In addition to ecological factors, evolutionary processes can determine the invasion success of a species. In particular, genetic admixture has the potential to induce rapid evolutionary change, which can result from natural or human-assisted secondary contact between differentiated populations. We studied the recent range expansion of zander in Germany focusing on the interplay between invasion and genetic admixture. Historically, the rivers Elbe and Danube harboured the most north-western source populations from which a north-westward range expansion occurred. This was initiated by introducing zander outside its native range into rivers and lakes, and was fostered by migration through artificial canals and stocking from various sources. We analysed zander populations of the native and invaded ranges using nuclear and mitochondrial genetic markers. Three genetic lineages were identified, which were traced to ancestral ranges. Increased genetic diversity and admixture in the invaded region highlighted asymmetric gene flow towards this area. We suppose that the adaptive potential of the invading populations was promoted by genetic admixture, whereas competitive exclusion in the native areas provided a buffer against introgression by novel genotypes. These explanations would be in line with evidence that hybridization can drive evolutionary change under conditions when new niches can be exploited. Contemporary environmental alterations are predominantly caused by human activity. As a result, populations may be reduced or increased in numbers, and the species as a whole may contract or expand its range. This, in turn, can lead to changes in species composition and biodiversity, particularly in invaded areas  individuals per site and 151.0 ± 140.0 (range = 31–433) individuals per river catchment. Microsatellites were amplified with a Mastercycle Gradient machine  using the Qiagen® Multiplex PCR Kit . Microsatellites MSL1–3, MSL4–6 and MSL7–9 were co-amplified, respectively, in multiplex PCR with 1.5 μL genomic DNA extract following the manufacturer's instruction, using reduced reaction volumes of 12.5 μL. Forward primers were 5′-labelled with fluorescence dyes D2, D3 and D4 . The cycling program started with 15 min at 95°C, followed by 35 cycles of 0.5 min at 94°C, 1.5 min at 57°C, 1.5 min at 72°C, and finished with 10 min at 72°C. Fragment analysis was performed with a CEQ 8000 machine  equipped with an eight capillary injection system. Chromatograms were evaluated with the appending software package GenomeLab GeXP Genetic Analysis System version 10.2 by applying the Fragment Analysis Module.Nine polymorphic microsatellites developed by Kohlmann and Kersten  as well cyt b analysis with an average sample size of 7.1 ± 5.1 samples per site (range = 1–20). Cyt b sequences were amplified with a Mastercycle Gradient machine  using a Maxima Hot Start PCR Master Mix  and zcyt-1F (5′-taatggcaagcctccgaaa-3′) and zcyt-2R (5′-ctgagctactaatgcaggatca-3′) as forward and reverse primers, respectively. PCRs were performed according to the manufacturer's instruction, using reduced reaction volumes of 20 μL. The cycling programme started with 4 min at 95 °C, followed by 35 cycles of 0.5 min at 95°C, 1.0 min at 51°C, 2.0 min at 72°C, finishing with 15 min at 72°C. Restriction analysis of the cyt b PCR fragment targeted the polymorphism described by Kohlmann et al. . These analyses were completed by a sequencing service  using the above mentioned primers and additionally zcyt-1R (5′-gtttaagccaagggggttgt-3′) and zcyt-2F (5′-ctcgattctttgccttccac-3′) to obtain two overlapping fragments.A subset of 387 samples was used in the n et al.  and was Presence of null alleles and large allele drop-outs were tested with MICROCHECKER 2.2.3 . The Neighbour Joining algorithm implemented in MEGA 5  to identify mutation patterns and possibly new haplotypes. Sequences from this study, together with known sequences of zander from different European regions, were used to construct a phylogenetic tree using the Neighbour Joining algorithm implemented in the software package MEGA 5. Sequences of the Volga zander amova analyses were conducted with Arlequin 3.5.1.2 , allelic richness (AR) and heterozygosity  were calculated with MSA 4.05  for all native populations together (N = 25). Next we tested for differences in genetic diversity (AR and HS independently) between the populations of the invaded and native areas by identifying values of the populations of the invaded areas that lay outside of the 95% confidence limits generated by the populations of the native areas. If more than one site was sampled in a river or lake in the invaded areas, the mean values were calculated for the respective water body and employed for comparison. Values of the populations of the invaded areas outside of the 95% CI of the native populations' values would be considered significantly different based on a 5% error threshold. Additionally, to further test for significant differences t-tests comparing the average AR and HS between the populations of the native (N = 25) and the non-native areas (N = 15) were conducted. (this test was not applicable to populations of single rivers and lakes due to the low number of sampling sites (N = 1–4) per water body).Number of alleles . Admixture of populations was calculated such that all individuals were assigned to each of the identified ancestral gene pools. Afterwards, their respective proportions of membership were computed. Values of populations within a catchment were partly pooled and plotted as pie-charts on a map of Germany to illustrate genetic admixture and distribution in a geographic context.STRUCTURE 2.3.2 , HE = 0.62 ±0.07 and HS = 0.61 ± 0.07 over all loci and populations  and an allelic richness of 4.5 ± 0.7 over all loci and populations . All hetulations .With resulations  deviatioR) and heterozygosity (HS) were chosen as estimators. When populations of single water bodies of the invaded areas  were compared with the populations of the native areas , they exhibited higher values in all cases. In most cases, these lay outside the 95% confidence interval (CI) of the native populations, and thus were considered significantly different ; HS = 0.67 ± 0.04 (±0.03); mean ± SD (± CI); indicated with data points ‘all’ in Fig. R = 4.26 ± 0.64 (± 0.25); HS = 0.60 ± 0.07 (±0.03); mean ± SD (± CI); indicated with solid lines in Fig. P < 0.001) when tested with a one-sided Student′s t-test.For a more detailed analysis of genetic diversity, allelic richness  trees  to high  bootstrap support. The Elbe-Oder group was further resolved into Elbe and Oder clades, but with rather low bootstrap support (49%). The Oder group formed a cluster together with, but clearly distinguished from populations of the brackish Baltic Sea populations (100% bootstrap support). The Elbe group split into two subgroups, one of which formed the sister group to the Eider/Nord-Ostsee-Kanal (NOK) clade. Within this clade, the brackish populations (NOK and adjacent waters) clearly separated as a sister group, as indicated by a moderately high bootstrap value (67%).Of the three measures used to estimate genetic distances – Dees Fig. . Becauseees Fig. . This waThe populations of the invaded areas separated the Danube clade from the Elbe-Oder clade. More precisely, the river Main and Lake Constance populations (both belonging to the Rhine catchment) appeared more closely related to the Danube clade, while the other Rhine populations (Rhine itself and Mosel), together with the reservoir Edersee (Weser catchment) and the Ems populations, appeared genetically closer to the Elbe-Oder group. Most zander populations of the invaded areas formed clusters as well, for example, Ems, Rhine and Lake Constance populations, though with very low bootstrap support.amova with all seven catchments (defined as groups) showed that most genetic variability was present on the population level (WPV in Table amova resulted in a decrease of within-population variability from 88.6% to 85.5%. The concomitant increase of the genetic variability among catchments (AGV) by about 31% clearly indicated a higher genetic diversity in the populations of the invaded compared with the native ranges (increase from 7.3% to 10.6%). Variation among populations (APV) within a catchment remained almost the same (4.1% vs 3.9%) in both amovas.amovas  and compared with sequences of zander from geographic regions outside Germany. Neighbour Joining analysis showed that no haplotypes other than A and B have been identified for Europe to date. All sequences grouped with one of these two main types, which represented sister clades . Moreovecyt b gene revealed conspicuous asymmetric distribution of admixed genetic ancestries indicating gene flow predominantly from the Danube, Elbe and Oder towards Rhine, Weser and Ems catchments. We discuss the population differentiation of zander as a result of different initial situations in the native versus the newly colonized ranges.We investigated the contemporary invasive spreading of zander in German inland waters, and analysed how this was possibly driven by admixture of different genetic lineages after multiple human-assisted secondary contacts. Analysis of nine species-specific microsatellites and the mitochondrial Microsatellite based genetic distance analysis identified the populations of the Danube, Oder and Elbe catchments as three distinct clades, indicating a divergent development of these populations after postglacial re-colonization of the native areas  that supports this idea. However, in our own study, we observed only small genetic differences between German brackish and freshwater populations (data not shown). Larsen et al. (Esox lucius populations in Denmark and observed very low introgression rates (<1%) despite intensive stocking with freshwater pike over more than four decades. Local adaptation might be an important reason for their findings, and might also be relevant in brackish zander populations.Within the groups of native zander, the brackish populations of the Oder  and Elbe catchments  exhibited particularly high proportions of native gene ancestry. Living in higher salinity waters and spawning in freshwater tributaries requires genetic adaptation, which may not be acquired ä et al.  found prn et al.  investigcyt b gene, which spread towards the other catchments, where generally the A haplotype dominated. Intriguingly, much like it was observed with microsatellite markers, gene flow seemed to spread foremost to the invaded areas, while the native areas appeared less (Elbe) or not at all admixed (Oder).Also the freshwater populations of the Danube catchment exhibited pronounced levels of native genetic ancestry. A more effective geographical isolation as compared with other catchments might be a reason here, protecting the local gene pool against foreign genetic influences. However, the Danube was likely the source of haplotype B of the mitochondrial Coregonus albula in Mehner et al. Salvelinus alpinus in Englbrecht et al. As an alternative cause for the observed asymmetric admixture patterns, unequal levels of stocking in the native versus the newly colonized areas might be considered. Unfortunately, we could not investigate this hypothesis further due to the lack of a comprehensive documentation on stocking activities in Germany. However, more and more evidence is accumulating that stocking in the presence of self-sustaining fish stocks is rarely successful in a range of freshwater fish species (vendace


