DOI: 10.5061/dryad.vq83bk3w5
title: Permafrost-thaw lake development in Central Yakutia: sedimentary ancient DNA and element analyses from a Holocene sediment record

description: In Central Yakutia (Siberia) livelihoods of local communities depend on
 alaas (thermokarst depression) landscapes and the lakes within.
 Development and dynamics of these alaas lakes are closely connected to
 climate change, permafrost thawing, catchment conditions, and land use. To
 reconstruct lake development throughout the Holocene we analyze
 sedimentary ancient DNA (sedaDNA) and biogeochemistry from a sediment core
 from Lake Satagay, spanning the last c. 10,800 calibrated years before
 present (cal yrs BP). SedaDNA of diatoms and macrophytes and microfossil
 diatom analysis reveal lake formation earlier than 10,700 cal yrs BP. The
 sedaDNA approach detected 42 amplicon sequence variants (ASVs) of diatom
 taxa, one ASV of Eustigmatophyceae (Nannochloropsis), and 12 ASVs of
 macrophytes. We relate diatom and macrophyte community changes to
 climate-driven shifts in water level and mineral and organic input, which
 result in variable water conductivity, in-lake productivity, and sediment
 deposition. We detect a higher lake level and water conductivity in the
 Early Holocene (c. 10,700–7000 cal yrs BP) compared to other periods,
 supported by the dominance of Stephanodiscus sp. and Stuckenia pectinata.
 Further climate warming towards the Mid-Holocene (7000–4700 cal yrs BP)
 led to a shallowing of Lake Satagay, an increase of the submerged
 macrophyte Ceratophyllum, and a decline of planktonic diatoms. In the Late
 Holocene (c. 4700 cal yrs BP–present) stable shallow water conditions are
 confirmed by small fragilarioid and staurosiroid diatoms dominating the
 lake. Lake Satagay has not yet reached the final stage of alaas
 development, but satellite imagery shows an intensification of
 anthropogenic land use, which in combination with future warming will
 likely result in a rapid desiccation of the lake.
description: SedaDNA was extracted using Dneasy PowerSoil
 and Dneasy PowerSoil Max DNA Isolation Kit. Extracted DNA was combined and
 concentrated using a GeneJet PCR purification Kit. Primers for the
 amplification of diatoms targeted a diagnostic short diatom metabarcode
 (primers: diat_rbcL705 and diat_rbcL808, Stoof-Leichsenring et al. 2012).
 For plant DNA metabarcoding we used standard primers targeting the
 chloroplast trnL P6 loop (Taberlet et al. 2007). PCRs for diatom and plant
 metabarcoding were run in three replicates along with No Template Controls
 (NTCs) to control chemical contamination of PCR chemicals. Purification of
 PCRs was done using MinElute. Samples containing diatoms and plants DNA
 were sequenced in paired-end mode (2x 150 bp) on an Illumina NextSeq 500
 platform at an external sequencing service. We used the Obitools pipeline
 as described in Boyer et al. 2015, but applied the updated version
 Obitools3 (see detailed usage description here:
 https://git.metabarcoding.org/obitools/obitools3). Diatom and plant EMBL
 reference databases were built by using in silico PCR (Ficetola et al.
 2010) with diatom and plant specific primers, respectively, on the EMBL
 Nucleotide Sequence Database (Release 143, April 2020). Plant DNA query
 sequences were matched against the plant EMBL Nucleotide Sequence Database
 and the Arctic and Boreal vascular plant and bryophytes database
 (Willerslev et al. 2014, Soininen et al. 2015, Sønstebø et al. 2010). The
 taxonomic assignment of diatoms was based on 98–100% similarity to at
 least one entry of the diatom EMBL reference database and taxonomic
 assignment of plants was based on a 100% similarity between query
 sequences and the arctic and plant EMBL reference database.
