Field notes from city streets

White clover in the lawn of North Hollywood Park (jby)

I spent this morning in Los Angeles city parks, pulling up clover. This attracted less attention than you might expect. Angelenos are, as a group, not inclined to bother people who aren’t doing anyone else any obvious harm, and honestly the range of activity in a typical LA park includes behaviors that are objectively odder (group yoga) and more damaging to the turf (pickup soccer games). The single fellow who shouted good morning to me followed up only with “happy hunting!” He may have thought I was looking for a lucky charm.

In fact I wasn’t counting leaflets, but trying to make sure I got to twenty more-or-less distinct patches of Trifolium repens, white clover. White clover is native to Europe, but it loves lawns, and it’s been introduced all over the world. Recently Ken Thompson, Marie Renaudin, and Marc Johnson reported something interesting about white clover populations in Toronto, New York, and Boston: close to the centers of those cities, clover samples were less likely to respond to injury by producing toxic hydrogen cyanide, HCN. HCN production has a simple genetic basis, so parallel loss of the phenotype is likely an evolved response. The HCN reaction defends against herbivores — but it’s also triggered by freezing damage, so HCN-producing plants in cold climates risk poisoning themselves. Thompson et al. hypothesized that winter road plowing in built-up urban areas exposes roadside clover to sharper cold than it would cope with in the countryside, insulated under snow cover. And it turns out that in a fourth city, Montreal, where there were below-freezing days with no snow cover at the sampling sites, clover populations didn’t show the pattern of reduced HCN production.


That’s an interesting story from northeast North America, but white clover grows worldwide. The Global Urban Evolution (GLUE) Project aims build on Thompson et al.‘s original four-city dataset with replicate transects in cities on every continent. I’m one of three biologists at institutions in greater Los Angeles (it’s a big city!) that have pitched in to take samples and test them for HCN content.

Collecting my samples has taken a little more thought than I’d initially figured on. The GLUE sampling protocol envisions relatively contained, convex urban areas that shade smoothly into suburbs and then countryside, with clover available pretty much anywhere along the way. The first problem with this, for me, is that the development of greater Los Angeles hasn’t so much sprawled outward from a central built-up area as it’s poured around and occasionally over the complex of coastal mountains that surround downtown LA on three sides. The second is that white clover doesn’t grown in truly natural rural areas beyond the limits of that development — it seems the only suitable habitat for T. repens in greater LA really is a regularly watered lawn.

Not just any lawn, either! I went through a couple of draft sampling plans, picking likely looking lawns out from Google Earth aerial imagery, only to find, upon showing up in person, that they’d be too well weeded, or closely mowed, for clover. Or just too small — many a median strip of grass has spreading patches of Medicago or Oxalis where white clover might otherwise move in. A first attempted collecting trip with a couple of my students turned up patch after patch of beautiful, carpet-like grass without a single trifoliate leaf to be seen.

That is, until we got to our first city park. It seems that the LA Department of Recreation and Parks provides just the right degree of lawn care — enough water, not too much re-seeding or mowing — to make white clover happy. So I went back to Google Earth, loaded the map layer with public park locations, and stitched together a transect from downtown north and west across the San Fernando Valley. It’ll take a few more days working with the students and on my own, but by the end of it I’ll have seen a pretty large sample of the public green spaces in the city.

Honeybee on white clover in Valley Village Park (jby)


Thompson KA, M Renaudin, MTJ Johnson. 2016. Urbanization drives the evolution of parallel clines in plant populations. Proc. Royal Soc. B 283, 20162180. doi: 10.1098/rspb.2016.2180

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What is DAS? A new tool to recover genomes from metagenomes

There are a lot of data out there, and if you haven’t already noticed the ‘omics train has steadily stayed its path through the fruitful (but challenging) world of metagenomics. Metagenomics offers the chance to unravel complex microbial communities without the need for individual cell sequencing or the isolation and cultivation of each and every member of the uncultured majority.