Thais clavigera, we determined the partial nucleotide sequences of the mitochondrial COI gene for 602 individuals sampled from 29 localities spanning almost the whole distribution of T. clavigera in the NW Pacific Ocean . Results from population genetic and demographic analyses  revealed a lack of genealogical branches or geographical clusters, and a high level of genetic (haplotype) diversity within each of studied population. Nevertheless, low but significant genetic structuring was detected among some geographical populations separated by the Changjiang River, suggesting the presence of geographical barriers to larval dispersal around this region. Several lines of evidence including significant negative Tajima’s D and Fu’s SF statistics values, the unimodally shaped mismatch distribution, and Bayesian skyline plots suggest a population expansion at marine isotope stage 11 , the longest and warmest interglacial interval during the Pleistocene epoch. The lack of genetic structure among the great majority of the NW Pacific T. clavigera populations may be attributable to high gene flow by current-driven long-distance dispersal of prolonged planktonic larval phase of this species.The present-day genetic structure of a species reflects both historical demography and patterns of contemporary gene flow among populations. To precisely understand how these factors shape current population structure of the northwestern (NW) Pacific marine gastropod,  The present-day genetic structure of a species reflects both historical demography  and patterns of contemporary gene flow among populations . An undeChelon haematocheilus , Pohang-si , Qingdao , Rizhao , Zhoushan , Wenzhou , Shenzhen  and Haikou ) were small, but statistically highly significant (P < 0.05)  . Pairwise Φ < 0.05) . These r = 1.00) .T. clavigera based on multiple lines of evidence from neutrality tests and the mismatch distribution  for 23 out of 29 populations. Similarly, Fu’s Fs statistic was significantly different from zero (P < 0.05) for all populations except for the Taiwan samples. Both tests of neutrality were significant  when samples were grouped as a single data set. The mismatch distribution was unimodal for all examined samples, supporting a model of sudden expansion  11, the longest and warmest interglacial interval of the past 500 kyr.We detected significant population expansion of ribution . Tajima’xpansion . The expy 400 ka . These eT. clavigera in the NW Pacific Ocean. We found that genetic variation among the NW Pacific populations was generally low, perhaps owing to a combination of high contemporary gene flow and recent common ancestry of haplotypes. Nevertheless, both AMOVA and pairwise ΦST analyses indicated weak, but significant genetic structure across the Changjiang River  between the northern and southern populations of the Changjiang River in our AMOVA analysis , only a few T. clavigera individuals were found; by contrast, near the southern mouth, T. clavigera were very abundant in the rocky seashore of the Zhoushan archipelago. This habitat discontinuity may have reduced effective gene flow between the northern and southern populations of the Changjiang River.In this study, the AMOVA analysis and pairwise Φs Tables  & 3 suggc larvae ,19. In ac larvae ,14. We oanalysis . As docuTetraclita japonica in the NW Pacific [Catomerus polymerus [T. clavigera, we found a lack of genetic structure across major ocean current systems, we found very low, but statistically significant genetic structure between the two major ocean circulation systems (CSCC and KSC) by AMOVA , but ΦSC  was equivalent and statistically significant . The lack of genetic structure between the two current systems may be attributable to high gene flow owing to the long spawning time and prolonged planktonic larval phase of T. clavigera. Our findings that the most common two haplotypes occur at almost every site support this high gene flow hypothesis. Some additional observations suggest that although no water mass from a sub-branch of the KSC reaches the SCS in the summer, in some years this does occur [T. clavigera spawning could occur from spring to summer (February to August) [T. clavigera larvae are likely to enter and mix into the CSCC system from the Taiwan coastline, which may increase gene flow between populations in the two circulation systems to some extent, therefore resulting in a lack of genetic differentiation.It has been reported that oceanographic patterns play an important role in maintaining genetic and phenotypic differentiation in the acorn barnacle  Pacific . Moreoveolymerus ; howeveres occur . Moreovees occur . In sout August) ,28; furtT. clavigera populations underwent a demographic expansion  coupled with relatively low nucleotide diversity. The retention of a surplus of rare COI variants may indicate a recent population expansion of T. clavigera; otherwise, these rare variants are predicted to be eliminated by genetic drift [T. clavigera in the NW Pacific [From haplotype network analysis for COI data, we found a complicated network pattern that suggested that xpansion . Also, tic drift . This su Pacific , an inte Pacific  and has  Pacific . The seq Pacific . Paleont Pacific –62. SuchT. clavigera is assumed to have occurred pre-LGM. These results differ from the traditional view of demographic expansion that it occurs during the period of LGM. Nevertheless, population expansion that occurred pre-LGM has been reported in species such as the cold-water barnacle Chthamalus challengeri [Concholepas concholepas [et al. [The Pleistocene glacial age, and particularly the last glacial maximum (LGM) approximately 20,000 years ago, had an important influence on the evolution and genetic structure of marine organisms. Many species in various marine realms appeared to arise at the beginning of LGM ; howeverllengeri  and the cholepas . In the  [et al.  also est [et al. ,64, fish [et al. ,66, and  [et al. ,68. ThesT. clavigera populations, we determined the partial sequence of the mt COI gene from 602 individuals sampled from 29 localities across the NW Pacific Ocean. We observed a high level of genetic diversity within each of sampled populations, and no significant genealogical branches or geographic clusters, suggesting high levels of gene flow among populations throughout the NW Pacific. Nevertheless, we detected low, but significant genetic differentiation that corresponds to habitat conditions and freshwater discharge from the Changjiang River. Since we used only a single mtDNA marker, further studies of T. clavigera using multiple nuclear markers are required to validate the observed genetic structure. Also, population genetic studies of other marine species with a planktonic larval phase in the region will provide additional insight into the phylogeographic patterns of NW Pacific organisms.To better understand contemporary genetic structure and historical demography of the NW Pacific S1 FigBoth NJ and Bayesian inference (BI) analyses yield the same topology. Bootstrap values for NJ (the former number) and the posterior probabilities for BI (the latter number) analyses are indicated at the nodes.(TIF)Click here for additional data file.


MC1R exhibits reduced diversity in African populations from high UVR regions compared to low-UVR non-African populations. However, MC1R diversity in non-African populations that have evolved under high-UVR conditions is not well characterized. Variation in human skin pigmentation evolved in response to the selective pressure of ultra-violet radiation (UVR). Selection to maintain darker skin in high UVR environments is expected to constrain pigmentation phenotype and variation in pigmentation loci. Consistent with this hypothesis, the gene ST was calculated between Melanesian populations and populations from the 1000 Genomes Project. The SNP rs2228479 was genotyped in a larger sample (n = 635) of Melanesians and tested for associations with skin and hair pigmentation.In order to test the hypothesis that MC1R variation has been constrained in Melanesians the coding region of the MC1R gene was sequenced in 188 individuals from Northern Island Melanesia. The role of purifying selection was assessed using a modified McDonald Kreitman’s test. Pairwise FST values calculated between the four islands sampled here indicate little regional substructure in MC1R. When compared to African, European, East and South Asian populations, Melanesians do not exhibit reduced population divergence (measured as FST) or a high proportion of haplotype sharing with Africans, as one might expect if ancestral haplotypes were conserved across high UVR populations in and out of Africa. The only common nonsynonymous polymorphism observed, rs2228479, is not significantly associated with skin or hair pigmentation in a larger sample of Melanesians.We observe three nonsynonymous and two synonymous mutations. A modified McDonald Kreitman’s test failed to detect a significant signal of purifying selection. Pairwise FMC1R in Northern Island Melanesia This absence of strong constraint, as well as the recent population history of the region, may explain the observed frequencies of the derived rs2228479 allele. These results emphasize the complex genetic architecture of pigmentation phenotypes, which are controlled by multiple, possibly interacting loci. They also highlight the role that population history can play in influencing phenotypic diversity in the absence of strong natural selection.The pattern of sequence diversity here does not support a model of strong selective constraint on The online version of this article (doi:10.1186/s12863-015-0277-x) contains supplementary material, which is available to authorized users. Human skin and hair pigmentation are highly variable traits that are controlled by multiple genetic loci –14. SkinSkin pigmentation is primarily determined by the amount, type, and distribution of melanin, one of the primary chromophores of the skin. Melanin  acts as both a barrier to and filter of UVR—it scatters UVR and limits its penetration of the epidermis , 23. ThiMC1R gene, which encodes the melanocortin-1 receptor, a 7-pass transmembrane G-protein coupled receptor found on the surface of melanocyte cells. When the hormone α-MSH binds to the MC1R, activation of adenyl cyclase results in increased levels of cAMP. This leads to an increase in the activity of tyrosinase, the rate-limiting enzyme of melanogenesis, as well as increased levels of tyrosinase-related proteins (TRP)-1 and −2 [MC1R gene harbors a high number of polymorphisms, including several loss-of-function mutations that are associated with reduced skin color, melanoma, freckling, and red or blond hair [MC1R mutations are also associated with a reduced DNA-repair capacity, possibly explaining the link between MC1R and melanoma risk [A genetic locus known to influence human skin and hair pigmentation is the 1 and −2 , 35, ultond hair –39. MC1Roma risk , 41.MC1R has received particular attention in studies of human evolution because of its unusual levels and patterns of sequence diversity. Unlike many loci, which exhibit higher levels of diversity in African populations, MC1R