What is the effective population size of Collared Flycatchers on Gotland?

Comparing different methods to estimate Ne.

How many individuals are there in a population? This seemingly simple question is one of the most daunting challenges in biology. Numerous methods have been developed to estimate population sizes, including transect counts, camera traps and genetic techniques. In the context of population genetics, researchers often refer to the effective population size (abbreviated as Ne). This concept can be interpreted as the number of breeding individuals in a population. Hence, the effective population size (Ne) is usually smaller than the actual or consensus population size (Nc).

The early population geneticists – Ronald A. Fisher and Sewall Wright – defined the effective population size as “the size of an idealized population that experiences the same rate of genetic drift and inbreeding as the population in focus.” The central role of genetic drift and inbreeding in this definition indicates that estimating Ne is easiest when working with small populations. As the population size increases, the effects of genetic drift and inbreeding become smaller, making it more difficult to accurately determine Ne. But that did not stop Krystyna Nadachowska‐Brzyska, Ludovic Dutoit and their colleagues from trying. They focused on a large island population of Collared Flycatchers (Ficedula albicollis) and recently published their findings in Molecular Ecology.

Genetic Drift

The researchers sequenced the genomes of 85 Collared Flycatchers (45 individuals from 1993 and 40 individuals from 2015). Next, they applied two sets of methods to estimate Ne. The first set of methods takes a temporal approach by analyzing the change of allele frequencies over time. The larger the changes in allele frequencies between generations, the smaller the effective population size. This effect is nicely illustrated by the simulation figures below. As the effective population size increases, you can see less fluctuations in allele frequencies over time.

As the population size increases (from left to right figure), the changes in allele frequencies become less pronounced. From: https://boundino.github.io/S188592web/drift.html

Playing Darts

The second set of methods relies on linkage disequilibrium (LD). This population genetic concept refers to the non-random association between alleles at different genetic loci. In an infinite population, each genetic locus follows its own independent path. But as the population size decreases, some genetic loci might start to follow similar trajectories and become associated with each other. You could compare this situation to throwing darts (i.e. the alleles) at a special target where each square represents an individual. In a small population, some darts will likely end up in the same square and become associated with each other. In a bigger population, however, the chances of hitting the same square are much smaller and fewer darts will be associated with each other. Hence, the degree of linkage disequilibrium can provide insights into the effective population size.

Comparing linkage disequilibrium with playing darts. Each square represents an individual and each dart corresponds to an allele. In a small population, chances are higher that two alleles end up in the same square and become associated with one another. © Jente Ottenburghs

The Best Method?

In the end, the researchers used three temporal methods and one LD-based method (see table below for the details). All three temporal methods gave similar results, suggesting an effective population size between 4000 and 7000 individuals. The LD-based method, however, was less reliable. It provided much higher estimates (between 20,000 and 35,000 individuals) with a large level of uncertainty (even extending into infinity). These findings show that it is feasible to estimate large effective population sizes (more than 1000 individuals) with genomic data, although LD-based methods should be used with caution.

Effective population size of the Collared Flycatcher population on Gotland, based on different methods. From: Nadachowska‐Brzyska, Dutoit et al. (2021).

References

Nadachowska‐Brzyska, K., Dutoit, L., Smeds, L., Kardos, M., Gustafsson, L., & Ellegren, H. (2021). Genomic inference of contemporary effective population size in a large island population of collared flycatchers (Ficedula albicollis). Molecular Ecology, 30(16), 3965-3973.

Featured image: Collared Flycatcher (Ficedula albicollis) © Andrej Chudy | Wikimedia Commons

Reclassifying Robins: Two new species on the Canary Islands

Multiple lines of evidence point to distinct species on Tenerife and Gran Canaria.

The European Robin (Erithacus rubecula) is inextricably linked to Christmas in many European countries. The origin of this association is unclear, but several explanations have been proposed. One possible reason, for example, concerns postmen in Victorian Britain. These postmen were known as “robins” because of their red-breasted uniforms. Artists usually illustrated Christmas cards with scenes related to the delivery of letters, such the red-breasted postmen. At some point, the artists started to draw the familiar little brown bird delivering letters instead of the postmen. Other explanations go back earlier in time and provide direct links with Christianity (see this article for several stories). Whatever the reason for Robin on your Christmas card, it seems like the perfect time to delve into the taxonomy of this small passerine.

