Beschreibung: The northern Humboldt Current upwelling system (HCS) belongs to the most productive marine ecosystems, providing five to eight times higher fisheries landings per unit area than other coastal upwelling systems. To solve this “Peruvian puzzle”, to elucidate the pelagic food-web structure and to better understand trophic interactions in the HCS, a combined stable isotope and fatty acid trophic biomarker approach was adopted for key zooplankton taxa and higher trophic positions with an extensive spatial coverage from 8.5 to 16°S and a vertical range down to 1,000 m depth. A pronounced regional shift by up to ∼5‰ in the δ15N baseline of the food web occurred from North to South. Besides regional shifts, δ15N ratios of particulate organic matter (POM) also tended to increase with depth, with differences of up to 3.8‰ between surface waters and the oxygen minimum zone. In consequence, suspension-feeding zooplankton permanently residing at depth had up to ∼6‰ higher δ15N signals than surface-living species or diel vertical migrants. The comprehensive data set covered over 20 zooplankton taxa and indicated that three crustacean species usually are key in the zooplankton community, i.e., the copepods Calanus chilensis at the surface and Eucalanus inermis in the pronounced OMZ and the krill Euphausia mucronata, resulting in an overall low number of major trophic pathways toward anchovies. In addition, the semi-pelagic squat lobster Pleuroncodes monodon appears to play a key role in the benthic-pelagic coupling, as indicated by highest δ13C’ ratios of −14.7‰. If feeding on benthic resources and by diel vertical migration, they provide a unique pathway for returning carbon and energy from the seafloor to the epipelagic layer, increasing the food supply for pelagic fish. Overall, these mechanisms result in a very efficient food chain, channeling energy toward higher trophic positions and partially explaining the “Peruvian puzzle” of enormous fish production in the HCS.

Beschreibung: Science communication is becoming increasingly important to connect academia and society, and to counteract fake news among climate change deniers. Online video platforms, such as YouTube, offer great potential for low-threshold communication of scientific knowledge to the general public. In April 2020 a diverse group of researchers from the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research launched the YouTube channel "Wissenschaft fürs Wohnzimmer" (translated to "Sitting Room Science") to stream scientific talks about climate change and biodiversity every Thursday evening. Here we report on the numbers and diversity of content, viewers, and presenters from 2 years and 100 episodes of weekly livestreams. Presented topics encompass all areas of polar research, social issues related to climate change, and new technologies to deal with the changing world and climate ahead. We show that constant engagement by a group of co-hosts, and presenters from all topics, career stages, and genders enable a continuous growth of views and subscriptions, i.e. impact. After 783 days the channel gained 30,251 views and 828 subscribers and hosted well-known scientists while enabling especially early career researchers to improve their outreach and media skills. We show that interactive and science-related videos, both live and on-demand, within a pleasant atmosphere, can be produced voluntarily while maintaining high quality. We further discuss challenges and possible improvements for the future. Our experiences may help other researchers to conduct meaningful scientific outreach and to push borders of existing formats with the overall aim of developing a better understanding of climate change and our planet.

Beschreibung: Progress in molecular methods has enabled the monitoring of bacterial populations in time. Nevertheless, understanding community dynamics and its links with ecosystem functioning remains challenging due to the tremendous diversity of microorganisms. Conceptual frameworks that make sense of time series of taxonomically rich bacterial communities, regarding their potential ecological function, are needed. A key concept for organizing ecological functions is the niche, the set of strategies that enable a population to persist and define its impacts on the surroundings. Here we present a framework based on manifold learning to organize genomic information into potentially occupied bacterial metabolic niches over time. Manifold learning tries to uncover low-dimensional data structures in high-dimensional data sets that can be used to describe the data in reduced dimensions. We apply the method to re-construct the dynamics of putatively occupied metabolic niches using a long-term bacterial time series from the Baltic Sea, the Linnaeus Microbial Observatory (LMO). The results reveal a relatively low-dimensional space of occupied metabolic niches comprising groups of taxa with similar functional capabilities. Time patterns of occupied niches were strongly driven by seasonality. Some metabolic niches were dominated by one bacterial taxon, whereas others were occupied by multiple taxa, depending on the season. These results illustrate the power of manifold learning approaches to advance our understanding of the links between community composition and functioning in microbial systems.

Beschreibung: Many current challenges involve understanding the complex dynamical interplay between the constituents of systems. Typically, the number of such constituents is high, but only limited data sources on them are available. Conventional dynamical models of complex systems are rarely mathematically tractable and their numerical exploration suffers both from computational and data limitations. Here we review generalized modeling, an alternative approach for formulating dynamical models to gain insights into dynamics and bifurcations of uncertain systems. We argue that this approach deals elegantly with the uncertainties that exist in real world data and enables analytical insight or highly efficient numerical investigation. We provide a survey of recent successes of generalized modeling and a guide to the application of this modeling approach in future studies such as complex integrative ecological models.