Apodemus draco, in order to assess the role of geologic and climatic events on the Tibetan Plateau in shaping its genetic structure. We sequenced mitochondrial cytochrome b (cyt b) sequences in 103 individuals from 47 sampling sites. In addition, 23 cyt b sequences were collected from GenBank for analyses. Phylogenetic, demographic and landscape genetic methods were conducted. Seventy-six cyt b haplotypes were found and the genetic diversity was extremely high . Five major evolutionary clades, based on geographic locations, were identified. Demographic analyses implied subclade 1A and subclade 1B experienced population expansions at about 0.052-0.013 Mya and 0.014-0.004 Mya, respectively. The divergence time analysis showed that the split between clade 1 and clade 2 occurred 0.26 Mya, which fell into the extensive glacial period . The divergence times of other main clades (2.20-0.55 Mya) were congruent with the periods of the Qingzang Movement (3.6-1.7 Mya) and the Kun-Huang Movement (1.2-0.6 Mya), which were known as the most intense uplift events in the Tibetan Plateau. Our study supported the hypothesis that the SEMTP was a large late Pleistocene refugium, and further inferred that the Gongga Mountain Region and Hongya County were glacial refugia for A. draco in clade 1. We hypothesize that the evolutionary history of A. draco in the SEMTP primarily occurred in two stages. First, an initial divergence would have been shaped by uplift events of the Tibetan Plateau. Then, major glaciations in the Pleistocene added complexity to its demographic history and genetic structure.The southeastern margin of the Tibetan Plateau (SEMTP) is a particularly interesting region due to its topographic complexity and unique geologic history, but phylogeographic studies that focus on this region are rare. In this study, we investigated the phylogeography of the South China field mouse,  The Tibetan Plateau is a recognized biodiversity hotspot et al.Carduelis flavirostris) and the black redstart (Phoenicurus ochruros) inhabited the northeastern margin of the Tibetan Plateau and maintained stable population sizes through glacial cycles. On the contrary, three other avian species , mostly distributed on the inner region of the plateau, experienced population expansions after glacial retreat Several studies have demonstrated that species distributed on the Tibetan Plateau retreated to the east side of the plateau during cold periods In this study, we focused on the southeastern margin of the Tibetan Plateau (SEMTP), including western Sichuan Province, northwestern Yunnan Province and eastern Xizang Autonomous Region (Tibet). The large rivers and mountains  in this region could be natural barriers for the postglacial colonization of small mammals . The magApodemus draco). Small rodents are good candidates for inferring biotic history from contemporary patterns of genetic variation because their short generation time and relatively limited mobility Apodemusb (cyt b) gene in six A. draco and 197 A. ilex with samples from Yunnan-Guizhou Plateau in China, they reported that A. ilex should be a valid species rather than synonym of A. dracoA. ilex was closely related to the complex geographical structures in southwest China In order to further assess the role of geologic and climatic events on the Tibetan Plateau in shaping the genetic structures of local fauna, we present a phylogeographic investigation of the South China field mouse (b gene sequence has been extensively and successfully used in phylogeographic studies for rodents b gene were applied to investigate the genetic variation and evolutionary history of A. draco. We examined: (1) the relationship between the genetic structure of A. draco and geologic or climatic events on the SEMTP, and (2) whether the SEMTP, especially the GMR, potentially functioned as a refuge during the Quaternary period. Details of the historical demography of A. draco revealed in this study could provide new insights into how this rodent and other small mammals in this region responded to the geologic or climatic events on the SEMTP.The cyt We only used wild rodents in this study. All samples were obtained following the regulations for the implementation of China on the protection of terrestrial wild animals (State Council Decree [1992] No. 13). All our observational and field studies and lab work were approved by Wildlife Protection Office, Sichuan Provincial Forestry Departments (China) and by the Ethics Committee of Sichuan University, China.b sequences from GenBank were retrieved , which were collected from an additional six sampling sites in the Sichuan and Yunnan Provinces  than our target sequences (1050 bp), thus we did not include them here. Detailed information on sampling localities is shown in One Hundred and three South China field mice were collected from 47 sampling sites in Sichuan Province . In addirovinces  [18], [3rovinces . Cyt b sb with the universal primers L14724/H15915 EXTaq buffer , 1.5 µl dNTP (2.5 mM each), 2.0 µl MgCl2 (25 mM), 1 µl of each primer (10 uM), 0.2 µl EXTaq polymerase , and approximately 200 ng total genomic DNA as template. PCR products were sequenced from both directions with the same PCR primers in an ABI PRISM 3730 DNA sequencer .Polymerase chain reaction (PCR) was used to amplify the sequences of cyt h) and nucleotide diversity (π) were estimated using DnaSP Ver. 5 STF with the software Arlequin 3.1 b on the website (http://mkt.uab.es) b of A. draco and A. latronum (Nos. GU908339-GU908394) were estimated and their ratio was compared for each species with the same ratio for fixed mutations between the species. The cyt b sequences of A. latronum were used here due to A. latronum was the sister taxon to A. draco.We aligned the sequences in software MEGA 4.0 b haplotypes: Bayesian inference (BI) and maximum parsimony (MP). Apodemus latronum  was used as outgroup due to it was the sister taxon to A. draco. In order to avoid the possible bias, A. flavicollis (AB032853) and A. sylvaticus (AB033695) were also chosen as outgroups. The best fit model of DNA substitution was obtained using MrModeltest2.2 b. Posterior distributions were obtained by the Markov Chain Monte Carlo (MCMC) method with one cold chain and three heated chains for 10,000,000 generations and sampled every 1000 generations. The first 25% were discarded as a conservative burn-in and the remaining samples were used to generate a 50% majority rule consensus tree. PAUP* 4.0 b 10 Two different methods of phylogenetic analysis were used to reconstruct phylogenetic relationships among cyt b haplotypes of A. draco using Network 4.6.0.0 Since the haplotype network could better detect the relationship among haplotypes, we also constructed a median-joining network (MJN) approach to depict relationships among the cyt http://ibdws.sdsu.edu) Ay, the average genetic distance between individuals in distance class y. Thus, Ay can be interpreted as the average genetic distance between pairs of individuals that fall within distance classes We used analysis of molecular variance (AMOVA) to estimate the geographical pattern of population subdivision using different grouping options b gene is estimated at 2% per Myr b in genus Apodemus is estimated at 2.4% per Myr b gene of rodents in general evolved several times faster than the standard rate. Moreover, Fan et al.A. latronum in their study. Monophyly of clades was not enforced, and runs were initiated on random starting trees. The MCMC chain was run for 50 million generations, sampling every 1,000 generations. The convergence of the chains to stationary was checked using Tracer 1.5 A. draco’ s divergence.Divergence times among the observed mitochondrial clades on phylogenetic tree were estimated by BEAST v1.7.1 b sequences. First, two tests of selective neutrality were calculated for each set (whole sample and each observed phylogenetic clade). Fu’s Fs2 statistics Fs and R2 were applied due to the fact that they have been suggested as the most powerful tests for detecting sudden population growth or contractions Fs is more powerful for large population sizes, whereas R2 works well for small ones r) b .The demographic history was tested by mismatch distributions and neutrality tests based on the cyt b gene sequences each consisting of 1050 sites . With sequences from the additional 23 individuals, we recovered 76 haplotypes from 126 individuals, and the overall h was 0.982, while the π was 0.0381  was larger than Dn/Ds (divergence), and the difference between them was not significant . The 50% majority consensus tree based on BI was shown in Based on the AIC test, the GTR+I+G model was chosen for the cyt Five major evolutionary clades, based on geographic locations, were identified: Maerkang (clade 2), northern Sichuan , southern Sichuan , and Daocheng county and Yunnan Province (clade 5) . HaplotyThe haplotype network also revealed five clades separated by 9 to 73 mutational steps , which wFST values for pairwise populations comparisons ranged from −0.026 to 0.985. Of the pairwise values obtained, only 3 of the 78 were nonsignificant (p<0.001) or 86% (p<0.001) of the overall variation. However, when we focused on the populations in clade 1, the result exhibited that among populations variation only accounted for 26% of the overall variation, but 48% of the overall variation was contributed by variation among samples (The nificant . The AMO samples .FST) and geographic distances was detected in first two datasets , indicating high levels of geographical structure for A.draco. But for the dataset only including 4 populations in clade 1, the result was non-significant . Spatial autocorrelation analysis (SAA) also found a significant relationship between genetic and geographic distances in different distance classes . In analyses performed using 10 distance classes, both the whole individuals dataset and without GenBank samples dataset were significant smaller than random expectations over the first two shortest distance classes . Then, when the geographic distance increased , both the datasets yielded significant values of Ay that were higher than random expectations. Similar results were obtained when 5 and 15 distance classes were used for analyses  in the Asian