Theobroma cacao L.) is indigenous to the Amazon basin, but is generally believed to have been domesticated in Mesoamerica for the production of chocolate beverage. However, cacao’s distribution of genetic diversity in South America is also likely to reflect pre-Columbian human influences that were superimposed on natural processes of genetic differentiation. Here we present the results of a spatial analysis of the intra-specific diversity of cacao in Latin America, drawing on a dataset of 939 cacao trees genotypically characterized by means of 96 SSR markers. To assess continental diversity patterns we performed grid-based calculations of allelic richness, Shannon diversity and Nei gene diversity, and distinguished different spatially coherent genetic groups by means of cluster analysis. The highest levels of genetic diversity were observed in the Upper Amazon areas from southern Peru to the Ecuadorian Amazon and the border areas between Colombia, Peru and Brazil. On the assumption that the last glaciation  had the greatest pre-human impact on the current distribution and diversity of cacao, we modeled the species’ Pleistocene niche suitability and overlaid this with present-day diversity maps. The results suggest that cacao was already widely distributed in the Western Amazon before the onset of glaciation. During glaciations, cacao populations were likely to have been restricted to several refugia where they probably underwent genetic differentiation, resulting in a number of genetic clusters which are representative for, or closest related to, the original wild cacao populations. The analyses also suggested that genetic differentiation and geographical distribution of a number of other clusters seem to have been significantly affected by processes of human management and accompanying genetic bottlenecks. We discuss the implications of these results for future germplasm collection and in situ, on farm and ex situ conservation of cacao.Cacao ( Theobroma cacao L.) Numerous studies have been dedicated to investigating the genetic diversity of cacao  in situ conservation populations of a species is in many cases the result of evolutionary processes running over hundreds to thousands of generations. Current intraspecific diversity patterns of many Amazonian plant species are at least in part a reflection of their distribution during the last period of glaciation , which had the greatest impact on the vegetation of northern South America in Pleistocenic history 2 conditions During the glacial-Holocene transition, evergreen rainforest distribution probably increased again owing to ameliorating climatic and COTheobroma and Herrania species, the pulp surrounding cacao seeds was a popular snack and was fermented to make an alcoholic beverage, and occasionally vinegar, by numerous Amazonian indigenous groups at the time of European contact For species with longstanding economic or livelihood importance like cacao and Brazil nut, varying levels of past human intervention contributed to shaping current spatial diversity patterns In addition, when considering recent findings concerning the biological characteristics of cacao in wild populations , and the aggregated occurrence of cacao trees in natural stands in situ, on farm and ex situ conservation of cacao.The present paper has three objectives. First we investigate the distribution of genetic diversity of cacao in Latin America  and identify areas holding the highest levels of genetic diversity under the assumption that these may correspond with areas where original wild populations can be found, or at least populations that are most related to the original wild populations cf. . Wild pohttp://www.ars.usda.gov/Research/docs.htm?docid=16432). This same dataset formed the basis for the paper of Motamayor et al A number of different data sources were analysed to produce the results presented in this paper. Most importantly, we used the open-access dataset elaborated by the Agricultural Research Service (ARS) of the United States Department of Agriculture (USDA), which contains microsatellite marker data from 1241 cacao individuals, evaluated over 106 loci (available at www.gbif.org) to obtain a more representative distribution of cacao growing sites in Latin America. After the previously described data cleaning, a total of 1333 cacao records were retained . We performed the same exercise for obtaining representative observation points for cacao’s wild relatives , Shannon information index and Nei gene diversity Grid-based spatial diversity analyses were performed using 10 minute grid cells (∼18 km at the equator), constructed in ESRI ArcMap 10, as the unit of analyses. To obtain sufficient and more evenly distributed data points for constructing high resolution maps of genetic diversity at a continental scale, we applied circular neighborhoods of one degree diameter around the locations of all the cacao trees considered in this paper, following van Zonneveld et al. The