Population genomics finds veritas in the demographic history of vino

“Jefferson” (left) and “Eaton” varieties of Vitis vinifera ssp. vinifera, from The Grapes of New York (Flickr: Biodiversity Heritage Library, left and right)

One of the more, hah, fruitful applications of genomic data has been in crop and livestock improvement. Biologists know that domesticating plants and animals for human use has involved powerful artificial selection — usually inadvertent at first, then intensive and deliberate. Compared to their wild ancestors, domesticated populations usually have more cultivation-friendly phenology and mating systems, produce more of the whatever feature it is that humans use, and even show behavioral changes. Genome sequencing lets us find the actual changes in the genetic code that underly those selected changes.

A nice new example of this work is online as a preprint at bioRxiv, which reports analysis of population genomic samples of cultivated and wild grapes. The paper’s coauthors, led by Yongfeng Zhou, are particularly interested in the fact that domestic grapes are perennials, propagated by cloning from cuttings. Clonal propagation is far and away the easiest route to domestication, especially of a perennial plant, because it skips over multi-year or multi-decade generation times, and it lets cultivators and breeders rapidly access useful traits in individual lines of the plant. But it also means that the cultivated population can rapidly lose genetic diversity — this is the reason bananas are particularly vulnerable to disease — and ongoing clonal propagation may allow a buildup of deleterious mutations. Comparing population samples from both cultivated and wild grapes lets Zhou et al. examine that “cost of domestication”.

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Friday Action Item: Help Puerto Rico, and Puerto Rican science

The landmark tower of the University of Puerto Rico, in 2011. (Flickr: davsot)

On Fridays while the current administration is in office we’re posting small, concrete things you can do to help make things better. Got a suggestion for an Action Item? E-mail us!

If you’ve so much at glanced at the news this week, you’re surely aware of the ongoing disaster in Puerto Rico. In the aftermath of Hurricane Maria, the entire island is without electrical power, and relief is hampered by basic transportation issues on top of outright negligence at the federal level, as the White House dawdled over a request for relief funds, lifting a shipping restriction it waived after Hurricane Irma hit Houston or even something as basic as sending a Navy hospital ship to assist. It’s easy to feel like we’re helpless to watch this disaster proceed — but there are ways to help, right now.

The News Hour has compiled a good rundown of organizations that can use your donations to help folks on the ground in the immediate future, especially United for Puerto Rico and the Hispanic Foundation. For the longer term, you can contribute to the recovery of Puerto Rico’s science community, specifically, by offering help — workspace or equipment or teaching — through Ciencia Puerto Rico. It’s going to be a long time before life is back to normal on the island, and they’ll need all the help they can get.

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The state of debates at the 2018 Society for Systematic Biologists meeting

This is a guest post from Ohio State PhD Candidate Megan Smith

On June 1-4, 2018, the Society for Systematic Biologists will host its third annual stand-alone meeting. The meeting will be held in Columbus, OH and will include workshops, lightning talks, and debates. The planning committee is hard at work on the schedule, and we’re interested in your input!

The aim of the debates is to discuss ideas and issues that are central to the field of systematic biology. In past meetings, debaters have presented a short introduction to the topic before engaging the audience and other discussants in the topic. If all goes well, audience members will talk as much as the debaters and moderators, and the discussion will push itself forward with little need for the pre-determined questions developed by the moderator. We hope that the debates will be engaging for graduate students, postdocs, and faculty alike, and a big part of designing discussions that will engage all levels of professionals is getting input from as many of you as possible about the topics that interest you.

These topics can be wide-ranging and could focus on both theoretical and empirical issues. For example, you might think about problems that you run into when using next-generation sequence data (or other data) to answer questions that interest you. Are you at a loss when you try to decide on a method to answer a particular question? In what ways are the methods falling short of the data, or vice-versa?

Last year’s four debates covered a wide-array of topics, varying in their focus on theoretical and practical issues. Scott Edwards and Gavin Naylor kicked off the debates by delving into the theory behind gene tree variation, and Mark Holder and Rachel Schwartz wrapped things up with a more applied question: how do I deal with my missing data? In between, Emily Jane McTavish and Matt Hahn argued about when we should use gene trees and when we should use species trees, and Frank Burbrink and Robb Brumfield discussed the ever-present question: what are we actually delimiting when we use ‘species delimitation’ methods?

This year, we hope to come up with just as wide an array of questions, and we hope that you’ll help us organize debates that will make you want to stand up, grab the mic, and get involved (which is highly encouraged)!

If you have ideas, please email them to one of the meeting organizers listed here or comment below. We can’t wait for another productive meeting!


Megan Smith (megansmth67@gmail.com)

Bryan Carstens (carstens.12@osu.edu)

Laura Kubatko (lkubatko@stat.osu.edu)

Marymegan Daly (daly.66@osu.edu)

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We have the technology. Is sequencing getting better, smaller, faster?

Okay, I know some version of the phrase “recent developments in rapid and affordable sequencing have made blah blah blah possible…” is something you’ve probably read 10,000 times. However, third-generation sequencing platforms have turned out to be pretty darn astounding. The leaders in this area include Pacific Biosciences (aka PacBio) as well as Oxford Nanopore Technologies (think MinION). Multiple studies have recently been making the most of the portable technology provided by Oxford Nanopore, and have used the MinION to analyze reptiles in the Ecuadorian Chocó rainforest, Arabidopsis species in the Croesor Valley in Wales, and microbes from the McMurdo Dry Valleys in Antarctica.