Far East. Results from our analyses were different from this study in several ways. First, in their study, the cyt b haplotypes from Yunnan Province did not cluster with any other haplotypes from Sichuan Province, and they formed a single lineage Sakka Apodemus draco exhibits a high level of population structure in our study. Most individuals from the same or nearby sampling localities clustered together, with the exception of individuals from Gongga Mountain (in both clades 1 and 2) and individuals from Jiajin Mountain (in different subclades of clade 1). The results of multiple landscape analyses further support the geographic structuring of genetic diversity in this species. As shown in A. draco was very complicated due to the complex geologic history and topography of the SEMTP. However, an unexpected pattern was observed in clade 4. Individuals from the central part of Sichuan Province, Zhongguo (population 5), clustered together with samples from southern Sichuan Province, including Jinyang, Zhaojue, Yuexi, Jiulong and Shimian Counties  and the Gongga Mountain (population 15), whereas the samples from Hongya County (population 9) and Emei Mountain (population 1), which were more close to the sampling sites in southern Sichuan, did not fall into clade 4 ( clade 4 . FurtherA. draco’s main splits (2.20–0.55 Mya) were congruent with the periods of the Qingzang Movement (3.6–1.7 Mya) and Kun-Huang Movement (1.2–0.6 Mya) A. draco would have been shaped by the uplift events of the Tibetan Plateau, and high levels of genetic diversity have beeen maintained as a result of these events. Although the divergence times in our study was rough estimates, this conclusion was supported by Sakka et al.A.draco corresponded to periods from 1.5–1.7 Mya to 2.0-2.2 Mya, and they further suggested that the complex land conditions could explain the divergence of A. draco in Sichuan Province.The estimated divergence times of A. draco to maintain stable population sizes.The Tibetan Plateau underwent four or five glaciations during the Quaternary period A. draco. The estimated divergence time showed that the split between clade 1 and clade 2 occurred at 0.26 Mya, which fell into the EGP (0.5–0.17 Mya). Thus, it was possible that the largest glaciation further isolated these groups, which could promote the differentiation. Similarly, Zhan et al.Ithaginis cruentus).Indeed, some recent phylogeographic studies have indicated that the large mountain ranges on the east edge of the Tibetan Plateau could provide substantial refugia for the local species during the Quaternary The IBD and SAA results showed that there was no clear relationship between geographic distance and genetic distance in the central region (clade 1), although there were many huge mountains. It seems that there is a certain connection between the pattern in clade 1 and the regional historical demography. Indeed, two of the subclades in clade 1 experienced population expansions.Subclade 1A was estimated to have experienced population expansions at about 0.052–0.013 Mya, after the EGP (0.5–0.17 Mya), and probably before the LGM (0.021–0.017 Mya). Most individuals of this subclade were collected from Hongya County (population 9). Its elevation is low, and the sampling site in this county (about 1800 m) was lower than other sampling sites (2450–3700 m) in subclade 1A. Thus, Hongya could have provided suitable habitats for species during the glacial period. During the population expansions, many individuals from nearby areas might have migrated into Hongya County. At the same time, some individuals in these regions could have dispersed into nearby areas. This expansion scenario could explain why several individuals from the Jiajin Mountain (population 6) appeared in subclade 1A.Taxus chinensis, Kingdonia uniflora, Cordyceps sinensis, Cercidiphyllum japonicum; Fauna: Ailuropoda melanoleuca, Panthera uncia, Pantholops hodysoni, Rhinopithecus roxellanae), leading to the conclusion that the GMR acted as a refuge during the cold periods The expansion of the subclade 1B was inferred to have happened at about 0.014-0.004 Mya, right after the LGM (0.021-0.017 Mya). Most individuals of subclade 1B were collected from Gongga Mountain (population 15). Gongga Mountain located at the eastern margin of the Tibetan Plateau, was an important glaciation centre in the eastern Tibetan Plateau. Here, remains of Quaternary glaciers are still widespread. Glacial accumulation and erosion landforms coexisted, which indicated this region has been glaciated many times Apodemus draco has been found at elevations from 800 m to 4000 m, and it occupies different microhabitats, such as broad-leaved forests, mixed broadleaf-conifer forests, scrublands, and meadows A. draco could have found available microhabitats in high elevations or simply retreated to lower elevations. Consequently, based on the characteristics of A. draco and the geologic history of the GMR, A. draco could have survived in many parts of the GMR even during the EGP and LGM. This conclusion could explain why individuals from GMR fell into different evolutionary clades  A. latronum) and Chinese scrub vole (Neodon irene) A. draco in clade 1 (Hongya county and GMR) in the central region, the discontinuous gene flow among different sampling regions could form the current observed pattern at clade 1.Two recent studies found the similar pattern in the blood pheasant (et al.et al.The present study demonstrated that the genetic differentiation of the South China field mouse’s population in the SEMTP not only was influenced by the complex geologic history and unique topography of the SEMTP, but also was influenced by the major glaciations in the Pleistocene. As a consequence, we hypothesize that its evolutionary history included two stages. First, an initial divergence would have been shaped by the uplift events of the Tibetan Plateau, and high genetic diversity was maintained by environmental and topographic complexity. Then, major glaciations in the Pleistocene added further complexity to its demographic history and genetic structure. The observed genetic differentiation pattern in this work is consistent with that of other recent studies focused on the eastern Tibetan Plateau


Presbytis), a genus of Asian leaf monkeys (Colobinae) with disputed taxanomy, and compared the structure of their loud-calls with a molecular genetic analysis.The degree to which loud-calls in nonhuman primates can be used as a reliable taxonomic tool is the subject of ongoing debate. A recent study on crested gibbons showed that these species can be well distinguished by their songs; even at the population level the authors found reliable differences. Although there are some further studies on geographic and phylogenetic differences in loud-calls of nonhuman primate species, it is unclear to what extent loud-calls of other species have a similar close relation between acoustic structure, phylogenetic relatedness and geographic distance. We therefore conducted a field survey in 19 locations on Sumatra, Java and the Mentawai islands to record male loud-calls of wild surilis (P.potenziani, P.comata, P.thomasi and P.melalophos. In a more detailed acoustic analysis of subspecies of P.melalophos, a further separation of the southern P.m.mitrata confirms the proposed paraphyly of this group. In concordance with their geographic distribution we found the highest correlation between call structure and genetic similarity, and lesser significant correlations between call structure and geographic distance, and genetic similarity and geographic distance.The acoustic analysis of 100 surili male loud-calls from 68 wild animals confirms the differentiation of In this study we show, that as in crested gibbons, the acoustic structure of surili loud-calls is a reliable tool to distinguish between species and to verify phylogenetic relatedness and migration backgrounds of respective taxa. Since vocal production in other nonhuman primates show similar constraints, it is likely that an acoustic analysis of call structure can help to clarify taxonomic and phylogenetic relationships. Presbytis (surilis) are exclusively arboreal animals, which inhabit tropical rainforest habitats of Sundaland, i.e., the Malay peninsula and the western Indo-Malay archipelago, comprising of Sumatra, Borneo, Java, the Mentawai islands and some smaller interjacent islands , populations 4,6,13 [P.m.mitrata], population 9 [P.m.melalophos], populations 14, 15 [P.m.bicolor], populations 17 [P.potenziani], populations 18, 19 [P.thomasi]; Figure x, y = 0.91), and lower significant correlations between genetic and geographical distance , and geographical distance and vocal structure .A Mantel test was performed to test the concordance between genetic distance, geographical distance and acoustic similarity. All populations where corresponding genetic data was available N = 17) were included in the analysis  derived from the above described northernmost distribution range of this subspecies, south of the Batang Hari river. Although much paler, the morphological appearance resembles the reddish P.m.melalophos more than the grayish white southern Sumatran P.m.mitrata  forming a distinct cluster. Unfortunately, genetic data from Bengkulu are lacking, but acoustically, the call types were more closely related to the Southern P.m.mitrata mainly due to the presence of inhalation elements. Historically different color morphs of P.m.melalophos were described, all of which are currently classified as synonyms of P.m.melalophos [Simia melalophos Raffles, 1821; syn. flavimanus Geoffroy, 1830), b) a foxy red northern form  from Solok [Semnopithecus ferruginneus Schlegel, 1876) and d) a golden buff variant  from Gunung Talamau (ca. 150 km northwards from Padang) [Presbytis, in particular in P.melalophos, has caused much debate over the past decades. Coloration might indicate relatedness, but can often be misleading, in particular, when no broad geographic sampling is available. Our data point out that the taxonomic ranking of some of these historically described taxa possibly should be reconsidered. However, the loud-calls from population 7 are only derived from two individuals and genetic data are missing. Therefore, further