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How to play Fantasy Football Confidence Pools

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The evolution of sensory divergence in the context of limited gene flow in the bumblebee bat

The sensory drive theory of speciation predicts that populations of the same species inhabiting different environments can differ in sensory traits, and that this sensory difference can ultimately drive speciation. However, even in the best known examples of sensory ecology driven speciation, it is uncertain whether the variation in sensory traits is the cause or the consequence of a reduction in levels of gene flow. Here we show strong genetic differentiation, no gene flow and large echolocation differences between the allopatric Myanmar and Thai populations of the world smallest mammal, Craseonycteris thonglongyai, and suggest that geographic isolation most likely preceded sensory divergence. Within the geographically continuous Thai population, we show that geographic distance has a primary role in limiting gene flow rather than echolocation divergence. In line with sensory driven speciation models, we suggest that in C. thonglongyai, limited gene flow creates the suitable conditions that favour the evolution of sensory divergence via local adaptation.

Grey shaded areas represent limestone areas and blue shaded areas represent inland water bodies (dams). C. thonglongyai distribution in Thailand (green) and Myanmar (dark blue) were taken from Puechmaille et al.16 A bat is represented next to each site where M. siligorensis was recorded. Myanmar colonies are labelled as M1, M3, M8, M13 and M14. Thailand colonies are labelled A [e] indicates a colony where only echolocation data were recorded, while [g] designates a colony with only genetic data.

(a) Distribution of echolocation calls recorded in Thailand (green; n=4,188) and Myanmar (blue; n=1,472). (b) Bayesian phylogenetic tree based on a 1.8 kb mitochondrial fragment of 602 samples and two different NUMTs (mitochondrial pseudogenes) showing the monophyly of the Thai (green) and Myanmar (blue) C. thonglongyai. The tree is rooted with two distinct NUMTs found in both populations. Bayesian posterior probabilities are shown below branches of interest. (c) Graphical representation of 196 Myanmar and 463 Thai individuals according to their probability to belong to each of the two clusters defined by STRUCTURE (black lines separate individuals sampled at different colonies). (d) Neighbour Joining tree showing the relationships between 196 Myanmar (in blue) and 463 Thai (in green) individuals based on 15 microsatellites (Da distance).

Principal component analysis (PCA) showing the population structure of individuals orientated along the Northwest direction with the first axis (explaining 8.46% of the variance) versus the (a) second axis (5.56%), (b) third axis (4.97%), (c) fourth axis (4.34%) and (d) fifth axis (4.24%). For each graph, the inset represents the eigenvalues of the two axes. Individuals from each of the 14 colonies are represented in different colours, and the colonies range from A in the Northwest to O in the Southeast. Colony P was excluded from the PCA, as only single individual was available from this site. (e) Relationship between genetic distance (Fst) versus geographic distance for the 12 colonies with echolocation and genetic data in Thailand (n=442 individuals). thonglongyai recorded at different colonies represented on the inset map. Colonies are arranged on the X axis according to their score at the first axis (explaining 98.9% of the variance) of a principal component analysis on their geographic coordinates (latitude, longitude). For each boxplot, the box represents the 0.25 quantile, median and 0.75 quantile. On either side of the box, the whiskers extend to the minimum and maximum. To showcase possible outliers, whiskers were shortened to a length of one time the box length. The colours in the plot correspond to the colours in the inset map.

Solid back lines represent the average expected distribution under a neutral model,
print cards against humanity, and the grey lines indicate the upper and lower 99% confidence interval for the expected. Each dot represents a microsatellite locus (the locus CTC1 is indicated by an arrow), red dots identifying candidates under positive selection,
cards against humanity review?, (a) for all investigated colonies, (b) for colonies North of the area of abrupt echolocation change (colonies A to F), and (c) for colonies South of the area of abrupt echolocation change (colonies G to O). Genotype frequencies are represented for Northern/Central (left) and Southern (right) colonies in Thailand, for two loci, (d) under selection (CTC1) or (e) evolving neutrally (CTC104).

