V = aMb between the variance V and mean number M of organisms in a quadrat. We examine the spatial distribution data of seven urban service facilities in 37 major cities in China, and find that Taylor’s Law is validated among all types of facilities. Moreover, Taylor’s Law is robust if we shift the observation window or vary the size of the quadrats. The exponent b increases linearly with the logarithm of the quadrat size, i.e. b(s) = b0 + A log (s). Furthermore, the ANOVA test indicates that b takes distinct values for different facilities in different cities. We decompose b into two different factors, a city-specific factor and a facility-specific factor (FSF). Variations in b can be explained to a large extent by the differences between cities and types of facilities. Facilities are more evenly distributed in larger and more developed cities. Competitive interchangeable facilities (e.g. pharmacy), with larger FSFs and smaller bs, are less aggregated than complementary services (e.g. restaurants).As one of the few generalities in ecology, Taylor’s power law admits a power function relationship  As a fundamental law in ecology, Taylor’s Law characterizes the fluctuation of the organisms’ spatial distribution. Based on the spatial data of urban facilities through the API of BaiduV = aMb between the between-sample variance in density V and the overall mean density M of a sample of organisms in an area. The exponent b describes the heterogeneity of the spatial or temporal distribution. For example, b = 1 corresponds to a Poisson distribution, b > 1 indicates the clumping of organisms.Taylor  describe2 A large body of literature has been devoted to this question, and many theoretical models have been introduced to explain Taylor’s Law. For instance, Andersen et al.  and i = 1, 2, …, 16) are computed using values of Ni,j, j = 1, 2, … for each sub-area i. In order to get a good estimation of mean and variance in each sub-area i, Ni,j should be bigger than 0 for a sufficient number of quadrats. In some cities, the numbers of certain facilities are not large enough, which may greatly increase the size of error in the estimation of the means and variances. To avoid this problem, we decide that if the total number of valid sub-areas is small, e.g. less than five (i.e. we have less than five pairs of means and variances), we cannot get a reasonably good estimation of the mean–variance relationship. We then have to give up our estimation for this given combination of facility and city.Each sub-area is further divided into 3.et al. [V = log a + b log M, we can judge whether or not Taylor’s power law is applicable to the urban facilities.It should be noted that in our study, every pair of mean and variance is estimated in an area using the same quadrat size. Hence, the potential relationship between the means and variances only reflects the fluctuation of the number of events in a quadrat of the given size, not a scaling rule where the number of events is measured in a series of expanding quadrats discussed by Wu et al. . Througha for J = 5 and figure 2b for J = 8. Remember that a sub-area is divided into J × J quadrats. J = 5 means that each quadrat in which the number of facilities Ni,j is counted is 2000 × 2000 m. J = 8 means that the size of each quadrat is 1250 × 1250 m. As we can see from Taking Beijing as an example, we draw the scatter plots of the means and variances for beauty salons, stadiums, schools and banks in figure 2b for each facility, we put together the values of exponent b for each facility in those 23 cities in J = 5 is used. We can see from b fall between 1 and 2, with a concentration around 1.5. b > 1 indicates that the facilities are aggregated. There are two cases for pharmacies that b < 1, it means that pharmacies in those two cities tend to be more uniformly distributed due to their competitive nature. There is a large fluctuation in the values of exponent b for different combination of facilities and cities. The main reason for the fluctuation lies in the fact that the distribution of urban facilities are driven by business nature of urban facilities, as well as social-economical environment of a city. We will decompose the exponent in the later part of the paper, which can explain up to 92% of the fluctuations. The rest may be attributed to the estimation error.The facilities, for which we can have more than five pairs of mean and variance to get a reasonable estimation of Taylor’s Law, are different from one city to the other city. There are 23 cities in which we can estimate Taylor’s Law for all the seven types of facilities. In order to give a clear picture of the exponent b from the left to the right. It seems that bigger and more developed cities have smaller values of b. In the next section, after we decompose the exponent b, we will plot the CSF against the population size.We organize the cities along the horizontal axis by population size from the highest to the lowest. One should understand that the population data are not accurate due to the rapid urbanization process in China. The population data reported in this study only include permanent residents, it does not include the migrating population who do not have the city Hukou. There is a slight tendency of increase in exponent b varies if we shift the locations of the observation windows of the quadrats.As a robust check, we will test if Taylor’s Law still holds and how the exponent J , the size of the quadrat is L = 10 000/J. We notice that for different choice of J, Taylor’s Law still holds by observing that the variance is a power function of the mean. J = 5 and J = 8. The results are similar for other choices of J.The whole study area of a city is covered by non-overlapping quadrats. For different choice of L/2 then to the right by L/2. By using the seven types of facilities in the 23 cities, we report the mean and standard deviation of b before we move the window and after we move the window in T-test to test the null hypothesis that b does not change if we shift the observation window. The p-values are larger than 0.05 for all Js, indicating that there is no significant difference in b. We will discuss how b varies when we use different sizes of the quadrats in the next subsection.Then we shift the location of the observation window to the maximum by moving the window up by 4.J (hence different sizes of the quadrats), we have demonstrated in the previous section that Taylor’s Law still holds. We have the following proposition on how the exponent b varies with the size of the quadrat. It is a result that can be derived from the combination of two scaling laws regarding mean–variance relationship, namely, Taylor’s Law and size-scaling law as studied in [Note that in this paper, the mean–variance relationship is obtained when the density of the events fluctuates across different sub-areas of a city, while the size of the quadrat is kept fixed. For different choices of udied in .The exponentbof Taylor’s Law measured in quadrats of sizes, we haveb(s) = b0 + A log (s), whereb0 ≡ b(s = 1) is the exponent measured in quadrats of unit size, andAis a constant to be decided by experiment.s is dimensionless, which does not have a unit. In other words, s is relative, measuring the ratio of the size of a quadrat relative to the base quadrat for which we let s = 1. For instance, at two different scales, we pick one and let the size be s = 1, the size of the other one relative to the first one is s. The formula tells how the exponents at the two different scales are related.It should be noted that s, the mean in the new quadrats is scaled by M(s) ∝ s2 in a two-dimensional space. Owing to the power law of the covariance density, i.e. the covariance density γ(r) is a power function of the physical distance r between two locations, γ(r) ∝ rc−. It is shown in [V ∝ sc4−. Thus, the mean–variance relationship is given by a power law V ∝ Mb′ with b′ = 2 − c/2. To differentiate the two power laws, we refer to the latter one as size-scaling law, and Taylor’s Law as the density-scaling law. We use b′ and b to refers to the exponent of the size-scaling law and the density-scaling law, respectively.To prove the proposition, we need to cite the other scaling invariant mean–variance relationship as studied in , if we vshown in  that thei and j with different densities. The density (i.e. the number of events in a unit area) in the two sub-areas is Mi and Mj, respectively. First we use quadrats of unit size to measure the mean and variance. According to Taylor’s Law, we haveAssume that there are two sub-areas s. In the new quadrats of sub-area i and j, the mean is Mi(s) = s2Mi and Mj(s) = s2Mj, respectively. According to the size-scaling law as shown in [i and j,Vi(s) and Vj(s) are the variance using the new quadrats (with size s) in sub-area i and j, respectively. Note that Vi and Vj are measured in the base quadrats when s = 1. b′ refers to the exponent of the size-scaling law. It may take different values in different sub-areas with different densities, so we use subscript i and j to refer to the difference.Then, we increase the quadrat size by shown in , we haveCombining the above three equations, we haves, we have the following two possible cases.Since Taylor’s Law still holds when the quadrats are increased in size by b′i − b′j = 0. The size-scaling law of mean–variance does not depend on the density of the events. Then log (Vi(s)) − log (Vj(s)) = b0(log (s2Mi) − log (s2Mj)). Thus b(s) = b0, i.e. we have the same exponent of Taylor’s Law at different scales of the quadrats. From J = 5 and J = 8. Case 1 is not true.Case 1, b′i − b′j = (A/2)(log (Mi) − log (Mj)). A is a constant. From equation (b(s) = b0 + A log (s).Case 2, equation , we haveJ, i.e. J = 4, 5, 6, 7, 8. Since the size of the sub-area is fixed as 10 000 × 10 000 m, the quadrat for a given J is of size 10 000/J. In bs for all facilities) are plotted against log (1/J). The five points, corresponding to the five different choices of J, are on a straight line with an upward slope. So, our conjecture is verified, i.e. b(s) = b0 + A log (s) with A > 0 for urban facilities.We can test if the above argument is true by using five different values of 5.b. In order to explain the fluctuation in the values of b for various facilities and in different cities, we can decompose the contributor affecting the number of a facility in the quadrats of a city into two major contributors: a city-specific contributor and a facility-specific contributor. Through the ordinary least-square regression, this decomposition may remove a proportion of estimation error due to insufficient sample quantity.As we have mentioned in §Xij = YiZj, where: (a) Xij stands for the quantity of the facility j , in a quadrat of city i ; (b) Yi represents the contribution from city-specific contributor and (c) Zj represents the contribution of facility-specific contributor. Here we do not specify any measurable variable or quantity for city-specific contributor or facility-specific contributor. Underlying Yi or Zj could be a complex function of many variables. We are only interested in the decomposition of the exponents b instead of the specific function form of the factors. Note that the independence and multiplicative assumption is supported by the results in 11Assume: (a) the number of a facility in a quadrat of a city is jointly determined by the city-specific contributor and facility-specific contributor, which are assumed to be independent from each other; (b) these two contributors satisfy the following equation for decomposition: Xij could be