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Abstract Emerging infectious diseases are a serious threat to wildlife populations, and there is growing evidence that host microbiomes play important roles in infection dynamics, possibly even mitigating diseases. Nevertheless, most research on this topic has focused only on bacterial microbiomes, while fungal microbiomes have been largely neglected. To help fill this gap in our knowledge, we examined both the bacterial and fungal microbiomes of four sympatric Australian frog species, which had different population‐level responses to the emergence of chytridiomycosis, a widespread disease caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd). We sequenced 16,884 fungal amplicon sequence variants (ASVs) and 41,774 bacterial ASVs. Bacterial communities had higher richness and were less variable within frog species than were fungal communities. Nevertheless, both communities were correlated for both ASV richness and beta diversity (i.e., frogs with similar bacterial richness and community composition tended to also have similar fungal richness and community composition). This suggests that either one microbial community was having a large impact on the other or that they were both being driven by similar environmental factors. For both microbial taxa, we found little evidence of associations between Bd (prevalence or intensity) and either individuals' ASVs or beta diversity. However, there was mixed evidence of associations between richness (both bacterial and fungal) and Bd, with high richness potentially providing a protective effect. Surprisingly, the relative abundance of bacteria that have previously been shown to inhibit Bd was also positively associated with Bd infection intensity, suggesting that a high relative abundance of those bacteria provides poor protection against infection.

Abstract Understanding broad trends in the distribution and composition of wetlands is essential for making evidence‐based management decisions. Determining temporal change in the extent of inundation in wetlands using remote sensing remains challenging and requires on‐ground verification to determine accuracy and precision. Therefore, optimization and validation of remote sensing methods in threatened wetlands is a high priority for their conservation. Despite their ecological importance in the landscape, we have little knowledge of the variation in the spatial extent of inundation in upland lagoons, a threatened ecological community in New South Wales, Australia. Our project developed locally trained algorithms to predict the extent of water and emergent vegetation using imagery from the Landsat‐5, ‐7, and ‐8 satellites. The best model for upland lagoons used shortwave infrared reflectance (performing better than normalized difference spectral indices), with model accuracy against validation transects greater than 95%. We applied the model to images from 1988 to 2020 across 58 lagoons to generate a dataset that demonstrates the variable water regime and vegetation change in response to local rainfall over 32 years such as in the lagoons. Our results reduce threats to a dynamic threatened ecological community by filling an important knowledge gap and demonstrate a valuable method to understand historical and current changes in the hydrology of dynamic wetland systems more broadly.