Taxonomic marker gene studies, such as the 16S rRNA gene, have been used to successfully explore microbial diversity in a variety of marine, terrestrial, and host environments. For some of these environments long term sampling programs are beginning to build a historical record of microbial community structure. Although these 16S rRNA gene datasets do not intrinsically provide information on microbial metabolism or ecosystem function, this information can be developed by identifying metabolisms associated with related, phenotyped strains. Here we introduce the concept of metabolic inference; the systematic prediction of metabolism from phylogeny, and describe a complete pipeline for predicting the metabolic pathways likely to be found in a collection of 16S rRNA gene phylotypes. This framework includes a mechanism for assigning confidence to each metabolic inference that is based on a novel method for evaluating genomic plasticity. We applied this framework to 16S rRNA gene libraries from the West Antarctic Peninsula marine environment, including surface and deep summer samples and surface winter samples. Using statistical methods commonly applied to community ecology data we found that metabolic structure differed between summer surface and winter and deep samples, comparable to an analysis of community structure by 16S rRNA gene phylotypes. While taxonomic variance between samples was primarily driven by low abundance taxa, metabolic variance was attributable to both high and low abundance pathways. This suggests that clades with a high degree of functional redundancy can occupy distinct adjacent niches. Overall our findings demonstrate that inferred metabolism can be used in place of taxonomy to describe the structure of microbial communities. Coupling metabolic inference with targeted metagenomics and an improved collection of completed genomes could be a powerful way to analyze microbial communities in a high-throughput manner that provides direct access to metabolic and ecosystem function.

Bacterial community structure can be combined with observations of ecophysiological data to build predictive models of microbial ecosystem function. These models are useful for understanding how function might change in response to a changing environment. Here we use five spring-summer seasons of bacterial community structure and flow cytometry data from a productive coastal site along the western Antarctic Peninsula to construct models of bacterial production (BP), an ecosystem function that heterotrophic bacteria provide. Through a novel application of emergent self-organizing maps we identified eight recurrent modes in the structure of the bacterial community. A model that combined bacterial abundance, mode and the fraction of cells belonging to the high nucleic acid population (fHNA; R²=0.730, P<0.001) best described BP. Abrupt transitions between modes during the 2013-2014 spring-summer season corresponded to rapid shifts in fHNA. We conclude that parameterizing community structure data via segmentation can yield useful insights into microbial ecosystem function and ecosystem processes.

The Southern Ocean and coastal Antarctica provide a variety of ecosystem services with benefits for humankind that are of regional and global importance. Despite being nearly uninhabited, increasing exploitation of natural resources, a growing human presence, and environmental change threaten the sustained provisioning of these services. Ecosystem service assessments have proven as a suitable tool to understand the relevance of ecosystems for human well-being and guide decision-making, but the fluid and transboundary nature of marine ecosystems poses challenges to analyzing ecosystem services in regions with large marine sections. New methods to objectively assess the supply of ecosystem services for such realms are needed, and this need is exemplified by the Antarctic Peninsula region which encompasses rich marine, coastal, and terrestrial ecosystems but faces growing impacts and needs for taking action. In this study we applied the matrix method, an expert-based approach that employs a tabular matrix of ecosystem services and service providing units (SPUs) to elicit expert knowledge and rate the actual supply of key ecosystems services from the Antarctic Peninsula region. Further, we tested the applicability of this method on conventional definitions of SPUs and on objectively defined physico-chemical seascape units for a subset of the study region. Our results show high variations in the estimated supply of ecosystem services for the Antarctic Peninsula region, both with respect to the applied data models and in terms of the assessed services. While cultural and regulating services received highest supply estimates, provisioning services were regarded less relevant for the study region. Further, experts' supply estimates were much lower for the tested physico-chemical seascape units than for bathymetrically regionalized marine areas. The results suggest that a more explicit elaboration of linkages between ecosystem functions and processes and of the actual supply of ecosystem services is required in order to tap the full potential of such seascape data models in the context of qualitative, expert-based ecosystem service assessments.