The European Robin is widely distributed – from western Siberia to the Macaronesian Islands – and shows considerable geographic variation, leading to the recognition of eight subspecies. A recent study in the journal Zoologica Scripta took at closer look at two subspecies on the Canary Islands (E. r. superbus on Tenerife and E. r. marionae on Gran Canaria). Based on genetics, song and plumage patterns, the researchers argued to elevate these subspecies to the species level.

Gathering Evidence

The study took an integrative approach to taxonomy, combining several lines of evidence to support the species status of the taxa. Let’s start with the genetic data. Phylogenetic analyses of the mitochondrial gene cytb uncovered three distinct lineages, representing birds from Europe (rubecula-lineage), Tenerife (superbus-lineage) and Gran Canaria (marionae-lineage). These three lineages showed substantial genetic divergence of more than 4%. This is well above the species-level threshold of 2% that is used for DNA-barcoding studies on birds (but see this blog post for the unreliability of only relying on these barcodes). Moreover, the researchers found no shared haplotypes among the lineages, suggesting that there is not gene flow between them (although this remains to be confirmed with nuclear data).

Next, the researchers compared 1413 songs of 60 individuals. These analyses uncovered numerous vocal differences – too many to list here – among the three genetic lineages. A discriminant function analysis (DFA) based on 24 song variables nicely separated the birds into three distinct clusters. Clearly, Robins from Europe, Tenerife and Gran Canaria sing different songs.

Finally, the birds from the Canary Islands (E. r. superbus and E. r. marionae) differ from their European counterparts in several plumage traits: (1) the presence of a pale eye ring, (2) a darker and greyer band of ash-grey on forecrown and from side of crown down to side of breast, (3) deeper rufous-chestnut face and chest, (4) darker, greyish olive upperparts, and (5) whiter belly and vent.

Analyses based on genetic data (left) and vocalizations (right) supported the recognition of three distinct Robin species.

Evolutionary History

As shown above, all lines of evidence converge upon the decision to split the European Robin into three species: E. rubecula, E. superbus and E. marionae. Apart from this taxonomic rearrangements, this study also reveals an interesting observation. At the end of the discussion, the researchers remark that:

The pattern of phylogenetic relationships of robins suggests that the colonisation of the extant robins in the Canary Islands was not the result of one wave but two or three.

Understanding how the European Robin colonized these islands is an exciting question to explore. And we don’t need a Christmas miracle to explain it. Just some solid science.

References

Sangster, G., Luksenburg, J. A., Päckert, M., Roselaar, C. S., Irestedt, M., & Ericson, P. G. (2022). Integrative taxonomy documents two additional cryptic Erithacus species on the Canary Islands (Aves). Zoologica Scripta, 51(6), 629-642.

Featured image: European Robin (Erithacus rubecula) © Francis C. Franklin | Wikimedia Commons

Do gut microbiota contribute to reproductive isolation between Nightingale species?

A recent study explores this intriguing hypothesis.

The expression “gut feeling” has received an entirely different meaning. Recent studies have documented how the microorganisms in an individual’s intestines can influence its behavior (see for example this paper). These findings suggest that gut microbiota might play an underappreciated role in several biological processes. They could even contribute to speciation. Experiments with fruit flies (Drosophila), for instance, indicated that lineage-specific microbiota influenced assortative mating among the flies, potentially giving rise to a premating barrier. Alternatively, microbiota could act after mating if hybrids suffer from incompatible microbiotic combinations. Regardless of the exact mechanism, it seems feasible that gut microbiota could play a role in the origin of new species. A recent study in the journal BMC Ecology and Evolution tested this idea in two closely related bird species: the Common Nightingale (Luscinia megarhynchos) and the Thrush Nightingale (L. luscinia).

