Descripción
The general objective of thE WP7 is to advance our understanding of the impacts of global change drivers - mainly climate change and exotic pathogens - on the above- and below-ground biodiversity of Mediterranean forests and silvopastoral agrosystems, and to use the resulting information to propose management tools aim to improve the resistance and resilience of these forests in scenarios of increasing abiotic and biotic stress. We will focus our research on forests and dehesas of evergreen Quercus species (Quercus suber and Quercus ilex) in Andalusia, due to their strategic ecological and economic importance and their current vulnerability status as a result of increasing aridity and the invasion of the aggressive exotic pathogen Phytophthora cinammomi.
Registros
Los datos en este recurso de evento de muestreo han sido publicados como Archivo Darwin Core(DwC-A), el cual es un formato estándar para compartir datos de biodiversidad como un conjunto de una o más tablas de datos. La tabla de datos del core contiene 506 registros.
también existen 1 tablas de datos de extensiones. Un registro en una extensión provee información adicional sobre un registro en el core. El número de registros en cada tabla de datos de la extensión se ilustra a continuación.
Este IPT archiva los datos y, por lo tanto, sirve como repositorio de datos. Los datos y los metadatos del recurso están disponibles para su descarga en la sección descargas. La tabla versiones enumera otras versiones del recurso que se han puesto a disposición del público y permite seguir los cambios realizados en el recurso a lo largo del tiempo.
Versiones
La siguiente tabla muestra sólo las versiones publicadas del recurso que son de acceso público.
¿Cómo referenciar?
Los usuarios deben citar este trabajo de la siguiente manera:
Gomez Aparicio L, García Garrido S (2023). Fungal members of the rhizosphere microbiota of Quercus ilex subsp. ballota. Version 2.1. Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC). Samplingevent dataset. https://doi.org/10.15470/jow7hd
Derechos
Los usuarios deben respetar los siguientes derechos de uso:
El publicador y propietario de los derechos de este trabajo es Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC). Esta obra está bajo una licencia Creative Commons de Atribución/Reconocimiento (CC-BY 4.0).
Registro GBIF
Este recurso ha sido registrado en GBIF con el siguiente UUID: 0360980c-a656-482a-a689-3c1f1ee3f7d9. Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC) publica este recurso y está registrado en GBIF como un publicador de datos avalado por GBIF Spain.
Palabras clave
samplingEvent; Biodiversity; Edafic Biodiversity
Contactos
- Punto De Contacto
- Originador
- Punto De Contacto
https://orcid.org/0000-0001-5122-3579
- Punto De Contacto
- Punto De Contacto
Cobertura geográfica
Huelva (Spain), Sevilla (Spain) and Córdoba (Spain)
| Coordenadas límite | Latitud Mínima Longitud Mínima [-90, -180], Latitud Máxima Longitud Máxima [90, 180] |
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Cobertura taxonómica
No hay descripción disponible
| Filo | Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota, Chytridiomycota |
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Datos del proyecto
The general objective of thE WP7 is to advance our understanding of the impacts of global change drivers - mainly climate change and exotic pathogens - on the above- and below-ground biodiversity of Mediterranean forests and silvopastoral agrosystems, and to use the resulting information to propose management tools aim to improve the resistance and resilience of these forests in scenarios of increasing abiotic and biotic stress.
