Resultados totales (Incluyendo duplicados): 46469
Encontrada(s) 4647 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359246
Dataset. 2023

INTERSTAGE SIMILARITIES IN HOST-PLANT PREFERENCE IN THE DIAMONDBACK MOTH (LEPIDOPTERA: PLUTELLIDAE) [DATASET]

  • Badenes-Pérez, Francisco Rubén
  • Heckel, David G.
Table S1: Test statistic and p-values of Kruskall–Wallis tests comparing differences in oviposition on four different oviposition substrates (cabbage, rape, pea, and aluminium foil) for the strains DBM-W, DBM-NOQA, DBM-C, and DBM-P of P. xylostella. The significance values have been adjusted by the Bonferroni correction for multiple tests. Significant p-values (p ≤ 0.05) are shown in bold type; Table S2: Comparison of the preference between abaxial and adaxial leaf surfaces among the three P. xylostella strains reared on cabbage (DBM-C), artificial diet (DBM-G88), and pea (DBM-P). Oviposition preference data were analyzed using a one-tailed, two-sample test of proportions (p ≤ 0.05) comparing the percentages of the total number of eggs laid on the abaxial side of leaves (n = 3–96, except in the case of C. papaya for DBM-C, where n = 2).; and Table S3: Total glucosinolate content (TOT) and content of aliphatic glucosinolates with sulfur-containing side chains (AS), other aliphatic glucosinolates (AO), benzenic glucosinolates (BEN), and indolic glucosinolates (IN) for each of the plant types tested (A). Glucosinolate richness (S), Shannon’s diversity index for the four glucosinolate classes (HA), Shannon’s diversity index for the relative concentrations of all individual glucosinolates (HB), and chemical complexity index for glucosinolates (CCI) for each of the plant types tested (B). Values based on means across replicates taken from Badenes-Pérez et al. 2020 [10]., Peer reviewed

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DOI: http://hdl.handle.net/10261/359246
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oai:digital.csic.es:10261/359246
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oai:digital.csic.es:10261/359246
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359249
Dataset. 2023

PLANT GLUCOSINOLATE CONTENT AND HOST-PLANT PREFERENCE AND SUITABILITY IN THE SMALL WHITE BUTTERFLY (LEPIDOPTERA: PIERIDAE) AND COMPARISON WITH ANOTHER SPECIALIST LEPIDOPTERAN [DATASET]

  • Badenes-Pérez, Francisco Rubén
Table S1: Pairwise comparisons in OPI between plant species after conducting Kruskal–Wallis tests; Table S2: Comparison between P. rapae and P. xylostella for the percentage of eggs laid on plant species tested compared to A. thaliana (n = 3). Significant differences are shown in bold type; Table S3: Significance of correlations between oviposition preference index in two-choice tests (OPI), total oviposition in no-choice tests (TO), and larval survival (LS) for P. rapae and P. xylostella in the plants tested; Table S4: Mean ± SE glucosinolate content (µmol g-1 plant dry weight) in the plants tested [37]; Table S5: Total glucosinolate content (TOT) and content of aliphatic glucosinolates with sulfur-containing side chains (AS), other aliphatic glucosinolates (AO), benzenic glucosinolates (BEN), and indolic glucosinolates (IN) for each of the plant types tested (A). Glucosinolate richness (S), Shannon’s diversity index for the four glucosinolate classes (HA), Shannon’s diversity index for the relative concentrations of all individual glucosinolates (HB), and chemical complexity index for glucosinolates (CCI) for each of the plant types tested (B) [37]; Table S6: Correlations between oviposition preference index (OPI), total oviposition (TO), and larval survival (LS) and glucosinolate richness (S), Shannon’s diversity index for the four glucosinolate classes (HA), Shannon’s diversity index for the relative concentrations of all individual glucosinolates (HB), glucosinolate complexity index (GCI), total glucosinolate content (TOT), aliphatic glucosinolates with sulfur-containing side chains (AS), other aliphatic glucosinolates (AO), benzenic (BEN), and indolic glucosinolates (IN) as shown by CATPCA analysis; Table S7: Pairwise comparisons in total oviposition in no-choice tests (TO) between plant species after conducting Kruskal–Wallis tests; Table S8: Comparison between P. rapae and P. xylostella for the percentage of eggs laid on the abaxial side of the leaves in the plant species tested (n = 3–96, except in the case of A. argenteum, C. bursa-pastoris, and T. majus for P. rapae and in the case of C. papaya and M. oleifera for P. xylostella, in which n = 2); Table S9: Pairwise comparisons in total oviposition in no-choice tests (TO) between plant species after conducting Kruskal–Wallis tests; Table S8: Pairwise comparisons in larval survival (LS) between plant species after conducting Kruskal–Wallis tests; Table S10: Origin of the seeds of the plant species tested; Figure S1: CATPCA plots showing the relationship between oviposition preference index (OPI) (A), total oviposition (TO) (B), and larval survival (LS) (C) and total glucosinolate content (TOT) and content of aliphatic glucosinolates with sulfur-containing side chains (AS), other aliphatic glucosinolates (AO), benzenic glucosinolates (BEN), and indolic glucosinolates (IN) in the plant species tested. Reference [37] is cited in the Supplementary Materials., Peer reviewed

