Saproxylic Fungal Communities in Boreal Forest, Finland, Oulanka, 2022-2023

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Os dados deste recurso de evento de amostragem foram publicados como um Darwin Core Archive (DwC-A), que é o formato padronizado para compartilhamento de dados de biodiversidade como um conjunto de uma ou mais tabelas de dados. A tabela de dados do núcleo contém 142 registros.

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This IPT archives the data and thus serves as the data repository. The data and resource metadata are available for download in the downloads section. The versions table lists other versions of the resource that have been made publicly available and allows tracking changes made to the resource over time.

Versões

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Como citar

Pesquisadores deveriam citar esta obra da seguinte maneira:

Shumskaya M, Lim J, Saarinen P K, Apgar S, Hoyte B, Nunez M, Gayathri M S, Vengine L, Salib C, Seidle M, Inoa A (2024). Saproxylic Fungal Communities in Boreal Forest, Finland, Oulanka, 2022-2023. Version 1.9. Kean University. Samplingevent dataset. https://ipt.gbif.us/resource?r=mycopinsfinland23-24&v=1.9

Direitos

Pesquisadores devem respeitar a seguinte declaração de direitos:

O editor e o detentor dos direitos deste trabalho é Kean University. This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.

GBIF Registration

Este recurso foi registrado no GBIF e atribuído ao seguinte GBIF UUID: 63283fef-d82f-40ba-9346-c4810e9690dc.  Kean University publica este recurso, e está registrado no GBIF como um publicador de dados aprovado por GBIF-US.

Palavras-chave

dead wood; molecular ecology; metabarcoding; Samplingevent; Specimen

Dados externos

Os dados de recurso também estão disponíveis em outros formatos

Contatos

Cobertura Geográfica

Oulanka Research Station https://eu-interact.org/field-sites/oulanka-research-station/ 25 km south of the Arctic Circle Sub-Arctic (Boreal zone) No permafrost

Cobertura Taxonômica

Saproxylic fungi from Ascomycetes and Basidiomycetes were identified from DNA extracted from saw dust of wooden pins (pine, spruce, birch) using MycoPins method (Shumskaya, 2023).

Cobertura Temporal

Dados Sobre o Projeto

The MycoPins (previously: ezDowels) project aimed to develop a rapid affordable protocol to monitor early colonization events in communities of boreal wood-inhabiting (saproxylic) fungi. Building upon research experiences of applicants, earlier INTERACT data, and the latest metabarcoding methods, setting and testing of sampling techniques was performed, as well as sample processing, data processing and analysis of the development of early dead wood fungal communities. The fieldwork carried out at INTERACT sites was followed by the laboratory analysis at Kean university. The project aimed to add a new monitoring method for INTERACT portfolio, and pave a way for a broader scale Remote Access project, and establish a well-documented internationally standardized data publishing routine for research stations to increase visibility and current Virtual Access through global data discoverability portals.

O pessoal envolvido no projeto:

Métodos de Amostragem

Sterilized wooden pins of softwood (pine and spruce) and hardwood (birch), each in a duplicate, were placed on the top soil of four different sampling sites: transect A - conifer forest with reindeer access, transect B - conifer forest without reindeer access, transect C - broadleaf forest with access to tourists, transect D - swamp. The pins were collected approximately every 2 weeks during summer and fall seasons between July 1, 2022 and October 6, 2023. Upon collection, the pins were dried for 2 hours at 45°C and stored at room temperature. Sawdust then was extracted by drilling and DNA was isolated from it. A set of 40 tagged primers for ITS fungal region (Clemmensen, 2016) were used to perform PCR with each DNA sample. The tagged amplicons were then mixed into a multiplex which was used for Next Generation Sequencing. The resultant sequence files were processed in SCATA pipeline (https://scata.mykopat.slu.se/). Species were identified using a curated fungi database UNITE Fungi v 9.0 (https://unite.ut.ee/) in SCATA using USEARCH algorithm, and then those that were not identified in it were identified using BLAST algorithm and Nucleotide database of NCBI (https://www.ncbi.nlm.nih.gov/) . Taxonomic IDs were aligned with the GBIF backbone taxonomy database, and fungal traits were assigned using the FungalTraits database (Põlme, 2020).

Descrição dos passos do método:

1. MycoPin placement Four 10 m wires (transects) were prepared with pin sets (MycoPins) attached to each one of them at every meter. Each MycoPin set consisted of 6 pins (a sextet): a pair of pine pins (softwood), a pair of birch pins (hardwood), and a pair of spruce pins (softwood). Each sextet was labeled with an individual number. Each transect was attached to a tree and then placed on the top soil with the pin sets buried under the top leaf and soil matter. One transect was placed at four different sampling sites: (A) An area of a boreal forest protected from grazing by reindeers. (B) An area of a boreal forest located next to A, but unprotected by reindeers. (C) An area of a mixed broadleaf forest, accessed by random visitors. (D) An area of a swamp protected from visitors.
2. Extraction and storage. One MycoPin sextet from each transect was located using the wire transect as a guide and collected every two weeks with exceptions for weather conditions. The collected sextets were dried in separate waxed paper bags for 2-3 hours at 45°C and stored dry at room temperature.
3. DNA isolation. The core of each pin from each sexted was drilled using a 2 mm fire-sterilized drill bit. The resultant sawdust was collected in a sterile centrifuge tube. The sawdust was then used to isolate genomic DNA using PowerSoil DNA Isolation kit from Qiagen (USA) according to the manufacturer instructions. Homogenization was performed using BeadBug homogenizers (BenchMark Scientific). DNA concentration was measured using NanoDrop (ThermoFisher). Genomic DNA was stored at −80°C.
4. PCR. Tagged primers for ITS2 fungal region were used to perform PCR according to Clemmensen (2016). While the forward and reverse primers were always the same, a pair of primers with a unique nucleotide tag was used to perform PCR for each DNA extracted from each MycoPin. The amplification was verified via agarose gel electrophoresis. The amplified DNA was purified and stored at −20°C. E.Z.N.A® Cycle Pure Kit (Omega Bio-tek) was used for the amplicons purification. Positive control was used to verify the PCR and subsequent NGS in a form of mock fungal community made of 12 plasmids (Palmer, 2018), negative control was used to exclude false-positive results in a form of water.
5. Next-Generation Sequencing. The amplified tagged DNA samples were combined at equal amounts of 100 ng to create a multiplex for next-generation sequencing. The multiplex was sequenced using AmpliconEZ service at Genewiz (Azenta Life Sciences, New Jersey, USA).
6. Bioinformatics. Two paired FASTQ files for each multiplex were analyzed using the following procedure:
   1. The FASTQ files were uploaded to SCATA pipeline (https://scata.mykopat.slu.se/) .
   2. A SCATA pipeline was used to exclude sequences of low quality, clustering of similar sequences, and identification of species using UNITE v. 9.0 (2023-07-18) fungi database. Clusters present in positive and negative controls were excluded.
      1. Sequence quality was parameterized to include only the following: (1) a 90% primer match on tag identification, (2) a minimum sequence length of 200, (3) a minimum base quality of 10, and (4) a minimum mean base quality of 20. Pipeline was configured to overlap and merge the FASTQ files. Kmer size for overlap search was set to 7. The minimum number of adjacent kmers to form high-scoring segment pairs during overlap search was set to 5. The minimum number of shared kmers to merge a read pair was set to 10.
      2. SCATA uses the USEARCH algorithm for clustering. Clustering distance was set to 0.015. The minimum proportion of the longest sequence in a sequence pair to consider for clustering was set to 0.85. Penalty for mismatch was set to 1. No penalty was set on an introduction of an open gap. However, a penalty of 1 is incurred for each succeeding gap. No weights were used for end gaps. Homopolymers longer than 3 before clustering were collapsed. No down sampling and no removal of low frequency genotypes were performed during clustering. Up to 3 representative sequences were reported for each cluster.
   3. Double clusters and clusters present in positive and negative controls were excluded.
   4. For each of the clusters without a match from the UNITE database, a BLAST search against NCBI database was performed. The search result with the lowest e-value and the highest percent identity was considered the best match species for the cluster. The BLAST match results with a score less than 200 were excluded. If there are multiple best matches, the first match in the best match list is selected.
   5. The abundance of the same species were amalgamated.
   6. Each species ID was aligned with the taxonomy of GBIF Backbone using statistical software R and rgbif package v. 3.7.9. Non-fungal species were rejected. Fungal species not identified on the genus-level, at the minimum, were also discarded. Fungal traits were assigned according to FungalTraits database (from an Excel sheet, supplementary data of Põlme, 2020). Historical weather data for each transect were gathered from Weatherstack (www.weatherstack.com).

Dados de Coleção

Citações bibliográficas

1. Shumskaya M, Lorusso N, Patel U, Leigh M, Somervuo P, Schigel D (2023) MycoPins: a metabarcoding-based method to monitor fungal colonization of fine woody debris. Mycokeys: 77-95. 10.3897/mycokeys.96.101033
2. Clemmensen KE, Ihrmark K, Durling MB, Lindahl BD (2016) Sample Preparation for Fungal Community Analysis by High-Throughput Sequencing of Barcode Amplicons. Methods in Molecular Biology (Clifton, NJ). Humana Press: New York, NY, USA, 61-88. 10.1007/978-1-4939-3369-3_4
3. Palmer JM, Jusino MA, Banik MT, Lindner DL (2018) Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. Peerj 6: e4925. 10.7717/peerj.4925
4. Abarenkov, K.; Zirk, A.; Piirmann, T.; Pöhönen, R.; Ivanov, F.; Nilsson, H.; Kõljalg, U.(2023): UNITE general FASTA release for Fungi 2. Version 18.07.2023. UNITE Community. 10.15156/BIO/2938068
5. Põlme, S., Abarenkov, K., Henrik Nilsson, R., Lindahl, B. D., Clemmensen, K. E., Kauserud, H., Nguyen, N., Kjøller, R., Bates, S. T., Baldrian, P., Frøslev, T. G., Adojaan, K., Vizzini, A., Suija, A., Pfister, D., Baral, H. O., Järv, H., Madrid, H., ... Pradeep, C. K. (2020). FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Diversity, 105(1), 1-16. 10.1007/s13225-020-00466-2

Metadados Adicionais