Resultados totales (Incluyendo duplicados): 418
Encontrada(s) 42 página(s)
Memoria Digital Vasca = Euskal Memoria Digitala
oai:www.memoriadigitalvasca.eus:10357/69928
Sound. 2024

EL PODCAST DE SANCHO EL SABIO. 1. KAPITULUA: SÍMBOLOS VASCOS

En este primer episodio, Santiago de Pablo y Virginia López de Maturana hablan sobre símbolos vascos. Descubriremos cuáles son los más importantes, su historia y la importancia que tienen para nuestra cultura. Además, conoceremos el origen del lauburu con Isabel Mellén y los fondos que atesora la Fundación Sancho el Sabio sobre el rock radical vasco con Ander Gondra. Gracias a Jabi Soto nos adentraremos en el fascinante mundo de los orígenes de la fotografía. ¡Descubre estos temas y mucho más en este primer episodio de El Podcast de Sancho el Sabio!

Proyecto: //
DOI: http://hdl.handle.net/10357/69928
Memoria Digital Vasca = Euskal Memoria Digitala
oai:www.memoriadigitalvasca.eus:10357/69928
HANDLE: http://hdl.handle.net/10357/69928
Memoria Digital Vasca = Euskal Memoria Digitala
oai:www.memoriadigitalvasca.eus:10357/69928
PMID: http://hdl.handle.net/10357/69928
Memoria Digital Vasca = Euskal Memoria Digitala
oai:www.memoriadigitalvasca.eus:10357/69928
Ver en: http://hdl.handle.net/10357/69928
Memoria Digital Vasca = Euskal Memoria Digitala
oai:www.memoriadigitalvasca.eus:10357/69928

RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
oai:ruja.ujaen.es:10953/1040
Dataset. 2020

ENCUESTAS REALIZADAS AL GRUPO DE CONTROL 1

  • Navarrete, Jose

Proyecto: //
DOI: http://hdl.handle.net/10953/1040
RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
oai:ruja.ujaen.es:10953/1040
HANDLE: http://hdl.handle.net/10953/1040
RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
oai:ruja.ujaen.es:10953/1040
PMID: http://hdl.handle.net/10953/1040
RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
oai:ruja.ujaen.es:10953/1040
Ver en: http://hdl.handle.net/10953/1040
RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
oai:ruja.ujaen.es:10953/1040

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/277997
Dataset. 2022

FILOGENIA DE LA FLORA PIRENAICA A NIVEL DE GÉNERO

  • Roquet, Cristina
  • García González, María Begoña
[Description of methods used for collection/generation of data] We built a genus-level phylogeny using the workflow proposed by Roquet et al. (2013). We downloaded from Genbank three conserved chloroplastic regions (rbcL, matK and ndhF) plus the ITS region for a subset of families, which we aligned separately by taxonomic clustering. [Methods for processing the data] We aligned all coding sequence clusters with MACSE (Ranwez et al. 2011) and non-coding ones with MAFFT (Katoh and Standlye 2013), and trimmed all alignments with TrimAl (Capella-Gutiérrez et al. 2009). We concatenated all alignments to obtain a supermatrix. We then conducted maximum-likelihood (ML) phylogenetic inference analyses with RAxML (Stamatakis 2014), applying the most appropriate partitioning scheme and substitution model obtained with PartitionFinder (Lanfear et al. 2012) and a supertree constraint at the family-level obtained with the online software Phylomatic v.3 (tree R20120829). Specifically, we performed 100 independent tree searches and selected the best ML tree (the one with the highest probability). [Instrument- or software-specific information needed to interpret/reproduce the data] Any software for manipulation or analysis of phylogenies such as R packages ape or picante. [Standards and calibration information, if appropriate] The best ML tree was dated applying the penalized likelihood method in treePL (Smith and O'Meara,2012) and the following node calibrations: we fixed the node corresponding to the ancestor of eudicots at 125 Ma based on the earliest eudicot fossil (Hughes and McDougall 1990), and applied minimum age constraints to 15 nodes based on fossil information extracted from Smith and Beaulieu (2010) and Bell et al. (2010). 5. Environmental/experimental conditions:, Agencia Estatal de Investigación, Project VULVIMON (Reference: CGL2017-90040-R)., Peer reviewed

DOI: http://hdl.handle.net/10261/277997, https://doi.org/10.20350/digitalCSIC/14719
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/277997
HANDLE: http://hdl.handle.net/10261/277997, https://doi.org/10.20350/digitalCSIC/14719
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/277997
PMID: http://hdl.handle.net/10261/277997, https://doi.org/10.20350/digitalCSIC/14719
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/277997
Ver en: http://hdl.handle.net/10261/277997, https://doi.org/10.20350/digitalCSIC/14719
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/277997