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DOI: http://hdl.handle.net/10261/359249
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oai:digital.csic.es:10261/359249
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oai:digital.csic.es:10261/359249
PMID: http://hdl.handle.net/10261/359249
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359262
Dataset. 2023

PLANT COMMUNITY TRAITS AND PHYLOGENY PREDICT SOIL CARBON AND NUTRIENT CYCLING IN MEDITERRANEAN MIXED FORESTS [DATASET]

  • Prieto Rubio, Jorge
  • Perea, A.
  • Garrido Sánchez, José Luis
  • Alcántara, Julio M.
  • Azcón González de Aguilar, Concepción
  • López-García, A.
  • Rincón, Ana
Table S1. Soil samples collected per plant species, forest (Jaén JA; Segura SE), dominant mycorrhizal type (Myc) of plants (arbuscular mycorrhizal AM; ectomycorrhizal ECM) and season (Autumn; Spring). Enzymatic activities (pmol mg-1 h-1 SOM-1) related to carbon (C), nitrogen (N) and phosphorous (P) cycles in the soils collected under the different plant species are shown. The enzymes corresponding to each cycle are: Labile C = β-glucosidase + cellobiohydrolase; Non-labile C = β-xylosidase + β-glucuronidase + laccase; N = chitinase + leucine-aminopeptidase; P = acid + alkaline phosphatases. Table S2. Linear Mixed-Effect Models testing the effect of season, forest and plant species on soil properties and C and nutrient cycling. Season and forest were fixed factors, while the site nested in forest and the plant species nested in site and forest were random factors. Significant interactions among factors were not detected. Labile C = β-glucosidase + cellobiohydrolase; Non-labile C = β-glucuronidase + β-xylosidase + laccase; N cycling = chitinase + leucine-aminopeptidase; P cycling = acid + alkaline phosphatase. Soil properties: pH, SOM = soil organic matter, soil moisture. χ12 values for fixed and random factors and Bonferroni corrected p-values ‘***’ p < 0.001, ‘**’ p < 0.01, ‘*’ p < 0.05, ‘.’ p < 0.10, are given; significant effects are noted in bold. The coefficient of determination (pseudo-R2, i.e., variance explained) is shown for both pools of fixed and random factors. Table S3. Plant phylogenetic composition and morpho-functional traits predictors of carbon and nutrient cycling in soils, analysed by Redundancy Analysis. A Redundancy Analysis was carried out per forest, a) Jaén and b) Segura, to determine the non-redundant phylogenetic PCoA axes and morpho-functional traits that explained responses of carbon and nutrient cycling. ANOVA results (F statistic and p-value) were extracted from each Redundancy Analysis and those significant (p < 0.05) compiled in this table (see also Figure 4). Fig. S1. Relationship between N cycling-related enzymatic activities with C cycling-related ones. Pearson correlations were carried out to relate N-related enzymes (chitinase + leucine amino-peptidase) with hydrolytic (β-glucosidase + cellobiohydrolase + β-glucuronidase + β-xylosidase) and oxidative (laccase) C-related enzymes. Each plot shows the correlation coefficient (r) with its p-value. Fig. S2. Local Moran’s Index (LMI) calculated for carbon and nutrient cycling and cycling ratios and mapped onto the plant phylogeny per forest, a) Jaén and b) Segura. Positive/negative LMI values indicate that closely relatives tend to show similar/dissimilar values for a given variable. Values marked in red indicate significant and marginal autocorrelations, i.e., p < 0.05 and 0.05 > p < 0.10, respectively. Hotspots of positive (i.e., phylogenetic conservatism) and negative (i.e., phylogenetic divergence) autocorrelation are differentiated with triangles and all are represented by LMI with p < 0.05. See correspondence between acronyms and plant names in Table S1. Labile C = β-glucosidase and cellobiohydrolase; Non-labile C = β-xylosidase, β-glucuronidase and laccase; N = chitinase and leucine-aminopeptidase; P = acid and alkaline phosphatases. Figure S3. Phylogenetic composition of plant communities. Principal Coordinates Analysis (PCoA) was run over the phylogenetic distance matrix of plant species studied per forest, Jaén and Segura. Eigenvector values (PCoA-n) that significantly explained responses of carbon and nutrient cycling were used in RDA analyses. Scores of PCoA axis were plotted by plant species at each forest, a) Jaén and b) Segura, and may locate taxa across the phylogeny on the PCoA axes. The total percentage of variance explained by significant PCoA axes was 3.4 % for Jaén and 0.2 % for Segura., Peer reviewed

