Summary from the authors: A linked-read approach to museomics: Higher quality de novo genome assemblies from degraded tissues

We aimed to sequence and compare all the DNA (eg., the genome) of a bunch of different deer mice (genus Peromyscus) species to understand how some deer mice survive in hot deserts with little to no water. A number of deer mice tissue samples were available through natural history museums, which house the raw materials for genetic and biodiversity investigations, but the samples had been collected many years earlier. Older samples produce lower quality DNA that has been broken into many pieces over time. Our normal sequencing procedure selectively removes small fragments of DNA, which would essentially throw away all the DNA we wanted to sequence for these older samples! To circumvent this, we were able to use a different DNA library preparation method called linked-read sequencing (LRS). LRS uses standard short-read sequencing technology, but adds additional information about the location of DNA fragments within the genome by bundling and barcoding DNA fragments that are located near each other prior to sequencing (eg., ‘links’ DNA fragments together in ‘genome-space’). We found that this method improves the overall quality and completeness of genome assemblies from historical tissue samples, in less time and with less effort than traditional shot-gun sequencing methods. This alternative method may be particularly valuable for building high-quality genome assemblies for extinct species for which there are no new samples being collected for or endangered species that are difficult to sample or collect. LRS adds to the suite of genomic methods that continue to unlock the secrets of natural history collections and enable fine-scale genetic measurement of change through time.

This summary was written by the study’s first author, Jocelyn Colella.

Read the full text here.

Video credit: Jocelyn Colella. Peromyscus in the field.

Full Text: Colella JP, Tigano A, MacManes MD. A linked-read approach to museomics: Higher quality de novo genome assemblies from degraded tissues. Mol Ecol Resour. 2020;20:871–881.

Interview with the authors: Evaluation of model fit of inferred admixture proportions

Admixture models are widely-used in population genetics, but they make several simplifying assumptions, which, if violated, could result in misleading estimates of individual ancestry proportions. In a recent paper published in Molecular Ecology Resources, Garcia-Erill and Albrechtsen introduce evalAdmix, a program for detecting poor fit of admixture models to empirical data. evalAdmix uses the correlations of the residual differences between true and predicted genotypes to detect poor fit; when the assumptions of the model are not violated, the residuals of a pair of individuals should be uncorrelated. In simulation studies and analyses of empirical datasets, evalAdmix was useful in identifying model violations due to gene flow from unsampled ghost populations, continuous variation, population bottlenecks, and an incorrect assumed number of ancestral populations. Read the full article here, and read below for an exclusive interview with lead author Genís Garcia-Erill.

Full text: Garcia-Erill G. and Albrechtsen A. Evaluation of model fit of inferred admixture proportions. Mol Ecol Resour. 2020;20:936–949.

Admixture model and evaluation with our method applied to worldwide human genetic variation. A. Admixture proportions inferred with ADMIXTURE assuming K=5 for all human populations from the 1000Genomes project. B. Evaluation of admixture model with the correlation of residuals performed with evalAdmix. Positive correlations are indicative of a bad model fit. The correlation of residuals shows that modelling with an ancestral population for each of the 5 major continental groups leads to a bad fit within most populations, and furthermore it gives additional information. For example we can see that the populations more genetically distant from the rest with which they are grouped, like Luhya in Webuye, Kenya (LWK) or Finish in Finland (FIN), have higher correlations of residuals, or it indicates the presence of substructure in some populations like the Gujarati Indians in Houston, TX (GIH).

What led to your interest in this topic / what was the motivation for this study?

The admixture model is one of the most used methods in population genetics, but it has already been known for some time that there are many potential issues with it. Specifically a recent study described very nicely different scenarios that can lead to wrong conclusions when applying the admixture model (Lawson et al. 2018). For example, they showed how multiple scenarios can lead to the same admixture results, and they also presented a method, badMixture, that can distinguish between those scenarios and evaluate model fit. However badMixture is quite difficult to apply, so we thought it would be interesting to develop an alternative method that could help in guiding the interpretation of admixture model results.