 Reference:  Boyer F, Mercier C, Bonin A, Le Bras Y, Taberlet P, Coissac E (2015) OBITOOLS: a unix-inspired software package for DNA metabarcoding. Mol Ecol Res 16: 176–182. 10.1111/1755-0998.12428. Ficetola, G.F., Coissac, E., Zundel, S. Riaz, S., Shehzad, W., Bessiere, J., Taberlet, P., Pompano, F. (2010) An In silico approach for the evaluation of DNA barcodes. BMC Genomics 11, 434. https://doi.org/10.1186/1471-2164-11-434 Soininen, E. M.; Gauthier, G.; Bilodeau, F.; Berteaux, D.; Gielly, L.; Taberlet, P.; Gussarova, G.; Bellemain, E.; Hassel, K.; Stenøien, H. K.; Epp, L.; Schrøder-Nielsen, A.; Brochmann, C.; Yoccoz, N. G. Highly Overlapping Winter Diet in Two Sympatric Lemming Species Revealed by DNA Metabarcoding. PLoS ONE 2015, 10 (1), e0115335. https://doi.org/10.1371/journal.pone.0115335. Sønstebø, J. H., Gielly, L., Brysting, A. K., Elven, R., Edwards, M., Haile, J., et al. (2010). Using next-generation sequencing for molecular reconstruction of past Arctic vegetation and climate. Mol. Ecol. Resour. 10, 1009–1018. doi: 10.1111/j. 1755-0998.2010.02855.x Stoof-Leichsenring K, Epp L, Trauth M, Tiedemann R (2012) Hidden diversity in diatoms of Kenyan Lake Naivasha: A genetic approach detects temporal variation. Mol Ecol 21: 1918–1930. 10.1111/j.1365-294X.2011.05412.x. Taberlet P, Coissac E, Pompanon F, Gielly L, Miquel C, Valentini A, Vermat T, Corthier G, Brochmann C, Willerslev E (2007) Power and limitations of the chloroplast TrnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res 35 (3): e14–e14. https://doi.org/10.1093/nar/gkl938. Willerslev E, Davison J, Moora M, Zobel M, Coissac E, Edwards ME, Lorenzen ED, Vestergård M, Gussarova G, Haile J, Craine J, Gielly L, Boessenkool S, Epp LS, Pearman PB, Cheddadi R, Murray D, Bråthen KA, Yoccoz N, Binney H, Cruaud C, Wincker P, Goslar T, Alsos IG, Bellemain E, Brysting AK, Elven R, Sønstebø JH, Murton J, Sher A, Rasmussen M, Rønn R, Mourier T, Cooper A, Austin J, Möller P, Froese D, Zazula G, Pompanon F, Rioux D, Niderkorn V, Tikhonov A, Savvinov G, Roberts RG, MacPhee RDE, Gilbert MTP, Kjær KH, Orlando L, Brochmann C, Taberlet P (2014) Fifty thousand years of Arctic vegetation and megafaunal diet. Nature 506 (7486): 47–51. https://doi.org/10.1038/nature12921.
description: The datasets* are prepared for
 the manuscripts: Baisheva et al. (2022): "Permafrost-thaw lake
 development in Central Yakutia – Sedimentary ancient DNA and element
 analyses from a Holocene sediment record" (submitted) and Glückler et
 al. (2022): "Holocene wildfire and vegetation dynamics in Central
 Yakutia, Siberia, reconstructed from lake-sediment proxies"
 (preprint). Also included is the processing of the raw sequencing data
 using bioinformatics tools. *The
 datasets were uploaded into two separate directories containing data and
 scripts. Each directory contains two main folders APMG_32 (Diatoms) and
 APMG_33 (Plants). Files of APMG_32 and APMG_33 after downloading have to
 be merged in the same folder, so the structure of datasets looks like as
 it is given below: 1)      APMG_32
 contains several folders and files of different format: <strong>00.   APMG-32_Metadata</strong>
 - Metadata information including lake geographic coordinates, sample
 depths and ages, laboratory codes and used primer tag combinations of
 forward and reverse primers to enable demultiplexing of the sequencing
 data <em>FILE:</em>   APMG-32_Metadata.xlsx contains information on sequencing (run number, type, device, mode, forward and reverse tags, read length). Also it includes information on individual samples: name, type, age, depth, extraction number, and PCR number, as well as sediment core name and core section number. <em>Format:</em> .xlsx <strong>01. Raw_data_APMG-32</strong> – Illumina sequencing raw data. <em>FILES</em>: 210602_NB501850_A_L1-4_APMG-32_R1.fastq.gz 210602_NB501850_A_L1-4_APMG-32_R2.fastq.gz <em>Format:</em> Illumina fastq format. The sequence files are compressed as .gz archives. Before using the data with the Obitools script (APMG_32_metabarcoding_rbcL_obi3_Dryad.sh) the datasets need to be uncompressed and converted into .fastq files. <strong>02. Reference_data_rbcl</strong> – Database used for taxonomic assignment of diatoms. <em>FILES:</em>   rbcl_embl143_db.fasta Obi3_rbcL_database_build.sh - Script for the conversion step. <em>Format</em>: .fasta and .sh. To use the rbcL database in the Obitools script (APMG_32_metabarcoding_rbcL_obi3_Dryad.sh), the rbcl_embl143_db.fasta needs to be converted to an obi3 database. <strong>03. OBITools_APMG-32</strong> – The metabarcoding pipeline for analyzing the raw sequencing data using OBITools3.  <em>FILES:</em>   APMG_32_metabarcoding_rbcL_obi3_Dryad.sh - Script to run OBITools3 pipeline with short descriptions and output data. APMG-32_embl143_rbcL.csv - Output file. APMG32_tagfile.txt - File contains primer combinations for demultiplexing with Obitools3 (see script: APMG_32_metabarcoding_rbcL_obi3_Dryad.sh). <em>Format:</em> .csv, .txt and .sh <strong>04. Final_resampled_data_APMG-32:</strong> <em>FILES:</em>   APMG-32_identitylevel0.98_wideformat.csv - Final count data. APMG-32_final_resampled_scientific_name.csv - Final dataset with filtering threshold of 98%, resampled to the minimal number of counts (n=2050), including diatoms and Nannochloropsis.  <em>Format:</em> .csv The file APMG-32_final_resampled_data.csv was used for further statistical analyses in Baisheva et al. (2022): "Permafrost-thaw lake development in Central Yakutia – Sedimentary ancient DNA and element analyses from a Holocene sediment record" (submitted).  2)      APMG_33 contains several folders and files of different format: <strong>00.   APMG-33_Metadata </strong>- Contains information on sequencing (run number, type, device, mode, forward and reverse tags, read length). Also it includes information on individual samples: name, type, age, depth, extraction number, and PCR number. As well as sediment core name and core section number. <em>FILE: </em>  APMG-33_Satagay2_metadata.xlsx <em>Format:</em> .xlsx  <strong>01.   Raw_data_APMG_33</strong> – Illumina sequencing raw data. <em>FILES:</em> 210602_NB501850_A_L1-4_APMG-33_R1.fastq.gz 210602_NB501850_A_L1-4_APMG-33_R2.fastq.gz <em>Format:</em> Illumina fast-q format. The sequence files are compressed as .gz archives. The archives can be uncompressed on linux OS using a gzip -d command. <strong>02. Reference_database_plants</strong> – Reference database to run OBITools pipeline with short instruction and script for the conversion step. <em>FILES:</em>   arctborbryo_gh.fasta gh_embl143_db_97.fasta Obi3_arctborbryo_database_build.sh Obi3_embl_database_build.sh <em>Format:</em> .fasta and .sh. To use the arctborbryo embl143 database in the Obitools script (APMG-33_obi3_script.sh), .fasta files need to be converted to an obi3 database. <strong>03. OBITools_APMG-33</strong> – The metabarcoding pipeline for analyzing the raw sequencing data using OBITools3.  <em>FILES:</em>   APMG33_arc_anno.csv - Output file. APMG33_embl143_anno.csv - Output file. APMG-33_obi3_script.sh APMG-33_tagfile.txt <em>Format:</em> .csv, .txt and .sh. OBITools_APMG-33 has two outputs as taxonomic assignment provided against the EMBL and Arctic databases.  <strong>04. Final_datasets_APMG-33</strong> - EMBL and Arctic assignments were  merged into the one dataset and filtered with 100% threshold. Final datasets separated into macrophytes and terrestrial plants.  <em>FILES: </em>  APMG-33_identitylevel100_wideformat.csv - Final count data. APMG-33_macrophytes_resampled_scientific_name.csv - Final dataset of separated macrophytes  and resampled to the minimal number of counts (n=1653).  APMG-33_terrestrial_families.csv - Final dataset of separated terrestrial plants. <em>Format:</em> .csv The file “APMG-33_macrophytes_resampled_scientific_name.csv” from output data was used for further statistical analyses in Baisheva et al. (2022): "Permafrost-thaw lake development in Central Yakutia – Sedimentary ancient DNA and element analyses from a Holocene sediment record" (submitted).  The file “APMG-33_terrestrial_families.csv” of separated terrestrial plants data was used for further statistical analyses in Glückler et al. (2022): "Holocene wildfire and vegetation dynamics in Central Yakutia, Siberia, reconstructed from lake-sediment proxies" (preprint).