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DOI: http://hdl.handle.net/10261/359262
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oai:digital.csic.es:10261/359262
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359272
Dataset. 2023

SUPPORTING INFORMATION STRUCTURE AND ECOLOGICAL FUNCTION OF THE INTERACTIONS BETWEEN PLANTS AND ARBUSCULAR MYCORRHIZAL FUNGI THROUGH MULTILAYER NETWORKS

  • Garrido Sánchez, José Luis
  • Alcántara, Julio M.
  • López-García, A.
  • Ozuna, Carmen
  • Perea, Antonio J.
  • Prieto Rubio, Jorge
  • Rincón, Ana
  • Azcón González de Aguilar, Concepción
Table S1. Location, altitude and dominant vegetation at each study site. Table S2. Samples distribution by species, season, region and site. Figure S3. Sequencing rarefaction curves and sampling completeness. Methods S4. Root processing, DNA extraction, sequencing and bioinformatic analyses. Table S5. A recruitment network represented by its recruitment matrix. Table S6. Description of main network metrics and indices used throughout the work. Methods S7. General metrics describing the plant-AMF networks studied. Methods S8. Null models for modularity. Table S9. Maximum likelihood tree of the sequences defining the AMF virtual taxa sampled in this study. Table S10. PERMANOVA exploring the effect of region, season, site and plant species on AMF community composition. Table S11. Bayesian glmm testing the influence of the degree of canopy plant species on the dissimilarity in AMF communities between canopy and recruit species., Peer reviewed

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DOI: http://hdl.handle.net/10261/359272
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359332
Dataset. 2024

SUPPORTING INFORMATION: FUNDAMENTALS OF POLARITONS IN STRONGLY ANISOTROPIC THIN CRYSTAL LAYERS

  • Voronin, Kirill V.
  • Álvarez-Pérez, Gonzalo
  • Lanza, Christian
  • Alonso-González, Pablo
  • Nikitin, Alexey Y.
Derivation of the dispersion relation in high-q approximation considering the TM modes only; description of the method of the calculation of the isofrequency curves by minimizing the losses; derivation of the expressions for the directions of the group velocity and the imaginary part of the wavevector; comparison of the IFC obtained with the different approaches; analytical expression for the field distribution; conditions for the surface or volume type of the mode; and derivation of the dispersion relation of the TE modes in the ultrathin-slab limit., Peer reviewed

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DOI: http://hdl.handle.net/10261/359332
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oai:digital.csic.es:10261/359332
PMID: http://hdl.handle.net/10261/359332
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359335
Dataset. 2024

WETYDAS: WEATHER TYPES DATASET IN SPANISH MAINLAND, V.2.0.0. [DATASET]

TIPOS DE TIEMPO EN ESPAÑA (1836-2015)

