Bees brought to their knees

As regular readers of TME will have read, this past summer was a whirlwind of sampling in which I took the briefest of holidays in the Southwest of England before attending the European Phycological Congress (read about the congress here and here).

I was able to do a little serendipitous Gracilaria searching and catch up with collaborators at the Marine Biological Association.

Salcombe algal hunting

Salcombe algal hunting

Science as art

Science as art

Declan Schroeder, a Senior Fellow at the MBA, shared with me some of the latest research from his group. It is an excellent departure for it’s not about a seaweed or even something marine!

Bees are the key pollinators for agriculture, with an economic value of be than 225 billion USD. But, in the last 50 years, millions of European honeybee colonies have collapsed due to the spread of the ectoparasite Varroa destructor and its affiliation with the Deformed Wing Virus (DWV), a single-stranded RNA virus.

As a result [of rapid replication and high error rates], many RNA viruses are highly genetically heterogeneous and exist within infected population structures known as quasispecies. It has been proposed that this gives these viral pathogens an increased ability to shift to a new environmental niche, such as a new host, as a suitable mutant is more likely to already exist if the opportunity arises.

Quasispecies can exist as swarms of mutants around one variant or as master variants with  their own swarm of mutants. It is the A variant of DWV that is implicated in colony collapse, but recently, DWV type B has been found to dominate the DWV population of honey bees in an isolated apiary in the UK (Mordecai et al. 2015a). DWV Type A leads to colony death, whereas Type B does not lead to the collapse of the colony. The role of the newly described Type C (Mordecai et al. 2015b) in overwintering colony losses is unclear.

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Workshop: Gene Conservation of Tree Species

(Flickr: tamasmatusik)

(Flickr: tamasmatusik)

From friend-of-the-blog Sean Hoban, an update about a workshop that should be of interest to molecular ecologists:

A reminder, the deadline to submit abstracts for the “Gene Conservation of Tree Species – Banking on the Future” conference, to be held mid May 2016, is December 1! This may be of interest to those working in conservation, forestry, genetics, adaptation, climate, botanic garden, or seed biology. There is already a great lineup of speakers and diverse partners involved:

The following topics are of particular interest:

  1. In-Situ Conservation
  2. Ex-Situ Conservation
    • Designing seed collections
    • Establishing and managing gene banks
    • Role of urban forests, arboreta and botanic gardens in tree genetic conservation
    • Maintenance of sample health ex situ
  3. Identification of ecosystems/species/ genes to be conserved
    • Threats and risk assessments
  4. Restoration of species/ecosystems of conservation concern
    • Assessing long term Impacts of tree conservation projects
  5. Tools and techniques for Tree Genetic Conservation
    • Databasing and managing information for germplasm (genetic conservation)
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Anti-predatory adaptations in sticklebacks and butterflies

Two recent studies analyze character shifts in response to different selection regimes – (1) Mullerian mimicry wing patterns in Heliconius butterflies, and (2) anti-intraguild-predator adaptations in armor and shape of threespine sticklebacks.

Mullerian mimicry in Heliconius butterflies. Image courtesy: Wikipedia (

Hoyal Cuthill and Charleston 2015 Wing patterning genes and coevolution of Mullerian mimicry in Heliconius butterflies: support from phylogeography, co-phylogeny and divergence times

The Heliconius butterfly radiation across the neotropics has been characterized extensively, especially in their fascinating diversity of wing colors and patterns, often exhibiting Mullerian mimicry. Recent publications of genomes from H. melpomene, H. eratus, and other species has helped localize this color/pattern variation to a handful of genomic regions. The idea that these genes may have co-evolved independently has however been contentious. Hoyal Cuthill and Charleston (2015) address this issue by comparative phylogenetic methods using ten genes across H. melpomene and H. eratus morphs. Their data comprised 127 butterflies from across the radiation, and ~6000 bp of sequences across genes involved in wing color/pattern variation. Population and genic phylogenies were reconstructed and compared revealed (1) single origin of red banding, and blue iridescence within each species, (2) reduced ability to recover phenotypic groupings from neutral makers, and (3) monophyly of H.m.melpomene color genes. Phylogeographic reconstructions suggest that both morphs originated in Amazonia, with added evidences that the adaptive radiation was prompted by Mullerian mimicry than allopatry.

