In this article, we summarize the work of the NASA Outer Planets Assessment Group (OPAG) Roadmaps to Ocean Worlds (ROW) group. The aim of this group is to assemble the scientific framework that will guide the exploration of ocean worlds, and to identify and prioritize science objectives for ocean worlds over the next several decades. The overarching goal of an Ocean Worlds exploration program as defined by ROW is to “identify ocean worlds, characterize their oceans, evaluate their habitability, search for life, and ultimately understand any life we find.” The ROW team supports the creation of an exploration program that studies the full spectrum of ocean worlds, that is, not just the exploration of known ocean worlds such as Europa but candidate ocean worlds such as Triton as well. The ROW team finds that the confirmed ocean worlds Enceladus, Titan, and Europa are the highest priority bodies to target in the near term to address ROW goals. Triton is the highest priority candidate ocean world to target in the near term. A major finding of this study is that, to map out a coherent Ocean Worlds Program, significant input is required from studies here on Earth; rigorous Research and Analysis studies are called for to enable some future ocean worlds missions to be thoughtfully planned and undertaken. A second finding is that progress needs to be made in the area of collaborations between Earth ocean scientists and extraterrestrial ocean scientists.

Dramatic decreases in the extent of Arctic multiyear ice (MYI) suggest this environment may disappear as early as 2100, replaced by ecologically different first-year ice. To better understand the implications of this loss on microbial biodiversity, we undertook a detailed census of the microbial community in MYI at two sites near the geographic North Pole using parallel tag sequencing of the 16S rRNA gene. Although the composition of the MYI microbial community has been characterized by previous studies, microbial community structure has not been. Although richness was lower in MYI than in underlying surface water, we found diversity to be comparable using the Simpson and Shannon's indices (for Simpson t=0.65, P=0.56; for Shannon t=0.25, P=0.84 for a Student's t-test of mean values). Cyanobacteria, comprising 6.8% of reads obtained from MYI, were observed for the first time in Arctic sea ice. In addition, several low-abundance clades not previously reported in sea ice were present, including the phylum TM7 and the classes Spartobacteria and Opitutae. Members of Coraliomargarita, a recently described genus of the class Opitutae, were present in sufficient numbers to suggest niche occupation within MYI.

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.