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Variation in the structure of host-associated microbial communities has been correlated with the occurrence and severity of disease in diverse host taxa, suggesting a key role of the microbiome in pathogen defense. However, whether these correlations are typically a cause or consequence of pathogen exposure remains an open question, and requires experimental approaches to disentangle. In amphibians, infection by the fungal pathogen Batrachochytrium dendrobatidis (Bd) alters the skin microbial community in some host species, whereas in other species, the skin microbial community appears to mediate infection dynamics. In this study, we completed experimental Bd exposures in three species of tropical frogs (Agalychnis callidryas, Dendropsophus ebraccatus,andCraugastor fitzingeri) that were sympatric with Bd at the time of the study. For all three species, we identified key taxa within the skin bacterial communities that were linked to Bd infection dynamics. We also measured higher Bd infection intensities in D. ebraccatus and C. fitzingeri that were associated with higher mortality in C. fitzingeri. Our findings indicate that microbially mediated pathogen resistance is a complex trait that can vary within and across host species, and suggest that symbiont communities that have experienced prior selection for defensive microbes may be less likely to be disturbed by pathogen exposure.

The auroral substorm has been extensively studied over the last six decades. However, our understanding of its driving mechanisms is still limited and so is our ability to accurately forecast its onset. In this study, we present the first deep learning‐based approach to predict the onset of a magnetic substorm, defined as the signature of the auroral electrojets in ground magnetometer measurements. Specifically, we use a time history of solar wind speed (V_x), proton number density, and interplanetary magnetic field (IMF) components as inputs to forecast the occurrence probability of an onset over the next 1 hr. The model has been trained and tested on a data set derived from the SuperMAG list of magnetic substorm onsets and can correctly identify substorms ∼75% of the time. In contrast, an earlier prediction algorithm correctly identifies ∼21% of the substorms in the same data set. Our model's ability to forecast substorm onsets based on solar wind and IMF inputs prior to the actual onset time, and the trend observed in IMFB_zprior to onset together suggest that a majority of the substorms may not be externally triggered by northward turnings of IMF. Furthermore, we find that IMFB_zandV_xhave the most significant influence on model performance. Finally, principal component analysis shows a significant degree of overlap in the solar wind and IMF parameters prior to both substorm and nonsubstorm intervals, suggesting that solar wind and IMF alone may not be sufficient to forecast all substorms, and preconditioning of the magnetotail may be an important factor.