Range expansions may create a unique spatial genetic pattern characterized by alternate genetically homogeneous domains and allele frequency clines. Previous attempts to model range expansions have mainly focused on the loss of genetic diversity during expansions. Using individual-based models, we examined spatial genetic patterns under two expansion scenarios, boundary-limited range expansions (BLRE) and phenotype-limited range expansions (PhLRE). Our simulation revealed that the genetic diversity within populations lost quickly during the range expansion, while the genetic difference accumulated between populations. Consequently, accompanying the expansions, the overall diversity featured a slow decrease. Specifically, during BLREs, high speed of boundary motion facilitated the maintenance of total genetic diversity and sharpened genetic clines. Very slight constraints on boundary motion of BLREs drastically narrowed the homogeneous domains and increased the allele frequency fluctuations from those levels exhibited by PhLREs. Even stronger constraints, however, surprisingly brought the width of homogeneous domains and the allele frequency fluctuations back to the normal levels of PhLREs. Furthermore, high migration rates maintained a higher total genetic diversity than low ones did during PhLREs. Whereas, the total genetic diversities during BLREs showed a contrary pattern: higher when migration was low than those when migration was high. Besides, the increase of migration rates helped maintain a greater number of homogeneous domains during PhLREs, but their effects on the number of homogeneous domains during BLREs were not monotonous. Previous studies have showed that the homogenous domains can merge to form a few broad domains as the expansion went on, leading to fewer homogeneous domains. Our simulations, meanwhile, revealed that the range expansions could also rebuild homogeneous domains from the clines during the range expansion. It is possible that that the number of homogeneous domains was determined by the interaction of merging and newly emerging homogeneous domains. Species range expansion is an important biological process, which has shaped global biodiversity patterns. Species expand their ranges from natal sites at some point in their evolutionary history The speed of expansions may vary considerably so that Nullmeier and Hallatschek Several previous studies have investigated the spatial genetic consequences of PhLREs. In general, a species' range expansion is often accompanied by loss of genetic diversity because of the founder effect The spatial genetic consequences of BLREs, although as important as those of PhLREs, have rarely been investigated, with the exception of simulations carried out by Nullmeier and Hallatschek In this study, we aim to explore different spatial patterns of genetic variations, specifically the number of genetically homogeneous domains and the fluctuations of allele frequencies, at the expansion front generated by the PhLREs and BLREs with varied speeds of boundary motion. We simulated the population genetic dynamics of neutrally evolved alleles at the advancing fronts using individual-based models (IBM). The impacts of migration and reproduction on the spatial genetic patterns were also examined.K), reproduction rate (r) and migration rate (m). As noted by Excoffier and Ray (2008), small populations tend to produce less defined homogeneous domains and broader transition zones, therefore we set K to 100 in our simulations, which was much greater than that used in Korolev et al.'s (2011) simulations (K = 30), and was within the range suggested by Excoffier and Ray's (2008) simulations. For reproduction, the number of offspring of each individual was drawn from independent Poisson distributions with means of r = 1.1, 2, 3, 5. Individuals were assumed to die immediately after reproduction. The migration rate m were set to 0.01, 0.1, 0.2, and 0.4, which lied within the range employed in the previous simulations r values  were tested with a constant m of 0.2. To test the effect of migration rate, different m values  were used with a constant r of 3. The individuals migrated with a probability of n demes. The value of n varied from 2 to 4, depending on the position of each deme with respect to the lattice boundaries.The simulations were carried out using a lattice with 75 row ×200 column grids. The populations comprised asexual, haploid individuals with discrete generations. Each individual genotype was typed by one neutral diallelic locus. We defined three population parameters: carrying capacity  in the unsuitable habitat. The unsuitable grids gradually turned into suitable, column by column, with the progression of generations. This process was used to imitate habitat expansion driven by environmental change. We employed six different speeds of boundary motion in BLREs, taking 600, 1000, 2000, 4000, 6000 and 7900 generations for all of the grids to change into suitable habitats. Other parameters were set as follows: r = 3, m = 0.2, and the initial allele frequency  = 0.5. Our simulation pilots showed that it took a mean of 533.6 generations for populations to expand from left to right and all populations reach K during a PhLRE, given r = 3 and m = 0.2. Therefore, 600 generations seemed to be a slight constraint on expanding populations. Additionally, the populations that do not expand make the extreme BLRE, which is usually referred as a process of isolation by distance Two range expansion scenarios, BLRE and PhLRE, were simulated. During PhLRE, all grids were designated as suitable habitats and range expansion occurred immediately after the simulation was initialized. During BLRE scenarios, only the first 20 columns were designated as suitable habitats at the initialization, while the others were all unsuitable habitat. The individuals can not reproduce  algorithm, which has been widely used in diverse fields of science to describe periodic patterns. The allele frequencies of a column of demes were considered as a wave curve p at index k, and k. The term of N is the number of demes in a column, and the highest frequency can be identified as N is odd in our simulations. A higher Where Genetic differentiation within and among populations at an expansion front was assessed by the average number of pairwise differences within populations (PX), the average number of pairwise differences between populations (PXY), and the corrected average pairwise difference between populations (Pc) Each parameter combination was simulated for 100 times. The counts and width of genetically homogeneous domain and clines, For both the PhLRE and BLRE, many narrow homogeneous domains emerged very quickly at the beginning of the expansions . CorrespThe PhLREs exhibited the narrowest clines, however slow boundary motion of BLREs broadened the clines . In the High migration rates broadened the clines but narrowed the homogeneous domains . It alsog PhLREs .Initial allele frequencies deviated from 0.5 accelerated the fixation and led to fewer but broader homogeneous domains and narrower clines , becauseThe simulations revealed that at the beginning of an expansion, pairwise differences within populations (PX) decreased quickly at the advancing front, while the corrected average pairwise difference between populations (Pc) increased quickly. The changing rate of PX and Pc soon decreased with the expansion , 3, 4, 5Generally, high migration rate increased the PX, but decreased the Pc . HoweverOur simulations showed stripe-like genetic patterns in expanded populations, which were equivalent to the genetic sectoring patterns established by previous studies The speed of boundary motion exerts great impacts on the spatial genetic pattern of the advancing front. For example, very slight constraints on boundary motion could drastically narrowed the homogeneous domains and increased the allele frequency fluctuations from those levels exhibited by PhLREs . In contThe average number of pairwise differences revealed a clear picture of changing spatial genetic patterns during expansions. The slow decrease in PXY, fast loss of PX, and fast increase in Pc at the expansion front suggested a process represented by slow loss of total diversity, quick loss of that within populations, and quick accumulation of genetic heterogeneity between populations corresponding to the emergence and merging of genetically homogenous domains.The speed of boundary motion also influenced the genetic diversity at advancing fronts. Slow boundary motion increased the diversity within populations but decreased the heterogeneity between populations. Though Nullmeier and Hallatschek Under PhLRE, though species ranges expanded most quickly, the total genetic diversity was intermediate among those during BLREs. For simulations with stationary habitats, the absence of founder effect maintained the total genetic diversity at the “non-expanding fronts”. These results indicated that PhLRE and non-expansion simulations can not be simply considered as BLREs with the quickest or the slowest boundary motions.Larix decidua) maintained high genetic diversity during slow range expansion due to global warming, which has usually been attributed to the intensive mixing of genes Lplateauto  =  2Km to detect a range expansion, where K was the carrying capacity and m was the migration rate. Furthermore, many factors, e.g. landscape heterogeneity and multiple introductions Some previous empirical studies have implied differences in genetic patterns between BLRE and PhLRE populations. For example, examinations of several invasive species have revealed a decrease in genetic variation at expansion fronts It is notable that spatial expansions, can not only lead to allele frequency clines by eroding genetically homogeneous domains, but also rebuild genetically homogeneous domains from those clines by the ongoing demixing . It is pHigh migration enhanced genetic diversity within populations but reduced heterogeneity between populations. Surprisingly, we found that high migration rate maintained a higher total genetic diversity than low migration rate during PhLREs, but the situation was reversed during BLREs, though Nullmeier and Hallatschek r diluted effects of migration by reproducing more individuals from local population, and thus hindered the formation of clines. This was exactly the case in BLREs, where front populations had more time to reproduce and migrate. High reproduction also increased the number of homogeneous domains and clines during PhLREs, but not so drastically during BLREs  may prove to be a powerful tool for the detection of spatial genetic structure. In evolutionary and ecological studies, the period of recurrent spatial pattern was often approximately evaluated by spatial autocorrelation analysis


