The international and interdisciplinary sea-ice drift expedition “The Multidisciplinary drifting Observatory for the Study of Arctic Climate” (MOSAiC) was conducted from October 2019 to September 2020. The aim of MOSAiC was to study the interconnected physical, chemical, and biological characteristics and processes from the atmosphere to the deep sea of the central Arctic system. The ecosystem team addressed current knowledge gaps and explored unknown biological properties over a complete seasonal cycle focusing on three major research areas: biodiversity, biogeochemical cycles, and linkages to the environment. In addition to the measurements of core properties along a complete seasonal cycle, dedicated projects covered specific processes and habitats, or organisms on higher taxonomic or temporal resolution in specific time windows. A wide range of sampling instruments and approaches, including sea-ice coring, lead sampling with pumps, rosette-based water sampling, plankton nets, remotely operated vehicles, and acoustic buoys, was applied to address the science objectives. Further, a broad range of process-related measurements to address, for example, productivity patterns, seasonal migrations, and diversity shifts, were made both in situ and onboard RV Polarstern. This article provides a detailed overview of the sampling approaches used to address the three main science objectives. It highlights the core sampling program and provides examples of habitat- or process-specific sampling. The initial results presented include high biological activities in wintertime and the discovery of biological hotspots in underexplored habitats. The unique interconnectivity of the coordinated sampling efforts also revealed insights into cross-disciplinary interactions like the impact of biota on Arctic cloud formation. This overview further presents both lessons learned from conducting such a demanding field campaign and an outlook on spin-off projects to be conducted over the next years.

The pennate diatom Phaeodactylum tricornutum is a model organism able to synthesize industrially-relevant molecules. Commercial-scale cultivation currently requires large monocultures, prone to bio-contamination. However, little is known about the identity of the invading organisms. To reduce the complexity of natural systems, we systematically investigated the microbiome of non-axenic P. tricornutum cultures from a culture collection in reproducible experiments. The results revealed a dynamic bacterial community that developed differently in “complete” and “minimal” media conditions. In complete media, we observed an accelerated “culture crash”, indicating a more stable culture in minimal media. The identification of only four bacterial families as major players within the microbiome suggests specific roles depending on environmental conditions. From our results we propose a network of putative interactions between P. tricornutum and these main bacterial factions. We demonstrate that, even with rather sparse data, a mathematical model can be reconstructed that qualitatively reproduces the observed population dynamics, thus indicating that our hypotheses regarding the molecular interactions are in agreement with experimental data. Whereas the model in its current state is only qualitative, we argue that it serves as a starting point to develop quantitative and predictive mathematical models, which may guide experimental efforts to synthetically construct and monitor stable communities required for robust upscaling strategies.

The Arctic Ocean is undergoing a major transition as a result of global warming, with uncertain consequences for its ecosystems. Our study introduces an integrated analytical approach using co-occurrence networks, convergent cross-mapping, and energy landscape analysis. Applied to four years of amplicon data from Fram Strait, located at the boundary between Arctic and Atlantic waters, our method identifies keystone species in seasonal microbial communities, elucidates causal interactions, and predicts stable configurations across changing environments. We find strong evidence for a “winter reset”, implying that organisms representing the spring bloom are largely determined by prevailing environmental conditions during winter. In addition, our analysis suggests that winter communities may adapt more readily to expected Atlantification than summer communities. These results highlight the utility of innovative time-series analyses in disentangling ecosystem dynamics. This approach provides critical insights into Arctic ecological interactions, dynamics, and resilience and aids in understanding ecosystem responses to environmental change.

Today massive amounts of sequenced metagenomic and metatranscriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding of biological systems analysed from different perspectives is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data are of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data have been successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.

The pennate diatom Phaeodactylum tricornutum is a model organism able to synthesise industrially-relevant molecules. Large-scale monocultures are prone to bio-contamination, however, little is known about the identity of the invading organisms. To gain insight into the bacterial community associated with diatoms, we translated the complexity of a natural system into reproducible experiments where we investigated the microbiome of P. tricornutum cultures. The results revealed a dynamic bacterial community that changed over time and in differing media conditions. We propose a network of putative interactions between P. tricornutum and the main bacterial factions, which is translated into a set of ordinary differential equations constituting a computational dynamic model. The proposed mathematical model is able to capture the population dynamics, further supporting the hypothesised interactions. The interdisciplinary approach implemented provides a framework for understanding the dynamics of diatom-associated microbial communities, and provides a foundation for further systematic investigations of host-microbe interactions.

Knock-out mutants are often used to study gene function by disrupting a specific gene and comparing the mutant to a wild-type strain. Reliable interpretation, however, requires that the two strains differ only by the intended mutation and that the observed phenotype is caused specifically by the deleted gene. In the highly polyploid cyanobacterium Synechocystis sp. PCC 6803, this is particularly challenging because incomplete segregation can mask genetic heterogeneity or secondary suppressor mutations. The genetic variation among laboratory wild-type lines can further confound phenotypic analyses. We show that these challenges can be addressed by routine strain validation via whole-genome sequencing (WGS). To this end, we developed and tested user friendly workflows for short-read (Illumina), long-read (Oxford Nanopore Technologies; ONT), and hybrid data, providing standardized quality control, variant calling, and structural variant detection. We benchmarked their performance in detecting single-nucleotide polymorphisms (SNPs), small indels, and structural variants using simulated datasets across different coverages and mixed populations. Applying the workflows to three Synechocystis sp. PCC 6803 wild-type lines revealed multiple sequence and structural differences relative to the reference genome, including previously undescribed genetic variants, underscoring the importance of documenting the strain background and the value of long-read sequencing. Characterization of two independent 6-phosphogluconate dehydrogenase ( gnd ) knock-out mutants and their complemented strains highlighted how a failed rescue can reveal a phenotype unrelated to the intended knock-out. An automated literature analysis revealed that only a minority of the investigated Synechocystis studies that used knock-out mutants included complementation as a control (39%), whereas this practice is more common in studies involving Escherichia coli (63%) and Saccharomyces cerevisiae (55%). Based on these results, we propose a practical guide for standardizing knock-out phenotyping in Synechocystis PCC 6803. Combined with accessible workflows for routine whole-genome validation, this framework aims to support more robust and reproducible knock-out studies in the future.