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Review

Evolution of Phytoplankton as Estimated from Genetic Diversity

1
Faculty of Science and Engineering, Environmental and Marine Biology, Åbo Akademi University, 20500 Turku, Finland
2
Faculty of Science and Engineering, Husö Biological Station, Åbo Akademi University, 22220 Emkarby, Finland
J. Mar. Sci. Eng. 2022, 10(4), 456; https://doi.org/10.3390/jmse10040456
Received: 26 February 2022 / Revised: 21 March 2022 / Accepted: 22 March 2022 / Published: 24 March 2022
(This article belongs to the Special Issue Marine Phytoplankton and Their Evolution)

Abstract

:
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.

1. Introduction

Primary production, the build-up of organic carbon, is considered the most valuable process in the global ocean, largely enabling life on Earth, as we know it. The major part of the marine food web depends on the inherent energy produced in this process by single-celled, photosynthetic pro- and eukaryotes, or so-called phytoplankton [1]. Eukaryotic phytoplankton, which are the main focus of this review, evolved 1.5 billion years ago and have been shaped by past evolutionary history and climate change ever since [2]. Three principal phytoplankton clades dominating the oceans today, dinoflagellates, coccolithophores, and diatoms increased in ecological relevance during the Mesozoic Era (251 to 65 mya). Phytoplankton has subsequently undergone significant adaptive radiation leading to an immense species diversity in today’s marine, freshwater, and terrestrial ecosystems [3]. It is estimated that diatoms alone, which account for 25% of primary production on Earth, may consist of 100,000 different species [4]. As a consequence of past climatic variations, such as ~7 °C warmer temperatures during the middle Miocene (~16 mya), followed by ocean cooling 14.7 mya, many species groups have evolved wide environmental tolerances and occur as cosmopolites in today’s ocean [5]. In the face of continued global warming, that has a rate an order of magnitude greater than the last glacial-to-interglacial period [6], this key planktonic realm is subjected to rapidly increased selection pressure, inevitably forcing phytoplankton species to undergo evolution, local extinction, or redistribution, posing cascading effects throughout the marine ecosystem [7,8,9,10]. As a consequence of anthropogenic activity and Global Change, also many other properties of the oceanic environment are changing rapidly. These include, for example, light, nutrients, oxygen, and CO2 [11,12]. In this context, and because phytoplankton are critical to the diversity of the marine biosphere in general [13], it is of central importance to understand the prerequisites of evolution in marine phytoplankton.
The relationship between genetic diversity and evolution has been reviewed in a population genetic framework previously by Rengefors et al. (2017) [14]. The purpose of this review is to focus on evolution and genetic diversity of marine phytoplankton that is relevant in the context of ongoing environmental change and include findings from experimental studies looking into the past and the future. I also focus on how we can make use of emerging approaches related to high throughput sequencing (HTS) to investigate the vast diversity within and between phytoplankton genomes. As this is a rapidly growing field, and it has partly been reviewed recently by others, I limit the scope of this review to mainly focus on results published during the last five years. When an organism undergoes an evolutionary change, making it more suitable to its environment, we often refer to the term adaptive evolution. In general, adaptive evolution is shaped by the interaction of population genomics, natural selection, and underlying biochemical constraints [15]. Thus, HTS data in combination with population genetic theory and targeted phenotyping experiments hold great potential for providing a more realistic understanding of evolution in phytoplankton as estimated from genetic diversity.
Adaptive evolution is underpinned by genetic changes in populations and may be characterized as the movement of a population towards a phenotype that best fits the present environment [16]. The tendency of adaptive evolution to occur is partly dependent on the level of genetic diversity the population harbors, either as standing variability or as new variability arising from mutations or recombination. For clarity, acclimation and evolutionary adaptation are two different, but related, processes. During acclimation, individual strains of phytoplankton display changed gene expression giving rise to short-term changes in the phenotype. This is called phenotypic plasticity and is common in phytoplankton inhabiting environmentally variable habitats [17]. When novel mutations are selected for and their relative allele frequency in a population increases, or when selection is acting on pre-existing genetic diversity (genotypic variability), evolutionary adaptation may occur [18,19]. However, not all evolution is adaptive in the sense that it is helpful for the organism. Evolution may also be neutral as a simple consequence of random genetic drift. Genetic drift refers to the change in the frequency of alleles in a population due to random sampling of organisms [20]. In the context of phytoplankton, this means that the alleles in a population are a sample of those in the ‘parents’, and that chance has a role in determining whether a given genetic variant survives and reproduces. In this way, genetic drift may cause genetic variants to disappear from populations and thus reduce genetic variation. On the ‘other side of the coin’, evolution may also be maladaptive, meaning a trait becomes more harmful than helpful for the organism as it evolves in a specific direction [17]. Sometimes the process of maladaptation may lead to ‘evolutionary traps’ as seen in seabirds ingesting floating plastics that mimic food items [21,22].
When predicting the ecological response of species to future environmental change, the role of adaptation has often been neglected, or included only indirectly [23,24]. For example, models predicting future species distributions of phytoplankton either build on occurrence data across environmental gradients, or on experimentally produced reaction norms of single genotypes, the latter falsely representing the phenotype of an entire species [25]. Thus, the significance of evolutionary potential as estimated from genetic diversity is not quantified in most contemporary studies [26]. At the same time, a current shift in methodology from genetic markers and single-gene approaches to genome-wide studies are helping us to identify variation in specific genes that is under selection from Global Change. This helps us to see evolution in marine phytoplankton in a new light.