Understanding the effects of actions undertaken by human societies on crop evolution processes is a major challenge for the conservation of genetic resources. This study investigated the mechanisms whereby social boundaries associated with patterns of ethnolinguistic diversity have influenced the on-farm distribution of sorghum diversity. Social boundaries limit the diffusion of planting material, practices and knowledge, thus shaping crop diversity To assess the effect of social boundaries, this study was conducted in the contact zone between the Chuka, Mbeere and Tharaka ethnolinguistic groups in eastern Kenya. Sorghum varieties were inventoried and samples collected in 130 households. In all, 297 individual plants derived from seeds collected under sixteen variety names were characterized using a set of 18 SSR molecular markers and 15 morphological descriptors. The genetic structure was investigated using both a Bayesian assignment method and distance-based clustering. Principal Coordinates Analysis was used to describe the structure of the morphological diversity of the panicles. The distribution of the varieties and the main genetic clusters across ethnolinguistic groups was described using a non-parametric MANOVA and pairwise Fisher tests. The spatial distribution of landrace names and the overall genetic spatial patterns were significantly correlated with ethnolinguistic partition. However, the genetic structure inferred from molecular makers did not discriminate the short-cycle landraces despite their morphological distinctness. The cases of two improved varieties highlighted possible fates of improved materials. The most recent one was often given the name of local landraces. The second one, that was introduced a dozen years ago, displays traces of admixture with local landraces with differential intensity among ethnic groups. The patterns of congruence or discordance between the nomenclature of farmers’ varieties and the structure of both genetic and morphological diversity highlight the effects of the social organization of communities on the diffusion of seed, practices, and variety nomenclature. Crop genetic diversity patterns result from selection, migration and genetic drift processes which are strongly influenced by human action. Recent studies combining linguistic, archeological and genetic data have unraveled the past domestication and diversification processes of crops such as banana Social boundaries contribute to the evolution of crop populations both directly, by determining seed flows, and indirectly, by inducing the divergence of seed selection practices Sorghum bicolor L. Moench) is an annual cereal extensively cultivated in smallholder farming systems because of its ability to grow under harsh climatic conditions. De Wet and Huckabay Sorghum , in an ethnolinguistic contact zone between Chuka, Tharaka and Mbeere groups . The thrth century, either because of a population increase or because of recurrent drought The three ethnic groups, Chuka, Tharaka and Mbeere, migrated to the study area by the end of the 19Sorghum bicolor), cowpeas (Vigna unguiculata), maize (Zea mays), mungo bean (Vigna radiata) and pearl millet (Pennisetum glaucum) are the main crop species grown in the area. Sowing is done either by hand-dibbling or by drilling, while plowing is done with animals. The different sorghum varieties are either grown in separate plots or mixed together within farmers’ fields. Improved varieties, mainly disseminated by the extension services of the Kenyan Ministry of Agriculture, have also been adopted by the farmers. They are cultivated together in the same field with the local varieties (or landraces). Farmers distinguish between short-cycle varieties that can be grown either from October to January or from March to June, and long-cycle varieties that are subjected to the ratooning practice The three ethnic groups manage low-input cropping systems that harbor high specific and infra-specific crop diversity. Cropping systems are based on cereals and legumes that are usually intercropped. Sorghum  and June 2011 (Long Rains cropping season), just before harvesting and prior to germplasm collection. The inventory of sorghum varieties was based on the local names as reported by women farmers who were in charge of sorghum selection in each of the 124 households surveyed. Indeed, grain crop farming comprising seed sowing, harvesting, selection and trading is ensured by women (Kaguru). As this variety was much more abundant than the others, we limited the number of samples collected. We thus sampled Kaguru variety randomly in a maximum of 19 households per ethnic group. One or two individual panicles of each variety were collected in each household cultivating it. The mean number of varieties collected per household was 1.5 . It was similar across ethnic groups, as well as the mean number of panicles of each variety sampled per household  or in July (74 households). Half of these 130 households were visited during the preliminary survey described above. They represented about half of the total number of households in the area, hence insuring a good representativeness. 60 households belonged to the Chuka ethnic group, 35 to the Mbeere and 35 to the Tharaka. In order to be representative of the sorghum population of each ethnic group, all the varieties grown in each household were collected, except a highly dominant variety of improved origin . Loci were distributed over the 10 chromosomes. DNA was extracted from dried leaves and the polymerase chain reaction amplifications were done following the procedure described previously Twenty-two pairs of primers were selected for their high polymorphism in central Kenya (unpublished data) and West Africa Four markers presenting either a high number of missing data, or low polymorphism (at a 99% threshold) were discarded from the analysis, so eighteen markers were kept, covering 9 chromosomes out of 10. The percentage of missing data for the 18 markers kept was 1%.Fifteen qualitative morphological traits were measured on the panicles of the 297 individuals that were genotyped . Eight madonis function in the R package veganadonis function partitions the distance matrix according to grouping factors (ethnic groups) and compares the sum of squared distances within groups  and between groups . A pseudo F-ratio is then computed and compared to its distribution under the null hypothesis simulated using 4000 random permutations of the raw data. Pairwise Fisher exact tests implemented in the R package fmsbp.adjust function.We characterized each household by its sorghum assemblage, which is the panel of co-occurring sorghum varieties that are cultivated by the household. The differentiation of sorghum assemblages across ethnic groups was tested using a non-parametric Multivariate Analysis of Variance .Genetic structure assuming sampling populations. Pairwise FSTp.adjust function in the R package stats).Analysis at individual level. Two complementary approaches, Bayesian clustering and Neighbor-Joining tree, were used to assess the genetic structure without defining a-priori populations. First, the genetic structure of sorghum populations was characterized using the Bayesian clustering algorithm implemented in STRUCTURE 2.3.3 software http://www.bioportal.uio.no). The admixture model with correlated allele frequencies was used, assuming that the genome of each individual resulted from the mixture of K ancestral populations. The estimated proportions of each individual’s genotype originating from each of the K ancestral populations (q) was calculated for K ranging from 2 to 10 ancestral populations (or clusters), with twenty runs for each K value. The burn-in period was set at 500 000 and 1 000 000 iterations were performed. The criterion suggested by Evanno et al. K values, was used to determine the most likely number of clusters (K). Second, a Neighbor-Joining tree q, hereafter admixture coefficient) was below a 0.8 threshold were considered as resulting from admixture between the populations. Individuals whose q value was equal to or above 0.8 for a population were assigned to that population (hereafter cluster). To explain the MMb genetic structure, the assignment of individuals to clusters thus defined was crossed with information concerning their origin and cycle length as reported by farmers during the collection of samples in situ. The occurrence frequencies of each MMb genetic cluster were then compared across ethnic groups using Pearson's Chi-squared test, and pairwise Fisher exact tests with False Discovery Rate (FDR) correction for multiple comparisons.For further analysis, individuals whose estimated proportion of genome originating from one population  using the R package Kaguru, (76% of the households), followed by Gadam (48% of the households), both of which are improved varieties. Ngirigacha, Mugeta, Mbura imwe, Muruge mbura ciiri, and Muruge mbura imwe were the most