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282691
Dataset. 2020

DATA FROM: NATURAL HAZARDS AND WILDLIFE HEALTH: THE EFFECTS OF A VOLCANIC ERUPTION ON THE ANDEAN CONDOR

  • Plaza, Pablo
  • Wiemeyer, Guillermo
  • Blanco, Guillermo
  • Alarcón, Pablo
  • Hornero-Méndez, Dámaso
  • Donázar, José A.
  • Sánchez-Zapata, José A.
  • Hiraldo, Fernando
  • De La Rosa, Jesús D.
  • Lambertucci, Sergio A.
Volcanic eruptions produce health changes in animals that may be associated with emitted gases and deposited ashes. We evaluated whether the Puyehue–Cordón Caulle volcanic eruption in 2011 produced health changes in the threatened Andean condor (Vultur gryphus) living in the area most affected by the eruption, north-western Patagonia. We studied clinical and biochemical parameters of condors examined before and after the eruption. We also examined concentrations of different metals and metalloids in the blood of individuals sampled after the eruption. The most common clinical abnormality associated with the eruptive process was irritating pharyngitis. In condors sampled after the eruption, blood concentrations of albumin, calcium, carotenoids and total proteins decreased to levels under the reference values reported for this species. We found different chemical elements in the blood of these condors after the eruption, such as arsenic and cadmium, with the potential to produce health impacts. Thus, the health of Andean condors was affected in different ways by the eruption; remaining in the affected area appears to have been costly. However, in comparison to other animal species, the health impacts were not as strong and were mainly related to food shortages due to the decrease in availability of livestock carcasses linked to the eruption. This suggests that condors dealt relatively well with this massive event. Future research is needed to evaluate if the health changes we found reduce the survival of this species, and if the cost of inhabiting volcanic areas has any ecological or evolutionary influence on the condor’s life history., Peer reviewed