  • Cortesi, Nicola
  • Peña Angulo, Dhais
[EN] WETYDAS v.2.0.0. (Weather Types Data set of Spain) contains 143 TXT archives localized by their coordinates in 20-century reanalyses grid (from 36º-44 lat North and 350º-5º longitude) covering 1836 to 2015; information include Year, Month, Day, Direction (in deg), WT.dir (8 classes, only dependent on direction), Hyb (separate between directional type, Pure A or C types, or Hybrid type), WT (final classification with 26 classes), WT.num (same as WT column but the names of the 26 WT classes are converted to numbers from 1 to 26; classes are ordered from NE = 1), WT10 (idem as WT but reclassified to 10 classes only: 8 directional types + C and A), WT10.num (idem as WT10 but converted in numbers from 1 to 10), SF (geostrophic southerly flow index), WF (geostrophic westerly flow index), F (total flow index: square root of SF^2+WF2^2), ZS (southerly shear vorticity), ZW (easterly shear vorticity) and Z (shear vorticity: ZS + ZW). For details of the computation see Trigo and DaCamara (2000) Int. Jr. Clim. https://doi.org/10.1002/1097-0088(20001115)20:13<1559:AID-JOC555>3.0.CO;2-5; [ES] WETYDAS v.2.0.0. (Weather Types Data set of Spain), contiene 143 archivos TXT con la clasificación de tipos de tiempo en los puntos de la malla del reanálisis 20-century (36º-44 lat North y 350º-5º longitude) period 1836- 2015; la información se incluye en columnas con el año, mes y día, la dirección (en grados), WT.dir (8 clasificación en ocho clases solamente acorde la direción del flujo), Hyb (identificación entre tipos direccionales, A Puro, C Puro o tipo Híbrido), WT (clasificación final en 26 tipos), WT.num (igual que WT pero numeradas las clases con NE = 1), WT10 (idem a WT con clases reclasificadas a 10 tipos, 8 direcionlaes + C y A), WT10.num (idem a WT10 convirtiendo a número de 1 a 10), SF (índice del flujo geostrófico sur), WF (índice del flujo geostrófico oeste), F (iínidce flujo total: raíz cuadrada de SF^2+WF2^2), ZS (shear vorticity meridional), ZW (shear vorticity del este) y Z (shear vorticity: ZS + ZW). Detalles de cálculo según Trigo and DaCamara (2000) Int. Jr. Clim. https://doi.org/10.1002/1097-0088(20001115)20:13<1559:AID-JOC555>3.0.CO;2-5. Validación del método aplicado a la malla del reanálisis en Fernández-Granja, J.A., Brands, S., Bedia, J. et al. Exploring the limits of the Jenkinson–Collison weather types classification scheme: a global assessment based on various reanalyses. Clim Dyn 61, 1829–1845 (2023). https://doi.org/10.1007/s00382-022-06658-7, [ES] Hemos calculado la nueva base de datos de alta resolución espacial empleando la clasificación de Jenkinson & Collison clasificación de tipos de tiempo a la Península Ibérica en la malla de presiones diaria del reanálisis del siglo (Enero-1836 a Diciembre 2015). Dada su resolución, 1ºx1º lat/long, WETYDAS v.2.0.0. contiene 143 archivos acorde las coordenadas de la ventana analizada (36º-44 lat Norte/ 350º-5º longitud). Los tipos de tiempo incluyen 8 direccionales puros, el tipo Anticiclónico y Ciclónico puros más 8 Ciclónicos híbridos y 8 Anticiclónicos Híbridos (total 26 tipos). Se recalculan reclasificaciones para evitar los tipos híbridos. Los casos no determinados fueron diseminados entre la clasificación., [EN] It has been applied the Jenkinson & Collison classification of Weather Types to Iberian Peninsula and Balearic Island by using the daily NOAA/CIRES/DOE 20th Century Reanalysis (V3) dataset (January-1836/December-2015). WETYDAS v.2.0.0. grid resolution/1.0ºx1.0 lat/long produces 143 series. Weather Types classification includes 8 directional pure, Anticyclonic and Cyclonic pure types, and combination of previous ones in the hybrid types. Non determines cases were spread homogeneously., No

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DOI: http://hdl.handle.net/10261/359335
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359343
Dataset. 2022

