–850 ppm: Schmitz & Bowring 2001) or SL2 (563.5 ± 3.2 Ma 206Pb/238U ID-TIMS weighted-mean age; [U]= ~518 ppm: Gehrels et al. 2008) was analyzed after every 91500 analyses. Reference material Plešovice (337.1 ± 2.0 Ma 206Pb/238U ID-TIMS weighted-mean age (Sláma et al. 2008; Horstwood et al. 2016), [U]= 465–1106 ppm: Sláma et al. 2008) or 94–35 (55.5 ± 1.5 Ma 206Pb/238U ID-TIMS weighted-mean age, [U]= 64–228 ppm: Klepeis et al. 1998) was analyzed after every third set of four to five unknowns of each aliquot for most samples, and used to assess and compare the accuracy of corrections based on the 91500, Fish Canyon and SL2 reference materials. Prior to analytical sessions, the coupled LA-HR-SC-ICP-MS system was manually tuned to optimize performance using 5 µm/s line scans of large fragments of the SL2 natural zircon reference material, with the laser otherwise set to parameters identical to the analytical session (see below). The tuning optimization routine involved adjusting torch position, then sample and HelEx gas flows to maximize signal (monitored by 238U cps) while minimizing oxide formation (monitored by UO/U) and inter-elemental fractionation (monitored by 232Th/238U). Optimized signal intensities were approximately 2x106 cps for 238U from SL2. UO/U values were ~0.3%, Th/U values were ~0.1 (very close to the accepted SL2 value of 0.13: Gehrels et al. 2008). During each analysis, the ablated material was transported in He carrier gas flowing at ~0.4 and ~0.1 liters/minute (LPM), respectively, from internal (MFC1) and external (MFC2) HelEx sample cell mass flow controllers downstream to the mass-spectrometer, where it was mixed with ~1 LPM of Ar sample gas in a PhotonMachines mixing bulb, and injected into the dry plasma source of a Thermo ELEMENT2 high-resolution single-collector mass-spectrometer, where the ablatant was ionized, passed through sample and skimmer cones, and then discriminated using the ELEMENT2’s double-focusing magnetic sector field mass analyzer. Samples T0, TM1, TM2, and T6: Analysis of each unknown and reference zircons involved the collection of 6 s of background data acquisition without the laser firing, followed by laser ablation of targeted zircons for 30 s at a laser repetition rate of 10–11 Hz and laser fluence of approximately 11 J/cm2, followed by at least 20 s for signal washout, baseline (blank) stabilization, and data compilation and recording. Signal intensity data were collected for masses 202, 204, 206, 207, 208, 232, and 238 using the ion counting mode of the ELEMENT2’s secondary electron multiplier (SEM) detector. Analysis of masses 206, 207, 208, 232, and 238 would switch automatically to analog mode above approximately 5 million cps. Mass 235 was determined by dividing the signal intensity of mass 238 by 137.818 (Horstwood et al. 2016). All samples except T0, TM1, TM2, and T6: Following the analyses of the first four tuff samples, our laboratory adjusted its protocol to optimize data collection, having discovered that data resolution does not significantly improve beyond ~12 s of grain ablation at ~10 Hz. Therefore, for all samples other than those aforementioned, we reduced ablation times to 16 s for all analyses. However, in order to increase the 206Pb counts required for an accurate 206Pb/238U age determined from young (i.e. low-daughter) grains, we employed a modified method that skipped data collection on 208Pb, and reduced dwell times on 207Pb and 232Th. We also employed a pre-ablation routine to remove any surface contamination introduced during the mounting, grinding and polishing procedures, or during storage and handling, and to ensure a horizontal exposed surface prior to ablation. Immediately prior to each analysis of unknown or reference zircon, 7 shots of a 50 µm circular laser spot were fired at a rate of 11 Hz at a position concentric with the (25 µm) ablation target, followed by 10 s delay for material to wash out and signal baselines to stabilize. Subsequent analysis of unknown and reference zircons involved the collection of 6 s of background data acquisition without the laser firing, followed by laser ablation of targeted zircons for 16 s at a laser repetition rate of 10–11 Hz and laser fluence of approximately 11 J/cm2, followed by at least 20 s for signal washout, baseline (blank) stabilization, and data compilation and recording. Signal intensity data were collected for masses 202, 204, 206, 207, 232, and 238 using the ion counting mode of the ELEMENT2’s secondary electron multiplier (SEM) detector. Analysis of masses 206, 207, 232, and 238 switched automatically to analog mode above approximately 2x106 cps. Mass 235 was determined by dividing the signal intensity of mass 238 by 137.818 (Horstwood et al. 2016). Data reduction methods Resulting data were reduced using the UPbGeochronology3 data reduction scheme of the Iolite (v. 2.4) software package (Paton et al. 2010) in the WaveMetrics IgorPro software environment. This approach subtracts background (‘blank’) signals, models and corrects inter-element downhole fractionation and instrument age-offsets and drift, and calculates individual analyses’ apparent ages, propagated uncertainties, and error correlations. We used an in-house data assessment table to exclude excessively discordant or uncertain ages. All volcanogenic and xenocrystic or detrital zircons with ages younger than 500 Ma had insufficient total counts of 207Pb to generate reliable 207Pb/206Pb ages for concordance calculations and filtering, so all grains with 206Pb/238U ages younger than 500 Ma were included without concordance filtering. Xenocrystic zircons with ages greater than 500 Ma that were less than 70% concordant (i.e. grains with 206Pb/238U ages younger than 70% of their 207Pb/206Pb ages) or more than 5% - 10% reverse discordant (i.e. grains with 206Pb/238U ages older than 105% of their 207Pb/206Pb ages were excluded from consideration as a result of likely disruption to an ideally closed U-Pb system, or poor matrix-matching between unknowns and reference materials. Zircons with excessive heterogeneity stemming from complex age zonation, abundant radiation damage, or fluid and crystal inclusions rarely generate reliable and meaningful grains. Thus, zircons with 206Pb/238U age uncertainties greater than 5% (at the 1σ uncertainty level) or 207Pb/206Pb age uncertainties greater than 10% (ibid.) were automatically excluded. Herein we employ a method that benefits from the objectivity of automatic and systematic data selection followed by grain-by-grain assessment of age-depth profiles. Processing began with the import of individual .FIN2 files written from .DAT and .INF files acquired from analysis of each unknown or reference zircon and its associated baseline and washout signals into the time-constrained reference frame of the IgorPro environment. Following data import, integration windows for baseline and ablatant signals were selected automatically by trimming 19 s (30 s for T0, TM1, TM2, and T6) from the end of each data file for baseline (aka background or blank) integrations, and 7 s and 3 s respectively from the start and end of each data file for ablation signals. Small offsets in analytical start times caused by operator error or computational delays compiling prior analyses’ data occasionally yielded inaccurately auto-selected integration windows. The occasional ablation of epoxy in insufficiently ground zircons or small zircons drilled through during standard ablation durations yielded similarly inappropriate windows. Manual adjustment of these windows was achieved by grain-by-grain data inspection, as was the elimination of analyses of grains known not to be zircon (e.g. by excessively low or unsteady signals, etc.). In the case of the former scenario, whenever possible care was taken to ensure that the start time of each ablatant integration window was spatially equivalent to those of grains with auto-selected windows in order to optimize the accuracy of down-hole fraction correction models (see below). Following data import, selection, and inspection of integration windows, Iolite-based data reduction involved: (1) subtraction of background signals from signals using an automatic (best-fit: see Paton et al. 2010) interpolation model; (2) determination of appropriate downhole-fractionation correction models by separately stacking the 206Pb/238U and 207Pb/235U (and 208Pb/232Th for T0, TM1, TM2, T6) downhole ratios of each of the primary reference zircon analyses, calculating best-fit exponential curves to those stacked datasets, and applying the resulting models to transform the isotopic ratios of analyzed ‘unknown’ zircons, ideally to optimize ratio steadiness; (3) estimation and correction of instrumental age-offsets and drift by comparison of determined (raw) and accepted (i.e. ID-TIMS) isotopic ratios of the primary reference zircon; and (4) calculation of final ages and values, including (a) propagated uncertainties determined from analyses of the primary reference zircon as pseudo-secondary standards, progressively removing them individually from the dataset, reprocessing the data, and calculating uncertainty, and (b) error correlations using the IgorPro StatsCorrelation function. See Paton et al. (2010) for further clarification and discussion of methods of Iolite data reduction of U-Pb zircon data. The aforementioned post-acquisition data processing in Iolite outputs 49 additional intermediate and final channels of data, including raw and corrected ratios and calculated ages using various relevant combinations of the seven input channels. Upon this processing, the steadiness of relevant daughter/parent (206Pb/238U) and daughter/daughter ratios (207Pb/206Pb) of each unknown were examined. Upon differentiating clearly inherited xenocrysts or detrital zircons (i.e. those with ages >30 Ma) from candidate first-cycle volcanogenic zircon, we calculated the weighted-mean ages and uncertainties, and accompanying mean squares of weighted deviates (MSWD) from each sample’s zircon age population(s). Mean spot ages were calculated by treating each laser spot as a separate age, regardless of the number of spots acquired from a given crystal. Weighted-mean grain ages were calculated using the average age of each grain’s spots (i.e. when a given crystal was analyzed by more than one laser spot). Mean spot and mean grain age uncertainties were calculated by quadrature. Each sample’s unfiltered mean spot ages and mean grain ages, and accompanying uncertainties and MSWD, were calculated from all zircon ages after excluding all clearly xenocrystic or detrital grains (i.e. those with ages >30 Ma). Filtered weighted-mean ages were calculated by iteratively excluding any ages outside two standard deviations of the unfiltered population. In the case of samples having multiple young (<30 Ma) age populations as indicated by subpopulations with non-overlapping 2 sigma uncertainties, we also calculated the weighted-mean age and uncertainty of the youngest population (‘cluster’). We also report the age of the youngest single-spot analysis acquired from each sample (Table 1 in the main text). \########################################################################################################### DATA-SPECIFIC INFORMATION FOR: 'Data 2.xlsx' 1\. Sample: Around 3 kg of rock tuffaceous levels T3. 2\. Geographic location of data colecction: Las Huayquerías del Este, San Carlos department, Mendoza Province, Argentina. 3\. Formation, fossil site and coordinates (latitude; longitude) of the collected sample: T3: Huayquerías Formation, Río Seco de la Isla Grande, 33°58'52.7"S, 68°27'00.8"W 4\. Number of sheets: 4 4.1. Sheet 1: Information about conditions of the laboratory. 4.2. Sheet 2: Results of analyses. A. Number of variables: 20 B. Tipe of case: spots (sample point) on zircon crystals through laser ablation C. Number of cases/rows in T3: 80 D. Variable List: \* Th (ppm) [Chemical composition]: The concentration of Th measured in the zircon crystal \* U (ppm) [Chemical composition]: The concentration of uranium measured in the zircon crystal \* Th/U (mass) [Chemical composition]: The relative concentrations of Th with respect to uranium measured in the zircon crystal \* 207Pb/235U [Radiogenic ratios]: The relative abundance of 207Pb with respect to 235U measured in the zircon crystal \* 2ѳ [Radiogenic ratios]: The 2s uncertainty of the measured 207Pb/235U ratio in the zircon crystal \* 206Pb/238U [Radiogenic ratios]: The relative abundance of 206Pb with respect to 238U measured in the zircon crystal \* 2ѳ [Radiogenic ratios]: The 2s uncertainty of the measured 206Pb/238U ratio in the zircon crystal \* 207Pb/206Pb [Radiogenic ratios]: The relative abundance of 207Pb with respect to 206Pb measured in the zircon crystal \* 2ѳ [Radiogenic ratios]: The 2s uncertainty of the measured 207Pb/206Pb ratio in the zircon crystal \* Rho XY [Radiogenic ratios]: \* Rho YZ [Radiogenic ratios]: \* 206Pb/238U [Isotopic age (Ma)]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 206Pb / 238U ratio measured from the zircon crystal. For crystals younger than ca. 1000 Ma, this apparent age is the most reliable \* 2ѳ [Isotopic age (Ma)]: The 2s uncertainty of the measured 206Pb/238U ratio in the zircon crystal \* 207Pb/235U [Isotopic age (Ma)]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 207Pb / 235U ratio measured from the zircon crystal \* 2ѳ [Isotopic age (Ma)]: The 2s uncertainty of the measured 207Pb/235U ratio in the zircon crystal \* 207Pb/206Pb [Isotopic age (Ma)]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 207Pb / 206Pb ratio measured from the zircon crystal. For crystals older than ca. 1000 Ma, this apparent age is the most reliable \* 2ѳ [Isotopic age (Ma)]: The 2s uncertainty of the measured 207Pb/206Pb ratio in the zircon crystal \* Preferred Age [Recommended Age]: Preferred (or recommended) age of the isotopic values obtained for each spot \* 2ѳ [Recommended Age]: The 2s uncertainty of the preferred age \* Disc. % (206/238)/(207/235): The degree of concordance between apparent ages calculated from the 206Pb / 238U ratio relative to those calculated from the 207Pb / 235U ratio, expressed as a percentage of the former divided by the latter E. Missing data codes: None F. Specialized formats or other abbreviations used: None 4.3. Sheet 3: Graphical results. 