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DNA sequence data shows that this “living fossil” isn’t so fossilized after all

A nautilus, very much alive and not replaced by mineral accretions at all, thank you. (Flickr: muzina_shanghai)

Living fossils are a tricky concept for evolutionary biology. In principle it seems simple: living organisms that closely resemble creatures seen in the fossil record going back millions of years. Usually they’re a single representative of a fossil record containing a multitude of diverse close relatives — survivors from a lost clade — and rare, or living in habitats that humans can’t easily access. As a consequence, living fossils’ shared ancestry with other living species goes back deep into the history of life, and they’re often treated as examples of “primitive” forms. The term has been applied to species including the coelacanth, dawn redwoods, ginkgo trees, and hoatzin.

However, as molecular ecologists well know, two organisms may look identical in terms of morphology, especially features like shells and skeletons that fossilize reliably, and still be quite different in ways less obvious to the human eye. Color or pheromones or behavior may change without leaving any fossil traces; and every living thing is locked in an ongoing coevolutionary race against parasites and pathogens, which necessitates rapid evolution at the molecular level. We can’t find evidence of these changes in fossils (well, except for relatively very recent ones), but we can see it in DNA sequence data from putative living fossils.

That’s the premise of a paper out ahead of print in Molecular Ecology, which reports the first substantial, genome-wide population genetic dataset for nautiluses. Nautiluses are cephalopod mollusks, most closely related to octopuses, with beautiful spiral shells. Though there are five recognized species in two genera, they are collectively considered living fossils, as the fossil record includes species from at least five other genera, and all of them are different enough from other cephalopods to rate placement in a separate suborder, the Nautiloidea. David Combosh, Sarah Lemer, and colleagues at Harvard, the University of Guam, and the University of Washington collected genome-wide SNP data from 140 samples of all five species using the ddRADseq protocol, and used it to take the first good look at nautilus diversity.

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Habitat-matching dispersal facilitates local adaptation

Migration disrupts local adaptation. At least, this is the first reaction I have when I consider these two processes. In fact, my initial thought is almost always: how strong does selection have to be to overcome gene flow? Gene flow can work to diminish adaptation by swamping out locally adapted alleles and homogenizing populations. Depending on the level of gene flow, selection may have to be quite strong to prevent the swamping of adaptive alleles. This line of thinking is, of course, too simplistic (For a review of gene flow and local adaptation, see Tigano and Friesen 2016).

First, gene flow can provide a source of adaptive genetic variation, known as adaptive introgression; think Heliconius butterflies. Secondly, migration and gene flow aren’t necessarily random. That is, individuals may preferentially migrate to habitats where their fitness is maximized. This can lead to adaptation instead of inhibiting it. This latter point is one I think is commonly overlooked and goes by names including habitat-matching dispersal, phenotypic sorting, matching habitat choice, directed movement, phenotype-dependent dispersal, and others (See Edelaar et al., 2008 for a good overview of the topic.). Habitat-matching dispersal can work to facilitate rather than hinder adaptation and is has a very different effect on adaptation than random dispersal.

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Current geography has little to say about gene flow during divergence

The terms we use to describe the geography of speciation are deceptively simple. Mention allopatry, parapatry, or sympatry, and most biologists will have a clear picture of the underlying conceptual model of range limits (and probably some strong opinions about their relative frequency). Yet from a genetic perspective, these definitions can often obscure more than they clarify. For example, it’s hard to know what exactly allopatry means in highly vagile species like seabirds. Sure, coastal populations in western and eastern Pacific might appear discrete on a map, but if it’s a relatively easy trip between the two areas for volant migrants, are they actually diverging in isolation? A population genetic approach to the same terms therefore rely on relative rates of gene flow, from none (allopatry) to a lot (sympatry). But as speciation is an inherently geographic process, this is also an imperfect solution. Whether these alternate definitions can cross predict has largely remained unclear.

Penalba et al’s new preprint addresses this question by looking at whether the geographic arrangement of contemporary bird species ranges predicts relative gene flow between species pairs during divergence. Using “suture zones” where eight Melaphagoid species pairs meet at biogeographic barriers across Northern Australia and New Guinea, the study collected SNP data and sequenced the mitochondrial gene ND2 to assess population structure, relative divergence, probability of migration throughout the speciation process, and the fit of various models of parapatric speciation, e.g. whether measures of genetic distance shows a linear or nonlinear increase. The authors also modeled changing species distributions through time with maximum entropy and environmental layers from the present, mid-Holocene, and last glacial maximum.

Figure 1 from Penalba et al. (2017).

If you’re familiar with the comparative phylogeography literature, their basic results are unsurprising: taxa varied widely in their divergence history, and their contemporary distributions did little to predict rates of gene flow during divergence. Among the many possible explanations for this discordance, an important one is the inherent lability of species ranges through time, even though they can appear as fixed, static entities on human timescales. (Losos and Glor’s 2003 paper provides a nice discussion of range dynamism over geologic time in a phylogenetic context.) Penalba et al.’s species distribution modeling provides support for this hypothesis by identifying distance and total range connectivity throughout all time periods as a better predictor of gene flow during speciation than current geography alone.  

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