What difficulties did you run into along the way?

My background is in Biology and I had limited experience in computer science and statistics when I started with this project, so most of the difficulties were related to my learning how to work in these two disciplines. The method itself was relatively straightforward, but in order for it to work properly we needed to find a way to correct the bias caused by the frequency estimation. The frequency correction is only a small part of the main article, but it was where we put most of the work during the development of the method; that ended up as a few pages full of equations in the supplementary material. Another aspect where I had to put considerable effort was in making the implementation, since again I did not have much experience in developing software that would (hopefully) be used by other people. That made me consider things I would not usually think about.

What is the biggest or most surprising innovation highlighted in this study?

I think the method itself is the main result of the study. As I said there is already a method to evaluate the admixture model fit, badMixture. However that method is rarely used, because it requires performing additional analyses with CHROMOPAINTER and also requires having data with good enough quality to at least call genotypes. The method we present is more generally accessible since it is based on information unique to the admixture model itself, meaning one can directly apply it to any data set to which the admixture model has been applied. So it provides what we think is a simple way, both in the application and in the interpretation, to evaluate the admixture model results.

Moving forward, what are the next steps in this area of research?

There are several directions in which this work could be expanded. Something we already spent some time on is trying to develop a more firm theoretical foundation for the correlation of residuals as a measure of model fit, for example expressing it in terms of individual-specific Fst and the distance between the populations from which they are sampled, in a framework similar to that in Ochoa and Storey (2018). In the end we could not figure out the math and left it as a short mention in the discussion, but that would be something very nice to do. We also could not find a good way to use the residuals to develop some sort of measure of model fit at a purely individual level (instead of depending on the relationship between pairs of individuals, as it does right now), and that would also be very nice to do. Moreover, individual frequencies can also be calculated using principal component analyses, so this method could be expanded to work as an evaluation of a PCA as a description of population structure. Finally what we are looking forward to the most is to see how the method is applied to different datasets and how that helps gain new scientific insights.

What would your message be for students about to start developing or using novel techniques in Molecular Ecology? 

I am myself a student who has very recently started developing and using novel techniques in Molecular Ecology, so I am not sure if I have enough experience and perspective to give any useful advice. But based on my limited experience, I would say that it is important not to be afraid to jump into new areas or fields where we feel like we might have too limited experience, and that often what at first seems very difficult will become more and more accessible and doable as we work on it.

What have you learned about methods and resources development over the course of this project?

I started working on this study during my Master studies, so it has been one of my first research experiences. Basically all I know about method development I learned during the course of this project, from the more practical skills related to developing and implementing a method to how to explain it, and make it accessible to the community that might be interested in using it. I realized that this can actually be very important, since it will affect how many people end up using it. Also, as a user of bioinformatics methods I really appreciate when I use a new method if it is easy to use and does not create too many problems.

Describe the significance of this research for the general scientific community in one sentence.

It is important to consider the assumptions of the methods we use, since relevant violations of the assumptions might result in misleading or even meaningless results.    

Describe the significance of this research for your scientific community in one sentence.

It makes it possible and easy to evaluate the model fit of the admixture model at the individual-level in almost any context in which the admixture model is currently used, so it can be applied before concluding a population is a mixture of others, or it can help to choose a meaningful number of ancestral populations.


Lawson, D. J., Van Dorp L., and Falush, D.. A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nature Communications 2018;9.1: 1-11.

Ochoa, A. and Storey, J. D. FST and kinship for arbitrary population structures I: Generalized definitions. BioRxiv 2016: 083915.

Interview with the authors: Habitat light sets the boundaries for the rapid evolution of cichlid fish vision, while sexual selection can tune it within those limits

Non-model organisms provide an interesting avenue to explore evolution in real time in natural populations. Here, we speak to Ralf F. Schneider and Sina J. Rometsch of University of Konstanz, Germany about their co-authored Molecular Ecology article, where they investigate sex‐specific opsin expression of several cichlids from Africa and the Neotropics which they coupled with data sets on sex‐specific body coloration, species‐specific visual sensitivities, lens transmission and habitat light properties. They illustrate how integrative approaches can address specific questions on the factors and mechanisms driving diversification, and the evolution of cichlid vision in particular.  Read on to get a behind-the-scenes view of this study.