frequent local varieties (landraces) . The most frequent variety was ndraces) .2,121 = 4.971, p-value  = 0.0002, R2 = 0.076). Pairwise Fisher exact tests confirmed that the frequency of three out of the five most frequent landraces differed significantly between ethnic groups, while the frequency of improved varieties  did not differ significantly between ethnic groups. Muruge mbura imwe and Mugeta were significantly less frequent in the Mbeere group than in the Chuka and Tharaka groups while Ngirigacha was significantly more frequent in the Mbeere group.The non-parametric perMANOVA showed that sorghum variety assemblages differed significantly between ethnic groups , even thK) identified by STRUCTURE was K = 4. Indeed, the log-probability of data increased up to K = 4, where it reached a plateau. This was congruent with Evanno’s ΔK curve which presented a clear peak for K = 4. The populations (clusters) inferred by STRUCTURE for K = 4 (q) above or equal to q = 0.8, and they were thus assigned to the corresponding cluster. The remaining 12% of the individuals were considered to result from admixture between clusters.The most likely number of populations  of the individual plants that were identified as long-cycle landraces by farmers during the collection were assigned to cluster B by STRUCTURE . A substThe morphological diversity was summarized by the PCoA . The twoeH) of Chuka and Tharaka sorghum populations were significantly higher than that of the Mbeere . Similar results were found for the unbiased allelic richness. ISF was very high in the three groups, yet it was significantly lower in the Chuka population as compared to those of both the Tharaka and the Mbeere , in relation with the higher heterozygosity found within the Chuka sorghum population (0.033) compared to the other two populations (0.022 for the Mbeere and 0.023 for the Tharaka).Various indexes were used to characterize the diversity displayed within each ethnic group . The unbGadam improved variety. The Pairwise G-tests showed that genetic differentiation was highly significant between the sorghum populations of the three groups, being highest between the Chuka and both the Tharaka  and Mbeere  populations and lowest between the Tharaka and Mbeere populations . The STF values between the sorghum populations of the three ethnic groups were low: 0.027 between the Chuka and Mbeere sorghum populations and 0.019 between the Chuka and Tharaka populations, both significant; and non significant between the Mbeere and Tharaka populations (STF = 0.010).An exact G-test of genetic differentiation of sorghum across ethnic groups was significant . The differentiation was clearer  when removing from the analysis the individuals assigned to cluster A, derived from the recent introduction of the No significant relationship was found between the genetic relatedness of individuals and their geographical distance. The partial Mantel test was not significant , and the correlogram did not display any significant spatial structure. Nevertheless, the spatial distribution of the four MMb genetic clusters was not uniform  and theyGadam improved variety, which has been disseminated in the area since 2009. Most of the other individuals assigned to cluster A were identified by farmers as Gadam , confirming the cluster A – Gadam correspondence. Yet cluster A also included 46% of varieties collected under other names, mainly Ngirigacha  and Mbura-imwe . As a result, cluster A was distributed throughout the study area and its spatial distribution appeared more uniform than that of the individuals designated by farmers as Gadam . Kaguru individuals originated uniformly from the study area  and in the Tharaka farms . Accordingly, the genetic diversity parameter estimates calculated for the Kaguru individuals collected in the Chuka farms were significantly higher than those for the Mbeere and Tharaka farms . The major share of the individuals assigned to cluster C was identified by farmers as an improved variety called udy area  and in tudy area , but werudy area . The proka farms . AltogetMuruge mbura ciiri, Mugana, Muthigo, Mucuri, Kathirigwa) and a few as short-cycle improved varieties . The latter individuals identified as improved varieties, both collected on-farm and stemming from certified seeds, formed a distinct genetic sub-group D’ on the Neighbor-Joining tree and STRUCTURE confirmed these results for K = 5. The rest of the individuals assigned to cluster D were distributed across three major sub-clusters . Mugana and Kathirigwa formed another branch (D2), and Mucuri a third one (D3). Hence, there was a clear correspondence between the farmers’ nomenclature and the genetic structure of individuals assigned to MMb cluster D, as well as with the structure of panicle morphological diversity , while fewer individuals bore that name in the Chuka (8%) and Tharaka (18%) populations. Cluster B accounted for a uniformly large share among the farmers of the Tharaka (39%), the Chuka (27%) and the Mbeere (27%) ethnic groups. It showed little internal sub-structure with no clear correspondence to farmers' varieties, and a morphological differentiation between Muruge mbura imwe and Mugeta (K = 4 formed a separate branch on the genetic Neighbor-Joining tree (B’) and their difference was confirmed by STRUCTURE for K = 5. It could be explained by their foreign origin, as farmers reported purchasing these seeds at a lowland market. A fifth individual assigned to cluster B for K = 4 formed a long branch (B”) indicative of a marked genetic differentiation. It was identified as Muthigo wa mwimbi which means that it was introduced from another ethnic group (Mwimbi).The MMb cluster B is both central and diverse on the basis of molecular markers as well as morphological traits. The individuals assigned to cluster B were mainly identified as local landraces bearing various local names, whose occurrence frequency differed across ethnic groups . Most ofd Mugeta . As the Our study showed that in a uniform agro-ecological environment, social boundaries associated with ethnolinguistic diversity patterns have impacted the distribution of sorghum varieties and their genetic spatial patterns. If seeds, knowledge and practices were freely exchanged across the three ethnic groups, we would expect their sorghum varieties to be similar and, because of their geographical proximity and similar environmental conditions, to display no genetic differentiation. Quite the contrary, we showed that ethnic groups maintained different sorghum landraces, whereas improved varieties were uniformly distributed across groups.The genetic diversity of sorghum in the area of study, as assessed with molecular markers, is organized in four major groups. These groups reflected the influence of improved variety dissemination and a differentiation in terms of cycle duration and phenology. The improved varieties (groups A and C) and short-cycle landraces (group B) collected on the area of study clustered with the Caudatum accessions from eastern Africa and central Africa of a reference set representing the worldwide sorghum genetic diversity . The lonKaguru variety showed more admixture with the local landraces in the Chuka sorghum population than in the Mbeere and Tharaka ones. As a result of the unbalanced frequency of the different genetic clusters across ethnic groups, the genetic differentiation of their sorghum populations was significant. The uneven distribution of named landraces across the Chuka, Tharaka and Mbeere ethnic groups is consistent with the results of Baco et al. The overall distribution patterns of sorghum diversity on our study site were clearly associated with the farmers’ ethnic partition. This genetic differentiation did not appear to result from isolation-by-distance as no significant relationship between the geographic distance and the genetic relatedness of individuals was detected. Long-cycle landraces formed a genetically distinct cluster which was more frequently encountered in the Chuka sorghum population than in the Tharaka sorghum population. The improved The field setting adopted in our study enabled us to limit the interference between socio-cultural factors and other environmental factors. Notably, climate and soil variations can influence the distribution of crop diversity. The climatic variation was neglectable on our study site regarding the limited gradient of altitude. In addition, we conducted a survey which did not highlight significant differences of soils’ physical properties among the areas inhabited by the three ethnic groups (data not shown). Furthermore, farmers did not report that some varieties were better adapted to particular types of soils. Hence, the interference between socio-cultural factors and other uncontrolled environmental factors remains much unlikely, even though it cannot be totally left out.The community-scale approach we used in this study revealed that social boundaries have contributed to the differentiation of sorghum populations across spatially-close ethnic groups living in the same agro-ecological environment. Such an approach is thus complementary to country or regional-scale studies. In addition, such an approach makes it possible to investigate the mechanisms behind the relationship by jointly analyzing the distribution of varieties and the structure of genetic and morphological diversity in relation to the social organization of the communities concerned.The ethnic identity of human groups is maintained by social boundaries that impede their cultural homogenization Muvia wa Chuka”, the sorghum of Chuka people. Moreover, certain sub-types within this cluster (sub-clusters D2 and D3) were not present in the Mbeere population and corresponding landraces were not inventoried in that ethnic group. The relation between the spatial distribution of the MMb genetic clusters and that of ethnic