4.4. Sheet 4: Information provided by the LA.TE. ANDES S.A. laboratory on the processing of this sample. 5\. Methodology for data acquisition We sampled one tuffaceous level for dating (T3), collecting c. 3 kg of rock, across the Huayquerías, Tunuyán, and Bajada Grande Formation. The tuff levels were georeferenced using Global Positioning System (GPS). Sample T3 was prepared and analysed by laser-ablation inductively coupled plasma mass-spectrometry by La.Te. Andes laboratory, Salta Province (Argentina). Zircon concentrates were obtained from samples using gravimetric, magnetic, and optical techniques (Fig. S5). Suitability for analysis was evaluated based on the mineralogy of each sample in the heavy mineral fraction and the morphological characteristics of the mineral phases, especially regarding the quantity and quality of zircons. The laboratory provided details of sample preparation, analysis, reduction, and filtering, as well as the age, isotopic ratio, and related data, and are included in Romano et al. (2023, data 2). Information provided by the LA.TE. ANDES S.A. laboratory on the processing of this sample can be found in sheet 4 of Data 2. \########################################################################################################### DATA-SPECIFIC INFORMATION FOR: 'Data 3.xlsx' 1\. Samples: Fossil specimens. 2\. Geographic location of data colecction: Las Huayquerías del Este, San Carlos department, Mendoza Province, Argentina. 3\. Number of sheets: 3 3.1. Sheet 1: Vertebrates: Information about the collected fossil vertebrate specimens. A. Number of variables: 15 B. Number of cases/rows: 1136 C. Variable List: \* Collection no.: Collection Number of each specimen housed in the repository of the Colección de Paleontología de Vertebrados del Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA-PV) in Mendoza, Mendoza Province, Argentina. \* Class: Taxonomic Class to which the fossil specimen belongs \* Superorder/Order: Taxonomic Superorder/Order to which the fossil specimen belongs \* Suborder/Infraorder/Superfamily: Taxonomic Suborder/Infraorder/Superfamily to which the fossil specimen belongs \* Family: Taxonomic Family to which the fossil specimen belongs \* Subfamily/Tribu: Taxonomic Subfamily/Tribu to which the fossil specimen belongs \* Genus: Taxonomic Genus to which the fossil specimen belongs \* Species/Taxon: Taxonomic Species to which the fossil specimen belongs or the highest level of systematic determination achieved \* Formation: Formation (lithostratigraphic unit) in which the fossil specimen was collected \* Section: Stratigraphic section in which the specimen was collected \* Fossil site (original names): Original name of the fossiliferous site where the specimen was collected \* Latitude: Latitude coordinates where the specimen was collected (WGS 84 datum , World Geodetic System 1984) \* Longitude: Longitude coordinates where the specimen was collected (WGS 84 datum , World Geodetic System 1984) \* Taxonomic justification: Description of the fossil specimen upon which its taxonomic assignment is based \* Determinated by: Author responsible for the taxonomic determination or publication where it is documented D. Missing data codes: None E. Specialized formats or other abbreviations used: None 3.2. Sheet 2: Footprints: Information about the collected fossil footprints. A. Number of variables: 10 B. Number of cases/rows: 2 C. Variable List: \* Collection no.: Collection Number of each specimen housed in the repository of the Colección de Paleontología de Vertebrados del Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA-PV) in Mendoza, Mendoza Province, Argentina. \* Material: Type of material used for replication to preserve the specimen \* Ichnogenus: Taxonomic Ichnogenus to which the fossil specimen belongs \* Ichnotaxa: Taxonomic Species to which the fossil specimen belongs \* Formation: Formation (lithostratigraphic unit) in which the fossil specimen was collected \* Section: Stratigraphic section in which the specimen was collected \* Fossil site (original names): Original name of the fossiliferous site where the specimen was collected \* Latitude: Latitude coordinates where the specimen was collected (WGS 84 datum , World Geodetic System 1984) \* Longitude: Longitude coordinates where the specimen was collected (WGS 84 datum , World Geodetic System 1984) \* Determinated by: Author responsible for the taxonomic determination or publication where it is documented D. Missing data codes: None E. Specialized formats or other abbreviations used: None 3.3. Sheet 3: Eggshells: Information about the collected fossil eggshells. A. Number of variables: 9 B. Number of cases/rows: 20 C. Variable List: \* Collection no.: Collection Number of each specimen housed in the repository of the Colección de Paleontología de Vertebrados del Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA-PV) in Mendoza, Mendoza Province, Argentina. \* Taxon: The highest level of systematic determination achieved \* Formation: Formation (lithostratigraphic unit) in which the fossil specimen was collected \* Section: Stratigraphic section in which the specimen was collected \* Fossil site (original names): Original name of the fossiliferous site where the specimen was collected \* Latitude: Latitude coordinates where the specimen was collected (WGS 84 datum , World Geodetic System 1984) \* Longitude: Longitude coordinates where the specimen was collected (WGS 84 datum , World Geodetic System 1984) \* Taxonomic justification: Description of the fossil specimen upon which its taxonomic assignment is based \* Determinated by: Author responsible for the taxonomic determination or publication where it is documented D. Missing data codes: n/a indicates that data on the taxonomy of the specimen is not available or does not correspond E. Specialized formats or other abbreviations used: None 4\. Methodology for data acquisition More than 1100 vertebrate specimens, now housed at IANIGLA-PV, were collected in situ over a ten-year period (2013–2019, 2022) from 21 different fossiliferous sites and stratigraphic levels in the Huayquerías and Tunuyán fms, Mendoza Province, Argentina. The collected specimens, with few exceptions, were georeferenced and their levels of origin were identified. Most specimens were determined by comparison with materials in Argentinean collections or bibliographic data, while others represent new taxa that will be described in future. The file contains data for each specimen including Collection Number, Taxonomy, Formation and section of provenance, fossil site of origin, GPS coordinates of Latitude and Longitude, and a brief taxonomic justification. \########################################################################################################### DATA-SPECIFIC INFORMATION FOR: 'Data 4.xlsx' 1\. Number of sheets: 28 1.1. Sheet 1: Fm - Original - taxa: Raw presence-absence data matrix for the different taxa identified in the Huayquerías and Tunuyán formations of the Huayquerías del Este A. Number of variables: 3 B. Number of cases/rows: 85 C. Variable List: \* Taxa: Taxonomic determination \* Huayquerías Fm: Taxon present (1) or absent (0) in the Huayquerías Formation \* Tunuyán Fm: Taxon present (1) or absent (0) in the Tunuyán Formation 1.2. Sheet 2: Fm - Per1App1OpA - taxa: Presence-absence data matrix for the different taxa identified in the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App1OpA (see below) A. Number of variables: 3 B. Number of cases/rows: 71 C. Variable List: \* Taxa: Taxonomic determination \* Huayquerías Fm: Taxon present (1) or absent (0) in the Huayquerías Formation \* Tunuyán Fm: Taxon present (1) or absent (0) in the Tunuyán Formation 1.3. Sheet 3: Fm - Per1App1OpB - taxa: Presence-absence data matrix for the different taxa identified in the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App1OpB (see below) A. Number of variables: 3 B. Number of cases/rows: 88 C. Variable List: \* Taxa: Taxonomic determination \* Huayquerías Fm: Taxon present (1) or absent (0) in the Huayquerías Formation \* Tunuyán Fm: Taxon present (1) or absent (0) in the Tunuyán Formation 1.4. Sheet 4: Fm - Per2App1OpA - taxa: Presence-absence data matrix for the different taxa identified in the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App1OpA (see below) A. Number of variables: 3 B. Number of cases/rows: 72 C. Variable List: \* Taxa: Taxonomic determination \* Huayquerías Fm: Taxon present (1) or absent (0) in the Huayquerías Formation \* Tunuyán Fm: Taxon present (1) or absent (0) in the Tunuyán Formation 1.5. Sheet 5: Fm - Per2App1OpB - taxa: Presence-absence data matrix for the different taxa identified in the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App1OpB (see below) A. Number of variables: 3 B. Number of cases/rows: 90 C. Variable List: \* Taxa: Taxonomic determination \* Huayquerías Fm: Taxon present (1) or absent (0) in the Huayquerías Formation \* Tunuyán Fm: Taxon present (1) or absent (0) in the Tunuyán Formation 1.6. Sheet 6: Fm - Original - Genera: Raw presence-absence data matrix for the different genera identified in the Huayquerías and Tunuyán formations of the Huayquerías del Este A. Number of variables: 3 B. Number of cases/rows: 50 C. Variable List: \* Genus: Genus-level determination \* Huayquerías Fm: Genus present (1) or absent (0) in the Huayquerías Formation \* Tunuyán Fm: Genus present (1) or absent (0) in the Tunuyán Formation 1.7. Sheet 7: Fm - Per1App1OpA - Genera: Presence-absence data matrix for the different genera identified in the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App1OpA (see below) A. Number of variables: 3 B. Number of cases/rows: 46 C. Variable List: \* Genus: Genus-level determination \* Huayquerías Fm: Genus present (1) or absent (0) in the Huayquerías Formation \* Tunuyán Fm: Genus present (1) or absent (0) in the Tunuyán Formation 1.8. Sheet 8: Fm - Per2App1OpA - Genera: Presence-absence data matrix for the different genera identified in the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App1OpA (see below) A. Number of variables: 3 B. Number of cases/rows: 46 C. Variable List: \* Genus: Genus-level determination \* Huayquerías Fm: Genus present (1) or absent (0) in the Huayquerías Formation \* Tunuyán Fm: Genus present (1) or absent (0) in the Tunuyán Formation 1.9. Sheet 9: Sections - Original - Genera: Raw presence-absence data matrix for the different genera identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este A. Number of variables: 8 B. Number of cases/rows: 50 C. Variable List: \* Genera: Genus-level determination \* HLo: Genus present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Genus present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* HuayFm: Genus present (1) or absent (0) in the Huayquerías Formation but without precise stratigraphic correlation \* TunFm: Genus present (1) or absent (0) in the Tunuyán Formation but without precise stratigraphic correlation \* TLo: Genus present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Genus present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Genus present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.10. Sheet 10: Sections - Per1App1 - Genera: Presence-absence data matrix for the different genera identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App1 (see below) A. Number of variables: 6 B. Number of cases/rows: 46 C. Variable List: \* Genus: Genus-level determination \* HLo: Genus present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Genus present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Genus present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Genus present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Genus present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.11. Sheet 11: Sections - Per1App2 - Genera: Presence-absence data matrix for the different genera identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App2 (see below) A. Number of variables: 6 B. Number of cases/rows: 46 C. Variable List: \* Genus: Genus-level determination \* HLo: Genus present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Genus present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Genus present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Genus present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Genus present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.12. Sheet 12: Sections - Per1App3 - Genera: Presence-absence data matrix for the different genera identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App3 (see below) A. Number of variables: 6 B. Number of cases/rows: 46 C. Variable List: \* Genus: Genus-level determination \* HLo: Genus present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Genus present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Genus present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Genus present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Genus present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.13. Sheet 13: Sections - Per2App1 - Genera: Presence-absence data matrix for the different genera identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App1 (see below) A. Number of variables: 6 B. Number of cases/rows: 46 C. Variable List: \* Genus: Genus-level determination \* HLo: Genus present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Genus