diversity is highest in Eurasian populations [MC1R can be attributed in part to a high mutation rate related to the elevated CpG content of the region [ulations , 43. Theulations , 43. It e region . This maMC1R in African and other high-UVR populations rests primarily on low levels of nucleotide diversity and the ratios of nonsynonymous to synonymous polymorphisms and divergent sites  [MC1R in non-African populations has been argued to be a signal of diversifying selection [MC1R in non-African populations [Evidence for purifying selection on A tests) –44. The election , althougelection . Tests oulations , 45, 46.MC1R has been extensively sequenced in populations living in low UVR regions. However, far less is known of MC1R sequence diversity in high UVR populations outside of Africa, including whether or not polymorphisms segregating in such populations may be responsible for variation in skin or hair pigmentation phenotype in these regions. Because MC1R is commonly associated with mutations that lead to a decrease in skin pigmentation, it is generally assumed that MC1R variation is tightly constrained by purifying selection in high UVR regions. However, it is possible that mutations leading to an increase in the synthesis of eumelanin may have been favored by positive selection. While such mutations in MC1R are believe to have played an important role in adaptation following the loss of body hair in hominins 1.2 mya [MC1R in high-UVR populations focus on the role of purifying, rather than positive, selection.Because of its association with several phenotypic traits in European (and to a lesser extent East Asian) populations,  1.2 mya , there iMC1R indicated that the gene was under strong functional constraint in populations from Papua New Guinea as well as Africa, as one might expect if purifying selection has acted to remove nonsynonymous variants resulting in lighter skin color [MC1R diversity in high-UVR non-African populations. In order to accurately assess the extent of variation at MC1R in Melanesia, a broader sample is critical, due to the complex population history of the region [MC1R variation is expected to have been tightly constrained during much of the first ~30,000–40,000 years of human habitation in Melanesia, the migration of Austronesian speakers into the region and their subsequent admixture with resident populations may have introduced nonsynonymous mutations more commonly found in low-UVR regions. If not quickly removed by strong purifying selection such variants may contribute to observed variation in pigmentation phenotype.Early reports of sequence variation in in color , 43 acroin color , resultie region –51. Arche region , 53, spre region . The rege region , 56 origTYRP1 gene [MC1R, known to influence skin and hair color in European and East Asian populations [Populations from Island Melanesia are darkly pigmented compared to populations of northern Europe and Asia , as expeRP1 gene , 59. VarRP1 gene . InsteadRP1 gene , 60. MC1ulations , 61, 62 MC1R gene in 188 Island Melanesians from four different islands: New Hanover, New Britain, New Ireland, and Bougainville. To our knowledge this is the most extensive survey of MC1R sequence variation in the region to date. We use these data to address three questions pertaining to the evolution of MC1R sequence variation in Northern Island Melanesian populations and the role of MC1R in shaping phenotypic diversity in the region. Specifically we set out to test the hypothesis that variation in MC1R has been constrained in these populations by purifying selection. We also characterized regional (inter-island) and global levels of variation at MC1R in order to assess the roles of selection and population history in shaping variation at MC1R in Northern Island Melanesia. Finally, we tested for associations between MC1R polymorphisms and quantitatively measured skin and hair pigmentation phenotype to evaluate the role of MC1R in shaping local pigmentation phenotype.Here we survey sequence variation in the coding region of the The pigmentation measurements reported here were originally collected as part of a larger study examining phenotypic variation and population history in Island Melanesia by H.L.N. and J.S.F conducted in 2000 and 2003. Individuals were sampled from islands throughout Northern Island Melanesia, with an emphasis on the islands of New Britain, New Hanover, New Ireland, and Bougainville Fig. . IndividIndividuals were assigned to categories according to sex, island, neighborhood, and linguistic phylum. In order to be assigned to a particular island, neighborhood, or linguistic phylum an individual and both of his or her parents needed to also be from that island/neighborhood or speak a language belonging to the same phylum. All individuals gave their informed consent to participate in the study, including the measurement of pigmentation data and the examination and publication of genetic variation. Institutional Review Board (IRB) approval for data collection and analyses were obtained from Temple University (IRB 99–226), The Pennsylvania State University (IRB 00 M558-2), and the Papua New Guinea Medical Research Advisory Committee. Approval was granted for all data collection sites. This was a collaborative project with the PNG Institute of Medical Research.M) index. The M index provides a quantitative measure of skin color that is due primarily to the effects of melanin alone [Quantitative measurements of skin and hair pigmentation were taken using the DermaSpectrometer  following the practices described in Norton et al. . The Derin alone .MC1R gene (951 bp) was amplified in the 191 Melanesian samples from the islands of Bougainville (N = 36), New Hanover (n = 60), New Britain (n = 32), and New Ireland (n = 57). Sequence was also obtained from three individuals who could not be clearly assigned to any one of these four islands (see p > 0.85) Melanesian sequences were later aligned with the chimpanzee (Pan troglodytes) MC1R sequence and African , East Asian , European , and South Asian  samples sequenced as part of the 1000 Genomes Project .The coding region of the In order to test for the effect of the derived allele at rs2228479 on skin and hair pigmentation in a larger sample of Melanesians we utilized published genotype and phenotype data from these individuals . To obtaMC1R sequence diversity and polymorphism and divergence were calculated in DNASp v5.10.1 [Summary statistics for  v5.10.1 , 68. Two v5.10.1 , which i v5.10.1 . Tajima’ v5.10.1 . A negat v5.10.1 . This moMC1R has been shaped by purifying selection in Island Melanesian populations we use a modification of the McDonald-Kreitman test, which compares the ratio of nonsynonymous to synonymous polymorphisms to the ratio of nonsynonymous to synonymous fixed differences [n = 3) to synonymous (n = 2) polymorphisms in Melanesians as well as for all other equally or less likely probabilities provides a test of this null hypothesis.In order to test the hypothesis that ferences . An advaferences , 75. Dueferences  and use ferences . The sumχ2 test implemented in DNASp [ST values for the coding region of MC1R were calculated using DNASp between each of the four Melanesian islands, between each island and each population from the 1000 Genomes Project, and between the Melanesian region as a whole 1000 Genomes African, East Asian, European, and South Asian regional groupings. Median-joining haplotype networks showing the relationship of MC1R haplotypes among Melanesian islands and between Melanesians and the 1000 Genomes samples were constructed using Network 4.613 [Allele frequency distinctions between islands and between linguistic phyla were tested for using a in DNASp . Tests oin DNASp . Pairwisrk 4.613 .MC1R nonsynonymous polymorphism, rs2228479, and skin and hair pigmentation in the Melanesian population were tested for in PLINK [M λ = −0.788, hair M λ = 2.93). The distribution of untransformed and transformed skin and hair M index values are depicted in Additional files Associations between the only common (frequency > 0.05) in PLINK  using liin PLINK . We alsoMC1R mutations observed here are rs2228479 (V92M), rs885479 (R163G), and rs2228478 (T314T), which occur at frequencies of 15.4, 4.5, and 22.3 % in the resequenced sample, respectively. MC1R haplotypes and their frequencies in the Melanesian sample are reported in Table In our sample of 188 sequenced Melanesians, we observed a total of five segregating sites: three of these were synonymous mutations, two were nonsynonymous. Of these five mutations, one (a nonsynonymous Ile → Leu mutation at position 89986456) is novel and has not been previously reported. However, this mutation is rare in the sample, occurring in three individuals . The most common N = 376) compared to the larger regional 1000 Genome populations (2 N range: 370–758), we randomly subsampled all regional populations  to obtain 370 haplotypes  from each and recalculated summary statistics. This process was repeated 100 times to obtain a range of π and θ values for each subsampled population  in the total Melanesian sample, as well as for each island and linguistic phylum separately. These are displayed in Table Mean levels of diversity and haplotype distribution across the four Melanesian islands sampled here can be found in Tables MC1R has been tightly constrained by strong purifying selection in Melanesian populations. Although site-frequency-spectrum based statistics such as Tajima’s D and Fay and Wu’s H are sensitive to demographic history, we also calculated values for these statistics in the full Melanesian sample, as well as for each individual Melanesian population , with the highest values reported for comparisons between New Hanover and New Britain (0.042), and the lowest between New Ireland and Bougainville (0.000) and between New Ireland and New Britain (0.000). By comparison, pairwise FST values at MC1R among the five European, two African, three East Asian, and five South Asian populations in the 1000 Genomes Project ranged from 0.001–0.065 (European) 0.000 (African), 0.022–0.089 (East Asian), and 0.000–0.004 (South Asian)  can be found on each of the 4 islands  = 0.027) and the highest with East Asians (FST(MEL-EAS) = 0.270). However, this high divergence between Melanesians and East Asians is characteristic of generally high levels of divergence between East Asians and the other 3 populations at MC1R . There is no evidence of a particularly strong affinity between Melanesians and the high-UVR African populations of the 1000 Genomes Project . The frequency of the derived allele also varied significantly between the Austronesian speaking (0.19) and non-Austronesian speaking (0.10) population sample .The only common nonsynonymous SNP observed in the Melanesian sample was the rs2228479 polymorphism. To better assess the frequency of the derived allele at this site across Northern Island Melanesia we genotyped this allele in 444 additional individuals from New Britain, New Hanover, New Ireland, and Bougainville. The frequency of the derived allele at this locus in each Melanesian island and population are reported in Table p = 3.494 × 10−5), possibly indicating significant population substructure, and because of previous reports of substructure in the region [p = 0.7201). Associations with skin pigmentation are