Resultados de investigación

Avian brood parasites are expected to select host nests according to characteristics that maximize offspring fitness, such as reduced probability of ectoparasitism. Spotless starlings (Sturnus unicolor) lay immaculate blue eggs that sometimes become brownish- spotted due to the activity of the ectoparasitic fly Carnus hemapterus on incubating birds. Therefore, conspecific parasitic females should adaptively avoid nests with spotted eggshells for parasitism. Here, we manipulated perceived risk of ectoparasitism by painting the eggs with either brown spots (similar to those due to ectoparasite activity), or blue spots. A third group of nests was maintained with immaculate eggshells. Nests with non-spotted eggshells showed the highest rate of brood parasitism, while nests with brownish or bluish spots on the eggshells were parasitized at a similar lower rate. These results suggest that brood parasitic females use the presence of spots on the eggshells in their selection of host nests. This study adds to the scarce evidence showing that brood parasitic birds select host nests with low risk of ectoparasitism and demonstrates that colour patterns of the eggshell (i.e. spottiness) of their potential hosts represent a major cue employed to appraise risk of ectoparasitism, Peer reviewed

70 páginas, 11 figuras, 5 tablas, 2 recuadros, 1 tabla A. -- Kattge, Jens [et al]. -- 729 authors contributed to writing. -- The first published is by John Wiley & Sons Ltd, Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives, Peer reviewed

Los datos pertenecen al trabajo: Gómez, E.J., Delgado, J.A., González, J.M. (2020): Persistence of microbial extracellular enzymes in soils under different temperatures and water availabilities Ecology and Evolution (Artícle in press), Figure 2. Decay rates as a function of water availability and temperature for extracellular enzymes from mesophiles and thermophiles at three different soils. Extracellular enzymes: A, B and C (upper row), glucosidases; D, E and F (central row), phosphatases; G, H and I (lower row), proteases. Left column (A, D and G), Seville soil (South Spain); Center column (B, E and H), Cadiz soil (South Spain); Right column (C, F and I), North Spain soil. Symbols: in red, decay of extracellular enzymes from thermophiles at 60ºC; in blue, decay of extracellular enzymes from mesophiles at 20ºC; in black, decay of extracellular enzymes from thermophiles at 20ºC. Points resulted from the average of triplicated samples. Error bars indicate a standard deviation, Figure 3. NMDS ordination of decay rates as a function of temperature, water availability and the soils for glucosidase (A), phosphatase (B) and protease (C) activities. Water activity is shown with brownish filled circles from dark to light in decreasing levels of water activity. Decays at 20ºC and 60ºC by enzymes from mesophilic (M20, down-pointing triangles, and M60, up-pointing triangles, respectively) and thermophilic (T20, squares, and T60, diamonds, respectively) microoorganisms are shown as unfilled symbols for each studied soil (Huesca soil [North Spain] in green, Cadiz soil [South Spain] in blue, Seville soil [South Spain] in red)., No

Contiene documentos en pdf con fotografías, protocolos de excavación, topografías e inventario resultados de los trabajos realizados en el yacimiento Alashawaia en 1995., Documentos de las excavaciones arqueológicas realizadas en 1995 en el yacimiento Alashawaia., Agencias financiadoras: Unión Europea, CSIC, CONICET, Ministerio de Educación y Ciencia (España) y Ministerio de Cultura (España)., Peer reviewed

