How (not) to review papers on inclusive fitness

Hamilton’s Rule

There are few evolutionary concepts as polarizing as Hamilton’s rule. Some researchers feel that there is no mathematical grounding for it, while others beg to differ. Yet empirical evidence in support of Hamilton’s rule is scarce (but check out this recent review).

Peter Nonacs and Miriam Richards’ recent call to arms in TREE suggests that this dearth of support is partially due to two things:

1) To some reviewers, Hamilton’s rule is on par with He Who Shall Not Be Named. (Well, more specifically, reviewers never agree on the correct best way to test it, so nothing gets published). Or, as Nonacs and Richards write:

“Our proposed solution is a simple admonition to reviewers: ‘Reflective, not reflexive critique, please!’”

2) Researchers don’t always assess the costs AND benefits of Hamilton’s rule. Moreover, they don’t publish all of the accompanying data so that reviewers (and readers) can come to their own conclusions.

In sum, we need reviewers to stop acting like their favorite stick-in-the-mud and authors to be transparent in presenting and analyzing their data.

Clearly more work is needed to generate a consensus about the correct way to both calculate inclusive fitness and advance our understanding of the diversity in social evolution. We urge reviewers to be constructive, not obstructive, in this process.

Nonacs P & Richards MH (2015) How (not) to review papers on inclusive fitness. Trends Ecol. Evol.

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l’oliva di mare: disturbance and genetic diversity

Seagrasses are important ecosystem-engineers of coastal regions around the world. Previous work has demonstrated the correlation of high genotypic diversity with resistance (e.g., Hughes and Stachowicz 2004) and resilience (e.g., Reusch et al. 2005).

In a recently accepted paper in Molecular Ecology, Jahnke, Olsen and Procaccini (2015) performed a meta-analysis of 56 meadows of Posidonia oceanica in which they tested for correlations of disturbance with genetic diversity.



Anthropogenic disturbances are the main threat to seagrass populations, but, P. oceanica  is a long-lived species. Past climate change may generate complex phylogeographic patterns that might result in

particular vulnerabilities under rapidly changing environmental stress.

Moreover, the longevity of species, like P. oceanica, can result in a temporal mismatch. In other words, a meadow may be characterized as healthy, but the allelic diversity may be slowly deteriorating.  

The authors advocate the necessity of placing genetic estimates from a single meadow in the context of a meta-population. The ability to sample at fine-scales and combine these data with connectivity matrices will be the way forward and enable an

understanding [of] the causes behind and evolutionary meaning of genetic diversity metrics for application in conservation management.


Hughes AR, Stachowicz JJ (2004) Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. PNAS, 101, 8998-9002.

Jahnke M, Olsen JL, Procaccini G (2015) A meta-analysis reveals a postive correlation between genetic diversity metrics and environmental status in the long-lived seagrass Posidonia oceanica. dpi: 10.1111/mec.13174

Reusch TBH, Ehlers A, Hämmerli A, Worm B (2005) Ecosystem recovery after climatic extremes enhanced by genotypic diversity. PNAS, 102, 2826-2831.

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F-statistics Manhattan Plots in R

Characterizing differentiation across individual genomes sampled from different populations can be very informative of the demographic processes that resulted in the differentiation in the first place. Manhattan plots have grown to be very popular representations of genome-wide differentiation statistics in recent literature. And what’s better? They’re surprisingly easy to make in R! In this post, I describe making these plots from scratch – starting with a VCF (Variant Call Format) file, which contains genotype information (and other meta data) across genomic positions.


Plot of genome-wide Fst using the qqman package in R.

