DOI: 10.5061/dryad.bk3j9kd9g
title: A time-lagged association between the gut microbiome, nestling weight and nestling survival in wild great tits

description: Natal body mass is a key predictor of viability and fitness in many
 animals. While variation in body mass and therefore viability of juveniles
 may be explained by genetic and environmental factors, emerging evidence
 points to the gut microbiota as an important factor influencing host
 health. The gut microbiota is known to change during development, but it
 remains unclear whether the microbiome predicts fitness, and if it does,
 at which developmental stage it affects fitness traits. We collected data
 on two traits associated with fitness in wild nestling great tits (Parus
 major): weight and survival to fledging. We characterised the gut
 microbiome using 16S rRNA sequencing from nestling faeces and investigated
 temporal associations between the gut microbiome and fitness traits across
 development at day 8 (D8) and day 15 (D15) post-hatching. We also explored
 whether particular microbial taxa were ‘indicator species’ that reflected
 whether nestlings survived or not. There was no link between mass and
 microbial diversity on D8 or D15. However, we detected a time-lagged
 relationship whereby the microbial diversity at D8 was negatively
 associated with weight at D15, while controlling for weight at D8.
 Indicator species analysis revealed that while several taxa were unique to
 birds that either survived or did not survive, there were no universal
 taxa that were consistently found across all birds within either survival
 group. This suggests that the presence of particular bacterial taxa may be
 sufficient, but not necessary for determining future survival, perhaps
 owing to functional overlap in microbiota. We highlight that measuring
 microbiome-fitness relationships at just one time point may be misleading,
 especially early in life. Instead, microbial-host fitness effects should
 be investigated longitudinally as there may be critical development
 windows in which key microbiota are established and prime host traits
 associated with nestling weight. Pinpointing which features of the gut
 microbial community impact on host fitness, and when during development
 this occurs, will shed light on population level processes and has the
 potential to support conservation.
description: <b><i>Field
 monitoring and microbiome sampling</i></b> Birds were sampled from nine nest
 box populations across Co. Cork, Ireland, five of these were
 mixed/deciduous habitats and four were coniferous habitats (see
 O'Shea et al. (2018)). We collected 204 faecal samples from 150
 nestling great tits from 54 nests (see below for the number of samples
 that were successfully sequenced and sample size per developmental stage)
 for 16S rRNA gene sequencing in order to map the gastrointestinal
 microbial communities. During April-June 2016, nest boxes were monitored
 to determine lay dates, hatching dates and nestling survival. Nestlings
 aged 8 days (+/- 1 day) (D8) and 15 days (D15) were placed into sterile
 holding bags inside a heated holding case. Coffee filters were used to
 line the bags in order to soak up uric acid from the faeces, as uric acid
 has the potential to affect downstream sequencing (Khan et al., 1991).
 Samples were collected from nestlings that defaecated naturally within
 15-20 minutes of being out of the nest before being returned to the nest.
 Although the aim was to have
 repeated samples for all individuals, not all birds survived, and not all
 birds produced samples within this time limit. Faecal sacks were
 opened using a sterile inoculation loop to release the faecal matter and
 place in a microcentrifuge tube containing 500uL of 100% ethanol. Samples
 were stored at -20<sup>o</sup>C within 8 hours of collection
 until DNA extraction. D8 birds were weighed, and uniquely individually
 identified by clipping the tip of one of their nails, avoiding the blood
 vessel (commonly known as the “quick”). D15 nestlings were weighed and
 ringed with a unique identifiable metal ring (British Trust for
 Ornithology) and matched against their unique nail clipping. Nestlings for
 which we had repeated samples (i.e. for both D8 and D15) are referred to
 as ‘repeat samples’. <b><i>DNA
 Extractions</i></b> Briefly, 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. A 0.10 - 0.20 g
 aliquot of each faecal sample was added to the kit, alongside two negative
 controls which were carried through to sequencing. The negative controls had low sequence
 reads and were not included in any subsequent analyses. We note that no
 decontamination methods were used, though they can be beneficial for
 samples with low biomass (Eisenhofer et al., 2019). Therefore,
 we recognise that our sequence data may contain some Amplicon Sequence
 Variants (ASVs) from the external environment, rather than the gut
 microbiome, although this should not have systematically biased our
 results. DNA was stored at -20<sup>o</sup>C. Full extraction
 methods are described in Supporting Information. <b><i>Illumina MiSeq
 sequencing</i></b> Full library preparation details
 are described in Supporting Information and in Davidson et al 2020
 Scientific Reports. 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.
 <b><i>Bioinformatics
 and statistical analysis</i></b> The DADA2 pipeline (Callahan et
 al., 2016) was used to process the raw sequencing data 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 higher than expected errors. Read errors were
 estimated before dereplication. Forward and reverse reads were merged to
 construct 'contig' sequences, these were used to construct a
 sequence table of ASVs, which 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). This is
 at 100% sequence identity in contrast to the lower resolution OTU method
 which groups sequences at 97% identity. ASVs allow greater sensitivity and
 specificity, better discrimination of ecological patterns than OTU's
 and are reusable across studies (Callahan et al., 2017). The DADA2 outputs were assembled
 into a single Phyloseq object (McMurdie & Holmes, 2013). Sequences
 identified as mitochondrial or chloroplast were removed. Further filtering
 of samples and ASVs took place in R. 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;10000
 reads, 9 samples) were removed leaving 195 (Day-8=81, Day-15=114, repeat
 samples=41) samples for the analysis. Alpha diversity (both Shannon and
 Chao1 diversity) was calculated using the ‘<i>estimate_richness’
 </i>function from the phyloseq package on the filtered dataset.
 Shannon diversity (Shannon, 1948) measured richness weighted by abundance
 (the evenness of a community) and Chao1 (Chao, 1984) measured richness,
 specifically estimating taxa abundance and rare taxa missed from under
 sampling.  
description: See README.txt for description of variables.
 Note, some data subsets were centred and scaled before
 analyses.