Skin microbiome correlates with bioclimate and Batrachochytrium dendrobatidis infection intensity in Brazil’s Atlantic Forest treefrogs

Abstract

In Brazil’s Atlantic Forest (AF) biodiversity conservation is of key importance since the fungal pathogen Batrachochytrium dendrobatidis (Bd) has led to the rapid loss of amphibian populations here and worldwide. The impact of Bd on amphibians is determined by the host's immune system, of which the skin microbiome is a critical component. The richness and diversity of such cutaneous bacterial communities are known to be shaped by abiotic factors which thus may indirectly modulate host susceptibility to Bd. This study aimed to contribute to understanding the environment-host–pathogen interaction determining skin bacterial communities in 819 treefrogs (Anura: Hylidae and Phyllomedusidae) from 71 species sampled across the AF. We investigated whether abiotic factors influence the bacterial community richness and structure on the amphibian skin. We further tested for an association between skin bacterial community structure and Bd co-occurrence. Our data revealed that temperature, precipitation, and elevation consistently correlate with richness and diversity of the skin microbiome and also predict Bd infection status. Surprisingly, our data suggest a weak but significant positive correlation of Bd infection intensity and bacterial richness. We highlight the prospect of future experimental studies on the impact of changing environmental conditions associated with global change on environment-host–pathogen interactions in the AF.

Host phylogeny and environment shape the diversity of the salamander skin microbiome

“…Here, we assessed the relative contributions of host habitat, climate, and host phylogenetic relationships to the diversity and structure of the skin microbiome in salamanders. In agreement with previous studies on amphibian skin microbiota, we found that host habitat (terrestrial/aquatic) 13,28 , precipitation, and seasonality play a major role in shaping the diversity of the salamander skin microbiota 9,14,15 . We also inferred that host phylogenetic relationships have an important effect in shaping these microbial communities, which contrasts with previous studies in which phylogenetic effects were minor 9,13,23 .…”

“…In other words, we uncovered a general tendency where the skin microbiota of closely related host species resembles each other more than those of host species drawn at random from the same tree. Recent meta-analyses spanning several amphibian families (mainly anurans) have found signi cant but weaker effects of host phylogeny (relative to other factors) using topological congruence analysis and other proxies of host phylogeny (i.e., nMDS of patristic distances) 9,11,12,14 . In these cases, the weaker phylogenetic signal probably stems from loss of statistical power because distances in microbiota compositions based on dendrograms or NMDS and raw (true) distances are moderately to poorly correlated 5 .…”

Signatures of functional bacteriome structure in a tropical direct-developing amphibian species

“…In contrast to consistent non-detections of Bd in H. binotatus , the highest Bd prevalence in our study was found in I. henselii , the only other species in our study with a terrestrial-breeding life history strategy, and which can be found side by side with H. binotatus in the field. While the skin bacteriomes of Atlantic Forest treefrog species tracked bioclimatic variables at large spatial scales [ 55 ], the finer spatial resolution of our study revealed significant species-level variation in host bacteriome attributes under constant biogeographic and climatic conditions, consistent with previous work showing differences in the skin bacterial communities of co-occurring amphibian species [ 56 – 59 ]. Compared to I. henselii , H. binotatus carried significantly higher overall bacterial alpha diversity (Fig.…”

Low microbiome diversity in threatened amphibians from two biodiversity hotspots

“…Environmental covariates included annual mean temperature (BIO1) and annual precipitation (BIO12), extracted for each sampling locality from WorldClim at 5-min spatial resolution, and Normalized Difference Vegetation Index (NDVI), averaged over the sampling period for each locality. Host species covariates included body length (maximum male snout-vent length [SVL]) [45] and geographic range area (log-transformed). For Brazil, we included DNA extraction method as an additional predictor of sOTU richness to account for variation attributable to differences in DNA extraction protocol among samples.…”