Individual Variation

Camille Sottas and her colleagues studied the gut microbiota of 18 Common Nightingales and 18 Thrush Nightingales. For both species, half of the individuals were collected in sympatric regions, while the other half originated from allopatric locations. This sampling design allowed the researchers to disentangle the different factors contributing to the diversity in gut microbiota. They nicely explain the reasoning behind potential patterns in the introduction: “A higher divergence [in gut microbiota] in sympatry would imply a stronger effect of habitat use or diet, while a higher divergence in allopatry would indicate a stronger effect of geographical region on the gut microbiota divergence.”

Sequencing the microorganism community at three sections of the intestine (duodenum, jejenum and ileum) uncovered twelve bacterial phyla and 126 genera. Analyses of the consequent diversity patterns revealed no significant differences between the species and their geographic origins. In fact, 79% of the variation in microbiota could be explained by individual differences. These results suggest that the gut microbiota composition is unlikely to contribute to reproductive isolation between these nightingale species.

Statistical analyses of different diversity measures in allopatry and sympatry revealed no significant differences in the gut microbiota of Thrush Nightingale (TN, in blue) and Common Nightingale (CN, in red). From Sottas et al. (2021).

Negative Results

The answer to the title of this blog post is thus a resounding no. Gut microbiota do not play a role in speciation (at least when it comes to these nightingales). You might be wondering why I decided to dedicate a blog post to a negative result. This decision aligns with the goal of this blog – and my personal mission – to generate attention for less “sexy” topics. Most newspapers and popular science websites tend to focus on scientific research that captures the imagination of the general public and generates clicks. A negative result is thus not that interesting for these attention mongers. From a scientific perspective, however, negative results are a crucial piece of the puzzle in our quest to advance human knowledge. Finding out that something does not work is also a relevant discovery (even though it will generate less publicity).

Most scientists are working extremely hard behind the scenes, and only a few will get the attention that they deserve. Indeed, most media attention and awards for the happy few are often the result of favoritism and knowing the right people. That is why I try to direct the spotlight to lesser-known scientists and the fruits of their unseen labor. Even if – or perhaps especially if – their results are negative.

References

Sottas, C., Schmiedová, L., Kreisinger, J., Albrecht, T., Reif, J., Osiejuk, T. S., & Reifová, R. (2021). Gut microbiota in two recently diverged passerine species: evaluating the effects of species identity, habitat use and geographic distance. BMC Ecology and Evolution, 21(1), 1-14.

Featured image: Common Nightingale (Luscinia megarhynchos) © Marcel Burkhardt | Wikimedia Commons

The role of positive selection during the genomic evolution of Flycatchers

Comparing four species of Ficedula flycatchers to unravel their genomic landscape of differentiation.

One of the most interesting debates in speciation research revolves around the genomic landscape of differentiation. Scan across the genomes of two closely related species and calculate the level of genetic differentiation as you go along. This exercise will probably reveal a heterogenous picture with some genomic regions that show little genetic differences, and other regions that are highly divergent. With some imagination, you can recognize a hilly landscape with valleys and peaks. Most research efforts have focused on the origin of the peaks in this landscape, so-called “genomic islands of differentiation”. What evolutionary processes underlie the formation of these differentiated regions?

Reproductive Isolation and Selection

The earliest studies interpreted these patterns in the context of speciation-with-gene-flow, suggesting that these genomic islands contain loci that contribute to reproductive isolation. When two species interbreed, these barrier loci are expected to be immune to introgression. Hence, they will diverge while the remainder of the genome is homogenized by introgression. This explanation might apply to some study systems, such as bean geese.

Alternatively, selection events might be responsible for origin of genomic islands. This can either be negative selection against recurring deleterious alleles (also known as background selection) or positive selection for beneficial alleles. Because peaks in genetic differentiation are often shared between species, some authors have argued that positive selection is not a reasonable explanation. The rationale is that positive selection is unlikely to occur in the same genomic regions over long evolutionary periods. Instead, background selection is presented as the dominant force shaping genomic landscapes. Because the genomes of birds are relatively stable – in terms of recombination rate and gene density – the targets of background selection will remain the same over millions of years.