If you think of deciphering metagenomes as trying to complete wildly complex puzzles that have all been jumbled together into one box, (as Sieber and colleagues suggested recently) it makes sense that it would be hard to sort them all out. This would be challenging because you (a) don’t know what the final pictures should be (b) aren’t sure how many full puzzles are in the box, and (b) are pretty dang certain there are pieces missing. To make pulling out pieces of interest (sequences) and completing one entire puzzle (genome) more feasible, bioinformatic tools have been developed. These tools put contigs from a metagenome into bins, which essentially means putting all the contigs that seem to be from one thing together, making it possible to eventually assemble (completely…or mostly…or partly) entire genomes.

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La vie en rouge … l’algue rouge

Best laid plans of a #NewPI … what happens to them?

Well, they often get triaged for more urgent things that were triaged earlier for more urgent things that were also triaged even earlier for more urgent things … and well you get the drift.

My lab and I have embarked on a summer of field work (not unlike the summer of 2015 that was highlighted on TME).

First leg of Summer 2018 was to France, for field work bookended by two evolutionary conferences. I will follow up this post with several posts on the work presented at the Conférence Jacques Monod Sex Uncovered: the evolutionary biology of reproductive systems held at the Station Biologique de Roscoff and then Marine Evolution 2018 in Strömstad, Sweden.

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Is the neutral theory dead?

Motoo Kimura. Source: WikimediaCommons

You might have noticed how the world of genetics was shaking as the giants of theoretical population genetics started discussing some of the most fundamental questions in the arena of Twittersphere. This happened after the publication of Andrew Kern and Matthew Hahn’s paper titled “The Neutral Theory in Light of Natural Selection“.

For the sake of all the early career scientists who might be under the false impression that they don’t understand anything, while all the fancy last authors have it figured out, I have decided that at least some of the Twitter gems shall be recorded.

I’m not going to walk into the lion’s den and try to argue for or against neutral theory or even comment on the new paper. I’m merely providing a record of what’s been said by others, and by the end of this post you might be more confused about neutral theory than ever (#sorrynotsorry).

I think this could be the best summary of the whole thing:

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Nominations Open for 2018 Molecular Ecology Prize

We are soliciting nominations for the annual Molecular Ecology Prize. The field of molecular ecology is young and inherently interdisciplinary. As a consequence, research in molecular ecology is not currently represented by a single scientific society, so there is no body that actively promotes the discipline or recognizes its pioneers. The editorial board of the journal Molecular Ecology therefore created the Molecular Ecology Prize in order to fill this void, and recognize significant contributions to this area of research. The prize selection committee is independent of the journal and its editorial board. The prize will go to an outstanding scientist who has made significant contributions to Molecular Ecology. These contributions would mostly be scientific, but the door is open for other kinds of contributions that were crucial to the development of the field. The previous winners are: Godfrey Hewitt, John Avise, Pierre Taberlet, Harry Smith, Terry Burke, Josephine Pemberton, Deborah Charlesworth, Craig Moritz, Laurent Excoffier, Johanna Schmitt, Fred Allendorf, Louis Bernatchez, and Nancy Moran. Please send your nomination with a short supporting statement (no more than 250 words; longer submissions will not be accepted) directly to Deborah Charlseworth ( by Friday 29 June 2018. With thanks on behalf of the Molecular Ecology Prize Selection Committee
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Chromosomal inversions and the maintenance of species barriers

Chromosomal inversions have long fascinated evolutionary biologists for their role in adaptation and speciation. These structural variants are abundant in natural populations and can have diverse evolutionary consequences. They can cause reproductive isolation through hybrid sterility or protect sets of co-adapted alleles from recombination, while inversions in genes or promoters can disrupt gene expression.

A recent review paper by Maren Wellenreuther and Louis Bernatchez published in TREE comprehensively summarises what is known about the biology of inversions. Wellenreuther & Bernatchez (2018) include a list of about two dozen species where inversions have been characterised in the wild, and come to some interesting conclusions about inversion size (important inversions are often large), age (they’re often old and pre-date speciation), and the evolutionary processes that act on them (balancing selection may be important).


Representative examples of taxa with well-characterised chromosomal inversions, and their evolutionary consequences. From Wellenreuther & Bernatchez (2018).

