Large‐scale drifting Sargassum biomass, known as golden tides, has caused considerable damage to the local environment and economy associated with the Yellow Sea of China. To understand the reoccurrence of Sargassum horneri golden tides in the Yellow Sea, large‐scale spatio‐temporal sampling was performed across nine cruises and five coastal surveys. Morphological data indicated that the floating S. horneri thalli with differing reproductive timing coexisted in the Yellow Sea. A total of 196 S. horneri samples had identical sequences of partial cox3 and rbcL‐S spacer region, revealing very low genetic diversity in the floating biomass. A total of 19 haplotypes for partial cox3 previously found in the Yellow Sea were not detected in our large‐scale sampling. Based on four novel mtDNA markers, the 196 samples could be further distinguished into two forms, which varied in proportions at various locations, but coexisted in each of the spatio‐temporal sampling. These results indicated that the floating Sargassum biomass in the Yellow Sea came from only two dominating haplotypes. The novel findings uncovered by this work will provide further insight into the underlying mechanisms of reoccurring golden tides in the Yellow Sea, and lead to the improved management of the Sargassum biomass.

An exploratory Life Cycle Assessment (LCA) was carried out to provide insight into the environmental impacts of using the green seaweed Ulva spp. as a feedstock, for production of bioplastic. The study focused on the production of lactic acid as a precursor of polylactic acid. The study was on the production process: (1) The cultivation of Ulva spp., in an Integrated Multitrophic Aquaculture system; (2) the processing of the biomass for solubilization of sugars; (3) the fermentation of the sugars to lactic acid; (4) the isolation of lactic acid from fermentation broth. The study identified environmental hotspots and compared an experimental seaweed production chain with conventional feedstocks. The main hotspot is derived from electricity consumption during seaweed cultivation. The impact of electricity consumption can be lowered by reducing energy use and sourcing renewable energy, and by improving the material efficiency in the product chain. To improve understanding of the process of production’s environmental impacts, future studies should broaden the system boundaries and scope of sustainability issues included in the environmental assessment.

The last few years have seen the advancement of high-throughput experimental techniques that have produced an extraordinary amount of data. Bioinformatics and statistical analyses have become instrumental to interpreting the information coming from, e.g., sequencing data and often motivate further targeted experiments. The broad discipline of “computational biology” extends far beyond the well-established field of bioinformatics, but it is our impression that more theoretical methods such as the use of mathematical models are not yet as well integrated into the research studying microbial interactions. The empirical complexity of microbial communities presents challenges that are difficult to address within vivo/in vitroapproaches alone, and with microbiology developing from a qualitative to a quantitative science, we see stronger opportunities arising for interdisciplinary projects integrating theoretical approaches with experiments. Indeed, the addition ofin silicoexperiments, i.e., computational simulations, has a discovery potential that is, unfortunately, still largely underutilized and unrecognized by the scientific community. This minireview provides an overview of mathematical models of natural ecosystems and emphasizes that one critical point in the development of a theoretical description of a microbial community is the choice of problem scale. Since this choice is mostly dictated by the biological question to be addressed, in order to employ theoretical models fully and successfully it is vital to implement an interdisciplinary view at the conceptual stages of the experimental design.

The ability of phototrophs to colonise different environments relies on robust protection against oxidative stress, a critical requirement for the successful evolutionary transition from water to land. Photosynthetic organisms have developed numerous strategies to adapt their photosynthetic apparatus to changing light conditions in order to optimise their photosynthetic yield, which is crucial for life on Earth to exist. Photosynthetic acclimation is an excellent example of the complexity of biological systems, where highly diverse processes, ranging from electron excitation over protein protonation to enzymatic processes coupling ion gradients with biosynthetic activity, interact on drastically different timescales from picoseconds to hours. Efficient functioning of the photosynthetic apparatus and its protection is paramount for efficient downstream processes, including metabolism and growth. Modern experimental techniques can be successfully integrated with theoretical and mathematical models to promote our understanding of underlying mechanisms and principles. This review aims to provide a retrospective analysis of multidisciplinary photosynthetic acclimation research carried out by members of the Marie Curie Initial Training Project, AccliPhot, placing the results in a wider context. The review also highlights the applicability of photosynthetic organisms for industry, particularly with regards to the cultivation of microalgae. It intends to demonstrate how theoretical concepts can successfully complement experimental studies broadening our knowledge of common principles in acclimation processes in photosynthetic organisms, as well as in the field of applied microalgal biotechnology.

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.