Given the spectacular correspondence between their phenotypes and geographic ranges, it is likely that H. erato and H. melpomene have been major partners in mimicry throughout their evolutionary history. However, wider interactions with other butterfly species, such as Heliconius timareta and Heliconius elevatus, may also have had an influence this mimicry system

Miller et al. 2015 Intraguild predation leads to genetically based character shifts in the threespine stickleback

Banded sculpin feeding on redbelly dace – Image courtesy:

Threespine sticklebacks (Gasterosteus aculeatus) have been studied extensively, as growing model organisms for recent evolutionary adaptation to changing environments. Miller et al. (2015) study evolutionary response to selection due to intra-guild predation in sticklebacks, and if this response is plastic or with a genetic basis using a common garden experiment in the presence of prickly sculpin (Cottus asper). Individuals from lakes with and without sculpin were grown in a common garden experiment and F1 crosses of the two were used in assessing maternal effects, which were then transferred to two treatment facilities – with and without sculpin, for 36 weeks. Numerous morphological and behavioral characteristics were then measured, and analyzed. Results from wildtype, and common garden sticklebacks indicated that there were several associations with presence or absence of sculpin and measured characteristics – higher armor traits, higher mean vertical position in the water column, decreased shoaling. Several of these characters were also determined to be plastic in marine sticklebacks, with potential maternal effects.

This study provides evidence that intraguild predation leads to evolutionary divergence among stickleback populations (Schluter and McPhail 1992). Phenotypic differences between lakes with and without sculpin have a clear genetic basis. Character shifts have occurred in parallel across replicated populations, therefore these differences are not due to chance.

Further genetic studies are required to characterize these adaptive traits in both species, and understanding how natural selection has contributed to their persistence.


Charleston, Michael. “Wing patterning genes and coevolution of Müllerian mimicry in Heliconius butterflies: support from phylogeography, co‐phylogeny and divergence times.” Evolution (2015). DOI:

Miller, Sara E., Daniel Metcalf, and Dolph Schluter. “Intraguild predation leads to genetically based character shifts in the threespine stickleback.” Evolution(2015). DOI:

Posted in adaptation, Coevolution, evolution, genomics, natural history, phylogenetics, phylogeography, population genetics, selection, speciation | Tagged , , , | Leave a comment

What’s a Wachapreague?

Heading north to Virginia (and our base of operations at the VIMS Eastern Shore Lab, ESL) was one of the easiest, in terms of travel and packing. Though maybe not the coolest ride around, a minivan doesn’t have 50 lb (23 kg) weight restrictions!

I got to bring along a different sidekick, our grad student, Ben, who had been busy helping keep the ship afloat in Charleston. Not sure four sites in four days, fast food and 4 am reveilles was a better alternative to a windowless lab!

The Delmarva Peninsula (composed of Delaware, Maryland, and Virginia) on the eastern side of the Chesapeake Bay, is an area rich in history and a culture built on the agricultural and seafood productivity of the region. The Virginia portion of the peninsula is a narrow strip of land that crests between the ocean and bay.

delmarva map

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Large predators, large data, large conservation issues


We are a diverse group here at The Molecular Ecologist. Melissa writes love letters to sponges. Stacy takes you on exotic kelp road trips. Arun gives you another excuse to spend the afternoon playing with R. I feel that it is my duty to hold down the fort for charismatic megafauna every once in a while.

So here you go, two new studies that use genetic data to solve some “big” animal problems in wildlife conservation.

The conservation challenge of accurate effective population sizes

The effective population size (Ne) is one of those foundational parameters in molecular ecology. It is also a sort of wolf in sheep’s clothing: a parameter so fundamental to other calculations, yet difficult to estimate accurately in the first place. In reality, generations overlap, sampling rarely takes place over long periods of time, and age-specific survival isn’t an easy metric to come by.

Measuring Ne is especially important in conservation scenarios, when the effective population size reveals much more about the evolutionary potential of a population than simply monitoring population sizes. In the most recent issue of Molecular Ecology, Kamath et al. use grizzly bears, a historical figurehead for predator conservation, to test the efficacy of different Ne estimators. The grizzly bear system in the Greater Yellowstone Ecosystem makes for a model group to determine Ne: isolated from other populations (no gene flow) and sampled intensively throughout multiple generations.

