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.
An international and interdisciplinary sea ice drift expedition, the ‘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 coverage of core properties along a complete seasonal cycle, dedicated projects covered specific processes and habitats, or organisms on higher taxonomic or temporal resolution. A wide range of sampling approaches from sampling, sea ice coring, lead sampling to CTD rosette-based water sampling, plankton nets, ROVs and acoustic buoys was applied to address the science objectives. Further, a wide range of process-related measurements to address e.g. productivity patterns, seasonal migrations and diversity shifts were conducted both in situ and onboard RV Polarstern. This paper 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 two habitat- or process-specific projects. First results presented include high biological activities in winter time 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 Arctic Ocean is one of the regions where anthropogenic environmental change is progressing most rapidly and drastically. The impact of rising temperatures and decreasing sea ice on Arctic marine microbial communities is yet not well understood. Microbes form the basis of food webs in the Arctic Ocean, providing energy for larger organisms. Previous studies have shown that Atlantic taxa associated with low light are robust to more polar conditions. We compared to which extent sea ice melt influences light-associated phytoplankton dynamics and biodiversity over two years at two mooring locations in the Fram Strait. One mooring is deployed in pure Atlantic water, and the second in the intermittently ice-covered Marginal Ice Zone. Time-series analysis of amplicon sequence variants abundance over a 2-year period, allowed us to identify communities of co-occurring taxa that exhibit similar patterns throughout the annual cycle. We then examined how alterations in environmental conditions affect the prevalence of species. During high abundance periods of diatoms, polar phytoplankton populations dominated, while temperate taxa were weakly represented. Furthermore, we found that polar pelagic and ice-associated taxa, such as Fragilariopsis cylindrus and Melosira arctica, were more common in Atlantic conditions, while temperate taxa, such as Odontella aurita and Proboscia alata, were less abundant under polar conditions. This suggests that sea ice melt may act as a barrier to the northward expansion of temperate phytoplankton, preventing their dominance in regions still strongly influenced by polar conditions. Our findings highlight the complex interactions between sea ice melt, phytoplankton dynamics, and biodiversity in the Arctic.
Zooplankton play a crucial role in the ocean’s ecology, as they form a foundational component in the food chain by consuming phytoplankton or other zooplankton, supporting various marine species and influencing nutrient cycling. The vertical distribution of zooplankton in the ocean is patchy, and its relation to hydrographical conditions cannot be fully deciphered using traditional net casts due to the large depth intervals sampled. The Lightframe On-sight Keyspecies Investigation (LOKI) concentrates zooplankton with a net that leads to a flow-through chamber with a camera taking images. These high-resolution images allow for the determination of zooplankton taxa, often even to genus or species level, and, in the case of copepods, developmental stages. Each cruise produces a substantial volume of images, ideally requiring onboard analysis, which presently consumes a significant amount of time and necessitates internet connectivity to access the EcoTaxa Web service. To enhance the analyses, we developed an AI-based software framework named DeepLOKI, utilizing Deep Transfer Learning with a Convolution Neural Network Backbone. Our DeepLOKI can be applied directly on board. We trained and validated the model on pre-labeled images from four cruises, while images from a fifth cruise were used for testing. The best-performing model, utilizing the self-supervised pre-trained ResNet18 Backbone, achieved a notable average classification accuracy of 83.9%, surpassing the regularly and frequently used method EcoTaxa (default) in this field by a factor of two. In summary, we developed a tool for pre-sorting high-resolution black and white zooplankton images with high accuracy, which will simplify and quicken the final annotation process. In addition, we provide a user-friendly graphical interface for the DeepLOKI framework for efficient and concise processes leading up to the classification stage. Moreover, performing latent space analysis on the self-supervised pre-trained ResNet18 Backbone could prove advantageous in identifying anomalies such as deviations in image parameter settings. This, in turn, enhances the quality control of the data. Our methodology remains agnostic to the specific imaging end system used, such as Loki, UVP, or ZooScan, as long as there is a sufficient amount of appropriately labeled data available to enable effective task performance by our algorithms.