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/282736
Dataset. 2020

DENITRIFICATION RATES IN MOUNTAIN LAKE SEDIMENTS

  • Palacín, Carlos
  • Catalán, Jordi
[Methods] Dr Carlos Palacin-Lizarbe made the datasets. For further details of the methods see the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). Further details on the denitrification rate measurement method are provided in the publication: Palacin-Lizarbe, C., Camarero, L. & Catalan, J. Estimating sediment denitrification rates using cores and N2O microsensors. J Vis Exp, e58553, doi:doi:10.3791/58553 (2018). Further details on the sediment molecular descriptors (DNA, 16S, nirS, nirK, nosZ1, and nosZ2) are provided in the publication: Palacin-Lizarbe, C. et al. The DNRA-denitrification dichotomy differentiates nitrogen transformation pathways in mountain lake benthic habitats. Front Microbiol 10, 1229, doi:10.3389/fmicb.2019.01229 (2019)., [Usage Notes] Dryad repository - https://doi.org/10.5061/dryad.j6q573n95 This document describes four datasets (tab-separated text format tables) related to measurements of denitrification rates in mountain lake sediments of the Pyrenees. Dr Carlos Palacin-Lizarbe made the datasets, anyone that uses these data should reference this DRYAD repository and the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). See this publication for further details on the methods used. Dataset 1: Sediment_denitrification_rates_DRYAD.txt Dataset containing all measured denitrification rates and incubation conditions. The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, denitrification_rate, nitrate_treatment, temperature_insitu, temperature_incubation, nitrate_actual_plus_added, glucose_added, habitat, site. Dataset 2: table_background_DRYAD_def.txt Dataset containing all measured denitrification rates and the background descriptors (landscape, water, sediment). This dataset contain missing values. The dataset contains the following variables: id_core, yymmdd_lake_sample, actual_denitrification_rate, nitrate_add_7_denitrification_rate, nitrate_add_14_denitrification_rate, nitrate_add_28_denitrification_rate, high_nitrate_add_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrmicroogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. Dataset 3: table_actual_denitrification_rates_modeled_DRYAD.txt Dataset containing the actual denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, actual_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of actual denitrification rates, we use the variables scaled; before being scaled some variables were square root (actual_denitrification_rate, DOC, DNA, X16S, nirS, and nirK) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Dataset 4: table_potential_denitrification_rates_modeled_DRYAD.txt Dataset containing the potential (28 microM nitrate added) denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, nitrate_add_28_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of potential denitrification rates, we use the variables scaled; before being scaled some variables were square root (nitrate_add_28_denitrification_rate, DOC, DNA, X16S, and nirS) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nirK, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Variables contained in the datasets: id_rate: identification code for each denitrification rate measurement is useful as a link between datasets. id_core: identification code for each sediment core is useful as a link between datasets. yymmdd_lake_sample: The datasets are sorted by this variable. Identification code for each sediment core is useful as a link between datasets. Each code corresponds to the date sampled (year, month, and day), and the Lake, and the number of the sediment core. E.g. 130701Llo3 correspond the 3rd core sampled in Llong Lake on 2013, July 1 (13 07 01). The following are the sampled Lakes, with its abbreviation in brackets: Bergus (Be), Bassa de les Granotes (Bgra), Contraix (Co), Gelat de Bergus (GBe), Llebreta (Lle), Llong (Llo), Plan (Pl), Podo (Po), Redon (Re), Redo Aiguestortes (ReAT), and Redon de Vilamos (ReVil). This variable is useful as a link between datasets. denitrification_rate (micromols N2O m-2 h-1): denitrification rate without identifying the nitrate treatment. actual_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured without any substrate addition. nitrate_add_7_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 7 microM nitrate and 1.5 g/L of glucose. nitrate_add_14_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 14 microM nitrate and 1.5 g/L of glucose. nitrate_add_28_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 28 microM nitrate and 1.5 g/L of glucose. high_nitrate_add_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding a high concentration of nitrate (>300 microM) and 1.5 g/L of glucose. nitrate_treatment: treatment of nitrate addition in the denitrification measurements. Treatments: actual (without any nitrate added), Add_7 (7 microM nitrate added), Add_14 (14 microM nitrate added), Add_28 (28 microM nitrate added), High_add (>300 microM nitrate added). temperature_incubation (Celsius degrees): constant temperature during the incubation of the denitrification measurement. nitrate_actual_plus_added (microM): actual (in situ) plus added nitrate concentration in the water overlying the sediment core. glucose_added (mg * L-1): added when nitrate was also added. site: Lake (code): Bergus (B), Contraix (C), Bassa de les Granotes (G), Gelat de Bergus (GB), Llebreta (Le), Llong (Lo), Plan (P), Podo (Po), Redon (R), Redo Aiguestortes (RA), and Redon de Vilamos (RV). latitude (N). longitude (E). altitude (m a.s.l.). lake_area (ha). catchment_area (ha). renewal_time (months): mean time that water spends in the lake, retention time and residence time are synonyms of renewal time. habitat: sediment type (code): littoral sediments from rocky areas (R), helophyte Carex rostrata belts (C), beds of isoetid (I) and elodeid (E) macrophytes, and non-vegetated deep (D) sediments. temperature_insitu (Celsius degrees): temperature measured in the water overlying the sediment core just after the sediment core was retrieved. nitrate (microM): nitrate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. nitrite (microM): nitrite concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. ammonium (microM): ammonium concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. sulphate (microM): sulphate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. DOC (mg*L-1): dissolved organic carbon concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. water_column_depth (m): depth of the water column in the sediment core sampling point. Coded as 0.5 for littoral habitats. sediment_layer_depth (cm): depth of the sediment layer described by molecular and abiotic features. organic_matter (%): sediment organic matter content determined by Lost on Ignition. nitrogen (%): sediment nitrogen content. d15N (parts per thousand): sediment delta15N. carbon (%): sediment carbon content. d13C (parts per thousand): sediment delta13C. carbon_nitrogen_ratio (a/a): sediment atomic ratio of the two elements. dry_weight_wet_weight_ratio: sediment dry weight to wet weight ratio. sediment_density (g * cm-3). sediment_grain_size (um): sediment, volume diameter of the mean-sized spherical particle (vol_weighted_mean parameter provided by Mastersizer 2000, Malvern Instruments Ltd, UK). DNA (ng * m-2): sediment DNA content, ng of DNA per m2 in the sediment layer (0-0.5 cm). X16S (copies * m-2): 16S rRNA gene copies per m2 in the sediment layer (0-0.5 cm). nirS (copies * m-2) : nirS gene copies per m2 in the sediment layer (0-0.5 cm). nirK (copies * m-2) : nirK gene copies per m2 in the sediment layer (0-0.5 cm). nosZ1 (copies * m-2) : nosZ1 gene copies per m2 in the sediment layer (0-0.5 cm). nosZ2 (copies * m-2) : nosZ2 gene copies per m2 in the sediment layer (0-0.5 cm)., During the last decades, atmospheric nitrogen loading in mountain ranges of the Northern Hemisphere has increased substantially, resulting in high nitrate concentrations in many lakes. Yet, how increased nitrogen has affected denitrification, a key process for nitrogen removal, is poorly understood. We measured actual and potential (nitrate and carbon amended) denitrification rates in sediments of several lake types and habitats in the Pyrenees during the ice-free season. Actual denitrification rates ranged from 0 to 9 μmol N2O m−2 h−1 (mean, 1.5 ± 1.6 SD), whereas potential rates were about 10-times higher. The highest actual rates occurred in warmer sediments with more nitrate available in the overlying water. Consequently, littoral habitats showed, on average, 3-fold higher rates than the deep zone. The highest denitrification potentials were found in more productive lakes located at relatively low altitude and small catchments, with warmer sediments, high relative abundance of denitrification nitrite reductase genes, and sulphate-rich waters. We conclude that increased nitrogen deposition has resulted in elevated denitrification rates, but not sufficiently to compensate for the atmospheric nitrogen loading in most of the highly oligotrophic lakes. However, there is potential for high rates, especially in the more productive lakes and landscape features largely govern this., Ministerio de Ciencia e Innovación, Gobierno de España, Award: CGL2010-19373. Ministerio de Economía, Industria y Competitividad, Gobierno de España, Award: CGL2016–80124-C2-1-P., Peer reviewed


Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/283203
Dataset. 2020

MODELING MULTIPARTITE VIRUS EVOLUTION: THE GENOME FORMULA FACILITATES RAPID ADAPTATION TO HETEROGENEOUS ENVIRONMENTS

  • Zwart, Mark P.
  • Elena, Santiago F.
[Methods] This submission contains all the R code used for numerical predictions and simulations presented in the paper. Selected simulation results are also included, to allow the reader quick access to results without having to run some of these (computationally intensive) scripts. [Usage Notes] The R code and simulation results are organized according to the figures presented in the data, to help easily find the relevant code., Multipartite viruses have two or more genome segments, and package different segments into different particle types. Although multipartition is thought to have a cost for virus transmission, its benefits are not clear. Recent experimental work has shown that the equilibrium frequency of viral genome segments, the setpoint genome formula (SGF), can be unbalanced and host-species dependent. These observations have reinvigorated the hypothesis that changes in genome-segment frequencies can lead to changes in virus-gene expression that might be adaptive. Here we explore this hypothesis by developing models of bipartite virus infection, leading to a threefold contribution. First, we show that the SGF depends on the cellular multiplicity of infection (MOI), when the requirements for infection clash with optimizing the SGF for virus-particle yield per cell. Second, we find that convergence on the SGF is very rapid, often occurring within a few cellular rounds of infection. Low and intermediate MOIs lead to faster convergence on the SGF. For low MOIs this effect occurs because of the requirements for infection, whereas for intermediate MOIs this effect is also due to the high levels of variation generated in the genome formula. Third, we explored the conditions under which a bipartite virus could outcompete a monopartite one. As the heterogeneity between environments and specificity of gene-expression requirements for each environment increased, the bipartite virus was more likely to outcompete the monopartite virus. Under some conditions changes in the genome formula helped to exclude the monopartite competitor, highlighting the versatility of the genome formula. Our results show the inextricable relationship between MOI and the SGF, and suggest that under some conditions the cost of multipartition can be outweighed by its benefits for the rapid tuning of viral gene expression., Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Award: 016.Vidi.171.061. Agencia Estatal de Investigación, Award: FEDER grant BFU2015-65037-P. Generalitat Valenciana, Award: PROMETEU/2019/012., Peer reviewed


Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/283209
Dataset. 2020

MARINE BIOMONITORING WITH EDNA: CAN METABARCODING OF WATER SAMPLES CUT IT AS A TOOL FOR SURVEYING BENTHIC COMMUNITIES?

  • Antich, Adrià
  • Palacín, Cruz
  • Cebrian, Emma
  • Wangensteen, Owen S.
  • Golo, Raül
  • Turon, Xavier
In the marine realm, biomonitoring using eDNA of benthic communities requires destructive direct sampling or the setting-up of settlement structures. Comparatively much less effort is required to sample the water column, which can be accessed remotely. In this study we assess the feasibility of obtaining information from the eukaryotic benthic communities by sampling the adjacent water layer. We studied two different rocky-substrate benthic communities with a technique based on quadrat sampling. We also took replicate water samples at distances from a few centimetres to 20 m from the benthic habitat. Using as marker a fragment of the Cytochrome c oxidase subunit I gene with universal primers, we obtained a total of 3,543 molecular operational taxonomic units (MOTUs) from the samples. The structure obtained in the two environments was markedly different, with Metazoa, Archaeplastida Rhodophyta and Stramenopiles being the most diverse group in benthic samples, and HacrobiaAlveolata, Metazoa and Alveolata Rhizaria in the water. Only 265 MOTUs (7.5%) were shared between benthos and water samples, and of these 180 MOTUs (5.1%) were identified as benthic MOTUs that left their DNA in the water. Most of them were found immediately adjacent to the benthos, and their number decreased and The distribution of these benthic shared MOTUs showed a decrease both in number of MOTUs and in number of reads as we moved apart from the benthic habitat. It was concluded that water eDNA, even in the close vicinity of the benthos, was a poor proxy for the analysis of benthic structure, and that direct sampling methods are required for monitoring these complex benthic communities via metabarcoding., Peer reviewed

