DOI: 10.5061/dryad.fttdz08x2
title: Individual variation in the avian gut microbiota: The influence of host state and environmental heterogeneity

description: The gut microbiota have important consequences for host biological
 processes and there is some evidence that they also affect fitness.
 However, the complex, interactive nature of ecological factors that
 influence the gut microbiota has scarcely been investigated in natural
 populations. We sampled the gut microbiota of wild great tits (Parus
 major) at different life stages allowing us to evaluate how microbiota
 varied with respect to a diverse range of key ecological factors of two
 broad types: (1) host state, namely age and sex, and the life history
 variables, timing of breeding, fecundity and reproductive success; and (2)
 the environment, including habitat type, the distance of the nest to the
 woodland edge, and the general nest and woodland site environments. The
 gut microbiota varied with life history and the environment in many ways
 that were largely dependent on age. Nestlings were far more sensitive to
 environmental variation than adults, pointing to a high degree of
 flexibility at an important time in development. As nestlings developed
 their microbiota from one to two weeks of life, they retained consistent
 (i.e., repeatable) among-individual differences. However these apparent
 individual differences were driven entirely by the effect of sharing the
 same nest. Our findings point to important early windows during
 development in which the gut microbiota are most sensitive to a variety of
 environmental drivers at multiple scales, and suggest reproductive timing,
 and hence potentially parental quality or food availability, are linked
 with the microbiota. Identifying and explicating the various ecological
 sources that shape an individual's gut bacteria is of vital
 importance for understanding the gut microbiota's role in animal
 fitness.  
description: <em><strong>Field
 monitoring and microbiota
 sampling</strong></em>
 Birds were sampled from nine
 small woodland fragments across Co. Cork, Ireland, five of which were
 mixed/deciduous and four coniferous woodlands (see O’Shea et al. (2018)). We collected 262 faecal samples from 204
 great tits from 63 nests (see table S1 for final sample sizes post
 bioinformatic processing) for 16S rRNA gene sequencing. Nest boxes were
 monitored during April–June 2016 to determine lay dates, hatching dates
 and nestling survival. Typically, all individuals in a nest were sampled
 except for cases where nestlings did not produce a sample within 10–15
 minutes of being placed in the sampling apparatus and risked becoming
 chilled or weak. Individual nestlings were sampled when they were 8 days
 old (+/- 1 day), and again, if they survived, when they were 15 days old
 (D8 and D15 birds respectively), at which point parents were also sampled.
 Birds were placed individually into sterile holding bags inside a heated
 holding case and naturally-produced faecal samples were collected. Urea
 has the potential to affect downstream sequencing and was removed through
 absorption by coffee filters placed as lining in the sampling bags
 (Khan et al.,
 1991). The faecal matter
 was collected within 15–20 minutes of placing birds in the sampling bag,
 after which birds were returned to the nest. Faecal sacks were ruptured
 immediately using a sterile inoculation loop and placed in a
 microcentrifuge tube containing 500uL of 100% ethanol. Samples were stored
 at -20°C within 8 hours of collection until DNA extraction. D8 nestlings
 were weighed, and individually identified by clipping the tip of one of
 their nails, taking care to avoid the blood vessel. Nestling birds were
 again weighed at D15 and ringed with a unique identifiable metal ring
 (British Trust for Ornithology). Samples from nestlings for which we had
 multiple samples (i.e. for both D8 and Day-15, n = 41) are referred to as
 ‘repeat samples’. Both parents were trapped on the nest where possible
 and, if not already ringed, were fitted with a British Trust for
 Ornithology ring, weighed, and aged as either ‘immature’ (first year
 breeding) or ‘mature’ (second year/+ breeding) and sexed using plumage
 indicators.
 <em><strong>DNA
 Extractions</strong></em>
 DNA was extracted from the
 dried faecal contents of all birds using the Qiagen QIAamp DNA Stool Kit,
 following the ‘Isolation of DNA from Stool for Pathogen Detection’
 protocol (June 2012 edition), with modifications described in Shutt et al.
 (2020) to accommodate dried avian faeces. DNA was
 stored at -20°C. Full extraction methods are described in the
 Supporting Information of Davidson et al. (2021).
 <em><strong>Illumina MiSeq
 sequencing</strong></em>
 Full library preparation
 details are described in the Supporting Information of Davidson et al.
 (2021). Briefly, the V3-V4 variable region of the 16S
 rRNA gene was amplified from the DNA extracts using the 16S metagenomic
 sequencing library protocol (Illumina). The DNA was amplified with primers
 specific to the V3-V4 region of the 16S rRNA gene which also incorporates
 the Illumina overhang adaptor. Samples were sequenced on the MiSeq
 sequencing platform (Clinical Microbiomics, Denmark), using a 2 x 300
 cycle kit, following standard Illumina sequencing protocols. Samples were
 randomized across plates to avoid systematic bias resulting from batch
 effects. Two negative controls were carried through to sequencing. These
 samples were subsequently excluded from any analyses following
 bioinformatic processing. These negative controls indicated there was a
 small number of potentially contaminant ASV’s (&lt;0.01% of ASV’s)
 detected by the decontam package (Davis et al., 2018). Therefore, our sequence data may contain some
 ASV’s from the external environment though this should not have
 systematically biased our results.
 <em><strong>Bioinformatics</strong></em> The DADA2 pipeline was used to process the raw sequencing data (Callahan et al., 2016) in R version 3.5 (R Core Team, 2019). Sequence quality was visually inspected. Sequences were trimmed to remove adapters and lower quality reads (median quality scores below 25-30 threshold) at the extremities of the sequence and filtered to remove sequences with expected errors above 1. Read errors were estimated before dereplication. Forward and reverse reads were merged to construct 'contig' sequences, which were used to construct a sequence table of Amplicon Sequence Variants (ASV's), which in turn counts the number of times each unique sequence is detected. The previous steps were performed for each run separately. Then the separate sequence tables were merged and chimeras removed using the 'consensus' method. Taxonomy was assigned to each ASV by RDP's Naive Bayes Classifier (Wang et al., 2007) against the Silva reference database (version 132)  (Quast et al., 2012). The DADA2 outputs were assembled into a single Phyloseq object (McMurdie & Holmes, 2013). Sequences identified as mitochondrial or chloroplast were removed. Sample completeness curves were plotted using vegan (Oksanen et al., 2019) and helped determine the lower cut-off for sample reads at 10,000 reads. Low read samples (&lt;10,000 reads, 11 samples) were removed leaving 195 (adult=51, Day-8=81, Day-15=114) samples for the analysis. Alpha diversity (both Shannon and Chao1 diversity) was calculated using the ‘<em>estimate_richness</em>’ function from the phyloseq package on the filtered dataset. After removing low read samples (&lt;10,000 reads, n = 11), chloroplast sequences and mitochondria sequences, there were 18,890,006 total reads clustered into 54,343 ASV’s in 246 samples (see table S1 for sample breakdown). Reads ranged from 10,220 to 557,336, with a mean of 76,789 reads per sample. For the analyses presented here, the reads were not rarefied prior to alpha diversity calculation or relative abundance analyses (McMurdie & Holmes, 2014; Willis, 2019). In case alpha diversity estimates were affected by library size, the alpha diversity analyses were rerun with a dataset rarefied to 10220 reads (the minimum library size after removing small libraries) and there was no change in the results.