As an example, I downloaded the variant calls for Chromosome 22 from the Phase 3 of the 1000 genome project (see link), and estimated Weir and Cockerham estimates of Fst for two populations (GBR – Great Britain, and YRI – Yoruba, a total of 199 individuals out of 2504) using VCFTools. The .weir.fst file produced by VCFTools contains pairwise Fst values for your specified window size. To do this, I used the command:

vcftools –vcf chr22.vcf –keep allindivs –out gbryri –weir-fst-pop gbrindivs –weir-fst-pop yriindivs

where allindivs is a file with individual ID’s of all individuals from the GBR, and YRI populations, and gbrindivs, and yriindivs are files with individual labels from GBR, and YRI respectively. I pulled out unique ID’s for all these individuals from the meta information made available on the same FTP site. Now onto plotting! While neat Manhattan plots can be created just by using R’s plot(), or qplot() functions, I found Stephen Turner’s “qqman” package to be very handy, and easy to use. Just as an example, I randomly replaced some of the chromosome 22 values from the output file above with chromosome number 1-3. Ideally, when you’re analyzing whole genome/transcriptome VCF’s, this shouldn’t be a problem. I also subset the data to avoid lines with NA values. Thereon, install the “qqman” package, and plot by:

fst<-read.table(“fst”, header=TRUE)
,snp="SNP",logp=FALSE,ylab=”Weir and Cockerham Fst”)

And voila! You have your genome wide Fst Manhattan plot! The “qqman” package also has plenty of options for changing the color, displaying chromosomes, etc, which Stephen Turner explains in his blog here. Good luck!

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Killer genetic differentiation

RAD whales

Like most of you out there, I sometimes get bogged down in literature, and the pressure to keep up with new methods can lead to a towering “to-read” folder. I feel forced to read many of these papers no matter how deep the stacks get due to the desire to keep up with new analyses or techniques.

But sometimes I read a paper just because it captures the basic passion for wildlife that made me interested in biology in the first place. That’s the case with this investigation into the population genomics of killer whales (!!!) by Andre Moura and colleagues in Molecular Ecology.

We test the hypothesis that populations representing sympatric ecotypes (e.g. residents and transients) will show patterns of differentiation that reflect selection at functional loci. More broadly, we investigate the hypothesis that in addition to the process of genetic drift, disruptive selection is driving the differentiation of killer whale ecotypes in sympatry.

Moura and colleagues used the largest set of molecular data for killer whales to test multiple demographic hypotheses and document genetic structure of whale populations across the globe:

Taken together, these data suggest that differentiation in sympatry is based in part on ecological processes, but that differentiation is likely being facilitated by the life history of killer whales, founder events and differentiation by drift.

A solid investigation using great data and analyses. But honestly, I was just in it for the whales.

Moura A.E., Roy Chaudhuri, Margaret A. Hughes, Andreanna J. Welch, Ryan R. Reisinger, P. J. Nico de Bruyn, Marilyn E. Dahlheim, Neil Hall & A. Rus Hoelzel (2014). Population genomics of the killer whale indicates ecotype evolution in sympatry involving both selection and drift, Molecular Ecology, 23 (21) 5179-5192. DOI:

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Panamanian golden frog skin microbiota predict ability to clear deadly infection

Panamanian golden frog (photo from Wikipedia)

The fungal skin infection, Batrachochytrium dendrobatidis (Bd), has pushed many amphibian species to the brink of extinction. One such species, the Panamanian golden frog, is likely extinct in the wild and has been maintained in captive breeding colonies since 2006. Successful reintroduction of this species hinges on the ability of the amphibians to fight off Bd infection. Continue reading

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A love letter to sponges

Aplysina fistularis. Photo courtesy of

Aplysina fistularis. Photo courtesy of

Like many kids interested in marine biology, growing up I wanted to work on sharks. After college I interned for a year at the Center for Shark Research at the Mote Marine Lab under the guidance of two great mentors, Jim Gelsleichter and Michelle Heupel. After my internship I started my Master’s degree with Mahmood Shivji, whose research focuses broadly on conservation genetics of sharks and billfish, but who had recently received funding from the National Coral Reef Institute. Mahmood asked me how felt about working on marine invertebrates instead of sharks and since I’m a go with the flow person (much like a sponge), I said, sure, why not? That was ten years ago and I’ve never looked back.

