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.

Post‐epizootic microbiome associations across communities of neotropical amphibians README

File structure: 

Pipelines
    Ecuador_pipeline (contains bacterial data and bioinformatic pipeline)
    Ecuador_pipeline ITS (contains fungal data and bioinformatic pipeline)

Statistical analysis 
    Question 1 (contains all data and script for analysis of BdqPCR data)
    Question 2 (contains all data and scripts for analysis of bacterial data and fungal data individually)
    Dual kingdom analysis (script for analysis of both datasets within a single microbiome)
    

Subfolder contents:

Pipelines
    Ecuador pipeline
        Database (Reference training set for bacterial taxonomic IDs)
        Fastq_plate1 (raw r16S data)
        Fastq_plate2 (raw r16S data)
        16S_pipeline.R (DADA2 pipeline for processing raw r16S data)
    Ecuador_pipeline ITS
        Plates (raw data and modified UNITE database used for fungal taxonomic IDs)
        Ecuador_pipeline.R (DADA2 pipeline for processing raw ITS2 data)

Statistical analysis 
    
    Question 1 
        Question 1.R (analysis script of qPCR data)
        Mixed Model Results.xlsx (output from Question1.R)
        Ecuador_v2020final.csv (raw data for Question1.R)
    
    Question 2 
        16S
            Question2_16S.R (analysis script of 16S microbiome data)
            Cooccurrence results_16S (output from coccurrence analysis in Question2_16S.R)
            Remaining files are input files for Question2_16S.R (see annotations within the script)
        ITS
            Question2_ITS.R (analysis script of 16S microbiome data)
            Cooccurrence results_ITS2 (output from coccurrence analysis in Question2_16S.R)
            Remaining files are input files for Question2_ITS.R (see annotations within the script)
        
    Dual kingdom analysis 
        Dual-kingdom analysis.R (script for analysis of both datasets within a single microbiome)