Revealing environmentally driven population dynamics of an Arctic diatom using a novel microsatellite PoolSeq barcoding approach

Heatwave responses of Arctic phytoplankton communities are driven by combined impacts of warming and cooling

Marine heatwaves are increasing in frequency and intensity as climate change progresses, especially in the highly productive Arctic regions. Although their effects on primary producers will largely determine the impacts on ecosystem services, mechanistic understanding on phytoplankton responses to these extreme events is still very limited. We experimentally exposed Arctic phytoplankton assemblages to stable warming, as well as to repeated heatwaves, and measured temporally resolved productivity, physiology, and composition. Our results show that even extreme stable warming increases productivity, while the response to heatwaves depends on the specific scenario applied and is not predictable from stable warming responses. This appears to be largely due to the underestimated impact of the cool phase following a heatwave, which can be at least as important as the warm phase for the overall response. We show that physiological and compositional adjustments to both warm and cool phases drive overall phytoplankton productivity and need to be considered mechanistically to predict overall ecosystem impacts.

Multiannual patterns of genetic structure and mating type ratios highlight the complex bloom dynamics of a marine planktonic diatom

Understanding the genetic structure of populations and the processes responsible for its spatial and temporal dynamics is vital for assessing species' adaptability and survival in changing environments. We investigate the genetic fingerprinting of blooming populations of the marine diatom Pseudo-nitzschia multistriata in the Gulf of Naples (Mediterranean Sea) from 2008 to 2020. Strains were genotyped using microsatellite fingerprinting and natural samples were also analysed with Microsatellite Pool-seq Barcoding based on Illumina sequencing of microsatellite loci. Both approaches revealed a clonal expansion event in 2013 and a more stable genetic structure during 2017-2020 compared to previous years. The identification of a mating type (MT) determination gene allowed to assign MT to strains isolated over the years. MTs were generally at equilibrium with two notable exceptions, including the clonal bloom of 2013. The populations exhibited linkage equilibrium in most blooms, indicating that sexual reproduction leads to genetic homogenization. Our findings show that P. multistriata blooms exhibit a dynamic genetic and demographic composition over time, most probably determined by deeper-layer cell inocula. Occasional clonal expansions and MT imbalances can potentially affect the persistence and ecological success of planktonic diatoms.

The pennate diatom Pseudo-nitzschia multistriata as a model for diatom life cycles, from the laboratory to the sea

Phytoplankton dynamics are regulated by external cues, such as light and nutrients, as well as by biotic interactions and endogenous controls linked to life cycle characteristics. The planktonic pennate diatom Pseudo-nitzschia multistriata, with a heterothallic mating system with two opposite mating types (MTs), represents a model for the study of diatom life cycles. P. multistriata is a toxic species, able to produce the neurotoxin domoic acid. First described in Japan in 1993, it was detected at the long-term monitoring station MareChiara (Gulf of Naples, Italy) in 1995. Since then, P. multistriata has been reported from several worldwide coastal sites. A large body of knowledge has been produced on its ecology, genetic diversity, and life cycle characteristics. The availability of these data, the ecological relevance of the Pseudo-nitzschia genus, and its controllable life cycle with a short generation time made it an ideal species to develop a genetic model system for diatoms. To enable functional studies, a 59 Mb genome sequence and several transcriptomic data were produced, and genetic transformation was optimized. These tools allowed the discovery of the first mating-type determining gene for diatoms. Gene expression studies and metabolomics analyses defined genes and molecules underpinning different phases of the process of sexual reproduction. This model system, developed to explore the genetics of diatom life cycles, offers the opportunity to parallel experimental observations in the laboratory using in situ meta-omics analyses along space and time, empowering knowledge on the biology and ecology of the genus.

Spatial and biological oceanographic insights into the massive fish-killing bloom of the haptophyte Chrysochromulina leadbeateri in northern Norway