The calls of C. thonglongyai (top panels) and M. siligorensis (bottom panels) were recorded from free flying bats in an open environment. The amplitude panels a and b show the pattern of calls over a period of 1 s for C. thonglongyai ((a), interval between calls=62 ms) and M. siligorensis ((b), interval between calls=87 ms). Panels c and e show in detail the amplitude for one call per species, whereas panels d and f show the fast Fourier transformation spectrogram of these same calls. Frequency change over time for one call per species can be seen in the spectrograms. Despite classical studies investigating the role of sensory ecology in speciation,3, 5 it has not yet been possible to confirm whether sensory divergence is initially responsible for limiting gene flow or results as a consequence of limited gene flow6. Typically, most classical research on speciation follows a retrospective approach, whereby the causes and mechanisms of speciation are inferred retrospectively after the speciation process is finished7. Although this approach has undeniably improved our understanding of speciation, it often lacks resolution at the early stages of the process,
stores that carry cards against humanity, either because after speciation is completed, the signal has been confounded by post speciation changes or simply because of the lack of historical ecological and/or behavioural data when the process was initiated8. Templeton9 suggested that closer to the speciation process, the greater the ability to focus upon the genetics of speciation leading to the view that research is needed at the population/species interface to uncover how speciation occurs7, 8, 10. Therefore, studying the population/species interface, using a glass approach, can reveal important aspects of speciation and how ecology, behaviour and genetics interact in different situations to cause the evolution of barriers to gene flow7, 11, particularly important in the case of sensory ecology driven speciation.

The majority of bats use sound (echolocation) to orient themselves in their environment, locate their food and communicate12. A bat echolocation signal and its ability to perceive this signal are functionally linked, as the bat must be physically able to hear the echoes of its outgoing calls13. Therefore, studying the signal or echolocation call in bats will inform our understanding of their sensory system and perception capabilities11, 13. Currently the genetic mechanisms that control echolocation are being uncovered and studies are starting to explore the role that echolocation has in communication and sexual selection14. Recent studies have suggested that changes in echolocation frequency (for example, Rhinolophus) are associated with assortative mating, reproductive isolation and ultimately speciation, regardless of external barriers to gene flow5. Therefore, bat species are an excellent mammalian model to study the role of sensory drive (that is, echolocation calls) in the speciation process11, 15.

One such species is Craseonycteris thonglongyai, a rare and endangered, charismatic bat species, considered as the World smallest mammal and restricted to a 2,000 km2 region that straddles the Thai border16. Currently, the Thai and Myanmar populations are disjointed, although the long term presence of suitable intervening karst landscape suggests that this may once have been a continuous distribution (Fig. 1). Despite being morphologically indistinguishable17, observed variation in echolocation calls between Thailand18 and Myanmar19 suggested that more than one species might be present. Therefore, studying both the allopatric and sympatric populations offers a unique opportunity to study the factors that influence population structure, gene flow and sensory trait divergence across an entire species range and at different evolutionary timeframes.

Grey shaded areas represent limestone areas and blue shaded areas represent inland water bodies (dams). C. thonglongyai distribution in Thailand (green) and Myanmar (dark blue) were taken from Puechmaille et al.16 A bat is represented next to each site where M. siligorensis was recorded. Myanmar colonies are labelled as M1, M3, M8, M13 and M14. Thailand colonies are labelled A [e] indicates a colony where only echolocation data were recorded, while [g] designates a colony with only genetic data.

