Nor climate, nor human impact factors: Chytrid infection shapes the skin microbiome of an endemic amphibian along a biodiversity hotspot
Data files
Sep 26, 2024 version files 86.32 KB
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Alpha_diversity.csv
9.44 KB
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Alpha_diversity.R
2.22 KB
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Anti_Bd.R
1.99 KB
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Beta_diversity.csv
24.02 KB
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Beta_diversity.R
3.15 KB
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Genero_vs_BD.txt
1.32 KB
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Negatives.csv
23.03 KB
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Positives.csv
19.02 KB
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README.md
2.12 KB
Abstract
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.
README: 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.