The following dataset shows 4 tables with the occurrence of CUPs and PPCPs in the different environmental matrices. For both shallow lakes, Al-Asfar and Al-Hubail, the sites 3, 4 and 5 were located in the shallow lake, while site 1 was located in irrigation channels, which provide wastewater (from farms, factories and/or domestic sewage) to each lake. Site 2 was located between the end of the irrigation channels and the mouth of each lake. Detailed information of each sampling site is provided in the related article [1]. Table 1 shows the occurrence of CUPS and PPCPs in water samples, while tables 2, 3 and 4 show the occurrence in sediments, soil and plants (wild flora Phragmites australis) respectively. In order to make the table easier to understand the data has been filtered, eliminating in each table, those compounds that were not detected in the sampling sites. A detailed list of the analyzed compounds is provided in the related article [1]. Furthermore, the CUPs acetochlor, acrinathrin, alachlor, atrazine, atrazine-deethyl, atrazine-deisopropyl, azinphos-ethyl, azinphos-methyl, buprofezin, carbofuran, chlotianidin, coumaphos, diclofenthion, dimethoate, diuron, 2,4-dimethylaniline (DMA), 2,4-dimethylphenylformamide (DMF), 2,4-dimethylphenyl-N′-methylformamidine (DMPF), ethion, etofenprox, fenthion, fenthion sulfoxide, fipronil, flumethrin, hexythiazox, malathion, methiocarb, metolachlor, molinate, omethoate, parathion-ethyl, parathion-methyl, propanil, propazine, pyriproxyphen, simazine, spinosyn A, spinosyn C, spinosyn D, terbumeton, terbumeton-deethyl, terbutryn and tolclophos-methyl and the PPCPs allopurinol, amoxicillin, chloramphenicol, furosemide, indomethacin, norfloxacin and thiamphenicol were not detected in the samples. In addition, the tables also show the total accumulated contamination for each contaminant and matrix, which provides and insight of the overall presence (and use) of each compound in the area., The data set presents the occurrence of 59 currently used pesticides (CUPs) and 33 pharmaceuticals and personal care products (PPCPs), from wetland areas, in Saudi Arabia, impacted by wastewater discharge. Wetlands are valuable ecosystems, but are very fragile and easily affected by anthropogenic pressure [1], [2], [3], [4], [5], [6]. The occurrence of organic contaminants provides understanding about their fate and possible risk for humans and environment. Up to our knowledge, this is the first report on the occurrence of the mentioned organic pollutants in shallow lakes in Saudi Arabia, and the first time these compounds are analyzed in wild flora. Samples of water, sediment, soil and plants were extracted via ultrasound assisted extraction (UAE) and solid phase extraction (SPE). The compounds determination was performed using ultra-high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS). Interpretation and discussion of the present dataset can be found in the article entitled “Pharmaceuticals, pesticides, personal care products and microplastics contamination assessment of Al-Hassa irrigation network (Saudi Arabia) and its shallow lakes”, The financial support from the project number (RSP-2019/11) King Saud University, Riyadh, Saudi Arabia. R. Álvarez-Ruiz acknowledges the Spanish Ministry of Science, Innovation and Universities and the ERDF (European Regional Development Fund) for his FPI grant BES-2016-078612., Peer reviewed

19 pages, 7 tables, 8 figures.-- This work is distributed under the Creative Commons Attribution 4.0 License., Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2), which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont–Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables related to the former’s variability. The global root mean square error (RMSE) of mapping TCO2 is relatively low for the two datasets (GLODAPv2.2019: 7.2 μmolkg-1; LDEO: 11.4 μmolkg-1) and also for independent data, suggesting that the network does not overfit possible errors in data. The ability of NNGv2LDEO to capture the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in 10 time series stations spread over different regions of the ocean (RMSE: 3.6 to 13.2 μmolkg-1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity, and oxygen from the World Ocean Atlas 2013 and phosphate, nitrate, and silicate computed from a neural network fed with the previous fields. The resolution is 1ºx1º in the horizontal, 102 depth levels (0–5500 m), and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution, and it is centered around the year 1995. The uncertainty of the climatology is low when compared with climatological values derived from measured TCO2 in the largest time series stations. Furthermore, a computed climatology of partial pressure of CO2 (pCO2) from a previous climatology of total alkalinity and the present one of TCO2 supports the robustness of this product through the good correlation with a widely used pCO2 climatology (Landschützer et al., 2017). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020), This research was supported by Ministerio de Educación, Cultura y Deporte (FPU grant no. FPU15/06026); Ministerio de Economía y Competitividad through the ARIOS (grant no. CTM2016-76146-C3-1-R) project, cofunded by the Fondo Europeo de Desarrollo Regional 2014–2020 (FEDER); and EU Horizon 2020 through the AtlantOS project (grant agreement no. 633211). Are Olsen was supported by the Norwegian Research Council through ICOS (grant no. 245927). Mario Hoppema was partly supported by the European Union’s Horizon 2020 program under grant agreement no. 821001 (SO-CHIC)., Peer reviewed