molecular genetic and bio-acoustic research based on a broader sampling is needed to draw final conclusions. Of great interest are the acoustic data of the Bornean taxa, in particular data of P.rubicunda. Based on molecular genetic results P.melalophos is even polyphyletic since P.rubicunda is nested within the P.m.sumatrana, P.m.bicolor, P.m.melalophos/central Sumatran P.m.mitrata clade [P.rubicunda and P.melalophos based on the red coat coloration [P.rubicunda is retained, species status of P.m.sumatrana, P.m.bicolor, P.m.melalophos will be consequently warranted, otherwise P.rubicunda has to be assigned as a subspecies of P.melalophos.Among lalophos . These ata clade . Previouloration  or in soloration . If specPresbytis migration pattern, which is largely in agreement with Wilson and Wilson [Presbytis occurred between P.thomasi and all other taxa, and P.thomasi colonized North Sumatra, which became isolated afterwards. The ancestor of the remaining taxa colonized first Borneo and later Sumatra. An early divergence of Bornean taxa is also supported by previous genetic studies [P.potenziani), the other split into the proto-P.melalophos lineage and into the southern P.m.mitrata/P.comata lineage  are not analysed in our study, previous publications show similarities in call structures of P.femoralis and P.thomasi [P.femoralis diverged relatively early from other lineages and, thus, the similar call structure of P.femoralis and P.thomasi might be a plesiomorphic feature. Up to this point the genetic, geographic and acoustic differences between populations increased. From this point onwards the geographic distances between populations decreased, because proto-P.melalophos subsequently transmuted into various present day subspecies, which were finally distributed across Sumatra as far as to the distribution range of P.thomasi in North Sumatra. Consequently, the geographic distance between P.thomasi and the remaining Sumatran populations decreased, while the genetic and the acoustic differences increased. Finally, the southern P.m.mitrata/P.comata lineage split into P.m.mitrata and P.comata that colonized Java. In this case we have a linear migration pattern and thus would expect a similar high correlation between acoustic structure, genetic and geographic distance, as it was currently shown in crested gibbons which are proposed to migrate in a linear fashion from North to South [The correlation between acoustic structure and genetic differences was higher than the correlation between acoustic structure and geographic distance. This pattern can be explained by the following proposed d Wilson . The ini studies ,32-34. Oe Figure . Althoug.thomasi ,41. Our .thomasi  shows thto South .Presbytis-specific trait. Crested gibbons show an ambiguous result [Surilis and gibbons are limited to rainforest habitats where the selection pressure forces an optimal adaptation of the structure of loud-calls for transmission over longer distances ,42. Sincs result , where aP.potenziani was regarded as most basal lineage [P.thomasi [P.potenziani derived much later [P.potenziani is therefore either the result of an analogous evolution or a pleisiomorphic Presbytis feature. To clarify this issue further research is needed and particularly genetic and acoustic data on the Bornean and Malaysian taxa will help to better understand the evolution and phylogeography of the genus.Interestingly,  lineage  and due .thomasi . Howeverch later . The speP.rubicunda seems to be similar to P.melalophos calls [P.rubicunda with P.melalophos [Presbytis taxa in Sumatra, and sympatry in Borneo [P.rubicunda originated on Sumatra and subsequently invaded Borneo during the middle Pleistocene via a proposed connection between both islands [P.chrysomelas, P.frontata and P.hosei. As a result of this second colonization, P.rubicunda is sympatric today with the three other species wherever their ranges overlap [For instance, the call structure of os calls  and, as lalophos . This cln Borneo . P.rubic islands . At this overlap .P.melalophos by differences in loud-call structure. Furthermore, acoustic analysis can be used as a tool to support proposed migration routes. These findings might also help to explain taxonomic relationships and migration backgrounds in other nonhuman primate taxa, as long as they have similar constraints in their vocal communication.In this study we have shown that vocal similarity highly correlates with genetic relatedness; these two measures also correlate significantly with geographic distance, but the strength of the relationship is lower. Accordingly, acoustic analysis of surili loud-calls has been proven to be a promising and powerful tool to support taxon-affiliation and phylogenetic relatedness. In addition, we were able to confirm the proposed paraphyly of P.thomasi, P.potenziani, P.melalophos and P.comata , information about the group composition and the appearance of the animals  were noted on data sheets whenever possible. All visual observations were made by using binoculars (8 × 32 Steiner Sky-Hawk).In 2007 and 2008 we conducted field surveys in 19 locations on Sumatra, Java and the Mentawai islands, and recorded male loud-calls of Since surilis intensively respond to stranger call playbacks , we usedFor the final data collection, playback treatments were amplified with a Vision David Speaker connected to a MP3-Player (Samsung YP-U3) from about 75 m distance of the focal group at a height of 2 m ,46. AfteP.thomasi and P.potenziani produce coughing elements at the beginning of the call. In P.thomasi, the successive elements rise in crescendo and increase in volume .Male surili loud-calls consist of iterations of single elements. s Figure . We consAVISOFT SASLAB Pro 5.1 was used to measure acoustic parameters and to generate spectrograms . To find the point with maximum energy at the beginning, ending and intermediate points of call elements in the spectrogram, the bounded reticule cursor tool of AVISOFT was used. To address different phases within loud-calls, each call was additionally divided into four quarters. Since all taxa produce exhalation elements, the amount of exhalation elements (Ex) was therefore divided by 4 and subsequently multiplied by 1, 2, 3 and 4, respectively. Odd numbers were rounded. If inhalation elements (In) were present; the second, third and fourth quarter always started with an exhalation element , we used the same 23 acoustic parameters for 71 loud-calls , we excluded acoustic variables that could not be obtained in the majority of loud-calls. In the first DFA we used 23 acoustic parameters for 100 loud-calls from all 19 populations .To test the statistical relationship between acoustic structure, and genetic and geographic distance matrices, we used a Mantel Test algorithm programmed in R . For the analysis we only used populations where acoustic and genetic data was available (N = 17). The acoustic similarity matrices were generated as described above. Geographic coordinates were obtained via GPS and the geographic distance matrices were calculated from the minimum distance of different groups as implemented in GenAlEx 6.4.1 . GenAlExDM designed the study, collected acoustic and genetic samples, did laboratory work, analyzed data and wrote the paper. JKH designed the study and wrote the paper. DR collected acoustic data and supported fieldwork. AW collected acoustic and genetic samples and did laboratory work. CR designed the study, did laboratory work, analyzed data and wrote the paper. KH designed the study, analyzed data and wrote the paper. All authors read and approved the final manuscript.Calculated values of the arithmetic mean and the standard derivation of measured variables (pdf).Click here for filePresbytis taxa from Sumatra, Java and the Mentawai islandsPhotographs of wild . Numbers refer to locations in Figure P.thomasi by Cedric Buttoz Girard, all others by Dirk Meyer & Ambang Wijaya) (tif).Click here for filePresbytis loud-call with examples for measured parameters (tif)Spectrogram of a .Click here for file


Picoides arcticus) and found a pattern of fine-scale genetic structure. We then tested for differences in spatial structure between sexes and detected a pattern consistent with male-biased dispersal. We also detected a temporal increase in relatedness among individuals within newly burned forest patches. Our results indicate that specialist species that outlive their ephemeral patches can accrue significant fine-scale spatial structure that does not necessarily affect spatial structure at larger scales. This highlights the importance of both spatial and temporal scale considerations in both sampling and data interpretation of molecular genetic results.An exciting advance in the understanding of metapopulation dynamics has been the investigation of how populations respond to ephemeral patches that go ‘extinct’ during the lifetime of an individual. Previous research has shown that this scenario leads to genetic homogenization across large spatial scales. However, little is known about fine-scale genetic structuring or how this changes over time in ephemeral patches. We predicted that species that specialize on ephemeral habitats will delay dispersal to exploit natal habitat patches while resources are plentiful and thus display fine-scale structure. To investigate this idea, we evaluated the effect of frequent colonization of ephemeral habitats on the fine-scale genetic structure of a fire specialist, the black-backed woodpecker ( The genetic structure of metapopulations is driven by habitat quality within patches, which influences the demographic success of a species, and movement of individuals, or gene flow, among habitat patches Silene dioca, and found temporal variation in the amount of genetic divergence among populations, with generally ‘young’ and ‘old’ populations being more divergent than populations of an intermediate age. While other previous research has shown that rapid habitat turnover can result in genetic homogenization across large spatial scales For a dynamic metapopulation, large-scale genetic structure will be the result of patterns of frequent colonization and extinction of patches and how that varies in space and time. Frequent colonization of habitat