present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Genus present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Genus present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Genus present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.14. Sheet 14: Sections - Per2App2 - Genera: Presence-absence data matrix for the different genera identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App2 (see below) A. Number of variables: 6 B. Number of cases/rows: 46 C. Variable List: \* Genus: Genus-level determination \* HLo: Genus present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Genus present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Genus present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Genus present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Genus present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.15. Sheet 15: Sections - Per2App3 - Genera: Presence-absence data matrix for the different genera identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App3 (see below) A. Number of variables: 6 B. Number of cases/rows: 46 C. Variable List: \* Genus: Genus-level determination \* HLo: Genus present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Genus present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Genus present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Genus present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Genus present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.16. Sheet 16: Sections - Original - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este A. Number of variables: 8 B. Number of cases/rows: 85 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* HuayFm: Taxon present (1) or absent (0) in the Huayquerías Formation but without precise stratigraphic correlation \* TunFm: Taxon present (1) or absent (0) in the Tunuyán Formation but without precise stratigraphic correlation \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.17. Sheet 17: Sections - Per1App1OpA - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App1OpA (see below) A. Number of variables: 6 B. Number of cases/rows: 69 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.18. Sheet 18: Sections - Per1App2OpA - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App2OpA (see below) A. Number of variables: 6 B. Number of cases/rows: 70 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.19. Sheet 19: Sections - Per1App3OpA - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App3OpA (see below) A. Number of variables: 6 B. Number of cases/rows: 70 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.20. Sheet 20: Sections - Per2App1OpA - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App1OpA (see below) A. Number of variables: 6 B. Number of cases/rows: 71 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.21. Sheet 21: Sections - Per2App2OpA - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App2OpA (see below) A. Number of variables: 6 B. Number of cases/rows: 71 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.22. Sheet 22: Sections - Per2App3OpA - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App3OpA (see below) A. Number of variables: 6 B. Number of cases/rows: 71 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.23. Sheet 23: Sections - Per1App1OpB - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App1OpB (see below) A. Number of variables: 6 B. Number of cases/rows: 85 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.24. Sheet 24: Sections - Per1App2OpB - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App2OpB (see below) A. Number of variables: 6 B. Number of cases/rows: 86 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.25. Sheet 25: Sections - Per1App3OpB - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per1App3OpB (see below) A. Number of variables: 6 B. Number of cases/rows: 87 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.26. Sheet 26: Sections - Per2App1OpB - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App1OpB (see below) A. Number of variables: 6 B. Number of cases/rows: 89 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.27. Sheet 27: Sections - Per2App2OpB - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App2OpB (see below) A. Number of variables: 6 B. Number of cases/rows: 89 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) 1.28. Sheet 28: Sections - Per2App3OpB - Taxa: Raw presence-absence data matrix for the different taxa identified in the sections of the Huayquerías and Tunuyán formations of the Huayquerías del Este applying the scenario Per2App3OpB (see below) A. Number of variables: 6 B. Number of cases/rows: 89 C. Variable List: \* Taxa: Taxonomic determination \* HLo: Taxon present (1) or absent (0) in the lower section of the Huayquerías Formation (HLo) \* HUp: Taxon present (1) or absent (0) in the upper section of the Huayquerías Formation (HUp) \* TLo: Taxon present (1) or absent (0) in the lower section of the Tunuyán Formation (TLo) \* TMid: Taxon present (1) or absent (0) in the middle section of the Tunuyán Formation (TMid) \* TUp: Taxon present (1) or absent (0) in the upper section of the Tunuyán Formation (TUp) ``` 2\. Missing data codes: None 3\. Specialized formats or other abbreviations used: None 4\. Methodology for data acquisition ``` Biostratigraphic analysis We divided the Huayquerías-Tunuyán stratigraphic succession to explore possible faunal turnover across the sequence. The Huayquerías Fm. was divided into a lower section (HLo, < c. 6.2 Ma) and an upper section (HUp, > c. 6.2 Ma), based on independent geochronological dates. The Tunuyán Fm. was divided into three sections of equivalent thickness, denominated TLo (lower), TMid (middle), and TUp (upper). Similarity analysis Different possible scenarios were considered in the analysis. We applied two different perspectives to deal with problems arising from taxa with open nomenclature. In perspective 1 (Per1), ambiguous taxa were excluded from the analysis when another unambiguous taxon existed, whereas in perspective 2 (Per2), we considered taxa with open nomenclature as unambiguously belonging to the unqualified taxon (cf. or aff.) to see how the results could be affected in case the presence of such taxa was corroborated in the future in different sections of the sequence where the presence of such taxon could not be confirmed for now due to the lack of intraspecific variability studies in some cases. In addition, both perspectives follow three different approaches. The first approach (App1) employs the raw data; the second (App2) includes ‘Lazarus taxa’. We consider that the absences of certain taxa in some levels may be caused by biases in the record, due to both the environmental monotony observed in the sedimentary sequence and the geological similarity between the different sections (see section Geological settings; in the main text), discarding also biogeographical differences in the Huayquerías del Este area, or due to a lower sampling of some sections. The third approach (App3) considers taxa without precise stratigraphic correlation collected from the Huayquerías or Tunuyán fms as they come from HUp or TLo when they were recorded in at least one section of the Tunuyán or Huayquerías Fm., respectively. This situation affects, on the one hand, 90 specimens from the Huayquerías Fm. collected in Huayquerías de la Horqueta Norte and Sur. The taxa represented there are considered as belonging to HUp as long as they appear in the Tunuyán Fm. from another fossil site; and, on the other hand, it affects 21 specimens collected from the Tunuyán Fm. in RS del Agua Escondida. The taxa represented there are considered as TLo if they occur in the Huayquerías Fm. of another fossil site. In this way we explore the effect of placing these fossils following the hypotheses about the correlation of these sites that we considered more probable (e.g. the regional geological observations regarding the arrangement of the strata and stratigraphic contacts of each of these sites). Furthermore, analyses at the species level were performed under two different alternatives: option A (OpA), the raw data; and option B (OpB), when two or more species of the same genus were identified, the taxonomic category of the genus was included as a separate entry. The latter aims to reduce the differences caused by the difficulty of determining specimens at the species level, as genera are less affected by the problems of assessing intraspecific variability in fossils. In addition, similarity analyses were performed both at the genus level only and genus/species level. For comparisons between stratigraphic formations, we applied the two perspectives (Per1–2), the first approach (App1), and option A (OpA) at the two levels of analysis, while the second alternative (OpB) was also explored with genus/species matrices. For comparisons between each pair of sections, the two perspectives (Per1–2), three approaches (App1–3), and option A (OpA) at only genus matrices, and all possible scenarios (Per1–2, App1–3, and OpA–B) at genus/species matrices were explored. ``` \########################################################################################################### Zircon geochronology We sampled ten tuffaceous levels for dating (T), collecting c. 3 kg of rock. The tuff levels were georeferenced using Global Positioning System (GPS). For samples other than T3, zircon aliquots were acquired through conventional density and magnetic separation procedures conducted in the School of the Earth, Ocean and Environment at the University of South Carolina (USA). U-Pb zircon geochronology was achieved by laser-ablation inductively coupled plasma mass-spectrometry (LA-ICP-MS) at the University of South Carolina’s Center for Elemental Mass Spectrometry. Sample preparation, analysis, reduction, and filtering methods are detailed in Appendix S1. Age, isotopic ratio, and related data can be found in Romano et al. (2023, data 1). Sample T3 was prepared and analysed by laser-ablation inductively coupled plasma mass-spectrometry by La.Te. Andes laboratory, Salta Province (Argentina). Zircon concentrates were obtained from samples using gravimetric, magnetic, and optical techniques (Fig. S5). Suitability for analysis was evaluated based on the mineralogy of each sample in the heavy mineral fraction and the morphological characteristics of the mineral phases, especially regarding the quantity and quality of zircons. The laboratory provided details of sample preparation, analysis, reduction, and filtering, as well as the age, isotopic ratio, and related data, and are included in Romano et al. (2023, data 2). Fossil sample More than 1100 vertebrate specimens, now housed at IANIGLA-PV, were collected in situ over a ten-year period (2013–2019, 2022) from 21 different fossiliferous sites and stratigraphic levels in the Huayquerías and Tunuyán fms. The collected specimens were georeferenced and their levels of origin were identified. Most specimens were determined by comparison with materials in Argentinean collections or bibliographic data, while others represent new taxa that will be described in future contributions (detailed in Romano et al. 2023, data 3). Similarity analysis We compared the taxonomic composition of the Huayquerías and Tunuyán fms by calculating their overall similarity, as well as between each pair of sections defined in both units. The similarity analysis is based on presence-absence matrices (Romano et al. 2023, data 4) analysed using the Corrected Forbes Coefficient (CFC). Different possible settings were considered in the analysis, including the raw data taxa, taxa under open nomenclature, and ‘Lazarus taxa’. In addition, to evaluate whether similarity is biased at the species level, we also employed this information at the genus level. For more details, see Supplementary Materials. Similarity analyses were performed using an R function provided by J. Alroy’s website (https://bio.mq.edu.au/~jalroy/Forbes.R). These analyses were computed in R (v4.2.2; R Core Team 2013) The Huayquerian Stage of the South American chronostratigraphic scheme (named for the Huayquerías del Este, Argentina) was originally based on a poorly known mammal association of six taxa from the Huayquerías Formation. We studied the geology, age and fauna of the Neogene sequence in this area, including the Huayquerías, Tunuyán and Bajada Grande formations. The sequence comprises a monotonous succession of synorogenic epiclastic sediments deposited under arid to semi-arid conditions. Zircon U–Pb dates from 10 tuffaceous levels (7.2–1.6 Ma) place deposition of the Huayquerías Formation during the late Tortonian or Messinian to early Zanclean, the Tunuyán Formation during the Zanclean–Piacenzian, and the Bajada Grande Formation during the Piacenzian–Calabrian. We present 43 and 31 new mammal taxon records for the Huayquerías and Tunuyán formations, respectively. Progressive faunal change was observed along the sequence. The first records of the Chaco tortoise Chelonoidis chilensis and the notoungulate Xotodon major, and the latest records of Interatheriidae and Typotheriopsis (notoungulates), Metacaremys calfucalel, Phtoramys hidalguense and Lagostomus pretrichodactyla (rodents), Chasicotatus ameghinoi and Macroeuphractus morenoi (xenarthrans) are reported. The faunal associations of the Huayquerías and lower Tunuyán formations are highly similar to each other, and to other coeval localities in Argentina. The Macroeuphractus morenoi Assemblage Biozone is proposed as the basis for redefining the Huayquerian Stage, due to the co-occurrence of three taxa with wide geographical distribution in southern South America: Macroeuphractus morenoi, Pseudotypotherium subinsigne and Lagostomus pretrichodactyla. The age of this biozone is constrained at c. 8–5 Ma in its type area. Microsoft Excel

