DOI: 10.5061/dryad.34tmpg4nm
title: Data for: Skin bacterial microbiome diversity predicts lower activity levels in female, but not male, guppies, Poecilia reticulata

description: While the link between the gut microbiome and host behaviour is well
 established, how the microbiomes of other organs correlate with behaviour
 remains unclear. Additionally, behaviour–microbiome correlations are
 likely sex-specific because of sex differences in behaviour and
 physiology, but this is rarely tested. Here, we tested whether the skin
 microbiome of the Trinidadian guppy, Poecilia reticulata , predicts fish
 activity level and shoaling tendency in a sex-specific manner.
 High-throughput sequencing revealed that the bacterial community richness
 on the skin (Faith's phylogenetic diversity) was correlated with both
 behaviours differently between males and females. Females with richer
 skin-associated bacterial communities spent less time actively swimming.
 Activity level was significantly correlated with community membership
 (unweighted UniFrac), with the relative abundances of 16 bacterial taxa
 significantly negatively correlated with activity level. We found no
 association between skin microbiome and behaviours among male fish. This
 sex-specific relationship between the skin microbiome and host behaviour
 may indicate sex-specific physiological interactions with the skin
 microbiome. More broadly, sex specificity in host–microbiome interactions
 could give insight into the forces shaping the microbiome and its role in
 the evolutionary ecology of the host.
description: These methods are modified from the manuscript:
 a) <em> Fish origin and maintenance</em>
 We used laboratory-bred descendants of guppies collected from the
 Caura River, Trinidad. Guppies were housed at densities of 1–2 fish per
 litre in 4.5-litre tanks in a recirculating system at 24 ± 1°C, on a 12
 L:12 D lighting schedule and fed daily on flake and
 <em>Artemia</em>. All conditions were kept identical for the
 experiment duration, conducted in five experimental batches between August
 and November 2017. b) <em>Behavioural experiment
 and microbiome sampling</em> To assay behaviours,
 we used a central glass aquarium ('test aquarium'), flanked on
 both short sides by smaller aquaria ('stimulus aquaria'), all
 with 5cm water depth. The stimulus aquaria were lit from above by 32cm LED
 strip lights (350lm, 5W, 4000k, MeRox® Technics). The test aquarium had a
 black Perspex lid, to which was fastened a GoPro camera (Hero4 Black
 Edition; GoPro Inc. San Mateo, CA). We placed a test fish (of either sex:
 n=18 females; 19 males) in an acetate cylinder in the centre of the test
 tank for a 5-minute settling period. We then remotely set the GoPro to
 record, lifted the settling cylinder using a pulley, and recorded a
 10-minute test period. For a subset of trials, we placed a shoal of three
 females in one of the stimulus tanks (side chosen haphazardly). The short
 sides of the test aquarium were one-way mirrors, such that test fish could
 see stimulus fish, but not vice versa. The experimental set-up was
 enclosed in a blackout curtain to ensure even lighting and prevent
 disturbance. The test aquarium was scrubbed with 70% ethanol, rinsed, and
 refilled between trials. We quantified "shoaling-tendency" as
 the proportion of trial time the test fish spent within the third of the
 test tank closest to the stimulus tank and "activity level" as
 the proportion of time the test fish actively swam throughout the 10
 minutes. One of four observers, blind to the treatment, recorded the
 activity levels and shoaling levels from the videos using JWatcher™ 1.0
 (www.jwatcher.ucla.edu). c) <em>Skin microbiome
 collection</em> The day after the behaviour
 trials, fish skin microbiome samples were collected. Fish were
 anaesthetised (0.02% tricaine methanesulfonate; PHARMAQ Ltd.,
 Fordingbridge, UK), washed twice with two separate 20ml aliquots of
 sterile water, then swabbed the body surface with a sterile cotton swab
 for 30 seconds. Between fish, all glassware was sterilised with 70%
 ethanol. The swabs were stored at -20 ºC until DNA extraction.
 d) <em>Microbiome inventory</em>
 We used QIAamp PowerFecal DNA Isolation Kit (Qiagen, Hilden,
 Germany) to extract DNA, following the manufacturer's protocol. To
 control for contaminants, we included four blank samples in extractions
 and sequencing. Extracted DNA was shipped on dry ice to the DNA Services
 Facility at the University of Illinois at Chicago (Chicago, IL, USA). All
 library preparation, amplification, and sequencing were performed on the
 Illumina Miseq platform (V4 region of the bacterial 16S rRNA gene). We
 filtered the raw sequences for quality, removed chimeric reads, merged
 forward and reverse reads and assigned the remaining reads to ASVs
 (Amplicon Sequence Variant) using the DADA2 pipeline in QIIME2 2019.7. We
 trimmed each read to 220 base pairs, removed all singleton reads and used
 FastTree to build a phylogenetic tree, using the SILVA database classifier
 release 138 to assign taxonomy. We removed all non-bacterial reads
 (archaea, chloroplast, or mitochondria) and any ASVs detected in control
 blank samples prior to analysis. d) <em>Data
 analysis</em> First, we calculated Faith's
 phylogenetic distance (Faith's PD) as a metric for skin microbial
 alpha diversity for each sample, and compared Faith's PD between
 female and male guppies using a Kruskal–Wallis test using
 QIIME2. To test for correlations between behaviour and
 microbiome alpha diversity, we used activity level or shoaling tendency as
 the response variable in two generalised linear mixed models (glmm) in the
 glmmTMB package, v. 1.1.2.3 (Beta error family and identity logit function
 within R v.4.1.0). As fixed main effects, we included test fish sex and
 length (controlling for sex differences), body condition (scaled mass
 index), Faith's PD, trial date and time of day, and whether there was
 a shoal present. We also included the two-way interaction between sex and
 Faith's PD. We used<em> visreg </em>and
 <em>ggplot2</em> for plotting.            
 To investigate correlates of skin microbiome bacterial community
 membership, we calculated the Unweighted UniFrac distance matrix in
 Qiime2, imported it into Primer v7, and built a distance-based linear
 model (DISTLM). This model included the same predictor variables as the
 glmm. We used permutation to assess statistical significance. We
 visualised the fitted DISTLM models in multidimensional space, using the
 distance-based redundancy analysis (dbRDA) in PRIMER v7 (see
 supplement). Finally, to test for associations between
 individual bacterial taxa and guppy activity level and shoaling tendency,
 we used MaAsLin2. Within our models we added trial date as a fixed effect
 to control for any compositional differences caused by difference in
 experimental date. This programme first uses boosting in a univariate
 pre-screen to identify taxa (arcsine square-root transformed relative
 abundances) potentially associated with a focal variable and then uses
 linear mixed-effects models to test for statistically significant
 associations using the Benjamini–Hochberg false discovery rate
 method.
description: We have included .csv files containing the data, and a .pdf
 README file including the R code and output we used to analyse the data,
 plus more details on the variables in the datasheets.