Develop a new algorithm and novel approaches with the aim of producing the best quality SSS product of the Arctic region of well-characterized accuracy using SMOS data. Data acquisition Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity). Time coverage 01 January, 2011 - 31 December, 2019. L2 time resolution 2 minutes. L3 time resolution 3 days, 9 days and 18 day. Maps frequency generation: Daily. Spatial coverage Latitude range: 50ºN-90ºN Longitude range: 180ºW-180ºE. Spatial resolution: 25 km. Spatial projection: Lambert Azimuthal Equal Area. Spatial grid WGS 84 / NSIDC EASE-Grid 2.0 North (EPGS:6931). Format NetCDF. Climate and Forecast (CF) conventions version: 1.6., Project Arctic+ salinity. Funding European Space Agency under contract 4000125590/18/I-BG, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Sea surface salinity; Sea surface salinity uncertainty; Sea surface salinity anomaly; Sea surface salinity quality flag, Peer reviewed

To investigate the oxidative effect of CPF on chicken breast proteins, peptidomics and proteomics analysis were used. For this, chicken breast samples were exposed to CPF and subjected to simulated gastrointestinal digestion. The identification of oxidized peptides from digested and undigested proteins were performed by LC MS/MS (nanoESI qQTOF). Prior to mass analyses undigested proteins were trypsinated. Data were analysed using MASCOT and ProteinPilot v 4.5 software. In this study, 90 and 107 oxidized peptides from digested proteins of control and exposed samples were identified, respectively. These peptides corresponding to 12 oxidized proteins. Meanwhile, 260 and 324 oxidized peptides from undigested proteins (control and exposed samples) were identified, which corresponding to 19 and 17 proteins, respectively. Collagen was protein more susceptible to oxidation promoted by chlorpyrifos in digested and undigested proteins. Identification of these oxidized proteins from simulated digestion provides an important insight about the impact of substances like certain veterinary drugs at trace concentrations on the nutritional value of chicken breast meat., Grant AGL2017-89381-R and FEDER funds from the Spanish Ministry of Economy, Industry and Competitiveness, and Ramón y Cajal postdoctoral contract by L.M. are acknowledged. Also COLCIENCIAS and University of Cartagena for financial support (2019 and 2020) to the project Code 1107-711-50102 are acknowledged., Peer reviewed