patches can increase or decrease the amount of divergence among occupied patches depending on the source of the propagules Little is known about the effects of ephemeral habitat patches on fine-scale genetic structure, or on changes in fine-scale structure through time. Patterns of fine-scale genetic structure can result from an individual’s behavior within patches such as the presence of juveniles prior to natal dispersal, variation in reproductive success among individuals in the population, and general patterns of natal and breeding dispersal including sex-biased dispersal and dispersal distance In habitat-tracking metapopulations, once an area is colonized, fine-scale genetic structure can build during the time the habitat is occupied due to the presence of family groups, limited dispersal distance, and timing of natal dispersal by juveniles. Large-scale genetic structure tends to reflect the long-term genetic signatures of these individual behaviors. That is, over time, individuals that depend on natural disturbances to create habitat will colonize, occupy and leave patches for new ones and the result of this behavior at the large scale will be reflected in the large-scale genetic structure. Fine-scale genetic structure within these recently ‘created’ habitat patches will reflect more recent behaviors by individuals occupying the current patches such as the distance the individual travelled to colonize the patch and the dispersal behavior of their offspring. Dispersal is often sex-biased Picoides arcticus) is perhaps the most commonly cited example of a fire specialist Wildfire has historically been the dominant force responsible for shaping numerous landscapes in both the western and boreal forests of North America. Consequently, many species are adapted to, and some are even dependent on, living in these early successional, postfire habitats. In North America, the black-backed woodpecker  in areas that have burned at moderate to high severity In previous research, we found black-backed woodpeckers had very low genetic differentiation among patches at extremely large spatial scales (>3500 km). In addition, male and female black-backed woodpeckers likely have different dispersal behaviors in terms of crossing large inhospitable habitats such as grasslands All necessary permits were obtained for the described study, which complied with all relevant regulations. This work was conducted with approval from The University of Montana’s Institutional Animal Care and Use Committee, United States Fish and Wildlife Service, Montana Fish, Wildlife and Parks, Oregon Department of Fish and Wildlife, and South Dakota Department of Game, Fish, and Parks. Samples were collected on public land in the US and Canada and necessary permissions were granted from Glacier National Park, Jasper National Park, Grands Jardins Park and the US Forest Service.A full description of sampling protocols, DNA extraction, genotyping protocols and analyses can be found in Pierson et al. To assess fine-scale structure within and among burned areas, two field locations in western Montana (Missoula: MSLA and Glacier: GL) were identified that had three burned areas within 50 km of each other . Only saAdult woodpeckers were captured at the nest site during the nestling stage to reduce the chance of nest abandonment caused by disturbance during the incubation stage. Birds were captured using either hoop nets placed over the entrance of the cavity after the adult had entered, or with mist nets targeting birds flying to the nest cavity. While both adults were sampled when possible, attempts were made to only capture one adult per day to minimize disruption of food delivery to young. Blood was collected from the brachial vein and/or 5–10 small back feathers were collected, birds were weighed and marked with a unique color-band combination and then released. Latitude and longitude of the nest locations were recorded.C111, C115, D118, RCW4 (added tail), RCW5, (Mullins and Haig pers. comm.); DIU3, DIU4, HrU2, Lox4, R) and ISF were calculated in FSTAT.Briefly, we used the following nine microsatellite loci: r) for a specified set of distance classes . Significant spatial structure is measured using both bootstrapping and permutation tests as described in Peakall et al. r and assumed class significance when the error bar did not cross zero, which is considered a conservative approach r fell outside the confidence envelope around the null hypothesis of r = 0. Permutation tests provide a robust estimate of significance when sample sizes are small because they use the entire data set We performed global spatial autocorrelation analyses to test within-patch dispersal patterns We initially conducted a global spatial autocorrelation (GSA) that included all samples across the study and divided the sample into bins of even sample sizes to determine the largest spatial scale over which genetic structure (autocorrelation between genetic and geographic distance matrices) could be detected among individuals. We then created frequency distributions of the distances among individuals within approximately 225 km of each other, the maximum distance at which structure was detected, and conducted a GSA using variable distance classes based on the peaks of these multimodal distributions lr) for each focal point and a specified subset of n neighboring points. We calculated a 2D LSA for the five nearest neighbors and permutation tests were used to calculate if the local autocorrelation was significantly larger than expected by chance. While multiple comparisons are involved in this type of analyses, Bonferroni corrections are not necessary because we are only looking at a small, specific subset of points P = 0.05 to indicate significant lr values. Next, we calculated mean genetic relatedness We used two different approaches to assess whether family groups may be present within burned areas. Only samples from burned areas in which at least 10 individuals were sampled over the course of the study were included in these analyses. First, we employed a two-dimensional local spatial autocorrelation (2D LSA) using Genalex6 which calculates a local autocorrelation , and finally pooled all samples from three consecutive years (year 3) and estimated mean genetic relatedness. We then visualized these estimates to assess if there was an increase in genetic relatedness indicated over time. Due to the small number of years and sites in this study and the cumulative nature of the samples, a formal analysis such as regression or a sign test is not appropriate to test for an increase over time. However, the goal of this approach was to simply determine if a trend may exist in order to inform future research agendas.E) ranged from 0.48–0.61, allelic richness (AR) ranged from 3.76–6.67, and FIS ranged from −0.107 to 0.054.We analyzed samples from 233 black-backed woodpeckers across North America . The ninr) values in distance classes up to 164 km (P = 0.01), with females having significantly positive r-values at larger distance classes than males  analysis to estimate fine-scale genetic structure. Black-backed woodpeckers displayed significantly positive genetic correlation  and values tended to be small but significant within burned areas  on samples in eight burned areas. All of the burned areas contained at least one individual with significantly positive atedness , and mosST <0.02, Our goal was to test if a disturbance-dependent specialist can accrue significant fine-scale genetic structure in the limited period of time that their natal habitat persists, despite displaying near panmixia over extremely large spatial scales. Frequent colonization events combined with a high rate of population turnover usually leads to a lack of genetic structure among subpopulations A significant positive genetic correlation among individuals is expected when dispersal is limited The presence of genetic structure at this finer scale suggests that a mechanism other than long-distance dispersal is responsible for the lack of structure at the larger scale and may provide general information on the spatial scale of the genetic neighborhood When sex-biased dispersal is present, fine-scale structure may be due to restricted dispersal in only one sex even though fine-scale structure is observed when the sexes are pooled. When examined alone, only the sex with restricted dispersal will display fine-scale structure and the sex that disperses will typically show either a lack of fine-scale structure or markedly less fine-scale structure relative to the sex with restricted dispersal Male-biased dispersal in black-backed woodpeckers is concurrent with the differences in behavior detected at the large scale, where female black-backed woodpeckers appeared to respond differently to crossing gaps outside the boreal forest r) compared to the population overall that appears to increase through time. This prediction was largely supported with all eight burned areas showing a relatively large number of genetic clusters with strong signals of genetic correlation . Black-backed woodpeckers did show evidence of increased genetic relatedness over time in 86% of the burned areas sampled  the location of the seven broad-scale study sites: AB: Jasper National Park, Alberta; MSLA: Missoula, Montana; GL: Glacier National Park, Montana; OR: Silver Lake, Oregon; EC: Eastern Canada, Québec Grands Jardins Park; SD: Black Hills, South Dakota; ID: central Idaho; and (b) the two study sites within western Montana that each have three areas that burned in 2003: Missoula – BLM: Black Mountain fire; BM: Boles Meadow fire; FC: Fish Creek fire and Glacier National Park – WC: Wedge Canyon fire; RB: Robert fire; TR: Trapper fire.(TIF)Click here for additional data file.Table S1Results from a one-tailed test for positive genetic autocorrelation (r), which is expected when there is limited dispersal, for both sexes, then for males and females separately. The number of pairwise comparisons (N) per distance class (km), estimated genetic correlation for each distance class (r), and the probability the estimated r is greater than expected based on 1000 permutations (P); significant values are indicated in bold.(DOCX)Click here for additional data file.