expressed as follows:ci between the variance V and the average contribution of the city-specific contributor, while the power function for facility-specific contributor is with exponent fj. This assumption can be represented by the following two equations:From the independent assumption, it is clear that the mean value of V = aMb, we can deriveij represents the estimation error in bij. We assume that ɛij is normally distributed and has the same variance, i.e. bij is decomposed into two components, namely, ci and fj in equation (ci the CSF and fj the FSF.Based on Taylor’s power function quations –5.4), w, wV = aMci and fj up to a constant since they always appear in a summation pair. For example, ci − c0 and fj + c0 are also the solutions. It suffices our purpose to examine whether CSFs (or FSFs) take distinct values for different cities (or facilities). To solve equation , Shanghai (0.23) and Guangzhou (0.23). These four are the largest cities ranking in the first tier in the Chinese cities. Our results suggest that in larger and more developed cities, we do not only have more facilities available to the citizen, but also the facilities are more evenly distributed so that citizens can better use them.As we can see from bij = ci + fj, given a value of ci in a city, the smaller the value of fj, the larger the values of bij and therefore larger Vij for a given mean Mij in the corresponding city. Larger variances imply greater differences among the numbers of facility j in different quadrats. At some place, the number is small, but at another place, the number can be very large which means that the facility tends to aggregate in space. On the contrary, when fj becomes larger, given the same value of mean, the variance falls, thus the distribution tends to behave more like the Poisson distribution, which implies a weaker aggregation. In our decomposition, the value of the FSF for restaurants is 0.45, which is the smallest, and for pharmacies it is 0.60, which is the largest. It shows that restaurants have the highest degree of aggregation; while pharmacies have the lowest degree of aggregation. Restaurants with different styles can coexist at one place; however, due to their interchangeability, pharmacies tend to avoid staying close to each other.For the values of the FSF, we need to understand from another perspective. Because 1/b, the positive correlation between CSF and population indicates that b decreases with population. It is worth mentioning that the conclusion is applicable to all type of facilities, since CSF is a component of b for all type of facilities. It should be noted that China is experiencing a rapid urbanization. The population data, reflecting the official record of permanent residents, does not include the migrating population. The latter becomes more and more important as a lot of Chinese people are migrating from rural areas to cities in recent decades. With larger CSFs and smaller bs, larger and more developed cities tend to have more facilities (more service) than small cities.In 6.6.1.b for various facilities in different cities.As indicated in tables To further investigate whether these differences have statistical significance, we use the two-way ANOVA test. The two-way ANOVA is an appropriate analysis method for a study with a quantitative outcome and two categorical explanatory variables.The two-way ANOVA test has the following assumptions: the sample in each cell (i.e. for each combination of levels of the two factors) is independent of the samples in the other cells, the sample in each cell comes from an (approximately) normal distribution and the populations corresponding to each cell have the same variance (from the homogeneous variance assumption). Our structural model for the two-way ANOVA without interaction is the no-interaction (additive) model. The additive model assumes that the effects on the outcome of a particular level change for one explanatory variable does not depend on the level of the other explanatory variable.b on two explanatory variables (city and facility). Similar to the decomposition, the statistical test on b for various facilities in different cities is carried out on the inverse of b, i.e. xi,j = 1/bi,j, similar to the factor decomposition. The test can be called a 23 × 7 ANOVA because those are the levels of the two categorical explanatory variables. The ANOVA test has the two null hypotheses as follows:H1: There is no difference of the values of b in different cities.H2: There is no difference of the values of b for various facilities.We report the result in F22,132 = 2.12 and F6,132 = 6.88, which all correspond to p < 0.05. We can reject the null hypotheses  and conclude that both CSF and FSF have significant effects on the exponent b in the power function.The purpose of the ANOVA test is to investigate the dependence of 7.V = aMb between the variance V and mean M of number of facilities in a quadrat. The distribution of urban facilities complies with Taylor’s Law. The same facilities in a city may help each other survive, while at the same time, they compete for various resources, which resembles the relationship between the organisms of a species in an area.Based on the dataset of spatial coordinates of the seven types of facilities in 37 major cities in China, we explore the micro-structure of these cities and study the characteristics of the distribution of urban facilities. We find that there is a power law function relationship b and explore the mechanism underlying the distribution of urban facilities, we decompose the inverse of exponent b into two different factors contributing to the numbers of facilities in a city, respectively: the CSF and the FSF. We find that the values of the CSF vary significantly between different cities, and different facilities have different degree of agglomeration. It is interesting to note that Beijing, Shanghai, Guangzhou and Shenzhen, the largest and most developed four cities in mainland China, have the largest b. Our results suggest that in larger and more developed cities, we do not only have more service facilities available to the citizen, but also the facilities are more evenly distributed so that citizens can better use them. Moreover, restaurants have the highest degree of agglomeration; while pharmacies have the lowest degree of agglomeration. These findings are consistent with our intuitive understandings of these cities and urban facilities.Furthermore, in order to study the key factors contributing to the difference between the values of exponent Economic activities are often geographically concentrated in particular cities or metropolitan areas, and there are many theories explaining why the concentration may occur –26. ElliThe analytical results in this paper are in line with the findings in the literature mentioned in the above paragraph. Beijing, Shanghai, Shenzhen and Guangzhou are generally acknowledged as the four most urbanized cities in China, and our analytical results show that these cities have the highest level of concentration of urban facilities. Glaeser & Kohlhase  show eviAs we have shown in the above analyses, the distribution of urban facilities resembles that of the organisms in ecosystems. Organisms feed on various resources, while facilities ‘feed on’ consumer demands. Organisms are prone to form groups, but the size of group varies between different species. For example, zebras and wildebeest form large herds, while the lions usually live in small groups. Urban facilities tend to agglomerate in an area, while as we can see from per capita, population density and health indicator, etc. Lastly, we could explore relevant theoretical frameworks that could help improve the distribution of urban facilities, thus facilitating sustainable development of cities.It is important for us to carry out further studies on the distribution of urban facilities, and the potential directions could lie in the following three aspects. Firstly, through combining spatial statistics, economic theories and other relevant fields, we could further explore the rationals and mechanisms underlying the distribution of urban facilities, and examine its impact on socioeconomic development in a city and the adjacent regions. Secondly, when we have sufficient panel data, we could examine the evolution of the distribution of urban facilities over both the time and space, and explore the relationship between the evolution process and the changes in socioeconomic development indicators, such as income


Gasterosteus aculeatus), breeding sympatrically in multiple lagoons on the Scottish island of North Uist. Using morphological data, targeted genotyping, and genome-wide single-nucleotide polymorphism data, we show that lagoon resident and anadromous ecotypes are strongly reproductively isolated with an estimated hybridization rate of only ∼1%. We use palaeoecological and genetic data to test three hypotheses to explain the existence of these species-pairs. Our results suggest that recent, purely ecological speciation from a genetically homogeneous ancestor is probably not solely responsible for the evolution of species-pairs. Instead, we reveal a complex colonization history with multiple ancestral lineages contributing to the genetic composition of species-pairs, alongside strong disruptive selection. Our results imply a role for admixture upon secondary contact and are consistent with the recent suggestion that the genomic underpinning of ecological speciation often has an older, allopatric origin.Ecological speciation has become a popular model for the development and maintenance of reproductive isolation in closely related sympatric pairs of species or ecotypes. An implicit assumption has been that such pairs originate (possibly with gene flow) from a recent, genetically homogeneous ancestor. However, recent genomic data have revealed that currently sympatric taxa are often a result of secondary contact between ancestrally allopatric lineages. This has sparked an interest in the importance of initial hybridization upon secondary contact, with genomic reanalysis of classic examples of ecological speciation often implicating admixture in speciation. We describe a novel occurrence of unusually well-developed reproductive isolation in a model system for ecological speciation: the three-spined stickleback ( The sympatric coexistence of closely related but reproductively isolated “species-pairs” represents a biological conundrum because it is difficult to explain how divergence is maintained in the absence of obvious barriers to gene flow. Ecological speciation, in which ecologically dependent natural selection drives the evolution of variation in adaptive traits that also influence reproductive isolation between ecotypes, has become a popular model to account for this problem . HoweverAnopheles species-complex  is common throughout their Holarctic range , Scottish Western Isles, like most of the rest of northern Europe, was likely colonized by marine stickleback following the melting of ice sheets ∼16,000 YBP . The isl2) Alternatively, genetic divergence evolved in allopatry, prior to colonization, and Uist experienced a double-invasion that was brought about by differential arrival times of different lineages/ecotypes (and therefore not necessarily related to RSL change), leading to secondary contact between two already distinct lineages. There is evidence that the west coast of Scotland may be a rare contact zone for ancient mitochondrial stickleback lineages that persisted in glacial refugia on either side of the Atlantic during the