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Abstract 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. 

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. 

Abstract Climate change is leading to phenological shifts across a wide range of species globally. Polar oceans are hotspots of rapid climate change where sea ice dynamics structure ecosystems and organismal life cycles are attuned to ice seasonality. To anticipate climate change impacts on populations and ecosystem services, it is critical to understand ecosystem phenology to determine species activity patterns, optimal environmental windows for processes like reproduction, and the ramifications of ecological mismatches. Since 1991, the Palmer Antarctica Long‐Term Ecological Research (LTER) program has monitored seasonal dynamics near Palmer Station. Here, we review the species that occupy this region as year‐round residents, seasonal breeders, or periodic visitors. We show that sea ice retreat and increasing photoperiod in the spring trigger a sequence of events from mid‐November to mid‐February, including Adélie penguin clutch initiation, snow melt, calm conditions (low winds and warm air/sea temperature), phytoplankton blooms, shallow mixed layer depths, particulate organic carbon flux, peak humpback whale abundances, nutrient drawdown, and bacterial accumulation. Subsequently, from May to June, snow accumulates, zooplankton indicator species appear, and sea ice advances. The standard deviation in the timing of most events ranged from ~20 to 45 days, which was striking compared with Adélie penguin clutch initiation that varied <1 week. In general, during late sea ice retreat years, events happened later (~5 to >30 days) than mean dates and the variability in timing was low (<20%) compared with early ice retreat years. Statistical models showed the timing of some events were informative predictors (but not sole drivers) of other events. From an Adélie penguin perspective, earlier sea ice retreat and shifts in the timing of suitable conditions or prey characteristics could lead to mismatches, or asynchronies, that ultimately influence chick survival via their mass at fledging. However, more work is needed to understand how phenological shifts affect chick thermoregulatory costs and the abundance, availability, and energy content of key prey species, which support chick growth and survival. While we did not detect many long‐term phenological trends, we expect that when sea ice trends become significant within our LTER time series, phenological trends and negative effects from ecological mismatches will follow.