It is essential to elucidate the relationship between metabolic and genomic diversity to understand the genetic regulatory networks associated with the changing metabolo-phenotype among natural variation and/or populations. Recent innovations in metabolomics technologies allow us to grasp the comprehensive features of the metabolome. Metabolite quantitative trait analysis is a key approach for the identification of genetic loci involved in metabolite variation using segregated populations. Although several attempts have been made to find correlative relationships between genetic and metabolic diversity among natural populations in various organisms, it is still unclear whether it is possible to discover such correlations between each metabolite and the polymorphisms found at each chromosomal location. To assess the correlative relationship between the metabolic and genomic diversity found in rice accessions, we compared the distance matrices for these two "omics" patterns in the rice accessions.1H-nuclear magnetic resonance (NMR). We found no correlation between global metabolic diversity and the phylogenetic relationships among the rice accessions (rs = 0.14) by analyzing the distance matrices  and the genetic distance on the basis of the RFLP markers. However, local correlation analysis between the distance matrices  against genetic distance matrices , generated clear correlations  at 34 RFLP markers.We selected 18 accessions from the world rice collection based on their population structure. To determine the genomic diversity of the rice genome, we genotyped 128 restriction fragment length polymorphism (RFLP) markers to calculate the genetic distance among the accessions. To identify the variations in the metabolic fingerprint, a soluble extract from the seed grain of each accession was analyzed with one dimensional This combinatorial approach will be valuable for exploring the correlative relationships between metabolic and genomic diversity. It will facilitate the elucidation of complex regulatory networks and those of evolutionary significance in plant metabolic systems. It is essential to elucidate the relationships between metabolic and genomic diversity to understand the genetic causes of phenotypic variation among natural populations. Visible and chemical variations associated with genomic diversity provide the information required to identify key genes associated with such phenotypic changes . PatternThe technical development of metabolite profiling has provided quantitative phenotypic information on the metabolome among strains and identified genes involved in metabolic networks . TherefoMetabolite quantitative trait loci (mQTLs) analysis using segregated populations has been applied to various plant species as a popular forward genetics approach ,5,7-9. AIn this study, to assess the correlative relationship between metabolic and genomic diversity found in 18 rice accessions selected from the world rice collection, we compared the distance relationships of accessions for these two omics patterns, globally, with marker polymorphisms vs. the metabolomic fingerprints of NMR spectra, as well as locally, for each chromosomal region vs. each spectra. To perform these analyses, we developed a robust procedure to explore the relationship between metabolic and genomic diversity. As a result, we present the first correlation map of metabolic and genomic diversity among rice accessions.To select the appropriate rice accessions for the comparison of metabolic profiles with patterns of genetic polymorphisms, we initially investigated the population structure of the world rice collection. The accessions of the world rice collection have been classified into three major groups, japonica, indica I, and indica II . To dete1H-nuclear magnetic resonance (NMR) analysis to elucidate the metabolic diversity among the accessions  between the distance matrices calculated from the 4.29- to 0.71-ppm 1H chemical shifts and genetic distance among accessions with polymorphisms at the 128 RFLP markers. We found no correlation between global metabolic diversity and the phylogenetic relationships among the rice accessions (rs = 0.14). Although the phylogenetic tree of the genetic distance matrix showed the expected 3 typical clusters  of two parental ecotypes, which had been selected from 14 ecotypes based on their metabolic profiles [To assess the global correlation between metabolic and genomic diversity, we calculated Spearman's coefficient of correlation . Therefore, in this analysis, we applied rs = 0.4 as the threshold to detect correlative combinations between metabolic and genomic diversity.We then calculated the 1H-13C NMR analysis . The lipid methylene signal (1.22 ppm) showed correlations with polymorphic sites at multiple chromosomal locations, with the highest located around the C43 RFLP marker at 92.8 cM on Chr. 5 , and the second highest around R1943 at 123.7 cM on Chr. 8 . The correlation peaks corresponding to each of the lipids  with Chr. 2 (153.6 and 95.7 cM), Chr. 5 , and Chr. 11 (123.3 and 85.8 cM) were in good accordance with the covalent bond networks within molecules of each compound. Furthermore, sucrose  with Chr. 1 (186.8 cM) and Chr. 4 (31.4 cM) also showed good accordance. Similar intramolecular covalent bond networks were reported in NMR experiments of biological metabolite mixtures, including covariance total correlation spectroscopy (TOCSY) analysis of insect venom [The presentation of the correlations as a 2D heat map showed large non-correlative areas studded with correlative areas, which can be explored in omics-wide comparisons. The heat map showed clear correlations between the profiles of soluble metabolites and local genomic diversity at each chromosomal location. To identify metabolites, we performed 2D ct venom .Local comparison between the dendrogram based on the correlative RFLP polymorphisms and NMR spectra showed a similar tree structure between both genetic and metabolic variation among the accessions. For example, the dendrogram of genetic polymorphisms around the C43 RFLP marker (92.8 cM on Chr. 5) and the diversity of the lipid methylene signal indicated the genetic and metabolic divergence between indica I and two other groups, japonica and indica II . The accessions were classified into three major groups by principal components analysis (PCA) of the RFLP data: japonica, indica I, and indica II [http://pritch.bsd.uchicago.edu/software/structure2_1.html) was also conducted on the world rice collection to determine the population structure in more detail, especially for the japonica type [The rice accessions were chosen from the RFLP-based Rice Discovery Research Set of rice germplasms, which contains 68 rice accessions and is developed and maintained by Genebank ndica II . Populatica type ,21. Basehttp://evolution.genetics.washington.edu/phylip.html; we used the distance matrix to calculate the coefficients of correlation with the metabolic diversity matrix and to calculate a phylogenetic tree by the UPGMA method using the "neighbor" program of PHYLIP. The physical position of each RFLP marker was retrieved from the Rice Annotation Project Database (RAP-DB) http://rapdb.dna.affrc.go.jp/[We used 128 RFLP markers to evaluate the genetic diversity of the 18 accessions by the Southern hybridization method of Kojima et al. (2005). Each of the marker alleles was scored as Nipponbare-type (1), Kasalath-type (2), or other types 3-7)  in the 1D NMR spectra was calculated by using the "dist" command of the R package. Genetic diversity among the 18 rice accessions was calculated from the genotype data of all alleles of the 128 scored RFLP markers. To assess the correlation between genome- and metabolome-wide diversity among the 18 accessions, we calculated Spearman's coefficient of correlation between the genetic and Euclidian distance matrices. Local metabolic diversity among the 18 accessions was calculated as a Euclidian distance matrix in each of the 104 bins. Local genomic diversity was also calculated as a genetic distance matrix using sliding bins that included three adjacent RFLP markers along each rice chromosome. To explore the local correlations between genomic and metabolic diversity, we calculated Spearman's 13C-labelled rice extracts were prepared as previously described methods with minor modifications [2O), heated at 65°C for 15 min, and centrifuged at 12,000 × g for 5 min. The supernatant (500 μL) was decanted into a 5 mm diameter NMR tube.ications ,28. In bZ-axis gradient, operating at 500.13 MHz for 1H frequency (176.061 MHz for 13C frequency), and the temperature of the NMR samples was maintained at 298 K. A total of 128 complex f1 (13C) and 1024 complex f2 (1H) points were recorded with 96 scans per f1 increment, resulting in a total recording time of about 6 h. The spectral window and offset frequency in the f1 dimension were 7042.593 Hz (40 ppm) and 11971.59 Hz (68 ppm), respectively. The spectral window in the f2 dimension was 11160.7 Hz (16 ppm). The offset frequency in the f2 dimension was 3330 Hz (4.75 ppm).Two-dimensional (2D) heteronuclear single quantum coherence (HSQC)  spectra 13C-labelled rice samples were processed using the NMRPipe software package [1H/13C chemical shift database [13C-HSQC peaks with peaks in the database. Our chemical shift data are continuously updated and are available on the PRIMe website http://prime.psc.riken.jp/[1H and 0.53 ppm for 13C were used in this study (Additional file The 2D HSQC spectra of the  package . To quandatabase . The datriken.jp/. The queKM and JK designed the project, performed data analysis, and drafted the manuscript. TF performed NMR data processing and analyses. EK provided RFLP polymorphism data and population structure of the rice core collection. KS served as the principal investigator of the project. All the authors have contributed to writing this manuscript and have read and approved the contents of the final submitted version.Figure S1. Rice seed material used in this study and their origins.Click here for fileTable S1. The scored genotype data from the 128 RFLP markers.Click here for fileTable S2. The numeric metabolite profiling data from rice seed extract based on the 1H NMR spectra.Click here for fileTable S3. The coefficients of correlation values between genetic and metabolic diversity in rice.Click here for fileFigure S2. Histogram of the correlation coefficient for the randomized data from 10,000 trials.Click here for fileTable S4. The data of the annotated metabolites.Click here for fileFigure S3. The 13C-HSQC spectra of seed extracts from the japonica and indica rice varieties.Click here for fileFigure S4. The 13C-HSQC spectrum from a seed extract of 13C-labelled Nipponbare.Click here for fileFigure S5. The correlative local genetic and metabolo-phenotypic diversity in rice.Click here for fileFigure S6. The correlative chromosomal regions for each NMR spectrum allocated on an RFLP map of rice.Click here for fileFigure S7. A schematic representation of the work flow used to explore the correlations between metabolic and genomic diversity with genetic resources.Click here for file