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    ZENODO; DRYAD
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      ZENODO; DRYAD
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    Authors: Bartrons, Mireia; Trochine, Carolina; Blicharska, Malgorzata; Oertli, Beat; +2 Authors

    We built an inventory (database) of Nature-based Solutions (NbS) actions (creation, restoration, and management) in ponds and pondscapes (ponds at the landscape scale) in a diversity of social-ecological settings to assess the best practices. We formulated an online questionnaire that was shared with pond stakeholders. The questionnaire asked general (e.g., number of ponds, area of the pondscape, etc.) and specific (e.g., costs of the action, stakeholders involved, etc.) information on the NbS action implemented, and on 11 associated Nature's Contributions to People (NCPs). Among the NCPs we included, for instance, habitat creation for biodiversity, regulation of climate, learning or physical and physiological experiences. The database contains information gathered through the questionnaire, research papers and relevant web pages and platforms. We used three different approaches to obtain information on NbS actions implemented in ponds/pondscapes and the associated NCPs mainly focusing on Europe and Uruguay: 1) the development of a user-friendly online questionnaire on NbS implemented in ponds/pondscapes and associated NCPs, which was shared in the form of a survey through the platform Survey Monkey with PONDERFUL members and pond Stakeholders; 2) the search of information in research papers; and 3) the search of information on web pages such as https://oppla.eu, https://renature-project.eu, https://climate-adapt.eea.europa.eu, https://una.city. We requested permissions from the respondents to make the data available.

