• SDSN - Greece
• Research data close
• Other research products close
• English close
• DIGITAL.CSIC close

[Methods] Data collection In order to identify phylogenetic and environmental correlates of plant mycorrhizal traits, we compiled four data sources: 1) plant species mycorrhizal trait data; 2) plant species occurrence data; 3) global climatic and soil environmental data and 4) plant phylogenetic information. The plant occurrence and environmental data were used to estimate plant species environmental associations. The environmental association data and phylogenetic information were then used to model variations in plant mycorrhizal trait expression (Figure 1). First, plant mycorrhizal trait data were obtained from the most up-to-date literature available, including data published by Harley & Harley (1987, 1990), Wang & Qiu (2006), Hempel et al. (2013), Bueno et al. (2017), Gerz et al. (2018) and Soudzilovskaia et al. (2020). We distinguished four plant mycorrhizal types (arbuscular (AM), ecto- (ECM), orchid (ORM), and ericoid (ERM) mycorrhiza, as defined by Smith & Read (2008)) and three plant mycorrhizal statuses (obligately (OM), facultatively (FM) and non-mycorrhizal (NM)). We compiled plant mycorrhizal trait data for 14,722 taxa at the species level (Appendix S2). Second, plant species occurrence records were retrieved from the Global Biodiversity Information Facility (GBIF; www.gbif.org) for 13,479 species that were present in both the standardised GBIF species list and the mycorrhizal trait species list. Third, plant occurrence data of 13,479 species were intersected with a 30 arc-seconds (approximately 1 km) global grid, with the presence or absence of each species in each cell recorded. A sensitivity test indicated that sampling 20 grid-cell records produced environmental parameter estimates that were representative of wider distribution areas (Appendix S1); retaining species recorded in ≥ 20 cells left 62,540,387 grid-cell level records for 11,770 species (Appendix S3). Environmental associations were approximated by intersecting the distribution data for each species with a raster stack of 54 environmental data layers (Table S1). Fourth, a phylogenetic tree containing the 11,770 species in our dataset was compiled, and phylogenetic signal in plant mycorrhizal traits was examined using the δ statistic (Borges et al., 2019). See Appendix S1 for details of datasets and data filtering. 710 dual mycorrhizal plant species (AM + ECM) were distinguished (Appendix S2), of which 665 were matched with geographic location information in GBIF. Except where stated otherwise, these species were grouped with ECM plant species in further analyses, reflecting ongoing controversy concerning the definition of dual mycorrhizal plant species (Teste et al., 2020; Brundrett, 2021a) and the fact that the niches of dual mycorrhizal plants most closely resemble those of ECM plants (Gerz et al., 2018). Mycorrhizal symbioses are known to strongly influence plant performance, structure plant communities and shape ecosystem dynamics. Plant mycorrhizal traits, such as those characterizing mycorrhizal type (arbuscular (AM), ecto-, ericoid, or orchid mycorrhiza) and status (obligately (OM), facultatively (FM), or non-mycorrhizal) offer valuable insight into plant belowground functionality. Here, we compile available plant mycorrhizal trait information and global occurrence data (~100 million records) for 11,770 vascular plant species. Using a plant phylogenetic mega-tree and high-resolution climatic and edaphic data layers, we assess phylogenetic and environmental correlates of plant mycorrhizal traits. We find that plant mycorrhizal type is more phylogenetically conserved than plant mycorrhizal status, while environmental variables (both climatic and edaphic; notably soil texture) explain more variation in mycorrhizal status, especially FM. The previously underestimated role of environmental conditions has far-reaching implications for our understanding of ecosystem functioning under changing climatic and soil conditions. Ramon y Cajal fellowship, Award: RYC2021-032533-I. European Research Council, Project ALFAwetlands. European Commission, Award: Centre of Excellence EcolChange. Alexander von Humboldt Foundation. Narodowa Agencja Wymiany Akademickiej, Award: PPN/ULM/2019/1/00248/U/00001. Estonian Research Council, Award: PRG1065.Estonian Research Council, Award: PRG1789.Estonian Research Council, Award: PRG741. Peer reviewed