2. Experimental Studies of Genetic Diversity and Evolution

Evolution and adaptation potential in phytoplankton have, in many cases, been studied using a “forward-in-time” method, i.e., experimental evolution [27]. The field of experimental evolution, defined here as the study of evolutionary changes occurring in experimental populations as a consequence of conditions imposed by the experimenter [28], has significantly increased our understanding of the adaptation potential and plasticity in microorganisms [26,29] and is believed to increase the understanding further, also more specifically for phytoplankton [25,30]. The main challenge within this field is that the observations are often based on “domesticated” study organisms, not necessarily mirroring how evolutionary processes would occur in the natural environment. Another limitation is the lack of any species interactions in monospecific experiments, which may drastically affect evolutionary potential [31]. However, the strength and value of these types of observations are that they can address the evolutionary and mechanistic processes in a controlled environment, teaching us about the inherent potential in nature.
The field of experimental evolution has shown us that phytoplankton can undergo rapid evolution (referring to evolution on shorter timescales than Global Change) to environmental change, such as temperature and CO2. Many studies have focused on model species, such as Emiliania huxleyi [32,33,34], Thalassiosira pseudonana [35,36], and Chlamydomonas reinhardtii [37] to quantify evolution in response to currently relevant selection regimes. The two most commonly studied selection pressures are thermal stress and ocean acidification. In Table 1, I highlight some examples of adaptive evolution in response to these environmental variables and provide information on evolution in traits or functions identified by the authors. When examining the relationship between evolution and genetic diversity in phytoplankton, it is evident that when populations adapt to a change, it will occur through a combination of selection acting on existing standing genetic variation within the species, as well as de novo mutations. This is especially well documented in a few papers where adaptation has been studied simultaneously at different levels of genetic diversity as the ‘starting material’. For example, Lohbeck et al., 2012 [32] where adaption to increased CO2 levels was observed separately, in both single- and multi-strain populations. With such an approach it is valid to conclude that adaptation can occur both through positive selection on de novo mutations (in single strain approach) and via sorting of multiple genotypes representing standing genetic diversity. It is, however, still commonplace to use only a single strain for adaptation assays making it difficult to evaluate the relative contribution of de novo mutations vs. standing genetic diversity to adaptive potential in phytoplankton. The examples shown in Table 1 mostly used single strains as starting material and support the role of de novo mutations in phytoplankton adaptation. These results do not exclude adaptive evolution from selection on standing genetic diversity as an important factor in natural populations. Intuitively, adaptive evolution from standing genetic diversity is even highly likely given the huge population sizes and genetic diversity levels observed in most phytoplankton species.
The examples were included here because they also provided some functional explanation for the observed adaptation. For example, increased photosynthetic capacity, as a consequence of downregulated respiration in higher temperatures [26,37] was identified as an evolving function in green algae. Altered thermal reaction norms, which were observed to evolve in diatoms [35,38] may initially sound oversimplified and self-evident when examining adaptation to thermal stress. However, this can mean slightly different things for different species. An altered thermal reaction norm in some species can mean shifting the critical thermal minimum and maximum, with no shift in the optimal temperature (Topt). This enables higher growth rates (µmax) at higher temperatures, but lower growth capacity in the lowest end of the thermal spectrum. In contrast, other species may adapt by increasing their µmax under warmer conditions and shifting their Topt [38].
Results emerging from studies within the field of experimental evolution show that phytoplankton species are likely to adapt all the time also in natural conditions. However, experimental evolution on its own may have little to do with factual processes taking place in the natural environment, making clear-cut predictions about adaptation and ecological effects in relation to environmental change extremely challenging [27]. In order to design appropriate studies within the field of experimental evolution, we also need to identify the relevant selective pressures caused by climate change.
The relatively young field of resurrection ecology offers a “backward-in-time” approach and can be defined as the revival and study of long-dormant organisms via hatching of dormant life stages. This enables the direct quantification of phenotypes over temporal scales exceeding the average human lifespan [41,42]. Many phytoplankton species form dormant resting stages that sediment at the sea floor after vegetative growth is ceased in the water column. This also means that only a small proportion of the total species diversity can be observed at any time in the water column [43], which has significantly hampered any analyses of diversity in marine phytoplankton. Seed banks of phytoplankton are considered archives of evolutionary history and have provided unique insights into past genetic changes in relation to environmental change [44,45,46]. Most previous studies conducted with resurrected phytoplankton [44,47,48] have used neutral genetic markers, such as microsatellite loci, for the quantification of genetic diversity and correlation to environmental change. For improving the understanding of adaptation processes in phytoplankton from the natural environment, documenting evolution from these hidden historic archives at the seafloor is a useful approach and has not been used to its full potential considering the latest molecular tools and approaches. In order to advance the field of resurrection ecology focused on phytoplankton, and for investigating the relationship between genetic diversity and evolution, applying re-sequencing of genomes of resurrected isolates from temporal series acquired from geologically dated sediment cores seem promising. We have very restricted knowledge about the genetic diversity of organisms buried in coastal sediments even though it has a high potential for the study of adaptation to environmental change. As seen above, rapid adaptation to singular environmental changes has been inferred for several phytoplankton species in experimental evolution studies. However, natural populations may experience a multitude of selection pressures simultaneously, which is complicated to simulate in experiments. By resurrection of millennia-old, pre-Anthropocene dormant resting stages of phytoplankton [49,50] and subsequent experimental work, we may be offered several advantages compared to studies of experimental evolution. Resurrected strains from temporal series across times of environmental change may be re-sequenced and analyzed for signatures of adaptation to documented changes in a region. This provides a more realistic understanding of the relationship between diversity and evolution as evolution that actually occurred can be quantified from natural populations. However, this requires access to reference genomes for ecologically relevant species, which is still lagging behind regarding eukaryotic phytoplankton.

3. Recent Population Genetic Studies Indicate Vast Diversity

As a consequence of mainly asexual reproduction, large population sizes and seemingly vast dispersal potential, marine phytoplankton populations were traditionally assumed to consist of one or only very few clonal lineages at any point of place and time [51]. However, population genetic studies using neutral genetic markers have repeatedly shown us that this is not the case (reviewed in [14]). Clonal diversity, which is available for evolution as a source of standing genetic diversity, is comparably high in phytoplankton species. Phytoplankton populations investigated using genetic markers actually display extremely low proportions of identical multi-locus genotypes (MLGs) in natural conditions [25], with a few exceptions [52,53]. Population genetic studies are continuously providing new evidence from natural populations supporting the understanding of vast genetic diversity in phytoplankton species [54,55,56,57,58,59,60]. These types of studies show us that populations can be genetically structured over small and large geographic areas and over temporal scales and that genetic diversity may function as a proxy for a species’ adaptive potential [61,62,63]. Subsequently, it is likely to be the reason for why phytoplankton survive in highly variable environments. On the other hand, high standing diversity may slow down evolution through clonal interference [64], something that has been hypothesized also from studies on natural populations [60]. The same study applied traditional microsatellite markers and Restriction-site DNA (RAD) sequencing for the study of genetic diversity in the marine dinoflagellate Alexandrium ostenfeldii in bloom vs. seed bank populations. The results suggested that a strong benthic–pelagic coupling, combined with frequent sexual reproduction, prevent the dominance of single clonal lineages. Population genetic studies of phytoplankton have, and probably will, significantly increase our understanding of genetic diversity and evolution of phytoplankton. In addition, recently developed approaches such as the PoolSeq barcoding [65] utilizing genetic markers without the need of single cell isolation and cultivation, entail the potential to improve our ability to follow intraspecific dynamics over space and time in natural populations. However, neutral markers are inherently restricted in the sense that they cannot provide knowledge about functional genes under selection. This is why utilizing population genomic approaches may be an important addition in the study of evolution and intra-species diversity in marine phytoplankton. In some cases, genomic data have offered a more realistic understanding of intraspecific diversity as shown for the ubiquitous unicellular marine phytoplankton species E. huxleyi. Resequencing of geographically separated genomes in this species contains surprisingly limited variability, likely because the range of population sizes exceeds the range of genetic diversity by many orders of magnitude [66].