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/283242
Dataset. 2020

DATA FROM: SPONTANEOUS FOREST REGROWTH IN SOUTH-WEST EUROPE: CONSEQUENCES FOR NATURE’S CONTRIBUTIONS TO PEOPLE

  • Martín-Forés, Irene
  • Magro, Sandra
  • Bravo-Oviedo, Andrés
  • Alfaro-Sánchez, Raquel
  • Espelta, Josep Maria
  • Frei, Theresa
  • Valdés-Correcher, Elena
  • Rodríguez Fernández-Blanco, Carmen
  • Winkel, Georg
  • Gerzabek, Gabriel
  • Hampe, Arndt
  • Valladares Ros, Fernando
[Methods] The dataset was collected by four different teams who took part in the project. It consisted on four case studies of forest regrowth (including expansion and densification) after rural abandonment with contrasting ecological and societal contexts. The study took place in Spain and France. Two landscapes are located in rural areas undergoing human exodus and forest expansion and densification; the other two, in peri-urban areas with intense land use and forest densification but negligible expansion. For each forest plot, we estimated variables related to ten out of the 18 main Nature's contributions to people (NCP) defined by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Regulating and material NCP were addressed using variables measured in the field as proxies. Non-material NCP were studied through stakeholder interviews. Thus, this dataset contains data on NCP associated with forest regrowth of Juniperus thurifera L., Fagus sylvatica L., Quercus ilex L., and Quercus robur L stands. It includes a total of 65 study plots and 2,837 individual trees. The considered NCP included four regulating, three material, and three non-material NCP as well as one NCP common to all categories. The four regulating NCP were habitat creation and maintenance; pollination and dispersal of seeds and other propagules; regulation of climate through biological carbon (C) sequestration and storage; and regulation of detrimental organisms and biological processes. The two material NCP were energy; and medicinal, biochemical and genetic resources. The three non-material NCP were learning and inspiration; physical and psychological experience; and supporting identities. The NCP common to all categories was the maintenance of options, reflected in maintaining biodiversity (estimated in this case with the Shannon diversity index). The dataset contains information at both individual and plot level. Habitat creation and maintenance (NCP1) was calculated by computing the spatial connectivity of the plots in Q. ilex and Q. robur stands. It was inferred by calculating the percentage cover of broadleaved forest in a circular buffer (radius = 500 m) around each plot. Pollination and the dispersal of seeds and other propagules (NCP2) was estimated by counting all seedlings and saplings in each plot and divided them by the plot area to obtain the density of saplings per hectare. Climate regulation in terms of biological carbon (C) storage and sequestration (NCP4) was estimated by the overall C stock contained in the trees of the study plots. We calculated the total biomass per tree using species-specific allometric equations that combine the dbh and the height of the sampled trees. We also calculated the C stock per tree multiplying the obtained biomass by the percentage of C in each species. The regulation of detrimental organisms and biological processes (NCP10) was assessed using the percentage of invertebrate herbivory. For all except the J. thurifera case study, we determined herbivore damages by visually estimating the percentage of leaf area removed by invertebrates. Energy provision (NCP11) was understood as the production of biomass-based fuels such as fuelwood. We estimated biomass input from thick and medium branches (i.e. the tree parts normally employed as fuelwood) for each tree within plots. Biomass input from thick and medium branches was calculated from allometric equations specific for each species. The provision of medicinal, biochemical and genetic resources (NCP14) includes the production of plant genes and genetic information. In each plot we quantified gene diversity corrected for sample size as proxy for NCP14. The maintenance of options (NCP18) includes the benefits associated with species diversity. We scored woody species richness and abundance and computed the Shannon diversity index as proxy. Additionally, interviews regarding social perceptions related to learning and inspiration (NCP15), physical and psychological experiences (NCP16) and supporting identities (NCP17) were conducted at the case study level. Please notice that the social perception dataset is not uploaded to ensure data privacy policy. More information and detailed Methodology can be found in Martín-Forés et al. (2020) People and Nature, in both the main text and the Supplementary Material S1. [Usage Notes] Some values are missing because the methodology was adjusted according to each case study. For more information please read the detailed information provided by Martín-Forés et al. (2020) People and Nature, in both the main text and the Supplementary Material S1., [Context] European forests are expanding and becoming denser following the widespread abandonment of farmland and rural areas. Yet, little is known about the goods and services that spontaneous forest regrowth provide to people., [Aims] We assessed the changes in nature’s contributions to people (NCP) from spontaneous forest regrowth, i.e. forest expansion and densification, in South-West Europe., [Methods We investigated 65 forest plots in four different landscapes with contrasting ecological and societal contexts. Two landscapes are located in rural areas undergoing human exodus and forest expansion and densification; the other two, in peri-urban areas with intense land use and forest densification but negligible expansion. For each forest plot, we estimated variables related to ten out of the 18 main NCP defined by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). Regulating and material NCP were addressed using variables measured in the field as proxies. Non-material NCP were studied through stakeholder interviews., [Results] Our results show across the cases that forest expansion and densification is generally associated with greater climate regulation and energy provision. Changes in other NCP, especially in non-material ones, were strongly context-dependent. The social perception of spontaneous forest regrowth was primarily negative in rural areas and more positive in peri-urban landscapes., [Conclusion] Passive restoration through spontaneous forest expansion and densification can enhance regulating and material NCP, especially when adaptive management is applied. To optimise NCP and to increase the societal awareness of and interest in spontaneous forest regrowth, the effects of this process should be analysed in close coordination with local stakeholders to unveil and quantify the many and complex trade-offs involved in rural or peri-urban social perceptions., BiodivERsA, Award: BiodivERsA3-2015-58. Agencia Estatal de Investigación, Award: PCIN-2016-055. Ministerio de Economía, Industria y Competitividad, Gobierno de España, Award: CGL2017-83170-R. Deutsche Forschungsgemeinschaft. BiodivERsA, Award: BiodivERsA3-2015-58., Peer reviewed


Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/283251
Dataset. 2020

DATA FROM: SORPTION AND DESORPTION OF BICYCLOPYRONE ON SOILS

  • Spokas, K. A.
  • Gámiz, B.
  • Schneider, S. K.
  • Hall, K. E.
  • Chen, Wenlin
[Methods] Bicyclopyrone sorption was determined on each soil in triplicate using the batch equilibration method with radiolabeled compounds. The 14C spiking solution for BCP was prepared by diluting 14C-bicyclopyrone [pyridinyl-3-14C; specific activity = 95.3 µCi mg-1; 8,880 Bq mmol-1; 99.5% purity] with unlabeled BCP (Syngenta Crop Protection, LLC) to result in a stock solution of 10 mg L−1 with an overall activity of 1,090 Bq mL-1 (65,450 DPM mL-1). A 0.5 mL aliquot of this spiking solution was added to 9.5 mL of 0.01 M CaCl2 to achieve a 10 mL solution volume. This solution (0.5 mg L−1 BCP) was added to 1 g of soil in a 20 mL glass scintillation vial for a series of 25 different USDA soil series. Samples were then placed horizontally on a reciprocal shaker and allowed to equilibrate for 24 h (180 rev min-1) in the dark. Following centrifugation (20 min, 1500 × g), 2 mL of supernatant was removed and filtered (0.45 μm). Then a 1 mL aliquot of the filtered solution was mixed with 5 mL of scintillation cocktail [EcoLite(+)™; MP Biomedicals, LLC, Solon, OH] and analyzed for 14C by liquid scintillation counting (LSC) (HITACHI AccuFLEX LSC-8000, GMI Ramsey, MN, 10-minute counting window). No statistically significant sorption of the BCP to scintillation vials, syringes or syringe filters was observed (98-100% recovery, data not shown). The sorbed concentration of BCP on the soil was estimated by the following: Cs=Ci-CemV , where Ci is the initial concentration (mg L-1), Ce is the equilibrium concentration (mg L-1), V is the total volume of the liquid phase (L), and m is the mass of the soil (g). The equilibrium concentration was determined by the LSC of the liquid phase using the following equation: Ce=LSCe-Blank * CiLSCi , where LSCe is the disintegrations per minute of the sample following equilibration with the soil, Blank is the disintegrations per minute of the scintillation cocktail and vial alone, Ci is the liquid phase concentration of the initial standard (mg L-1), and LSCi are the disintegration of the corresponding BCP standards (without soil added). The partitioning coefficient (KD) is estimated by the following: KD= CSCe . Sorption Isotherms – Multiple Concentrations Sorption isotherms were generated for 7 selected soils which were selected based on the initial screening above for a range of observed KD values. The isotherm was determined utilizing initial BCP solution concentrations of 0.1, 0.5, 1, 2.5, 5, 10, and 20 mg L−1. Individual samples were run in duplicate using methods analogous to the single concentration batch KD determination described earlier. Equilibrium liquid (Ce) and solid (Cs) concentrations were then analyzed by fitting to different linear forms of four sorption models: Langmuir, Freundlich, Temkin, and the Dubinin-Radushkevich models (Horsfall, Spiff, & Abia, 2004; Hunt & He, 2015). [Usage Notes] Excel file with separate worksheets for the batch-isotherm (KD) and the multi-point isotherm datasets., Bicyclopyrone is a herbicide that is targeted for the control of herbicide-resistant weeds. However, there is a lack of extensive data on its sorption and factors that control its sorption in the soil system. In this study, we evaluated a series of 25 different soils, with a variety of soil properties to assess if an empirical relationship could be developed to predict the sorption coefficient for bicyclopyrone. Overall, there were no statistically significant relationships observed with organic carbon, cation exchange capacity, or clay content. There solely was a moderate negative correlation with soil pH (R=-0.65). Additionally, Freundlich isotherm analysis suggests that the KD could be adequate to characterize the sorption behavior for the range of soils evaluated here., Peer reviewed