1. What led to your interest in this topic / what was the motivation for this study? 

Cichlid fishes are an amazing system to work with. They are one of the most species rich vertebrate families and adapted to a wide range of ecological niches. This is reflected in outstanding phenotypic diversity in numerous traits. Their striking variation in body coloration, which can sometimes differ considerably between the sexes, has even been acknowledged in their German name “Buntbarsche”, which translates to colorful perches. Moreover, their visual system is intriguingly complex, because cichlids possess a total of seven opsin genes that allow for color vision (humans only have three). While sexual selection has been recognized as a major driver in the evolution of cichlid body coloration (coloration being the “sender”), less is known about what shapes visual sensitivities (the receiving end of the system) in cichlids. Therefore, we were interested in whether the phenotypic diversity found across cichlid visual sensitivities is primarily driven by sexual selection (e.g., vision co-evolves with body colors), or whether environmental factors, such as light availability and water turbidity, turn out to have a stronger effect on the evolution of cichlid vision.

2. What difficulties did you run into along the way? 

The main challenge in addressing the question on whether the light environment (“ecological selection”) or conspecifics’ body colorations (“sexual selection”) are driving the diversification in cichlid vision was that both potential drivers are very complex and can be challenging to quantify. The visual environment across cichlids’ habitats varies tremendously, and is dependent on factors, such as water depth and turbidity. Coloration patterns often change across the fish’s body and only photospectrometric measurements across the whole range of visible light wavelengths can objectively quantify a color. Moreover, the fish’s visual system is highly complex. Visual sensitivity can be modified physiologically or by phenotypic plasticity by a number of factors including expressing different subsets of the seven opsin genes, changing their expression level, lens filtering etc. Combining all this information, either obtained from our own experiments or from published studies, in a common framework allowing for meaningful statistical analyses was challenging.

3. What is the biggest or most surprising innovation highlighted in this study? 

Most surprising, in terms of results, was to us that we did not find sexual dimorphism in opsin expression in any cichlid – not even those with very strong dimorphism in body coloration (such as in Pseudotropheus lombardoi (attached photo), where females are blue and males are yellow). While we did expect that evaluating mating partners based on their body color would favor associated sexual dimorphism in the visual system, this seemingly has not (yet?) happened in these fish. In terms of methodology, our study integrates a complex data-set on ecological and physiological parameters that can affect the visual sensitivity. This allowed us to evaluate the potential interactions of these parameters in a very comprehensive way.

4. Moving forward, what are the next steps in this area of research?

In our study, we show that a wide range of data can be integrated in a single model, which allowed us to investigate interactions among variables that are rarely used in a common framework. Thus, we encourage future studies to also consider comprehensive approaches when addressing questions concerning the visual ecology of these (or other) fish, if this information is available or obtainable. Additionally, while we have a relatively good understanding of how visual information is perceived by cichlids, there is only very little information on how visual information is processed in the neuronal circuitry of the eye and later in the brain. Understanding signal processing in cichlid eyes will provide a new information layer for evolutionary ecologists to work with.

5. What would your message be for students about to start developing or using novel techniques in Molecular Ecology? 

In the last decades, due to ever-increasing computational power, storage capacities and high-throughput techniques, such as next-gen sequencing, large amounts of data can be more reasonably collected and are accessible by more researchers. New methods can benefit from incorporating these data into analysis pipelines to consolidate them or broaden their scope. Being aware of available data can thus be very useful. However, it is also important to us to stress that approaching a scientific question from several angles and across biological disciplines that don’t frequently communicate is often the soundest approach. Classical lab methods, such as in situ hybridization or histology, as well as cutting edge techniques, such as Crispr/Cas9, can provide valuable validation/falsification of formulated hypotheses.