| Título | Sustainability for Mediterranean Hotspots in Andalusia integrating LifeWatch ERIC (SUMHAL). Working package 7: Improving sustainability of Mediterranean forests and silvopastoral agrosystems under climate change |
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| Identificador | LIFEWATCH-2019-09-CSIC-4, POPE 2014-2020 |
| Fuentes de Financiación | This study was funded by MICINN through European Regional Development Fund [SUMHAL, LIFEWATCH-2019-09-CSIC-13, POPE 2014-2020] and by the Spanish Ministry of Economy, Industry and Competitiveness [AGL2015-66048-C2-1-R; RTI2018-098015-B-I00]. To be referred from 2023 onwards as SUMHAL, LIFEWATCH-2019-09-CSIC-4, POPE 2014-2020. |
| Descripción del área de estudio | We will focus our research on forests and dehesas of evergreen Quercus species (Quercus suber and Quercus ilex) in Andalusia, due to their strategic ecological and economic importance and their current vulnerability status as a result of increasing aridity and the invasion of the aggressive exotic pathogen Phytophthora cinammomi. |
| Descripción del diseño | Soil at 5 to 20 cm depth attached to the secondary roots of every tree (Quercus ilex subsp. ballota) was collected, transported on ice and frozen at -80 ºC until processed. The DNA from each sample was extracted using DNeasy Power Soil Pro kit (QIAGEN) according to the manufacturer’s instructions. V3-V4 16S rRNA and ITS2 regions from Bacteria and Fungi kingdoms, respectively, were amplified. Likewise, in order to study the presence of Phytophthora species, amplicon libraries using the Phytophthora-specific primers that amplify ITS1 region were created using a nested PCR approach. The libraries were sequenced with Illumina MiSeq platform using 2 x 275 bp paired-end reads. |
Personas asociadas al proyecto:
Métodos de muestreo
The Illumina paired-end raw sequences were processed using the freely available bioinformatics software QIIME 2 version 2022.2.0 (Bolyen et al., 2019). The sequences from each target (bacteria, fungi or Phytophthora spp.) and each sequencing run were processed equally but separately throughout the analysis. The sequences were trimmed by implementing cutadapt (Martin, 2011) in QIIME 2 with q2-cutadapt plugin and trim-paired command. Chimeric sequences were identified and deleted after quality filtering and de-noising using the Divisive Amplicon Denoising Algorithm 2 (DADA2) pipeline implemented in QIIME 2 with q2-dada2 plugin and denoise-paired command (Callahan et al., 2016). The resulting amplicon sequence variants (ASVs) identified were curated using mumu by removing taxonomically redundant and erroneous ASVs. This involved constructing a database of the ASV sequences using makeblastdb application from the BLAST v.2.9.0 software suite, from which match lists were created using the blastn algorithm (query coverage: 80; percent identity cutoff: 84). These match lists and the ASV feature tables were input into the mumu algorithm to produce curated ASV tables. Singletons were excluded from the analysis. Taxonomic classification of the ASVs for bacteria and fungi was performed with pre-trained Naive Bayes classifiers and the q2-feature-classifier plugin (Bokulich et al., 2018). For the bacterial dataset, the SILVA v.138 database (Quast et al., 2013) was applied using a pre-trained classifier specifically curated for the sequenced 16SV3V4 region. In the case of the fungal dataset, the UNITE dynamic database v.8.3 (Abarenkov et al., 2021), which has been manually curated by experts in these particular fungal lineages, was used. Sequences assigned to mitochondria, chloroplasts and archaea were removed using the q2-taxa plugin in QIIME 2 and a taxonomy-based filtering step using the qiime taxa filter-seqs and qiime taxa filter-table commands.
| Área de Estudio | Soil at 5 to 20 cm depth attached to the secondary roots of every tree (Quercus ilex subsp. ballota) was collected, transported on ice and frozen at -80 ºC until processed. The DNA from each sample was extracted using DNeasy Power Soil Pro kit (QIAGEN) according to the manufacturer’s instructions. V3-V4 16S rRNA and ITS2 regions from Bacteria and Fungi kingdoms, respectively, were amplified. Likewise, in order to study the presence of Phytophthora species, amplicon libraries using the Phytophthora-specific primers that amplify ITS1 region were created using a nested PCR approach. The libraries were sequenced with Illumina MiSeq platform using 2 x 275 bp paired-end reads. |
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Descripción de la metodología paso a paso:
- The taxonomic assignment to the ASVs identified in the analysis of Phytohthora species was performed by generating a reference database from a combination of sequences from five different sources: the UNITE dynamic database v.8.3 (consisting of 58,440 eukaryotic sequences), reference sequences from phytophthoradb (http://www.phytophthoradb.org/; 340 sequences), reference sequences from Phytopthora-id (http://Phytophthora-id.org; 270 sequences), 174 sequences of Phytophthora spp. from the database generated in Riddell et al. (2019), and 39,701 sequences from Genbank matching the search "oomycota 'internal transcribed spacer'". In the latter case, the taxonomy for the Genbank accessions was obtained using the taxonomizr package (v 0.10.2) in R v 4.2.2. Finally, the reference sequence database, namely the combined fasta file (98,925 sequences), and the associated taxonomy description file were imported into QIIME 2 and used together with the qiime feature-classifier plugin and the classify-consensus-blast command. As a first step, the sequences of the potential Phytophthora ASVs were aligned against the custom reference database using strict homology parameters (query coverage: 90; percent identity cutoff: 99) to ensure that successful matches belong to a Phytophthora species. The unaligned ASVs were submitted to a second step with relaxed parameters (query coverage: 75; percent identity cutoff: 65). The third step consisted of comparing the unassigned ASVs from the second step to the entire NCBI non-redundant protein database (release-255) using default parameters. In the fourth step, the low confidence ASVs assigned in the second and third steps were concatenated, aligned with MAFFT, and used to construct a maximum likelihood (ML) phylogenetic tree using RAxML (v 8.2.12; Stamatakis, 2014). The tree was inferred employed a general time reversible substitution model with a computational work–around (GTRCAT) without rate heterogeneity with a correction for ascertainment bias. Statistical support was calculated by applying bootstrap runs in an automated approach (autoMRE), where RaxML executes a maximum of 1000 BS replicate searches, although convergence may occur earlier. The best-scoring ML tree of the search analysis was then visualized using the software FIGTREE version 1.4.4 (Rambaut, 2018).