Fifty years of grizzly bear samples (N = 729) were genotypes at 20 microsats and Ne estimations were compared to those from corresponding demographic data. Estimators of Ne included:

  • Parentage assignments (EPA)
  • linage disequilibrium (LDne)
  • Sibship estimator (SA in COLONY)
  • Variance Ne (in NEESTIMATOR)
  • Temporal Approach (in GONe)
  • Likelihood-based temporal (in MLNe)

So, what’s best? Well, it depends on your data of course. Small sample sizes greatly reduce the power of EPA and SA methods compared to LD methods. However, EPA methods are one of the only approaches that is applicable to age-structured populations. Essentially, knowing the strengths and weaknesses of all these methods and comparing their results can increase your confidence in the “right” Ne.

The good news for you megafauna lovers out there, regardless of methodology: grizzly bears have bounced back in a big way by almost quadrupling their effective population size in just a few decades. Predictably, this new data also has some eager to knock that number back down.

Outbreeding and the demise of the dingo

The hybridization of native and introduced species can cause an avalanche of sticky conservation issues. What if the hybrids are better off than either species? What if the hybrids aren’t likely to spread around? What if hybridization results in complete extinction of a native species? A particularly interesting application of this native/introduced hybrid scenario is also featured in the recent issue of Molecular Ecology: Australian dingoes and domestic dogs.

Ever since dogs came to Australia with European settlers two hundred years ago, interbreeding between dingoes and dogs has been observed and the proportion of hybrid animals corresponds to those areas where dogs (and humans) have been the longest. Unlike some other hybrids between native and invasive species, the traits that we humans have selected for in domestic dogs (smaller brains, weaker jaws, reduced hearing ability) are likely bad news for dingoes, which currently occupy an ecologically-important role as a trophic regulator across most of Australia.

Stephens et al. conduct a continent-wide survey of hybridization between dingoes and dogs using both Bayesian clustering and log-of-odds methods that were compared to simulated hybrid data. They find that almost half of their 3,600 samples can be classified as “pure” dingos, but all regions demonstrate some percentage of introgression from domestic dogs. This introgression in much more pronounced in eastern and coastal Australia, where human influence provides the highest density of domestic dogs and the longest periods of interaction.

Figure taken from Stephens et al. (2015). Displays

Figure taken from Stephens et al. (2015). Displays the “purity” of dingos across Australia as assessed by log-of-odds (left) and Bayesian clustering (right)

While the result seems hopeful (there are still refugia for pure dingoes), the sheer spread of this hybridization and the likelihood of further human-assisted interactions between dogs and dingoes is intimidating. However, the documentation of this pattern now may prevent the headline of the future: “Scientists: the pure dingo is no more”


Kamath, P. L., Haroldson, M. A., Luikart, G., Paetkau, D., Whitman, C., & Manen, F. T. (2015). Multiple estimates of effective population size for monitoring a long‐lived vertebrate: an application to Yellowstone grizzly bears. Molecular Ecology.

*Stephens, D., Wilton, A. N., Fleming, P. J., & Berry, O. (2015). Death by sex in an Australian icon: a continent‐wide survey reveals extensive hybridisation between dingoes and domestic dogs. Molecular Ecology.

*Postscript: Current leader, by a wide margin, for my favorite title of the year.


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Keeping up with the kelps

After we returned from Japan, we embarked on sampling both coasts of North America (but more on that soon!) and Europe. July, August and September blurred together, punctuated by lugging heavy bags weighed down with silica gel and bad airline food.

In the next few posts, I’ll be concluding my travel/photologues with a few highlights, in no particular order, from these trips.

France is always a good idea, so we’ll start there.

IMG_3663 IMG_3710

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Clinal genomic variation in Drosophila species

Two recent manuscripts describe adaptive evolutions to clinal/latitudinal variations in Drosophila species to supplement a growing wealth of recent studies on geographic variation and adaptive evolution in natural populations of fruitflies (eg. see Kao et al. 2015, Zhao et al. 2014).