suggestive (0.0635), but not significant. These should also be interpreted with caution, since this analysis does not account for potential substructure within island groupings [p > 0.05 for all tests).We tested for an association between genotype at rs2228479 and quantitatively measured skin and hair color using this expanded sample. Mean skin and hair values  are found in Table e region , we testroupings . rs22284MC1R, in a large sample of individuals from Northern Island Melanesia in order to test the following hypotheses: a) that MC1R variation is constrained by purifying selection in Melanesia, as it is in other high UVR populations; b) that, given this selective constraint, Melanesians should appear more closely related to other high-UVR populations  than to populations with which they share a more recent common history , and c) that nonsynonymous variants in MC1R are significantly associated with skin and/or hair pigmentation phenotype in Melanesian populations.The populations sampled here are all from Northern Island Melanesia, a region where UVR is high and variation in skin phenotype and at pigmentation loci is expected to be constrained by purifying selection. While pigmentation is generally dark, notable variation in both skin and hair pigmentation is observed , indicatMC1R would be constrained in Melanesian populations, particularly in non-Austronesian speakers, as the ancestors of modern non-Austronesian speakers have been resident in the region for ~ 30 kya. Surprisingly, out of the five segregating sites that we observed in this Melanesian sample, three are nonsynonymous polymorphisms. One of these is rare (occurring at a frequency of < 1 % in the sample), while the two others, rs2228479 and rs885479, occur at frequencies of 15.4 and 4.5 % in the sequenced sample. A modified McDonald’s Kreitman’s test  does not support a model of purifying selection. Tajima’s D and Fay and Wu’s H values, while slightly negative, also do not indicate a significant departure from neutrality, suggesting that MC1R in Melanesians has not been subject to either purifying or positive selection. This absence of strong purifying selection is surprising given the high UVR levels in the region [MC1R in this Melanesian population sample it is likely that purifying selection has influenced variation at other pigmentation loci.Given the high levels of UVR in Melanesia, we expected that variation in e region . Howeverp = 0.553. This differs from observations of Harding et al. [MC1R haplotypes in Africans, and zero nonsynonymous polymorphisms. Using those data, the authors estimated that the probability of observing zero nonsynonymous polymorphisms out of five total polymorphisms was 0.0198, causing them to reject a model of neutral evolution in Africans. Similarly low levels of variation were observed in Africans by Rana et al. [Interestingly, when we apply this modified McDonald-Kreitman’s test to the African 1000 Genomes data we also fail to reject a model of neutrality. Ten segregating sites are observed among the LWK and YRI samples, 4 of which are nonsynonymous and 6 of which are synonymous. The likelihood of observing such a pattern is g et al. , who obsa et al. . Howevera et al.  reporteda et al. . These rMC1R. However, levels of pairwise FST between islands are relatively low (0.000–0.031) suggesting that MC1R is not significantly differentiated among islands. This is also visualized in the Melanesian MC1R haplotype networks  and high levels of haplotype sharing between Melanesian and African populations. Here we observe an FST value of 0.088 between our sequenced Melanesian sample and the African populations (LWK and YRI) of the 1000 Genomes Project. Without comparisons to FST values for other genes sequenced in the same population samples it is difficult to assess whether this is unusually high or low compared to average levels of divergence between these specific populations. However, previous studies have reported the mean FST between a single Melanesian (Nasioi) and African (Mende) population to be 0.182, based on allele frequencies at ~11,000 SNPs distributed throughout the genome [ST values range from 0.199–0.283 [ST values here between Melanesians and Africans may be lower than average levels of genome-wide divergence between populations from these two regions. However, if MC1R function were highly conserved across high-UVR populations we would expect to observe much lower values, as well as a high degree of exclusive Melanesian-African haplotype sharing, which is not evident  of individuals from four islands in Northern Island Melanesia, and find little indication of strong purifying selection acting on this gene. We also find little evidence of high levels of inter-island diversity in the region, contrary to other loci [ST values between our Melanesian sample and African populations sequenced in the 1000 Genomes Project indicate lower than average levels of divergence, we do not find strong evidence that ancestral MC1R haplotypes have been conserved in populations from these two high UVR regions. The only common nonsynonymous MC1R mutation observed in the Melanesian sample, rs2228479, is not associated with pigmentation phenotype in the region, possibly due to epistatic effects at other loci. These epistatic effects along with recent migrations of East Asian individuals via the Austronesian expansion can explain the observed patterns of MC1R diversity across the region. Pigmentation is a complex phenotype that exhibits extensive variation within and between populations, and is controlled by multiple interacting genetic loci. While MC1R has a strong influence on pigmentation in several non-African populations, it does not play a significant role in shaping variation in populations from Northern Island Melanesia. Clearly a better understanding of pigmentation candidate gene diversity in Melanesian populations will be necessary in order to better characterize the genetic architecture of the pigmentation phenotype in this region.In this study we sequenced the her loci . While FThe DNA sequences supporting the material analyzed here are available in the GenBank repository, with accession numbers KT863240- KT863426.


Dendroctonus ponderosae Hopkins), a major pine forest pest native to western North America, has extended its range north and eastward during an ongoing outbreak. Determining how the MPB has expanded its range to breach putative barriers, whether physical (nonforested prairie and high elevation of the Rocky Mountains) or climatic , may contribute to our general understanding of range changes as well as management of the current epidemic. Here, we use a panel of 1,536 single nucleotide polymorphisms (SNPs) to assess population genetic structure, connectivity, and signals of selection within this MPB range expansion. Biallelic SNPs in MPB from southwestern Canada revealed higher genetic differentiation and lower genetic connectivity than in the northern part of its range. A total of 208 unique SNPs were identified using different outlier detection tests, of which 32 returned annotations for products with putative functions in cholesterol synthesis, actin filament contraction, and membrane transport. We suggest that MPB has been able to spread beyond its previous range by adjusting its cellular and metabolic functions, with genome scale differentiation enabling populations to better withstand cooler climates and facilitate longer dispersal distances. Our study is the first to assess landscape-wide selective adaptation in an insect. We have shown that interrogation of genomic resources can identify shifts in genetic diversity and putative adaptive signals in this forest pest species.The mountain pine beetle (MPB;  Dendroctonus ponderosae Hopkins; Chaunus marinus; Locusta migratoria; Rapid range expansion is an extremely important biological phenomenon that is only beginning to be understood at the molecular level. Species invasions into new ecosystems can have direct and indirect impacts at community, ecological, and population scales  and haveThe MPB is an ecologically important endemic species that ranges across the pine forests of western North America . HistoriPinus contorta Dougl. Ex Loud.), hybridizes with a novel host, jack pine . Of the 159 annotated SNP-containing sequences, BLAST2GO analyses returned 118 sequences with at least one cellular component term and 139 with at least one molecular function or biological process term. Alignments of the 1,032 SNP-containing genomic DNA (gDNA) to complimentary DNA (cDNA) suggest that 142 (14%) were exonic, 52 (5%) intronic, and 712 (69%) intergenic (the remaining 126 [12%] were unclassified).From the panel of 1,032 SNPs, flanking sequences corresponding to 159 (15%) returned annotations using BLAST2GO, of which 153 had unique BLAST hit descriptions. Each annotated SNP-containing sequence was assigned at least one GO term within the cellular component, molecular function, and/or biological process ontologies, based on the most similar sequence identified by BLAST. We specified a minimum similarity threshold of 95% for BLAST hits when identifying annotations; however, several SNPs returned annotations below this threshold (see In order to distinguish genome-wide effects (such as inbreeding) from the locus-specific effects (such as selection and mutation) measured later, we first assessed the level of genetic diversity present in the data. Allele frequency calculations revealed that in >95% of SNPs the least common allele occurring in a given population had a frequency >10% , suggesting a low probability of obtaining false positives. Tests for deviation from Hardy–Weinberg proportions showed that 21 of the 1,032 manually scored SNPs had significant heterozygote deficits . Genotypic linkage disequilibrium (GLD) among SNPs across populations indicated that 494 SNPs had highly significant linkage ; hence, 247 SNPs  were excluded to assure independence of loci. Subsequent analyses were performed using a subset of the 1,032 SNPs in which Hardy–Weinberg equilibrium (HWE) discordant and GLD SNPs were removed, leaving a total of 764 SNPs.FST estimates provide a measure of population differentiation. Pairwise population FST estimates ranged from 0.011 between FOX and GRP (northwestern AB) to 0.092 between FSJ and WHI  (FST of 0.038 (SE ± 0.005) indicates that genetic differentiation among populations is relatively low overall. FIS, or inbreeding coefficient values ranged from 0.0212 at MAP to −0.050 at LAC, suggesting that the levels of inbreeding are also relatively low. Although measures of genetic diversity varied from 0.3378 WHI to 0.3885 LAC , and 0.3422 WHI to 0.389 VMT , suggesting that sampled individuals did not exhibit significant genetic similarity. These tests facilitated the removal of loci whose aberrant behavior could affect estimates of population structure.K = 2. These clusters partitioned the 15 sites  in northern BC and AB from the 12 sites  found in the south .Investigations of population-level genetic structure allow the identification of genetically similar individuals and groups, irrespective of geographic location. Using the method of Frt = 0.080, Fsr = 0.015, Fst = 0.094, Fis = 0.024, respectively; P ≤ 0.002). Plots of K = 3 for all 27 sites maintained a northern and southern cluster, while assigning MAP and WHI to a third