groups suggests that social boundaries limit the diffusion of planting material. Indeed, in most rural societies, seed exchanges depend on social networks as trust is required for seed transactions Long-cycle landraces formed a distinct MMb genetic cluster, whose frequency differed across ethnic groups. It was more frequent among the Chuka than among the Tharaka, and, interestingly, these results confirmed farmers’ reports stating that long-cycle landraces were “Gadam variety was genetically distinct from the landraces and showed limited introgression from the other genetic clusters. It was genetically uniform and complied with certified control. However, farmers also gave the names of local and already known variety to individuals that have the same genetic profile as Gadam, an improved variety. This can be explained by a morphological similarity. Yet it raises the question of the consequences this will have for the on-farm evolution of the improved variety. Kaguru, for instance, which was introduced in the area 10–15 years ago, seems to have evolved differently across ethnic groups. High admixture was detected between Kaguru and the local landraces in the Chuka population, resulting in a range of genetically diverse materials still called Kaguru, while this variety was found to be genetically more uniform in the Mbeere and Tharaka populations. As a result, the genetic diversity of Kaguru, as identified by farmers, was greater in the Chuka population than in the Mbeere and Tharaka populations. According to the farmers, the variety was introduced simultaneously in the three ethnic groups but little information is available concerning the origin of the seed lots. The divergence of the Kaguru variety across ethnic groups within a few decades could thus be the result of differences in their management practices, be it in planting  or in seed selection. The higher admixture rate between Kaguru and the local landraces among the Chuka could be due to more intense gene flows within fields or to less stringent selection practices. As our observations suggest that the cropping systems used by the three ethnic groups were similar, the hypothesis of different selection practices is more likely. Cases of divergent selection practices between geographically close communities were observed by Pressoir and Berthaud In contrast to the case of some landraces, improved varieties were uniformly distributed and their frequencies did not differ between ethnic groups. The recently introduced Mugeta and Muruge mbura imwe, mainly grown by the Chuka and Tharaka, corresponded to two distinct morphological groups while Ngirigacha, which is mainly grown by the Mbeere, was distributed over the entire PCoA plot  first, which is common in patrilineal and patrilocal societies in situ. Crop diversity patterns, thus result not only from an interaction between genetic and environmental factors, G × E, but from a three-way interaction G × E × S, where ‘‘S’’ stands for effects of the social boundaries Our study highlights the importance of local short-scale studies to investigate farm crop evolution processes. To date, emphasis has been placed on the effect of agro-ecological conditions on crop evolution processes, as in the study of the evolution of wild plants. The influence of the cultural diversity and social organization of farming communities has consequently been neglected, although the major role of smallholders in the management of crop diversity has been acknowledged A further study is now needed to probe the mechanisms involved. Notably, the link between seed exchange networks and social organization deserves more investigation to confirm whether seed exchanges are confined within ethnic groups. This would explain why the diffusion of long-cycle landraces is more limited than that of short-cycle landraces. Moreover, further comparison of the local sorghum nomenclature and classification systems (folk taxonomy) across ethnic groups would make it possible to test whether their definition of landraces differs, and whether it influences their seed selection practices.The uneven distribution of the genetic clusters across ethnic groups within a restricted geographic area highlights the need to take the social relationship and exchanges into account in the characterization, collection, and conservation of crop diversity. Accounting for the impact of human practices on crop populations would help capture their diversity more efficiently and, to this end, ethnic contact zones are of major interest for their potentially high genetic diversity. This study paves the way for participatory plant breeding as it shows that farmers’ individual choices concerning planting material are not only determined by agro-ecological conditions or economic interest, but also by their cultural background.Figure S1Diagram displaying the rain seasons and the growth-cycle of sorghum on our study site. Inventories’ dates are symbolized by the letter I (orange points) and collections’ dates by the letter C (red points).(TIF)Click here for additional data file.Figure S2Comparison between the inventory of varieties and their sampling. (A) Percentage of households where each variety was sampled for the genetic diversity study on a total of 130 households. (B) Linear correlation between the proportions of households where each variety was inventoried  and where it was collected  in each ethnic group.(TIF)Click here for additional data file.Figure S3Neighbor-Joining tree based on the genetic dissimilarity among the individuals sampled on our study site (in Black) and the accessions of a global reference set . The genetic dissimilarities were calculated on 16 SSRs using the simple matching index. The sorghum individuals sampled on our study site are displayed in black. The genetic assignment  or unassignment (Unassigned - q≤0.8) of our individuals is indicated on the figure. Colors represent the ten genetic groups identified in Billot et al. 2013, and described as following by the authors: “Group 1 [Dark orange] included Caudatum, Caudatum-Bicolor and Durra from Eastern Asia; Group 2 [Light orange] encompassed Durra and Bicolor from the Indian subcontinent, while Group 3 [Light green] exhibited Durra from Eastern Africa. Bicolor and Durra-Bicolor from Eastern Africa were assigned in Group 4 [Light blue]. Group 5 [Dark blue] included Guinea and Guinea margaritiferum from Western Africa and Bicolor from North America. Group 6 [Red] appeared as a well-separated group made predominantly of Guinea accessions from western Africa, accompanied by intermediate race Durra-Caudatum materials from western Africa while Group 7 [Magenta] was made essentially of materials collected from eastern Africa and central Africa generally classified as race Caudatum . Group 8 [Dark green] was a small and heterogeneous group made of Durra and Caudatum race accessions from central Africa. Group 9 [Pink] was made essentially of Guinea race accessions from the Indian subcontinent and southern/eastern Africa with Guinea-Caudatum (GC) intermediate race accessions from various parts of Africa. Group 10 [Purple] was made almost exclusively of accessions from southern Africa of race Kafir or intermediate race Kafir-Caudatum (KC).” Unassigned individuals in the global reference set are displayed in grey.(TIF)Click here for additional data file.Figure S4Structure of the morphological and genetic diversity within the MMb clusters B (top) and D (bottom). (A) Plot of the two first axes of the Principal Coordinates Analysis (PCoA) done on the sorghum plants assigned to the MMb cluster B and based on 15 panicle morphological traits. The first axis (x) expresses 35.1% of the total variation and the second axis 13.1%. Varieties are displayed using the following color code: Blue: Mugeta, purple: Muruge mbura imwe, green: Mbura imwe, brown: Ngirigacha, Red: Gadam, yellow: Kaguru, salmon: Muthigo wa mwimbi. (B) Neighbor-Joining tree based on the genetic dissimilarity among individuals assigned to the MMb cluster B calculated on 18 SSRs using the simple matching index. (C) Plot of the two first axes of the Principal Coordinates Analysis (PCoA) done on the sorghum plants assigned to the MMb cluster D and based on 15 panicle morphological traits. The first axis (x) expresses 51.5% of the total variation and the second axis 18.0%. Varieties are displayed using the following color code: Yellow: Serendo, orange: Musalama, light-pink: Kathirigwa, Fushia: Mugana, Greenish blue: Mucuri, dark-blue: Muruge mbura ciiri, black: Muthigo, blue: Mugeta. (D) Neighbor-Joining tree based on the genetic dissimilarity among individuals assigned to the MMb cluster D calculated on 18 SSRs using the simple matching index.(TIF)Click here for additional data file.Table S1Summary of the sampling of planting material. Mean number of varieties collected per household (Mean no. varieties/household) and mean number of samples of each variety collected per household (Mean no. samples/variety/household) in each ethnic group, followed by their standard error (SE).(DOCX)Click here for additional data file.Table S2Summary of information and genetic diversity estimates per locus. Minimum and maximum size of alleles (Size), chromosome where the locus is located (Ch), percentage of missing data per locus (Miss), number of sampled alleles (NAl), He: unbiased gene diversity, ISF: Fixation index.(DOCX)Click here for additional data file.Table S3Morphological descriptors used for panicle description.(DOCX)Click here for additional data file.Table S4Results from the perMANOVA comparing the effect of ethnic groups on sorghum variety assemblages. Df: degrees of freedom, Ssq: sequential sum of squared distance between individuals and their group’s centroïd, Mean Ssq  =  Ssq/Df, F.Model: pseudo F ratio, R2: coefficient of determination [Ssq Etnic group/Ssq Total].(DOCX)Click here for additional data file.