Pleistocene , making 3) Finally, prior allopatric divergence may have existed, as in (2), but rather than reproductive isolation having reached completion prior to secondary contact, species pairs could have formed as a direct result of admixture between lineages upon secondary contact. This hypothesis predicts that Uist was colonized by multiple, prediverged stickleback populations; that there is evidence for recent admixture between those populations and one or both of the species-pair ecotypes is genetically admixed in relation to putative parental populations. To test these three hypotheses, we reconstruct the Holocene marine inundation history of a series of coastal lagoons on Uist and use targeted mitochondrial sequencing alongside genome-wide SNP data to investigate the demographic history and genetic relationships of species-pairs inhabiting those lagoons. By consideration of putative mechanisms for these three hypotheses, we shed light on the underlying mechanisms of speciation in postglacial fish.Assessing the role of spatial isolation in speciation is notoriously problematic because inferring spatial distributions of historic populations is difficult over long time-scales . Howevern = 43). The LDA predicted the salinity of all elemental samples from Obse, Faik, and Strm long cores with posterior probabilities of >0.99. LDA predictions indicated that all three currently brackish lagoons transitioned from freshwater conditions during the time spanned by our sediment sequences , deposited under known salinity conditions, was used as a calibration series to create a discriminant function to predict the salinity under which older sediment was deposited. Surface (upper 20 cm) freshwater and marine  sediment deposits clustered distinctly and separately along a single linear discriminant axis LD1, a and jacequences b–e, but equences  online.Radiocarbon dating of macrofossil material indicated that the Faik long core spanned the Holocene period with basal sediment ∼13,097–13,289 cal. YBP  online. fig. 2t = 280.94, df = 230.65, P < 0.0001, 2 = 4.26, df = 5, P = 0.5128). In the 239 euthanized individuals, we measured the sizes of a further six elements of body armor  with anadromous fish being considerably larger than lagoon resident fish (Ectodysplasin A (Eda) locus, which has two key alleles; LEda (low) and CEda (complete), that generally give rise to low- and completely plated phenotypes, respectively , accounting for 99% of variation in plate number (n = 55). Lagoon resident fish (n = 33) were fixed for the LEda allele and anadromous fish were approaching fixation for the CEda allele with only 1 out of the 21 genotyped individuals possessing a heterozygous, rather than homozygous CC genotype  haplotypes across the 76 Uist species-pair individuals  sequenced in this study, 19 of which are, to the best of our knowledge, previously undescribed. Cyt b and control region (CR) sequences were submitted separately to GenBank under accession numbers MG602878–MG602914 and MG602915–MG602951, respectively. We extended our genetic analysis to include 126 individuals from allopatric freshwater populations using sequences taken from φST permutation values indicated significant genetic differentiation in mitochondrial haplotypes between all three ecotypes , with anadromous fish being particularly differentiated from the other two ecotypes , with the Trans-Atlantic lineage comprising 47% of anadromous, but only 3% and 6% of lagoon resident and freshwater resident populations, respectively  from Obse on Uist, a nearby freshwater resident ecotype from Scad (Uist) and an Atlantic marine population from Nyps in Iceland  of allele frequencies based on all  SNPs reveal that all four populations form distinct and separate genetic clusters c. The prF3,64 = 33.842, P < 0.0001), but post hoc pairwise comparisons indicated that this was driven by reduced heterozygosity in freshwater resident fish, compared with the other three ecotypes  and with random substitution of biallelic sites . This is consistent with an introgression event from marine fish into the anadromous population.Fifth, jacknifed ABBA BABA tests, with populations arranged to test for introgression from the marine population into either lagoon resident or anadromous populations , indicated a significantly positive value of Sixth, Bayesian coalescent-based estimates of the species-tree indicated a largely consistent consensus tree topology  with anadromous fish diverging recently from lagoon residents  also implied substantial genetic segregation between sympatric anadromous and lagoon resident ecotypes. All of the methods we used to assess population structure identified the two ecotypes as distinct and separate genetic groups, with very little indication of ongoing admixture. Furthermore, our coancestry estimates showed that some anadromous individuals from the species-pair shared more common ancestry with both an isolated inland freshwater population on Uist that is ∼10 km away through water and an allopatric marine population ∼1,000 km away in Iceland, than they did with sympatric lagoon resident fish, strongly suggesting that the two sympatric ecotypes form two largely independently evolving lineages. Outlier analyses also identified numerous regions of the genome that are under putative selection between lagoon resident and anadromous fish. When mapped back to the annotated genome, many of those regions fall within genes that are of known functional and ecological importance , and somWe found little evidence to support the hypothesis that the evolution of species-pairs on Uist is solely explained by the “classic” double-invasion model of postglacial speciation in fish. This hypothesis states that two colonizations from the same, genetically homogeneous marine population, made as a result of changes in spatial isolation driven by changes in RSL, coupled with ecologically based divergent selection in allopatry, are exclusively responsible for the formation of species-pairs. Applied to Uist species-pairs, this theory made four testable predictions. First that there is evidence for strong, ecologically based selection between ecotypes; second, the species-pairs originated from multiple colonizations from a genetically homogeneous “stock” marine population; third that they are postglacial in age; and fourth, that there was a double-peak in RSL since the retreat of the Pleistocene glaciers allowing a double-invasion with spatial isolation.Eda, which is associated with lateral-plate morph in stickleback , but rather is approximately the same as estimates of the timing of deglaciation of the island . This isWe also hypothesized that Uist may have been colonized by lineages that were already reproductively isolated prior to colonization and that secondary contact, with or without admixture could be responsible for the evolution of species-pairs on North Uist. These hypotheses predicted that Uist was colonized by multiple prediverged lineages, there was a differential genetic origin of ecotypes, the prior genetic divergence existed before the colonization of Uist (and thus must predate the retreat of the Pleistocene glaciers on Uist), and either there was no evidence for recent genetic admixture, or one or both of the species-pair ecotypes is genetically admixed in relation to putative parental populations.Our analyses confirmed that Uist is a meeting place for two predominantly allopatric, ancient mitochondrial lineages: the Trans-Atlantic and European lineages , which dVma21) gene as being under strong selection between lagoon resident and anadromous fish. The function of Vma21 is likely related to ATP synthesis , which should result in trans-Atlantic mitochondrial haplotypes infiltrating freshwater resident populations, and yet this largely appears not to be the case. Interestingly, our outlier analysis identified six SNPs (by far the most within any one gene) within the vacuolar H+-ATPase  stickleback to investigate the genetic relationships between ecotypes. Our analyses of the autosomal genome-wide SNP set indicated that the anadromous population on Uist is genetically admixed, with genomic input from lagoon resident  and Atlantic marine populations. Genetic admixture can provide novel combinations of genes on which selection can act, and is most likely to be involved in speciation when recombinant phenotypes are better adapted to a given niche than either parental species, allowing admixed individuals to exploit environments that are unavailable to either parent species . We therIn a phylogeny of Uist stickleback populations (with Icelandic marine fish as the outgroup), Uist anadromous fish do not approximate the marine founders of the island, as would be expected by traditional models of stickleback dispersal . Rather,We have identified unusually strong reproductive isolation between sympatric anadromous and lagoon resident stickleback ecotypes, in multiple lagoons on the Scottish Hebridean island of North Uist. We tested three hypotheses, which were not mutually exclusive, to explain how such strong reproductive isolation has evolved. While we cannot completely rule out contributions from any of our three models, our results indicate that the “classic” explanation for more pronounced reproductive isolation in stickleback, an ecological speciation model driven by postglacial changes in RSL, is unlikely to be responsible for speciation in the present case. Instead, our results suggest that the most parsimonious explanation probably involves genetic admixture upon secondary contact between multiple colonizing lineages/ecotypes that provided the basis for strong  divergent selection. These findings are in line with much recent research that is beginning to suggest that cases of seemingly recent, purely ecologically based speciation are actually cryptic examples of speciation that has a much older genetic basis, that developed allopatrically . This stWe collected sediment core sequences for RSL change reconstructions from species-pairs lagoons during two field trips to Uist in 2013 and 2015  online. Briefly, to reconstruct changes in RSL on Uist over the Holocene period, we precisely mapped the elevation of three species-pair lagoons, collected long sediment sequences from them, and predicted past changes in salinity using a discriminant function trained with modern Uist sediment samples. We then radiocarbon dated ancient sediment samples to date marine—freshwater/freshwater—marine transitions. For details, see To quantify morphological differentiation, stickleback were sampled from six species-pair lakes on Uist: Faik, Obse, Duin, Trun, Strm, and Dheo  gene and a partial fragment of the D-loop CR, which are known to resolve ancient mitochondrial lineages present in the Atlantic and the seas around Europe (To investigate whether North Uist is a meeting place for multiple ancient mitochondrial lineages, we sequenced 76 species-pair individuals collected from five lagoons (Faik, Obse, Duin, Trum, and Strm, See d Europe . We obtad Europe . We thend Europe  to ensuradegenet (D statistics, both with biallelic sites removed and with random substitutions of biallelic sites using custom R scripts (see To test the hypothesis that admixture may have been important in the evolution of species-pairs, we conducted a variety of analyses using the genome-wide SNP data . We atteadegenet  package adegenet  on data adegenet  to assesadegenet . Fifth, ipts see , implemeipts see . Finallyipts see . For furMolecular Biology and Evolution online.msz161_Supplementary_DataClick here for additional data file.