This paper is a clear reminder of the importance of inversions  in evolution. Reading the review, however, made me wonder just how common inversions are in speciation, adaptation, and the maintenance of species differences, or whether their perceived importance is biased by their ease of detection. In many genetic analyses inversions stand out like a sore thumb. They can be spotted in genome-wide alignments, or may be seen as large regions of divergence in outlier scans. Inversions are also obvious in genetic maps where you see clusters of markers with reduced recombination. Making a causal link between inversions and adaption is harder, but there are many cases where quantitative trait loci (QTL) co-occur with inversions and implicate their role in adaptation (e.g. Lowry & Willis, 2010).

This question about the ubiquity of inversions in speciation studies is partly addressed in a recent paper by Davey et al. published in Evolution Letters. Davey et al. (2017) investigated whether species barriers between closely-related hybridizing taxa are always maintained by inversions, and answered this with a resounding “no”. While this shouldn’t come as much of a surprise, it is useful case study that brings balance to the recent inversion-heavy evolutionary literature.

In their study, Davey et al. (2017) use genetic mapping, genome assemblies, and long-read sequencing to look for inversions that differ between two sympatric species of the widely studied butterfly genus Heliconius. They showed hybridising H. cydno and H. melpomene completely lack any large inversions (over 50Kb in length) that are likely to be involved in maintaining co-adapted gene complexes. Their use of high quality genome assemblies and long-read sequence data make it unlikely that any large inversions would be overlooked. The authors conclude that: “This suggests that hybridization is rare enough and mate preference is strong enough that inversions are not necessary to maintain the species barrier”.

So, how often are inversions involved in speciation? Clearly we need more studies investigating the genomic basis of species differences in order to answer this question. But we should also remember that the prevalence of studies that implicate inversions in adaptation and speciation are likely (at least in part) to be due to the ease of detecting inversions, and the methodological constraints in finding individual loci underlying divergence. In Heliconius butterflies, and no doubt many other organisms, divergence has occurred in the face of homogenizing gene flow despite lacking major inversions. Future studies investigating the genetics of speciation and adaptation should broaden the search to other genic modifiers of recombination rate (reviewed in Ortiz-Barrientos et al. 2016), to more fully understand how the recombination landscape influences speciation, while also continuing the search for elusive speciation genes.


Davey JW, Barker SL, Rastas PM, et al. (2017) No evidence for maintenance of a sympatric Heliconius species barrier by chromosomal inversions. Evolution Letters 1, 138-154.

Lowry DB, Willis JH (2010) A widespread chromosomal inversion polymorphism contributes to a major life-history transition, local adaptation, and reproductive isolation. PLoS Biol 8, e1000500.

Ortiz-Barrientos D, Engelstädter J, Rieseberg LH (2016) Recombination rate evolution and the origin of species. Trends in Ecology & Evolution 31, 226-236.

Wellenreuther M, Bernatchez L (2018) Eco-evolutionary genomics of chromosomal inversions. Trends in Ecology & Evolution 33, 427-440.



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Scriptable evolutionary simulations in SLiM 2

Both empirical and theoretical population genetics are increasingly dependent on evolutionary simulations. How did historical processes lead to the patterns of genetic variation observed in your data set? How do selection, recombination, and drift interact to shape the genome during population divergence? These and many other questions that require statistical model testing or that would be analytically intractable can benefit from the judicious application of tools designed to model evolutionary processes in silico. As a result, our conclusions are increasingly dependent on the strength of these tools, how we apply them, and the assumptions they make. Using programs that emphasize flexibility and reproducibility give us the best shot at minimizing the potential pitfalls to this approach.

SLiM 2 (an acronym for “Selection on Linked Mutations) is a relatively new and unusually buzzed-about framework that achieves all of these goals. Though SLiM 1.0 was launched 2013, its successor is less an update than what the developers refer to as a ground-up rewrite. The primary feature that distinguishes it from its own ancestor and from most (but not all; see simuPOP) other simulators out there is its scriptabilty. What this means is that you interact with SLiM 2 through a novel, integrated programming language called Eidos. By design, its syntax has strong similarities to R and Python, which most users will probably be familiar with.

