10.5061/DRYAD.VQ83BK3W5
Baisheva, Izabella
0000-0002-9330-1122
Alfred Wegener Institute for Polar and Marine Research
Levina, Sardana
0000-0002-7653-7379
North-Eastern Federal University
Biskaborn, Boris K.
0000-0003-2378-0348
Alfred Wegener Institute for Polar and Marine Research
Vyse, Stuart A.
Alfred Wegener Institute for Polar and Marine Research
Heim, Birgit
0000-0003-2614-9391
Alfred Wegener Institute for Polar and Marine Research
Pestryakova, Luidmila
0000-0001-5347-4478
North-Eastern Federal University
Glückler, Ramesh
0000-0003-1800-8601
Alfred Wegener Institute for Polar and Marine Research
Herzschuh, Ulrike
0000-0003-0999-1261
Alfred Wegener Institute for Polar and Marine Research
Stoof-Leichsenring, Kathleen R.
0000-0002-6609-3217
Alfred Wegener Institute for Polar and Marine Research
Permafrost-thaw lake development in Central Yakutia: sedimentary ancient
DNA and element analyses from a Holocene sediment record
Dryad
dataset
2022
FOS: Earth and related environmental sciences
sedaDNA
metabarcoding
lake sediment core
Central Yakutia
Siberia
Permafrost
alaas
thaw-lake
Diatoms
Plants
macrophytes
Earth-Surface Processes
Aquatic Science
European Research Council
https://ror.org/0472cxd90
Glacial Legacy: 772852
The Ministry of Education and Science of the Russian Federation
https://ror.org/00ghqgy32
FSRG-2020-0019
German Academic Exchange Service
https://ror.org/039djdh30
AWI INSPIRES (International Science Program for Integrative Research)*
German Academic Exchange Service e.V.
91775743
AWI INSPIRES
Earth Systems Knowledge Platform (ESKP) of the Helmholtz Foundation
the European Research Council
Glacial Legacy: 772852
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI)
2022-07-29T00:00:00Z
2022-07-29T00:00:00Z
en
https://doi.org/10.5194/egusphere-2022-395
https://doi.org/10.1007/s10933-023-00285-w
https://doi.org/10.5281/zenodo.6937268
5440845561 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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.
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: 00. APMG-32_Metadata - 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 FILE: 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. Format:
.xlsx 01. Raw_data_APMG-32 – Illumina sequencing raw data. FILES:
210602_NB501850_A_L1-4_APMG-32_R1.fastq.gz
210602_NB501850_A_L1-4_APMG-32_R2.fastq.gz Format: 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. 02.
Reference_data_rbcl – Database used for taxonomic assignment of diatoms.
FILES: rbcl_embl143_db.fasta Obi3_rbcL_database_build.sh - Script for
the conversion step. Format: .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. 03.
OBITools_APMG-32 – The metabarcoding pipeline for analyzing the raw
sequencing data using OBITools3.
FILES: 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). Format: .csv, .txt and .sh 04.
Final_resampled_data_APMG-32:
FILES: 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. Format: .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: 00. APMG-33_Metadata -
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.
FILE: APMG-33_Satagay2_metadata.xlsx Format: .xlsx 01.
Raw_data_APMG_33 – Illumina sequencing raw data. FILES:
210602_NB501850_A_L1-4_APMG-33_R1.fastq.gz
210602_NB501850_A_L1-4_APMG-33_R2.fastq.gz Format: 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. 02.
Reference_database_plants – Reference database to run OBITools pipeline
with short instruction and script for the conversion step.
FILES: arctborbryo_gh.fasta gh_embl143_db_97.fasta
Obi3_arctborbryo_database_build.sh Obi3_embl_database_build.sh Format:
.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. 03. OBITools_APMG-33 – The metabarcoding pipeline for
analyzing the raw sequencing data using OBITools3.
FILES: APMG33_arc_anno.csv - Output file. APMG33_embl143_anno.csv -
Output file. APMG-33_obi3_script.sh APMG-33_tagfile.txt Format: .csv, .txt
and .sh. OBITools_APMG-33 has two outputs as taxonomic assignment provided
against the EMBL and Arctic databases. 04. Final_datasets_APMG-33 - EMBL
and Arctic assignments were merged into the one dataset and filtered with
100% threshold. Final datasets separated into macrophytes and terrestrial
plants. FILES: 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. Format: .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).