cluster. Principal coordinates analysis (PCoA) (B). These results suggest a signal of population structure among southern and northern clusters; however, the signal is not particularly strong, indicating that some degree of connectivity remains between the subpopulations.Analysis of molecular variance (AMOVA) assesses allele frequencies and the rate of fixation to determine whether subpopulations are part of a single large, randomly mating population. The results from an AMOVA indicate that genetic variance was partitioned as follows: 8% among northern and southern clusters, 1% among sites within clusters, 2% among individuals within sites, and 88% among individuals (s (PCoA)  supportss (PCoA) B. These Population Graphs  allows gThe population graph in In order to understand the relative contributions of selective adaptation to genome-wide variation across the landscape, we used outlier detection tests. Using BayeScan, 68 outliers were identified across the 27 populations, all of which were under directional selection according to the alpha values obtained. Using Lositan, 179 outliers were identified across the 27 populations, of which 30 SNPs were identified as being under balancing selection. Using the 12 southern sites as populations presented more outlying SNPs  than when using the 15 northern sites as populations . However, all SNPs identified from the independent analyses of sites within each subset were directionally selected. Outlying SNPs were not detected when the data were partitioned into the two main clusters identified by STRUCTURE . supplementary table S1, Supplementary Material online) were consistently identified by the different analysis methods within the southern subset (12 populations), whereas 11 SNPs  were consistently identified in the full data set (27 populations). With respect to Splign alignments, 30 SNPs were identified as being within exonic regions, whereas 138 were intergenic and 10 intronic. Five SNPs were consistently identified as outliers among methods and subsets.Of the 208 unique outliers detected, 32 SNPs were annotated by BLAST2GO, and of these 32 SNPs, two were identified as being under balancing selection by Lositan. The 32 SNPs fell into GO categories in the following manner: Eight SNPs were assigned a cellular component, 16 assigned a molecular function, and eight were assigned a biological process. Based on these annotations, the majority of outlying SNPs appear to encode a protein with a metabolic function. No significant enrichment of GO groups was identified. Among outlying, annotated SNPs, two  of MPB presence interspersed with light damage around KPE, KOY, MAP, and WHI prior to 1999 . The endA). In the course of expanding northward in interior BC, the MPB populations have undergone significant change in their genomic composition between southern and northern populations.Our independent data largely confirms the previous studies, using a much larger sample of variation across the MPB genome, and provides a more detailed reconstruction of the potential dispersal routes. The most likely dispersal routes appear to have been as follows: 1) Historical expansion from the southwest (MAP) toward northwestern BC (HOT), with rapid population size increases in central and northern BC, followed by range expansion east into northern AB  and 2) slower expansion from CPS in all directions within southeastern BC and AB, contacting the northern expansion front in the region of VMT to produce a local peak in genetic diversity A. In theThe correspondence between our data and the results from mtDNA  and micrWeak genetic structure has been consistently identified within the current MPB expansion through western Canada . Our resThe current MPB outbreak and range expansion throughout western Canada has been described as originating from multiple sources within southwestern BC . In contMAP and WHI, two sites from southwest BC, may be the most established and isolated sites for MPB in Canada see , based oTwo features are noteworthy within this pattern of dispersal. First, the assignment of individuals using population priors based on two clusters indicates that individuals remain relatively admixed at VMT and WEG, sites in central BC that are close to the Rocky Mountains. This admixture, and high level of genetic diversity and connectivity, suggests that gene flow is more prevalent in this area. It is possible that gene flow is higher because, over time, southern individuals are slowly channelled northward through forested river valleys within the Rocky Mountains from southeastern BC. In contrast, a number of northern individuals are likely to have dispersed slightly south over time as the population expanded east, creating an area around the northern Canadian Rocky Mountains that may be subject to constant migration. Second, HOT was identified as one of the sites with the least connectivity . As HOT Drosophila melanogaster, for example. Of the annotated SNPs, the majority appear to be nonsynonymous changes that match via BLAST to protein sequences, with several grouping into three broad categories: 1) Cholesterol/sterol association, 2) ion transport, and 3) actin contraction.The number of SNPs detected as being under selection within any given partition of the data was well within the 6–10% commonly observed in other taxa . HoweverIn insects, cholesterol plays an important role in many molecular and cellular processes, including the synthesis of sterols, cellular membrane components, and the regulation of developmental genes . InsectsSeveral SNPs were annotated to genes that act in transporting ions across membranes. Membrane transport is essential for a number of cellular processes as it regulates the diffusion of ions and molecules among cells for use in molecular and metabolic functions . One sucTribolium castaneum, shows sequential development and adult plasticity of axonal outgrowths, many of which are associated with olfactory processing pathways and higher cognitive functions  association in southwestern BC, presumably with continued or historic gene flow from populations in the United States. Genetic connectivity and structure also suggest that the MPB has expanded east and north within western Canada, with areas around HOT and VMT being of particular significance as potential source populations for the current outbreak and high genetic admixture, respectively. Outlier detection suggests that the Canadian MPB range expansion may continue as populations are currently exhibiting signals of selection. These signals suggest ongoing adaptation of metabolic and cellular processes that could potentially allow them to withstand colder temperatures, shift developmental timing, and facilitate longer dispersal flights. However, at this early stage, further research is required in other systems to provide validation for what we have identified here. Furthermore, experimental trials that assess the expression effects of outlying SNPs are warranted to verify the function and implications of identified SNPs in the MPB system. In spite of the need for validation and verification, our results illustrate the efficacy of genetic surveys to provide insight into selective processes that may lead to adaptation. These results also suggest that the MPB threat requires consistent, targeted management.supplementary table S2, Supplementary Material online). Genotyping collection sites were selected throughout AB and BC, Canada. Sites were chosen to reflect historical Canadian endemic (ca. 1959–1998), epidemic, and recent expansion front occurrences (P. contorta) to hybrids of lodgepole × jack pine (P. banksiana). A total of 27 genotyping sites, or local populations, were represented by MPB samples collected during 2006–2008 and 2010 , and one for population-level genotyping using GoldenGate technology (referred to as genotyping sites). urrences . These sand 2010 . All colgDNA was extracted from adult MPBs as outlined in http://www.genome.jp/kegg/, last accessed April 22, 2014) amino acid sequences from pathways of interest. Genes involved with thermoregulation, cardiac regulation, olfaction, metabolism, pheromone production, growth, and detoxification were prioritized because they were hypothesized to be influenced by changes in host or climate. SNPs were filtered in CLC Genomics Workbench 5.0.1 to conform with the requirements for Illumina GoldenGate genotyping such that 1) suitable SNPs were biallelic; 2) 50 bases of flanking sequence, that did not contain another SNP, were on either side of the target SNP; and 3) each SNP had a minimum GoldenGate design score of 0.6. In total, 1,536 SNPs with the highest design scores were selected for further genotyping.Candidate genes with physiological functions were selected from transcriptome assemblies derived from sequenced cDNA libraries , 2013 anIllumina GoldenGate genotyping was conducted (McGill University and Génome Québec Innovation Centre) on 576 samples from 27 Canadian sites. Of the 576 samples genotyped, 6 were negative controls (blanks), 12 were MPB samples repeated from the Illumina SNP discovery sequencing described above (providing a means of comparison between the sequencing and GoldenGate technologies), 7 were replicated within GoldenGate and run as positive controls, and 551 were new MPB individuals. All DNA samples were standardized using Qubit  flourometery and milliQ to a concentration of 20 ng/µl prior to genotyping.Genotypes were initially scored using automated BeadStudio Genotyping Module v3.2 . The software relies on two main measures of quality: 1) GenTrain score—a measure of how well the samples cluster into each allele for a particular SNP and 2) GenCall score—essentially the measure of reliability associated with each genotype call in which lower scores indicate low reliability based on the position of the sample in the clustering. We specified acceptance of SNPs with a GenTrain score >0.25 as scores below this indicate that the genotype cluster separation is of poor quality compared with other SNPs being assessed . After thttp://blast.ncbi.nlm.nih.gov, last accessed April 22, 2014) of the class Insecta were identified using the BLAST2GO portal  on the 1,536 GoldenGate genotyping panel were extracted using CLC Genomics Workbench 5.0.1. The most similar sequences from NCBI BLASTX databases  and expected (HE) heterozygosity under HWE, GLD, genetic diversities (Qinter and Qintra), and F-statistics were performed in GenePop Version 4.1.3  tests were also employed  was plotted using the geostatistical kriging method in ArcMap 10.0 .Genetic dissimilarity among populations was investigated using PCoA in GenAlEx 6.4 . In addi4. The best estimate of K, or population cluster, was determined by the ΔK method described by STRUCTURE was used with the admixture model for a total of 10 runs with 20 iterations per run, setting burn-in and MCMC repetitions to 1 × 10Fis), among individuals within populations (Fst), among populations within clusters identified from STRUCTURE  (Fsr), and among clusters  (Frt). Tests for significant departure from the null hypothesis that subpopulations are part of a single large, random mating, genetic population were performed using 999 random permutations.The total genetic variance was assessed using a hierarchical AMOVA in GenAlEx 6.4. Data were partitioned at four levels—within individuals .