Towards Population Genomics of Phytoplankton

Metabarcoding studies targeting the ribosomal RNA (rRNA) 16S and 18S genes in environmental DNA (eDNA) surveys has significantly increased our understanding of marine phytoplankton diversity in the recent decade [67,68]. However, the evolutionary significance of the discovered taxonomic and functional variability is still largely unknown. The rRNA genes are appropriate for community level investigations but generally do not provide sufficient genetic resolution to reveal intraspecific diversity patterns. Species with identical 16S or 18S rRNA sequences may have highly divergent genomes and phenotypes [69,70,71,72]. Even though not numerous yet, studies with a genome-wide approach are steadily increasing. These are believed to aid in a better understanding of how selection affects patterns of variation, and in identifying what genes are involved when phytoplankton evolve to new conditions. These technologies have shown that phytoplankton adapt genomically to contrasting nutrient conditions [73,74,75] by gene gain and loss. This is information that is out of reach in traditional population genetics. Genome-wide approaches utilizing eDNA include metagenomics [76,77] and metatranscriptomics [78]. Other approaches, requiring laboratory isolation or sorting of cells, such as single-cell genomics [79] have revealed previously underestimated functional diversity and unknown interactions in uncultivated protists [80,81].
These technologies emerging in the field of phytoplankton population genomics are facilitated by approaches primarily developed for studying prokaryotic diversity [82,83] and comparative microbiome studies [84]. On one hand, studies utilize eDNA in combination with reference genomes of cultivated organisms, such as [85] for studying genome adaptation across the seascape. This approach is based on shotgun sequencing of eDNA, where resulting sequence reads are aligned to a previously established reference genome. The complete genome sequences created for Prochlorococcus and Synechococcus [86] have, for instance, been utilized in this way for investigating how genomic adaptation regulates phosphate uptake [76]. The genomic content in these organisms was significantly altered in reciprocal transplant experiments carried out in the nutrient-rich Labrador Sea in the NE Atlantic and the ultra-oligotrophic Sargasso Sea in the S Atlantic. Interestingly, these two phytoplankton lineages seem to adapt their genomes rapidly in terms of the frequency of P acquisition genes, which can be directly linked to shifts in nutrient uptake rates between two contrasting regions. When such metagenome data is collected across vast geographical areas and integrated with trait-based modeling (as in [87]) it can even be used to predict regional conditions in terms of elemental composition of marine plankton communities. This means that the study of genomic variation and adaptation can reveal regions with nutritional challenges (for example iron, N, P limitation) in the global marine ecosystem [85]. Hence, the value of sequence-based, comparative population genomic approaches is that they can provide us with more functional understanding than before. However, for eukaryotic phytoplankton this is still hampered by a lack of relevant gene annotations. Recent comparative studies with a genome-wide scope are also in line with population genetic studies and conform to early work on full genome sequencing of phytoplankton [88,89,90]. Namely, it is increasingly clear that phytoplankton are characterized by considerable sequence variation between homologous chromosomes, likely translating into high levels of intra-species variation in natural populations and a high potential for adaptive evolution.
A significant obstacle for sequence-based studies that aim for comparative analyses is the lack of relevant reference sequences when it comes to eukaryotic phytoplankton. The situation is significantly better for marine prokaryotic phytoplankton [91]. Studies with a pure re-sequencing or re-mapping approach of –omics data are still limited by the low number of established reference genomes available for ecologically relevant eukaryotic phytoplankton. Free-living planktonic phytoplankton with full genome sequences include the dinoflagellate Polarella glacialis [92], the pelagophyte Aureococcus anophagefferens [93], Cryptophytes, such as Chroomonas mesostigmatica [94], Cryptomonas paramecium [95], Guillardia theta [96] (nucleomorph genome), the eustigmatophyte Nannochloropsis gaditana [97], the coccolithophorid Emiliana huxleyi [98], and the diatoms Fragilariopsis cylindricus [99], Phaeodactylum tricornutum [89], Pseudo-nitzschia multiseries [100], P. multistriata [101], Thalassiosira pseudonana [88], T. oceanica [90], and Skeletonema marinoi [102]. In addition, initiatives such as the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP) [103], providing over 650 assembled and functionally annotated transcriptomes (396 unique strains assigned to 210 distinct genera) was an important addition and facilitates comparative studies of intra-species diversity and evolution [104]. However, as most of the included taxa in MMETSP represent cultivated strains, the immense diversity of uncultivated phytoplankton remains out of reach.

4. Environmental Genomics Target Uncultivated Eukaryotic Phytoplankton without Previous Reference Genomes

The circumglobal Tara Ocean expedition (2009–2012) revealed that >90% of the most abundant eukaryotic protists in the marine environment do not match any known species, and most of them are represented by lineages without any cultivated representatives [105]. This seriously complicates the assessment of how variable selection pressures are structuring genomic variability in natural populations of species that may have a much more important ecological role in the global ocean than what was previously appreciated [106,107]. Recent studies adopting a metagenomic approach show that such previously undescribed genomic diversity can be targeted by the reconstruction of metagenome-assembled genomes (MAGs) [108,109,110]. This is similar to previous work applied on samples from, e.g., the brackish Baltic Sea using eDNA for the reconstruction of prokaryotic MAGs [111]. Other recent studies have focused on developing much needed bioinformatic pipelines for quantification of intra-species genomic variability from metagenomic data [82,112,113,114] and have largely highlighted the importance of adaptive divergence as a structuring mechanism in the evolution of uncultivated bacterioplankton. Eukaryotic MAGs are of lower quality compared to re-sequenced genomes of cultivated isolates, but they offer genome-wide information about the diversity of phytoplankton from natural ecosystems. One challenge is that eukaryotic genomes are much more complex than those of prokaryotes, which means we cannot expect to use only the same tools for exploring the eukaryotic diversity. The ecological context of eukaryotic protists is also significantly different from that of bacteria, meaning that using only the genome data of eukaryotes cannot produce the same insights about, e.g., ecological functioning as it does in prokaryotes [115]. However, more in-depth analyses of the population genomic diversity and differentiation in eukaryotic MAGs may provide novel insights into what environmental drivers are dictating the evolution and metapopulation structure also in uncultivated eukaryotes.

5. Conclusions

In this mini-review, I discussed how marine phytoplankton have evolved and are evolving to continuously changing environmental conditions. I examined standing genetic variation and de novo mutations as assessed from experimental studies and investigations in natural populations. Recent population genetic studies support the understanding of high genetic diversity in marine phytoplankton, facilitating a high adaptive potential in the face of rapidly changing selection pressures. The field of resurrection ecology, i.e., the revival of up to millennia-old resting stages of diatoms offer new possibilities for investigating diversity and evolution in this organism group. Additionally, the emerging approaches utilizing metagenome and transcriptome data covered here are revolutionizing the study of genomic variation and evolution in phytoplankton. The reconstruction of MAGs has already provided novel insights in marine prokaryotic phytoplankton. This may be especially relevant for the study of many eukaryotic species that we are currently not able to cultivate in laboratory conditions.

Funding

This research was funded by the Academy of Finland (grant number 321609) and by the Swedish Cultural Foundation (grant number 176762).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

I thank Giannina Hattich and three anonymous reviewers for kindly providing valuable comments on the manuscript.