Here we used both a and glass approach to untangle the early drivers of speciation in allopatric and sympatric populations of C. thonglongyai, the sole representative of the monotypic bat family Craseonycteridae. To illuminate the early stages of the speciation process, we examined the ecological and genetic factors that could have limited gene flow and driven local adaptation in these unique populations. We specifically focus on the role that echolocation has in this process. We show that currently no gene flow is occurring between the geographically isolated allopatric populations in Thailand and Myanmar, and infer that geographic isolation most likely preceded the echolocation divergence. Within the sympatric populations in Thailand, we identified geographic distance rather than echolocation divergence as the major factor shaping population structure across the entire range. GU247601 Supplementary Tables S1 and S2) and collected acoustic data from throughout the species range (Fig. 1). Intensive trapping, sampling and acoustic recording were carried out in Thailand and Myanmar (Fig. 1). These data confirmed the marked and significant difference in echolocation calls between Thailand and Myanmar (Thailand: N=4,188, 70.125 kHz and Myanmar: N=1,472, 76.875 kHz, generalized linear mixed model, P=0.002; Fig. 2a; Supplementary Fig. S1). Bayesian phylogenetic analyses based on a 1.8 kb mitochondrial fragment of 602 samples and two different NUMTs (mitochondrial pseudogenes) showed that the Thai and Myanmar populations were reciprocally monophyletic with a posterior probability of 1.00 (Fig. 2b). This clear Thai split was confirmed at the nuclear level by screening all individuals (n=659) for 15 microsatellites (Fst=0.49, P Fig. 2c and five nuclear single nucleotide polymorphisms (SNPs), which showed almost complete lineage sorting (Supplementary Fig. S2). The overall strong differentiation of all markers investigated combined with the complete lineage sorting of the mitochondrial fragment (Fig. 2b), two nuclear SNPs (Supplementary Fig. S2) and the absence of shared alleles at one microsatellite locus, strongly advocate for a total absence of gene flow between the allopatric Thai and Myanmar populations. Relying on the molecular clock, the split between the two populations is dated to approximately 0.4 Mya (95% highest probability density, 0.268 Mya). Genetic diversity indices of the Myanmar population are significantly reduced at the mitochondrial (Thailand, Hs=0.92; Myanmar, Hs=0.36; permutation test, P=0.006) and nuclear (Thailand, Hs=0.56; Myanmar, Hs=0.43; permutation test, P=0.014) levels compared with the Thai population. Similarly, nucleotide diversity of the mitochondrial DNA fragment calculated per site in Thailand and Myanmar differ strongly with more than 10 fold differences (Thailand: average min 0.0028 Myanmar: average min 0.00013 indicating that the Myanmar population probably originated from a small number of individuals, either as a consequence of a strong bottleneck or more likely via a founder effect. However, to fully elucidate whether echolocation variation drove the isolation of the two populations or evolved after their split requires knowledge about distribution of species and the echolocation frequency of the population at the time of divergence. As this is not available, we turned to the sympatric Thai population to address this question.

(a) Distribution of echolocation calls recorded in Thailand (green; n=4,188) and Myanmar (blue; n=1,472). (b) Bayesian phylogenetic tree based on a 1.8 kb mitochondrial fragment of 602 samples and two different NUMTs (mitochondrial pseudogenes) showing the monophyly of the Thai (green) and Myanmar (blue) C. thonglongyai. The tree is rooted with two distinct NUMTs found in both populations. Bayesian posterior probabilities are shown below branches of interest. (c) Graphical representation of 196 Myanmar and 463 Thai individuals according to their probability to belong to each of the two clusters defined by STRUCTURE (black lines separate individuals sampled at different colonies). (d) Neighbour Joining tree showing the relationships between 196 Myanmar (in blue) and 463 Thai (in green) individuals based on 15 microsatellites (Da distance).

The magnifying glass approachAs the first steps of speciation occur at the population/species interface7, 8, 9, 20, we investigated the recent spatio temporal history of the Thai population and the relationship between genetic structure and echolocation frequency at the population level. To investigate past changes in distribution, we assessed whether the Thai population has been stable, increased in size (demographic expansion) or spatially expanded (spatial expansion)21 using mismatch distribution analyses of mitochondrial fragments (n=462). A model of stable population size was rejected for all Thai colonies (non overlapping 99% confidence intervals of and two mutation parameters that are related to the effective size of the population before and after a putative expansion, respectively). While the Southern Thai colonies (H, I, J and L, M, N, O, Fig. 1) fitted both models of demographic and spatial expansion equally well, the Central (E, F and G, Fig. 1) and Northern colonies (A, B and D, Fig. 1) fitted a model of spatial expansion but not a model of pure demographic expansion (Supplementary Fig. S3). Principal component analysis of microsatellite data showed a strong population structure orientated along the Northwest direction (Fig. 3a This structure was confirmed by a Mantel test showing a very strong correlation between geographic and genetic distances (Mantel test; r=0.95, R2=0.90, P Fig. 3e). This strong correlation could theoretically result from different evolutionary histories such as secondary contact between two previously isolated populations or spatial expansion from a single population. Although the secondary contact scenario cannot be excluded, mismatch distributions do not support the presence of two isolated populations, but rather suggest that a single population was most likely present in the Southern part of their range and recently expanded northwards. Analyses that simulate the Thai population spatially expanding under a wide range of migration rate and population growth rate show that the strong isolation by distance pattern observed (Fig. 3e) can result from a recent spatial expansion when gene flow is restricted to neighbouring populations (Supplementary Table S3 and Fig. S4). The average dispersal distance estimated from the slope of the regression between genetic distance and geographic distance was 2.2 km, indeed demonstrating extreme localized dispersal.