6. What have you learned about methods and resources development over the course of this project? 

It was great to see how this study evolved: one question and technique lead to another until we finally aimed at developing an analysis frame-work for the complex data-sets that are obtained in visual ecology of (cichlid) fishes. This comprised changing and further developing our pipeline while analyzing the data. Several preliminary pipelines had to be discarded as they did not properly address our core hypotheses. Thus, an important lesson for us was that it can take a while until a newly developed analysis pipeline does actually what one envisioned roughly at the beginning of the project. Overall, collaborating in a team with members of quite different backgrounds such as ecology, molecular biology and data science and working in a large and well-established lab made it possible to learn and apply new techniques.

7. Describe the significance of this research for the general scientific community in one sentence.

Evaluating the relative strengths of natural vs. sexual selection is a very interesting question and these two forces are often very hard to disentangle, but using a set of multidisciplinary approaches combined with a comprehensive statistical analysis allowed us to show that in narrow light environments visual sensitivity is tuned to exploit all available light, while broader light environments allow for more specialized visual sensitivities.

8. Describe the significance of this research for your scientific community in one sentence.

We show that ambient light is a prime driver for the evolution of visual sensitivities through natural selection in cichlid fishes, whereas sexual selection seems to finetune the observed diversity within the limits set by natural selection.

9. How has COVID-19 affected work in your group?

For the last two months we’ve all been confined to working from home which – on the plus side – allowed us to dedicate more time to data analyses and finishing manuscripts, but on the down side required lab experiments to be currently on hold – unfortunately.

Full paper: Schneider, R. F., Rometsch, S. J., Torres-Dowdall, J., & Meyer, A. (2020). Habitat light sets the boundaries for the rapid evolution of cichlid fish vision, while sexual selection can tune it within those limits. Molecular Ecology.

Interview with the authors: Applying genomic data in wildlife monitoring

Massive parallel sequencing has led to an explosion of sequence data in recent years. However, the methods used to obtain such data are usually high-cost and time-intensive, and often require high-quality samples. This creates limits as to whether and how well such data can be used by researchers working in applied conservation science. Here, we speak to Alina von Thaden about her recent study in Molecular Ecology Resources. Using European wildcats as a case study, Alina and co-authors present a relatively low-cost and time-efficient workflow for the development and optimisation of microfluidic SNP panels, which can be used to obtain SNP data from minimally invasive samples. Beyond outlining the workflow and its applications, they go so far as to estimate the costs of their pipeline, providing valuable practical information for conservation scientists. Read on for an in-depth view of this study.

Monitoring elusive European wildcats (Felis silvestris) is heavily reliant on noninvasively collected DNA samples. Photo credit: Annsophie Schmidt.

What led to your interest in this topic / what was the motivation for this study? 

We are mainly working on genetic monitoring of large carnivores and most of our research is based on noninvasively collected wildlife samples such as hairs, faeces and saliva traces. The field demands for very fast and reliable genetic analyses of samples with degraded DNA. And since funding is generally sparse in applied conservation, our methods need to be cost-effective and suitable for high-throughput approaches.

Genomic tools, on the other hand, usually involve large amounts of data, complex bioinformatic pipelines and typically rely on samples with high-quality DNA. We have been looking into ways to combine the advantages of genomics with the challenges of conservation monitoring. For some years now, we have been working with microfluidic arrays combined with reduced SNP panels and wanted to share our experiences with other labs interested in applying them.

What difficulties did you run into along the way? 

Setting-up and optimizing methodological resources comes along with several challenges – but there is a lot to learn! Most important to me was to remain skeptical about the results and to constantly validate them through analyzing the data from several perspectives and with different software. The validation of the technology also took a lot of extra lab hours, but we are confident that the workflow and guidelines that we present now will save others a lot of hands-on time and costs when optimizing SNP panels for degraded samples.

What is the biggest or most surprising finding from this study? 