The dataset contains all the chemical data collected for the work “Mechanisms controlling explosive-effusive transition of Teide-Pico Viejo dome eruptions”. The samples were taken from Pico Cabras dome (Tenerife), including deposits of the sub-plinian fallout (explosive phase) and the lava flow and dome (effusive phase). The samples were analysed with a Scanning Electron Microscope (SEM) Merlin Compact ZEISS with an EDS Bruker detector (QUANTAX-XFlash6-30mm2-129eV) at the Institut des Science de la Terre d’ Orleans (ISTO) Measures Physiques laboratory (Centre National de la Recherche Scientifique, Orleans, France). Additionally, we performed a micro X-Ray Flourescence (Micro-XRF) analysis using the XTRACE detector (Tube Rh – filtres Al, Ti, Ni) connected to the SEM, which was calibrated against inner Br glassy standards containing different amounts of Br (Faranda et al., 2019) in order to analyse Br in sodalite and glass in the pumice samples. Mineral phase compositions were obtained with an electron microprobe SX-FIVE, also at the ISTO. An acceleration voltage of 15kV, a sample current of 6nA, and a counting time of 10s were used. For the glasses, a defocused beam of 10μm and 20μm was used, whereas for minerals a focused beam was employed. Whole-rock data was obtained by analysing two glass samples resulting from the two-step melting (with grinding in between) of lava and pumice samples at 1400ºC for 4 hours in total time, respectively. The aim of the study was contributing to the understanding of the mechanisms that control the transition between explosive and effusive dynamics in dome-forming eruptions at T-PV stratovolcanoes and propose a conceptual model for the magmatic reservoir of Pico Cabras prior to eruption. In this dataset we collect all de geochemical data obtained in the study. The Excel file consists of seven sheets, each one corresponding with one of the principal phases found in the samples: feldspars, pyroxenes, biotites, magnetites, ilmenites, sodalites and glasses (including the whole-rock analysis resulting from the obsidian obtained in the melting of the samples). The four analysed samples were given a International GeoSample Number (IGSN) for their correct identification. IEODG0001, IEODG0002 and IEODG0004 are samples from the effusive phase of Pico Cabras dome eruption (the later corresponding to an obsidian sample from the dome itself). IEODG0003 are pumices samples from the explosive phase in which minerals and glass fragments were selected for analysis., The dataset contains all the chemical data collected for the work “Mechanisms controlling explosive-effusive transition of Teide-Pico Viejo dome eruptions”. The samples were taken from Pico Cabras dome (Tenerife), including deposits of the sub-plinian fallout (explosive phase) and the lava flow and dome (effusive phase). The samples were analysed with a Scanning Electron Microscope (SEM) Merlin Compact ZEISS with an EDS Bruker detector (QUANTAX-XFlash6-30mm2-129eV) at the Institut des Science de la Terre d’ Orleans (ISTO) Measures Physiques laboratory (Centre National de la Recherche Scientifique, Orleans, France). Additionally, we performed a micro X-Ray Flourescence (Micro-XRF) analysis using the XTRACE detector (Tube Rh – filtres Al, Ti, Ni) connected to the SEM, which was calibrated against inner Br glassy standards containing different amounts of Br (Faranda et al., 2019) in order to analyse Br in sodalite and glass in the pumice samples. Mineral phase compositions were obtained with an electron microprobe SX-FIVE, also at the ISTO. An acceleration voltage of 15kV, a sample current of 6nA, and a counting time of 10s were used. For the glasses, a defocused beam of 10μm and 20μm was used, whereas for minerals a focused beam was employed. Whole-rock data was obtained by analysing two glass samples resulting from the two-step melting (with grinding in between) of lava and pumice samples at 1400ºC for 4 hours in total time, respectively. The aim of the study was contributing to the understanding of the mechanisms that control the transition between explosive and effusive dynamics in dome-forming eruptions at T-PV stratovolcanoes and propose a conceptual model for the magmatic reservoir of Pico Cabras prior to eruption. In this dataset we collect all de geochemical data obtained in the study. The Excel file consists of seven sheets, each one corresponding with one of the principal phases found in the samples: feldspars, pyroxenes, biotites, magnetites, ilmenites, sodalites and glasses (including the whole-rock analysis resulting from the obsidian obtained in the melting of the samples). The four analysed samples were given a International GeoSample Number (IGSN) for their correct identification. IEODG0001, IEODG0002 and IEODG0004 are samples from the effusive phase of Pico Cabras dome eruption (the later corresponding to an obsidian sample from the dome itself). IEODG0003 are pumices samples from the explosive phase in which minerals and glass fragments were selected for analysis., This study has been partially funded by EC Grant EVE (ref: DG ECHO H2020 826292) and was financially support by the Equipex-Planex ANR-11-EQPX-0036 and Labex-Voltaire from Orléans., Excel file: Dorado et al., (2020). Pico Cabras products chemistry. 7 sheets: Feldspar, Piroxene, Biotite, Magnetite, Ilmenite, Sodalite, Glass., No

Set of files with the consolidated first version of #pubarchMED bibliography. With over 1000 references and downloadable in different formats. Compiled by Jaime Almansa-Sánchez and Pedro Suárez-López, but open for collaboration and use. You ca USE it freely; We welcome you to COLLABORATE with further references to add., A collection of references about public archaeology and archaeological heritage management in/from the Mediterranean., Report pdf; Bibliography in: rtf, Zotero rdf, Refworks txt, LaTeX bib, Endnote xml, csv; README, txt, No