Dipsosaurus dorsalis and Sauromalus ater) and two species of carnivorous lizards (Crotaphytus bicinctores and Gambelia wislizenii) that vary in population size at the same locality, and comparing metrics of genetic diversity. Contrary to neutral expectations, results from four independent loci showed levels of diversity were usually higher for species with smaller population sizes. This suggests that selective processes may be having an important impact on intraspecific diversity in this reptile community, although tests showed little evidence for selection on the loci sequenced for this study. It is also possible that idiosyncratic histories of the focal species may be overriding predictions from simple neutral models. If future studies show that lack of correlation between population size and genetic diversity is common, methods using genetic diversity to estimate population parameters like population size or time to common ancestor should be used with caution, as these estimates are based on neutral theory predictions.Understanding the causes of genetic variation in real populations has been elusive. Competing theories claim that neutral vs. selective processes have a greater influence on the genetic variation within a population. A key difference among theories is the relationship between population size and genetic diversity. Our study tests this empirically by sampling two species of herbivorous lizards ( Neutral Theory predicts that most molecular genetic variation found in populations is selectively neutral, and is shaped primarily through the random processes of mutation and genetic drift  with long generation times, essentially controlling for factors other than trophic level and habitat niche. The two crotaphytids are predators, and often prey on smaller lizards Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This is an interesting study that explores the relationship between population size and genetic diversity with four sympatric species- two herbivorous and two carnivorous lizards. I have a few broad and some specific comments.IntroductionGenerally, the introduction seems to be guided by the results that the authors got. If the motivation behind the study was to test whether neutral or selective forces shape genetic variation, this system and study design may not be most suited to address that.In the introduction, the authors could focus more on the factors affecting genetic diversity in natural populations. It would be useful to have more information about both the intrinsic and extrinsic factors that affect genetic diversity. Authors mention the intrinsic factors of body size, mutation rates, generation times, etc. But extrinsic factors, such as extent and continuity of available habitat, do not find any mention.66-67 Please elaborate on/ give examples of the confounding variables that you mentionMethods91-92 It would be good to mention the body sizes here. People unfamiliar with the species and the region might find it useful.ResultsIt would be nice to have a map showing study area with sample locations of the different species in different coloursIt would be nice to compare generalist and specialist species within a trophic level instead of pooling them across the two levels as done in table 3.A lot of information in the first paragraph under the ‘Mitochondrial DNA’ subheading can be moved to methods.283-288 This information should go under the collection section in methods. Please also mention the distance between the different collection sites. Why was only one species sampled from Zzyzx? Other species do not occur in that area or were they not sampled for a reason?Discussion361-363 ‘The generalist species would be predicted to have a larger population size, since more usable habitat is available to them, and therefore they should have higher genetic diversity under the neutral theory. However, the data show no evidence for this pattern either.’ The authors cannot ignore the trophic level while making this statement. It is not okay to club the carnivore and herbivore generalists since population size is impacted by a combination of both- specialization and trophic level. Does the statement imply that the generalist herbivore in their study had a larger population size than the specialist carnivore? If yes, then its okay to club them in the same category and comment on expectations based on neutral theory. Whereas if they expect the population size in the following order: Carnivore specialist< carnivore generalist< herbivore specialist< herbivore generalist, the above statement cannot be made. The authors say nothing about the order in which they expect the population sizes to be for their study species. It should be explicitly mentioned in the introduction.G. wislizenii has the lowest genetic differentiation and the highest genetic diversity based on multiple estimators. Does it also, among the study species, have a more continuous distribution and larger range?Grundler et al (2019) does not find a reference in your paper. This recent study looks at the relationship between genomic diversity and abundance, occupancy and habitat specialization using genomic data from 30 species of lizards from arid Australia.Reviewer #2: This study presents valuable data on genetic diversity, with an interesting ecological component that seeks to shed light on the still equivocal relationship between heterozygosity and population size. As such, it represents an important contribution to the field. However, there are several major points that I feel need reworking. My recommendation is publication after major revision, which should address the following issues:1. The bulk of the data are presented in tables, which in my opinion lessens both the digestibility and impact of the results. The single figure that is presented could be improved with a key that indicates population size category of each of the species, such that readers can immediately pair heterozygosity with abundance/trophic category.2. There are issues with the presentation of abundance data, which I feel impedes reproducibility of the study. The abundance data on which the authors base population size classification include a census study that I was unable to access; observational anecdotes ; and coarse proxies. Proxies can be both necessary and valuable, but should be supported by more data or literature reference than found here. Similarly, observations of abundance should be supported with stronger justification as is done in Hague and Routman (2016) (frequently referenced in the current manuscript).3. Because of the above two issues, I found it necessary to read Hague and Routman (2016) in conjunction with the manuscript in question. Much of the data and ecological context for the current study is presented in the 2016 manuscript. The two studies together present an interesting picture of the ambiguity of the relationship between genetic diversity and population size, and highlight the need for further study of ecological traits and genetic diversity. The current study seems to be a logical continuation of the 2016 project, and Figure 1 is a good example of the integration of data from each of these projects. However, the current manuscript could be improved by elaborating on the combined results in the discussion.Specific comments in attached document.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, AttachmentPONE-D-19-14658_reviewer.docxSubmitted filename: Click here for additional data file.AttachmentPLOS_review_trophic_gendiv.docxSubmitted filename: Click here for additional data file. 2 Sep 201928 August 2019Dear Dr. Dutta: We would like to thank you and the reviewers for your careful review of our manuscript. We have revised the manuscript incorporating most of the comments from the reviews. This cover letter details the responses, organized by reviewer. The reviewers’ comments are in quotes and smaller font, followed by our response.Reviewer 1 “Generally, the introduction seems to be guided by the results that the authors got. If the motivation behind the study was to test whether neutral or selective forces shape genetic variation, this system and study design may not be most suited to address that.” This is the original motivation for this study, which is part of a larger study on multiple species in the same area. This study adds to work we have previously published  that examines genetic diversity in sympatric populations of lizards. We are a Master’s only institution, and to accommodate the shorter time period that those students matriculate, individual projects need to be smaller than one would expect from a Ph.D. institution. We feel that the total system is actually quite appropriate for this question, but of course the comparison of genetic diversity to population size is only part of testing neutrality vs. selection, and we anticipate using next generation sequencing to test for the fingerprint of selection in future work. It should be noted that the main stimulus for theorists to develop selective theories for DNA sequence diversity is the supposed lack of correlation, predicted by neutral theory, between population size and genetic diversity when comparing taxa from very different parts of the tree of life. We are simply arguing that comparison of populations of closely related species from identical areas would be a better test of neutral theory. Such comparisons should be done by many different labs for taxonomically divergent groups before a definitive conclusion can be drawn about genetic diversity vs. population size.“Authors mention the intrinsic factors of body size, mutation rates, generation times, etc. But extrinsic factors, such as extent and continuity of available habitat, do not find any mention.”“66-67 Please elaborate on/ give examples of the confounding variables that you mention” The paragraph the reviewer is referring to is from Line 65-70. This is our attempt to justify 1) using taxonomically related species, and 2) using sympatric species, based on the criticisms from the previous paragraphs. We suggest that “focusing on a taxonomically related group” eliminates confounding variables that the reviewer correctly identifies as “intrinsic”. But we also suggest that studying species from a single area reduces some “extrinsic” forces like different geological histories or levels of gene flow (lines 69-71). We have altered the language to make this clearer. “91-92 It would be good to mention the body sizes here. People unfamiliar with the species and the region might find it useful. “ We have added body sizes here.  “It would be nice to have a map showing study area with sample locations of the different species in different colours.” We have added a map to show the desert region (satellite view) and the collection localities of each species. “It would be nice to compare generalist and specialist species within a trophic level instead of pooling them across the two levels as done in table 3.” This comparison can be made from Table 3, but we have made it more explicit by showing the results for each species/gene combination as a Figure. . The pooled comparison is to show that even if we are wrong about the relative population sizes and both of the habitat specialists have lower population sizes, population size still does not explain the differences in genetic diversity. “A lot of information in the first paragraph under the ‘Mitochondrial DNA’ subheading can be moved to methods” We have moved some of this info to Methods, leaving only aspects which were done because of the specific results that were obtained.