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    ZENODO
    Dataset . 2023
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    Data sources: Sygma; ZENODO
    ZENODO
    Dataset . 2023
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      ZENODO
      Dataset . 2023
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      Data sources: Sygma; ZENODO
      ZENODO
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    Authors: Lundgren, Erick; Bergman, Juraj; Trepel, Jonas; le Roux, Elizabeth; +6 Authors

    Literature screening and digitization This meta-analysis was part of a larger effort to understand megafauna impacts on multiple facets of ecosystems (e.g. including soil nutrients, invertebrates, etc). This ensured that the dataset included plant responses that were also measured in studies focused on other response variables (e.g., spider diversity). We searched Web of Science with a string of search terms that included the common names and Latin genera of all terrestrial mammalian megafauna species (common names from HerbiTraits v1.2 (Lundgren et al. 2021)) separated with an ‘OR’ operand, along with the following search terms: “disturb*, graz*, brows*, impact*, effect, affect, disrupt, facilitate, invasi*, ecosystem*, vegetat*, plant*, fauna*, reptil*, amphib*, bird*, rodent*, fish*, invertebrat*, insect*, soil*, carbon, climate, albedo, river*, riparian, desert*, forest*, tundra, decomposition, grassland*, savanna*, chaparral, scrub, shrub, diversity, heterogeneity, extinction, richness, environment, reptile*, ecolog*, hydrolog*, disturbance, density, biodiversity, response*, ecosystem, herbaceous, canopy, germination, cover, pollinator*, tree, nutrient*, understorey, erosion, grass*, vegetation, community, exclosure, competition, effect*, abundance, productivity”. To reduce unrelated results we also included a Web of Science category filter (“WC”) of “ECOLOGY OR ZOOLOGY OR ENVIRONMENTAL SCIENCES OR BIODIVERSITY CONSERVATION OR EVOLUTIONARY BIOLOGY OR GEOGRAPHY PHYSICAL OR REMOTE SENSING OR PLANT SCIENCES OR MULTIDISCIPLINARY SCIENCE OR FORESTRY OR ENTOMOLOGY OR MARINE & FRESHWATER BIOLOGY OR MYCOLOGY OR BIOLOGY OR OCEANOGRAPHY OR ORNITHOLOGY OR BEHAVIORAL SCIENCES OR FISHERIES”. The Web of Science review was concluded on the 18th of February 2021 and returned 60,537 studies. We removed duplicate studies using the fuzzy matching algorithm with the function ‘find_duplicates’ in the R package ‘revtools’ (version 0.4.1) (Westgate 2019). After removing duplicates, our final search returned 46,825 studies. Title screening reduced the number of studies to 2,369. We screened the full text of these studies to only include studies focused on wild megafauna (≥45 kg) and that compared areas with low versus high megafauna densities due to exclosures, policy-driven differences (hunting versus no-hunting in adjacent properties), and differences in introduction or eradication histories (adjacent islands with and without megafauna). Some studies compared areas with and without focal megafauna populations for unknown reasons (e.g., a site with and without horses with no indication of why horses might be absent (Robertson et al. 2019)), which were excluded due to low confidence in the ultimate drivers of observed differences. We excluded all before-after comparisons (e.g., a plot measured prior to exclosure construction and then afterwards) because of the high rates of change in many systems through time (via afforestation, shifts in climate, succession, etc.). Studies that excluded megafauna but also all vertebrates were excluded. Two additional studies reported data from extremely limiting resources (i.e., wetlands in deserts). These were excluded given that such scenarios should be analyzed separately, for which we did not have sufficient sample size. Studies that evaluated the effects of megafauna on transplants or agricultural crops (including plantations) were not digitized. Studies that included an appropriate comparison and reported a central tendency (mean or median), a measurement of error (standard deviation, standard error, variance, etc), and sample size were digitized (n=154). This literature list was supplemented by the literature contained in other relevant meta-analyses (Daskin and Pringle 2016, Eldridge et al. 2020) and those encountered in the bibliographies of the studies we digitized. Given the limited number of studies from oceanic islands and regarding widely distributed introduced species (feral pigs, goats) in our initial Web of Science search, we conducted focused Google Scholar searches on July 15th, 2022 with the following terms: “ungulate impacts island*”, “introduced goat impact island*”, “introduced deer impact*”, “feral camel impact*”, “wild OR feral boar OR hog OR swine impact*”, “feral cattle impact*”, “invasive ungulate hawaii OR guam OR new zealand OR pacific island OR new caledonia OR galapagos OR caribbean OR oceanic island” and a Web of Science search on the 22nd of December 2022 using the search string “herbivore* AND plant* AND response*”. This uncovered an additional 482 studies of which 66 studies were fit for inclusion, leading to a total of 221 studies in our final dataset. We digitized central tendencies (mean, median), error (standard deviation, standard error, interquartile ranges), and sample sizes for each response (diversity, richness, and abundance) in each study. We used ImageJ to extract data from figures (Schneider et al. 2012). Interquartile ranges and medians (e.g., as extracted from boxplots) were converted to means and standard deviation using the function qe.mean.sd in the package ‘estmeansd’ version 1.0.0 (McGrath et al. 2022). Means and SD/SE were reported by 213 studies (3,846 observations) while 11 studies (149 observations) reported medians and interquartile ranges. We also digitized relevant covariates from the text, which included time since treatment (e.g., exclosure construction, introduction, eradication, etc), study coordinates (latitude, longitude), megafauna density (standardized to kg per hectare), relative abundance of megafauna (in the case of multispecies megafauna communities and if density was not provided), and the scale of measurement (treated both as area, m2, and maximum measurement length, m, to allow the comparison of transects to plots). If study coordinates were not exactly provided, we extracted latitude and longitude from the approximate center of each study location in Google Maps. Maximum measurement length was calculated as either the hypotenuse of square/rectangular plots, the length of transects, or the diameter of circular plots. Distributions of megafauna traits, environmental variables (see below), and methodological variables were similar between native and introduced megafauna communities in our final dataset. We treated measurements of species richness and species diversity (e.g., Shannon Weiner index) as ‘diversity’ responses and density estimates (individual plants per plot), % cover, and biomass as measurements of abundance. Analyzing these responses alone led to similar results. We excluded seed abundance and diversity responses, given that seedbanks can be at disequilibrium from realized plant communities. We included all true plant species, excluding multicellular algae and lichen. Effect sizes Given the presence of negative values and zeros in our dataset, we calculated effect sizes using Hedges’ g, a unitless measure of standardized mean difference between groups. Each effect size was associated with sampling variance calculated from the sample size and standard deviation of each observation. Effect sizes and sampling variances were calculated with the function ‘escalc’ in the R package ‘metafor’ (version 3.5-12) (Viechtbauer 2010). Megafauna and plant nativeness Megafauna nativeness was based on study author designations or IUCN range maps (17), if unreported. While many communities had both native and introduced megafauna present, the vast majority of studies only manipulated (excluded) the introduced megafauna, which was possible because of body size differences or through eradication. Only one study manipulated both native and introduced megafauna (Ward‐Jones et al. 2019). Given that the majority of megafauna biomass in this study consisted of introduced megafauna, we classified this study as introduced. Excluding it (only relevant for abundance analyses) led to similar results. The evolutionary exposure of study sites to megafauna (i.e., oceanic islands versus continents and offshore islands) was determined using PHYLACINE v1.2 range maps- (Faurby et al. 2018). We considered New Zealand, which possessed avian megafauna, an oceanic island without coevolutionary history with mammalian megafauna (due to distinctive foraging strategies of avian versus mammalian herbivores). However, counting New Zealand as an offshore island led to similar results. The nativeness of collective plant responses was assigned as reported by the authors (1,864 observations from 104 studies). In cases where plant nativeness was unspecified (2,136 observations from 155 studies) we evaluated nativeness based on author-provided flora descriptions of the study site by referring to the Plants of the World Online (POWO n.d.) and the study site location. If introduced plants were described in the study system, we described the study as mixed (and thus excluded it) unless the introduced plants collectively constituted <5% relative abundance (cover, biomass, density), as reported by authors, in which case we counted these systems as ‘native’. From this, we were able to classify an additional 1,718 observations from 113 studies as native (1,499 observations, 97 studies), mixed (218 observations, 15 studies), and introduced (1 observation, 1 study). A final portion of studies did not provide site flora descriptions (418 observations, 42 studies). These studies generally came from large, well-protected landscapes (e.g., Kruger National Park, Arctic tundra). We treated these responses as native. The nativeness of individual plant species, relevant only to plant abundance responses, was extracted from the Plants of the World Online (POWO n.d.), as above. Plant taxonomy was standardized with the Taxonomic Name Resolution Service (TNRS) (Boyle et al. 2013). Coevolutionary history and coevolutionary novelty The coevolutionary history between megafauna and the biomes to which they have been introduced was determined using biome maps from Olson et al. 2001 (Olson et al. 2001). Introduced megafauna were considered ’coevolved’ with the biome if they would have occurred in the absence of human-caused extinctions and range contractions (e.g., Equus ferus caballus in North America), based on PHYLACINE v1.2’s (Faurby et al. 2018) megafauna distributions in the absence of extinctions and range contractions, or if the megafauna species was native elsewhere within the focal biome, as in the case of megafauna introduced to offshore islands within their native continent. Species-level coevolutionary history between megafauna and individual plant species was determined by comparing plant distributions (POWO n.d.) to PHYLACINE range maps (Faurby et al. 2018). In cases of multiple introduced megafauna, we based this on the dominant megafauna species, with dominance determined by relative biomass. Functional and phylogenetic novelty were calculated by identifying coevolved megafauna communities, in the absence of Late Pleistocene extinctions and range contractions, for each study location from PHYLACINE v1.2 range maps (Faurby et al. 2018). Functional novelty was calculated as the Gower distance to the most functionally similar coevolved megafauna. Gower distances were calculated using the function ‘gowdis’ (R package ‘FD’, version 1.0-12.1) (Laliberté et al. 2014) from key megafauna functional traits that determine their effect on the environment (provided by HerbiTraits (Lundgren et al. 2021)). These included body mass (log10 scale), two ordinal dietary traits (graminoid consumption, browse consumption), fermentation type (converted to an ordinal variable describing fermentation efficiency), three non-exclusive binary habitat use variables (aquatic, terrestrial, arboreal), a categorical variable describing limb morphology (plantigrade, digitigrade, unguligrade). Variable weightings followed (Lundgren et al. 2020). Phylogenetic novelty was defined as the cophenetic distance between the introduced megafauna and the most closely related megafauna in the absence of human-caused extinctions and range contractions using the function ‘pd’ (R package ‘ape’ version 5.6-2, (Paradis and Schliep 2019), with the phylogeny provided by PHYLACINE v1.2 (Faurby et al. 2018)). For both phylogenetic and functional novelty, we identified the distances between the introduced megafauna and the most similar prehistoric ‘coevolved’ megafauna. This value was relativized by the introduced species’ relative biomass in their community and then averaged across all introduced megafauna. Relative biomass estimates were calculated from relative abundance or absolute density estimates per species, which were reported for 78.4% of data points. Environmental covariates Environmental covariates were extracted for each study location by buffering each study location by 5 km and using the function ‘extract’ from the R package ‘terra’ (version 1.7-6) (Hijmans 2023). Values were averaged across the 5 km buffer. Specifically, we extracted values of net primary productivity (Zhao et al. 2005), maximum annual temperature and precipitation (Fick and Hijmans 2017), and the human footprint index (Venter et al. 2016). The human footprint index was available for both 1993 and 2009. We thus used values closest to the year the data was collected or, if unreported, the year the study was published (n=78 studies). For studies reporting data over multiple years, the year was adjusted for the time when the individual response was collected based on commencement of study and collection interval. Megafauna community functional traits To understand how megafauna functional traits influence their effects on plants, we evaluated key megafauna functional traits for all species in our dataset. Given that 80 studies (1,433 observations) manipulated multiple species of megafauna, we analyzed megafauna functional trait summaries at a community level. We did this by multiplying species trait values by their relative biomass in their community (0–1) and then calculating the maximum and mean of these traits (henceforth ‘community-weighted’). Mean trait values reflect overall community tendencies, while the possibility that ecological outcomes may be shaped more by extremes (while accounting for relative biomass) is captured by maximum trait values. Traits were extracted from HerbiTraits (Lundgren et al. 2021) and included body mass, proportion of megafauna biomass with hindgut fermentation (which has distinct effects on ecosystems relative to foregut fermentation, (Alexander 1993)), and dietary preference for graminoids (grasses and allies). Note that while our meta-analysis focused on megafauna ≥45 kg, some studies excluded smaller herbivores as well. These herbivores were included in trait summaries if ≥10 kg in mass, for which trait data was available. Browse and graminoid (grass and allies) consumption are the two primary axes of dietary differentiation in herbivores. Browse and graminoid consumption were available from HerbiTraits as two non-exclusive variables ranging from 0 (avoided) to 3 (highly preferred). To synthesize these variables into a single measure, we relativized each species’ graminoid consumption value by multiplying it by their relative biomass (0-1) within their community. We then divided this value by the sum of relativized browse and graminoid consumption. This variable was used in conjunction with the plant growth form of each response, categorized as forb, woody, or graminoid. Species-level plant growth forms were derived from the World Checklist of Vascular Plants and from the ‘growthform’ package (Zanne et al. 2015). Muzzle widths were extracted from (Pérez–Barbería and Gordon 2001, Mendoza et al. 2002). Species-level muzzle widths were absent for 42 species of 114 herbivore species in our dataset (36.8%). We used genus-level averages for the 25 of these species for which genus-level data was available. A remaining 17 species without genus-level estimates were minor members (median 13% relative biomass) of speciose herbivore communities in 40 observations (4 studies). For these observations, we excluded these particular species, calculating muzzle width summaries with the other species present only. Elephants (Loxodonta africana and Elephas maximus), on the other hand, were important components (by biomass) of 154 data points (10 studies). Muzzle width is a poor proxy for dietary selectivity in elephants since these animals use their trunk to forage and can both be selective and consume large quantities of biomass. We thus assigned elephants the same muzzle width as black rhinos (Diceros bicornis) because of their similar body mass and diet. The final dataset included community-weighted muzzle width estimates for all but 56 abundance responses (1.7%) and 13 diversity responses (1.7%), which were excluded from analysis. Finally, we assigned each megafauna into functional groups to evaluate whether functional group richness shaped megafauna impacts. Functional groups were based on combinations of body mass bins (10–45 kg, 45–100 kg, 100–1,000 kg, ≥1,000 kg), dietary guilds (browser, mixed feeder, grazer), and fermentation type (foregut, hindgut, and simple gut). 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Remote Sensing of the Environment 95:164–176. # Data for Functional traits - not nativeness - shape the effects of large mammalian herbivores on plant communities --- This dataset consists of meta-analytic data on plant diversity and abundance responses to mammalian megafauna (>45 kg). This data can be used (as in manuscript) to assess whether there are differences between introduced and native megafauna in their effects on plants and to assess how megafauna functional traits, environmental variables, and so on, influence megafauna effects on plants. ## Description of the Data and file structure This repository includes the following data files. Note that NA and blank cells are considered equivalent in all csv files. 'Plant_Abundance.csv' = Each row is an observed effect of megafauna on plant abundance. 'Plant_Diversity.csv' = Each row is an observed effect of megafauna on plant diversity. This dataset and 'Plant_Abundance.csv' have the same columns but are separated for convenience. 'Model_Comparison_Guide.csv' = This dataset has a row for every model comparison used in manuscript. These comparisons were multi-tiered, comparing a null model to a base model (containing a factor of interest) and then this base model to a model with that factor of interest + nativeness. The filepath to each of these model objects is provided in the 'dir' columns ('dir_null', 'dir_base', and 'dir_nativeness') and the formulas for each model are provided in the 'formula' columns. 'models.zip' = This archive contains each rma.mv model object as an .Rds file, which can be loaded with readRDS() in R after loading the 'metafor' package. 'Plant_Metadata.csv' = This csv file contains a description of each column name in the 'Plant_Abundance.csv' and 'Plant_Diversity.csv' datasets as well as the variable names in all model objects (in 'models.zip'), which themselves are trained on the data in 'Plant_Abundance.csv' and 'Plant_Diversity.csv' 'Model_Comparison_Metadata.csv' = This csv file contains a description of each column name in the 'Model_Comparison_Guide.csv' file. The repository includes the following R scripts: 0_Helper_Scripts.R contains functions used in the other scripts. 1_Run_Models.R contains code to run all models. Note that this script should be used cautiously as some models can take days to run on a personal computer. 2_Analyze_and_Plot.R contains code to create figures and interpret models. ## Sharing/access Information Please cite: Lundgren et al. Functional traits - not nativeness - shape the effects of large mammalian herbivores on plant communities. Science. Large mammalian herbivores (megafauna) have experienced extinctions and declines since prehistory. Introduced megafauna have partly counteracted these losses yet are thought to have unusually negative effects compared to native megafauna. Using a meta-analysis of 3,995 plot-scale plant abundance and diversity responses from 221 studies, we found no evidence that megafauna impacts were shaped by nativeness, ‘invasiveness’, ‘feralness’, coevolutionary history, or functional and phylogenetic novelty. Nor was there evidence that introduced megafauna facilitate introduced plants more than native megafauna. Instead, we found strong evidence that functional traits shaped megafauna impacts, with larger-bodied and bulk-feeding megafauna promoting plant diversity. Our work suggests that trait-based ecology provides better insight into interactions between megafauna and plants than concepts of nativeness. R version 4.2.1 as well as a variety of packages, including data.table, metafor, multcomp, ggplot2, broom, tidyr, and dplyr.