The Antarctic marine environment is a dynamic ecosystem where microorganisms play an important role in key biogeochemical cycles. Despite the role that microbes play in this ecosystem, little is known about the genetic and metabolic diversity of Antarctic marine microbes. In this study we leveraged DNA samples collected by the Palmer Long Term Ecological Research (LTER) project to sequence shotgun metagenomes of 48 key samples collected across the marine ecosystem of the western Antarctic Peninsula (wAP). We developed an in silico metagenomics pipeline (iMAGine) for processing metagenomic data and constructing metagenome-assembled genomes (MAGs), identifying a diverse genomic repertoire related to the carbon, sulfur, and nitrogen cycles. A novel analytical approach based on gene coverage was used to understand the differences in microbial community functions across depth and region. Our results showed that microbial community functions were partitioned based on depth. Bacterial members harbored diverse genes for carbohydrate transformation, indicating the availability of processes to convert complex carbons into simpler bioavailable forms. We generated 137 dereplicated MAGs giving us a new perspective on the role of prokaryotes in the coastal wAP. In particular, the presence of mixotrophic prokaryotes capable of autotrophic and heterotrophic lifestyles indicated a metabolically flexible community, which we hypothesize enables survival under rapidly changing conditions. Overall, the study identified key microbial community functions and created a valuable sequence library collection for future Antarctic genomics research.

The transition from winter to spring represents a major shift in the basal energy source for the Antarctic marine ecosystem from lipids and other sources of stored energy to sunlight. Because sea ice imposes a strong control on the transmission of sunlight into the water column during the polar spring, we hypothesized that the timing of the sea ice retreat influences the timing of the transition from stored energy to photosynthesis. To test the influence of sea ice on water column microbial energy utilization we took advantage of unique sea ice conditions in Arthur Harbor, an embayment near Palmer Station on the western Antarctic Peninsula, during the 2015 spring–summer seasonal transition. Over a 5-week period we sampled water from below land-fast sea ice, in the marginal ice zone at nearby Palmer Station B, and conducted an ice removal experiment with incubations of water collected below the land-fast ice. Whole-community metatranscriptomes were paired with lipidomics to better understand how lipid production and utilization was influenced by light conditions. We identified several different phytoplankton taxa that responded similarly to light by the number of genes up-regulated, and in the transcriptional complexity of this response. We applied a principal components analysis to these data to reduce their dimensionality, revealing that each of these taxa exhibited a strikingly different pattern of gene up-regulation. By correlating the changes in lipid concentration to the first principal component of log fold-change for each taxa we could make predictions about which taxa were associated with different changes in the community lipidome. We found that genes coding for the catabolism of triacylglycerol storage lipids were expressed early on in phytoplankton associated with a Fragilariopsis kerguelensis reference transcriptome. Phytoplankton associated with a Corethron pennatum reference transcriptome occupied an adjacent niche, responding favorably to higher light conditions than F. kerguelensis. Other diatom and dinoflagellate taxa had distinct transcriptional profiles and correlations to lipids, suggesting diverse ecological strategies during the polar winter–spring transition.

abstract The marine coastal region makes up just 10% of the total area of the global ocean but contributes nearly 20% of its total primary production and over 80% of fisheries landings. Unicellular phytoplankton dominate primary production. Climate variability has had impacts on various marine ecosystems, but most sites are just approaching the age at which ecological responses to longer term, unidirectional climate trends might be distinguished. All five marine pelagic sites in the US Long Term Ecological Research (LTER) network are experiencing warming trends in surface air temperature. The marine physical system is responding at all sites with increasing mixed layer temperatures and decreasing depth and with declining sea ice cover at the two polar sites. Their ecological responses are more varied. Some sites show multiple population or ecosystem changes, whereas, at others, changes have not been detected, either because more time is needed or because they are not being measured.

Microbial ecological functions are an emergent property of community composition. For some ecological functions, this link is strong enough that community composition can be used to estimate the quantity of an ecological function. Here, we apply random forest regression models to compare the predictive performance of community composition and environmental data for bacterial production (BP). Using data from two independent long-term ecological research sites-Palmer LTER in Antarctica and Station SPOT in California-we found that community composition was a strong predictor of BP. The top performing model achieved an R2 of 0.84 and RMSE of 20.2 pmol L-1 hr-1 on independent validation data, outperforming a model based solely on environmental data (R2 = 0.32, RMSE = 51.4 pmol L-1 hr-1). We then operationalized our top performing model, estimating BP for 346 Antarctic samples from 2015 to 2020 for which only community composition data were available. Our predictions resolved spatial trends in BP with significance in the Antarctic (P value = 1 × 10-4) and highlighted important taxa for BP across ocean basins. Our results demonstrate a strong link between microbial community composition and microbial ecosystem function and begin to leverage long-term datasets to construct models of BP based on microbial community composition.