“283-288 This information should go under the collection section in methods. Please also mention the distance between the different collection sites. Why was only one species sampled from Zzyzx? Other species do not occur in that area or were they not sampled for a reason?” We only collected Crotaphytus from Zzyzx because the species is uncommon enough that we had to collect from a different locality to get a sample size that approached that of the two herbivorous species. Gambelia was also less common than the herbivorous species, but is not common at Zzyzx. That is why we collected Gambelia at Kelso Dunes to help fill out this sample. When the study started, we were expecting that Gambelia and Crotaphytus would be less variable than the more common herbivorous species, even when combining samples from different areas, and therefore the combined samples would be a conservative test. This turned out not to be true, so we needed to do the analysis with and without combined samples, as described in the manuscript in the Results, subsection Population subdivision.“361-363 ‘The generalist species would be predicted to have a larger population size, since more usable habitat is available to them, and therefore they should have higher genetic diversity under the neutral theory. However, the data show no evidence for this pattern either.’ The authors cannot ignore the trophic level while making this statement. It is not okay to club the carnivore and herbivore generalists since population size is impacted by a combination of both- specialization and trophic level. Does the statement imply that the generalist herbivore in their study had a larger population size than the specialist carnivore? If yes, then its okay to club them in the same category and comment on expectations based on neutral theory. Whereas if they expect the population size in the following order: Carnivore specialist< carnivore generalist< herbivore specialist< herbivore generalist, the above statement cannot be made. The authors say nothing about the order in which they expect the population sizes to be for their study species. It should be explicitly mentioned in the introduction.” We have rewritten this to be clearer that we were referring to a possible alternative contrast based on habitat specialization only, not the original hypothesis that carnivores are less common than herbivores and that within this contrast, habitat specialists are rarer than generalists. That is, we did not get the result that the observed-to-be-rarer species, the carnivorous lizards, were less diverse than the more common, herbivorous species. So one way to get our unexpected result would be if the habitat specialists as a group were actually less common than the habitat generalists. But that contrast did not match neutral theory explanations either. In fact, our genetic diversity results are not consistent with any contrast based on proxies for population size and neutral theory. “G. wislizenii has the lowest genetic differentiation and the highest genetic diversity based on multiple estimators. Does it also, among the study species, have a more continuous distribution and larger range?” We have included additional discussion of the possible role of gene flow in the Discussion. G. wislizenii does have a larger range than the three other focal species, although this may be an artifact of lower taxonomic/biogeographic attention that this species has received. For example, a mtDNA study (COIII) by Orange et al showed that the Mojave/Great Basin/Colorado plateau populations are separated from the Chihuahuan Desert populations by 6.1% sequence divergence. This suggests that gene flow from the Chihuahuan populations is unlikely to be influencing diversity in the more western populations. If the Chihuahuan Desert populations were designated as a separate species, there would not be as much difference in range sizes. The difficulty is that funding thorough multi-locus biogeographic studies is not a priority, so these studies may never be done. “Grundler et al (2019) does not find a reference in your paper. This recent study looks at the relationship between genomic diversity and abundance, occupancy and habitat specialization using genomic data from 30 species of lizards from arid Australia. “ I only became aware of the Grundler et al. paper after we submitted the manuscript. We have incorporated some discussion of their paper. Reviewer 2“The bulk of the data are presented in tables, which in my opinion lessens both the digestibility and impact of the results. The single figure that is presented could be improved with a key that indicates population size category of each of the species, such that readers can immediately pair heterozygosity with abundance/trophic category” We have added figures to correct this, as stated under “Reviewer 1”“There are issues with the presentation of abundance data, which I feel impedes reproducibility of the study. The abundance data on which the authors base population size classification include a census study that I was unable to access; observational anecdotes ; and coarse proxies. Proxies can be both necessary and valuable, but should be supported by more data or literature reference than found here. Similarly, observations of abundance should be supported with stronger justification as is done in Hague and Routman (2016) (frequently referenced in the current manuscript).” The abundance data are not great. The published survey  is mostly consistent with my 20+ years of observation at this locality, but they did not show some very abundant taxa, such as Coleonyx variegatus, as being among the most common species, suggesting a collecting method bias or year to year variation in apparent abundance. We found a publication that includes the Wallace can trap data from the Zzyzx area, and we have added that to our revision. However, can traps are not effective at collecting Sauromalus, so the data include only 2 specimens of this species. We have added discussion of the fact that it took 4 years to collect the carnivore samples and much of that collecting was focused specifically on the carnivore species, whereas the herbivore sampling took less than two years and was done early in the study, when we were collecting all species of lizards and therefore not always in the best microhabitats for the two herbivore species that are the focus of this manuscript.“Because of the above two issues, I found it necessary to read Hague and Routman (2016) in conjunction with the manuscript in question. Much of the data and ecological context for the current study is presented in the 2016 manuscript. The two studies together present an interesting picture of the ambiguity of the relationship between genetic diversity and population size, and highlight the need for further study of ecological traits and genetic diversity. The current study seems to be a logical continuation of the 2016 project, and Figure 1 is a good example of the integration of data from each of these projects. However, the current manuscript could be improved by elaborating on the combined results in the discussion.”  We have elaborated on the combined results in the discussion.“L41-43 – what are the hypotheses for deviations from expectation in these studies? Good to have context here about why we might not expect things to correlate” We added a statement that the authors credit natural selection for the deviations from neutral expectations. However, the possibility of coalescent variation  and variation in mutation rates is also present.L59 – not sure about the use of “haphazard” here; larger scale analyses are important for other reasons, especially considering impacts of population connectivity on genetic diversity We eliminated the term haphazard.L117-118 – references for this? We have added two references for this statement.L118-120 – References for census studies are inaccessible. I feel more detail is needed here to justify the validity of qualitative data – observations on abundance across seasons? Over periods of how long? Etc. Even a figure plotting the cited census data with genetic diversity would be valuable. We have added more information about our justification for relative population sizes, including another reference to Wallace’s can trap study. .L124 – how large was the main collection area, and what are overall geographic ranges like for these species? More detail on connectivity of these species with surrounding populations would be useful when considering genetic diversity and population size   We have no data on the connectivity among surrounding populations  DoneTables summarized into figures would be helpful, especially Table 3. Much of this appears in fig 1, but the summary of information could be improved; cannot immediately tell which are small pops, which are large pops, which trophic level the species represent, etc. It’s not a lot to keep track of or to find in the text, but figures should be interpretable by themselves. Done (see response to Reviewer 1 for the same criticism).“L236 - Tables 4 and 5 are identical” Oops! The correct Table 5 has been added.“L334 – references for eco data? Found in open habitat doesn't necessarily = generalist…if rocky habitat is common then rock specialists could also be common…”small fraction of total habitat” – any kind of quantification available? What is the rest of the habitat characterized by? Ecological specialization is a very interesting and important consideration, and a valuable addition to the study, but it does feel a little too coarse as it stands. It would be helpful to at least include some references to strengthen inferences made here. “ We have added language to our discussion of the collection maps to discuss the fact that the area of suitable habitat for the rock dwelling species is less than that of the species that prefer the open areas. In addition, the habitat generalists also make use of the rock areas in addition to the open areas. Habitat specialization is simply more justification for our ranking of relative population sizes. .L386-387 - Reasoning behind “likely lack of statistical power”? Authors have a good number of individuals - do they mean due to number of species? Could use a little clarification This is not a “number of species” problem. We discuss this a bit in the results section . This test relies on the ability to differentiate a multi-modal distribution (of pairwise nucleotide mismatches among sampled copies of a locus) from a unimodal distribution. Differentiating distributions require a large number of sampled copies to have strong statistical power. In this case we are trying to infer a population expansion by detecting a unimodal distribution. In the statistical test, the unimodal distribution is the null hypothesis, so that failure to reject the null could be due to population expansion or lack of power.L395 – need some background here to contextualize coalescent variance  Coalescent variance is the term to describe the variation in TMRCA (Time to Most Recent Common Ancestor) among loci sampled from a common ancestor. As a result, it follows that there can be differences in genetic diversity of a given locus sampled from different populations/species just because the locus has a recent TMRCA in one population and a more ancient TMRCA in another population, simply by chance. I believe that the term “coalescent variance” is in common use in population genetics, but we can include a paragraph like the one in this response if the editor feels that it would be useful. For this revision we have defined it in a few words in the text on its first mention  as “coalescence variation”, which I have changed to “coalescent variance” for consistency.L407 – It would be valuable to situate the results of this study better in the context of previous results – Hague and Routman (2016) showed clear relationship between diversity and pop size but now we have conflicting relationship when we throw smaller pops into the mix – what could be going on here? This is especially interesting and merits additional study with all species to look for ecological correlations – and so should be mentioned with more detail in the discussion We have elaborated on the discussion to discuss the other species for which we have data and the paper mentioned by reviewer 1.Associate Editor Dutta “I agree with the reviewers that the manuscript needs a (i) map with details of sampling locations and habitat types, (ii) explicitly stated hypothesis and predictions of this manuscript, (iii) changing some tables to figures for better presentation of the results, and (iv) better presentation of abundance and habitat specialization proxies. In addition, I would suggest to clearly and explicitly state the expectations as mentioned by one of the reviewers. For example, make a statement at the end of the introduction that “We tested neutral vs selection theories for relationship between genetic diversity and (a) body size, (b) population size, (c) trophic level, and (d) habitat specialization. Our expectation is that ….. under population size, genetic diversity would be …Carnivore specialist< carnivore generalist< herbivore specialist< herbivore generalist” Items i, iii, and iv are address in responses to Reviewers 1 and 2. We have added language to address explicit hypotheses and expectations.