previously described circular neighborhood technique does not in itself eliminate the sampling bias that was already present in the original dataset: the number of trees per grid cell after the replication exercise varied between 1 and 244. To allow for standardized and statistically sound comparisons of genetic parameters between grid cells containing different numbers of trees, these parameters were calculated on bootstrapped subsamples (without repetition) of trees per grid cell. The bootstrap approach generates highly similar results as the rarefaction methodology that is commonly used for correcting the sample bias in allelic richness calculations To visualize continental-scale gradients in the genetic diversity of cacao and enhance our understanding of how geographical and environmental features structure this genetic diversity, we applied a spatial principal components analysis (sPCA) k-means algorithm with increasing values of k on the PCA-transformed data (i.e. the 939 genotyped trees) and then compares different clustering solutions using Bayesian Information Criterion (BIC). It is important to bear in mind that in many (most) cases the ultimate choice of the optimal k value is user-defined. In this respect Jombart k, but some values of k are better, more efficient summaries than others We used adegenet to identify different genetic groups in the cacao dataset. To identify the optimal number of clusters this package runs the To compare the climatological niches occupied by the different clusters, we extracted the values of the 19 bioclim variables and altitude for each of the sampled trees corresponding to their location from the respective 2.5 minute raster maps. Furthermore, to allow visualizing the environmental niches of the different clusters in two-dimensional space, we applied a Principal Coordinates Analysis (PCoA) by means of R packages Vegan 1.17–12 and BiodiversityR 1.5 www.worldclim.org/current) and projected it on past and future climate scenarios at the same resolution. The Area Under Curve value obtained was 0.912, pointing to good model performance. For past climate conditions we used the average of the two downscaled climate models of the Last Glacial Maximum  obtained from the WorldClim database (www.worldclim.org/past). It is highly probable that processes of expansion and constriction of species distributions occurred also in times preceding the LGM http://ccafs-climate.org/). We restricted the modeled distributions visualized on maps to the maximal threshold value generated by Maxent  varied between 8 and 670, and values obtained for the Area Under Curve were higher than 0.9 , suggesting good model performance in all cases. Next we made binary (presence-absence) maps of each of the species at the same resolution, applying Maxent’s ten percentile training presence threshold values and overlaid these rasters so as to obtain a map showing for each cell the number of Theobroma species with modeled distribution in that cell. A map of the observed species richness of genus Theobroma (i.e. showing the distribution of records extracted from GBIF) was developed in DIVA-GIS at resolution of 10 minutes and a circular neighborhood of one degree.For developing a map of the modeled species richness of genus Theobroma is observed in southern Nicaragua, Costa Rica and western Panama  on the one hand and the Upper Amazon regions of Ecuador, Northern Peru and southern Colombia , as well as northeastern Brazil and southern Venezuela . In any case, only in the western Amazon, a certain level of correspondence seems to exist between the areas where the highest levels of locally common alleles (and other genetic parameters) were observed and some potential refugia . As arguA Mantel test comparison of the geographical distances with Nei’s genetic distance between individual cacao trees showed strong evidence for isolation by distance , which was expected given the continental scale of the dataset. A better visual representation of continental-scale gradients in the genetic diversity of cacao is obtained through projection of the results of a spatial Analysis of Principal Components (sPCA) on a map of the study area . InteresThe 10 clusters obtained from k-means clustering in adegenet  correspoSomewhat surprisingly, and as opposed to the result obtained by Highest cluster diversity is observed in northeastern Peru, in the region around and to the south of Iquitos, with up to 4–5 different clusters occurring in this relatively small area . Up to tLarge variation exists in the measured genetic parameters from one cluster to another . ClusterThe remaining clusters are grouped together more or less in accordance with the geographical range they occupy. Cluster 6 and 10 are separated by a relatively small genetic distance (0.44) and both occur closest to the Andean foothills . ClusterThe different clusters occupy different ecological niches . A Mantel test confirmed the relation between genetic distance (Nei) and environmental distance (Euclidean) . The PCoA ordination diagram in in situ conservation status of cacao in light of future climate change, we modeled its potential distribution based on the average of 19 currently available downscaled climate models for 2050 and the A2 greenhouse gas emission scenario. Although we do not expect this model to give a 100% trustworthy high resolution map of the 2050 niche suitability of cacao, through the use of a high threshold value (see methodology) it should give us a fair approximation of the main areas where the species will have a high likelihood of surviving. To assess the Theobroma species are the upstream areas of the western Amazon, and to a lesser extent the southern part of Central America ) seems to be located in the southern part of Amazonian Peru since it is the only prevailing cluster  and 6 anThe center of origin of cluster 8 (Iquitos (+Purus)) may well be located in the upper Amazon region of northwestern Brazil, close to the corresponding refugium . As for Cluster 10 (Curaray) is restricted to Amazonian Ecuador and may have developed from a part of the original cacao population that was isolated during the LGM and resulted in relatively high values of genetic parameters observed in some parts of this region . The facThe remaining cluster that is notable in terms of observed values for genetic parameters is cluster 1 (Purus) whose potential origin is located in the Brazilian state of Acre . This clThe clusters that follow the previous ones in terms of high values registered in the genetic parameters are clusters 4 (Marañon-Amazon River) and 5 (Amelonado). It is likely that the genetic differentiation of these clusters was triggered by human interference Cluster 9 (Nanay) is a small cluster located in the northern part of the Peruvian Amazon . It seemCluster 7 (Marañon-Rondônia) is located in Rondônia and is geographically and genetically associated with cluster 1 Purus; . AlmeidaCluster 3 (Guianas) mainly occurs in eastern South America . The relet al. Cluster 2 corresponds to the Criollo cultivar. This cluster shows the lowest levels of genetic diversity in the dataset, most likely resulting from genetic bottlenecks that accompanied human selection and domestication processes Based on the highest genetic affinity of cluster 2 with cluster 6 ) found in the present analysis, it seems that the Criollo cultivar may be derived from an original southern Peruvian genepool . The Criollo cluster shows also higher affinity with the northernmost subcluster of cluster 6  of whichMost of the main clusters described above occupy different ecological niches. This could imply that trees from a particular niche  may contain alleles that give them competitive advantage to survive in that niche, compared to trees from sites with different environmental conditions. Such information can be useful when planning collection trips in search of interesting traits for potential use in cacao breeding programs.in concreto, interesting areas for collection of cacao germplasm would be the green areas in The likelihood of encountering interesting genetic material for breeding is higher where levels of  genetic diversity attain their maximal values simply because the high neutral diversity implies that populations did not experience genetic bottlenecks Chuncho variety from Cuzco) also contain interesting functional traits.However, some nuancing is in place here, because the neutral genetic variation measured here is not generally linked with genetic variation and population differentiation at quantitative, adaptive traits ex situ collections. This has not yet been done and is important to avoid duplication. Furthermore, ex situ conservation needs to be complemented by in situ and on farm conservation of diverse wild and cultivated populations, respectively In any case, it is important to stress that prior to planning new collection missions, it is essential to obtain a full understanding of what is currently conserved in existing ex situ collections). Standardized use of molecular markers is crucial in this respect This study has shown the merit of using spatial diversity analysis to uncover patterns in genetic marker data that may remain ‘hidden’ when using more classical approaches to population genetics. The approach adopted has allowed us to begin to separate the possible contributions of climate change, geography, history and culture to the current distribution of genetic diversity in cacao. Such modeling procedures may be applicable to other crops as well. Although the extensive dataset used in this paper allowed a better understanding of the spatial distribution of genetic diversity in cacao, several questions remain unanswered. Further validation the hypotheses we have put forward here can probably best be achieved through a combined application of alternative modeling approaches e.g.  and grou