Oncorhynchus mykiss) using a multigenerational data of 45,900 individuals from the Finnish national breeding programme. We also tested whether or not microenvironmental sensitivity has been changed as a correlated genetic response when genetic improvement for growth has been practiced over five generations. The animal model analysis revealed the presence of genetic heterogeneity both in body weight and its residual variation. Heritability of residual variation was remarkably lower (0.02) than that for body weight (0.35). However, genetic coefficient of variation was notable in both body weight (14%) and its residual variation (37%), suggesting a substantial potential for selection responses in both traits. Furthermore, a significant negative genetic correlation (−0.16) was found between body weight and its residual variation, i.e., rapidly growing genotypes are also more tolerant to perturbations in microenvironment. The genetic trends showed that fish growth was successfully increased by selective breeding (an average of 6% per generation), whereas no genetic change occurred in residual variation during the same period. The results imply that genetic improvement for body weight does not cause a concomitant increase in microenvironmental sensitivity. For commercial production, however, there may be high potential to simultaneously improve weight gain and increase its uniformity if both criteria are included in a selection index.Microenvironmental sensitivity of a genotype refers to the ability to buffer against non-specific environmental factors, and it can be quantified by the amount of residual variation in a trait expressed by the genotype’s offspring within a (macro)environment. Due to the high degree of polymorphism in behavioral, growth and life-history traits, both farmed and wild salmonids are highly susceptible to microenvironmental variation, yet the heritable basis of this characteristic remains unknown. We estimated the genetic (co)variance of body weight and its residual variation in 2-year-old rainbow trout ( Early phases of selective breeding can generate rapid genetic responses in farmed animals. This typically involves genetic improvement of mean performance in the direction of selection. It is well established that many concurrent improvements in animal husbandry, including nutrition, housing and veterinary practices, accompany the genetic enhancement in animal performance. Additionally, trait heterogeneity can evolve over time, for example via increased or reduced susceptibility of individuals to variable and unmeasured microenvironmental factors. Understanding the genetic basis of such concurrent changes in quantitative traits reveals how selection influences the ability of individuals to respond to unpredictably fluctuating environmental conditions via developmental mechanisms, and helps us to explain the persistence of phenotypic variability within populations.Microenvironmental sensitivity refers to an individual’s ability to be buffered against local non-specific environmental factors  and subtle developmental noise, and it is considered synonymous to developmental instability Permanent changes in microenvironmental sensitivity are possible only when there is additive genetic variation for residual variation. In other words, different genotypes should produce differently variable progenies. The recent evidence from both wild and farmed animals imply that genotypes indeed differ in their amount of residual variation of traits Previous work has concentrated on terrestrial vertebrates and laboratory model species, which greatly differ from aquatic species, and from salmonids in particular. Salmonids have a multitude of characteristics that make the genetic analysis of microenvironmental sensitivity in growth important. In aquaculture production, new populations and species are constantly introduced in intensive captive breeding, providing an opportunity to investigate the genetic effects of artificial selection . We first estimated the proportion of genetic variation in residual variation for body weight in fish being maintained in the same location. By providing a common macroenvironment across year classes and by using the animal model, we ensured that residual variation can be regarded as microenvironmental sensitivity  that results from non-systematic environmental factors and internal developmental noise. Second, we estimated the genetic correlation between the additive genetic effects for body weight and its residual variation. Finally, by estimating genetic trends that quantify genetic responses across multiple generations, we investigated the effects of selective breeding for body weight on the genetic change in microenvironmental sensitivity.To investigate the inheritance of microenvironmental sensitivity and its genetic responses across generations when directional selection is performed for improved growth, we analyzed multigenerational pedigreed data covering ten year classes and 46 546 individuals from the Finnish breeding programme for rainbow trout, All procedures involving animals were approved by the animal care committee of the Finnish Game and Fisheries Research Institute (FGFRI).The data originated from the Finnish national rainbow trout breeding programme maintained by the FGFRI and MTT Agrifood Research Finland. The breeding nucleus is held at the Tervo Fisheries Research and Aquaculture station in Central Finland .The phenotypic data included 45 900 records of body weight from individuals born during 1992–2002 and reared at the same freshwater nucleus station. The fish represented eight year classes and belonged to two subpopulations with four successive generations (Pop I and Pop IIa) The generation interval of the study population was 3–4 years. Annual selection of breeding candidates was made using a multitrait selection index with main emphasis on improved growth The selection index has consisted of best linear unbiased predictions of breeding values for body weight measured at the age of 2 and 3 years (since 1992), maturity age (since 2001) Full-sib egg batches were incubated separately, and at the eyed-egg stage, they were transferred to one or two 150-liter indoor family tanks. Hatching of eggs occurred in June. During the following winter, after six months of growing in the family tanks, equal amount of fingerlings (of 50–100 g body weight) from each family tank were haphazardly sampled and individually tagged with passive integrated transponders  and then transferred to a flow-through earth-bottomed raceway at the Tervo station. The fish were fed with commercial dry feed. In Finland, year is highly seasonal and the effective growing season lasts from early May to late October.n =45 900). The number of individuals within each year class ranged between 2 518–10 753. The proportion of sexually matured (2+) males in the entire data-set was 14.9%, whereas no mature females were found.After the second growing season, the two-year-old fish were individually weighed to the nearest 1 g (mean 1020±315 (SD) g, n =457 sires) were selected for the analysis. Large family sizes are needed to obtain accurate and unbiased genetic parameters and estimated breeding values (EBVs) for residual variation To improve the reliability of genetic parameters for residual variation, only sire families with at least 35 offspring , k = family tan k × year number), i = number of animals), and sf. The common environment effect is modeled without the pedigree information. The values for Akaike’s Information Criteria (AIC) The estimation of genetic parameters and genetic trends was conducted using a bivariate animal model i for A is the additive genetic relationship matrix with additive genetic variance Ι is the identity matrix with homogeneous residual variance The second trait was microenvironmental sensitivity which was quantified by the log-transformed squared residual values, Because the residuals of the model 1 are used as an input variable for the model 2, the model for the mean and the residual variation was iteratively solved by conducting 30 consecutive bivariate analyses. At each iterative round, µ is the phenotypic mean of the population. GCV describes the propensity of the trait to respond to selection, that is, its evolvability Heritability of weight mean was calculated as Heritability of residual variation was calculated as et al.The approximate standard errors for estimated variance components and variance ratios were calculated using ASReml. The standard error of n=30 rounds). The observed oscillation is inherent to the statistical model used and is mainly due to an interplay between vice versa).REML log-likelihood values and the parameter estimates for body weight were found to remain relatively stable across the 30 iterative rounds, whereas To investigate whether or not genetic changes in mean body weight and its mircroenvironmental sensitivity occurred during selective breeding, genetic trends