Unlike temperate soils, where N limits primary productivity, cold and dry soils exposed by retreating glaciers are P-limited.

While past research has studied forest succession on decadal timescales, ecosystem responses to rapid shifts in nutrient dynamics within the first months to years of succession after fire (e.g., carbon (C) burn-off, a pulse in inorganic nitrogen (N), accumulation of organic matter, etc.) have been less well documented. This work reveals how rapid shifts in nutrient availability associated with fire disturbance may drive changes in soil enzyme activity on short timescales in forest secondary succession. In this study, we evaluate soil chemistry and decomposition extracellular enzyme activity (EEA) across time to determine whether rapid shifts in nutrient availability (1–29 months after fire) might control microbial enzyme activity. We found that, with advancing succession, soil nutrients correlate with C-targeting β-1,4-glucosidase (BG) EEA four months after the fire, and with N-targeting β-1,4-N-acetylglucosaminidase (NAG) EEA at 29 months after the fire, indicating shifting nutrient limitation and decomposition dynamics. We also observed increases in BG:NAG ratios over 29 months in these recently burned soils, suggesting relative increases in microbial activity around C-cycling and C-acquisition. These successional dynamics were unique from seasonal changes we observed in unburned, forested reference soils. Our work demonstrates how EEA may shift even within the first months to years of ecosystem succession alongside common patterns of post-fire nutrient availability. Thus, this work emphasizes that nutrient dynamics in the earliest stages of forest secondary succession are important for understanding rates of C and N cycling and ecosystem development.

Antarctic cryoconite holes, or small melt-holes in the surfaces of glaciers, create habitable oases for isolated microbial communities with tightly linked microbial population structures. Viruses may influence the dynamics of polar microbial communities, but the viromes of the Antarctic cryoconite holes have yet to be characterized. We characterize single-stranded DNA (ssDNA) viruses from three cryoconite holes in the Taylor Valley, Antarctica, using metagenomics. Half of the assembled metagenomes cluster with those in the viral family Microviridae (n = 7), and the rest with unclassified circular replication associated protein (Rep)-encoding single-stranded (CRESS) DNA viruses (n = 7). An additional 18 virus-like circular molecules encoding either a Rep, a capsid protein gene, or other unidentified but viral-like open reading frames were identified. The samples from which the genomes were identified show a strong gradient in microbial diversity and abundances, and the number of viral genomes detected in each sample mirror that gradient. Additionally, one of the CRESS genomes assembled here shares ~90% genome-wide pairwise identity with a virus identified from a freshwater pond on the McMurdo Ice Shelf (Antarctica). Otherwise, the similarity of these viruses to those previously identified is relatively low. Together, these patterns are consistent with the presence of a unique regional virome present in fresh water host populations of the McMurdo Dry Valley region.

Understanding when and why new species are recruited into microbial communities is a formidable problem with implications for managing microbial systems, for instance by helping us better understand whether a probiotic or pathogen would be expected to colonize a human microbiome. Much theory in microbial temporal dynamics is focused on how phylogenetic relationships between microbes impact the order in which those microbes are recruited; for example, species that are closely related may competitively exclude each other. However, several recent human microbiome studies have observed closely related bacteria being recruited into microbial communities in short succession, suggesting that microbial community assembly is historically contingent, but competitive exclusion of close relatives may not be important. To address this, we developed a mathematical model that describes the order in which new species are detected in microbial communities over time within a phylogenetic framework. We use our model to test three hypothetical assembly modes: underdispersion (species recruitment is more likely if a close relative was previously detected), overdispersion (recruitment is more likely if a close relative has not been previously detected), and the neutral model (recruitment likelihood is not related to phylogenetic relationships among species). We applied our model to longitudinal human microbiome data, and found that for the individuals we analyzed, the human microbiome generally follows the underdispersion (i.e., nepotism) hypothesis. Exceptions were oral communities and the fecal communities of two infants that had undergone heavy antibiotic treatment. None of the datasets we analyzed showed statistically significant phylogenetic overdispersion.

Background: Understanding the factors that influence microbes’ environmental distributions is important for determining drivers of microbial community composition. These include environmental variables like temperature and pH, and higher-dimensional variables like geographic distance and host species phylogeny. In microbial ecology, “specificity” is often described in the context of symbiotic or host parasitic interactions, but specificity can be more broadly used to describe the extent to which a species occupies a narrower range of an environmental variable than expected by chance. Using a standardization we describe here, Rao’s (Theor Popul Biol, 1982. https://doi.org/10.1016/0040-5809(82)90004-1, Sankhya A, 2010. https://doi.org/10.1007/s13171-010-0016-3 ) Quadratic Entropy can be conveniently applied to calculate specificity of a feature, such as a species, to many different environmental variables.Results: We present our R packagespecificityfor performing the above analyses, and apply it to four real-life microbial data sets to demonstrate its application. We found that many fungi within the leaves of native Hawaiian plants had strong specificity to rainfall and elevation, even though these variables showed minimal importance in a previous analysis of fungal beta-diversity. In Antarctic cryoconite holes, our tool revealed that many bacteria have specificity to co-occurring algal community composition. Similarly, in the human gut microbiome, many bacteria showed specificity to the composition of bile acids. Finally, our analysis of the Earth Microbiome Project data set showed that most bacteria show strong ontological specificity to sample type. Our software performed as expected on synthetic data as well.Conclusions: specificityis well-suited to analysis of microbiome data, both in synthetic test cases, and across multiple environment types and experimental designs. The analysis and software we present here can reveal patterns in microbial taxa that may not be evident from a community-level perspective. These insights can also be visualized and interactively shared among researchers usingspecificity’s companion package,specificity.shiny.