First of all, after years of developing the framework, we applied it to a new SNP panel designed for dog-wolf hybridization assessment (to be published) and found that the lab work for generating a new ready-to-use marker panel took us only a few weeks. To see the approach being proved effective was great and encouraged us to share it with the community.

Secondly, a large proportion of noninvasively collected samples could be run without or with only very few genotyping errors as compared to more traditional microsatellite-based genotyping (see also von Thaden et al. 2017). This has direct implications for genotyping costs and thus promotes the broader establishment of a genomic technology in applied conservation.

Alina von Thaden collecting reference samples of European wildcat (Felis silvestris) for testing a newly developed SNP panel. Photo credit: Annsophie Schmidt.

Moving forward, what are the next steps for this research? 

One of our next steps is to apply the technology to historical samples from museum collections. Additionally, we are going to implement the SNP panel from our current paper in routine genetic monitoring of European wildcats in Germany.

We currently develop other reduced SNP panels for a variety of endangered species in our lab, such as dormice and European bison. Besides neutral variation, we also aim to integrate functional markers, such as SNPs associated with disease susceptibility.

Further, we will test alternative platforms that will allow generating larger SNP sets for degraded samples. Ultimately, our long-term goal is the effective implementation of an “applied genomic wildlife monitoring” approach.

What would your message be for students about to start their first research projects in this topic? 

Get in contact with other groups working in this area! Sharing ideas and experience really helps to shape your project and refine the aims of your research. Most people are very cooperative and happy to contribute or answer questions.

What have you learned about science over the course of this project? 

Perseverance and tenacity. When exploring new directions the research journey may well become bumpy and lead you somewhere else than you initially expected. But it’s worth it – keep your goal in mind and be ready to rethink your strategy.

Describe the significance of this research for the general scientific community in one sentence.

Bridging the gap between genomics and applied conservation is a key prerequisite for effective wildlife management, especially in the light of rapid biodiversity declines.

Describe the significance of this research for your scientific community in one sentence.

We demonstrate how reduced SNP panels can be efficiently developed and optimized for genotyping based on degraded wildlife samples.


von Thaden, A., Cocchiararo, B., Jarausch, A., Jüngling, H., Karamanlidis, A. A., Tiesmeyer, A. … Muñoz-Fuentes, V. (2017). Assessing SNP genotyping of noninvasively collected wildlife samples using microfluidic arrays. Scientific Reports, 7, 83.

Full paper

von Thaden, A., Nowak, C., Tiesmeyer, A., Reiners, T. E., Alves, P. C., Lyons, L. A., … & Hegyeli, Z. (2020). Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism (SNP) panels. Molecular Ecology Resources.


Interview with the authors: Strong divergent selection at multiple loci in two closely related species of ragworts adapted to high and low elevations on Mount Etna

Non-model organisms provide an interesting avenue to explore evolution in real time in natural populations.Here, we speak to Edgar Wong of Department of Plant Sciences, University of Oxford, UK about his Molecular Ecology article, which investigated speciation in two closely related Senecio species, S. aethnensis and S. chrysanthemifolius, which grow at high and low elevations, respectively, on Mount Etna, Sicily and form a hybrid zone at intermediate elevations.  Wong and his co-authors found an extremely strong selection (up to 0.78) against hybrids in the system. This estimate is one of the highest reported in literature, and much higher than the one reported in the same system in the past. Read on to get a behind-the-scenes view of this study.

1. What led to your interest in this topic / what was the motivation for this study?

Speciation and hybridisation have always been interesting topics to me. In the case of Senecio on Mount Etna, they have an especially fascinating story: first, Mount Etna is a relatively young mountain (less than half a million years old), and previous research hypothesized that the formation of the mountain led to the divergence of the two species, Senecio aethnensis and S. chrysanthemifolius. These species are thought to be a rare example of clear-cut, recent speciation subject to divergent selection – the formation of new species driven by adaptation to distinct conditions – high- and low-elevations in our study. Second, botanists around 300 years ago brought some live Senecio specimens of the plants from Mount Etna back to the UK, and led to hybrid speciation of S. squalidus that has since spread all over the UK (although crossing experiments using plants from Mount Etna suggested hybrid breakdown). A lot is still unknown about the plants both on Mount Etna and in the UK. Hence, I was intrigued to find out unknown aspects in the system and focused on the species on Mount Etna.