Proyecto: //

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285150
Dataset. 2020

DATA FROM: MEGAFAUNA BIOGEOGRAPHY EXPLAINS PLANT FUNCTIONAL TRAIT VARIATION IN THE TROPICS

  • Dantas, Vinícius de L.
  • Pausas, J. G.
[Methods] We compiled data on the presence of spines, SLA, WD, HMax and presence of spines for Afrotropical and Neotropical savanna and forest woody species (trees and shrubs), from literature and herbarium sources (a list of the data sources is found in Appendix 1). We first compiled SLA and WD data from the primary literature and calculated species means at the biogeographic scale. Then, for Afrotropical species, we searched for HMax and spine data in JSTOR Global Plant (http://plants.jstor.org/) and in the African Plant Database of the Conservatoire et Jardin botaniques de la Ville de Genève and South African National Biodiversity Institute, using the list of species for which we obtained SLA and WD data as reference. For Neotropical species we obtained HMax and presence of spine information from the NeoTropTree dataset (Oliveira-filho, 2017) and Flora do Brasil (2020) for all the available species recorded in Brazilian savanna (Cerrado) and forest (Amazon and Atlantic forest) biomes. For spinescence, we only considered species with detailed descriptions of stem and branch features. We classified species as savanna, forest, or generalist (occurring in both savanna and forest) species, based on the study site descriptions reported in the literature sources from which the data were acquired, and on Mendonça et al. (2008). We only considered species that were consistently classified as forest or savanna, excluding species reported to occur in both ecosystem types, to better pinpoint the patterns and simplify the results. We also classified species according to the biogeographic region in which they occur as Afrotropical or Neotropical species (based on the data reference sources). Introduced species were also excluded using occurrence information from the flora websites and datasets used to compile height and spine data. We obtained data for number and proportion of African geoxylic plants from Maurin et al., (2014). This data is based on the flora of the Zambesian region, a savanna dominated region that includes 12 African countries. Maurin et al., (2014) present two datasets, a sampled dataset, with 53 geoxyles out of the 1400 woody species, and a provisional list of 266 African geoxylic suffrutices taxa occurring south of the Equator. We did not use the latter because an accurate quantification of the southern African flora was not provided. However, a preliminary estimate based on Germishuizen et al. (2003) indicates a total of 8169 woody taxa for southern Africa (including trees, shrubs, dwarf shrubs, subshrubs and suffrutex, but excluding scrumbers, as the proportion of woody stems was not reported). Based in these figures, we found that the first and second datasets represent a similar proportion of geoxyles for African woody species (4 and 3 %, respectively), and would provide very similar results in the statistical analyses. Thus, we only report the results for the sampled species of Maurin et al., (2014). For the Neotropical savanna region, we searched for information on stem and underground organs for subshrub species in the checklist of Mendonça et al. (1998). The list comprises 6429 savanna plant species from the Cerrado region (the largest Neotropical savanna-dominated region) and represents an older version of a more recent checklist with almost twice the number of plant species (but more difficult to work with because only the printed version is available; Mendonça et al., 2008). We then searched for information in plant species descriptions compiled by the Rio de Janeiro Botanical garden and publicly available in Portuguese at the Flora do Brasil (2020) website. We only considered information for species containing detailed descriptions of aerial and underground structures. We found information of this sort for 220 subshrubs out of the 816 subshrub species in the checklist, of which 101 were geoxyles (according to the definition used by Maurin et al., 2014). Based in the observed proportion (46%), we estimated the number of geoxyles among the 816 subshrubs to be 376 species, from a total of 3599 woody species. Thus, the comparison is between savanna regions, not actual savanna or forest vegetation (unlike the comparison for other traits), and only includes subshrub geoxyles, to match the criteria used by Maurin et al., (2014). [Environmental Data] We obtained decimal geographic coordinates for the species for which we obtained WD, SLA and HMax in GBIF.org (28 February 2020; see reference list for doi) and the R package “rgbif”. In order to exclude very close occurrences and, thus, match the resolution of the available satellite-derived environmental data (see below), we rounded the decimal coordinates to include only three decimal digits and then remove repeated species occurrences. We also excluded coordinates falling outside Africa, South and Central Americas, and outside the following biomes (according to Dinerstein et al. 2017): Tropical and Subtropical Moist and Dry Broadleaf Forests; Tropical and Subtropical Grasslands, Savannas and Shrublands; Montane Grasslands and Shrublands; Tropical and