Peromyscus leucopus) populations sampled along an urbanization gradient in the New York City metropolitan area. Genomewide variation was estimated as a proxy for evolutionary potential using more than 10 000 single nucleotide polymorphism (SNP) markers generated by ddRAD‐Seq. We found that genomewide variation is inversely related to urbanization as measured by percent impervious surface cover, and to a lesser extent, human population density. We also report that urbanization results in enhanced genomewide differentiation between populations in cities. There was no pattern of isolation by distance among these populations, but an isolation by resistance model based on impervious surface significantly explained patterns of genetic differentiation. Isolation by environment modeling also indicated that urban populations deviate much more strongly from global allele frequencies than suburban or rural populations. This study is the first to examine loss of genomewide SNP variation along an urban‐to‐rural gradient and quantify urbanization as a driver of population genomic patterns.Urbanization results in pervasive habitat fragmentation and reduces standing genetic variation through bottlenecks and drift. Loss of genomewide variation may ultimately reduce the evolutionary potential of animal populations experiencing rapidly changing conditions. In this study, we examined genomewide variation among 23 white‐footed mouse ( Humans exert an outsized influence on ecosystems  in the New York City (NYC) metropolitan area:Genomewide variation within populations is inversely related to urbanization of the surrounding landscape.Urbanization of the landscape results in greater genomic structure and differentiation between populations.‘Resistance distances’ and ‘ecological distances’ resulting from urbanization are better predictors of genetic differentiation than geographic distances between populations.In this study, we test the following interrelated predictions about the population genomics of white‐footed mice . Traps within grids were placed 15 m apart, and grids were located several hundred meters apart to avoid trapping close relatives. We collected ear punches or tail clippings from up to 25 mice at each site, and stored the tissue in 80% EtOH before transfer to a ‐20 C freezer in the laboratory. Animal handling procedures were approved by Fordham University's Institutional Animal Care and Use Committee (Protocol No. JMS‐13‐03).n = 12 sites), suburban (n = 4), and rural (n = 7) sites in the NYC metropolitan area based on the levels of development  available from the US Census Bureau. Buffers were created in ArcGIS v10.1  around each site's GPS coordinates with radii of 500 m, 1000 m, 1500 m, and 2000 m. These buffers were chosen because they are relevant to the typical lifetime dispersal distance of many white‐footed mice; for example, no mice over a 40‐year study of one woodlot dispersed to the nearest neighboring woodlot <1.5 km away . To avoid including redundant information in downstream analyses, we removed any variables that exhibited We generated SNP genotypes for 233 individuals using a double‐digest RADseq protocol , number of mismatches allowed between RAD loci for a single individual (M = 3), and the number of mismatches allowed when building the catalog (n = 2). After building the initial catalog of loci, we used the populations script in Stacks to filter loci for those that occurred in at least 22/23 sampling sites (P = 22) and in at least 50% of individuals at each site (r = 0.5) to a minimum coverage of 7X (m = 7). These parameter values imply that each base pair position was sequenced at least 668X across the entire dataset. Stacks calculates the likelihood of the two most common genotypes at each site given a maximum‐likelihood estimate of the sequencing error rate, and then uses a likelihood ratio test to identify the most likely genotype at that position. Full details of the models underlying Stacks are given in Catchen et al.  with one randomly selected SNP from each locus (–write_random_SNP), and generated summary statistics such as observed heterozygosity, nucleotide diversity, and pairwise FST between all populations. These summary statistics were calculated for the sites that were polymorphic in at least one population .As an initial check on the quality of our Illumina sequence data, we analyzed the raw reads in FastQC Andrews . Subsequn et al. . The popWe loaded the RAD loci and individual data from Stacks into a MySQL database and visualized the output using the Stacks webserver. Based on the results of the first pipeline run, we removed 26 individuals because their small number of reads resulted in very small SNP datasets and excessive missing genotypes compared to other samples. Highly related individuals in our dataset could also bias downstream analyses. We avoided relatives at our urban sites using relatedness values from previous microsatellite studies of the same individuals , nucleotide diversity (π), number of private loci, and the percent of polymorphic loci. We examined genetic (dependent) variables using seven candidate general linear models (GLMs) consisting of combinations of human population size and percent impervious surface estimated using different geographic buffers as described above (500 m and 2000 m): an intercept‐only model, four univariate models with one variable measured at one buffer size, and two bivariate models including both human population and impervious surface estimated for the same buffer size. Human population size and percent impervious surface were ln‐transformed prior to analysis to improve normality. If a model performed substantially better than the intercept‐only model, then we interpreted that result as evidence of a statistical effect of the urbanization variable(s) on genetic diversity within white‐footed mouse populations. We calculated maximum‐likelihood estimates of model parameters for each model, and then ranked models using values of AICc, the corrected Akaike's information criterion  of each model. For the best GLMs, we examined the statistical significance of the urbanization coefficients. We also examined scatterplots  to visualize membership of individuals in different clusters. Genomewide SNP datasets often have power to discriminate between all groups, and results from analyses such as DAPC may reflect hierarchical structure. Thus, we ran DAPC on the full dataset as well as subsets of sampling sites to investigate hierarchical structure.To investigate whether urbanization results in greater genetic differentiation between populations, we used discriminant analysis of principal components (DAPC) in the R package chooseK.py) also calculates heuristic scores for detecting the range of the most likely values of K. After identifying likely values of K, we reran fastSTRUCTURE with the more computationally demanding logistic prior model on a smaller subset of K values. The most likely values of K were determined in ADMIXTURE using its cross‐validation procedure  approach and includes several graphing functions for evaluating success of the MCMC posterior parameter estimation. To confirm adequate mixing and convergence of chains, we examined traces and marginal distributions for all parameters. We also examined acceptance rates for MCMC parameter estimation, and when these rates were too high or low, we adjusted the tuning parameters and reran the analysis for 5–10 million steps. For the final runs, we calculated the median and 95% credible intervals for the K, that measures the strength of drift and lack of fit of each population to the model. To address population history, we also ran the beta‐binomial model for the two IBE scenarios above. For these models, we examined the posterior distributions of the ΦK values  to identify outlier populations.The simple BEDASSLE model assumes identical variance of allele frequencies about the global mean allele frequency. However, populations deviate from the global mean for a number of demographic reasons , and outlier populations can have a strong influence on posterior distributions. To account for this variation, BEDASSLE includes a beta‐binomial model that estimates an additional parameter, ΦST between all pairs of populations. Simulated FST values can then be compared to the observed FST values to examine the models’ ability to describe patterns in the real data.To test the relative fit of the models to our data, we generated 1000 posterior predictive samples for each model and compared the simulated data to our observed data in BEDASSLE. BEDASSLE includes a function that randomly draws parameter values from the posterior distributions and simulates new datasets. These simulated datasets are then used to calculate FThree lanes of Illumina HiSeq 2 × 100 PE sequencing produced a total of 1.16 billion reads, of which 936.64 million reads (80.9%) passed initial quality filters. Reads were filtered out due to ambiguous barcode sequences (56.3%), low‐quality scores (40.9%), and ambiguous RAD tags (2.8%). The catalog generated by STACKS included 880 898 RAD loci with at least 7X coverage, but this number was reduced to 14 930 after requiring loci to be present in 22/23 populations and at least 50% of individuals within each population. All populations were well‐represented in the final dataset, with averages of 4 to 10 individuals genotyped per locus across the 23 populations . The only exceptions to these trends were relatively high diversity values for the Pelham Bay (PB) population in NYC, and low values for suburban Saxon Woods (SW), the rural Cary Institute (CIE) and Minnewaska Reserve  ranged from 0.894 to 0.957, and the average observed heterozygosity from 0.054 to 0.181 Table . If all Sampling sites could be distinguished by their combinations of human population size and percent impervious surface Fig. B. The ruAICc confirmed that nearly all GLMs with one or more urbanization variables described variation in genomic diversity better than intercept‐only models, except for human population size estimated at a 500‐m buffer  and suburban (LCC) population, and a high of 0.145 between a suburban (MRG) and urban (RR) population. Most values were between 0.05 and 0.10. Some NYC populations exhibited many FST > 0.10, particularly RR and JB, as did one suburban population (MRG). A simple Mantel test revealed no significant IBD .Pairwise n = 23 as the optimal number of principal components to retain for DAPC analysis . The first two discriminant functions distinguished two isolated NYC sites, Jamaica Bay (JB) and Fort Tilden (FT), from the other populations. Discriminant function three separates out a cluster of the other populations on Long Island , a cluster of NYC populations in the middle  with suburban populations just below them , and rural populations  at the bottom  and cross‐validation in ADMIXTURE identified K = 2 as the most likely number of evolutionary clusters among the 23 white‐footed mouse populations we sampled. CLUMPAK confirmed that individual assignment to the two clusters was highly correlated across the two methods  relative to the suburban  and rural populations , with the exception of two rural populations that also had high values . The simple IBE model using resistance distances did not converge after trying many different combinations of prior values. The beta‐binomial model did converge, producing a median αE:αD ratio of 0.0001 and 95% negligible impact on genetic differentiation compared to geographic distances, which contradicts the IBR results. Posterior predictive sampling indicated that this beta‐binomial model performed moderately well, but systematically underpredicted observed FST < 0.05  were significantly associated with F were sigThis study is the first to our knowledge to examine the influence of urbanization on genomewide SNP variation in a city‐dwelling species. We found that populations of white‐footed mice along an urban‐to‐rural gradient exhibit a negative correlation between genomic variation and urbanization in and around their habitat. Populations within NYC also exhibited greater genetic differentiation from one another than pairs of populations in rural areas. IBR and IBE models based on urbanization explained a greater proportion of pairwise population differentiation overall than IBD by some metrics. NYC populations deviated more strongly from global mean allele frequencies than rural populations, indicating that urbanization has substantially altered the evolutionary trajectories of urban wildlife . Habitat networks that promote gene flow in cities can be constructed from even small gardens and green spaces not explicitly dedicated to biodiversity  under accession number SRP067131. Bi‐allelic SNP genotypes, allele counts, and sample sizes are available in various formats on the Dryad digital repository Table S1. Correlation coefficients calculated between % impervious surface and human population sizes calculated at buffers around study sites of 500 m, 1 km, 1.5 km, and 2 km.Click here for additional data file.Table S2. Summary population genomic statistics calculated for all nucleotide positions (variant and fixed).Click here for additional data file.Table S3. Matrix of pairwise FST  and great‐circle geographic distance  calculated between all pairs of 23 populations.Click here for additional data file.Figure S1. Cross‐validation  to identify the optimal number of principal components to retain for DAPC without overfitting4.Click here for additional data file.Figure S2. Compoplot/bar plot result from DAPC analysis on all 23 populations.Click here for additional data file.Figure S3. Scatterplot of first two discriminant functions from DAPC for populations on Long Island.Click here for additional data file.Figure S4. Cross‐validation of results from ADMIXTURE for K = 1 – 12.Click here for additional data file.Figure S5. Cumulative current map from isolation by resistance (IBR) modeling in Circuitscape. Lighter areas represent landscape cells with higher cumulative predicted current .Click here for additional data file.