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      ZENODO; DRYAD
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    Authors: Ji, Aoshuang; Tomazzeli, Orlando; Palancar, Gustavo G.; Chaverot, Guillaume; +4 Authors

    This dataset includes the correlated-k table (both 4- and 12- terms, named as 'k4_O2SR.dat' and 'k12_O2SR.dat', respectively) used in our 1-D photochemical model for parameterizing O2 absorption cross sections in the O2 Schumann-Runge (SR) bands (175–205 nm). Transmission data are saved in files 'Transmission_.csv'. Photolysis frequencies and rates for gases calculated from the 1-D photochemical model can be accessed in files 'PHOTO_.csv'. Matlab is used to plot figures, and the code is in 'OLD_CORRK.m'. '.zip' files should be decompressed for using 1-D output data. Those data are comparisons of different O2 photolysis parameterizations (AF82 band model, Old exponential sum, WACCM, Correlated-k) at the SR bands with/without scattering in the 1-D photochemical model. Specifically, in '1PAL.zip' and '0001PAL.zip', '00' refers to WACCM without scattering, '01' refers to WACCM with scattering, '10' refers to correlated-k without scattering, '11' refers to correlated-k with scattering. 'OLD_1PAL.zip' refers to the 1-D model with old exponential sum fits. 'AF_1PAL.zip' refers to the 1-D model with AF82 band model. All the files can be open with MATLAB code 'OLD_CORRK.m' and be used to plotting all the figures in the paper 'A Correlated-k Parameterization for O2 Photolysis in the Schumann-Runge Bands'.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Bañuelos, María-José; Morán-Luis, María; Mirol, Patricia; Quevedo de Anta, Mario;

    # Tracking movements in an endangered capercaillie population using DNA-tagging This dataset contains the information on the individual birds identified, and the relative positions of their captures and recaptures. The coordinates have been shifted to a non-geographical reference, although maintaining the original dimensions, to comply with the Spanish guidelines for treatment of information on endangered species. ## Description of the data and file structure \- **all_locations.csv**: this is a comma-separated dataset that contains all locations of individuals (column ***indv***) included in the study. The coordinates (fields ***x_relative*** and ***y_relative***) resulted from shifting the original coordinates to a non-geographical reference, although maintaining the original dimensions, which would allow repeating the spatial treatment of the data. Also included are fields ***year*** (year of the Spring survey) and ***sex***. In the latter males are coded as ***M***, females as ***F***. \- **max_dist_movements.csv**: this is a comma-separated dataset that contains the locations to and from corresponding to maximum distance movements registered for each individual. Fields ***indv*** and ***sex*** are as above. Field ***unique_id*** identifies each movement. Field ***scope*** identifies whether the movement was recorded *within* a mating season, or *between* mating seasons. Fields ***year_from*** and ***year_to*** identify mating seasons. Fields ***x_relative_from***, ***y_relative_from***, ***x_relative_to***, ***y_relative_to*** identify the relative coordinates (see above) for those movements. \- **caper_moves_esurge.csv**: This is a comma-separated dataset, where each line corresponds to individual capture-recapture histories of Cantabrian capercaillie, as used in multi-event modeling software E-SURGE, and following its notation. Fields ***H:c1***, ***H:c2***, ***H:c3***, correspond to capture sessions of Spring 2009, 2010 and 2011, respectively. Coding in those fields corresponds to possible events of the CR multi-event model, indicated as follows: ***1 - Captured in t in the same site as at t-1*** ***2 - Captured in t in a different site as at t-1*** ***3 - Captured in t but not captured in t-1 (thus no movement information)*** ***0 - Not captured (thus no movement information)***. Field ***S:*** indicates the sample size of each capture session (=1, since each row corresponds to the encounter history of a single individual). Two classes of individual were considered (males and females), and are coded as group effect in field ***$COV:sex***. Knowing the location and movements of individuals at various temporal and spatial scales is an important facet of behavior and ecology. In threatened populations, movements that would ensure gene flow and population viability are often challenged by habitat fragmentation. Also in those endangered populations capturing and handling individuals to tag them, or to obtain tissue samples, can present additional challenges. DNA tagging, i.e. non-invasive individual identification of samples, can reveal movement patterns. We used fecal material genetically assigned to individuals to indirectly track movements of a large-bodied, endangered forest bird, Cantabrian capercaillie (Tetrao urogallus cantabricus). We wanted to know how the birds were using the fragmented forest landscape, and whether they showed fidelity to display areas. We used multi-event capture-recapture models to estimate fidelity to display areas among three consecutive mating seasons. We identified 127 individuals, and registered movements of 22 females and 48 males. Most observed movements were as expected relatively short, concentrated around display areas. We did not find differences in movement distances between females and males within mating seasons, or between them. Fidelity to display areas among seasons was 0.62 (± 0.12 SE) for females and 0.77 (± 0.07 SE) for males. The best CR model suggested no sex or season effects. Several longer movements, up to 9.9 km, linked distant display areas, demonstrating that Cantabrian capercaillies were able to move between different parts of the study area, complementing previous studies on gene flow. Those longer movements may be taking birds out of the study area, and into historical capercaillie territories, which still include substantial forest cover. The non-invasive DNA tagging approach provided a much larger sample size than would have been feasible with direct tracking. Lack of information on the social status of individuals and timing of movements are some disadvantages of DNA tagging.

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    ZENODO; DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite; ZENODO
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      ZENODO; DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite; ZENODO
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    Authors: Chiuffo, Mariana; Hierro, Jose L.;

    # Abiotic and biotic contexts shape the effect of disturbance on non-native plant invasion **Chiuffo&Hierro_Oikos2023_Dataset**. Dataset containing the data supporting the paper 'Abiotic and biotic contexts shape the effect of disturbance on non-native plant invasion' (Chiuffo and Hierro, Oikos 2023). This dataset was collected in disturbed and nearby non-disturbed communities growing in sandy and sandy loam soils in the Parque Luro Provincial Reserve (36º 57’ S, 64º 17’ W), La Pampa province, Argentina. **Dataset includes:** * Environment (that is, closed woodland, open woodland, or grassland). * Site (i.e. number of replicates per environmental context). * Distance (-5: distance from disturbance border, survey conducted on disturbed communities, 0-300m: distances from disturbance). * Community (i.e. disturbed or non-disturbed). * Plot (plot number, 1-5 per distance). * Non-native cover (percentage). * Total cover (i.e. native and non-native species cover percentage). * Non-native richness (i.e. number of non-native species). * Native richness (i.e. number of native species). * Photosynthetic active radiation (PAR, percentage of full light). * Soil type (i.e. sandy or sandy loam). * x: latitude (decimal degrees). * y: longitude (decimal degrees). Making predictions about when and where a given mechanism of invasion will be weak or strong is crucial for the effective management of non-native species. Despite the importance of disturbance on invasion, our understanding of how variation in abiotic and/or biotic conditions may modify the disturbance-invasion relationship is scarce. Here, we aimed to evaluate how abiotic (soil type) and biotic (tree and shrub cover) contexts affect the disturbance-invasion relationship in disturbed and nearby non-disturbed communities in the semi-arid open forest of central Argentina (ca. 36° S) using field sampling. We found that abiotic context modulated non-native species success in disturbed communities, whereas both abiotic and biotic context modulated success in nearby non-disturbed communities. These findings suggest that the plant invasion-disturbance relationship is context-dependent. Our results hint at the possibility that the significance of disturbance in predicting invasion might diminish as the importance of abiotic filters increases.