were determined for both traits and for both subpopulations separately. The genetic trends were obtained by plotting the average estimated breeding values  suggests that there is notable genetic potential in microenvironmental sensitivity of body weight.Heritability for body weight was moderate (0.35), whereas the common environment ratio was low (0.05) . GeneticThere was a slight but significant negative genetic correlation between body weight and its residual variation  in 2-year-old rainbow trout. This is in marked contrast with the moderate heritability of 0.35 for body weight. The low heritability estimate of microenvironmental sensitivity is somewhat surprising as large within- and between-family variation in fish growth is created by multiple factors, some of them presumably exhibiting substantial genetic variation. However, the notable genetic coefficient of variation  indicates the presence of substantial additive genetic variation for microenvironmental sensitivity. Regarding most life-history traits, the low heritabilities yet paradoxically high evolvability are attributed to the high residual variation accumulating from the variable underlying physiological and behavioral traits Diplostomum spp.) in our population may create genetic variation in microenvironmental sensitivity. Some families remain uninfected while others have both infected and uninfected individuals, and the infected individuals exhibit reduced growth There are several factors that can maintain genetic variation in microenvironmental sensitivity in the population under study. First, high initial growth rates and energy resources are related to increased probability of early maturation in salmonids GCVE was in the range of those found in chickens, mice, pigs and rabbits (25–50%) Although rainbow trout, among other salmonids, possess a capacity of considerable growth and life-history strategy variation both across and within families It is possible that similar to life-history traits GCV for trait means GCVE was over two times higher than GCV for body weight, suggesting a good opportunity to obtain reduction in random environmental variation by selection.The low heritability estimate observed here does not necessarily indicate that microenvironmental sensitivity would be weakly responsive to selection. Heritability, the ratio of additive genetic variance to phenotypic variance, is one predictor of genetic potential to selection responses, though in this context, genetic coefficient of variation provides a more reasonable measure of evolvability, similar to DrosophilaSome selection experiments and breeding programmes have obtained considerable genetic responses in traits with low heritability (e.g., developmental stability in To our knowledge, this study is the first multigenerational breeding experiment on aquatic organisms to assess the correlated genetic effect of strong directional selection on microenvironmental sensitivity of a trait. The genetic correlation between body weight and its residual variation was negative, implying that a high trait value was linked to a slightly reduced microenvironmental sensitivity. This combined with the low heritability of residual variation predicts only a weak decreasing microenvironmental sensitivity across successive generations in response to selection for rapid growth. However, the genetic trend for microenvironmental sensitivity remained stable or slightly elevated over the course of the selection period, while body weight mean displayed a 6% genetic increase per generation. These results together indicate that genetic improvement of body weight does not make rainbow trout more sensitive to microenvironmental perturbations. This is important animal welfare issue, because increase in size heterogeneity would lead to serious challenges in animal husbandry.et al.Drosophila fruitfly showed that phenotypic variation can be substantially higher in the lines selected for high and low abdominal bristle number relative to the unselected base population Intense mass selection based on individuals’ own phenotype is expected to increase phenotypic variation within a population even when there is no additive genetic correlation between trait and its residual variation In conclusion, heterogeneity of residual variation in rainbow trout growth was found to be partly under genetic control. This implies the possibility for selection to favor genotypes with low variability when constancy across microenvironmental conditions is important. The negative genetic relationship between body weight and its microenvironmental sensitivity presumably facilitates improving weight gain and simultaneously increasing its uniformity/robustness if both objectives are incorporated into a selection index. In addition, increasing the growth potential of fish does not seem to cause a concomitant change in the trait’s microenvironmental sensitivity.


Our ability to be conscious of the world around us is often discussed as one of the most amazing yet enigmatic processes under scientific investigation today. However, our ability to imagine the world around us in the absence of stimulation from that world is perhaps even more amazing.Our capacity to re-experience objects or scenarios that we've encountered before, and to notice new things about those experiences, is itself remarkable. But perhaps more remarkable still is our ability to experience objects or events that do not exist in the world, through our imagination. This is perhaps one of the fundamental abilities that allow us successfully to plan, run dress rehearsals of future events, re-analyze the past—and even simulate or fantasize events that may never happen. In short, it could be argued that this ability is one of the main factors that have allowed us as a species to dominate our planet so profoundly.Empirical research into mental imagery has seen a recent surge, which is partly a result of new neuroscientific methods and their clever application—but is also due to the discovery and application of additional sorts of objective methods to investigate this inherently internal and private process.Here we introduce an inspiringly broad range of work that focuses on mental imagery. This ebook contains the work from a broad range of researchers in different fields, both empirical work and reviews. Chapters range from the role of imagery in music, biomechanics, and mathematics to the functions of the cerebral hemispheres in imagery and imagery's effects on sensory perception.This collection provides a cohesive and broad-spectrum addition to the rapidly growing field of mental imagery. This set of articles provides theoretical insights and an overview of the state of empirical understanding, where it is heading, and how mental imagery relates to other cognitive and sensory functions.


Xiphias gladius was examined based on 2231 individual samples, collected mainly between 2009 and 2010, among three major sampling areas within the Indian Ocean , Atlantic (two sites) and Pacific (one site) Oceans using analysis of nineteen microsatellite loci (n = 2146) and mitochondrial ND2 sequences (n = 2001) data. Sample collection was stratified in time and space in order to investigate the stability of the genetic structure observed with a special focus on the South West Indian Ocean. Significant AMOVA variance was observed for both markers indicating genetic population subdivision was present between oceans. Overall value of F-statistics for ND2 sequences confirmed that Atlantic and Indian Oceans swordfish represent two distinct genetic stocks. Indo-Pacific differentiation was also significant but lower than that observed between Atlantic and Indian Oceans. However, microsatellite F-statistics failed to reveal structure even at the inter-oceanic scale, indicating that resolving power of our microsatellite loci was insufficient for detecting population subdivision. At the scale of the Indian Ocean, results obtained from both markers are consistent with swordfish belonging to a single unique panmictic population. Analyses partitioned by sampling area, season, or sex also failed to identify any clear structure within this ocean. Such large spatial and temporal homogeneity of genetic structure, observed for such a large highly mobile pelagic species, suggests as satisfactory to consider swordfish as a single panmictic population in the Indian Ocean.Genetic population structure of swordfish  Acanthocybium solandriThunnus alalungaMakaira nigricansTetrapturus albidusXiphias gladius with two subdivisions in the Pacific Ocean . Nevertheless, when population structure was considered and when the assessment focused on the southwest Indian Ocean as an independent stock – a case considered by the India Ocean Tuna Commission (IOTC) on the basis of the fishery data The identification of genetic structure in the Indian swordfish should be of importance in term of fish management as one of the challenging issues commonly recognized is to match the artificial spatial scale of stock assessment with the natural spatial structure of the species The present study aims to determine the swordfish genetic population structure in the Indian Ocean. For this purpose, an intensive sampling was conducted over the whole Indian Ocean, at several periods during two consecutive years (2009–2010). We examined genetic variation of more than two thousands swordfish using newly developed genetic markers, supposedly more discriminating than older ones: 19 microsatellite loci Sampling for the present study focused on the Indian Ocean (IO) – as defined by international conventions as the waters delineated from the Atlantic Ocean by the 20° east meridian, from the Pacific by the meridian of 146°55′ east and a southern limit at 60°S Initial sampling strategy was to sample one hundred fish per zone at two targeted seasons  over two consecutive years (2009 and 2010). Initial sampling seasons were defined based on known information of swordfish reproductive condition in IO  following the manufacturer instructions. A 1007 bp fragment