Organismen von morgen im Experiment von heute – experimentelle Evolution mit Phytoplankton

Der globale Wandel erfasst zunehmend auch die Weltmeere und ihre Lebewelt. Insbesondere Erwärmung und Ozeanversauerung könnte den Beginn der Nahrungskette beeinträchtigen, die mikroskopisch kleinen Pflanzen des Phytoplanktons. Diese Einzeller tragen zur Hälfte der gesamten Biomasse - Produktion auf unserem Planeten bei. Das allermeiste Wissen über ihre Reaktionen auf die globalen Umweltveränderungen hat die Meeresbiologie aus Kurzzeit-Experimenten. Doch die Organismen von morgen könnten auch ganz anders auf die neuen Bedingungen reagieren, sofern sie sich evolutiv anpassen. Der Vortrag stellt den neuen Ansatz von Evolutionsexperimenten in der Meereskunde vor. Dieser ermöglicht es, die Organismen von morgen heute im Labor zu untersuchen. Wie erwartet können sich einige Arten rechtzeitig anpassen, ihre Anpassungsrate ist dabei schneller als die prognostizierten Umweltänderungen. In einem Ausblick werden die evolutionsbiologischen Konzepte auch auf andere Bereiche in der Meeresbiologie übertragen wie die Fischerei

1. Wochenbericht AL509

Wochenbericht FS Alkor Reise 509, Fahrtabschnitt 15.05 – 20.05.201

Cruise Report AL509 R.V. Alkor

Dates of Cruise: 15.05. – 30.05.2018 Areas of Research: Physical, chemical, biological and fishery oceanography Port Calls: Riga. Latvia, 22.05.201

1. Wochenbericht AL522

Wochenbericht FS Alkor Reise 522 "Ostsee Mai", 1. Fahrtabschnitt 15.05 – 25.05.201

Eco-Evolutionary Interaction in Competing Phytoplankton: Nutrient Driven Genotype Sorting Likely Explains Dominance Shift and Species Responses to CO2

How ecological and evolutionary processes interact and together determine species and community responses to climate change is poorly understood. We studied long-term dynamics (over approximately 200 asexual generations) in two phytoplankton species, a coccolithophore (Emiliania huxleyi), and a diatom (Chaetoceros affinis), to increased CO2 growing alone, or competing with one another in co-occurrence. To allow for rapid evolutionary responses, the experiment started with a standing genetic variation of nine genotypes in each of the species. Under co-occurrence of both species, we observed a dominance shift from C. affinis to E. huxleyi after about 120 generations in both CO2 treatments, but more pronounced under high CO2. Associated with this shift, we only found weak adaptation to high CO2 in the diatom and none in the coccolithophore in terms of species’ growth rates. In addition, no adaptation to interspecific competition could be observed by comparing the single to the two-species treatments in reciprocal assays, regardless of the CO2 treatment. Nevertheless, highly reproducible genotype sorting left only one genotype remaining for each of the species among all treatments. This strong evolutionary selection coincided with the dominance shift from C. affinis to E. huxleyi. Since all other conditions were kept constant over time, the most parsimonious explanation for the dominance shift is that the strong evolutionary selection was driven by the experimental nutrient conditions, and in turn potentially altered competitive ability of the two species. Thus, observed changes in the simplest possible two-species phytoplankton “community” demonstrated that eco-evolutionary interactions can be critical for predicting community responses to climate change in rapidly dividing organisms such as phytoplankton

Long-term dynamics of adaptive evolution in a globally important phytoplankton species to ocean acidification

Marine phytoplankton may adapt to ocean change, such as acidification or warming, because of their large population sizes and short generation times. Long-term adaptation to novel environments is a dynamic process, and phenotypic change can take place thousands of generations after exposure to novel conditions. We conducted a long-term evolution experiment (4 years = 2100 generations), starting with a single clone of the abundant and widespread coccolithophore Emiliania huxleyi exposed to three different CO2 levels simulating ocean acidification (OA). Growth rates as a proxy for Darwinian fitness increased only moderately under both levels of OA [+3.4% and +4.8%, respectively, at 1100 and 2200 μatm partial pressure of CO2 (Pco2)] relative to control treatments (ambient CO2, 400 μatm). Long-term adaptation to OA was complex, and initial phenotypic responses of ecologically important traits were later reverted. The biogeochemically important trait of calcification, in particular, that had initially been restored within the first year of evolution was later reduced to levels lower than the performance of nonadapted populations under OA. Calcification was not constitutively lost but returned to control treatment levels when high CO2–adapted isolates were transferred back to present-day control CO2 conditions. Selection under elevated CO2 exacerbated a general decrease of cell sizes under long-term laboratory evolution. Our results show that phytoplankton may evolve complex phenotypic plasticity that can affect biogeochemically important traits, such as calcification. Adaptive evolution may play out over longer time scales (>1 year) in an unforeseen way under future ocean conditions that cannot be predicted from initial adaptation responses

An improved filtering algorithm for big read datasets and its application to single-cell assembly

Background: For single-cell or metagenomic sequencing projects, it is necessary to sequence with a very high mean coverage in order to make sure that all parts of the sample DNA get covered by the reads produced. This leads to huge datasets with lots of redundant data. A filtering of this data prior to assembly is advisable. Brown et al. (2012) presented the algorithm Diginorm for this purpose, which filters reads based on the abundance of their k-mers. Methods: We present Bignorm, a faster and quality-conscious read filtering algorithm. An important new algorithmic feature is the use of phred quality scores together with a detailed analysis of the k-mer counts to decide which reads to keep. Results: We qualify and recommend parameters for our new read filtering algorithm. Guided by these parameters, we remove in terms of median 97.15% of the reads while keeping the mean phred score of the filtered dataset high. Using the SDAdes assembler, we produce assemblies of high quality from these filtered datasets in a fraction of the time needed for an assembly from the datasets filtered with Diginorm. Conclusions: We conclude that read filtering is a practical and efficient method for reducing read data and for speeding up the assembly process. This applies not only for single cell assembly, as shown in this paper, but also to other projects with high mean coverage datasets like metagenomic sequencing projects. Our Bignorm algorithm allows assemblies of competitive quality in comparison to Diginorm, while being much faster. Bignorm is available for download at https://git.informatik.uni-kiel.de/axw/Bignorm