DOI: 10.5061/dryad.08kprr559
title: Microtus agrestis microbiome reads, taxonomy and related animal metadata

description: 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.
description: 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 <em>Microtus
 agrestis</em>, alongside relevant animal metadata and bacterial OTU
 taxonomy. Also, there is a phylogenetic tree data of the bacterial OTUs
 sequenced.
description: 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
 <em>phyloseq.</em>
description: # Microtus agrestis microbiome reads, taxonomy and related animal metadata
 Dataset DOI: [10.5061/dryad.08kprr559](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 ###### Sheet 1 -
 "OTU\_Read\_Counts" 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. ###### Sheet 2 -
 'OTU\_Taxonomy' 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. ###### Sheet 3 - 'MetaData' 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.