A bloom of the fish-killing haptophyte Chrysochromulina leadbeateri in northern Norway during May and June 2019 was the most harmful algal event ever recorded in the region, causing massive mortalities of farmed salmon. Accordingly, oceanographic and biodiversity aspects of the bloom were studied in unprecedented detail, based on metabarcoding and physico-chemical and biotic factors related with the dynamics and distribution of the bloom. Light- and electron-microscopical observations of nanoplankton samples from diverse locations confirmed that C. leadbeateri was dominant in the bloom and the primary cause of associated fish mortalities. Cell counts by light microscopy and flow cytometry were obtained throughout the regional bloom within and adjacent to five fjord systems. Metabarcoding sequences of the V4 region of the 18S rRNA gene from field material collected during the bloom and a cultured isolate from offshore of Tromsøy island confirmed the species identification. Sequences from three genetic markers (18S, 28S rRNA gene and ITS region) verified the close if not identical genetic similarity to C. leadbeateri from a previous massive fish-killing bloom in 1991 in northern Norway. The distribution and cell abundance of C. leadbeateri and related Chrysochromulina species in the recent incident were tracked by integrating observations from metabarcoding sequences of the V4 region of the 18S rRNA gene. Metabarcoding revealed at least 14 distinct Chrysochromulina variants, including putative cryptic species. C. leadbeateri was by far the most abundant of these species, but with high intraspecific genetic variability. Highest cell abundance of up to 2.7 × 10⁷ cells L ^- 1 of C. leadbeateri was found in Balsfjorden; the high cell densities were associated with stratification near the pycnocline (at ca. 12 m depth) within the fjord. The cell abundance of C. leadbeateri showed positive correlations with temperature, negative correlation with salinity, and a slightly positive correlation with ambient phosphate and nitrate concentrations. The spatio-temporal succession of the C. leadbeateri bloom suggests independent initiation from existing pre-bloom populations in local zones, perhaps sustained and supplemented over time by northeastward advection of the bloom from the fjords.

Bray-Curtis (AFD) differentiation in molecular ecology: Forecasting, an adjustment ( A A), and comparative performance in selection detection

Geographic genetic differentiation measures are used for purposes such as assessing genetic diversity and connectivity, and searching for signals of selection. Confirmation by unrelated measures can minimize false positives. A popular differentiation measure, Bray-Curtis, has been used increasingly in molecular ecology, renamed AFD (hereafter called BCAFD). Critically, BCAFD is expected to be partially independent of the commonly used Hill "Q-profile" measures. BCAFD needs scrutiny for potential biases, by examining limits on its value, and comparing simulations against expectations. BCAFD has two dependencies on within-population (alpha) variation, undesirable for a between-population (beta) measure. The first dependency is derived from similarity toG ST andF ST . The second dependency is that BCAFD cannot be larger than the highest allele proportion in either location (alpha variation), which can be overcome by data-filtering or by a modified statistic A A or "Adjusted AFD". The first dependency does not forestall applications such as assessing connectivity or selection, if we know the measure's null behavior under selective neutrality with specified conditions-which is shown in this article for A A, for equilibrium, and nonequilibrium, for the commonly used data type of single-nucleotide-polymorphisms (SNPs) in two locations. Thus, A A can be used in tandem with mathematically contrasting differentiation measures, with the aim of reducing false inferences. For detecting adaptive loci, the relative performance of A A and other measures was evaluated, showing that it is best to use two mathematically different measures simultaneously, and that A A is in one of the best such pairwise criteria. For any application, using A A, rather than BCAFD, avoids the counterintuitive limitation by maximum allele proportion within localities.

Evolution of Phytoplankton as Estimated from Genetic Diversity

Phytoplankton are photosynthetic, single-celled organisms producing almost half of all oxygen on Earth and play a central role as prey for higher organisms, making them irreplaceable in the marine food web. As Global Change proceeds, imposing rapidly intensifying selection pressures, phytoplankton are forced to undergo evolution, local extinction, or redistribution, with potentially cascading effects throughout the marine ecosystem. Recent results from the field of population genetics display high levels of standing genetic diversity in natural phytoplankton populations, providing ample ‘evolutionary options’ and implying high adaptive potential to changing conditions. This potential for adaptive evolution is realized in several studies of experimental evolution, even though most of these studies investigate the evolution of only single strains. This, however, shows that phytoplankton not only evolve from standing genetic diversity, but also rely on de novo mutations. Recent global sampling campaigns show that the immense intraspecific diversity of phytoplankton in the marine ecosystem has been significantly underestimated, meaning we are only studying a minor portion of the relevant variability in the context of Global Change and evolution. An increased understanding of genomic diversity is primarily hampered by the low number of ecologically representative reference genomes of eukaryotic phytoplankton and the functional annotation of these. However, emerging technologies relying on metagenome and transcriptome data may offer a more realistic understanding of phytoplankton diversity.