Cyclina sinensis was investigated to test the validity of these two hypotheses for intertidal species in three marginal seas—the East China Sea (ECS), the South China Sea (SCS), and the Japan Sea (JPS).The marginal seas of northwestern Pacific are characterized by unique topography and intricate hydrology. Two hypotheses have been proposed to explain genetic patterns of marine species inhabiting the region: the historical glaciations hypothesis suggests population genetic divergence between sea basins, whereas the Changjiang River outflow hypothesis suggests genetic break in line with the Changjiang Estuary. Here the phylogeography of bivalve ST were observed between different marginal sea populations. Network analyses illustrated restricted distribution of haplogroups/sub-haplogroups to sea basins, with a narrow secondary contact zone between the ECS and SCS. Demographic expansion was inferred for ECS and SCS lineages using mismatch distributions, neutral tests, and extended Bayesian Skyline Plots. Based on a molecular clock method, the divergence times among COI lineages were estimated dating from the Pleistocene.Fragments of two markers  were sequenced for 335 individuals collected from 21 populations. Significant pairwise ΦC. sinensis populations is congruent with the historical isolation of sea basins rather than the putative Changjiang River outflow barrier. The large land bridges extending between seas during glaciation allowed accumulation of mutations and subsequently gave rise to deep divergent lineages. The low-dispersal capacity of the clam and coastal oceanography may facilitate the maintenance of the historical patterns as barriers shift. Our study supports the historical glaciations hypothesis for interpreting present-day phylogeographical patterns of C. sinensis, and highlights the importance of historical glaciations for generating genetic structure of marine coastal species especially those with low-dispersal abilities in northwestern Pacific.The phylogeographical break revealed for  A series of marginal seas that has historically connected and isolated Asia from the Pacific Ocean contributes to the intricate geography of the northwestern Pacific The historical separation of the sea basins was reported to have dramatically influenced the current genetic distribution of various marine species 3Cellana toreumaCyclina sinensisSargassumC. sinensis), and they had the abilities to transgress the barrier and dispersed over a wide range. Thereby, the barrier effect of the Changjiang River outflow on gene flow of intertidal species (the Changjiang River outflow hypothesis) is still a controversial issue, and additional data are needed to appraise the influence cautiously.However, the simple picture of genetic distribution drawn from climate changes may be obscured by other environmental factors. Oceanic currents operating during the pelagic phase theoretically facilitate the dispersal of larval and enhance high population connectivity C. sinensis, widely distributed in East Asia, provides an ideal case with which to address the relative influence of historical glaciations and the Changjiang River outflow on the genetic structure of intertidal species. The two factors may affect population structure of C. sinensis on different temporal and spatial scales, resulting in different patterns with regard to group partitioning and location of secondary contact zones. This bivalve is considered a euryhaline species because it inhabits broad salinity ranges from muddy sand beaches in the intertidal zone to estuaries where freshwater enters sea C. sinensis have home ranges no wider than 80 m C. sinensis particularly suitable to disentangle the complex interactions of various factors on genetic structure in marginal seas. Indeed, several studies have emerged with aims to reveal the genetic distribution of C. sinensisThe bivalve C. sinensis in three marginal seas  of East Asia by intensive sampling with an emphasis on the coast of China. Based on the analysis of mitochondrial cytochrome oxidase I (COI) and nuclear ribosomal internal transcribed spacers 1 (ITS-1) markers variation, we specifically aimed at elucidating the detailed genetic patterns, and testing the validity of the historical glaciations and Changjiang River outflow hypotheses for the clam. By addressing these issues, we expect to gain better understanding of the genetic distribution as well as evolutionary mechanisms for marine coastal species in the northwestern Pacific.In the present study, we surveyed the phylogeographical structure of Cyclina sinensis were collected between October 2005 and January 2010. We obtained 335 individuals from 21 localities representing the geographical distribution of this species in East Asia, with a range of 5–23 individuals per site  based on following conditions: initial denaturation at 94°C for 3 min, followed by 35 cycles of denaturation at 94°C for 1 min, annealing at 50°C for 1 min, and extension at 72°C for 1 min, and a final extension at 72°C for 5 min.A fragment of the