Giraffa tippelskirchi), Reticulated (G. reticulata) and Rothschild's  giraffe lineages in East Africa are morphologically and genetically distinct, yet in Kenya their ranges abut. This raises the question of how divergence is maintained among populations of a large mammal capable of long-distance travel, and which readily hybridize in zoos. Here we test four hypotheses concerning the maintenance of the phylogeographic boundaries among the three taxa: 1) isolation-by-distance; 2) physical barriers to dispersal; 3) general habitat differences resulting in habitat segregation; or 4) regional differences in the seasonal timing of rainfall, and resultant timing of browse availability. We used satellite remotely sensed and climate data to characterize the environment at the locations of genotyped giraffes. Canonical variate analysis, random forest algorithms, and generalized dissimilarity modelling were employed in a landscape genetics framework to identify the predictor variables that best explained giraffes' genetic divergence. We found that regional differences in the timing of precipitation, and resulting green-up associated with the abundance of browse, effectively discriminate between taxa. Local habitat conditions, topographic and human-induced barriers, and geographic distance did not aid in discriminating among lineages. Our results suggest that selection associated with regional timing of events in the annual climatic cycle may help maintain genetic and phenotypic divergence in giraffes. We discuss potential mechanisms of maintaining divergence, and suggest that synchronization of reproduction with seasonal rainfall cycles that are geographically distinct may contribute to reproductive isolation. Coordination of weaning with green-up cycles could minimize the costs of lactation and predation on the young. Our findings are consistent with theory and empirical results demonstrating the efficacy of seasonal or phenologically dictated selection pressures in contributing to the reproductive isolation of parapatric populations.Masai ( Population divergence and speciation can result from genetic drift in geographic isolation, or from spatially variable natural selection 2Giraffa camelopardalis and Giraffa rothschildi, and we will thus follow their suggestion by treating Rothschild's giraffe as G. camelopardalis. In East Africa, divergence between Masai (G. tippelskirchi), Reticulated (G. reticulata), and Rothschild's  giraffe lineages is supported by strong genetic structure in mtDNA and microsatellites Giraffes range from the Sahel to South Africa, living in scrub and savannah habitat in loose social groups with home range sizes between 5 and 992 kmGiven the apparent absence of geographical barriers to dispersal, the striking genetic differentiation among these giraffes suggests that environmental or behavioural mechanisms limit gene flow. Here we consider four scenarios for the maintenance of divergence among the East African giraffe taxa, focusing on geographic and environmental parameters in a first-order assessment of their relative importance in discriminating between the three taxa. We make no claims concerning the environmental, orographic, or other conditions that initiated divergence among these taxa in the Pleistocene. Rather, we restrict our assessment to late Holocene to modern processes that contribute to the maintenance of the current, nearly complete reproductive isolation among the giraffe lineages. To this end, we conduct multivariate and spatially non-explicit as well as spatially explicit analyses to evaluate four hypothesized isolating scenarios: 1) isolation-by-distance; 2) the presence of barriers to dispersal, limiting gene flow; 3) spatial habitat differences that do not represent differences in timing of the seasons; and 4) differences in the seasonal timing of precipitation in relation to green-up.Hypothesis I – Isolation-by-distance is the effect of diminishing genetic relatedness with increasing distance, and could potentially be important when dispersal is limited relative to the overall size of the range. Even though isolation-by-distance appears to be an unlikely force maintaining divergence between parapatric taxa, simulations suggest that under some circumstances parapatric speciation is possible solely due to limited dispersal distances and the accumulation of genetic incompatibilities Connochaetes taurinus) Aepyceros melampus) a priori ruling out any potential evolutionary process, we have nevertheless included both hypotheses in our analyses.Hypothesis II – Geographic barriers to dispersal – and as a result gene flow – between the three giraffe species are not obvious, but they have been implicated – most notably the Rift Valley – in the divergence of other large mammals, including wildebeest , LAI annual minimum (LAImin), and LAI annual range . These LAI metrics provide spatial information on vegetation density.Based on Moderate Resolution Imaging Spectroradiometer (MODIS) measurements on board of NASA's TERRA and AQUA satellites, we used the vegetation continuous field (VCF) product as a measure of the percentage of tree canopy cover http://www2.jpl.nasa.gov/srtm/) digital elevation data, aggregated from the native 90 m resolution to 1 km (available from the WorldClim group http://www.worldclim.org).In addition to these optical remote sensing products, we included microwave QSCAT data available in three-day composites at 2.25 km resolution http://bioinformatics.org/~typhon/populations) and Bayesian clustering in STRUCTURE Giraffes (n = 429) from 51 locations throughout the ranges of the three focal species  were colTo test the hypothesis that the giraffe taxa occupy different habitats, a canonical variate analysis (CVA) was performed with CANOCO 4.5 To further test whether differences in the timing of rainfall could effectively differentiate among the three giraffe taxa as defined by genetic cluster membership, and to assess their importance relative to environmental variables that do not represent timing of seasons, we used random forest algorithms  and bioclimatic and remote sensing variables. After verification that only monthly rainfall data were important contributors to the model, we computed a random forest model with only monthly rainfall variables, which was used in the subsequent predictive step. Random forest models were run with 20,000 trees , variable importance was computed (importance  =  TRUE), and default settings for the remaining parameters were used.An imbalance in the number of records within a class  can bias random forest predictions, and cause high error rates in the classification of the rare class e.g., . This phI) given as an output from random forests. For each variable n, its variable importance score nI is divided by the maximum variable importance score maxI, resulting in a model improvement ratio (MIR) ranging between 0 and 1. In subsequent random forest models, variables with MIR smaller than a set threshold are withheld from the predictor variable set, and the resulting out-of-bag error rate is compared to that of the full model. We iteratively removed variables below MIR thresholds of 0-1at increments of 0.1.Although random forests can be used with a large number of predictor variables, the out-of-bag error rate can be increased when many variables are included that do not contribute to explaining the response variable. To minimize the out-of-bag error rate while at the same time minimizing the number of predictor variables included, Murphy et al. Highly correlated predictor variables can potentially bias random forest results e.g., . To evalTo create a spatially explicit prediction of taxon distributions based on our random forest model, we extracted the values of the twelve monthly rainfall variables for 10,000 random points in a wide range of our study area. We used these points and our random forest model for only monthly rainfall data to predict (‘predict’ function in randomForest package) which taxon would be present at each of the 10,000 randomly drawn locations. The results were plotted on a map of the study area and compared visually to species distribution maps of the three taxa to evaluate the concordance between predicted and observed ranges. Because the available species distribution maps are rather crude approximations of the true ranges, we did not compute a percent overlap between predicted and observed ranges.A priori assignment of populations into one of the three study taxa would appear justified, because genetic evidence suggests clear divergence among those groups rand. ΔD is compared to ΔDrand, based on which the predictor variable is either retained or dropped. Generalized dissimilarity models were run using an Avenue script in ArcView v 3.2 in conjunction with a SPlus v 4 script obtained from the authors of GDM To assess the level of population divergence, we used genetic distances  among the sixteen populations from our study taxa that were typed for fourteen microsatellite loci ™ Global Population Database. Oak Ridge, TN: Oak Ridge National Laboratory. Available from http://www.ornl.gov/landscan). Although the relation between human population density and anthropogenic pressure varies from one region to the other , with cost distances based on either altitude or population density. Importance of any of those variables in a model would implicate its role in maintaining divergence among the three giraffe taxa. To evaluate cross-correlations among predictor variables, in the remaining four models the following subsets of the predictor variables were entered: only monthly precipitation variables, only geographic distance, or only one of the two cost distances. The percentages of the variation explained by each model were compared to assess which parameter set best explained the observed genetic variation.To further investigate hypothesis IV , we assessed whether regional differences in the timing of green-up are related to reproductive timing in giraffes. Unfortunately, data on calving times in giraffes is largely lacking for our study taxa, and only available for two distinct genetic groups of Masai giraffe We first assessed the roles of differences in the timing of green-up as well as general habitat differences in differentiating the three giraffe taxa in East Africa, by means of a canonical variate analysis (CVA) on a set of environmental variables pertaining to 51 locations where giraffe samples have been genetically