Conflicts of Interest

The author declares no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Falkowski, P.G.; Barber, R.T.; Smetacek, V. Biogeochemical Controls and Feedbacks on Ocean Primary Production. Science 1998, 281, 200–206. [Google Scholar] [CrossRef] [PubMed][Green Version]
  2. Beaufort, L.; Bolton, C.T.; Sarr, A.-C.; Suchéras-Marx, B.; Rosenthal, Y.; Donnadieu, Y.; Barbarin, N.; Bova, S.; Cornuault, P.; Gally, Y.; et al. Cyclic evolution of phytoplankton forced by changes in tropical seasonality. Nature 2022, 601, 79–84. [Google Scholar] [CrossRef]
  3. Falkowski, P.G.; Katz, M.E.; Knoll, A.H.; Quigg, A.; Raven, J.A.; Schofield, O.; Taylor, F.J.R. The Evolution of Modern Eukaryotic Phytoplankton. Science 2004, 305, 354–360. [Google Scholar] [CrossRef] [PubMed][Green Version]
  4. Mann, D.G.; Vanormelingen, P. An Inordinate Fondness? The Number, Distributions, and Origins of Diatom Species. J. Eukaryot. Microbiol. 2013, 60, 414–420. [Google Scholar] [CrossRef][Green Version]
  5. Henderiks, J.; Bartol, M.; Pige, N.; Karatsolis, B.; Lougheed, B.C. Shifts in Phytoplankton Composition and Stepwise Climate Change during the Middle Miocene. Paleoceanogr. Paleoclimatol. 2020, 35, e2020PA003915. [Google Scholar] [CrossRef]
  6. Masson-Delmotte, V.; Schulz, A.M.; Abe-Ouchi, J.; Beer, A.; Ganopolski, J.F.; Rouco, G.; Jansen, E.; Lambeck, K.; Luterbacher, J.; Naish, T.; et al. Information from paleoclimate archives. In Climate Change 2013—The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge Univ. Press: New York, NY, USA, 2013; pp. 383–464. [Google Scholar]
  7. Thomas, M.K.; Kremer, C.T.; Klausmeier, C.A.; Litchman, E. A Global Pattern of Thermal Adaptation in Marine Phytoplankton. Science 2012, 338, 1085–1088. [Google Scholar] [CrossRef] [PubMed][Green Version]
  8. Barton, A.D.; Irwin, A.J.; Finkel, Z.V.; Stock, C.A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl. Acad. Sci. USA 2016, 113, 2964–2969. [Google Scholar] [CrossRef] [PubMed][Green Version]
  9. Jensen, L.; Mousing, E.A.; Richardson, K. Using species distribution modelling to predict future distributions of phytoplankton: Case study using species important for the biological pump. Mar. Ecol. 2017, 38, e12427. [Google Scholar] [CrossRef]
  10. Chivers, W.J.; Walne, A.W.; Hays, G. Mismatch between marine plankton range movements and the velocity of climate change. Nat. Commun. 2017, 8, 14434. [Google Scholar] [CrossRef] [PubMed]
  11. Boyd, P.W.; Doney, S.C.; Strzepek, R.; Dusenberry, J.; Lindsay, K.; Fung, I. Climate-mediated changes to mixed-layer properties in the Southern Ocean: Assessing the phytoplankton response. Biogeosciences 2008, 5, 847–864. [Google Scholar] [CrossRef][Green Version]
  12. Van De Waal, D.B.; Litchman, E. Multiple global change stressor effects on phytoplankton nutrient acquisition in a future ocean. Philos. Trans. R. Soc. B 2020, 375, 20190706. [Google Scholar] [CrossRef] [PubMed]
  13. Martin, R.E.; Servais, T. Did the evolution of the phytoplankton fuel the diversification of the marine biosphere? Lethaia 2019, 53, 5–31. [Google Scholar] [CrossRef]
  14. Rengefors, K.; Kremp, A.; Reusch, T.B.; Wood, A.M. Genetic diversity and evolution in eukaryotic phytoplankton: Revelations from population genetic studies. J. Plankton Res. 2017, 39, 165–179. [Google Scholar] [CrossRef][Green Version]
  15. Olson-Manning, C.F.; Wagner, M.R.; Mitchell-Olds, T. Adaptive evolution: Evaluating empirical support for theoretical predictions. Nat. Rev. Genet. 2012, 13, 867–877. [Google Scholar] [CrossRef][Green Version]
  16. Fisher, R.A. The Genetical Theory of Natural Selection, A complete variorum ed.; Bennett, J.H., Ed.; Oxford University Press: Oxford, UK, 1999. [Google Scholar]
  17. Collins, S.; Rost, B.; Rynearson, T.A. Evolutionary potential of marine phytoplankton under ocean acidification. Evol. Appl. 2013, 7, 140–155. [Google Scholar] [CrossRef] [PubMed][Green Version]
  18. Hoffmann, A.A.; Sgrò, C.M. Climate change and evolutionary adaptation. Nature 2011, 470, 479–485. [Google Scholar] [CrossRef]
  19. Becks, L.; Ellner, S.P.; Jones, L.E.; Hairston, N.G., Jr. Reduction of adaptive genetic diversity radically alters eco-evolutionary community dynamics. Ecol. Lett. 2010, 13, 989–997. [Google Scholar] [CrossRef] [PubMed]
  20. Masel, J. Genetic drift. Curr. Biol. 2011, 21, R837–R838. [Google Scholar] [CrossRef] [PubMed][Green Version]
  21. Robertson, B.A.; Rehage, J.S.; Sih, A. Ecological novelty and the emergence of evolutionary traps. Trends Ecol. Evol. 2013, 28, 552–560. [Google Scholar] [CrossRef]
  22. Brady, S.P.; Bolnick, D.I.; Angert, A.L.; Gonzalez, A.; Barrett, R.D.; Crispo, E.; Derry, A.; Eckert, C.G.; Fraser, D.J.; Fussmann, G.; et al. Causes of maladaptation. Evol. Appl. 2019, 12, 1229–1242. [Google Scholar] [CrossRef][Green Version]
  23. Ford, D.A.; van der Molen, J.; Hyder, K.; Bacon, J.; Barciela, R.; Creach, V.; McEwan, R.; Ruardij, P.; Forster, R. Observing and modelling phytoplankton community structure in the North Sea. Biogeosciences 2017, 14, 1419–1444. [Google Scholar] [CrossRef][Green Version]
  24. Sekerci, Y.; Petrovskii, S. Global Warming Can Lead to Depletion of Oxygen by Disrupting Phytoplankton Photosynthesis: A Mathematical Modelling Approach. Geosciences 2018, 8, 201. [Google Scholar] [CrossRef][Green Version]
  25. Godhe, A.; Rynearson, T. The role of intraspecific variation in the ecological and evolutionary success of diatoms in changing environments. Philos. Trans. R. Soc. B Biol. Sci. 2017, 372, 20160399. [Google Scholar] [CrossRef][Green Version]
  26. Padfield, D.; Yvon-Durocher, G.; Buckling, A.; Jennings, S.; Yvon-Durocher, G. Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecol. Lett. 2016, 19, 133–142. [Google Scholar] [CrossRef][Green Version]
  27. Elena, S.F.; Lenski, R. Evolution experiments with microorganisms: The dynamics and genetic bases of adaptation. Nat. Rev. Genet. 2003, 4, 457–469. [Google Scholar] [CrossRef]