The SLiMGui in action

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Walking, galloping, and sauntering towards genetic differentiation

“This validates, at a major scale (across all vertebrates), what a handful of studies have found within narrow taxonomic groups…”

My citation manager has a special folder—elegantly named “TEACHING??”—where papers get stored for eventual use in a classroom. These papers tend to have a similar set of characteristics that unite them, mainly that they illustrate broad concepts simply and interestingly. Depending on how much foundational knowledge you have on a subject, your reaction to reading these papers can/should fall between “Oh, that makes sense” to “Ah, I get it now” to “Well, yeah, of course”.

A new addition to my “TEACHING?”** folder was this paper by Iliana Medina and colleagues, “Walk, swim or fly? Locomotor mode predicts genetic differentiation in vertebrates.”

The idea? The number of species and the rate at which they’ve appeared is far greater for organisms on land compared to those in water. But here on Earth, there is a much larger amount of water in which to diversify compared to terrestrial environments. So what gives?

One of the primary hypotheses that explain this phenomenon relies on how much resistance terrestrial and aquatic species undergo when moving around. Picture the classic model of allopatric speciation: a species exists within a certain range, a barrier (picture a long, transparent wall made by humans) appears that separates what was one range into two or more, and the separated populations diverge from one another. These barriers are more likely on land (mountains, rivers, etc) compared to the sky and ocean.

Medina et al. tested this hypothesis by gathering two key pieces of data from almost 500 published studies on geographic patterns of vertebrates: a common metric of genetic variance among populations of each species (Fst) and the mode of locomotion of each species. They used these data to look for statistical associations between locomotion type and genetic differentiation while taking into account phylogenetic relationships, molecular data type, and geographic scale.

As you might expect, locomotion is significantly associated with genetic differentiation. Vertebrates that move terrestrially (mostly mammals and reptiles in this study) show greater differentiation between populations at smaller scales than vertebrates that fly or swim. This pattern even holds when looking at mammals alone, where all three modes of reproduction are well-represented.

Figure 1 from Medina et al. (2018). On the left, Phylogenetic relationships between 327 vertebrate species, colored by their mode of locomotion (green = walk, blue = swim, red = fly). On the right, a visualization of the association between genetic structure (measured with either microsatellite or mitochondrial data) and geographic distance across the three type of locomotion. 

Importantly, one big caveat here is that Fst is not a direct genetic measurement of dispersal. Therefore, there is no doubt that other life history traits are influencing the patterns of genetic variation among populations of all these diverse vertebrates. However, on this scale, locomotion is certainly a powerful part of explaining why diversification happens differently by land:

This link implicitly underlies the assumption that a greater variety of geographical features can act as barriers to terrestrial dispersal, resulting in a higher incidence of reproductive isolation among populations on land. This idea has been used to explain why speciation rates are higher on land than in the sea…..Our study also clarifies the idea that it is not gross environment type per se – land vs. sea – that is key in affecting dispersal ability, rather it is the mode of locomotion used by species.

You might also notice a major group of vertebrates are absent from that tree, the amphibians. Because amphibians often split their lives between aquatic and terrestrial locomotion, I can imagine how difficult it would be to classify them in an analysis like this. However, it would have still been interesting to see how they compare to their vertebrate relatives. We’ll just have to see if future students catch that omission as well.



Medina, Iliana, Georgina M. Cooke, and Terry J. Ord. “Walk, swim or fly? Locomotor mode predicts genetic differentiation in vertebrates.” Ecology Letters (2018).

**Maybe I should ask Kathryn to provide a sequel to her sampling naming post, “Best practices in folder management”?


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Best practices in sample naming

Wherein I try to save me from myself

Let’s imagine a young scientist, bursting to the seems with enthusiasm and schemes to uncover the secrets of the biological world. Everything is new and she learns as she goes! Let’s call her… Kathryn.

Imagine past-Kathryn. She’s busy, she has things to do. She’s setting up a major experiment or planing out a collecting trip spanning thousands of miles. She has a crew of undergrads and precious volunteers to manage. When a sample came into her hand she named it in an expeditious fashion and moved on.

