Microtus agrestis microbiome reads, taxonomy and related animal metadata
Data files
Feb 07, 2023 version files 4.63 MB
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OTU_Read_Counts.xlsx
4.11 MB
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README.txt
5.14 KB
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vole_mb_phylo.tree
516.50 KB
Apr 03, 2025 version files 5.92 MB
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OTU_Read_Counts.xlsx
5.39 MB
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README.md
5.97 KB
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vole_mb_phylo.tree
516.50 KB
Abstract
Mammalian gastrointestinal microbiomes are highly variable, both within individuals and across populations, with changes linked to time and ageing being widely reported. Discerning patterns of change in wild mammal populations can therefore prove challenging. We used high-throughput community sequencing methods to characterise the microbiome of wild field voles (Microtus agrestis) from faecal samples collected across 12 live-trapping field sessions, and then at cull. Changes in α- and β-diversity were modelled over three timescales. Short-term differences (following 1–2 days captivity) were analysed between capture and cull, to ascertain the degree to which the microbiome can change following a rapid change in environment. Medium-term changes were measured between successive trapping sessions (12–16 days apart), and long-term changes between the first and final capture of an individual (from 24 to 129 days). The short period between capture and cull was characterised by a marked loss of species richness, while over medium- and long-term in the field, richness slightly increased. Changes across both short and long timescales indicated shifts from a Firmicutes-dominant to a Bacteroidetes-dominant microbiome. Dramatic changes following captivity indicate that changes in microbiome diversity can be rapid, following a change of environment (food sources, temperature, lighting etc.). Medium- and long-term patterns of change indicate an accrual of gut bacteria associated with ageing, with these new bacteria being predominately represented by Bacteroidetes. While the patterns of change observed are unlikely to be universal to wild mammal populations, the potential for analogous shifts across timescales should be considered whenever studying wild animal microbiomes. This is especially true if studies involve animal captivity, as there are potential ramifications both for animal health, and the validity of the data itself as a reflection of a ‘natural’ state of an animal.
Dataset DOI: 10.5061/dryad.08kprr559
Description of the data and file structure
Field Vole Microbiome Data and Associated Metadata
This dataset includes two files, pertaining to faecal and caecum samples collected from a wild population of the field vole, Microtus agrestis from Kielder forest, Northumberland, UK. These samples were taken both longitudinally, during live-trapping of the animals, and at cull of the animals, after 1-2 days captivity. The bacterial microbiome within these samples was sequenced and used in analysis, with distinct taxa being designated a unique OTU (operational taxonomic unit) number.
Firstly, an Excel workbook (.xslx) containing three worksheets - a complete list of bacterial OTU read counts, a record of taxonomic information for each OTU, and a record of metadata associated with samples.
Secondly, a phylogenetic tree file (.TREE), containing phylogenetic information on the OTUs
Detailed file description
Files and variables
File: vole_mb_phylo.tree
Description: The file 'vole_mb_phylo.tree' is a TREE file containing phylogeny information of bacterial OTUs, which can be used with the R analysis software, and associated packages including 'phyloseq' and 'qiime'
File: OTU_Read_Counts.xlsx
Description: The file 'OTU_Read_Counts.xlsx' is an Excel workbook, containing bacterial OTU read counts, taxonomy of bacterial OTUs and host animal metadata, on three separate worksheets
Variables
Within the first worksheet, 'OTU_Read_Counts', each row is a sample, and the three leftmost columns give information on the sample in that row.
'PIT_TAG' refers to the unique passive integrated transponder number for each individual vole, and can be used to group multiple samples from the same individual. Individuals which were not tagged will show 'n/a' in this column.
The second column, 'SAMPLE_ID', gives a unique identifier for each sample.'SAMPLE_TYPE' distinguishes whether the sample in question was faeces taken during live-trapping ('Faeces_Longitudinal'), faeces taken at cull ('Faeces_Cross_Sectional') or caecum taken at cull ('Caecum')
The remaining 1321 columns give read counts for each OTU present in the microbiome of each sample.