Four Flycatchers

To discriminate between background selection and positive selection, it would make sense to choose on a study system where introgression plays a minor role. That is why Madeline Chase and her colleagues focused on two independent species pairs of Ficedula flycatchers: the Pied Flycatcher (F. hypoleuca) and the Collared Flycatcher (F. albicollis), and the Red-breasted Flycatcher (F. parva) and the Taiga Flycatcher (F. albicilla). First, they constructed the genomic landscapes of these species pairs and identified the location of numerous genomic islands of differentiation (based on the summary statistic F_ST). Next, the researchers performed several tests for positive selection, namely Fay and Wu’s H and the composite likelihood test (CLR). The analyses revealed that most selective sweeps coincided with the previously identified peaks in F_ST. These findings suggest that positive selection plays an important role in shaping the genomic landscape of these flycatchers (similar to patterns uncovered in Sporophila Seedeaters using a different method).

Overview of the genomic landscape of differentiation for four flycatcher species. Different circles indicate a variety of summary statistics. The most relevant ones for this blog post are F_ST and D_XY. From: Chase et al. (2021).

Recurrent Selection

Now that we know that positive selection is involved, we can go one step further: when did these selective sweeps happen? Are these genetic signatures the result of repeated selection in these species and their ancestral population (i.e. recurrent selection model) or do they represent species-specific selection after speciation occurred (i.e. selection in allopatry model). Some researchers proposed that you can discriminate between these models by looking at three different summary statistics: F_ST, D_XY and π (see this blog post for a detailed explanation). The rationale behind this approach was nicely described in the paper:

Because D_XY is unaffected by current levels of diversity, under the selection in allopatry scenario, D_XY is expected to be similar both within and outside of F_ST peaks. However, when selection has recurrently impacted a region from the common ancestor of two species, ancestral diversity will have also been reduced, leading to a reduction in D_XY in F_ST peaks.

Calculating D_XY across the species pairs indicated that this summary statistic was consistently lower in the F_ST peaks for Pied and Collared Flycatcher. A pattern that is consistent with recurrent selection. In the Red-breasted and Taiga Flycatcher comparison, however, this was not the case: D_XY was higher in the F_ST peaks. What is going on here? The researchers think that as species differentiation proceeds, substitutions become fixed in certain genomic regions – perhaps due to positive selection – resulting in a higher D_XY value. Because Red-breasted and Taiga Flycatcher diverged before Pied and Collared Flycatcher, they have had more time to accumulate substitutions and inflate their D_XY. The shared F_ST peaks might thus still be the outcome of recurrent selection. Hence, the timescale of species divergence is important to keep in mind when interpreting these summary statistics.

Finally, the researchers also uncovered several lineage-specific signatures of selection that seemed to coincide with changes in local recombination rates. All in all, the patterns uncovered in this study highlight the interplay of positive selection and recombination in the evolution of genomic landscapes of differentiation.

References

Chase, M. A., Ellegren, H., & Mugal, C. F. (2021). Positive selection plays a major role in shaping signatures of differentiation across the genomic landscape of two independent Ficedula flycatcher species pairs. Evolution, 75(9), 2179-2196.

Featured image: Red-breasted Flycatcher (Ficedula parva) © Nppgrandmeadow | Wikimedia Commons

Does disrupted gene expression cause hybrid sterility in Flycatchers?

Taking a closer look at gene expression in the testis.

Every student of speciation should be familiar with the Bateson-Dobzhansky-Muller (BDM) model of genetic incompatibilities. Most evolutionary biologists can probably explain the rationale behind this model, but not everyone will know its interesting history (and why I chose to list these three names). The model was formulated by Dobzhansky (1934) and further developed by Muller (1942). However, Bateson (1909) already published an essentially identical model, apparently unknown to Dobzhansky and Muller, to explain the “secret of interracial sterility”. The BDM-model is very intuitive. Here is the short version from my PhD thesis:

Consider two allopatric populations diverging independently, with the same ancestral genotype AABB in both populations. In one population, a mutation (A -> a) appears and goes to fixation, resulting in aaBB, which is fertile and viable. In the other population, another mutation (B -> b) appears and goes to fixation, resulting in AAbb, which is also fertile and viable. When these populations meet and interbreed, this will result in the genotype AaBb. Alleles a and b have never “met” each other and it is possible that allele a has a deleterious effect that becomes apparent when allele b is present, or vice versa. Over evolutionary time, numerous of these incompatibilities may arise, each possibly contributing to hybrid sterility or unviability.