2. What difficulties did you run into along the way?

One big difficulty was that Asteraceae (which Senecio belongs to) is notorious for being hard to extract clean DNA. It was a struggle to extract good-quality DNA for this study, which was resolved in the end. Also, we only had a draft genome for the hybrid species, S. squalidus, which limited the scope of analyses we could carry out. Luckily, we managed to find some interesting, highly differentiated genes that might be underlying speciation and adaptation.

3. What is the biggest or most surprising innovation highlighted in this study?

The most surprising finding in our study is that we estimated an extremely strong selection (up to 0.78) against hybrids in the system. This estimate is one of the highest reported in literature, and much higher than the one reported in the same system in the past. Such strong selection was surprising to us because hybrids between the two species are (apparently) happily growing at intermediate elevations between the typical habitats of ‘pure’ S. aethnensis and S. chrysanthemifolius. We think this strong cumulative selection on multiple loci works together with intrinsic incompatibility to maintain the phenotypic and genotypic divergence between the two target species.

4. Moving forward, what are the next steps in this area of research?

In the future, we hope to identify the environmental and ecological selective forces that had shaped this system. We also hope to characterise the genetic aspect of the species by improving the genome assembly and study more in detail the intrinsic incompatibility between the two target species (such as hybrid breakdown). With more data on both extrinsic and intrinsic processes, we can integrate these findings to get a more comprehensive picture of reproduction isolation in this system.

5. What would your message be for students about to start developing or using novel techniques in Molecular Ecology?

I would say to spend enough time understanding the experimental techniques and different types of data analyses (and the theories behind them). Most importantly, make sure that the type of data you generate are suitable for answering your research questions. As a graduate student myself, I would also suggest not to rush your work and not get transfixed on certain issues/ problems along the way – taking a step back and asking for advice and opinions from other researchers are always helpful in getting another perspective, which often helps to find a solution.

6. What have you learned about methods and resources development over the course of this project?

The type of data I used and subsequent data analyses were all new to me when I started the project, so there is no doubt I learnt a great deal about handling new types of data and how to analyse it. Another thing I have learnt is that there are always newer or ‘better’ technologies and methods that give you more data and/ or data with higher accuracy. It is inevitable that sometimes you would be worried whether what you have is not good enough. However, I have come to realise that more isn’t always better and there will always be more advanced methods; the most important thing is to use what you have and try to answer your research questions.

7. Describe the significance of this research for the general scientific community in one sentence.

Non-model organisms inform us a lot about evolutionary processes such as hybridisation, adaptation and speciation.

8. Describe the significance of this research for your scientific community in one sentence.

Strong multifarious selection could be crucial in maintaining species divergence despite on-going gene flow.

Summary from the authors: Interdependent sensory systems regulate larval settlement in a marine sponge

In the ocean, pelagic larvae that settle onto the seafloor and metamorphose into an adult directly regulate the ecology and evolution of all benthic communities. To settle, larvae of most species need to encounter specific biochemical cues that indicate an optimal environment, and many also prefer to settle in the dark. It appears likely, then, that larval responses to light and to biochemical cues are closely linked, but exactly how this happens at a molecular level is largely unexplored.

We explored how changes in gene expression regulate larval settlement in a marine sponge. We find that these larvae naturally settle at twilight, and that this is directly related to the expression of receptors and signalling pathway components. Further, we find that constant light prevents larval settlement via blocking the ability of larvae to respond to biochemical cues. Our data provide the first suggestions of candidate genes and molecular pathways that may regulate the way in which light can directly affect larval settlement. Our findings in a sponge, one of the earliest branching extant animal lineages, raises the possibility that larval responses to light and to biochemical cues might be a mechanism regulating settlement across the animal kingdom.