  28. Kawecki, T.J.; Lenski, R.E.; Ebert, D.; Hollis, B.; Olivieri, I.; Whitlock, M.C. Experimental evolution. Trends Ecol. Evol. 2012, 27, 547–560. [Google Scholar] [CrossRef][Green Version]
  29. Schlüter, L.; Lohbeck, K.T.; Gutowska, M.A.; Gröger, J.P.; Riebesell, U.; Reusch, T.B.H. Adaptation of a globally important coccolithophore to ocean warming and acidification. Nat. Clim. Chang. 2014, 4, 1024–1030. [Google Scholar] [CrossRef]
  30. Collins, S.; Boyd, P.W.; Doblin, M.A. Evolution, Microbes, and Changing Ocean Conditions. Annu. Rev. Mar. Sci. 2020, 12, 181–208. [Google Scholar] [CrossRef]
  31. Hattich, G.S.I.; Listmann, L.; Govaert, L.; Pansch, C.; Reusch, T.B.H.; Matthiessen, B. Experimentally decomposing phytoplankton community change into ecological and evolutionary contributions. Funct. Ecol. 2022, 36, 120–132. [Google Scholar] [CrossRef]
  32. Lohbeck, K.T.; Riebesell, U.; Reusch, T.B.H. Adaptive evolution of a key phytoplankton species to ocean acidification. Nat. Geosci. 2012, 5, 346–351. [Google Scholar] [CrossRef]
  33. Lohbeck, K.T.; Riebesell, U.; Reusch, T.B.H. Gene expression changes in the coccolithophore Emiliania huxleyi after 500 generations of selection to ocean acidification. Proc. R. Soc. B Biol. Sci. 2014, 281, 20140003. [Google Scholar] [CrossRef][Green Version]
  34. Listmann, L.; Leroch, M.; Schlüter, L.; Thomas, M.; Reusch, T.B.H. Swift thermal reaction norm evolution in a key marine phytoplankton species. Evol. Appl. 2016, 9, 1156–1164. [Google Scholar] [CrossRef][Green Version]
  35. O’Donnell, D.R.; Hamman, C.R.; Johnson, E.C.; Kremer, C.T.; Klausmeier, C.A.; Litchman, E. Rapid thermal adaptation in a marine diatom reveals constraints and trade-offs. Glob. Chang. Biol. 2018, 24, 4554–4565. [Google Scholar] [CrossRef] [PubMed]
  36. Schaum, C.-E.; Buckling, A.; Smirnoff, N.; Studholme, D.J.; Yvon-Durocher, G. Environmental fluctuations accelerate molecular evolution of thermal tolerance in a marine diatom. Nat. Commun. 2018, 9, 1719. [Google Scholar] [CrossRef][Green Version]
  37. Schaum, C.-E.; Barton, S.; Bestion, E.; Buckling, A.; Garcia-Carreras, B.; Lopez, P.; Lowe, C.; Pawar, S.; Smirnoff, N.; Trimmer, M.; et al. Adaptation of phytoplankton to a decade of experimental warming linked to increased photosynthesis. Nat. Ecol. Evol. 2017, 1, 94. [Google Scholar] [CrossRef] [PubMed]
  38. Jin, P.; Agustí, S. Fast adaptation of tropical diatoms to increased warming with trade-offs. Sci. Rep. 2018, 8, 17771. [Google Scholar] [CrossRef] [PubMed][Green Version]
  39. Schaum, C.-E.; Rost, B.; Collins, S. Environmental stability affects phenotypic evolution in a globally distributed marine picoplankton. ISME J. 2016, 10, 75–84. [Google Scholar] [CrossRef] [PubMed][Green Version]
  40. Tong, S.; Gao, K.; Hutchins, D.A. Adaptive evolution in the coccolithophore Gephyrocapsa oceanica following 1000 generations of selection under elevated CO2. Glob. Chang. Biol. 2018, 24, 3055–3064. [Google Scholar] [CrossRef] [PubMed][Green Version]
  41. Weider, L.J.; Jeyasingh, P.D.; Frisch, D. Evolutionary aspects of resurrection ecology: Progress, scope, and applications—An overview. Evol. Appl. 2018, 11, 3–10. [Google Scholar] [CrossRef] [PubMed][Green Version]
  42. Ellegaard, M.; Clokie, M.R.J.; Czypionka, T.; Frisch, D.; Godhe, A.; Kremp, A.; Letarov, A.; McGenity, T.J.; Ribeiro, S.; Anderson, N.J. Dead or alive: Sediment DNA archives as tools for tracking aquatic evolution and adaptation. Commun. Biol. 2020, 3, 169. [Google Scholar] [CrossRef][Green Version]
  43. Foissner, W. Protist diversity and distribution: Some basic considerations. Biodivers. Conserv. 2008, 17, 235–242. [Google Scholar] [CrossRef]
  44. Härnström, K.; Ellegaard, M.; Andersen, T.J.; Godhe, A. Hundred years of genetic structure in a sediment revived diatom population. Proc. Natl. Acad. Sci. USA 2011, 108, 4252–4257. [Google Scholar] [CrossRef] [PubMed][Green Version]
  45. Miyazono, A.; Nagai, S.; Kudo, I.; Tanizawa, K. Viability of Alexandrium tamarense cysts in the sediment of Funka Bay, Hokkaido, Japan: Over a hundred year survival times for cysts. Harmful Algae 2012, 16, 81–88. [Google Scholar] [CrossRef]
  46. Ellegaard, M.; Godhe, A.; Ribeiro, S. Time capsules in natural sediment archives—Tracking phytoplankton population genetic diversity and adaptation over multidecadal timescales in the face of environmental change. Evol. Appl. 2018, 11, 11–16. [Google Scholar] [CrossRef] [PubMed][Green Version]
  47. Hinners, J.; Kremp, A.; Hense, I. Evolution in temperature-dependent phytoplankton traits revealed from a sediment archive: Do reaction norms tell the whole story? Proc. R. Soc. B 2017, 284, 20171888. [Google Scholar] [CrossRef] [PubMed][Green Version]
  48. Lundholm, N.; Ribeiro, S.; Godhe, A.; Nielsen, L.R.; Ellegaard, M. Exploring the impact of multidecadal environmental changes on the population genetic structure of a marine primary producer. Ecol. Evol. 2017, 7, 3132–3142. [Google Scholar] [CrossRef][Green Version]
  49. Sanyal, A.; Larsson, J.; van Wirdum, F.; Andrén, T.; Moros, M.; Lönn, M.; Andrén, E. Not dead yet: Diatom resting spores can survive in nature for several millennia. Am. J. Bot. 2022, 109, 67–82. [Google Scholar] [CrossRef]
  50. Girault, M.; Siano, R.; Labry, C.; Latimier, M.; Jauzein, C.; Beneyton, T.; Buisson, L.; Del Amo, Y.; Baret, J.-C. Variable inter and intraspecies alkaline phosphatase activity within single cells of revived dinoflagellates. ISME J. 2021, 15, 2057–2069. [Google Scholar] [CrossRef]
  51. Finlay, B.J. Global Dispersal of Free-Living Microbial Eukaryote Species. Science 2002, 296, 1061–1063. [Google Scholar] [CrossRef] [PubMed][Green Version]
  52. Tesson, S.V.M.; Montresor, M.; Procaccini, G.; Kooistra, W.H.C.F. Temporal Changes in Population Structure of a Marine Planktonic Diatom. PLoS ONE 2014, 9, e114984. [Google Scholar] [CrossRef][Green Version]