Principal component analysis (PCA) showing the population structure of individuals orientated along the Northwest direction with the first axis (explaining 8.46% of the variance) versus the (a) second axis (5.56%), (b) third axis (4.97%), (c) fourth axis (4.34%) and (d) fifth axis (4.24%). For each graph, the inset represents the eigenvalues of the two axes. Individuals from each of the 14 colonies are represented in different colours, and the colonies range from A in the Northwest to O in the Southeast. Colony P was excluded from the PCA, as only single individual was available from this site. (e) Relationship between genetic distance (Fst) versus geographic distance for the 12 colonies with echolocation and genetic data in Thailand (n=442 individuals). 4). As body size and echolocation frequency are correlated across species22, we used forearm length as a proxy of body size and showed that body size differences are unlikely to explain the observed echolocation difference (Spearman rank correlation, P=0.53). We then used causal modelling on resemblance matrices23, 24 to investigate the combination of factors driving genetic differentiation between colonies. We first evaluated the set of diagnostic statistical tests of the seven possible organizational models, which included geographic distance, the presence of barriers (areas without limestone), echolocation distance (measured by the absolute echolocation difference) and genetic distance (measured by the Fst index calculated on 14 microsatellites; Supplementary Information). Only one organizational model, which included geographic distance, echolocation distance and genetic distance,
cards against humanity cards, was fully supported by all statistical expectations (Supplementary Table S4). The six possible models of causal relationships between geographic distance, echolocation distance and genetic distance were then compared with the predictions of the models23. Five models were not supported by the data, as one or several of their predictions were not realized (Supplementary Table S5), while one model was fully supported. This latter model supports the primary influence of geographic distance on genetic distance, which itself influences echolocation distance.

Graph showing the variation in frequency of free flying C. thonglongyai recorded at different colonies represented on the inset map. Colonies are arranged on the X axis according to their score at the first axis (explaining 98.9% of the variance) of a principal component analysis on their geographic coordinates (latitude, longitude). For each boxplot, the box represents the 0.25 quantile, median and 0.75 quantile. On either side of the box, the whiskers extend to the minimum and maximum. To showcase possible outliers, whiskers were shortened to a length of one time the box length. The colours in the plot correspond to the colours in the inset map.

Echolocation and driftTo investigate if this change in echolocation could result from echolocation drift, we assessed the short and long term population sizes in the North/Centre and South of Kanchanaburi. We used the actual average colony size16 as a proxy for short term population size, and mitochondrial and nuclear genetic diversity found within colonies as a proxy for long term population size; high genetic diversities are being associated with large effective population sizes and reduced drift, whereas low genetic diversities are being associated with reduced population sizes and increased drift25. The actual average colony size (South: 275; North/Centre: 248) did not significantly differ between Northern/Central and Southern colonies (Wilcoxon Mann rank sum test, n=20, P=0.19). A one sided permutation test showed that the Northern/Central colonies had significantly reduced mitochondrial (North/Centre, Hs=0.87; South, Hs=0.94; P=0.01) and nuclear (North/Centre, Hs=0.52; South, Hs=0.57; P=0.04) gene diversities when compared with the Southern colonies. These short term and long term population sizes suggest that the Northern/Central colonies, which most likely spatially expanded, were equally or more prone to drift than the Southern colonies, and therefore, if drift had a role in echolocation frequency change, the Northern/Central colonies should show similar or higher variation across colonies than their Southern counterparts. Contrary to this expectation, C. thonglongyai in the Northern/Central part of the range shows little echolocation vari

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