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    DRYAD; ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO; Datacite
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      DRYAD; ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO; Datacite
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    Authors: Cockle, Kristina; Gomez, Milka; Ferreyra, Carlos; Di Sallo, Facundo; +1 Authors

    To understand the evolution, life-history trade-offs, and population ecology of cavity nesters, it is critical to identify the avian lineages and circumstances in which birds excavate tree cavities. Woodcreepers (Furnariidae: Dendrocolaptinae; 56 species) are considered non-excavators dependent on existing cavities. We overturn this assumption by providing definitive evidence that the Lesser Woodcreeper (Xiphorhynchus fuscus, 23 g) is a facultative tree-cavity excavator. From 2007 to 2022 in the Atlantic forest of Misiones, Argentina, they nested in pre-existing tree crevices (4 nests), or excavated in trunks of large-diameter trees or stumps in advanced stages of decay (mean: 58 cm diameter; range: 22–121 cm; 22 nests). Nest entrances were vertically elongated and chambers were usually pocket-like, excavated in the exterior of the trees (sapwood), with floors that curved along the trees' circumference. Excavating woodcreepers pulled out elongated, fibrous pieces of decayed wood with a spongy texture, tapping only when inside cavities. Published and online photographs of nests of Xiphorhynchus species suggest that excavation may be widespread in the genus. Our observations that woodcreepers tore out elongated pieces of spongy wood (rather than hammering) are consistent with the idea that their long, thin bills are more resistant to torsion and less resistant to impact compared to the stouter bills of other excavators in Passeriformes and Piciformes. Research has tended to focus on birds with chisel-shaped bills, perforating harder sapwood to create nesting chambers in the center of heartrot-infected trees (resulting in typical woodpecker cavities, with circular floors). We hypothesize that Lesser Woodcreepers have adopted an alternative strategy, selecting large trunks with soft outer wood (sapwood), stopping their excavation radially if they reach harder wood, and then expanding the nest chamber laterally. Furnariidae may offer a useful model family for understanding ecological and evolutionary factors that influence cavity excavation. # Nest cavity excavation by Lesser Woodcreeper Xiphorhynchus fuscus (Aves: Furnariidae: Dendrocolaptinae) [https://doi.org/10.5061/dryad.p8cz8w9x5](https://doi.org/10.5061/dryad.p8cz8w9x5) The dataset contains four videos of Lesser Woodcreepers (Xiphorhynchus fuscus) excavating nest cavities in trees at Parque Provincial Cruce Caballero, San Pedro, Misiones, Argentina. All videos were made by Carlos Ferreyra and/or Milka Gomez. ## Description of the data and file structure The file names correspond to Trees 1, 2 and 3 identified in the corresponding journal article. ## Sharing/Access information N/A We studied Lesser Woodcreeper at Cruce Caballero Provincial Park (San Pedro department, Misiones, Argentina; 26°31'S 54°00'W; 550–600 m asl). Carlos Ferreyra and Milka Gomez found trees with woodcreeper excavation activity. They made videos of excavating woodcreepers using a camera in hand, camera mounted on tripod, or iPhone (original) coupled to a Celestron telescope. Videos were trimmed for length.

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    ZENODO; DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite; ZENODO
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      ZENODO; DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite; ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Fabrizio, Messina; Ashim, Pramanik; Alice, Sciortino;

    Carbon dots are carbon-based nanoparticles renowned for their intense light-emitting capabilities covering the whole visible light range. Here, we overcome these problems by solvothermally synthesizing carbon dots starting from Neutral Red, a common red-emitting dye, as a molecular precursor. The obtained nanoparticles are highly luminescent in the red region, with a quantum yield comparable to that of the starting dye. Most importantly, the nanoparticle carbogenic matrix protects the Neutral Red molecules from photobleaching under ultraviolet excitation while preventing aggregation-induced quenching, thus allowing solid-state emission inside PVA. Finally, the dye-based carbon dots demonstrate stable and efficient random lasing emission in the red region.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    This record includes the two main datasets necessary to run the GSI tutorial presented here: GUESS The 10-member ensemble background files generated using the WRF-ARW numerical model for a regional domain centered in the center and northern Argentina. For more information about the model configuration see https://doi.org/10.1016/j.atmosres.2022.106456 The 00 subfolder includes a 10-member ensemble and the ensemble mean to run the GSI system using the ENKF version. The 01 to 10 subfolders include the background at the analysis time and files every 10 minutes inside the assimilation window to run the GSI system using the fGAT method. OBS Meteorological observations in bufr format. cimap.20181122.t12z.01h.prepbufr.nqc is derived from a prepbufr file available at https://rda.ucar.edu/datasets/ds337.0 plus observations from private automatic meteorological weather networks in Argentina. abig16.20181122.t12z.bufr_d was generated using GOES-16 data available at: The other radiance observations comes from the Global Data Assimilation System (GDAS) Model: https://www.nco.ncep.noaa.gov/pmb/products/gfs/ 1bamua.20181122.t12z.bufr_d ssmisu.20181122.t12z.bufr_d 1bhrs4.20181122.t12z.bufr_d airsev.20181122.t12z.bufr_d mtiasi.20181122.t12z.bufr_d 1bmhs.20181122.t12z.bufr_d atms.20181122.t12z.bufr_d satwnd.20181122.t12z.bufr_d

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    Authors: Rovere, Alessio; Rubio Sandoval, Karla Zurisadai; Ryan, Deirdre D.; Richiano, Sebastian; +5 Authors

    This repository contains the supplementary information and raw data annexed to the manuscript "Quaternary and Pliocene sea-level changes at Camarones, central Patagonia, Argentina", authored by Karla Rubio-Sandoval et al. and submitted for consideration in the journal Quaternary Science Reviews. The folder contains the following items. 1. Raw_data.xlsxThis is an excel file that includes all survey and analytical data in several sheets, briefly described hereafter. - GNSS data. Data surveyed with differential GNSS in the field.- Sea level index points. Datapoints used as sea-level index points, and associated calculations of paleo Relative Sea Level.- AAR Summary. Table summarising the main results of the AAR analyses.- AAR complete sheet. The complete set of analytical data done for the Amino Acid Racemization dating.- Radiocarbon data. The analytical results of radiocarbon dating.- Literature ages. A compilation of the Electron Spin Resonance and U-series ages published for the Camarones site.- Transects. Topographical transects extracted from the TanDEM-X Digital Elevation model and referred to the GEOIDEAR 16 geoid.- Distance plot. Data for plotting Relative Sea Level vs distance along the coast of the sea-level index points described in the manuscript. 2. Holocene (folder)This folder contains two excel files ("Area_Camarones_Accepted.xlsx" and "Area_Camarones_Rejected.xlsx") that include the Holocene data described in the paper compiled following the standard HOLSEA template. 3. Runup_modellingThis folder contains three folders, each with a Jupyter notebook (.ipynb) and datasets to perform the runup calculations described in the manuscript.

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    ZENODO
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      ZENODO
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    Authors: Romano Muñoz, Cristo Omar; Garrido, Alberto C.; Barbeau Jr, David L.; Vera, Rocío B.; +18 Authors