A.H. Sturtevant, discoverer of Drosophila simulans, in the Drosophila stock-room of the Kerchoff Laboratories. Image courtesy:

Bergland et al. 2015 – Secondary contact and local adaptation contribute to genome-wide patterns of clinal variation in Drosophila melanogaster

Previous studies have recorded geographical clines in D. melanogaster, particularly to do with hardier, yet less fecund individuals at higher latitudes versus tropical relatives. However, their ancestral demography, particularly to do with colonization history to North America and Australia remains contentious – with two plausible histories – one of secondary contact and adaptation, versus ancestral admixture and recent colonization. Using genome-wide data from D. melanogaster sampled across continents (~32 populations), Bergland et al. (2015) show through simulations, estimates of admixture using Patterson’s D statistics, and Fst outliers that (a) African populations exhibited greatest diversity among populations analyzed, (b) clustering of North American and Australian populations between African and European populations, indicative of secondary contact between the latter two, also supported by simulated ancestral proportions, (c) negative correlation of African ancestry versus positive correlation of European ancestry with latitude in both North American and Australian populations, (d) Western African populations as most likely sources of African components of both North American and Australian flies, and (e) numerous SNP’s identified to be significant outliers in Fst in North America, versus none in Australia.

…the recent demographic history of this species in North America and Australia is collinear with both local adaptation within these newly colonized continents and among the ancestral ranges.

Machado et al. 2015 – Comparative population genomics of latitudinal variation in D. simulans and D. melanogaster

JW Meigen – German entomologist that described Drosophila melanogaster in his seminal 1830 publication, Systematische Beschreibung der bekannten europäischen zweiflügeligenInsekten

Using similar analyses, Machado et al. 2015 compare clinal variation in two Drosophila species – D. simulans and D. melanogaster, with predicted lower variation in D. simulans predicted through previous phenotypic studies. Using > 2 million SNP’s identified across 267 female flies of D. simulans (and comparable data from D. melanogaster from the previous study), measure genetic differentiation, diversity, a generalized linear model for clinal variation in allele frequencies and latitude, and enrichment of similar variation in the two species. Their results suggest (a) greater clinal variation in SNP’s as predicted in D. melanogaster (3.7%), than D. simulans (2.5%) with strong association of this variation with genetic differentiation (Fst), (b) consistency of this clinal variation over time, (c) no significant enrichment for shared clinal variants (SNP’s) in the two species, but 56% of shared clinal genes in both species, (d) increased clinal variants on the X than on autosomes in D. simulans, compared to D. melanogaster, and (e) weaker pattern of isolation by distance in D. simulans than D. melanogaster.

We argue that one contributing factor to these differences is the ability of the two species to overwinter in temperate climates, causing differences in bottlenecks and migrations. However, despite differences in demography, we do see an enrichment of shared clinal genes between the two species, suggesting that climate-associated selection might act on similar genes and phenotypes in the two taxa.



Bergland, Alan O., et al. “Secondary contact and local adaptation contribute to genome-wide patterns of clinal variation in Drosophila melanogaster.” bioRxiv(2015): 009084., also Molecular ecology (2015). DOI: 10.1111/mec.13455

Machado, Heather E., et al. “Comparative population genomics of latitudinal variation in D. simulans and D. melanogaster.” Molecular ecology (2015). DOI: 10.1111/mec.13446

Kao, Joyce Y., et al. “Postmating reproductive barriers contribute to the incipient sexual isolation of the United States and Caribbean Drosophila melanogaster.” Ecology and evolution 5.15 (2015): 3171-3182. DOI: 10.1002/ece3.1596

Zhao, Li, et al. “Origin and spread of de novo genes in Drosophila melanogaster populations.” Science 343.6172 (2014): 769-772. DOI: 10.1126/science.1248286

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2D Posterior Density Plots in R

I have been grappling with visualizing two dimensional histograms of posterior density distributions of parameters, as estimated by one of your favorite programs – IMa2, MIGRATE-n, MSVAR, etc. All these programs print out distributions of estimated parameters, and here’s a neat and innovative way to visualize them in two dimensions. As an example, I used the output of MSVAR v.1.3 for some simulated microsatellite data with a bottleneck – that can be accessed at this link. A little Googling led me to this great post on five different ways to build two dimensional histograms in R – I use the hexbinplot() function here to obtain my plots. Feel free to play around with the other methods, and program outputs!

rf <- colorRampPalette(rev(brewer.pal(11,'Spectral')))
r <- rf(32)
hexbinplot(V6~V4,data=hpars,xlab="Log10(Current population size N0)",
ylab="Log10(Past population size Na)",colramp=rf)

2D density plot of estimated ancestral and current population sizes using MSVAR.