PLANT AFFINITY TO EXTREME SOILS AND FOLIAR SULPHUR MEDIATE SPECIES-SPECIFIC RESPONSES TO SHEEP GRAZING IN GYPSUM SYSTEMS [DATASET V2]

  • Cera, Andreu
  • Montserrat-Martí, Gabriel
  • Luzuriaga, Arantzazu L.
  • Pueyo, Yolanda
  • Palacio, Sara
21679463.zip contains: Cera et al. PED.R; Supplementary files_25112022.docx; data_Cera et al. Plant Ecology and Diversity.xlsx, Plants growing on extreme soils have mainly been described in relation to their adaptations to edaphic conditions, although herbivores may also be an important factor in these ecosystems. Gypsum soils occur in drylands often where livestock practices occur. However, it is unknown whether plant traits related to gypsum soil constraints are associated with resistance to herbivory. In order to assess whether gypsum specialist species might be favoured at higher grazing levels and to detect the traits involved, we evaluated the responses of gypsum specialists vs. generalists to three intensities of livestock pressure. We analysed the relative cover shifts of species along a livestock gradient, and variation in canopy height, canopy area, leaf carbon (C), nitrogen (N), and sulphur (S), specific leaf area (SLA) and leaf dry matter content (LDMC). We found that gypsum-specialists responded by increasing or maintaining their cover at medium and high grazing pressure, whereas most generalists responded by decreasing it. Gypsum-specialists showed higher leaf S than generalists, regardless of grazing intensity. All species showed similar patterns for traits linked to loss of above-ground biomass when grazing increased. Plant affinity to gypsum soils mediates vulnerability to grazing with foliar S possibly being a defence trait., This work was supported by Gobierno de España [MICINN, CGL2015-71360-P, CGL2016-80783-R and PID2019-111159GB-C31]; by European Union’s Horizon 2020 [H2020-MSCA-RISE-777803]; and by Consejo Superior de Investigaciones Científicas [COOPB20231]. AC and SP were funded by a FPI fellowship [MICINN, BES-2016-076455] and a Ramón y Cajal Fellowship [MICINN, RYC-2013-14164], respectively., Peer reviewed

DOI: http://hdl.handle.net/10261/359343
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oai:digital.csic.es:10261/359343
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359368
Dataset. 2024

MOPREDAS_ANNUAL_BOOKS, V.1.0.0. [DATASET]

MOPREDAS LIBROS ANUALES (1916-1950)

  • González Hidalgo, José Carlos
  • Peña-Angulo, Dhais
  • Beguería, Santiago
  • Trullenque Blanco, Víctor
  • Cortesi, Nicola
[ES] MOPREDAS_Annual_Books_1916-1950 se compone de los datos digitalizados de los Libros Resúmenes Anuales editados por los sucesivos Servicios Meteorológicos de España en el periodo 1916-1950; [EN] MOPREDAS_Annual_Books_1916-1950 is a compilation of monthly precipitation data digitalized from Annual Books 1916-1950., [ES] Mopredas_Annual_Books_1916-1950 es el resultado de un proceso de digitalización de los datos publicados en los Libros Resúmenes Anuales editados por los servicios meteorológicos de España, periodo 1916-1950. Combinados con los datos incluidos en el BNDC han sido empleados para el desarrollo de las mallas de alta resolución denominadas MOPREDAS_1916-2015 y subsiguientes versiones, [EN from original) ] Mopredas_Annual_Books_1916-1950 is the result of a long digitalization procedure of Annual Books edited by Spanish Meteorological Services; the compilation includes hundreds of data not at present included in the Banco Nacional de Datos del Clima and increase the spatial and temporal density of precipitation records in pre-1950 period. Combined with BNDC after identification of digitalized data with BNDC observatories, it has been uses to develop the new high spatial density grid MOPREDAS_1916-2015 and versions, CGL2014-52135-C3-3-R Desarrollo de índices de sequía sectoriales: mejora de la monitorización y alerta temprana de las sequías en España DESEMON. Gobierno de España y UE, CGL2011-27574-C02-01 Impactos Hidrológicos del Calentamiento Global en España HIDROCAES Gobierno de España y UE, CGL2008-05112-C02-01/CLI. Proyecto Cambio climático: base de datos de precipitaciones, análisis de tendencias e impactos en los sistemas naturales Gobierno de España y UE, Three .csv files, No, Manual