Sponges (phylum Porifera) have a global distribution and are found in both fresh and saltwater from polar seas to tropical coral reefs. There are over 8,000 valid species with an estimated 4,000 left to be described. Sponges are sessile as adults and disperse through a mobile larvae phase. Some sponge species are hermaphroditic and some have separate sexes. Many species have internal fertilization and brood their larvae to an advanced developmental stage while other species broadcast spawn their gametes and fertilization takes in the water column. Sponges come in a range of shapes, colors, textures, and sizes. In this post, I highlight some of the amazing research conducted on sponges focusing on topics related to molecular ecology, phylogenetics, and evolution, with some other fun facts thrown in.

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Haploid-diploidy, a (brief?) history

Haploid-diploid life cycles are not only good exercise for the brain, but they’re also fantastic study systems to investigate a myriad of questions.

Yet, the majority of molecular studies have focused on the diploid-dominated life cycles of animal and plant taxa. In these organisms, the meiotically-produced haploid gametes immediately fuse to form a new diploid individual. In other words, the haploid stages never become functional, independent organisms.

In contrast, seaweeds, mosses, ferns and some fungi, have life cycles in which there is an alternation between separate, free-living individuals that differ in ploidy levels and reproductive modes. Unlike diploid-dominant plants and animals, the haploid stage in a haploid-diploid life cycle becomes an independent, functional organism with somatic development. Mature haploid adults produce female and/or male gametes that fuse to produce new diploid individuals. The mature diploids undergo meiosis, in which spores are produced and develop into new haploid individuals. Thus, each phase is dependent on the other to complete the sexual life cycle.

What impacts do these life cycles have on genetic structure or on mating systems? Is dioecy really a good proxy for outcrossing in these species (e.g., intergametophytic selfing can still occur, Klekowski 1969)? Why are they maintained, when theoretically, selection should eventually favor either diploidy or haploidy, but not both (Mable & Otto 1998).

There are many variations on the haploid-diploid theme found in mosses, ferns, fungi and seaweeds. As mentioned in this post in the context of colonizing species, we also have a very preliminary understanding of mating system variation and genetic structure in these organisms.

As a mini-review, I’ve compiled a brief summary of what we know, with a slight emphasis on seaweeds (unsurprisingly).

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Posted in DNA barcoding, domestication, evolution, genomics, haploid-diploid, natural history, population genetics, selection, speciation | Tagged , , , , , | Leave a comment

British fineSTRUCTURE

Leslie et al. (2015) provide an analysis of genome-wide SNP data from over 2,000 individuals in the United Kingdom in a paper out this week in Nature.



The population structure in the UK was limited with FST estimates averaged 0.0007, with a maximum of 0.003. But, unlike earlier studies, they used a new method for detecting fine-scale population structure called fineSTRUCTURE (Lawson et al. 2012).

In contrast to commonly used approaches, fineSTRUCTURE explicitly models the correlation between nearby SNPs and uses extended multi-marker haplotypes throughout the genome. This substantially increases its power to detect subtle levels of genetic differentiation.

They found a pattern of genetic differentiation that was concordant with geography. Their genetic clustering did not take into account geographic location of the samples, thus providing confidence that they had detected real population differentiation occurring at fine scales. For example, in the southwest of England, they were able to distinguish Cornwall from Devon.

Then, they compared genetic structure to a European data set in order to further describe genetic differences due to different patterns of migration and admixture from other populations outside the UK.

There has been a long debate about the Saxon replacement of the existing populations in the present day UK. Using their ancestry profiles and another analytical tool called GLOBETROTTER (Hellenthal et al. 2014), they provide evidence for the Saxon migration, but exclude the possibility of long-term replacement by the Saxons.

Interestingly, they did not find clear genetic evidence of a vast Danish Viking occupation of a large swathe of England, nor a generalized Celtic population in the non-Saxon parts of the UK. For example, one might expect Cornwall to resemble the other Celctic corners of the UK, but it was more similar to Devon and Central and Southern England.


Hellenthal et al. (2014) A genetic atlas of human admixture historyScience 343, 747751.

Lawson et al. (2012) Inference of population structure using dense haplotype dataPLoS Genet. 8e1002453.

Leslie et al. (2015) The fine-scale genetic structure of the British population. Nature, 519, 309–314.