“I suggest to refrain from reference to ‘our lab’ and replace it with our previous research (citation) or previous research (citation).” Done.“Also, please be consistent in how you use and present the species names: and Genus species, or G species, or G.species, or Gs: choose one and be consistent with it please.” We have corrected any deviations from the traditional usage of scientific names: the first use of the name is written out fully and thereafter the genus name is abbreviated as a single letter and only the species name is written out fully. The exception is for readability of the new figures: we adopted the use of a 4 letter code for the species that consists of the first two letters of the genus and species names, e.g. CRBI for Crotaphytus bicinctores. This is defined in the figure legends. If the editor would like us to be consistent across text and figures, we could easily adopt the 4 letter codes in the text as well.Sincerely,Eric Routman References:Hague MTJ, Routman EJ. Does population size affect genetic diversity? A test with sympatric lizard species. Heredity. 2016 Aug 26;116:92-98.Micheletti S, Parra E, Routman EJ. Adaptive color polymorphism and unusually high local genetic diversity in the Side-Blotched Lizard, Uta stansburiana. PLoS One. 2012 Oct 25;7(10):e47694.AttachmentRutherford et al cover letter for revision.docxSubmitted filename: Click here for additional data file. 4 Oct 2019Genetic variation across trophic levels: a test of the correlation between population size and genetic diversity in sympatric desert lizardsPONE-D-19-14658R1Dear Dr. Routman,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,Trishna DuttaAcademic EditorPLOS ONEAdditional Editor Comments :Dear Dr. Routman,Congratulations on submitting a well-addressed reply answering the issues raised by the two reviewers and myself. I find that you have addressed a majority of the issues satisfactorily, and I am happy to accept this manuscript for publication in PLOSONE.My only minor comments are: (1) I encourage you to insert an inset in Fig1 (map of the study area), and adjust marker transparency so that the underlying collection locations can be visualized.(2) Page 19, L 368: First word should be Indeed, but is ndeed. Please correct this.Thank you and CongratulationsRegards,Trishna DuttaReviewers' comments: 22 Nov 2019PONE-D-19-14658R1 Genetic variation across trophic levels: a test of the correlation between population size and genetic diversity in sympatric desert lizards Dear Dr. Routman:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. For any other questions or concerns, please email Thank you for submitting your work to PLOS ONE.With kind regards,PLOS ONE Editorial Office Staffon behalf ofDr. Trishna Dutta Academic EditorPLOS ONE


Oxytropis ochrocephala, its symbiotic endophyte Alternaria oxytropis, and the mycotoxin swainsonine. Host genetic variation was characterized by microsatellite markers. Endophyte infection rate and swainsonine levels were determined by PCR and HPLC, respectively. Genetic markers defined two distinct host populations and revealed that host genetics were significantly correlated with geographical location, elevation, and precipitation. As the host diverged, symbiotic interactions were reduced or failed to produce detectable swainsonine in one host population. Host genotype and precipitation had a significant impact in shaping swainsonine production at the population level. This study highlights the effect of host genotype in influencing this interaction in locoweeds. Many plant endophytes produce mycotoxins, but how host genetic variation influences endophyte colonization and mycotoxin production under natural conditions is poorly understood. This interaction has not been fully considered in many previous studies which used controlled experiments with agronomic or model plant species. Here, we investigated this interaction in a naturally occurring forb (a locoweed species)  Fungal endophytes are ubiquitously associated with almost all plant species. These associations represent 400 Myr of co-evolution from when plant ancestors colonized and started to adapt to terrestrial ecosystems ,2,3. AltPlant fungal symbiosis is commonly considered mutualistic largely because in many cases such interactions improve fitness through increased growth and reproduction, abiotic stress tolerance, or herbivore deterrence . HerbivoLolium perenne) and Epichloё (anamorph Neotyphodium) species, largely because such symbiosis makes the grass poisonous to herbivores and, thus, is detrimental to agriculture. However, this endophyte–grass interaction has also provided an ideal model for ecologists who are focusing on co-evolution and agronomists interested in forage improvement. Such studies have provided real insights into the fundamental underlying mechanism but their relevance to other common grassland species needs to be defined [The variety of toxic alkaloids and the extent of harmful effects on herbivores may be determined by both host and endophyte genotypes ,7. Indee defined . Additio defined . Thus, aOxytropis ochrocephala is a perennial native forb widely distributed in western China [Alternaria sect. oxytropis (Ascomycota) [A. oxytropis is categorized as a Class 1 endophyte [Oxytropis and Astragalus spp.) and is strictly vertically transmitted [A. oxytropis has been posited as commensal [A. oxytropis and swainsonine content does not increase following mechanical damage  of the plant, so, any defensive response in locoweeds is not likely to be improved by this symbiosis. rn China . It formomycota) ,13,14, womycota) . A. oxytndophyte  as it innsmitted . A. oxytommensal  leads toA. oxytropis [A. oxytropis strains (chemotypes), producing either high or low levels of swainsonine. Thus, the level of swainsonine production in subsequent generations can be influenced by the abundance and types of different chemotypes [A. oxytropis chemotypes onto two endophyte-free locoweed accessions showed no noticeable effect on the amount of swainsonine produced. Although the authors suggested no role for host genotype on swainsonine production, the role of host genotypes in sorting the endophyte from different genetic backgrounds or chemotypes in the next generation has not been explored.Currently, the role of the host genotype in influencing the initial interaction is unclear. Swainsonine concentration in a host plant depends partially on the abundance of xytropis . Additioemotypes . Cross-iO. ochrocephala has evolved, and whether host genotype influences endophyte colonization and mycotoxin production in naturally evolved populations. Additionally, we consider how environmental variables could be an important additional determining factor. Thus, we quantified plant genetic variation, endophyte infection rate, and mycotoxin concentrations in the wild Chinese forb O. ochrocephala from sites across western China. In this paper, we consider how We demonstrate that while the host had diverged into two populations, host genotype influenced the host’s ability to support swainsonine-producing endophytes. In turn, swainsonine levels were affected by both host genetics and environment. For host genetic variation and swainsonine production, precipitation was suggested to be the major environmental variable. O. ochrocephala samples from 33 accessions gathered from western China. Genetic diversity was calculated at 14 loci (Ho) was 0.474 (0.3016~0.652) whereas the expected heterozygosity (He) was 0.766 (0.484~0.887). The average polymorphic information content (PIC) was 0.73 (0.41~0.82), which indicated a satisfactory discriminative power for the primer sets. F statistics inbreeding coefficients were FIS: 0.176, FST: 0.253, and FIT: 0.385. The overall gene flow (Nm) equaled 0.738 < 1. This low gene flow indicated limited gene flow amongst the sampled accessions. Fourteen polymorphic EST-SSR (Expressed Sequence Tags- Simple Sequence Repeats) primer sets were used to characterize genetic diversity in 368  14 loci  and 192 O. ochrocephala. The model value of ΔK was found to be at K = 2, suggesting that genetic differentiation is best explained if two distinct, large-scale populations were assumed to exist  dendrogram constructed based on Nei’s genetic distance showed a consistent pattern of genetic relationships , analysis of molecular variance (AMOVA) showed that 72.36% of the total genetic variation could be attributed to “within accession” variation, 17.95% to “amongst accessions within populations” and 9.69% to “amongst populations” variation . Genetic variation in ariation . The fixn = 20) and 2 , but swainsonine concentrations differed significantly . In order to compare the ability to support endophyte symbiosis and to produce swainsonine in host plants, we measured endophyte infection rates and swainsonine concentration in each accession . There ir = 0.523, p < 0.001, and n = 528 pairwise, r = 0.424, p < 0.001, and n = 528 pairwise, r = 0.606, p < 0.001, and n = 33) and population clustering . This demonstrated the effect of isolation-by-distance and altitudinal environmental variation on the genetic divergence seen in the two populations of O. ochrocephala.We tested whether environment  plays an important role in plant genetic variation. Additionally, we tested whether G x E (genetic x environment interactions) acted to influence patterns of endophytic symbiosis and mycotoxin biosynthesis. Therefore, we performed a series of pairwise correlation analyses between each two factors from the environmental and genetics, and endophyte infection rate and swainsonine concentration. Results showed that geographical distance  and precipitation  changed with elevation (p < 0.001) average annual precipitation (392 mm) than pop 2 (694 mm). The correlation coefficients between annual precipitation and genetic distance , population partition , and clustering  were significant at each level of genetic hierarchy. Thus, we suggest that precipitation plays a key role in driving plant genetic divergence in O. ochrocephala. Such patterns were not observed in annual temperature, though it was moderately correlated with expected accession heterozygosity . Higher temperature may therefore influence the reproduction of O. ochrocephala by changes in flowering, pollination, and the setting of seeds, which can contribute to heterozygosity levels.Altitudinal environmental variation is often linked with changes in temperature and precipitation. Across our sampling