This model has been mostly applied to mutations in protein-coding genes, but could be extended to the regulation of gene expression. Regulatory regions come in two main types: cis-regulatory elements that are linked to nearby genes and trans-regulatory elements that affect distant genes (millions of DNA-letters apart). Interacting cis- and trans-regulatory elements often evolve in concert, and a mutation in one element can be compensated by a mutation in the other element. When species have experienced different compensatory mutations and interbreed, the gene expression in hybrids might be disturbed, leading to sterility or unviability.

The Bateson-Dobzhansky-Muller model of genetic incompatibilities. From: Wikipedia.

Sterile Males

A recent study in the journal Genome Research applied this reasoning to hybrids between Pied Flycatcher (Ficedula hypoleuca) and Collared Flycatcher (F. albicollis). These two species diverged about one million years ago and interbreed in several locations, including the Swedish island of Öland (where the group of Anna Qvarnström has been monitoring the breeding populations for numerous years). Previous work showed that male hybrids are infertile due to the production of abnormal sperm cells. Could male sterility be the result of disrupted gene expression due to mismatches between cis- and trans-regulatory elements? To answer this question, the researchers took a closer look at gene expression patterns in five Pied Flycatchers, five Collared Flycatchers and three natural hybrids.

The analyses focused on misexpression in hybrids, which can be detected by gene expression levels in hybrids that are either higher or lower than any of the parental species. The researchers reported “evidence for abundant hybrid misexpression in heart, kidney, and liver but not in brain or testis.” In addition, more detailed analyses of genes involved in spermatogenesis did not reveal misexpression in hybrids. All in all, this study could not provide evidence that disrupted gene expression in the testis causes sterility in hybrid males. However, the high levels of misexpression in other tissues could contribute to lower hybrid fitness in other ways.

Typical sperm from a collared flycatcher (a) and a pied flycatcher (b), compared to abnormal sperm from two hybrid flycatchers, indicated by arrows (c-f). From: Ålund et al. (2013) Biology Letters.

Evolution at Two Levels

Although the testis showed no clear signs of misexpression in hybrids, this tissue did experience the highest level of divergence in gene expression between Pied and Collared Flycatcher. More research will be needed to unravel the exact changes in gene expression and their contribution to male sterility, but it seems unlikely that mismatches between cis- and trans-regulatory elements play a major role.

Despite the “negative” result, this study nicely highlights the potential involvement of regulatory changes in evolution and the formation of new species. In 1975, Mary-Claire King and A. C. Wilson already drew attention to the contrast between evolution at the sequence level and changes in patterns of gene expression. Focusing on human evolution, they noted that “a relatively small number of genetic changes in systems controlling the expression of genes may account for the major organismal differences between humans and chimpanzee.” At the time, we did not have the methods to explore how regulatory changes shape evolutionary trajectories. The development of new techniques, such as RNAseq, provide exciting opportunities to understand how changes in gene expression contribute to the origin of new species. What a wonderful time to be an evolutionary biologist.

References

Mugal, C.F., Wang, M., Backström, N., Wheatcroft, D., Ålund, M., Sémon, M., McFarlane, S.E., Dutoit, L., Qvarnström, A. & Ellegren, H. (2020). Tissue-specific patterns of regulatory changes underlying gene expression differences among Ficedula flycatchers and their naturally occurring F1 hybrids. Genome Research, 30(12), 1727-1739.

Featured image: Collared Flycatcher (Ficedula albicollis) © Andrej Chudy | Wikimedia Commons