    This README file was generated on 2023-12-19 by Cristo O. Romano. \########################################################################################################### GENERAL INFORMATION 1\. Title of Dataset: Data from ‘Redefining the Huayquerian Stage (Upper Miocene to Lower Pliocene) of the South American chronostratigraphic scale based on biostratigraphical analyses and geochronological dating’ 2\. Author Information A. Corresponding Author Contact Information Name: Cristo O. Romano Institution: Instituto Argnetino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA) / Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Address: Mendoza, Mendoza Province, Argentina Email: romano.cristo@gmail.com B. Co-authors Information Name: Alberto C. Garrido Institution: Museo Provincial de Ciencias Naturles Prof. Dr Juan Olsacher (MOZ) / Centro de Investigación en Geociencias de la Patagonia (CIGPat) Address: Zapala, Neuquén Province, Argentina Name: David L. Barbeau Jr Institution: School of the Earth, Ocean & Environment, University of South Carolina Address: Columbia, South Carolina, USA Name: Rocío B. Vera Institution: Instituo de Estudios Andinos 'Don Pablo Groeber' (IDEAN), Estudios Paleobiológicos en Ambientes Contienteales, Universidad de Buenos Aires (UBA) / Facultad de Ciencias Exactas y Naturales, Departamento de Ciencis Geológicas, Laboratoio de Paleontología de Vertebrados (UBA) / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Ricardo Bonini Institution: Instituto de Investigacioñnes Arqueológicas y Paleontológicas del Cuaternario Pampeano (INCUAPA), Faculta de Ciencias Sociales, Universidad Nacional del Centro de la Provincia de Buenos Aires / CONICET Address: Olavarría, Buenos Aires Province, Argentina Name: Alberto Boscaini Institution: Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA), Departamento de Ecología, Genética y Evolución, Faculta de Ciencias Exactas y Naturales, UBA / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Esperanza Cerdeño Institution: IANIGLA / CONICET Address: Mendoza, Mendoza Province, Argentina Name: Laura E. Cruz Institution: División Paleontología Vertebrados, Museo Argentino de Ciencias Naturales Bernardino rivadavia (MACN) / Laboratroio de Anatomía y Biología Evolutiva de los Vertebrados (LABEV-UNLu), Departamento de Ciencias Básicas, Universidad Nacional de Luján (UNLu) / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Graciela I. Esteban Institution: Instituto Superior de Correlación Geológica (INSUGEO), Facultad de Ciencias Naturales e Instituto Miguel Lillo, Universidad Nacional de Tucumán Address: San Miguel de Tucumán, Tucumán Province, Argentina Name: Marcelo S. de la Fuente Institution: Instituto de Evolución, Ecología Histórica y Ambiente (IDEVEA) / CONICET Address: San Rafael, Mendoza Province, Argentina Name: Marcos Fernández-Monescillo Institution: Cátedra y Museo de Paelontología, Faculta de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba / CONICET Address: Córdoba, Córdoba Province, Argentina Name: Juan C. Fernicola Institution: División Paleontología Vertebrados, MACN / LABEV-UNLu / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Verónica Krapovickas Institution: IDEAN / UBA / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: M. Carolina Madozzo-Jaén Institution: INSUGEO / CONICET Address: San Miguel de Tucumán, Tucumán Province, Argentina Name: M. Encarnación Pérez Institution: Museo Paleontológico Egidio Feruglio (MEF) / CONICET Address: Trelew, Chubut Province, Argentina Name: François Pujos Institution: IANIGLA / CONICET Address: Mendoza, Mendoza Province, Argentina Name: Luciano Rasia Institution: División Paleontología Vertebrados, Museo de La Plata, Universidad Nacional de La Plata / CONICET Address: La Plata, Buenos Aires Province, Argentina Name: Guillermo Fm. Turazzini Institution: Laboratorio de Morfología Evolutiva y Paleobiología de Vertebrados, Departamento de Biodiversidad y Biología Experimental, Faculta de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA) / CONICET Address: Ciudad Autónoma de Buenos Aires, Argentina Name: Bárbara Vera Institution: Centro de Investigación Esquel de Montaña y Estepa Patagónica (CIEMEP) / CONICET Address: Esquel, Chubut Province, Argentina Name: Ross D. E. MacPhee Institution: Department of Mammalogy, American Museum of Natural History Address: New York, NY, USA Name: Analía M. Forasiepi Institution: IANIGLA / CONICET Address: Mendoza, Mendoza Province, Argentina Name: Francisco J. Prevosti Institution: Museo de Ciencias Antropológicas y Naturales, Universidad Nacional de La Rioja (UNLaR) / CONICET Address: La Rioja, La Rioja Province, Argentina 3\. Date of data colection (time range): 2013-2019 4\. Geographic location of data colecction: Las Huayquerías del Este, San Carlos department, Mendoza Province, Argentina. 5\. Information about funding sources that supported the collection of the data: This research was supported by ANPCYT (Projects PICT 2015-966, 2019-2874), Argentina \########################################################################################################### SHARING/ACCESS INFORMATION 6\. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain 7\. Links to publications that cite or use the data: Romano, C. O., Garrido, A. C., Barbeau, D. L., Vera, R. B., Bonini, R., Boscaini, A., Cerdeño, E., Cruz L. E., Esteban, G. I., de la Fuente, M. S., Fernández-Monescillo, M., Fernicola, J. C., Krapovickas, V., Madozzo-Jaén, M. C., Pérez, M. E., Pujos, F., Rasia, L., Turazzini, G. F., Vera, B., MacPhee, R. D. E., Forasiepi, A. M. & Prevosti, F. J. (in press). Redefining the Huayquerian Stage (Upper Miocene – Lower Pliocene) of the South American chronostratigraphic scale based on biostratigraphical analyses and geochronological dating. Papers in Palaeontology, DOI: 10.1002/spp2.1539 8\. Links to other publicly accessible locations of the data: None 9\. Links/relationships to ancillary data sets: None 10\. Was data derived from another source? No A. If yes, list source(s): NA 11\. Recommended citation for this dataset: Romano, C. O., Garrido, A. C., Barbeau, D. L., Vera, R. B., Bonini, R., Boscaini, A., Cerdeño, E., Cruz L. E., Esteban, G. I., de la Fuente, M. S., Fernández-Monescillo, M., Fernicola, J. C., Krapovickas, V., Madozzo-Jaén, M. C., Pérez, M. E., Pujos, F., Rasia, L., Turazzini, G. F., Vera, B., MacPhee, R. D. E., Forasiepi, A. M. & Prevosti, F. J. (in press). Data from: 'Redefining the Huayquerian Stage (Upper Miocene – Lower Pliocene) of the South American chronostratigraphic scale based on biostratigraphical analyses and geochronological dating.' Dryad Digital Repository, https://doi.org/10.5061/dryad.ngf1vhj0t \########################################################################################################### DATA & FILE OVERVIEW 12\. File List (description of the data and file structure) A- Data 1. Excel file with raw U-Pb zircon geochronology data of tuffaceous samples (except T3), from Huayquerías del Este, Mendoza Province, Argentina. B- Data 2. Excel file with raw U-Pb zircon geochronology data of T3 tuff sample, from Huayquerías del Este, Mendoza Province, Argentina. Including information about the methodology (provided by the laboratory). C- Data 3. Excel file with detailed information on the fossil specimens collected in the Huayquerías del Este, Argentina. D- Data 4. Excel file with presence-absence matrices for similarity analysis. \[Access this dataset on Dryad] 13\. Relationship between files, if important: The presence and absence matrices in Data 4 are based on the specimens listed in Data 3. 14\. Additional related data collected that was not included in the current data package: None 15\. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA \########################################################################################################### DATA-SPECIFIC INFORMATION FOR: 'Data 1.csv' 1\. Samples: Around 3 kg of rock from each of the 10 tuffaceous levels (T): T1, T2, T4, T5, T6, T10, T11, TM1, and TM2. 2\. Geographic location of data colecction: Las Huayquerías del Este, San Carlos department, Mendoza Province, Argentina. 3\. Formation, fossil site and coordinates (latitude; longitude) of the collected samples: T0: Huayquerías Formation, Río Seco de la Horqueta, 33°50'00.4"S, 68°28'59.6"W T1: Bajada Grande Formation, Río Seco de la Isla Grande, 33°58'50.6"S, 68°26'31.6"W T4: Bajada Grande Formation, Río Seco de la Isla Grande, 33°58'43.4"S, 68°26'15.8"W T5: Bajada Grande Formation, Cerro Parvitas, 33°44'30.2"S, 68°40'55.8"W T6: Huayquerías Formation, Río Seco de la Última Aguada, 33°54'23.8"S, 68°27'18.4"W T10: Huayquerías Formation, Río Seco de Los Pajaritos, 33°55'28.5"S, 68°26'51.0"W T11: Tunuyán Formation, Río Seco de Los Pajaritos, 33°44'30.2"S, 68°40'55.8"W TM1: Huayquerías Formation, Río Seco del Carrizalito, 33°54'58.1"S, 68°32'55.0"W TM2: Huayquerías Formation, Río Seco del Carrizalito, 33°54'53.9”S, 68°33'02.1"W 4\. Number of variables: 23 5\. Tipe of case: spots on zircon crystals through laser ablation 6\. Number of cases/rows by sample (T): T0: 69 T1: 36 T4: 33 T5: 22 T6: 26 T10: 39 (big crystals) and 24 (small crystals) T11: 51 TM1: 38 TM2: 22 7\. Variable List: \* analysis: spot on zircon crystal identifier \* 207Pb/235U [ISOTOPIC RATIOS]: The relative abundance of 207Pb with respect to 235U measured in the zircon crystal. \* prop. 2s no sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 207Pb / 235U ratio in the zircon crystal expressed as a percent, including analytical uncertainties and propagated uncertainties but not including systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction \* prop. 2s w sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 207Pb / 235U ratio in the zircon crystal expressed as a percent, including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 206b/238Pb [ISOTOPIC RATIOS]: The relative abundance of 206Pb with respect to 238U measured in the zircon crystal \* prop. 2s no sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 206Pb / 238U ratio in the zircon crystal expressed as a percent, including analytical uncertainties and propagated uncertainties but not including systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction \* prop. 2s w sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 206Pb / 238U ratio in the zircon crystal expressed as a percent, including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 206/238 vs 207/235 error correlation [ISOTOPIC RATIOS]: A measurement of the covariance between the measured 206Pb/238U and 207Pb/235U ratios \* 238U/206Pb [ISOTOPIC RATIOS]: The relative abundance of 238U with respect to 206Pb measured in the zircon crystal \* prop. 2s no sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 238U / 206Pb ratio in the zircon crystal expressed as a percent, including analytical uncertainties and propagated uncertainties but not including systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction \* prop. 2s w sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 238U / 206Pb ratio in the zircon crystal expressed as a percent, including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 207Pb/206Pb [ISOTOPIC RATIOS]: The relative abundance of 207Pb with respect to 206Pb measured in the zircon crystal \* prop. 2s no sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 207Pb / 206Pb ratio in the zircon crystal expressed as a percent, including analytical uncertainties and propagated uncertainties but not including systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction \* prop. 2s w sys (%) [ISOTOPIC RATIOS]: The 2s uncertainty of the measured 207Pb / 235Pb ratio in the zircon crystal expressed as a percent, including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 238/206 vs 207/206 error correlation [ISOTOPIC RATIOS]: A measurement of the covariance between the measured 238U / 206Pb and 207Pb / 206Pb ratios \* [U] (ppm) [ELEMENTAL CONCENTRATIONS]: The concentration of uranium measured in the zircon crystal \* U/Th [ELEMENTAL CONCENTRATIONS]: The relative concentrations of uranium with respect to Th measured in the zircon crystal \* 207Pb/235U age (Ma) [APPARENT AGES]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 207Pb / 235U ratio measured from the zircon crystal \* prop. 2s w sys (Myr) [APPARENT AGES]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 207Pb / 235U ratio measured from the zircon crystal \* 206Pb/238U age (Ma) [APPARENT AGES]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 206Pb / 238U ratio measured from the zircon crystal. For crystals younger than ca. 1000 Ma, this apparent age is the most reliable \* prop. 2s w sys (Myr) [APPARENT AGES]: The 2s uncertainty of the age calculated from the measured 206Pb / 238U ratio in the zircon crystal expressed in millions of years (Myr), including analytical uncertainties, propagated uncertainties, and systematic uncertainties. Analytical uncertainties capture the natural variability within the zircon. Propagated uncertainties incorporate uncertainty related to in-run analytical age uncertainty of the primary reference material, and the correction of down-hole fractionation and instrument drift during data reduction. Systematic uncertainties include excess variance determined from long-term analysis of a monitor reference material, decay constant uncertainties, and primary reference material age uncertainties \* 207Pb/206Pb age (Ma) [APPARENT AGES]: The apparent age of the zircon crystal in millions of years ago (Ma) when calculated from the 207Pb / 206Pb ratio measured from the zircon crystal. For crystals older than ca. 1000 Ma, this apparent age is the most reliable \* prop. 2s w sys (Myr) [APPARENT AGES]: The 2s uncertainty of the age calculated from the measured 207Pb / 206Pb ratio in the zircon crystal expressed in millions of years (Myr), including analytical uncertainties, propagated uncertainties, and system