  53. Ruggiero, M.V.; D’Alelio, D.; Ferrante, M.I.; Santoro, M.; Vitale, L.; Procaccini, G.; Montresor, M. Clonal expansion behind a marine diatom bloom. ISME J. 2018, 12, 463–472. [Google Scholar] [CrossRef]
  54. Sassenhagen, I.; Gao, Y.; Lozano-Duque, Y.; Parsons, M.L.; Smith, T.B.; Erdner, D.L. Comparison of Spatial and Temporal Genetic Differentiation in a Harmful Dinoflagellate Species Emphasizes Impact of Local Processes. Front. Mar. Sci. 2018, 5, 393. [Google Scholar] [CrossRef]
  55. Sefbom, J.; Kremp, A.; Rengefors, K.; Jonsson, P.R.; Sjöqvist, C.; Godhe, A. A planktonic diatom displays genetic structure over small spatial scales. Environ. Microbiol. 2018, 20, 2783–2795. [Google Scholar] [CrossRef] [PubMed]
  56. Gao, Y.; Sassenhagen, I.; Richlen, M.L.; Anderson, D.M.; Martin, J.L.; Erdner, D.L. Spatiotemporal genetic structure of regional-scale Alexandrium catenella dinoflagellate blooms explained by extensive dispersal and environmental selection. Harmful Algae 2019, 86, 46–54. [Google Scholar] [CrossRef] [PubMed]
  57. Paredes, J.; Varela, D.; Martínez, C.; Zúñiga, A.; Correa, K.; Villarroel, A.; Olivares, B. Population Genetic Structure at the Northern Edge of the Distribution of Alexandrium catenella in the Patagonian Fjords and Its Expansion along the Open Pacific Ocean Coast. Front. Mar. Sci. 2019, 5, 532. [Google Scholar] [CrossRef][Green Version]
  58. Postel, U.; Glemser, B.; Alekseyeva, K.S.; Eggers, S.L.; Groth, M.; Glöckner, G.; John, U.; Mock, T.; Klemm, K.; Valentin, K.; et al. Adaptive divergence across Southern Ocean gradients in the pelagic diatom Fragilariopsis kerguelensis. Mol. Ecol. 2020, 29, 4913–4924. [Google Scholar] [CrossRef]
  59. Gäbler-Schwarz, S.; Hinz, F.; Kirst, G.O.; Uttieri, M.; Medlin, L.K. Local environment shapes adaptation of Phaeocystis antarctica to salinity perturbations: Evidence for physiological resilience. J. Exp. Mar. Biol. Ecol. 2021, 538, 151527. [Google Scholar] [CrossRef]
  60. Jerney, J.; Rengefors, K.; Nagai, S.; Krock, B.; Sjöqvist, C.; Suikkanen, S.; Kremp, A. Seasonal genotype dynamics of a marine dinoflagellate: Pelagic populations are homogeneous and as diverse as benthic seed banks. Mol. Ecol. 2021, 31, 512–528. [Google Scholar] [CrossRef]
  61. Barrett, R.D.; Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 2008, 23, 38–44. [Google Scholar] [CrossRef] [PubMed]
  62. Schlüter, L.; Lohbeck, K.T.; Gröger, J.P.; Riebesell, U.; Reusch, T.B.H. Long-term dynamics of adaptive evolution in a globally important phytoplankton species to ocean acidification. Sci. Adv. 2016, 2, e1501660. [Google Scholar] [CrossRef] [PubMed][Green Version]
  63. Ørsted, M.; Hoffmann, A.A.; Sverrisdóttir, E.; Nielsen, K.L.; Kristensen, T.N. Genomic variation predicts adaptive evolutionary responses better than population bottleneck history. PLoS Genet. 2019, 15, e1008205. [Google Scholar] [CrossRef]
  64. Pepin, K.M.; Wichman, H.A. Experimental evolution and genome sequencing reveal variation in levels of clonal interference in large populations of bacteriophage φX174. BMC Evol. Biol. 2008, 8, 85. [Google Scholar] [CrossRef] [PubMed][Green Version]
  65. Wolf, K.K.E.; Hoppe, C.J.M.; Leese, F.; Weiss, M.; Rost, B.; Neuhaus, S.; Gross, T.; Kühne, N.; John, U. Revealing environmentally driven population dynamics of an Arctic diatom using a novel microsatellite PoolSeq barcoding approach. Environ. Microbiol. 2021, 23, 3809–3824. [Google Scholar] [CrossRef]
  66. Filatov, D.A. Extreme Lewontin’s Paradox in Ubiquitous Marine Phytoplankton Species. Mol. Biol. Evol. 2018, 36, 4–14. [Google Scholar] [CrossRef] [PubMed]
  67. Penna, A.; Casabianca, S.; Fernandez, A.; Guerrac, F.; Vernesid, C.; Scardi, M. Analysis of phytoplankton assemblage structure in the Mediterranean Sea based on high-throughput sequencing of partial 18S rRNA sequences. Mar. Genom. 2017, 36, 49–55. [Google Scholar] [CrossRef]
  68. Hu, Y.; Karlson, B.; Charvet, S.; Andersson, A.F. Diversity of Pico- to Mesoplankton along the 2000 km Salinity Gradient of the Baltic Sea. Front. Microbiol. 2016, 7, 679. [Google Scholar] [CrossRef]
  69. Tang, C.Q.; Leasi, F.; Obertegger, U.; Kieneke, A.; Barraclough, T.G.; Fontaneto, D. The widely used small subunit 18S rDNA molecule greatly underestimates true diversity in biodiversity surveys of the meiofauna. Proc. Natl. Acad. Sci. USA 2012, 109, 16208–16212. [Google Scholar] [CrossRef] [PubMed][Green Version]
  70. Bachy, C.; Dolan, J.; Lopez-Garcia, P.; Deschamps, P.; Moreira, D. Accuracy of protist diversity assessments: Morphology compared with cloning and direct pyrosequencing of 18S rRNA genes and ITS regions using the conspicuous tintinnid ciliates as a case study. ISME J. 2013, 7, 244–255. [Google Scholar] [CrossRef][Green Version]
  71. Beisser, D.; Graupner, N.; Bock, C.; Wodniok, S.; Grossmann, L.; Vos, M.; Sures, B.; Rahmann, S.; Boenigk, J. Comprehensive transcriptome analysis provides new insights into nutritional strategies and phylogenetic relationships of chrysophytes. PeerJ 2017, 5, e2832. [Google Scholar] [CrossRef][Green Version]
  72. Majda, S.; Boenigk, J.; Beisser, D. Intraspecific Variation in Protists: Clues for Microevolution from Poteriospumella lacustris (Chrysophyceae). Genome Biol. Evol. 2019, 11, 2492–2504. [Google Scholar] [CrossRef] [PubMed]
  73. Martiny, J.B.H.; Jones, S.E.; Lennon, J.T.; Martiny, A.C. Microbiomes in light of traits: A phylogenetic perspective. Science 2015, 350, aac9323. [Google Scholar] [CrossRef][Green Version]
  74. Scanlan, D.J.; Ostrowski, M.; Mazard, S.; Dufresne, A.; Garczarek, L.; Hess, W.R.; Post, A.F.; Hagemann, M.; Paulsen, I.; Partensky, F. Ecological Genomics of Marine Picocyanobacteria. Microbiol. Mol. Biol. Rev. 2009, 73, 249–299. [Google Scholar] [CrossRef] [PubMed][Green Version]
  75. Morris, J.J.; Lenski, R.E.; Zinser, E.R. The Black Queen Hypothesis: Evolution of Dependencies through Adaptive Gene Loss. mBio 2012, 3, e00036-12. [Google Scholar] [CrossRef] [PubMed][Green Version]