Subtropical Coniferous Forests; and, Deserts and Xeric Shrublands. This was directed at minimizing errors, standardizing the latitude ranges and biomes considered for each biogeographic regions, and to exclude flooded ecosystems in which plant relationships with climate and soil are likely different. Thus, from the initial approximately 2,8 million occurrences, we only used 87,739 occurrences, and the number of coordinates per species varied from 1 to 1432. Based on these coordinates, we obtained climate, soil and fire data for each occurrence location from global datasets. We obtained climate data from WorldClim 2 (Fick & Hijmans, 2017), soil data from SoilGrid (250 m of spatial resolution; Hengl et al. 2017), and fire data from the MODIS product MCD14ML collection 6 v.3 (Giglio et al., 2018). We used mean annual precipitation and temperature, as well as rainfall seasonality for the years 1970-2000, as climate variables; cation exchange capacity, organic carbon content, weight percentages of clay (<0.0002 mm), silt (0.0002–0.05 mm), and sand particles (0.05–2 mm), as well as the volumetric percentage of coarse fragments (>2 mm), as soil variables; and fire count per area (as a proxy for fire frequency) and radiative power (a proxy for fire intensity) as fire variables. Soil variables were the averages between two depth, 0.05 and 2 m. Fire data was obtained from a circular area of 5 km centered on the occurrence coordinates between the years 2000 and 2019 (both included). For each species and biogeographic region, we calculated the overall means as an indicator of species habitat preferences as defined by their average position in environmental niche space. [Usage Notes] The dataset that is made available here cosists of two files in .csv format. The first is the complete trait dataset for specific leaf area (sla; mm2.mg-1), wood density (woo; g.cm-3), HMax (m) and Spines (yes/no). The list of reference sources for trait data is presentes in the end of this note. Other abreviations in this file are: ref.sla: reference sources for sla data; ref.woo: reference sources for wood density data; ref.hmax: reference sources for hmax data; mat: mean annual temperature; map: mean annual precipitation; rs: rainfall seasonality; nfires5: number of fires per 5 km area (our proxy for fire frequency); avgfrp: average fire radiative power (our proxy for fire intensity); cec: soil cation exchange capacity; orc: soil organic carbon content; cly: weight percentage of clay particles (<0.0002 mm) in the soil; slt: weight percentage of silt particles (0.0002–0.05 mm) in the soil; snd: weight percentage of the sand particles (0.05–2 mm) in the soil; crf: volumetric percentage of coarse fragments (>2 mm) in the soil. The second file attached is a dataset of Geoxyle species (geox; y(yes)/n(no)) for a subset of the Brazilian Cerrado species., [Aim] Biomes can diverge substantially in plant functional traits and disturbance regimens among regions. Given that Neotropical and Afrotropical regions have contrasting histories of the megafauna (because of the Holocene megafaunal extinction in the Neotropics), we hypothesize that they should harbour plants with different traits in relationship to herbivory and fire, especially in savannas. We predicted that herbivory resistance traits should be more prominent in Afrotropical savanna plants and fire resistance in Neotropical savanna plants., [Location] Tropics., [Time period] Not applicable., [Major taxa studied] Angiosperms (woody)., [Methods] We compiled data for five key plant functional traits (wood density, specific leaf area, maximum tree height, spinescence and proportion of geoxyles) for forest and savanna woody species from the two distant regions (Afrotropics and Neotropics). We related these data to climate, soil and fire variables and tested predictions for megafauna selection., [Results] Spines and high wood density were more common among Afrotropical than Neotropical savanna species and species from the two forests. Moreover, the Neotropical savanna region contained more geoxyles than the Afrotropical savanna region. Finally, Afrotropical species were taller than Neotropical species. These differences were consistent with our predictions for trait selection by the megafauna, and these patterns did not change when considering climate, soil and fire regimens in the models., [Main conclusions] Our results highlight the great potential of these traits for summarizing disturbance strategy axes in tropical woody species and suggest that global variation in plant traits is unlikely to be understood fully without consideration of historical factors, especially the direct and indirect impacts of megafauna., Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (Finance Code 001), Award: 88887.311538/2018-00., Peer reviewed

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DOI: http://hdl.handle.net/10261/285150
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285150
HANDLE: http://hdl.handle.net/10261/285150
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285150
PMID: http://hdl.handle.net/10261/285150
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285150
Ver en: http://hdl.handle.net/10261/285150
Digital.CSIC. Repositorio Institucional del CSIC
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