of the mitochondrial ND2 gene was amplified by PCR using the primers and recommended conditions defined in Bradman et al. 2, 2 µM of each dNTPs, 0.3 µM of each primer, 0.5 U of SilverStar Polymerase Taq (Eurogentec), 25 ng of genomic DNA. Cycling parameters were 93°C for 3 min, followed by 35 cycles of 93°C for 30 s, 50–62°C for 50 s, and 72°C for 50 s and a final elongation at 72°C for 30 min. PCR were amplified separately and electrophoresed in three multiplex panels. Amplified fragments were separated on an ABI Prism 3100 genetic analyser. Alleles were scored using a co-migrating size standard  and identified using GENE MAPPER v4 (Applied Biosystems Inc.).Nineteen microsatellite loci were also used, three from i.e. swordfish sampled in a given area at a given time; each sample set was named as XXX_00_## .Data analysis was first conducted on the whole dataset to identify global level of structure. We also defined spatially and temporarily stratified sampling sets (h) and nucleotide (π) diversities and Fu’s Nall), and the observed (Ho) and expected (He) heterozygosities Rs) using the rarefaction process of the standArich package  for R isF as estimated by Weir and Cockerham 5 burn-in samples/generations and 5.105 iterations) for K values from 1 to 5.For ND2 sequences, haplotype  for ND2 sequences and SMOGD http://www.geodistance.com website) were plotted against genetic distance (using Фst/(1− Фst) for mitochondrial data or FST/(1− FST) for microsatellite data following the recommendations of Rousset ncf package (available at http://onb.ent.psu.edu/onb1/R).For both ND2 sequence and microsatellite data, the analysis package ARLEQUIN 3.5 a priori grouping based on geographical or temporal proximity, within or between oceans. The software SAMOVA 1.2 a priori group definition and identify geographic partitions that maximize genetic differences between groups and geographic homogeneity within groups; it was tested for K group values ranging from 1 to 4, with 100 annealing replicates each time.ARLEQUIN 3.5 Finally, the same analytical approaches  were processed to assess whether or not sex of individual had an effect on the genetic structure.A total of 2 231 swordfish were sampled from the three major study areas during this study . Sampling details are provided in d) and mean nucleotide diversity (π) were high, respectively 0.886 (±0.04) and 0.0022 (±0.0004), and similarly high within each sample set  of 2 001 swordfish. Approximately 48% of these 282 haplotypes were represented more than once. Mean haplotype diversity  varying from 5.8 for MAD_10_1 to 6.50 for COR_09_1 . FixatioST was 0.006 (p<0.001) when considering all the samples; it decreased to 0.001 and was non-significant (p>0.05) when considering only the swordfish sampled within the IO . Pairwise genetic distance estimates (ФST) between sample sets are summarized by a neighbor-joining (NJ) tree in Appendix F4 in ST values were observed for the NAM sample sets (mean ФST = 0.098), followed by COR and AFS comparisons (0.012 and 0.010 respectively). Consistent with these significant differentiation results, values of Jost’s D were very high when comparisons included NAM samples (D >0.25), even if the highest values of Jost’s D regarded AFS_10_3 (mean D = 0.74). An AMOVA analysis undertaken with grouping made per ocean also demonstrated a small but significant level of structure between oceans . The likelihood decreases when estimates were made with more than one pool (over ten independent simulations: LnP(D) for K = 1 and K = 2 were −109850 and −110440, respectively). When considering two genetic pools, the mean posterior probability per individual is 0.50 (±0.04) providing more evidence against subdivision.ST was 0.0028 (p<0.001) when considering all the sample sets and 0.0026 (p<0.001) when considering only the swordfish sampled within the IO suggesting the same low level of structure within and between oceans. The neighbor-joining tree based on the pairwise FST estimates .Overall FCT and FCT <0.001, p>0.05; see a priori geographic grouping, between-group variance was maximized when one sample set was considered isolated from all the others.We considered only the IO sample sets, excluding the five ones from the Atlantic Ocean (NAM and AFS) and the two from Pacific Ocean (COR). Irrespective of which marker was examined, the AMOVA analysis conducted within the IO showed that more than 99% of the variance was observed within the samples with no variance significantly associated with the partition into any kind of grouping . No significant differences were observed between years or seasons sampled at any site  neither there was significant differences observed between different sites within a season . Looking at microsatellite pairwise FST values (Appendix S2), 139 upon the 240 comparisons appeared significant . However, values of Jost’s D were very low with only 19 values above D = 0.01 (Appendix S2) at the intra IO level and no clear pattern of structure could be detected  were low and not significant, even between the most distant areas  when considering all the samples from this area; it was still not significant and decreased to 0.0009 (p>0.05) when considering only females and increased to 0.0020 (p>0.05) when considering males. A similar trend but significant was observed with microsatellite data as overall FST was 0.0023 (p<0.001) when considering all the samples, decreased to 0.0010 (p<0.05) when considering only the females and increased to 0.0040 (p<0.005) when considering the males. This trend indicated a higher genetic homogeneity between females than between males at the scale of the SWIO.Sex and maturity stage information were only available for localities in the South West Indian Ocean region see . Most ofXiphias gladius within the Indian Ocean (IO). The results obtained from both ND2 sequences and microsatellites on multiple sampling areas over multiple seasons detected some level of genetic heterogeneity but failed to demonstrate evidence that swordfish have multiple discrete populations within the IO. All the analysis focusing on the important spatially and temporally stratified sampling done in the SWIO failed to identify any significant structure. These results are consistent with the existence of a single panmictic population in the Indian Ocean population and thus they contradict previous studies suggesting the presence of at least two potential genetic populations i.e. long distance migration observed for IO swordfish using tag-recapture approach This study aimed as a first step to evaluate genetic structure of the swordfish MONS) in the north and the Indian South Subtropical Gyre Province (ISSG) ALME - which bordered the north west of the MONS province; Appendix F1 in ALME a serious candidate for a discrete feeding area; unfortunately, our sampling scheme did not allow us to identify whether this northwestern area is a specific foraging ground for some IO swordfish. Further exploration into the foraging behaviour of the swordfish, a deep-dwelling predator, with specialization on prey sizes and species The existence of at least two distinct stocks observed for this species in the Atlantic and Pacific Oceans in extenso is another argument suggesting a homogeneous single panmictic population in the IO. In the present case, the more pronounced structure observed with mitochondrial data could also underline the fact that our microsatellite loci were not as discriminating as they were expected to be e.g. SNPs (single nucleotide polymorphisms), or scan genomics approaches and detection of outlier loci Genetic analysis made by sex also failed to reveal any structure, however there was an indication of a higher genetic homogeneity between females than between males at the scale of the SWIO (significant using the nuclear marker but not using the mtDNA one). The fact that the genetic information given by the two genders is not the same could indicate a sex-biased dispersal in which gene flow between populations is accomplished primarily by one gender Analyses of mitochondrial ND2 sequences and microsatellite polymorphism indicated significant isolation between oceans. Both molecular markers showed a significant level of genetic variance associated when comparing samples by Oceans, with a level of differentiation of the IO higher with the Atlantic Ocean (AO) than the Pacific Ocean (PO).ST values observed between PO sample sets and the Indian sample sets are elements that strongly indicated an Indo-Pacific differentiation. Our results also showed that a significant part of genetic variance is associated with the Indo-Pacific differentiation . One of the criteria previously used to discuss the interoceanic differentiation and consider the swordfish from Indo-Pacific as one population was the shared absence of Clade II in both oceans Indo-Pacific swordfish was until now considered to belong to a unique stock ST values observed between NAM and Indian sample sets are elements that strongly indicated an Indo-Atlantic differentiation. Such differentiation observed is consistent with previous studies Thunnus obesusIstiophorus platypterus, and the blue marlin Makaira nigricansSphyrna lewiniChelonia mydasDermochelys coriaceaST and FST). The sample sets AFS_09_1 (July - November 2009) and AFS_10_2 (April 2010) showed a mtDNA signature more related to the AO population while the two others sets (AFS_10_1 and AFS_10_3) were more similar to IO genetic signature  that decreased to less than 5% in the IO and absent in the PO and the consequent high ФFile S1containing: Appendix F1: Geographic location of sampling areas and of the biogeographic provinces . Appendix F2: Swordfish sampling schedule. Appendix F3: Mismatch distribution for swordfish based on 1007 bp of the ND2 sequences. Appendix F4: Neighbor-joining trees showing the relationship between sample sets on the basis of pairwise genetic distances. Appendix S1: ND2 sequences pairwise ФST and D Jost’s values. Appendix S2: Microsatellites pairwise FST and D Jost’s values.(DOCX)Click here for additional data file.