  76. Martiny, A.C.; Ustick, L.; Garcia, C.A.; Lomas, M.W. Genomic adaptation of marine phytoplankton populations regulates phosphate uptake. Limnol. Oceanogr. 2020, 65, S340–S350. [Google Scholar] [CrossRef][Green Version]
  77. Ustick, L.J.; Larkin, A.A.; Garcia, C.A.; Garcia, N.S.; Brock, M.L.; Lee, J.A.; Wiseman, N.A.; Moore, J.K.; Martiny, A.C. Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation. Science 2021, 372, 287–291. [Google Scholar] [CrossRef]
  78. Casey, J.R.; Boiteau, R.M.; Engqvist, M.K.M.; Finkel, Z.V.; Li, G.; Liefer, J.; Müller, C.L.; Muñoz, N.; Follows, M.J. Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology. Sci. Adv. 2022, 8, eabl4930. [Google Scholar] [CrossRef]
  79. Sieracki, M.E.; Poulton, N.; Jaillon, O.; Wincker, P.; De Vargas, C.; Rubinat-Ripoll, L.; Stepanauskas, R.; Logares, R.; Massana, R. Single cell genomics yields a wide diversity of small planktonic protists across major ocean ecosystems. Sci. Rep. 2019, 9, 6025. [Google Scholar] [CrossRef] [PubMed][Green Version]
  80. Seeleuthner, Y.; Coordinators, T.O.; Mondy, S.; Lombard, V.; Carradec, Q.; Pelletier, E.; Wessner, M.; Leconte, J.; Mangot, J.-F.; Poulain, J.; et al. Single-cell genomics of multiple uncultured stramenopiles reveals underestimated functional diversity across oceans. Nat. Commun. 2018, 9, 310. [Google Scholar] [CrossRef] [PubMed]
  81. Yoon, H.S.; Price, D.C.; Stepanauskas, R.; Rajah, V.D.; Sieracki, M.E.; Wilson, W.H.; Yang, E.C.; Duffy, S.; Bhattacharya, D. Single-Cell Genomics Reveals Organismal Interactions in Uncultivated Marine Protists. Science 2011, 332, 714–717. [Google Scholar] [CrossRef]
  82. Nayfach, S.; Rodriguez-Mueller, B.; Garud, N.; Pollard, K.S. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res. 2016, 26, 1612–1625. [Google Scholar] [CrossRef][Green Version]
  83. Delmont, T.O.; Kiefl, E.; Kilinc, O.; Esen, O.C.; Uysal, I.; Rappé, M.S.; Giovannoni, S.; Eren, A.M. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife 2019, 8, e46497. [Google Scholar] [CrossRef] [PubMed]
  84. Schloissnig, S.; Arumugam, M.; Sunagawa, S.; Mitreva, M.; Tap, J.; Zhu, A.; Waller, A.; Mende, D.R.; Kultima, J.R.; Martin, J.; et al. Genomic variation landscape of the human gut microbiome. Nature 2013, 493, 45–50. [Google Scholar] [CrossRef][Green Version]
  85. Garcia, C.A.; Hagstrom, G.I.; Larkin, A.A.; Ustick, L.J.; Levin, S.A.; Lomas, M.W.; Martiny, A.C. Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry. Philos. Trans. R. Soc. B Biol. Sci. 2020, 375, 20190254. [Google Scholar] [CrossRef][Green Version]
  86. Biller, S.J.; Berube, P.; Berta-Thompson, J.W.; Kelly, L.; Roggensack, S.E.; Awad, L.; Roache-Johnson, K.H.; Ding, H.; Giovannoni, S.J.; Rocap, G.; et al. Genomes of diverse isolates of the marine cyanobacterium Prochlorococcus. Sci. Data 2014, 1, 140034. [Google Scholar] [CrossRef] [PubMed]
  87. Moreno, A.R.; Hagstrom, G.I.; Primeau, F.W.; Levin, S.A.; Martiny, A.C. Marine phytoplankton stoichiometry mediates nonlinear interactions between nutrient supply, temperature, and atmospheric CO2. Biogeosciences 2018, 15, 2761–2779. [Google Scholar] [CrossRef][Green Version]
  88. Armbrust, E.V.; Berges, J.A.; Bowler, C.; Green, B.R.; Martinez, D.; Putnam, N.H.; Zhou, S.; Allen, A.E.; Apt, K.E.; Bechner, M.; et al. The genome of the diatom Thalassiosira pseudonana: Ecology, evolution, and metabolism. Science 2004, 306, 79–86. [Google Scholar] [CrossRef][Green Version]
  89. Bowler, C.; Allen, A.E.; Badger, J.H.; Grimwood, J.; Jabbari, K.; Kuo, A.; Maheswari, U.; Martens, C.; Maumus, F.; Otillar, R.P.; et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 2008, 456, 239–244. [Google Scholar] [CrossRef]
  90. Lommer, M.; Specht, M.; Roy, A.-S.; Kraemer, L.; Andreson, R.; Gutowska, M.A.; Wolf, J.; Bergner, S.V.; Schilhabel, M.B.; Klostermeier, U.C.; et al. Genome and low-iron response of an oceanic diatom adapted to chronic iron limitation. Genome Biol. 2012, 13, R66. [Google Scholar] [CrossRef] [PubMed][Green Version]
  91. Sibbald, S.J.; Archibald, J.M. More protist genomes needed. Nat. Ecol. Evol. 2017, 1, 145. [Google Scholar] [CrossRef]
  92. Stephens, T.; Ragan, M.; Bhattacharya, D.; Chan, C.X. Genome Assemblies and the Associated Annotations for Polarella glacialis CCMP1383 and CCMP2088; The University of Queensland: Brisbane, Australia, 2020. [Google Scholar] [CrossRef]
  93. Gobler, C.J.; Berry, D.L.; Dyhrman, S.T.; Wilhelm, S.W.; Salamov, A.; Lobanov, A.V.; Zhang, Y.; Collier, J.L.; Wurch, L.L.; Kustka, A.B.; et al. Niche of harmful alga Aureococcus anophagefferens revealed through ecogenomics. Proc. Natl. Acad. Sci. USA 2011, 108, 4352–4357. [Google Scholar] [CrossRef][Green Version]
  94. Moore, C.E.; Curtis, B.; Mills, T.; Tanifuji, G.; Archibald, J.M. Nucleomorph Genome Sequence of the Cryptophyte Alga Chroomonas mesostigmatica CCMP1168 Reveals Lineage-Specific Gene Loss and Genome Complexity. Genome Biol. Evol. 2012, 4, 1162–1175. [Google Scholar] [CrossRef]
  95. Tanifuji, G.; Onodera, N.T.; Wheeler, T.J.; Dlutek, M.; Donaher, N.; Archibald, J.M. Complete Nucleomorph Genome Sequence of the Nonphotosynthetic Alga Cryptomonas paramecium Reveals a Core Nucleomorph Gene Set. Genome Biol. Evol. 2011, 3, 44–54. [Google Scholar] [CrossRef] [PubMed]
  96. Curtis, B.A.; Tanifuji, G.; Burki, F.; Gruber, A.; Irimia, M.; Maruyama, S.; Arias, M.C.; Ball, S.; Gile, G.H.; Hirakawa, Y.; et al. Algal genomes reveal evolutionary mosaicism and the fate of nucleomorphs. Nature 2012, 492, 59–65. [Google Scholar] [CrossRef]
  97. Radakovits, R.; Jinkerson, R.E.; Fuerstenberg, S.I.; Tae, H.; Settlage, R.E.; Boore, J.L.; Posewitz, M.C. Draft genome sequence and genetic transformation of the oleaginous alga Nannochloropsis gaditana. Nat. Commun. 2012, 3, 686. [Google Scholar] [CrossRef] [PubMed][Green Version]
  98. Read, B.A.; Kegel, J.; Klute, M.J.; Kuo, A.; Lefebvre, S.C.; Maumus, F.; Mayer, C.; Miller, J.; Monier, A.; Salamov, A.; et al. Pan genome of the phytoplankton Emiliania underpins its global distribution. Nature 2013, 499, 209–213. [Google Scholar] [CrossRef][Green Version]
