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ABSTRACT In metabarcoding studies, Linnaean taxonomy assignments of Operational Taxonomic Units (OTUs) or Amplicon Sequence Variants (ASVs) underpin many downstream bioinformatics analyses and ecological interpretations of environmental DNA (eDNA) datasets. However, public molecular databases (i.e., SILVA, EUKARYOME, BOLD) for most microbial metazoan phyla (nematodes, tardigrades, kinorhynchs, etc.) are sparsely populated, negatively impacting our ability to assign ecologically meaningful taxonomy to these understudied groups. Additionally, the choice of bioinformatics parameters and computational algorithms can further affect the accuracy of eDNA taxonomy assignments. Here, we use twoin silicodatasets to show that taxonomy assignments using the 18S rRNA gene can be dramatically improved by curating Linnaean taxonomy strings associated with each reference sequence and closing phylogenetic gaps by improving taxon sampling. Using free‐living nematodes as a case study, we applied two commonly used taxonomy assignment algorithms (BLAST+ and the QIIME2 Naïve Bayes classifier) across six iterations of the SILVA 138 reference database to evaluate the precision and accuracy of taxonomy assignments. The BLAST+ top hit with a 90% sequence similarity cutoff often returned the highest percentage of correctly assigned taxonomy at the genus level, and the QIIME2 Naïve Bayes classifier performed similarly well when paired with a reference database containing corrected taxonomy strings. Our results highlight the urgent need for phylogenetically informed expansions of public reference databases (encompassing both genomes and common gene markers), focused on poorly sampled lineages that are now robustly recovered via eDNA metabarcoding approaches. Additional taxonomy curation efforts should be applied to popular reference databases such as SILVA, and taxon sampling could be rapidly improved by more frequent incorporation of newly published GenBank sequences linked to genus‐ and/or species‐level identifications. 

Abstract Networks of weakly coupled oscillators had a profound impact on our understanding of complex systems. Studies on model reconstruction from data have shown prevalent contributions from hypernetworks with triplet and higher interactions among oscillators, in spite that such models were originally defined as oscillator networks with pairwise interactions. Here, we show that hypernetworks can spontaneously emerge even in the presence of pairwise albeit nonlinear coupling given certain triplet frequency resonance conditions. The results are demonstrated in experiments with electrochemical oscillators and in simulations with integrate-and-fire neurons. By developing a comprehensive theory, we uncover the mechanism for emergent hypernetworks by identifying appearing and forbidden frequency resonant conditions. Furthermore, it is shown that microscopic linear (difference) coupling among units results in coupled mean fields, which have sufficient nonlinearity to facilitate hypernetworks. Our findings shed light on the apparent abundance of hypernetworks and provide a constructive way to predict and engineer their emergence. 

Abstract Bloom‐forming gelatinous zooplankton occur circumglobally and significantly influence the structure of pelagic marine food webs and biogeochemical cycling through interactions with microbial communities. During bloom conditions especially, gelatinous zooplankton are keystone taxa that help determine the fate of primary production, nutrient remineralization, and carbon export. Using the pelagic tunicateDolioletta gegenbaurias a model system for gelatinous zooplankton, we carried out a laboratory‐based feeding experiment to investigate the potential ecosystem impacts of doliolid gut microbiomes and microbial communities associated with doliolid faecal pellets and the surrounding seawater. Metabarcoding targeting Bacteria and Archaea 16S rRNA genes/Archaea) and qPCR approaches were used to characterize microbiome assemblages. Comparison between sample types revealed distinct patterns in microbial diversity and biomass that were replicable across experiments. These observations support the hypothesis that through their presence and trophic activity, doliolids influence the structure of pelagic food webs and biogeochemical cycling in subtropical continental shelf systems where tunicate blooms are common. Bacteria associated with starved doliolids (representative of the resident gut microbiome) possessed distinct low‐biomass and low‐diversity microbial assemblages, suggesting that the doliolid microbiome is optimized to support a detrital trophic mode. Bacterial generaPseudoalteromomasandShimiawere the most abundant potential core microbiome taxa, similar to patterns observed in other marine invertebrates. Exploratory bioinformatic analyses of predicted functional genes suggest that doliolids, via their interactions with bacterial communities, may affect important biogeochemical processes including nitrogen, sulphur, and organic matter cycling. 

Marine multicellular organisms host a diverse collection of bacteria, archaea, microbial eukaryotes, and viruses that form their microbiome. Such host-associated microbes can significantly influence the host’s physiological capacities; however, the identity and functional role(s) of key members of the microbiome (“core microbiome”) in most marine hosts coexisting in natural settings remain obscure. Also unclear is how dynamic interactions between hosts and the immense standing pool of microbial genetic variation will affect marine ecosystems’ capacity to adjust to environmental changes. Here, we argue that significantly advancing our understanding of how host-associated microbes shape marine hosts’ plastic and adaptive responses to environmental change requires (i) recognizing that individual host–microbe systems do not exist in an ecological or evolutionary vacuum and (ii) expanding the field toward long-term, multidisciplinary research on entire communities of hosts and microbes. Natural experiments, such as time-calibrated geological events associated with well-characterized environmental gradients, provide unique ecological and evolutionary contexts to address this challenge. We focus here particularly on mutualistic interactions between hosts and microbes, but note that many of the same lessons and approaches would apply to other types of interactions. 

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project.