DOI: 10.5061/dryad.547d7wmg3
title: Data from: Wolbachia impacts microbiome diversity and fitness-associated traits for Drosophila melanogaster in a seasonally fluctuating environment

description: The microbiome contributes to many different host traits, but its role in
 host adaptation remains enigmatic. The fitness benefits of the microbiome
 often depend on ecological conditions, but theory suggests that
 fluctuations in both the microbiome and environment modulate these fitness
 benefits. Moreover, vertically transmitted bacteria might constrain the
 ability of both the microbiome and host to respond to changing
 environments. Drosophila melanogaster provides an excellent system to
 investigate the impacts of interactions between the microbiome and the
 environment. To address this question, we created field mesocosms of D.
 melanogaster undergoing seasonal environmental change with and without the
 vertically transmitted bacteria, Wolbachia pipientis. Sampling temporal
 patterns in the microbiome revealed that Wolbachia constrained microbial
 diversity. Furthermore, Wolbachia and a dominant member of the microbiome,
 Commensalibacter, were associated with differences in two higher-order
 fitness traits, starvation resistance and lifespan. Our work here suggests
 that the interplay between the abiotic context and microbe-microbe
 interactions may shape key host phenotypes that underlie adaptation to
 changing environments. We conclude by exploring the consequences of
 complex interactions between Wolbachia and the microbiome for our
 understanding of eco-evolutionary processes that shape host-microbiome
 interactions.
description: # data for Wolbachia impacts microbiome diversity and fitness-associated
 traits for Drosophila melanogaster in a seasonally fluctuating environment
 [https://doi.org/10.5061/dryad.547d7wmg3](https://doi.org/10.5061/dryad.547d7wmg3) Data was generated from a study of Drosophila undergoing seasonal adaptation in NJ during summer-fall 2019. ## Description of the data and file structure **Data for temperature data loggers** are in files called "`dl_N.csv`", with N = 1-8 to note the different cages. Each column for the data loggers is as follows: S/N = arbitrary sorting, Time = time the measurement was collected, Temperature = temperature in Celsius, Humidity%RH = %relative humidity.  **The following files are for microbiome data (generated using QIIME2 and exported to analyses/visualization in R)**: `field19_total_meta.tsv` is the metadata that relates sequencing library to collected metadata. The columns are as follows: flyid = individual fly, BarcodeSequence = is for demultiplexing, for.sort = an arbitrary sorting variable, plate.pos = position within 96 well plate during DNA extraction and library prep, pcr.id = internal note for when PCR took place, cage = cage from where the fly collected, wolbachia = wolbachia status, timepoint = discrete variable for collection time, datecollected = date collected (each timepoint matches a date), protK = protK treatment during DNA extraction, day_since_start = time since the field experiment started, merge.group = variable that merges together the +/- protK from individual flies, seq.run = sequencing run, arb.sort = another arbitrary sorting variable.  `otu_matrix.csv` is the ASV matrix (rows are ASVs, columns are individual flies) `taxonomy_fullnames.tsv` links ASV ID to taxonomic assignment. Please note that there are empty cells in this taxonomy file that are used to filter out later. The Rcode will later fill in empties. Rows are ASVs, columns are the different taxonomic ranks.  `tree.nwk` is phylogenetic tree `field2019_cleanF.Rds `is the phyloseq object with the cleaned up data (contaminants removed, protK merged, remaining wolbachia removed) **The data for Wolbachia frequency over time:**\ `wolbachia_screen.csv` For wolbachia_screen.csv, columns are as follows: cage : cage the fly was collected from the field, date = collection date, sample = sample ID from each cage per time point, wolbachia  = y (yes, presence of band during PCR check), n (no, no band).  **The data for the three starvation timepoints**: starv1_clean.csv for Day 96 starv2_clean.csv for Day 116 starv3_clean.csv for Day 127 For all of the starv files columns are as follows: fly.id = individual fly measured, camera = camera used to film, imageplate = plate used to image flies, imagewell = position in the 24 well plate, hour = hour of death, minute = minute of death, timetodeath = adds hour and minute together, cage = cage the fly was collected from in the field, sex = sex of fly, wolbachia = wolbachia status, full.treat = combines cage + sex + wolbachia status, code = 2 for survminer (all dead) **The data for the three starvation timepoints**: `isofemales_lifespan.csv` contains lifespan measurements at the end of the season (Day 127).  The columns are as follows: tube = tube ID that matched an individual fly, death day = date that fly died, start. date = date of collection, days.alive = length fly was alive, cage = cage from where the fly was collected, wolbachia = wolbachia status, code = 2 for survival package (all are 2) **The data from statistical model outputs that were later visualized** `ph_starv_coxestimates.csv` contains the estimates from the Cox models to visualize effect size over time. The columns are as follows: timepoint = timepoint from where the flies were collected, coefficient = model coefficient, se = standard error, group = model term from either Wolbachia or Commensalibacter `tops_bc_modstats.csv` contains the variance explained by fixed effects in the analysis of community turnover in the top10 most abundant bacteria. The columns are as follows: model = how many bacteria were included in the statistical model (i.e., top04 = top 4 most abundant bacteria, top05 = top 5 most abundant bacteria, etc), r2 marg = marginal r-squared value from fitted model, r2 con = conditional r-squared value from fitted model, p val season = the p-value for the estimate for time since start of the experiment, p val wolb = the p-value for the estimate for the wolbachia term, p val interact = the p-value for the time * wolbachia interaction term, interact.sig = whether or the not the interaction term was significant (p&lt;0.05).  **FASTQ files:** fastq files correspond to the "field19_total_meta.tsv". files with "-read-1.fastq" are forward, while "-read-3.fastq" are reverse.  ## Sharing/Access information Sequence is uploaded here.  ## Code/Software `field2019_qiimecode.txt` contains code run to generate ASV and taxonomy assignments using QIIME2 v2020.6 `field2019_v4_share.Rmd` is the R markdown containing all analyses. The following packages are required: phyloseq, ggplot2, tidyverse, RColorBrewer, ggpubr, scales, decontam, vegan, lubridate, btools, lmerTest, lme4, forcats, survival, survminer, coxme, and mgcv