  99. Mock, T.; Otillar, R.P.; Strauss, J.; McMullan, M.; Paajanen, P.; Schmutz, J.; Salamov, A.; Sanges, R.; Toseland, A.; Ward, B.J.; et al. Evolutionary genomics of the cold-adapted diatom Fragilariopsis cylindrus. Nature 2017, 541, 536–540. [Google Scholar] [CrossRef] [PubMed]
  100. Armbrust, E.V.; Parker, M.S.; Rocap, G.; Jenkins, B.; Bates, S.S. Pseudo-Nitzschia Multiseries CLN-47 Genome Sequence, Assembly v1. 2011. Available online: https://genome.jgi.doe.gov/Psemu1/Psemu1.home.html (accessed on 24 February 2022).
  101. Basu, S.; Patil, S.; Mapleson, D.; Russo, M.T.; Vitale, L.; Fevola, C.; Maumus, F.; Casotti, R.; Mock, T.; Caccamo, M.; et al. Finding a partner in the ocean: Molecular and evolutionary bases of the response to sexual cues in a planktonic diatom. New Phytol. 2017, 215, 140–156. [Google Scholar] [CrossRef][Green Version]
  102. Johansson, O.N.; Töpel, M.; Pinder, M.I.M.; Kourtchenko, O.; Blomberg, A.; Godhe, A.; Clarke, A.K. Skeletonema marinoi as a new genetic model for marine chain-forming diatoms. Sci. Rep. 2019, 9, 5391. [Google Scholar] [CrossRef]
  103. Keeling, P.J.; Burki, F.; Wilcox, H.M.; Allam, B.; Allen, E.E.; Amaral-Zettler, L.A.; Armbrust, E.V.; Archibald, J.M.; Bharti, A.K.; Bell, C.J.; et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the Functional Diversity of Eukaryotic Life in the Oceans through Transcriptome Sequencing. PLoS Biol. 2014, 12, e1001889. [Google Scholar] [CrossRef][Green Version]
  104. Caron, D.A.; Alexander, H.; Allen, A.E.; Archibald, J.M.; Armbrust, E.V.; Bachy, C.; Bell, C.J.; Bharti, A.; Dyhrman, S.T.; Guida, S.M.; et al. Probing the evolution, ecology and physiology of marine protists using transcriptomics. Nat. Rev. Microbiol. 2017, 15, 6–20. [Google Scholar] [CrossRef] [PubMed]
  105. De Vargas, C.; Audic, S.; Henry, N.; Decelle, J.; Mahé, F.; Logares, R.; Lara, E.; Berney, C.; Le Bescot, N.; Probert, I.; et al. Eukaryotic plankton diversity in the sunlit ocean. Science 2015, 348, 1261605. [Google Scholar] [CrossRef][Green Version]
  106. Cuvelier, M.L.; Allen, A.E.; Monier, A.; McCrow, J.P.; Messié, M.; Tringe, S.G.; Woyke, T.; Welsh, R.M.; Ishoey, T.; Lee, J.-H.; et al. Targeted metagenomics and ecology of globally important uncultured eukaryotic phytoplankton. Proc. Natl. Acad. Sci. USA 2010, 107, 14679–14684. [Google Scholar] [CrossRef][Green Version]
  107. Biard, T.; Stemmann, L.; Picheral, M.; Mayot, N.; Vandromme, P.; Hauss, H.; Gorsky, G.; Guidi, L.; Kiko, R.; Not, F. In situ imaging reveals the biomass of giant protists in the global ocean. Nature 2016, 532, 504–507. [Google Scholar] [CrossRef][Green Version]
  108. Vorobev, A.; Dupouy, M.; Carradec, Q.; Delmont, T.O.; Annamalé, A.; Wincker, P.; Pelletier, E. Transcriptome reconstruction and functional analysis of eukaryotic marine plankton communities via high-throughput metagenomics and metatranscriptomics. Genome Res. 2020, 30, 647–659. [Google Scholar] [CrossRef]
  109. West, P.T.; Probst, A.J.; Grigoriev, I.V.; Thomas, B.C.; Banfield, J.F. Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Res. 2018, 28, 569–580. [Google Scholar] [CrossRef] [PubMed][Green Version]
  110. Priest, T.; Orellana, L.H.; Huettel, B.; Fuchs, B.M.; Amann, R. Microbial metagenome-assembled genomes of the Fram Strait from short and long read sequencing platforms. PeerJ 2021, 9, e11721. [Google Scholar] [CrossRef] [PubMed]
  111. Hugerth, L.; Larsson, J.; Alneberg, J.; Lindh, M.V.; Legrand, C.; Pinhassi, J.; Andersson, A.F. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biol. 2015, 16, 279. [Google Scholar] [CrossRef][Green Version]
  112. Costea, P.I.; Munch, R.; Coelho, L.P.; Paoli, L.; Sunagawa, S.; Bork, P. metaSNV: A tool for metagenomic strain level analysis. PLoS ONE 2017, 12, e0182392. [Google Scholar] [CrossRef] [PubMed][Green Version]
  113. Olm, M.R.; Crits-Christoph, A.; Bouma-Gregson, K.; Firek, B.A.; Morowitz, M.J.; Banfield, J.F. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat. Biotechnol. 2021, 39, 727–736. [Google Scholar] [CrossRef]
  114. Sjöqvist, C.; Delgado, L.F.; Alneberg, J.; Andersson, A.F. Ecologically coherent population structure of uncultivated Bacterioplankton. ISME J. 2021, 15, 3034–3049. [Google Scholar] [CrossRef]
  115. Keeling, P.J.; Campo, J.D. Marine protists are not just big bacteria. Curr. Biol. 2017, 27, R541–R549. [Google Scholar] [CrossRef][Green Version]
Table 1. Examples of studies from the field of experimental evolution showing adaptive evolution in phytoplankton as a response to increased temperature or CO2.
Table 1. Examples of studies from the field of experimental evolution showing adaptive evolution in phytoplankton as a response to increased temperature or CO2.
Selection PressureSpeciesDiversity LevelGenerations/TimeEvolved Traits/FunctionsReference
TemperatureChlorella vulgaris1100Downregulation of respiration[26]
C. reinhardtii8–1010 yearsIncreased photosynthetic capacity and reduced susceptibility to photoinhibition[37]
T. pseudonana1300Transcriptional regulation, oxidative stress, redox homeostasis[36]
T. pseudonana1350Altered thermal reaction norm[35]
Chaetoceros sp., Thalassiosira sp., Synedra sp.1200–600Altered thermal reaction norm[38]
CO2E. huxleyi1/6 500Increased calcification[32]
Ostreococcus tauri16 400Reduced cell size and C:N ratio [39]
E. huxleyi1500pH regulation[33]
Gephyrocapsa oceanica11000Reduced growth rates, enhanced POC, PON, decrease in PIC:POC ratio[40]
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Sjöqvist, C. Evolution of Phytoplankton as Estimated from Genetic Diversity. J. Mar. Sci. Eng. 2022, 10, 456. https://doi.org/10.3390/jmse10040456

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Sjöqvist, Conny. 2022. "Evolution of Phytoplankton as Estimated from Genetic Diversity" Journal of Marine Science and Engineering 10, no. 4: 456. https://doi.org/10.3390/jmse10040456

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