2D density plot of estimated ancestral and current population sizes using MSVAR.

And voila! Simple, yet fun and intuitive visualization of densities!

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On getting empirical with the obvious

Sewall Wright knew how to pose for cool science portraits

Sewall Wright, titan of genetic drift and master of posing for cool science portraits

I’ve been thinking lately about the value in doing “simple” things. As a PhD student, my time is constantly incentivised by productivity (what am I doing right now that is working towards a publication?). But that doesn’t jive well with most general types of curiosity. Not the “big idea” types of questions, the little stuff: I wonder what happens if I change this parameter? Can I do this in R? Does that pattern happen in plants…birds…crocodiles….?

Paradoxical to these daily feelings, simple questions often beget great scientific impact.

A nice example is providing empirical evaluation of established theory. I, like many students, am guilty of lapping up most textbook theory as if it was handed to me by God on a stone tablet. That’s why I’m thankful for investigators like Jennifer Lohr and Christoph Haag, who recently provide a comprehensive experiment published in Evolution that explains how a fundamental aspect of molecular ecology (population size) relates to three big evolutionary co-factors (genetic load, inbreeding depression, hybrid vigor).

If you’ve taken an popgen course, a good evolution class, or read some primary literature on genetic drift and population size, here are the predictions you’d likely make:

Population size goes down, genetic drift increases, so:

  1. genetic load increases
  2. hybrid vigor increases
  3. inbreeding depression decreases

These factors have some pretty clear implications for the evolution of things like dispersal and the investment in local adaptation, and the associated theoretical literature that these predictions stem from is dense. But empirical evaluations are rare and have been limited by different effects between traits, which may relate to local adaption. Lohr and Haag use groups of Daphnia that have varying levels of genetic diversity to conduct outbreeding/inbreeding trials, establish clones, and measure life history traits.

The result? Well, yeah, it is what you’d expect.

As genetic diversity decreases, hybrid vigor increases, genetic load increases, and inbreeding depression decreases. While not surprising, these authors went to great lengths providing evidence for theory central to the way we think about how population size influences evolutionary trajectory.

Even if you already have a good feel for these ideas, the introduction of this paper is worth reading for clarity alone. Maybe you could pass it along to your own students or maybe even suggest they find another theory to test themselves. Something “simple” might be sitting right under their nose.


Lohr, J. N., & Haag, C. R. (2015). Genetic load, inbreeding depression and hybrid vigor covary with population size: an empirical evaluation of theoretical predictions. Evolution. DOI: 10.1111/evo.12802

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A Nice opinion on confronting uncertainty and modeling it for GBS data

Just over a week ago, I had the opportunity to work in Chris Nice‘s lab at Texas State University. I was accompanied by one of our MS students, Ben, and my colleague, Erik Sotka, to prep libraries for a genomic survey of a certain alga I’ve a penchant to write about. We also were there to prep a library with Torrance Hanley, a postdoc in the Kimbro and Hughes labs at Northeastern.

Chris walked us through each step as we embark on our first population genomic projects. We got to talking about analyses and issues I’ve written about before. In addition, we got to talking about times in which Bayesian approaches, such as STRUCTURE, may not be appropriate (i.e., when there are strong departures from HWE) and possible ways to get around this in the future!

I asked Chris to offer his opinion and write a small piece for TME. Et voilà 

Population genomics is certainly progressing as a field and there seems to be about as many ways to do things as there are labs doing them. Several methods for library construction have been reviewed recently with some good discussions (Andrews & Luikart, 2014; Puritz et al., 2014; Andrews et al., 2014). One area that has not received as much attention is the downstream analytical details – once you have your sequence reads.

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