Now imagine current-Kathryn. She has been BURNED. Who was this capricious imp of chaos that decided this awkward and error prone sample naming system? How is she supposed to tell one person’s O7_4L from another persons 01-Al? Can she even trust her own handwriting? At every turn a different data handling system/program/manipulator has choked on some aspect or another – how many different iterations of these names exist, reflecting into infinity, like one mirror facing another?

Infinity mirror effect. (Wikimedia Commons: Elsamuko)


Let’s learn from her struggles, shall we?

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DNA extraction for PacBio sequencing

PacBio is emerging as the favoured sequencing approach for assembling high-quality reference genomes. But the big issue with PacBio sequencing is that to get long sequence reads you need to start with high molecular weight DNA. For my first foray into PacBio sequencing back in 2016 I sent a single DNA sample from the parasitic plant Euphrasia  that I’d extracted from silica dried tissue with a standard commercial column-based DNA extraction kit (Qiagen DNeasy Plant Mini Kit). I did all that I could to minimise shearing by using wide-bore tips and by not vortexing the sample. The DNA looked fine when run on an agarose gel, with a single band above 20Kb, and with no smear that would indicate shearing.

The PacBio sequence data I got back from this DNA sample was disappointing. Most of the sequences were incredibly short, the size distribution showed a peak at less than 2 Kb, and  few reads were over 20 Kb. It seemed that the initial gel picture wasn’t really capturing the integrity of the DNA, and DNA damage such as breaks or nicks were present. This damage causes the polymerase to fall off during PacBio sequencing and results in short or failed reads.

PacBio read length distribution of a column-based plant DNA extract.

For my second attempt, I sent my silica dried tissue sample to a commercial company that offers a high molecular weight DNA extraction service (there are many companies to choose). I paid a hefty $1500 for them to extract DNA from a sample using their own proprietary DNA isolation protocol (similar to this). While I’d normally extract DNA myself, in this case I was short of time before some grant money ran out. The DNA they extracted looked excellent when run on a gel, with a smear above 50 Kb.

This time the PacBio data I got back was much better. While there were plenty of short fragments that are of little use, there is a good proportion over 10Kb and 20Kb, and the tail of read lengths is really long. There’s even a single 140Kb read! While the comparison between the read length distributions of the two libraries isn’t exactly like-for-like (the sequencing centres performed different size selections), I’ve now seen for myself the massive impact of DNA integrity on the quality of long-read sequence data.

PacBio read length distribution of a high molecular weight plant DNA extract.

What have I learnt from this experience?

  1. I think many of us need to reconsider our reliance on basic quality control (QC) checks for DNA samples. My QC checks usually involve measuring total yield using a fluorescent assay such as the Qubit, and the size distribution of DNA run on an agarose gel or a Tapestation/Bioanalyser. I don’t think any of these clearly show DNA breaks or nicking, though it may be indicated by a smear below a band on a gel. Perhaps we’ll have to accept that even what appears to be the ‘perfect’ DNA sample may perform poorly, and that we need to treat our DNA very carefully. Or perhaps we’ll have to adopt additional QC measures to look for DNA breaks or nicks.
  2. While there has been a massive and necessary shift from lab skills to bioinformatic skills, this has reminded me that lab skills are still important. There are a massive number of protocols for extracting high molecular weight DNA. Just about all of them forgo the easy-to-use extraction kits (putting DNA through a regular column is a bad idea if it is intended for long-read sequencing), with many protocols returning to old fashioned DNA extractions used for BAC sequencing. These protocols are often technically challenging and involve many stages, as well as species-specific optomisation. Perhaps the move to high molecular weight DNA extractions and long-read sequencing will require us to spend more time in the lab.
  3. Recent years have seen greater use of museum specimens and dried specimen collections for genetic analysis. I can’t help but think that many of these collections will prove not to be useful for these new long-read sequencing approaches and whole genome assembly. This may not be absolute—my freshly collected silica dried plant sample worked fine—but in some cases we may need to get back in the field and recollect samples for genomic analyses.

What is your experience with DNA extraction for PacBio sequencing? Let me know @alex_twyford.

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