sites, both annual temperature (levation . Pop 1 hA. oxytropis (A. oxytropis infection (compared to 5% in pop 1), and also 30% of the accessions with A. oxytropis symbiosis had no detectable swainsonine (10% in pop 1). Chi-square test of the distributions of the four types of endophyte/swainsonine combination (Endo+ Toxic+/Endo+ Tox−/Endo− Tox+/Endo− Tox−) are not equal in the two populations (p = 0.0095). This suggests that in the two populations, the ability to produce swainsonine by endophytes may shift toward being more stochastic in pop 2.In terms of endophyte colonization, the data showed that at the accession level, host genetics and environment are not correlated with the endophyte infection rate of xytropis . Neverthxytropis  showed tr = 0.218, p < 0.001, and n = 450 pairwise), it became more obvious at higher levels of genetic divergence  and also correlated with geographical distance . A further multiple regression analysis showed that the three significantly correlated variables  explained 39%  of the total variation in swainsonine concentration. Taken together, these data indicate that G x E affects the production of swainsonine.Although the correlation between swainsonine and host genetics is not strong when considering genetic distance . Around three-quarters of this genetic variation could be attributed to within-accession variation and the rest among accessions or populations (FST = 0.276). This accorded with the pattern of genetic variation seen in O. campestris var. chartacea (Fassett’s locoweed) in the United States [F statistics. As the overall genetic variation is high, inbreeding is likely to have occurred due to the geographical isolation of the surveyed accessions and accompanying limited gene flow (Nm < 1). Low gene flow is consistent with the isolation-by-distance pattern based on the correlation between genetic distance and geographical distance. Interestingly, genetic distance is also correlated with elevation.In this study, we examined the genetic variation in a wild locoweed plant d States . The oveST for mixed breeding or selfing/clonal propagation is 0.18 and 0.36 (corresponds to median Nm of 1.17 and 0.45), respectively [ST (0.253) and Nm (0.738) in O. ochrocephala fall in between the above two, indicating both mixed breeding and clonal strategies are employed. Although Oxytropis spp. are generally considered to be cross pollinated, some species can be self-compatible [Previous studies indicated that in plants the median Fectively . The FSTmpatible .O. ochrocephala split into two genetically distinct populations. AMOVA analysis suggests significant genetic differentiation at three hierarchical levels: within accession, among accession, and between the two genetic populations. The two populations were allopatrically isolated by the Qinling Mountains  primers were designed based on transcriptome sequencing data previously generated in our laboratory . Fourteew/v) NaOH to adjust the pH to 9–10. After centrifugation, n-butyl alcohol was added, and the solution was extracted eight times. The n-butyl alcohol phase residue was recovered, combined, and evaporated. The residue was dissolved in 1 mL methanol and filtered for further analysis. Three independent extractions were performed for each accession.Swainsonine concentration was measured for each accession. Leaf samples of the same weight from every individual within one accession were pooled together and the levels of swainsonine measured. Swainsonine extraction was performed as previously described . In brie2PO4 (pH 7.0) buffer/acetonitrile . Flow conditions were 0.8 mL/min with the column maintained at room temperature. Swainsonine concentrations were calculated from peak areas obtained at 205 nm with reference to the standard peak area. The detection limit of swainsonine was 0.005 mg/mL. Swainsonine concentrations were calculated and represented as a millesimal of the dry weight of plant leaves (SW‰).Swainsonine concentration was analyzed on a HPLC using a Hypersil ODS C18  column  according to the method of Duan et al. . Pure swA. oxytropis infection rate (see below for explanation). PCR was performed using ITS5 (5′-GGA AGT AAA AGT CGT AAC AAG G-3′) and OR1 (5′-GTC AAA AGT TGA AAA TGT GGC TTG G-3′) primers and PCR products were digested with AVA II endonuclease and separated on agarose gels. Crucially, the A. oxytropis ITS  sequence can be digested by AVA II into ~380 and 200 bp size bands. As many amplicons could only be partially digested by AVA II, we randomly selected and sequenced 90 samples  to validate the identity of the PCR amplicons , the effective number of alleles (Ne), and the expected (He) and observed (Ho) heterozygosities, inbreeding coefficient (F statistics), and polymorphic information content (PIC).Genetic diversity was calculated for all loci using POPGENE version 1.31  includinFst) were performed using Arlequin version 3.11 [DISTRUCT [AMOVA analyses and differentiation index (ion 3.11 . A phyloion 3.11  was consion 3.11 . Bootstrion 3.11 . Potentiion 3.11 . STRUCTUDISTRUCT . EvolutiDISTRUCT .n < 5), accession GS1, GS10, and QH1 were excluded from the correlation analyses relating to endophyte infection rate and swainsonine concentration. Multiple regression tests were also performed in SPSS, in which swainsonine levels were assigned as response variable and all the others with significant association assigned as explanatory variables. Differences in the means of the two populations were analyzed in SPSS by one-way ANOVA.Pairwise genetic distances between all accessions were calculated using GenAlEx version 6.5 . CorrelaWe further wanted to infer how the ability to produce swainsonine varied during host divergence, therefore, we compared in each accession the endophyte infection rate and swainsonine content. Based on whether they contain endophyte and swainsonine or not, all the accessions were classified as four types: Endophyte+ Toxic+/Endo+ Tox−/Endo− Tox+/Endo− Tox−. To determine whether the distributions of the frequencies of the four types in the two populations are equal a Chi-square test was performed in SPSS.


Neuroimaging provides a unique tool to investigate otherwise difficult-to-access mental processes like visual imagery. Prior studies support the idea that visual imagery is a top-down reinstatement of visual perception, and it is likely that this extends to object processing. Here we use functional MRI and multi-voxel pattern analysis to ask if mental imagery of cars engages the fusiform face area, similar to what is found during perception. We test only individuals who we assumed could imagine individual car models based on their above-average perceptual abilities with cars. Our results provide evidence that cars are represented differently from common objects in face-selective visual areas, at least in those with above-average car recognition ability. Moreover, pattern classifiers trained on data acquired during imagery can decode the neural response pattern acquired during perception, suggesting that the tested object categories are represented similarly during perception and visual imagery. The results suggest that, even at high-levels of visual processing, visual imagery mirrors perception to some extent, and that face-selective areas may in part support non-face object imagery. Visual imagery is usually defined as visual perception in the absence of external stimuli. Colloquially, people refer to imagery as “seeing with the mind’s eye.” Visual imagery mirrors perception both in its neural correlates and its behavioral effects –3. Many Though most of the functional MRI (fMRI) work on visual imagery has focused on regions in early visual cortex . Critically, to address our main question, whether imagery of non-face objects at the subordinate-level recruits face-selective areas, we included cars and recruited a sample of participants with above-average car recognition. Based on prior work, we expected cars to be represented in FFA in these participants, since this area is the most robust predictor of behavioral performance in individuation judgments for cars –28. AnotWe asked if imagery of faces and cars engages face-selective fusiform regions in above-average car recognizers, basing this prediction on evidence that perception of faces and cars engage these regions in comparable populations. In recent work measuring individual differences in the vividness of mental imagery, we found that the vividness of domain-specific imagery (for cars) relates to that of domain-general imagery, but not to perceptual or semantic knowledge ability levels with cars . TherefoTo address this question, we used MVPA to investigate the information that spatial patterns of neural responses in face-selective regions contain about object categories, when above-average car recognizers imagined faces, cars and a selection of familiar objects. Essentially, MVPA quantifies reproducible spatial patterns of activity that can discriminate between different conditions  indicatby males . Thirty-3). During the first participant’s scan,174 slices were acquired, and for all following participants’ scans, 190 slices were acquired. All functional scans were acquired using standard gradient-echo echoplanar T2*-weighted imaging . By using a comparable functional voxel size (3 mm on a side) to that typical at lower field strengths, we were able to capitalize on the higher field strength (7T) to achieve better signal-to-ratio than would occur at lower field strengths )The MRI portion of the experiment began with a structural run, followed by 2 functional localizer runs and 5 experimental runs. Each participant performed the same tasks with the same stimuli in the same sequence. The localizer runs consisted of 15 16-second blocks with an 8-second block of fixation between each 16-second block, beginning and ending with the task. Each block presented stimuli from either the face, object, or scrambled category and each image was displayed for 900 ms with a 100 ms temporal gap between presentations. Participants completed a 1-back task to detect the one image repeated per block, which they indicated with a button press (right hand index finger). The second localizer run was identical to the first except for the order in which the blocks and stimuli were presented. Each localizer lasted 6 minutes.For both imagery and perception runs, 4 stimuli were chosen from each category  to use in a given run . These iDuring perception runs, stimuli were presented with a grey fixation cross superimposed over the stimuli, and participants determined if the image was displaced up or down relative to the screen’s center. On each trial, images were displaced either 75 pixels upwards or downwards from the screen’s center, but were always centered horizontally . Particiwww.brainvoyager.com), in-house Matlab scripts, and LibSVM [The HR structural scans were normalized to Talairach space . All fund LibSVM . FunctioLocalizer data were submitted to a general linear model (GLM) with regressors for each stimulus category. Face-selective ROIs were defined using the Face>Object contrast from the localizer GLM. We distinguished between two face-selective fusiform areas: a more posterior FFA1 and a more anterior FFA2 ,30.3) for univariate analyses or 27 functional voxels (729 mm3) for MVPA using in-house Matlab scripts. This script created ROIs by growing regions to include the next-highest contiguous activated voxel until a given size (in this case 4 or 27) was reached. Functional voxels belonging to more than one ROI were removed from both ROIs.As is common when defining these functional regions ,33,34, wMVPA was conducted using a linear support-vector machine (SVM). Because we used different subordinate-level exemplars for each run and a blocked design, we trained our classifiers at the