DOI: http://hdl.handle.net/10261/359368
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359368
HANDLE: http://hdl.handle.net/10261/359368
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359368
PMID: http://hdl.handle.net/10261/359368
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Ver en: http://hdl.handle.net/10261/359368
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359377
Dataset. 2022

SUPPLEMENTAL INFORMATION FOR: POPULATION DIFFERENCES IN THE LENGTH AND EARLY-LIFE DYNAMICS OF TELOMERES AMONG EUROPEAN PIED FLYCATCHERS

  • Kärkkäinen, Tiia
  • Laaksonen, Toni
  • Burgess, Malcolm
  • Cantarero, Alejandro
  • Martínez-Padilla, Jesús
  • Potti, Jaime
  • Moreno, Juan
  • Thomson, Robert L.
  • Tilgar, Vallo
  • Stier, Antoine
Table S1. Results from DNA concentration and purity quantification using ND-1000-Spectrophotometer (mean ± sd). Large standard deviations for average concentration values are due to variation in tissue quantity among samples. All samples were however diluted to the concentration of 2.5 ng/µl before telomere length estimation. Three linear models (Concentration/[260/289]/[260/230] as dependent variable with Kenward-Roger approximation for degrees of freedom) were ran to test the differences among populations. Differences in DNA concentration were not statistically significant (F5, 531=1.02, p=0.40) while the differences in both 260/280 (F5, 531=2.45, p=0.03) and 260/230 (F5, 531=8.70, p<.0001) ratios reached statistical significance. Including the ratio-values as covariates in the statistical analyses presented in the main text with telomere length as dependent variable did not change the results or conclusion, thus these covariates were removed from the final models to reduce model parameters., Table S2. Population specific (Mean ± sd) efficiencies and Cq-values for control gene (SCG) and telomere (TELO) assays. Three linear models (SCG Cq/SCG Efficiency/TELO Efficiency as dependent variable with Kenward-Roger approximation for degrees of freedom) were ran to test the differences among populations. Differences in SCG Cq-values were not statistically significant (F5, 528=1.48, p=0.20) while the differences in both SCG (F5, 528=8.11, p<.0001) and TELO (F5, 528=12.66, p<.0001) efficiencies reached statistical significance. Including both assay efficiencies as covariates in the statistical analyses presented in the main text with telomere length as dependent variable did not change the results or conclusion, thus these covariates were removed from the final models to reduce model parameters., Table S3. Results of linear mixed models explaining the effects of Age class and Population on telomere length using subsets of the whole data including samples analyzed only with a) QuantStudio or b) MicPCR, Figure S1. Locations of the study sites; breeding area of the pied flycatcher in Eurasia shown in orange. Birds from all populations are expected to migrate through Iberian Peninsula and west coast of Africa to their Sub-Saharan non-breeding grounds described in Ouwehand et al. 2016 (black circle; Finnish and Estonian birds blue circle; English and Spanish birds red circle). Map modified from: BirdLife International. 2018. Ficedula hypoleuca. The IUCN Red List of Threatened Species 2018: e.T22709308A131952521. https://dx.doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22709308A131952521.en. Downloaded on 10 August 2021., Figure S2. Illustrating the telomere lengths (T/S ratios) of the same sample measured both with QuantStudio and MicPCR. Telomere length estimates are consistently somewhat higher for MicPCR (15 out of 20 samples) accounting for somewhat low agreement repeatability of 0.851 (95% Cl [0.66, 0.94], P<0.001) between the two machines., Figure S3. Individual raw telomere length values (T/S ratio) per population and age class. See sample sizes for Population: Nestling/Fledgling/Adult in the caption for Figure 1., Figure S4. Relative telomere length in six pied flycatcher populations across a north-south gradient in Europe, from the early nestling period (Nestling; 5 days after hatching), to fledging (Fledgling; 12 days after hatching) and adulthood (Adult; end of the rearing period) using subsets of data including rTL values obtained only with a) QuantStudio, or b) MicPCR. Values are estimated marginal means based on z-scored telomere length values ± s.e.m. Sample sizes [for Population: Nestling/Fledgling/Adult] are a) Oulu: 16/15/29; Turku: 15/13/32; Kilingi-Nõmme: 18/17/31; East Dartmoor: 17/17/31; La Hiruela: 23/12/33; Valsaín: 19/17/39, and b) Oulu: 3/4/12; Turku: 6/6/9; Kilingi-Nõmme: 4/3/12; East Dartmoor: 6/5/14; La Hiruela: 12/7/19; Valsaín: 5/6/10., Figure S5. Associations between migration distance (km) and relative telomere length (mean based on z-scored values) in the pied flycatcher fledglings (12 days after hatching; circles) and adults (averaged breeding pair; squares). Standard errors of the means (± sem) have been added to illustrate the population variation in telomere length. Fledgling values (circles) have been moved slightly to the right to clarify the error bars. Populations from the shortest migration distance to the longest: Spain (average of Valsaín and La Hiruela, red), England (East Dartmoor, yellow), Estonia (Kilingi-Nõmme, green), southern Finland (Turku, blue), and northern Finland (Oulu, purple)., Figure S6. Pied flycatcher chick body mass adjusted for clutch size at day 5 (A), day 12 (B) and growth rate (Δ mass between days 12 and 5; C) in six populations across a north-south gradient in Europe. Statistically significant differences after Tukey-Kramer adjustment for multiple comparisons are indicated with different letters. Values are estimated marginal means ± s.e.m. Sample sizes [for Population: Day5/Day12/Growth] are: Oulu, Finland: 19/19/17; Turku, Finland: 21/19/18; Kilingi-Nõmme, Estonia: 22/20/19; East Dartmoor, England: 20/22/18; La Hiruela, Spain: 33/19/18; Valsaín, Spain: 24/23/21., Figure S7. Change in relative telomere length during nestling period in the pied flycatcher (Δ telomere length between days 12 and 5) in six populations across a north-south gradient in Europe. The effect of population was marginally significant (p = 0.06) in explaining variation in early-life telomere change (see results for details). Values are estimated marginal means based on z-scored telomere length values ± s.e.m. Sample sizes [for Population] are: Oulu, Finland: 17; Turku, Finland: 18; Kilingi-Nõmme, Estonia: 19; East Dartmoor, England: 21; La Hiruela, Spain: 18; Valsaín, Spain: 21., Table of Contents: Table S1. Results from DNA concentration and purity quantification (p. 2).-- Table S2. Population specific efficiencies and Cq-values for control gene and telomere assays (p. 3).-- Table S3. Results of linear mixed models explaining the effects of Age class and Population on telomere length using subsets of the data (p. 4).-- Figure S1. Locations of the study sites (p. 5).-- Figure S2. Illustrating the telomere lengths of the same sample measured both with two qPCR machines (p. 6).-- Figure S3. Individual raw telomere length values (p. 6).-- Figure S4. Telomere length values in different populations using subsets of the data (p. 7).-- Figure S5. Associations between migration distance and telomere length (p. 8).-- Figure S6. Pied flycatcher chick body mass adjusted for clutch size (p. 9).-- Figure S7. Change in telomere length during nestling period (p. 10)., Peer reviewed

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DOI: http://hdl.handle.net/10261/359377
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Digital.CSIC. Repositorio Institucional del CSIC
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Dataset. 2024

SUPPLEMENTAL MATERIAL: TIGHT-BINDING MODEL WITH SUBLATTICE-ASYMMETRIC SPIN-ORBIT COUPLING FOR SQUARE-NET NODAL LINE DIRAC SEMIMETALS

  • Orozco-Galvan, Gustavo S.
  • García-Fuente, Amador
  • Barraza-López, Salvador
Supplemental Material contains a description of the on-site SOC, a MATLAB program to reproduce the bands of the 16-orbital model and to obtain the parameter $\eta$ from {L\"{o}wdin's} partition technique, and a TB electronic dispersion of slabs using the 16-orbital model., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/359374
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359374
HANDLE: http://hdl.handle.net/10261/359374
Digital.CSIC. Repositorio Institucional del CSIC
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PMID: http://hdl.handle.net/10261/359374
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/359374
Ver en: http://hdl.handle.net/10261/359374
Digital.CSIC. Repositorio Institucional del CSIC
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