typed  [7]. The2 <0.95), but this is of little to no influence on our random forest models. In each regression tree only one of the two correlated variables is picked as the most important variable. The presented importance scores are a summary of many tree regressions, and are an indication of how often each variable is used in a regression tree. Months known to be important in discriminating regional climate proved to be informative in the random forest model: February is associated with maximum precipitation in southern Kenya and Tanzania; July and August with maximum precipitation in north-western Kenya and Uganda; and March and October correspond to the post-equinoxal precipitation in north-eastern Kenya, Somalia, and eastern Ethiopia. Remote sensing and climate variables that do not capture the timing of seasons were relatively unimportant in the random forest model  in addition to the satellite remote sensing and climate variables that capture general habitat characteristics to construct a random forest model st model , suggestst model . The onest model . In an aTo explicitly assess the effects of isolation-by-distance or dispersal barriers as well as those of environmental heterogeneity, we also implemented a distance-based methodology using generalized dissimilarity modeling GDM . The fulFor differences in the timing of seasons to have biological meaning with respect to reproduction, calving times should also show differences between taxa. Tests of the null model of a uniform distribution of births across the year using the frequency of calving times from two distinct genetic groups within Masai giraffe We have investigated the potential current geographic and environmental factors that may contribute to maintaining divergence between giraffe taxa in East Africa. The results of our simultaneous tests of hypotheses presented here suggest that, among the factors investigated, regional differences in timing of maximum rainfall are of primary importance. Even though general habitat differences, dispersal barriers, and isolation-by-distance also appear to contribute to inter-taxon differentiation, our analyses suggest they play less important roles.Previous studies have shown that parapatric speciation is possible when dispersal distances are limited and genetic incompatibilities accumulate, resulting in reproductive isolation of parapatric populations Studies using a resistance surface to compute cost distances are often subject to an oversimplification of the relation between the habitat matrix and gene flow, and our study is no exception. To obtain a better model of gene flow as a function of the habitat matrix, Shirk et al. Locally adaptive responses to divergent habitat could provide selective pressures favouring endemic populations over migrants or hybrids. The most significant habitat variables that are not related to the timing of the seasons in canonical variate analyses were Bio12  and Bio15 . Rothschild's giraffe occupy habitat that generally gets more rainfall during the year than the regions occupied by Masai  and Reticulated giraffe . Similarly, Rothschild's giraffe habitat is characterized by smaller seasonal differences in rainfall than that of Reticulated and Masai giraffe. The low levels of rainfall in Reticulated habitat result in the lowest observed vegetation cover during the greenest time of the year (NDVIgreen), as well as small differences in greenness between the seasons (LAIrange). Nevertheless, there is large overlap in these variables between the three giraffe species, and they are therefore not effective predictors of genetic divergence.Although the association between patterns of green-up and giraffe taxa suggests a role for geographic differences in the seasonal timing of rainfall in the maintenance of differentiation, the historical processes of initial giraffe divergence remain an open question. The ∼ 23,000 year precession cycle and associated ∼ 100,000 year climate cycle strongly influence African precipitation and is known to have resulted in changes in precipitation intensity multiple times in the region since the early to middle Pleistocene The striking correlation between seasonal timing of rainfall and genetic divergence among giraffe taxa might be explained by different, but not necessarily mutually exclusive mechanisms. First, genetic divergence might be related to a synchronization of the reproductive cycle with regional timing of rainfall. For available data, a uniform distribution of births across the year was rejected, which is supported by independent data from the Serengeti Synchronization of the reproductive cycle with the timing of rainfall may involve selective advantages related to the condition of the female at the time of conception, increased growth rates and predation avoidance for calves, and quick recovery of the female at time of weaning. As a result of births peaking late in the dry season, weaning of giraffe young occurs at a time that fresh browse becomes available Peaking birth rates during the dry season An alternative mechanism that might explain the relation between genetic divergence and seasonal timing of rainfall could involve seasonal variation in habitat use as a response to differences in the timing of maximum rainfall and the associated availability of browse. Resource tracking has clear selective advantages and is one of the likely underlying causes of migration on both small and large scales. Although giraffes are capable of travelling large distances In our hypothesis of ecologically mediated maintenance of population divergence, differences in reproductive timing need not act alone. For instance, mate recognition mechanisms may also contribute to isolation. In this context, differences in pelage pattern may serve as visual cues in mate choice, possibly through imprinting on the conspecific pelage pattern during the early stages of life We have shown that among the predictive variables considered, regional differences in the seasonal timing of rainfall and the associated timing of green-up best discriminate among the three East African giraffe taxa, and that general habitat differences, dispersal barriers, and geographic distance do so less effectively. One explanation for this striking relation may be related to reproductive asynchrony, suggesting regional adaptation of the reproductive cycle to the differential timing of green-up. This scenario might represent a form of ecologically-mediated reproductive isolation consistent with a growing body of work that suggests that selection can produce or maintain the divergence between ecologically distinct groups Figure S1Response curves for variables entered in generalized dissimilarity models that were selected in the model as important in explaining the observed variation. The maximum value of each variable is indicative of its importance in the model. Response curves are shown for models of Nei's D  and Fst  genetic distances with a set of predictor variables consisting of monthly precipitation, geographic distance, and a cost distance based on either altitude and ruggedness  or human population density .(TIF)Click here for additional data file.Figure S2Monthly calving frequencies of giraffe in the Serengeti (solid line) and Nairobi National Parks (broken line). Adapted from (TIF)Click here for additional data file.


In heterogeneous landscapes, physical barriers and loss of structural connectivity have been shown to reduce gene flow and therefore lead to population structuring. In this study, we assessed the influence of landscape features on population genetic structure and gene flow of a semiaquatic species, the muskrat. A total of 97 muskrats were sampled from three watersheds near Sudbury, Ontario, Canada. We estimated population genetic structure using 11 microsatellite loci and identified a single genetic cluster and no genetic differences were found among the watersheds as a result of high levels of gene flow. At finer scales, we assessed the correlation between individual pairwise genetic distances and Euclidean distance as well as different models of least cost path (LCP). We used a range of cost values for the landscape types in order to build our LCP models. We found a positive relationship between genetic distance and least cost distance when we considered roads as corridors for movements. Open landscapes and urban areas seemed to restrict but not prevent gene flow within the study area. Our study underlines the high-dispersal ability of generalist species in their use of landscape and highlights how landscape features often considered barriers to animal movements are corridors for other species. Contemporary population structure can be affected by ecological barriers and decreased structural connectivity between optimal habitat patches  and assessed the effect of landscape features on population genetic structure. This semiaquatic rodent is widespread across North America and uses a wide range of freshwater habitats such as streams, marshes and lakes  and the presence of anthropogenic features . More specifically, because of the biology of the species, we predicted that muskrats will show population genetic structure which will reflect the watershed structure and that genetic differentiation should be greater between than within watersheds.N = 97) were live trapped in three watersheds located in Sudbury District, Ontario, Canada during May, June and July of 2008 . The total number of adults was 77, of which 54 were males and 23 were females. Analyses of genetic structure with or without juveniles had no consequences for the resulting output (data not shown); therefore, we maintained all samples in our analyses.Sex determination was performed following the methods described in Ermakov et al.  using thDNA was extracted using QIAGEN DNeasy tissue kits. All individuals were genotyped at 11 microsatellite loci  following Laurence et al. . PCR proMICRO-CHECKER v2.2.3  and Rousset's a (ar) which were calculated using Microsatellite Analyzer (MSA) , observed (Ho), and expected (He) heterozygosity. Allelic richness was calculated using rarefaction in HP-RARE to control for differences in samples sizes , and the variance of these assignment indices (vAIc), implemented in FSTAT v.2.9.3  values. Second, we also performed a Principal Component Analysis (PCA), a multivariate method adapted to genetic markers, implemented in the Adegenet package in R . However, resistance values are often