category level (e.g. cars vs. objects instead of Kia Forte vs. Honda Accord). We used MVPA to answer two different questions. In an initial series of classification analyses, we asked whether distinct image categories led to different representations in each ROI. Specifically, for each classification, a leave-one-run-out approach was used in which one run was used as test data and the remaining runs were used as training data. Classification rates represent the proportion of times the classifier produced a correct prediction. Because we are interested in object representations during visual imagery, we first focus on our imagery condition. To analyze this imagery condition, the classifier was trained on two runs and tested on one, for the three possible iterations. For the overall classification performance report, an average of these three iterations was calculated. MVPA was done for each participant individually, and results were averaged across participants within equivalent ROIs to form group results. If the classifier is able to distinguish one category from another, this provides evidence that the two categories are represented differently during imagery within that ROI. Most importantly, we were interested in whether imagined cars could be decoded from imagined objects in face-selective regions.Successful car vs. object decoding during imagery cannot, however, tell us if cars are imagined in a manner similar to the way they are perceived–decoding could be based on any difference in the representations during the imagery conditions. To address this question directly, in a second set of analyses we trained a classifier for each participant using data acquired during the imagery condition and then tested the classifier on data acquired during the perception condition. Prior work with designs that collected equal amounts of imagery and perceptual data obtained better cross-task classification when training on imagery than when training on perception . These aIndependent RFX GLMs were fit to the imagery and perception runs with each category , convolved with the canonical hemodynamic response function, included as a separate regressor. Parameter weights were calculated for each voxel, then averaged across all voxels composing an ROI. We examined all three possible pairwise contrasts of our three stimulus categories: face versus car, face versus object, and car versus object.https://figshare.com/s/24fc1314649a6e2b2899. Average performance on each behavioral test is reported in Data are available at t(19) = 4.98 p ≤ .001, d = 2.28; Matching Task: t(19) = 8.71; p ≤ .001, d = 4.00). While these tasks are not precisely matched in difficulty, in large unscreened samples the VETs have highly similar means (see [t(19) = 12.14; p ≤ .001, d = 5.57).As expected (given that we screened based on VET-Car and matching task-Car performance), participants performed significantly better with cars than non-car categories (VET: (ans (see ). Our paPerformance on behavioral tasks in the scanner indicated that participants were attending to the stimuli. The average performance on the n-back task during the first and second localizer runs (not including scrambled images) was 79% (SD = 19%) and 93% (SD = 8%), respectively. Average performance on the up/down displacement task during the first and second perception runs was 83% (SD = 26%) and 86% (SD = 17%), respectively.To grow regions suitable for MVPA, we created ROIs composed of 27 functional voxels. Though past work has shown that ROI sizes of around 100 voxels produce optimal MVPA performance , some ofr’s = -.17 –.27, p’s > .24). This is unsurprising given that we intentionally selected participants with high car recognition ability.To test if activity patterns for imagined faces, cars and objects were distinguishable, we performed MVPA on the imagery task  separately for each ROI and each participant. This addresses whether it is possible to decode cars versus objects in face-selective areas. The classifier achieved above chance performance in both lFFA2 and lFFA1, as well as rFFA1 . DecodinIn object-selective regions, the classifier could decode imagined faces versus imagined objects and imagined cars versus imagined objects in bilateral PHG2 and lLOC . ImagineTo ask whether the categorical differences we observed in imagery reflected the representations evoked by these categories during perception, we trained a classifier with data from the imagery runs and tested its ability to decode the perception runs. Central to our question, we were able to distinguish perceived cars from perceived objects based on imagery information in bilateral FFA2 . AdditioIn all object-selective regions, perceived faces were decoded from perceived objects based on imagery information, and perceived cars were also distinguished from perceived objects in all but bilateral LOC ROIs . All objThe average parameter weights are reported in While our main predictions concerned the MVPA results, we explored the possibility that our task led to particularly small univariate responses due to repetition suppression. Repetition suppression has been used as a tool to characterize functional specificity of neuronal populations, since neurons tend to adapt most to their preferred stimuli ,55. PrevThe present task required displacement judgments (which are not highly attention-demanding) and a great deal of stimulus repetition (each image repeating 12 times per run). This combination likely fostered repetition suppression, and perhaps to a greater extent within category, for cars and faces, than for objects, since cars are visually  more homogeneous. Thus, given both the stimulus characteristics and the fact that face-selective areas of individuals with above-average car recognition ability should be more selective for cars than for objects, we might expect more repetition suppression for cars and faces than objects, resulting in a paradoxically low average activation for these categories.p < .0001, ηp2 = .64). We unpacked this interaction with a run x condition ANOVA in each type of ROI. In face-selective ROIs, there was a run x category interaction , with more habituation for faces and cars than for objects. The same interaction was also significant in the PHG ROIs , but in this case it reflected more habituation for objects than for faces and cars. The interaction was not significant in LOC ROIs .We investigated this possibility by calculating parameter weights for each run using fixation as a baseline , these We set out to determine if the FFA can support imagery of non-face objects at the subordinate-level. To answer this question, we compared how well classifiers could distinguish between imagined faces, cars and objects in a sample of above-average car recognizers. We found that a classifier trained and tested on imagery runs can distinguish between imagined cars and imagined objects in some face-selective regions, suggesting that car representations exist in face-selective areas during visual imagery.In addition, a classifier trained on imagery runs can successfully distinguish perceived cars from perceived objects in the anterior FFA (FFA2), an area that has shown the most robust car expertise effects in prior work ,34 and aIn fact, the results generally suggest that faces and cars can be decoded from objects in several non-face selective areas. Such results are consistent with the general idea that categorical representations are distributed . The paThough our main analyses focused on distinguishing imagined cars from imagined objects when training and testing on imagery data, we should note that this within-task (imagery to imagery) classifier appeared to show somewhat weaker decoding of faces from objects than cars from objects. That is, imagined faces vs. imagined objects decoding was only above statistical significance in one face-selective region (lFFA2). We do not wish to over-interpret this relatively weak decoding for faces in this condition, because we do not have sufficient statistical power to show that decoding in lFFA2 is larger than that in other face-selective ROIs or that the decoding for cars was stronger than that for faces. Instead, we would emphasize that the cross-trained analyses (training on imagery and testing on perception) demonstrate that, in most face-selective areas, representations of imagined faces and objects are sufficiently similar to representations of their perceived counterparts to allow training of a classifier that can decode representations of perceived faces and objects. Perhaps what these results highlight is that there may be factors at play that we do not presently have a good way to measure. For instance, we know little about the relative difficulty of imagining objects from different categories, such as cars and faces, which could recruit different strategies, thereby facilitating decoding. While we can measure the neural correlates of imagery, there are few methodological approaches that allow us insight into the quality of the images our participants generate, aside from their similarity to perceptual representations.Though we made no predictions about the face versus car classification, we note that previous work found a negative correlation between car recognition ability and face versus car classification performance . Here, wWe did not systematically compare within- and across-task analyses, because the across-task analysis uses more data . However, a qualitative comparison suggests that decoding is not always best when more data is used, so it is interesting to speculate on this qualitative comparison. In the extreme, if the representations used during perception and imagery were identical, then we would expect across-task classification to always be better than within-task classification because more data is available. This is generally the case for the contrast of faces vs. objects, where decoding was better in across- than within-task analyses in almost every ROI (see to the extent that the representations used during imagery and perception differ. It is interesting that within-task decoding appears to outperform across-task decoding mainly in the car vs. object comparison. This could mean that some of what our participants do during imagery may not overlap with perception (more for cars than faces and objects), and yet still be category specific (sufficient to distinguish cars from objects). For instance, despite being asked to imagine the specific images used during the perception task, each car expert may have his or her own preferred representation of the car models we used . This is obviously speculative but it presents a challenge to the suggestion by Cichy et al [However, another consideration when comparing the across- and within-task analyses is that the across-task analysis trains and decodes on different tasks, whereas the within-task analysis uses a leave-one-out approach in a homogeneous set of runs. In that sense, even though the within-task analysis uses less data, it may provide better decoding than the across-task analysis hy et al  that allt(122) = 1.05; p = .30, d = 0.23, larger unselected sample of 104 from [In our univariate analysis, we did not entirely replicate previous findings –6 of gre104 from ). Regard104 from ). Additi104 from ,27,28). Our results provide new evidence that visual imagery mirrors visual perception, not only in early visual areas , but alsS1 FigAverage accuracies of the across-task classifier Click here for additional data file.S1 TableFixation is used as the baseline. Run 2 parameter weights that are significantly lower than Run 1 parameter weights (one-tailed) are denoted with an asterisk.(DOCX)Click here for additional data file.S2 Tablet-tests are one-tailed.In both Tables (DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.