Referencias bibliográficas
- Abarenkov, K., Zirk, A., Piirmann, T., Pöhönen, R., Ivanov, F., Nilsson, R.H., Kõljalg, U., 2021. UNITE QIIME release for Fungi. Version 10.05.2021. [WWW Document]. UNITE Community. URL https://doi.plutof.ut.ee/doi/10.15156/BIO/1264708 (accessed 1.17.23)
- Bokulich, N.A., Kaehler, B.D., Rideout, J.R., Dillon, M., Bolyen, E., Knight, R., Huttley, G.A., Gregory Caporaso, J., 2018. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90. https://doi.org/10.1186/s40168-018-0470-z
- Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet, C.C., Al-Ghalith, G.A., Alexander, H., Alm, E.J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J.E., Bittinger, K., Brejnrod, A., Brislawn, C.J., Brown, C.T., Callahan, B.J., Caraballo-Rodríguez, A.M., Chase, J., Cope, E.K., Da Silva, R., Diener, C., Dorrestein, P.C., Douglas, G.M., Durall, D.M., Duvallet, C., Edwardson, C.F., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J.M., Gibbons, S.M., Gibson, D.L., Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G.A., Janssen, S., Jarmusch, A.K., Jiang, L., Kaehler, B.D., Kang, K.B., Keefe, C.R., Keim, P., Kelley, S.T., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M.G.I., Lee, J., Ley, R., Liu, Y.-X., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B.D., McDonald, D., McIver, L.J., Melnik, A.V., Metcalf, J.L., Morgan, S.C., Morton, J.T., Naimey, A.T., Navas-Molina, J.A., Nothias, L.F., Orchanian, S.B., Pearson, T., Peoples, S.L., Petras, D., Preuss, M.L., Pruesse, E., Rasmussen, L.B., Rivers, A., Robeson, M.S., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S.J., Spear, J.R., Swafford, A.D., Thompson, L.R., Torres, P.J., Trinh, P., Tripathi, A., Turnbaugh, P.J., Ul-Hasan, S., van der Hooft, J.J.J., Vargas, F., Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y., Wang, M., Warren, J., Weber, K.C., Williamson, C.H.D., Willis, A.D., Xu, Z.Z., Zaneveld, J.R., Zhang, Y., Zhu, Q., Knight, R., Caporaso, J.G., 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9
- Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869
- Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12. https://doi.org/10.14806/ej.17.1.200
- Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glöckner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41, D590–D596. https://doi.org/10.1093/nar/gks1219
- Rambaut, A., 2018. FigTree.
- Riddell, C.E., Frederickson-Matika, D., Armstrong, A.C., Elliot, M., Forster, J., Hedley, P.E., Morris, J., Thorpe, P., Cooke, D.E.L., Pritchard, L., Sharp, P.M., Green, S., 2019. Metabarcoding reveals a high diversity of woody host-associated Phytophthora spp. In soils at public gardens and amenity woodlands in Britain. PeerJ 7, e6931. https://doi.org/10.7717/PEERJ.6931/SUPP-3
- Stamatakis, A., 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033
Metadatos adicionales
| Identificadores alternativos | 10.15470/jow7hd |
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| 0360980c-a656-482a-a689-3c1f1ee3f7d9 | |
| https://ipt.gbif.es/resource?r=fungal-rhizo-quercus |