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dN(eutralist) = dS(electionist) Part 3

In a previous post, I discussed the phenomenon of background selection, which results in rapid expungement of neutral alleles linked to loci under purifying or negative selection, and conversely, the rapid fixation of neutral variants that are linked to loci of high fitness (hitchhiking during a selective sweep, positive selection). Both processes lead to an overall reduction in genomic diversity at neutral sites (eg. Charlesworth et al. 1993).

In highly inbred populations, theory predicts the efficacy of selection to be lower, due to a fall in the effective population size. This fits well into the theory of sexual system evolution – particularly the transition from cross- to self-fertilization, which is often seen as an “evolutionary dead-end” (accumulation of deleterious mutations, small population sizes, limited ability to adapt to changing environments). Evolutionary persistence of selfers is thus hypothesized due to the phenomenon of “purging”, or strong negative selection against deleterious mutations. This seems contradictory – we expect lower efficacy of selection in smaller selfing populations, and expect greater efficacy of selection to purge recessive deleterious mutations to persist nonetheless.

Eichornia paniculata; Panel C (Top) shows an outcrossing flower, versus (Bottom), a selfer. Image courtesy: The Barrett Lab,

In a recent publication, Arunkumar et al. (2015) analyze the distribution of fitness effects (DFE’s) across different selective classes of new mutations in outcrossing and selfing populations of the aquatic flowering plant, Eichhornia paniculata. In short, they (1) simulate genomic datasets under varying outcrossing rates, population sizes, recombination rates, dominance, and compare the DFE’s of outcrossing versus selfing populations, and (2) sequence E. paniculata transcriptomes from selfing and outcrossing populations, identify variants, and compare strengths of selection at each variant site. Results from their study (a) show more power to detect purging in selfing populations with increasingly recessive mutations (lower dominance), (b) with increasing dominance, greater proportion of nearly neutral mutations, compared to outcrossing populations, and (c) with variability in both dominance, and selection coefficients, they detect the presence of both strongly deleterious (purging) variants, and variants under relaxed purifying selection (weakly deleterious). Also, the sequencing, and the simulation studies found an overall decline in fitness of selfers, and that selfers accumulated more non-synonymous mutations, than outcrossers.

In conclusion, both simulated and the empirical data show evidence of purging (due to purifying selection against recessive deleterious mutations), reduced efficacy of selection (due to reduced effective population sizes) in selfing populations, and that these patterns are distributed across a variety of dominance and selection coefficients across selfing E. paniculata genomes. In yet another standstill, the neutralist-selectionist debate continues.


Arunkumar, Ramesh, et al. “The Evolution of Selfing Is Accompanied by Reduced Efficacy of Selection and Purging of Deleterious Mutations.” Genetics(2014): genetics-114. DOI:

Charlesworth, Brian, M. T. Morgan, and D. Charlesworth. “The effect of deleterious mutations on neutral molecular variation.” Genetics 134.4 (1993): 1289-1303.

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Reviewing the reviews: Twelve years of Landscape Genetics

Landscape genetics has grown feverishly since its first formal definition in 2003 (Manel et al). The beauty of combining genetic, environmental, and spatial variation to answer biological questions sure is alluring, and the quest for improving the methodology of landscape genetics has been a reoccurring theme.

Entire issues are devoted to the subject in Evolution, Molecular Ecology, and Landscape Ecology. It is a broad field with a lot to talk about, and that comes with quite a few reviews over the years. Almost every review paper includes summaries of the topic and a dedicated section of future issues. Tracking the disparities between the “past” and “future” between review articles can give you an informative trajectory of a field: what problems are solved, what problems linger on, and what might not get solved any time soon.

Data return from SCOPUS using the search terms (landscape AND genetics). You can go play with the data yourself here.

Data return from SCOPUS using the search terms (landscape AND genetics). You can go play with the data yourself here.

In a (perhaps foolish) effort to distill some of the major issues down to a coffee break read, I’ve chosen some of the most influential reviews on landscape genetics (or landscape genomics) and broke down the evolution of the following components: the questions, the data, and the analyses.

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