DOI: 10.5061/dryad.pg4f4qrnb
title: Post-epizootic microbiome associations across communities of neotropical amphibians

description: Microbiome-pathogen interactions are increasingly recognised as an
 important element of host immunity. While these host-level interactions
 will have consequences for community disease dynamics, the factors which
 influence host microbiomes at larger scales are poorly understood. We here
 describe landscape scale pathogen-microbiome associations within the
 context of post-epizootic amphibian chytridiomycosis, a disease caused by
 the panzootic chytrid fungus Batrachochytrium dendrobatidis. We undertook
 a survey of Neotropical amphibians across altitudinal gradients in Ecuador
 ~30 years following the observed amphibian declines and collected skin
 swab-samples which were metabarcoded using both fungal (ITS-2) and
 bacterial (r16S) amplicons. Our data reveals marked variation in patterns
 of both B. dendrobatidis infection and microbiome structure that are
 associated with host life history. Stream breeding amphibians were most
 likely to be infected with B. dendrobatidis. This increased probability of
 infection was further associated with increased abundance and diversity of
 non-Batrachochytrium chytrid fungi in the skin and environmental
 microbiome. We also show that increased alpha diversity and the relative
 abundance of fungi is lower in the skin microbiome of adult stream
 amphibians compared to adult pond breeding amphibians, an association not
 seen for bacteria. Finally, stream tadpoles exhibit lower proportions of
 predicted protective microbial taxa than pond tadpoles, suggestive of
 reduced biotic resistance. Our analyses show that host breeding ecology
 strongly shapes pathogen-microbiome associations at a landscape scale, a
 trait that may influence resilience in the face of emerging infectious
 diseases.
description: Post‐epizootic microbiome associations across communities of
 neotropical amphibians README File
 structure:  Pipelines<br>     Ecuador_pipeline
 (contains bacterial data and bioinformatic pipeline)<br>   
  Ecuador_pipeline ITS (contains fungal data and bioinformatic
 pipeline) Statistical analysis <br>     Question
 1 (contains all data and script for analysis of BdqPCR data)<br>   
  Question 2 (contains all data and scripts for analysis of bacterial data
 and fungal data individually)<br>     Dual kingdom analysis (script
 for analysis of both datasets within a single microbiome)<br>   
   <br> Subfolder contents:
 Pipelines<br>     Ecuador pipeline<br>       
  Database (Reference training set for bacterial taxonomic IDs)<br>
         Fastq_plate1 (raw r16S data)<br>         Fastq_plate2 (raw
 r16S data)<br>         16S_pipeline.R (DADA2 pipeline for processing
 raw r16S data)<br>     Ecuador_pipeline ITS<br>         Plates
 (raw data and modified UNITE database used for fungal taxonomic
 IDs)<br>         Ecuador_pipeline.R (DADA2 pipeline for processing
 raw ITS2 data) Statistical analysis <br>   
  <br>     Question 1 <br>         Question 1.R (analysis
 script of qPCR data)<br>         Mixed Model Results.xlsx (output
 from Question1.R)<br>         Ecuador_v2020final.csv (raw data for
 Question1.R)<br>     <br>     Question 2 <br>       
  16S<br>             Question2_16S.R (analysis script of 16S
 microbiome data)<br>             Cooccurrence results_16S (output
 from coccurrence analysis in Question2_16S.R)<br>           
  Remaining files are input files for Question2_16S.R (see annotations
 within the script)<br>         ITS<br>           
  Question2_ITS.R (analysis script of 16S microbiome data)<br>       
      Cooccurrence results_ITS2 (output from coccurrence analysis in
 Question2_16S.R)<br>             Remaining files are input files for
 Question2_ITS.R (see annotations within the script)<br>       
  <br>     Dual kingdom analysis <br>         Dual-kingdom
 analysis.R (script for analysis of both datasets within a single
 microbiome)<br>     <br>