This data corresponds to an inter-laboratory study (5 labs) applying a harmonized SPME-GC-MS method to analyze volatile compounds on 15 samples. The data shows the min-max concentration and the repeatability and reproducibility results. This work is relevant to know the performance of this method to be applied as a supporting tool for sensory assessment of virgin olive oil. The data are presented for 18 volatile compound that were selected for their contribution to positive attributes (fruitiness) and sensory defect. This work was developed in the context of the project OLEUM “Advanced solutions for assuring authenticity and quality of olive oil at global scale”, funded by the European Commission within the Horizon 2020 Programme (2014–2020, GA no. 635690). The data set consists of: - a numerical quantitative file saved both in .xlsx and .ods formats: “OLEUM_Dataset_SPME-GC-MS_xlsx.xlsx”, “OLEUM_Dataset_SPME-GC-MS_ods.ods”. - a README file: “OLEUM_Dataset SPME-GC-MS_README_rtf.rtf”.-- Content of the file OLEUM_Dataset_SPME-GC-MS_xlsx/ods: SPME-GC-MS: This sheet presents the repeatability and reproducibility values, expressed as relative standard deviation (RSD%), for three quantification methods (QM1, QM2 and QM3). The three quantification methods are described in Casadei et al., 2021 (Food Control, Vol. 123, 2021, 107823, p. 107823. doi: 10.1016/j.foodcont.2020.107823). Additionally, the concentrations (min and max) determined by the 5 labs for each of 15 samples are shown. OLEUM (Advanced solutions for assuring the overall authenticity and quality of olive oil), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 635690; http://www.oleumproject.eu/. Peer reviewed

These data were generated in our lab, with the help of several laboratory assistants and master students, within the OLEUM project (http://www.oleumproject.eu): ‘Advanced solutions for assuring the authenticity and quality of olive oil at a global scale’ . The general objectives of the Project focus on olive oil fraud detection in a way that it improves the existing analytical methods and it has developed new strategies of analysis. It has been organized in seven work packages distributed in a number of tasks. This work is within Work Package 4 (‘Analytical solution addressing olive oil authentication issues’), and it concentrates on the detection of illegal blends, i.e. of illicit processing (deodorization) in extra virgin olive oil (EVOO) In our endeavor to detect soft deodorized olive oil in EVOO we have focused on the convenience of using two new factors (R1 and R2) obtained as a result of combining the total DAG content and the free acidity value of the samples under the microscope. We chose this methodology following our trend of using well-known, widely reputable routine parameters, avoiding in this way more complicated strategies that, although commonly used in the field of olive oil authentication, could require a more specific personnel training and laboratory apparatus. According to this, we demonstrated that since there is a relationship between free acidity and DAG concentration (both of them come from triacylglyceride hydrolysis and/or biosynthesis), and that such relationship breaks once the oil has gone through a refining process (free fatty acids are removed during the deodorization stage), it was possible to detect the presence of soft deodorized oil in EVOO by using a mathematical combination of both measurements at least to a certain extent. We actually proposed two factors to confirm the absence of soft deodorized oils in EVOO: R1 (10 x free acidity/DAGexp) >/= 0.23 and R2 (DAGexp-DAGtheor) < 0, in genuine EVOO, and we established that such approach was useful to detect the presence of soft deodorized olive oil when this was at least at 30 % in the mixture (Food Chemistry, volume 330, 2020, 127226). Samples were provided by Fera (Fera Science Ltd, Sand Hutton, York) and Institut des Corps Gras (ITERG, Canéjan, France). Excel file consisting of 14 sheets in which the calculation of the total diacylglycerol (DAG) content of a number of defective, non-defective, and soft deodorized olive oil samples, and of some of their corresponding mixtures, are given as examples. Data also show free acidity values and the theoretical DAG composition, together with the calculation of the R1 and R2 factors. It also includes the three resulting tables published as part of Food Chemistry, volume 330, 127226. Data must be read and interpreted within the context of such publication. Original chromatograms are not given since they are considered confidential. The Project has received funding from the European Commission within the Horizon 2020 Program (2014–2020), GA no. 635690. Peer reviewed

