DOI: 10.5061/dryad.2547d7wwj
title: Nor climate, nor human impact factors: Chytrid infection shapes the skin microbiome of an endemic amphibian along a biodiversity hotspot

description: The amphibian skin microbiome is a key component of the host’s innate
 immune system. However, we lack a complete understanding of the mechanisms
 by which different drivers can alter this function by modulating the
 microbiome’s structure. Our aim was to assess the extent to which
 different host attributes and extrinsic factors influence the structure of
 the skin microbiome. Skin bacterial diversity was examined in 148
 individuals of the four-eyed frog (Pleurodema thaul) from 16 localities
 spanning almost 1,800 km in latitude. Alpha (richness) and beta diversity
 and abundance of bacterial amplicon sequence variants were used to
 describe its structure. Predictors associated with the host (developmental
 stage, genetic lineage, individual Batrachochytrium dendrobatidis [Bd]
 infection status) and the landscape (current climate, degree of
 anthropogenic disturbance) were used in the statistical modelling in an
 information theoretical approach. Bd infection and host developmental
 stage were the only predictors affecting microbiome richness, with Bd+
 individuals and adults and juveniles having higher richness than Bd- ones
 and tadpoles, respectively. High diversity in Bd+ individuals is not
 driven by bacterial families with known anti-Bd properties. Beta diversity
 was not affected by Bd infection and is mostly a consequence of bacterial
 family turnover, rather than nestedness. Finally, for those bacterial
 families that are known to have inhibitory effects on chytrid, Bd-
 individuals are slightly more diverse than Bd+ ones. Our study confirms an
 association between Bd infection and the host developmental stage with the
 skin microbiome of P. thaul. Unexpectedly, macroclimate and human impact
 factors do not seem to play a role in shaping the amphibian skin
 microbiome. Our study exemplifies that focusing on a single host-parasite
 system over a large geographic scale can provide important insights into
 the factors driving host-parasite-microbiome interactions.
description: # Nor climate, nor human impact factors: chytrid infection shapes the skin
 microbiome of an endemic amphibian along the Chilean biodiversity hotspot
 ## Description of the Data and file structure The dataset consists of 3
 CSV files with the corresponding R-scripts. ### Alpha diversity The
 dataset "Alpha_diversity.csv" contains 148 individual bacteriome
 richness data for 16 different localities as ell as other relevant
 information associated with each of the 148 sampled individuals. ####
 Column names SR: bacteriome family richness (cuantitative: number of
 families) BD: whether the individual was infected or not with Bd
 (categorical: yes/no) HIF: Human footprint for a locality (cuantitative:
 no units) Year: year when the sample was collected (cuantitative)
 LOCATION: locality of sampling (categorical) Lineage: host genetic lineage
 of the sampled individual (categorical) bio1: annual mean temperature
 (cuantitative: ºC) bio4: temperature seasonality (cuantitative: ºC/100)
 bio12: annual mean precipitation (cuantitative: mm) age: larvae, juvenile,
 adult (categorical) Alpha_diversity.R: contains the script for the
 statistical analyses and figure 1. ## Beta diversity There are three
 different datasets for this analysis: "Beta_diversity.csv",
 "Positives.csv", and "Negatives.csv". Each dataset
 consists of 496 columns and 16 rows, representing the presence or absence
 of 496 bacterial families found in each of the 16 localities. The
 positives.csv and negatives.csv files partition the presence or absence of
 each family between individuals who tested positive or negative for Bd.
 Beta_diversity.R: contains the script for the statistical analyses and
 figure 2. ## Anti Bd The dataset "Genero vs BD.txt" consists of
 2 columns and 52 rows. The columns represent individuals who tested
 positive for Bd (Bd_yes) or negative (Bd_no). Each row corresponds to the
 genera of isolates known to have inhibitory effects on Bd. Each cell
 contains the number of reads for each combination of genus and Bd-positive
 or Bd-negative status. Anti_Bd.R: contains the script for the statistical
 analyses and figure 3.