chosen arbitrarily and the range of these values are also variable  (Pike We compared pairwise genetic distances to Euclidean distances and the different effective distances (33 LCP models) using partial Mantel tests with 10,000 permutations, which were calculated using the package ecodist version 1.2.7 (Goslee and Urban DR) Pike .P < 0.001 and Oz22 P < 0.0001). These two loci were removed from further analyses so all the results presented were obtained using nine microsatellite loci.No evidence of genotyping errors was found and two loci (Oz08 and Oz22) were suspected to show presence of null alleles . We did not detect LD after Bonferroni corrections and two out of 11 loci were not in HWE after sequential Bonferroni corrections (Oz08 P > 0.05) and was highly diverse in all three regions ; however, it was not significant. The test for heterogeneity did not detect a difference in spatial genetic structure patterns between sexes (0.200 ≤ P ≥ 0.991) indicating homogeneity between the spatial correlograms.The spatial autocorrelation analyses of cumulative distance classes showed similar patterns of genetic structure in both sexes Fig. B. Both src  were not significantly different from random for all the distance classes  was suggested by STRUCTURE. Although the highest LnP(K) was detected at K = 5 (average LnP(K) = −4025), suggesting the presence of five clusters, the proportion of individuals ancestry (q) was low (0.377 ± 0.178). These results indicate that K = 5 is not the true K and the number of genetic cluster is one. Similar results were obtained using the spatial Bayesian clustering method TESS. Comparable results indicating one cluster were also obtained using the PCA  and between East Wanapitei and Panache (FST = 0.0164 P ≤ 0.05) but not between Junction creek and Panache (FST = 0.0001).A single cluster (ar and Dps) and the Euclidean distance were significant , but showed weak Mantel r  were found between Dps and LCP models that considered the roads as facilitator models for muskrat movements  did not demonstrate genetic structure at the fine geographical scale. Underlying patterns of isolation by distance can make it difficult to interpret the results from Bayesian clustering methods    populations. The authors explained this isolation by distance pattern and the lack of effect of landscape fragmentation on gene flow by the high effective population size of this species and the barrier being too recent to affect such a large population.Contrary to our hypothesis, our results showed that population genetic structure was not influenced by the landscape structure at a fine geographical scale. However, genetic distances among individuals were partly explained by landscape features. We detected only one population in our study area and no barriers were identified which seems to support the idea that no landscape features seemed to prevent gene flow. However, when comparing the different models, models with high cost for “open landscape and urban areas” and “forest” were the models with the highest significant partial mantel's r, which seems to indicate that they do prevent, to some extent, gene flow. Open landscape and urban areas and to some extent forest seemed to restrict but not prevent gene flow in muskrat. Muskrat movements are influenced by the landscape, but gene flow was not prevented by the landscape characteristics in our study and no landscape barrier was identified. Although Ahlers et al.  found thNeovison vison) did not reflect the watershed structure and that gene flow was not influenced by connection between waterways. We observed similar results in muskrats, indicating the high dispersal ability of these semiaquatic species and their capacity to use terrestrial corridors for dispersal. Different dispersal strategies are predicted in generalist species as opposed to specialist ones. Habitat specialists may be more affected by landscape structure than generalists; however, Centeno-Cuadros et al. . Most studies report on the negative impact of human activities on dispersal due to landscape fragmentation  may be considered as the equivalent of FST-based measures and is more appropriate for examining relationships with historical landscape data (Balkenhol et al. ar associated with contemporary landscape data. The choice of resistance cost for the landscape types can affect the results of landscape genetic studies as the relative cost value will change the model sensitivity (Rayfield et al. We found significant patterns using In conclusion, population genetic structure of muskrat was not influenced by landscape composition, and landscape features had a limited effect on gene flow. Muskrats had the capacity to use terrestrial pathways between watersheds and roads were not a barrier to movements. On the contrary, it seems that roads may be used as corridors for movements. Our study suggests that semiaquatic species may be less sensitive to landscape fragmentation than species more restricted to aquatic environments such as amphibians.


Trachypithecus francoisi, T. poliocephalus and T. leucocephalus, which are all characterized by different pelage coloration predominantly on the head and shoulders. Therefore, we sequenced a 395 bp long fragment of the mitochondrial control region from 178 T. francoisi, 54 T. leucocephalus and 19 T. poliocephalus individuals, representing all extant populations of these three species. We found 29 haplotypes in T. francoisi, 12 haplotypes in T. leucocephalus and three haplotypes in T. poliocephalus. T. leucocephalus and T. poliocephalus form monophyletic clades, which are both nested within T. francoisi, and diverged from T. francoisi recently, 0.46-0.27  and 0.50-0.25 million years ago . Thus, T. francoisi appears as a polyphyletic group, while T. leucocephalus and T. poliocephalus are most likely independent descendents of T. francoisi that are both physically separated from T. francoisi populations by rivers, open sea or larger habitat gaps. Since T. francoisi populations show no variability in pelage coloration, pelage coloration in T. leucocephalus and T. poliocephalus is most likely the result of new genetic mutations after the split from T. francoisi and not of the fixation of different characters derived from an ancestral polymorphism. This case study highlights that morphological changes for example in pelage coloration can occur in isolated populations in relatively short time periods and it provides a solid basis for studies in related species. Nevertheless, to fully understand the evolutionary history of these three langur species, nuclear loci should be investigated as well.To understand the evolutionary processes leading to the diversity of Asian colobines, we report here on a phylogenetic, phylogeographical and population genetic analysis of three closely related langurs,  Primates exhibit striking examples of skin and pelage color variation. Closely related species often exhibit marked color differences, especially in Colobinae Pygathrix, Rhinopithecus, Nasalis, Simias, Presbytis, Trachypithecus and Semnopithecus) Trachypithecus comprises up to 20 species, which are grouped into various species groups according to similarities in morphology, ecology, behavior, distribution and genetics Trachypithecus francoisi langur species group Trachypithecus francoisi, T. poliocephalus and T. leucocephalus occur in nearby distribution areas, but differ extremely in pelage coloration, and are thus a good example to study evolutionary processes of pelage coloration variation in colobine monkeys.Colobine monkeys (subfamily Colobinae) are a diverse group of Old World primates with 59–78 species grouped in up to 10 genera T. francoisi, T. poliocephalus, T. leucocephalus, T. francoisi populations . On the other side, it can be hypothesized that T. leucocephalus and T. poliocephalus share a common ancestor because both are more similar in pelage coloration than either species is to T. francoisi (hypothesis 2) As the habitats of rancoisi , it is hT. francoisi, T. leucocephalus and T. poliocephalus, with the aim to (1) elucidate the phylogenetic, phylogeographical and demographical history of these three taxa, (2) detect which and when physical barriers isolated these relatively large-bodied primates, (3) evaluate the level and partitioning of genetic variation within and among them, and (4) describe phylogeographic relationships among extant populations and identify factors influencing genetic divergence and phenotypical differences. Although we used only a maternally inherited marker, we predict that our findings will provide deep insights into the evolutionary history of these three species, which are characterized by female philopatry To assess both hypotheses, we have analyzed sequence variation in the hypervariable region I (HVI) of the mitochondrial control region of T. francoisi individuals were provided by Guangxi Wuzhou Inbreeding Center, and 20 and three frozen blood samples of T. francoisi and T. leucocephalus were provided by Nanning Zoo and Shanghai Safari Park, respectively. Blood collection and invasive plucking of hairs was performed during routine health checks. In Guangxi Wuzhou Inbreeding Center, Nanning Zoo and Shanghai Safari Park, family units were housed in single indoor cages (4–6 m×5–6 m×6 m). Outdoor play cages for 3–5 family units are equipped with swing and trees. Langurs were fed with fruits, vegetables and leaves, which they were used to eat in the wild three times per day; thus, they were never deprived of water or food. Periodically, they obtained multi-vitamine supplement. Samples were collected by institutional staff and directors gave permission to use them in our study. Collection of fecal and blood samples adhered to the American Society of Primatologists (ASP) Principles for the Ethical Treatment of Non-Human Primates .Our work was conducted according to relevant Chinese, Vietnamese and international guidelines, including countries where we analyzed samples. Approval for sample collection in the wild was obtained from the China Wildlife Conservation Association, Chinese State Forestry Administration and the Vietnamese National Forest Protection Department. Fecal samples from 165 wild animals were collected non-invasively without disturbing, threatening or harming the animals during field surveys. Twenty-seven tissue samples were obtained from deceased individuals found in the wild. Hair samples from 36 T. francoisi, 54 T. leucocephalus, 19 T. poliocephalus) from 17 forest lots (lots 1–17) representing all of the extant populations of these three langurs were collected , and approx. 