These data were generated in our lab, with the help of several laboratory assistants and master students, within the OLEUM project (http://www.oleumproject.eu): ‘Advanced solutions for assuring the authenticity and quality of olive oil at a global scale’ . The general objectives of the Project focus on olive oil fraud detection in a way that it improves the existing analytical methods and has developed new strategies of analysis. It has been organized in seven work packages distributed in a number of tasks. This work is within Work Package 4 (‘Analytical solution addressing olive oil authentication issues’), and it concentrates on the study of legal blends between olive oils and other vegetable oils. The notion of legal blends comes from the authorization of the European Commission to market blends of olive oil with other vegetable oils and to emphasize the presence of olive oil in a place other than in the ingredient list, only if it accounts for at least 50 % of the blend (Commission Implementing Regulation (EU) No 29/2012 of 13 January 2012 on marketing standards for olive oil. Official Journal of the European Union L12, 14-21). This evinced a key weakness in the olive oil control chain: The lack of analytical methods to demonstrate the amount of such oil in declared mixtures. In this way, it was the purpose of this work to look for an analytical strategy that helped us to confirm if the amount of olive oil in a label-claimed blend was at least 50 %. In order to do that we used two of the most representative seed oils: normal type and high oleic sunflower oils (NTSO and HOSO, respectively), mixed on the one hand with olive oil (OO), and on the other hand with extra virgin olive oil (EVOO), at 60:40, 50:50, and 40:60 v/v proportions. We demonstrated that there was no need of developing new methods of analysis but that it was enough to combine four of the official purity parameters described in the legislation (International Olive Council (2016). Trade standard applying to olive oils and olive pomace oils. COI/T.15/NC No 3/Rev. 11, 1-17). Those parameters were: triacylglycerols (TAG), acyclic saturated hydrocarbons (SHC), free sterols (FS), and tocopherols (TCPH). They were also organized them in the form of decisional trees in a way that the blend who claimed to be composed of at least 50 % olive oil must comply not just with one but with the four of them (Food Chemistry, volume 315, 15 June 2020, 126235). Samples were provided by Fera (Fera Science Ltd, Sand Hutton, York) Excel file consisting of 12 sheets in which the composition of a number of olive oil and sunflower oil samples and their corresponding mixtures are given. Data show triacylglycerol, sterol, tocopherol, and linear aliphatic hydrocarbon contents. It also includes the five resulting tables published as part of Food Chemistry, volume 315, 15 June 2020, 126235. Data must be read and interpret within the context of such publication. The Project has received funding from the European Commission within the Horizon 2020 Program (2014–2020), GA no. 635690 Peer reviewed

This data set contains the underlying data of the scientific publication: Casadei, Enrico; Valli, Enrico; Aparicio-Ruiz, Ramón; Ortiz-Romero, Clemente; García-González, Diego L.; Vichi, Stefania; Quintanilla-Casas, Beatriz; Tres, Alba; Bendini, Alessandra; Gallina Toschi, Tullia; 2021; Peer inter-laboratory validation study of a harmonized SPME-GC-FID method for the analysis of selected volatile compounds in virgin olive oils. Food Control 123, 107823. https://doi.org/10.1016/j.foodcont.2020.107823. In the context of supporting the panel test in the classification of virgin olive oils, the qualitative and quantitative analysis of a number of volatile compounds responsible for their aroma is of great importance. Herein, the data obtained from three laboratories that analyzed the same samples are presented with the view to develop an inter-laboratory validation study of a harmonized solid-phase micro-extraction coupled with gas-chromatography with flame ionized detector (SPME-GC-FID) method for determination of selected volatile compounds. The dataset presents the results of the validation study. Three quantification strategies were considered. Repeatability showed a mean relative standard deviation (RSD%) lower than 14% except for ethyl propanoate, 3-methyl-1-butanol, 1-octen-3-ol, and (E)-2-decenal. Linearity was satisfactory (R2 > 0.90) for all compounds when the calibration curves were corrected by the internal standard. The results are showed for 18 volatile compounds that were selected for being for volatile markers of the main sensory defects of virgin olive oil. Peer reviewed EC - H2020.

This publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676876 (GenTree). This work was also supported by the Swiss Secretariat for Education, Research and Innovation (SERI) under contract No. 6.0032. We want to thank to Olivier Ambrosio, Francisco Auñón, Virgilijus Baliuckas, Eduardo Ballesteros, Giorgina Beffa, Sabine Brodbeck, William Brunetto, Jurata Buchovska, Fernando del Caño, Andreas Fera, Rene Graf, Berit Gregorsson, Markus Hartmann, Andreas Helmersson, Enja Hollenbach, Jan Philipp Kappner, Johannes Lambertz, David Lopez-Quiroga, Jérémy Marchon, David Matter, Benjamin Meier, Helge Meischner, Pekka Närhi, Daniel Nievergelt, Juri Nievergelt, Anne Eskild Nilsen, Hans Nyeggen, Geir Østreng, Sebastian Richter, Christoph Rieckmann, Marcus Stefsky, Sergio San Segundo, Ivan Scotti, Jørn Henrik Sønstebø, Arne Steffenrem, Jussi Tiainen, Anne Verstege and Mikael Westerlund for their support during the field campaigns. We are also grateful to all the forest owners and national administrations for providing sampling permissions. The dataset presented here was collected by the GenTree project (EU-Horizon 2020), which aims to improve the use of forest genetic resources across Europe by better understanding how trees adapt to their local environment. This dataset of individual tree-core characteristics including ring-width series and whole-core wood density was collected for seven ecologically and economically important European tree species: silver birch (Betula pendula), European beech (Fagus sylvatica), Norway spruce (Picea abies), European black poplar (Populus nigra), maritime pine (Pinus pinaster), Scots pine (Pinus sylvestris), and sessile oak (Quercus petraea). Tree-ring width measurements were obtained from 3600 trees in 142 populations and whole-core wood density was measured for 3098 trees in 125 populations. This dataset covers most of the geographical and climatic range occupied by the selected species. The potential use of it will be highly valuable for assessing ecological and evolutionary responses to environmental conditions as well as for model development and parameterization, to predict adaptability under climate change scenarios. Update notice Author Correction: The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe (Scientific Data, (2020), 7, 1, (1), 10.1038/s41597-019-0340-y) Scientific Data, Volume 7, Issue 1, 1 December 2020, Article number 114 Peer reviewed 7 Pág.

This data set contains the underlying data of the scientific publication: Aparicio-Ruiz R., Barbieri S., Gallina Toschi T., García-González D.L. (2020). Formulations of Rancid and Winey-Vinegary Artificial Olfactory Reference Materials (AORMs) for Virgin Olive Oil Sensory Evaluation. Foods, 9 (12), 1870, https://doi.org/10.3390/foods9121870. Panel test is the only sensory method included in international regulations of virgin olive oils and its application is compulsory. At present, there is no reference material (RM), in the strict sense of the term, to be used as a validated standard for sensory defects of virgin olive oil with which tasters can be trained. Usually, real samples of virgin olive oils assessed by many panels for the International Olive Council (IOC) ring tests are used as materials of reference in panel training and control. These data correspond to the work carried out to formulate RM that emulate rancid and winey-vinegary defects found in virgin olive oils with the aim of providing reproducible RMs that can be prepared on demand. Under the criteria of representativeness, verified with the advice of the IOC, aroma persistence, and simplicity in formulation, two RMs for winey-vinegary and rancid were obtained by diluting acetic acid and ethanol (winey-vinegary defect) and hexanal (rancid defect) together with other compounds that are used to modify aroma and avoid non-natural sensory notes. Content of the file OLEUM_Portable_ReferenceMaterials_VOO.xlsx/.ods: • Volatile markers: this sheet contains data of volatile markers of the virgin olive oils sensory defects winey-vinegary and rancid, sensory characteristics and corresponding odor threshold relating to an oil matrix. • Relative areas: this sheet contains data of relative areas of volatile compounds selected to emulate winey-vinegary and rancid defects in VOOs in a set of 60 samples and in RMs provided by the International Olive Council for each of the defects (RM IOC). • Formulations RM AV: this sheet contains data of the main formulations (volatile compounds and concentrations in mg/kg) emulating winey-vinegary aroma in virgin olive oil and evaluation by assessors in terms of suitability as possible RM. • Formulation RM R: this sheet contains the data of the main formulations (volatile compounds and concentrations in mg/kg) emulating rancid aroma in virgin olive oil and evaluation by assessors about their eligibility as possible RM. OLEUM (Advanced solutions for assuring the overall authenticity and quality of olive oil), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 635690; http://www.oleumproject.eu/. Aparicio-Ruiz, R.; Barbieri, Sara; Gallina Toschi, Tullia; García-González, Diego L. Peer reviewed

Time Series of NDVI derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, the product and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/ European Commission: ECOPOTENTIAL - ECOPOTENTIAL: IMPROVING FUTURE ECOSYSTEM BENEFITS THROUGH EARTH OBSERVATIONS (641762) Peer reviewed