(Left) Scanning electron micrograph of an Amphimedon queenslandica larva. A ring of very long cilia, which are associated with photosensory pigment cells, are clearly visible at the posterior end of the larva. Photo credit: Sally Leys. (Right) Tahsha Say in the field on Heron Island Reef flat, Great Barrier Reef, Australia.

Full article: Say, TE, Degnan, SM. Molecular and behavioural evidence that interdependent photo ‐ and chemosensory systems regulate larval settlement in a marine sponge. Mol Ecol. 2020; 29: 247– 261.

This summary was written by the study’s first author,TE Say.

Victoria Sork awarded the 2020 Molecular Ecology Prize

The Molecular Ecology Prize Committee is pleased to announce that the 2020 Molecular Ecology prize has been awarded to Dr. Victoria Sork, Distinguished Professor in Ecology and Evolutionary Biology, Dean of Life Sciences, and Director of the Mildred E. Mathias Botanical Garden at University of California Los Angeles. Throughout her career, Dr. Sork has made substantial and diverse scientific contributions to the field of molecular ecology – from working to build the foundation of landscape genetics, to pioneering the use of molecular markers in tracking plant dispersal, to unraveling the genomic and epi-genomic basis of climate adaptation in non-model organisms. With well over 100 publications, she has proven herself to be a preeminent scholar in her field for decades, while serving as a role model and mentor for many early career scientists, and as a continual advocate for increasing diversity and inclusion in STEM.

Dr. Sork joins the previous winners of the Molecular Ecology Prize: Godfrey Hewitt, John Avise, Pierre Taberlet, Harry Smith, Terry Burke, Josephine Pemberton, Deborah Charlesworth, Craig Moritz, Laurent Excoffier, Johanna Schmitt, Fred Allendorf, Louis Bernatchez, Nancy Moran, Robin Waples, and Scott Edwards.

Summary from the authors: A metagenomic assessment of microbial eukaryotic diversity in the global ocean

Marine microbial eukaryotes are key components of planktonic ecosystems in all ocean biomes. They are, along with cyanobacteria, responsible for nearly half of the global primary production, and play important roles in food-web dynamics as grazers and parasites, carbon export to the deep ocean, and nutrient remineralization. Currently, one of the most common approaches to survey their diversity is sequencing marker genes amplified from genomic DNA extracted from microbial assemblages. However, this approach requires a PCR step, which is known to introduce biases in microbial diversity estimates. One alternative to overcome this issue involves exploiting the taxonomic information contained in metagenomes, which use massive shotgun sequencing of the same DNA extracts with the goal of assessing the putative functions of environmental microbes.

In this study we investigated the potential of metagenomics to provide taxonomic reports of marine microbial eukaryotes. The overall diversity reported by this approach was similar to that obtained by amplicon sequencing, although the latter performed poorly for some taxonomic groups. We then studied the diversity of picoeukaryotes and nanoeukaryotes using 91 metagenomes from surface down to bathypelagic layers in different oceans, unveiling a clear separation of taxonomic groups between size fractions and depth layers.

Overall, this study shows metagenomics as an excellent resource for taxonomic exploration of marine microbial eukaryotes.

Summary of the relevance of main eukaryotic taxonomic groups within two size fractions of marine plankton (picoeukaryotes [0.2-3 µm] and nanoeukaryotes [3-20µm]) and in two different layers of the global ocean (photic [0-200 m] and aphotic [200-4000m]) as seen by metagenomics. The median of the relative abundance was calculated for each taxonomic group with samples from the 4 categories (pico-photic, pico-aphotic, nano-photic, nano-aphotic) and dots represent these median values transformed to a 0-100 scale. Dots are then colored based on the category where the taxonomic group is most relevant.

This summary was written by the study’s first author, Aleix Obiol.

Full article:
Obiol, A., Giner, C. R., Sánchez, P., Duarte, C. M., Acinas, S. G., & Massana, R. (2020). A metagenomic assessment of microbial eukaryotic diversity in the global ocean. Molecular Ecology Resources.