Sedimentary DNA of a human‐impacted lake in Western Canada (Cultus Lake) reveals changes in micro‐eukaryotic diversity over the past ~200 years
Data files
May 14, 2024 version files 1.68 MB
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ASVTable_CultusLakeSedimentCore.csv
303.19 KB
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README.md
3.21 KB
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SampleDescription_CultusLakeSedimentCore.csv
2.14 KB
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TaxonomyTable_CultusLakeSedimentCore.csv
509.53 KB
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V7-18SrRNAsequences_CultusLakeSedimentCore.fasta
858.64 KB
Abstract
Although the use of genetic analyses of sedimentary DNA to track changes in biodiversity has increased over the last decade, questions remain as to how well DNA captures past ecological conditions. Even less is known about how extracellular and intracellular DNA are archived in lake sediments and whether the two fractions yield similar information. Here we characterized the changes of micro‐eukaryotic communities over the past ~200 years in Cultus Lake (British Columbia, Canada), for which a rich body of limnological data and a pre‐existing multi‐proxy paleolimnological study exist. We generated and analyzed 18S rRNA gene amplicons and found that extracellular and intracellular DNA provided different insights, with the preservation of extracellular DNA compromised in sediments older than ~30 years. Principal Coordinates and indicator species analyses based on intracellular DNA showed that changes in micro‐eukaryotic diversity occurred at similar time periods as those identified with the classical paleolimnological study. For instance, decreases of Opisthokonta amplicons occurred during years with elevated numbers of sockeye salmon spawners, which might be associated with an increase of herbivory by juvenile sockeye salmon. Furthermore, two diatom species identified morphologically exhibited similar temporal dynamics to two diatom taxa identified genetically, suggesting that sedimentary DNA can track past diatom species changes as well as micro‐eukaryotic community changes. Overall, our study provides insights into the use of extracellular and intracellular DNA in sedimentary records and showed that sedimentary DNA enriches our understanding of micro‐eukaryotic community changes over centennial time scales.
README: Sedimentary DNA of a human‐impacted lake in Western Canada (Cultus Lake) reveals changes in micro‐eukaryotic diversity over the past ~200 years
Description of the data and file structure
This following data files were submitted:
1) SampleDescription_CultusLakeSedimentCore.csv: a sample description with subsample of sediments and age estimation
2) TaxonomyTable_CultusLakeSedimentCore.csv: the taxonomy of the ASV identified with the 18S rRNA gene primers
3) V7-18SrRNAsequences_CultusLakeSedimentCore.fasta: the fasta file with the DNA sequence of each ASV
4) ASVTable_CultusLakeSedimentCore.csv: the number of sequences of the ASVs in each sample
SampleDescription_CultusLakeSedimentCore.csv
SampleID: Number given to the sample for its location in the core. The A means that it was the A replicate analysed for metabarcoding (collected A and B replicates), and S or P means that it is extracellular (supernatant) or intracellular (pellet). S and P were used because of the process to separate both DNA fraction during the DNA extraction, i.e. after addition of NaP buffer to deadsorb the extracellular to the sediment matrix and centrifugation. The supernatant contains mostly extracellular DNA while the pellet contained intracellular DNA.
SedimentIntervalDepth_Top_cm: the top of the sediment interval collected in cm; 0cm being the top of the core (more recent sediments)
SedimentIntervalDepth_Bottom_cm: the bottom of the sediment interval collected in cm
SedimentInterval_MidDepth_cm: the middle of the sediment interval collected in cm
AgeModelEstimatedYear: The estimated year of the sediment sample according to the Constant rate of supply (CRS) model
SedimentYear: year of the sediment sample used for statistical analyses
DNAfraction: indicates whether the DNA was extracted as extracellular of intracellular DNA
TaxonomyTable_CultusLakeSedimentCores.csv
asvCode: Code of ASV attributed during the bioinformatic process
The taxonomy of the ASVs includes the levels of Kingdom, Phylum, Subphylum, Class, Order, Family, Genus and Species. The taxonomy was obtained using the reference database PR2 – SSU rRNA gene version 4.10.0 (Guillou et al. 2013).
Guillou, L., Bachar, D., Audic, S., Bass, D., Berney, C., Bittner, L., Boutte, C., Burgaud, G., de Vargas, C., Decelle, J., del Campo, J., Dolan, J. R., Dunthorn, M., Edvardsen, B., Holzmann, M., Kooistra, W. H., Lara, E., le Bescot, N., Logares, R., ... Christen, R. (2013). The protist ribosomal reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Research, 41, D597–D604. https://doi.org/10.1093/ nar/gks1160
V7-18SrRNAsequences_CultusLakeSedimentCores.fasta
This fasta file contained the asv code and its associated V7 18S rRNA gene sequence.
ASVTable_CultusLakeSedimentCore.csv
SampleID: same as in the file SampleDescription_CultusLakeSedimentCore.csv
DNAfraction: indicates whether the DNA was extracted as extracellular of intracellular DNA
The other columns represent each asv and the lines contained the number of sequences of the ASVs for each sample.
Methods
Intracellular and extracellular DNA was extracted from sediment samples using the NucleoSpin® Soil kit according to the manufacturer instructions (Macherey-Nagel, Düren, Germany). Beforehand, a phosphate buffer was used to de-adsord the extracellular DNA fraction from sediment particles and the first steps of the commercial DNA extraction kit involving lysis were skipped to avoid further degradation of the extracellular DNA. PCR amplification were performed to amplify the V7 region of the 18S rRNA gene and the samples were sent to Genome Quebec for library preparation and paired-end (2 x 250bp) sequencing on a MiSeq Illumina instrument. The MiSeq reads were then trimmed and filtered, and the paired-end reads were merged with the package dada2 in R software. Taxonomic assignment was performed using the Protist Ribosomal Reference database (PR2) - SSU rRNA gene database. For more details about the methods, refer to Gauthier et al. (2022; doi: 10.1002/edn3.310).