Rhipicephalus appendiculatus transmits the apicomplexan protozoan parasite Theileria parva, which causes East coast fever (ECF), the most economically important cattle disease in eastern and southern Africa. Recent analysis of micro- and minisatellite markers showed an absence of geographical and host-associated genetic sub-structuring amongst field populations of R. appendiculatus in Kenya. To assess further the phylogenetic relationships between field and laboratory R. appendiculatus tick isolates, this study examined sequence variations at two mitochondrial genes, cytochrome c oxidase subunit I (COI) and 12S ribosomal RNA (rRNA), and the nuclear encoded ribosomal internal transcribed spacer 2 (ITS2) of the rRNA gene, respectively.The ixodid tick vs field populations) or the mammalian host species. This finding was supported by the results obtained from the analysis of 12S rDNA sequences. Analysis of molecular variance (AMOVA) indicated that 90.8 % of the total genetic variation was explained by the two haplogroups, providing further support for their genetic divergence. These results were, however, not replicated by the nuclear transcribed ITS2 sequences likely because of recombination between the nuclear genomes maintaining a high level of genetic sequence conservation.The analysis of 332 COI sequences revealed 30 polymorphic sites, which defined 28 haplotypes that were separated into two distinct haplogroups (A and B). Inclusion of previously published haplotypes in our analysis revealed a high degree of phylogenetic complexity never reported before in haplogroup A. Neither haplogroup however, showed any clustering pattern related to either the geographical sampling location, the type of tick sampled  contains supplementary material, which is available to authorized users. Ixodes uriae [Hyalomma rufipes and Amblyomma hebraeum can influence the geographical distribution of the adult stages of these two ixodid ticks [Knowledge relating to the intra- and inter-population genetic structure and variability amongst parasitic populations is important in understanding the dispersal and transmission dynamics of the pathogens they transmit. Several factors, including climate, host diversity, degree of tolerance of host species and control and management practices affecting host behavior are all thought to influence spatial distribution patterns of ticks . The intes uriae , while tid ticks .Rhipicephalus appendiculatus is a three-host tick species whose ability to survive in a particular locality is determined by climatic conditions [Syncerus caffer), Waterbuck (Kobus ellipsiprymnus), Eland (Taurotragus oryx), Greater kudu (Tragelaphus strepsiceros) and other large bovids [Lepus capensis). Rhipicephalus appendiculatus is of major economic importance as the vector of the protozoan parasite Theileria parva, which causes East coast fever (ECF) in cattle [Rhipicephalus appendiculatus also transmits Theileria taurotragi to cattle from Eland (Taurotragus oryx) causing benign bovine theileriosis, Anaplasma marginale resulting in bovine anaplasmosis, the nairovirus inducing Nairobi sheep disease, and Rickettsia conorii resulting in tick typhus in humans [R. appendiculatus often congregate, anemia and toxicosis that results in enhanced susceptibility to other diseases [nditions , 8 and inditions , 10. It e bovids . The larn cattle . Rhipicen humans ). Heavy diseases .R. appendiculatus. These include diapause in R. appendiculatus in southern Africa, which has not been observed in east African populations [R. appendiculatus in southern and eastern Africa, which were thought to represent two phylogeographically differentiated lineages [R. appendiculatus in Kenya [Several studies suggest that phenotypic diversity exists between different populations of ulations , differeulations , 14, veculations  and in rulations . Morphollineages , 17–19. lineages . A recenin Kenya .R. appendiculatus have been maintained as laboratory stocks for sporozoite production and as representatives of field genotypes. For example, the standard laboratory stock of R. appendiculatus (designated Muguga) has been used to produce the Muguga cocktail vaccine against T. parva [R. appendiculatus revealed differences in infection rates [T. parva [R. appendiculatus which were less diverse than their field counterparts [Several populations of T. parva , 23. Preon rates , and susT. parva , 25. Recterparts . SelectiR. appendiculatus in Africa is determined by ecoclimatic factors, the genetic variability within the species remains poorly investigated. To further assess the phylogenetic relationships between field and laboratory R. appendiculatus tick stocks, this study examined sequence variation at the cytochrome c oxidase subunit I (COI) gene, 12S rDNA and the bi-parentally inherited ribosomal nuclear ITS2 region. The phylogenetic relationships, demographic dynamics and the partition of genetic diversity and structure amongst populations of R. appendiculatus were investigated.While the distribution of R. appendiculatus [R. appendiculatus were used to sequence the mitochondrial COI gene. From the 332 samples, a subset of 93 samples from 12 populations was used to sequence the 12S rRNA gene while 87 ticks from the same subset were used to sequence the nuclear ITS2 gene spacer , one in Zimbabwe  and two in Zambia ; the remaining eight stocks were collected in Kenya. The ticks had been identified following standard morphological criteria [The study used tick samples that had previously been described in earlier studies on population genetics of iculatus , 26. Gencriteria –31. Deta2, 0.1 μM of each dNTP, 0.25 pmol of each primer, 1.25 U of Taq DNA polymerase (Promega) and 50 ng of template DNA. The PCR cycling profiles involved an initial denaturation at 95 °C for 5 min followed by 35 cycles of 94 °C for 1 min, annealing for 1 min  was used to extract genomic DNA following minor modifications to the protocol (see ). COI gehttp://blast.ncbi.nlm.nih.gov/Blast.cgi).All sequence chromatograms were visually inspected and the sequences edited manually using the CLC Main Workbench 6.8.3 . The sequences were then trimmed to remove low quality reads at the 5' and 3' ends. Consensus sequences for each gene were generated from the sequenced fragments. Prior to analyses, all sequences were trimmed to uniform sizes . Multiple sequence alignments were performed for each gene using ClustalW2 in CLC Main Workbench. Species identity was investigated and confirmed via BLASTN searches on the NCBI database  two groups of sequences, i.e. those from areas grazed exclusively by cattle vs those from areas co-grazed by cattle and wildlife; (iii) two groups of sequences, i.e. those from areas grazed exclusively by cattle vs those from areas grazed exclusively by wildlife; (iv) two groups of sequences, i.e. those from areas co-grazed by wildlife and cattle vs those from areas grazed exclusively by wildlife; (v) two groups of sequences, i.e. field stocks vs laboratory stocks; (vi) three groups of sequences defined on the basis of the host species, i.e. cattle vs mixed cattle-wildlife vs wildlife, respectively; and (vii) amongst the groups identified by the phylogenetic and median-joining network analysis.Sequences were collapsed into haplotypes, following multiple sequence alignments, using DnaSP v5.10.01 . GeneticFS [D [Demographic dynamics were inferred from mismatch distribution patterns –40 of COFS  and TajiFS [D , 43 statR. appendiculatus, median-joining (MJ) network [Phylogenetic reconstruction was performed using the COI gene employing the Maximum Likelihood (ML) algorithm implemented in MEGA v6.0 . The bes network  was consR. appendiculatus .The 332 samples used in this study generated 558 bp of high quality consensus COI sequences. Their molecular identity was confirmed via BLASTN searches against the NCBI’s non-redundant nucleotide sequence database. The BLASTN searches returned high values of sequence similarity (97–100 %) with those of archived n = 12) were exclusively of one haplotype (Hap_8). The average number of haplotypes across the 22 study populations was 28 and on average, 20, 14, 7 and 5 haplotypes were observed in tick populations sampled from areas grazed by cattle only, co-grazed by cattle and wildlife, grazed by wildlife only and in laboratory stocks, respectively  in Ruma (RUM2) to 0 in nine laboratory stocks with an average value of 0.802 ± 0.014 . The MJ network also revealed two groups that were separated by 12 mutation steps. The cluster of haplotypes in the ML tree and MJ network did not differ between the two algorithms. We therefore designated the two groups as haplogroups A and B, respectively. Haplogroup A clustered 19 haplotypes including Hap_4, the haplotype with the highest frequency, whereas haplogroup B contained nine haplotypes, which included Hap_1, the haplotype with the second highest frequency. Two median vectors (mv) were observed among the two haplogroups , another (sub-haplogroup I-B) comprised nine Kenyan haplotypes and one from Rwanda, while the third (sub-haplogroup I-A) was made up of five haplotypes from Kenya, two from Rwanda and four from Zambia’s eastern province. This result suggests higher variation in R. appendiculatus, especially in haplogroup A, and a higher degree of phylogenetic complexity in this haplogroup not revealed in the studies of Mtambo et al. [To test if the COI haplotypes generated in our study clustered with those of t groups , we recot groups . We usedt groups . Our hapo et al. , 18.Fig.a priori  and had a smooth distribution   with no significant variation around the curve . For this group, Tajima’s D statistic was positive (D = 0.767) but this was not significant (P = 0.819), while Fu’s FS parameter was negative (FS = -0.959) and not significant (P = 0.447). For the laboratory stocks, the observed pattern also deviated significantly from that expected . For this group of ticks, both Tajima’s D statistic  and Fu’s FS parameter  were positive but not significant. This suggests that either the field populations have a weak signal of expansion or are in demographic equilibrium, whereas the laboratory-bred stocks have been subject to an anthropogenic bottleneck and/or genetic drift. The two peaks observed in the overall dataset and in the field and laboratory stocks, respectively suggested the existence of