The Arctic Ocean is experiencing unprecedented changes because of climate warming, necessitating detailed analyses on the ecology and dynamics of biological communities to understand current and future ecosystem shifts. Here, we generated a four-year, high-resolution amplicon dataset along with one annual cycle of PacBio HiFi read metagenomes from the East Greenland Current (EGC), and combined this with datasets spanning different spatiotemporal scales (Tara Arctic and MOSAiC) to assess the impact of Atlantic water influx and sea-ice cover on bacterial communities in the Arctic Ocean. Densely ice-covered polar waters harboured a temporally stable, resident microbiome. Atlantic water influx and reduced sea-ice cover resulted in the dominance of seasonally fluctuating populations, resembling a process of “replacement” through advection, mixing and environmental sorting. We identified bacterial signature populations of distinct environmental regimes, including polar night and high-ice cover, and assessed their ecological roles. Dynamics of signature populations were consistent across the wider Arctic; e.g. those associated with dense ice cover and winter in the EGC were abundant in the central Arctic Ocean in winter. Population- and community-level analyses revealed metabolic distinctions between bacteria affiliated with Arctic and Atlantic conditions; the former with increased potential to use bacterial- and terrestrial-derived substrates or inorganic compounds. Our evidence on bacterial dynamics over spatiotemporal scales provides novel insights into Arctic ecology and indicates a progressing Biological Atlantification of the warming Arctic Ocean, with consequences for food webs and biogeochemical cycles.
Multiomics approaches need to be applied in the central Arctic Ocean to benchmark biodiversity change and to identify novel species and their genes. As part of MOSAiC, EcoOmics will therefore be essential for conservation and sustainable bioprospecting in one of the least explored ecosystems on Earth.
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.
Spinal cord injury (SCI) is a rare condition, which even after decades of research, to date still presents an incurable condition with a complex symptomatology. An SCI can result in paralysis, pain, loss of sensation, bladder and sexual dysfunction, and muscle degeneration, to name but a few. The large number of publications makes it difficult to keep track of current progress in the field and of the many treatment options that have been suggested and are being proposed with increasing frequency. Scientific databases with user-oriented search options will offer possible solutions, but they are still mostly in the development phase. In this meta-analysis, we summarize and narrow down SCI therapeutic approaches applied in pre-clinical and clinical research. Statistical analyses of treatment clusters—assorted after counting annual publication numbers in PubMed and ClinicalTrials.gov databases—were performed to allow the comparison of research foci and of their translation efficacy into clinical therapy. Using the example of SCI research, our findings demonstrate the challenges that come with the accelerating research progress—an issue that many research fields are faced with today. The analyses point out similarities and differences in the prioritization of SCI research in pre-clinical versus clinical therapy strategies. Moreover, the results demonstrate the rapidly growing importance of modern (bio-)engineering technologies.
Deciphering how microbial communities are shaped by environmental variability is fundamental for understanding the structure and function of ocean ecosystems. Thus far, we know little about the structuring of community functionality and the coupling between taxonomy and function over seasonal environmental gradients. To address this, we employed autonomous sampling devices and in situ sensors to investigate the taxonomic and functional dynamics of a pelagic Arctic Ocean microbiome over a four-year period. We demonstrate that the dominant prokaryotic and microeukaryotic populations exhibit recurrent, unimodal fluctuations each year, with community gene content following the same trend. The recurrent dynamics within the prokaryotic microbiome are structured into five temporal modules that represent distinct ecological states, characterised by unique taxonomic and metabolic signatures and connections to specific microeukaryotic populations and oceanographic conditions. For instance, Cand. Nitrosopumilus and the machinery to oxidise ammonia and reduce nitrite are signatures of early polar night, along with Radiolarians. In contrast, late summer is characterised by Amylibacter, sulfur compound metabolism and diverse Haptophyta lineages. Exploring the composition of modules further along with their degree of functional redundancy and the structuring of genetic diversity within functions over time revealed seasonal heterogeneity in environmental selection processes. In particular, we observe strong selection pressure on a functional level in spring while late polar night features weaker selection pressure that likely acts on an organismal level. By integrating taxonomic, functional, and environmental information, our study provides fundamental insights into how microbiomes are structured under pronounced environmental variability in understudied, yet rapidly changing polar marine ecosystems.