Within the second worksheet, 'OTU_Taxonomy', each row is an OTU.
The first column, 'OTU', gives the unique ID of the OTU in question.
The next seven columns give the taxonomic group of that OTU, from Kingdom level, through Phylum, Class, Order, Family, Genus and lastly species.
For OTUs which can only be classified to a certain taxonomic level, any remaining cells in that row will read 'n/a'
For example, if an OTU can only be identified to Phylum level, the cells for Class through to Species for that OTU will read 'n/a'
The ninth column contains the RDP classifier confidence level for that OTU.
The tenth column contains the RDP classifier sequence used to classify that OTU
Please note that there are far more OTUs listed in this taxonomy than are present in the study population.
Within the third worksheet, 'MetaData', each row is a sample
The column 'PIT_TAG' refers to the unique passive integrated transponder number for each individual vole, and can be used to group multiple samples from the same individual. Individuals which were not tagged will show 'n/a' in this column
The second column, 'SAMPLE_ID' gives a unique identifier for each sample
The third column 'SAMPLE_TYPE' distinguishes whether the sample in question was faeces taken during live-trapping ('Faeces_Longitudinal'), faeces taken at cull ('Faeces_Cross_Sectional') or caecum taken at cull ('Caecum')
The fourth column 'Session' refers to the number of trapping session in which a longitudinal faecal sample was taken
The fifth column 'Date' refers to the date on which the sample was taken, in the format 'DD/MM/YYYY'
The sixth column 'Julian_date' refers to the day of the year 2017 in which the sample was taken
The seventh column 'Lens_weight(g)' refers to the paired dry mass in grams of the animal's eye lenses, listed for cross-sectional samples only, for use as a proxy for age
The seventh column, 'S.obvelata.Infection' gives a TRUE/FALSE indicator of whether the pinworm Syphacia obvelata was found in the gastrointestinal tract of the animal in question, listed for cross-setional samples only
The eighth column, 'Taepeworm.Infection' gives a TRUE/FALSE indicator of whether tapeworms were found in the gastrointestinal tract of the animal in question, listed for cross-setional samples only
The ninth column 'Age' gives a categorisation of an animal's age, with categories 'Juvenile', 'Sub-Adult' and 'Mature', made on rough visual inspection of body size
The tenth colum 'Sex' provides the animal's sex, with 'F' indicating female, and 'M' indicating male
The eleventh column 'Mass(g)' gives the body mass of the animal in grams
The twelth column, 'Snout_to_Vent_Length(cm)' gives the body length of the animal from tip of the snout to the base of the tail in centimetres
The thirteenth column, 'SMI', gives the scaled mass index of the animal as a measure of body condition, with calculation from 'New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method' - Peig & Green 2009
Code/software
OTU_Read_Counts.xlsx - can be opened with Microsoft Excel, or in R using the readxl package
vole_mb_phylo.tree - can be opened and analysed with the R packages 'phyloseq' and 'qiime'
Version Changes
2nd April 2025 - added two columns to Sheet 2 ('OTU_Taxonomy') of OTU_Read_Counts.xlsx. Column 'RDP_Confidence' gives the confidence value of the OTU classification, and column 'Classifier_Sequence' gives the sequence used to classify the OTU.
This dataset comprises read count data of bacterial operational taxonomic units (OTUs) sequenced from faeces and caecum samples, taken from a wild population of the field vole Microtus agrestis, alongside relevant animal metadata and bacterial OTU taxonomy. Also, there is a phylogenetic tree data of the bacterial OTUs sequenced.
The database can be opened with Microsoft Excel, with read counts, metadata and taxonomy found on separate worksheets. Both the database and TREE file can be opened and analysed in the open-source statistics software R, using packages like phyloseq.