Description
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.
Enregistrements de données
Les données de cette ressource données d'échantillonnage ont été publiées sous forme d'une Archive Darwin Core (Darwin Core Archive ou DwC-A), le format standard pour partager des données de biodiversité en tant qu'ensemble d'un ou plusieurs tableurs de données. Le tableur de données du cœur de standard (core) contient 472 enregistrements.
1 tableurs de données d'extension existent également. Un enregistrement d'extension fournit des informations supplémentaires sur un enregistrement du cœur de standard (core). Le nombre d'enregistrements dans chaque tableur de données d'extension est illustré ci-dessous.
Cet IPT archive les données et sert donc de dépôt de données. Les données et métadonnées de la ressource sont disponibles pour téléchargement dans la section téléchargements. Le tableau des versions liste les autres versions de chaque ressource rendues disponibles de façon publique et permet de tracer les modifications apportées à la ressource au fil du temps.
Versions
Le tableau ci-dessous n'affiche que les versions publiées de la ressource accessibles publiquement.
Comment citer
Les chercheurs doivent citer cette ressource comme suit:
Gómez Aparicio L, Zamora Ballesteros C, Gil Martinez M, García Garrido S (2023). Phytophthora members of the rhizosphere microbiota of Quercus ilex subsp. ballota. Version 2.4. Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC). Samplingevent dataset. https://doi.org/10.15470/hcvbbv
Droits
Les chercheurs doivent respecter la déclaration de droits suivante:
L’éditeur et détenteur des droits de cette ressource est Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC). Ce travail est sous licence Creative Commons Attribution (CC-BY) 4.0.
Enregistrement GBIF
Cette ressource a été enregistrée sur le portail GBIF, et possède l'UUID GBIF suivante : 3cf6d773-e669-4535-b16a-1f7a3e23d281. Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC) publie cette ressource, et est enregistré dans le GBIF comme éditeur de données avec l'approbation du GBIF Spain.
Mots-clé
Samplingevent; Biodiversity; Edafic Biodiversity
Contacts
- Créateur ●
- Personne De Contact ●
- Chercheur Principal
https://orcid.org/0000-0001-5122-3579
- Créateur
- Créateur
- Créateur
Couverture géographique
Huelva (Spain), Sevilla (Spain) and Córdoba (Spain)
| Enveloppe géographique | Sud Ouest [-90, -180], Nord Est [90, 180] |
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Couverture taxonomique
N/A
| Kingdom | Fungi |
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| Phylum | Pseudofungi |
| Class | Oomycetes |
| Order | Peronosporales |
| Family | Peronosporaceae |
Données sur le projet
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.
| Titre | 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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| Identifiant | LIFEWATCH-2019-09-CSIC-4, POPE 2014-2020 |
| Financement | 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. |
| Description du domaine d'étude / de recherche | 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. |
| Description du design | 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. |
Méthodes d'échantillonnage
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.
| Etendue de l'étude | 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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Description des étapes de la méthode:
- 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).
Citations bibliographiques
- 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) https://doi.org/10.15470/hcvbbv
- 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
Métadonnées additionnelles
| Identifiants alternatifs | 10.15470/hcvbbv |
|---|---|
| 3cf6d773-e669-4535-b16a-1f7a3e23d281 | |
| https://ipt.gbif.es/resource?r=irnas-phytophthora |