two groups of ticks. The two peaks were found to correspond to the two haplogroups revealed by the ML and MJ analyses. We therefore performed mismatch analysis for each haplogroup . Both had negative Tajima’s D  and Fu’s FS  values (Table D (P = 0.017) and Fu’s FS (P <0.001) attained significance for haplogroup A, the values for haplogroup B did not  , and three minor (each defined by one sequence). Following ML phylogeny analysis, the five haplotypes clustered into two haplogroups which were identical to those generated from the COI gene.The 12S rRNA gene and ITS2 region were sequenced from a subset of the 332 A 1149 bp fragment of the ITS2 region was amplified from 87 individuals derived from different mitochondrial haplotypes. Three haplotypes were observed. One contained 67 sequences and the other two contained nine and 11 sequences, respectively. These ITS sequences did not cluster into groups corresponding to the COI or 12S rDNA haplogroups. The five 12S rDNA haplotype sequences were deposited in the GenBank database under accession numbers KX276945-49 and those of the three ITS2 haplotypes unde accession numbers KX276950-52.R. appendiculatus found in Kenya through the analysis of the mitochondrial COI and 12S rRNA genes and the nuclear transcribed ribosomal ITS2 fragment. COI gene has been and continues to be widely used as a marker for DNA barcoding to discriminate between closely related taxa [R. appendiculatus speciation [R. appendiculatus samples from Kenya.This study assessed the genetic relationships between populations of ted taxa –52. Evolted taxa , 54. Theted taxa –58. It heciation , 18, 59 R. appendiculatus in eastern and southern Zambia but these were not detected by the ITS2 sequences. The low resolution afforded by ITS2 has also been reported in Amblyomma hebraeum and Hyalomma rufipes [In reconstructing the phylogenetic history of a species, the use of multiple genetic markers targeting different regions of the genome, is a better strategy in order to overcome the drawbacks of using a single marker, while increasing the accuracy of inference , 61. Her rufipes .R. appendiculatus, the overall mean number of nucleotide differences was 6.8647 ± 3.2391 and the mean haplotype and nucleotide diversities were 0.802 ± 0.014 and 0.0123 ± 0.0064, respectively. Cangi et al. [A. hebraeum, an ixodid tick with a wider vertebrate host range, but a comparable level of haplotype and nucleotide diversity among isolates of 0.96 and 0.009, respectively, relative to the much more host-specialized H. rufipes. We also observed a high level of intra- and inter-population genetic diversity among the study populations. The values were much higher in the field ticks compared to the laboratory stocks, which were, by definition, subject to founder effects and population bottlenecks. The high diversity in field ticks is most probably the result of admixture between different geographic populations facilitated by the translocation of domestic animals either as trade items or through exchange following socio-cultural traditions. Indeed, no phylogeographic structure was revealed between the R. appendiculatus populations analysed in this study as revealed by either ML or MJ network analysis. In an earlier study, Kanduma et al. [R. appendiculatus (wild bovidae) within the wildlife areas considered in this study is limited since these areas are fenced.From the analysis of 332 COI sequences of a et al.  observedThe laboratory stocks investigated here have been maintained as closed populations for over 30 years. It is therefore not surprising that they exhibited low levels of genetic diversity due to inevitably high levels of inbreeding. In spite of their inbred status, AMOVA revealed a negative value of genetic differentiation between the field and laboratory stocks implying that the two groups are much more related than might be expected. There are several potential explanations. First, that the inbreeding in the laboratory stocks has not resulted in a drastic reduction in their allelic variation; secondly, that variation present in the laboratory stocks is well represented in the field stocks; and thirdly, the induced bottleneck and genetic drift which could be due to inbreeding and small effective population sizes have not altered drastically their allelic composition.R. appendiculatus in some parts of Africa and it was suggested that they may represent geographically differentiated lineages, that may have diversified as a result of distinct selective pressures. For instance, ticks found in southern Africa  and those found in eastern Africa  were thought to constitute two geographically isolated groups of ticks that can be discriminated based on morphological, ecological and epidemiological differences [R. appendiculatus in Kenya as defined by mitochondrial haplotype. These two haplogroups however exhibited no phylogeographic structure or correlation with the type of host species from which the ticks were collected or the evolutionary and breeding history of the species (field populations relative to laboratory stocks). Although we did not estimate the divergence time between the two genetic groups, it is possible that their divergence is not recent because they were observed among inbred laboratory stocks which were initially collected from populations of field ticks up to 50 years ago. AMOVA revealed that 90.8 % of the total genetic variation was explained by divergence within the two major haplogroups. While different host species have been shown to influence the spatio-genetic structure of other tick species, such as Ixodes uriae [R. appendiculatus collected from different mammalian hosts was low (4.94 %). By contrast, the between population variation exceeded 35 %, whereas the variation between individuals within populations ranged between 9.3 and 52.3 %. This demonstrates low genetic differentiation between populations of R. appendiculatus sampled from different hosts suggesting minimal host specialisation. This suggests that genetic differentiation amongst tick populations in Kenya is a phenomenon primarily of ancestral differentiation between the two haplogroups and that recent reproductive isolation and the exploitation of different mammalian hosts has, to date, played a relatively minor role in driving this differentiation. The fact that the two major haplogroups that we have identified clusters together with representative haplotypes of R. appendiculatus from southern Africa [T. parva transmission dynamics, requires further investigation. Further investigation is also required into origin and possible evolutionary forces driving the occurrence of multiple sub-haplogroups within haplogroup A.Morphological , physiolferences , 18. In n Africa , 18, sugR. appendiculatus in Kenya by assessing the mismatch distribution patterns for the overall dataset, the field and laboratory stocks and within the two haplogroups identified by the ML and MJ network analysis. The results for the overall dataset, field populations and the two haplogroups suggest that these three groups of R. appendiculatus have passed through a demographic expansion perhaps associated with range expansion of a founder population.We investigated the demographic dynamics of R. appendiculatus. Rhipicephalus appendiculatus is a generalist tick, although Cape buffalo is the main wild host reservoir, and cattle are the preferred domestic hosts of the adult and nymphal instars [R. appendiculatus is important in the design of sustainable control strategies, since different tick populations may be characterised by differences in vector competence, acaricide resistance and susceptibility to infection with T. parva. In future it will be important to establish how the phenotypes of the two R. appendiculatus haplogoups identified in this study differ, particularly with respect to acquisition and transmission of ECF.The findings of this study may have taxonomic implications and suggest the potential for incipient speciation in  instars , 64. SucR. appendiculatus over the ITS2. Based on these two genes, two distinct and well-differentiated haplogroups which have passed through a demographic expansion perhaps associated with range expansion of a founder population exist in Kenya. These two haplogroups have no phylogeographic structure or correlation with their mammalian host species or the evolutionary and breeding history of the species. There is a wide geographical distribution range of these two haplogroups in eastern and southern Africa. These findings may have important taxonomic implications and may point to an ongoing speciation of R. appendiculatus in sub-Saharan Africa. It would be important to establish if the two haplogroups have any associated phenotypic differences which might influence parameters such as T. parva acquisition and transmission dynamics. In addition, identifying evolutionary forces driving the observed genetic differentiation may help explain the apparent population expansion of the two haplogroups within the sub-Saharan region.COI and 12S genes are superior genetic markers for intra-species population genetic studies in c oxidase subunit I; CSIRO, Australia’s Commonwealth Scientific and Industrial Research Organisation; DAAD, German academic exchange service; DNA, deoxyribonucleic acid; dNTP, deoxyribonucletide triphosphate; dNTPs, deoxyribonucleotide triphosphates; ECF, east coast fever; ICARDA, International Centre for Agricultural Research in the Dry Areas; icipe, International Centre of Insect Physiology and Ecology; ILRI, International Livestock Research Institute; ITS2, Internal transcribed spacer 2; KWS, Kenya Wildlife Service; MJ, median joining; ML, maximum likelihood; mv, median vector; NCBI, National Center Bioinformatics; NEPAD, new partnership for Africa’s development; PCR, polymerase chain reaction; rDNA, ribosomal deoxyribonucleic acid; SFSA, Syngenta Foundation for Sustainable Agriculture; SSD, sum of squares deviationAMOVA, analysis of molecular variance; ARC-OVI, Agricultural Research centre- Onderstepoort Veterinary Institute; AusAID, Australian Agency for International Development; AWARD, African Women in Agricultural Research and Development; BecA, Biosciences eastern and central Africa; BLASTN, basic local alignment search tool; bp, base pair; CIDA, Canadian International Development Agency; COI, cytochrome