10 ng total DNA extract. Forty cycles were run on a Perkin-Elmer Cetus 9700 DNA thermocycler with pre-denaturing at 95°C for 15 min; denaturing at 95°C for 1 min, annealing at 56°C for 1 min, extension at 72°C for 1 min; and a final 10 min extension step at 72°C. Positive (DNA extracted from blood) and negative (water) controls were used to check PCR performance and contamination xL Genetic Analyzer. To avoid errors in amplification and sequencing, PCR amplifications of all samples were performed twice or more, and products were sequenced from both strands.A 395 bp long fragment of the HVI region was amplified and sequenced with the primers 5′-AAC TGG CAT TCT ATT TAA ACT AC-3′ and To exclude contaminations of the dataset with nuclear integrations of mitochondrial fragments (“numts”), we mainly used material in which nuclear DNA is highly degraded (feces) To prevent cross-species contamination, benches and plastic ware were cleaned with 10% bleach and sterile water, and then exposed to UV light for 30 min. The surface of muscle samples was also exposed to UV light (30 min). DNA extraction, PCR amplification and sequencing was conducted in separate laboratories and repeated with random samples after several months. Further, negative controls were clean and sequences from independent analyses were identical.h) and nucleotide diversities (π) T. delacouri and T. obscurus were used as outgroups. MODELTEST 3.06 Sequences were aligned using ClustalX T. obscurus and the T. francoisi group was calibrated using a log-normal distribution so that the earliest possible sampled age corresponds to 2.40  million years ago (mya), and (2) a normal distribution with mean of 0.64 mya and a standard deviation (SD) of 0.213 for the time to most recent common ancestor (TMRCA) of T. delacouri and the ancestor of T. francoisi, T. leucocephalus and T. poliocephalus. We applied these molecular-based calibration points, because no fossil data are available. The uncorrelated log-normal model was used to estimate substitution rates for all nodes in the tree with uniform priors on the mean  and standard deviation  of this model. In addition, we employed the Yule process of speciation as the tree prior with the ingroup assumed to be monophyletic with respect to outgroups. Each BEAST analysis consisted of 20 million generations with a random starting tree and sampling every 1,000 generations. Log files from each run were imported into Tracer 1.5 Molecular dating was conducted with BEAST 1.5.3 Using the full data set, we estimated major demographic changes using BEAST. The Bayesian Skyline Plot (BSP) model was applied to examine a number of different population sizes through time and to run a smoothing procedure to visualize historical population size changes θ = 2 Nefμ, where Nef is the female effective population size and μ is the mutation rate per site per generation, was estimated using π according to the relationship E(θ) = π θW. The estimator π uses the recent population as the inference population, whereas θW uses the historical population as the inference population. We also calculated the maximum likelihood estimates of θ for variable population sizes, denoted here as θvar, jointly with the growth parameter g using the program FLUCTUATE 1.4 θW as a starting parameter for MCMC simulations. Stability of the parameter estimation was ensured by conducting ten short MCMC runs of 4,000 steps each and five long chains of length 400,000, with a sampling increment of 20 and one independent re-run. Secondly, a mismatch analysis was conducted using ARLEQUIN 3.0 Hri) Hri and SSD was assessed by parametric bootstrapping  and a significant value was taken as evidence for departure from the estimated demographic model of sudden population expansion. Third, Tajima’s D, Fu and Li’s D* Fs2RMacaca fuscata) To test the hypothesis of demographic expansion, the population parameter φCT value.The spatial analysis of molecular variance conducted with SAMOVA 1.0 T. francoisi and T. leucocephalus are separated by the Ming River and Zuo River, and both are wider than 100 m, rivers with a width of more than 100 m were lined out in the map and regarded as putative barriers for langurs. Mantel tests To visualize the spatial distribution of landscapes, we collected SPOT5 satellite imagery of the year 2005 (China Remote Sensing Satellite Ground Station) and developed a vegetation-mapping model with the software ARCGIS . Karst scrub was identified as suitable habitat for langurs rd, 193rd and 205th site). We defined 29 haplotypes (B01–B29) in T. francoisi, 12 haplotypes (W01–W12) in T. leucocephalus and three haplotypes (G01–G03) in T. poliocephalus  and T. poliocephalus (G01–G03) formed monophyletic clades, which were nested within a polyphyletic T. francoisi (B01–B29) clade. The network shows a similar pattern  and 61 transitions (ts)  and 2.872 (P = 0.810), respectively  and a relatively high θvar (0.10±0.001) were found  and the time after expansion was estimated at 0.15-0.06 mya. A recent population expansion for T. leucocephalus was also suggested by the BSP  for the 12 groups:  [Lot03] [Lot04]  [Lot08] [Lot09] [Lot10] [Lot11] [Lot12] [Lot13]  [Lot17]. These results suggested that this pattern is the most parsimonious geographical subdivision. 83.34% of the genetic diversity is found between groups, 7.39% existed among sampling lots within groups and only 9.27% existed within sampling lots.Haplotypes displayed very strong geographical specificity, consistent with population clustering based on geographic partitioning. Only one (B05) of the 29 eir lots . For T. r = 0.49, P<0.01). The Mantel test for the effect of barriers on genetic distance confirmed a strong influence on gene flow and explained 59.3% of the genetic differentiation among sampling locations . The partial Mantel test revealed a significant positive correlation between genetic distance and the presence of barriers  after controlling for the effect of Euclidean geographical distance. 58.4% of genetic distance was determined by the presence of barriers. Thus, we conclude that barriers as habitat gaps, rivers or open sea form a stronger barrier to gene flow than Euclidean geographical distance alone.Genetic distances among groups were calculated by pairwise (PiXY-(PiX+PiY)/2) and the Euclidean geographical distances were estimated by GIS analysis . PairwisT. francoisi is much larger than that of the other two species and we also found more haplotypes. T. leucocephalus is confined to a narrow triangular karst hill region of 200 km2 in southern Guangxi Province , China and is separated from T. francoisi by the Ming and Zuo Rivers  and she hybridized with a T. leucocephalus male producing several offspring . All hybrid offspring showed pelage coloration characteristics of T. leucocephalus, which might indicate that white pelage on the head, crest and tail tip is a dominant character.Noteworthy, T. francoisi haplotype B19 was introduced in Lot 14, which we were able to confirm in our study. However, to avoid confusion we excluded this data from further analysis. If other cases of human-mediated gene flow among populations occurred in the past remains speculative. However, haplotypes in all three species show strong geographical specificity and only two haplotypes were found in more than one lot , suggesting that if human-mediated gene flow occurred at all (besides the case mentioned above), it was very limited.As a result of this release, the T. francoisi was estimated  and males migrate at time of sexual maturity Besides ecological factors, also social structure largely influences population genetic structure, which is mediated mainly by social behavior  T. francoisi is classified as “Endangered” and T. poliocephalus and T. leucocephalus as “Critically Endangered” by the IUCN Red List T. francoisi was widely distributed in karst forests of tropical and subtropical south-western China  and northern Vietnam T. francoisi has experienced a dramatic decline of 85% in population size and 70% in distribution. Prior to 1980, the species was found in 23 different counties in China and numbered 8,000–10,000 animals. In 2007, it was estimated that there were only 1,900–2,150 animals left in the wild T. leucocephalus and T. poliocephalus is even more dire with estimates of only 580–620 and a maximum of 70 animals in the wild, respectively T. leucocephalus in Lot 14 and Lot 15 has resulted in population increase T. francoisi  is found between these lots. Subpopulations that are highly divergent must be protected so as to safe guard their unique genetic diversity.Our study shows that these langurs face a serious problem: habitat fragmentation and limited if any gene flow. We posit that geographical structuring of the populations is a direct issue for conservation. Species characterized by limited mobility and strong population genetic structure, such as the three herein studies species, are more prone to suffer from substantial loss of genetic diversity as a result of local extinction T. leucocephalus and T. poliocephalus populations, which became later fixed due to isolation. Thus, physical isolation could have provided the evolutionary potential for the divergence of different species, and even resulted in speciation and the significant diversity seen in Asian colobines today. However, since we analyzed only mitochondrial DNA, male-mediated gene flow is not captured by our study. Thus, to fully understand the evolutionary history of these and other species, nuclear loci should be included in future studies as well.For all these three langurs, habitat is fragmented by habitat gaps, river catchments and sea barriers. Our study indicates that such barriers have played a key role in shaping the present-day population structure of species and populations, and that gene flow between them appears to be strongly impeded by these barriers. Mutations causing pelage coloration changes might have occurred in ancestral Table S1Summary of haplotype distributions of HVI region sequences of T. francoisi, T. leucocephalus and T. poliocephalus.(DOC)Click here for additional data file.Table S2Genetic and geographical distances between the 17 Lots (a) and categorical distance matrix describing presence or absence of habitat gaps among sampling groups (b).(DOC)Click here for additional data file.