COI gene was amplified for all samples with the universal primers of Folmer et al. AcarTo confirm the COI results, the ITS-1 region was amplified for 110 individuals from 12 populations  using thh), nucleotide diversity (π), and mean number of pairwise differences (k). The evolutionary model that best fitted the two loci was determined with jModelTest Sequences of COI and ITS-1 were assembled and edited by the DNASTAR software  separately, then aligned with CLUSTAL_X 1.81 using default settings Networks of haplotypes were constructed with 95% maximum parsimony threshold in TCS version 1.21 −1) −1) To confirm the phylogenies among haplotypes, Bayesian inferences (BI) were performed in MrBayes version 3.1 ST as implemented in ARLEQUIN 3.5. Because the HKY model given by jModelTest is not available in ARLEQUIN, the Tamura-Nei model (with a gamma distribution for ITS-1) was used to correct the genetic distances. The significance of each pairwise comparison was tested with 10 000 random replicates, and a standard Bonferroni correction was applied for multiple tests Pairwise genetic divergences between populations were assessed using the fixation index ΦST between different sea populations (see results), we tentatively partitioned populations into three groups consistent with sea basins. The significance of Φ-statistic analogs  was evaluated with 10 000 random permutations. The feasibility of this pre-defined groupings were then examined using a spatial analysis of molecular variance as implemented in SAMOVA version 1.0 K, here 2–10) using a simulated annealing approach to maximize the variance (FCT) among those groups.Hierarchical analyses of molecular variance AMOVA,  were use−1) and a generation time of 1 year After identifying lineages using phylogenetic methods, extended Bayesian Skyline Plots (EBSPs) were executed to examine population size changes of lineages in Beast v1.7 SFR2As a comparison to the EBSP, we also calculated Fu's π = 0.0044 in Xiangshui, site 10) and sharp different haplotype diversity  as well as mean number of pairwise differences (k = 25.64).The final ITS-1 alignment  was 544 −1, the split times between the ECS and SCS COI lineages occurred about 74 000 to 254 000 years ago, while the divergence time for ECS/JPS and SCS/JPS dating from 750 000 to 2 570 000 and 720 000 to 2 460 000 years ago, respectively. All these dates fall into the range of Pleistocene period from 11 700 to 2 588 000 years ago Two haplogroups were formed in the COI network: one group comprised three haplotypes in JPS , which was highly concordant to the pre–defined grouping in AMOVA. For the COI data, the populations were primarily divided into two clusters . When only China group considered, the highest variation among group appeared for two groups (FCT = 0.440) mainly coinciding with the ECS and SCS (except for Xiamen population in the ECS assigned to SCS). These results in SAMOVA validated the pre-defined groupings in AMOVA based on sea basins. Hierarchical analyses of AMOVA groupings indicated that most of the genetic variation could be attributed to variation among groups , followed by variance within populations . Only 5.7% for COI and 1.0% for ITS-1 were due to variation among populations within groups, but the variance components at all levels were statistically significant  have experienced significant population size changes . The 95%FS and the Ramos-Onsins and Rozas' R2 (P < 0.05), implying a demographic expansion event under the neutral model. Mismatch distribution analyses for all lineages (except for ECS COI lineage) uniformly displayed unimodal distributions  to Xiamen (site 16). No significant relationship was found between genetic and geographic distances for either ECS or SCS populations, indicating a non-IBD model of gene exchange within each sea. The IBD pattern observed for all China samples was mainly due to genetically divergent populations from either side of the suture zone were included. Although the CCC connects the ECS and SCS with a common velocity of 20 cm/s in summer Postglacial range expansions might bring formerly isolated lineages into secondary contact at suture zones Cyclina sinensis in East Asia. No significant barrier effect of the Changjiang River outflow was revealed in the study. Detailed comparative investigations on other intertidal species are still needed to test and complement the perspective. Empirical studies in the future, focusing on the barrier effect of the Changjiang River discharge, should be cautiously carried out by sufficient sampling around the estuary and proper analyses. The present study contributes to the better understanding of historical processes of coastal species and disentangles complex interactions of various factors on generating biodiversity in the northwestern Pacific.Our results supported the historical glaciations hypothesis for shaping the present-day phylogeographical structure of Table S1The final alignment of 110 ITS-1 sequences.(NEX)Click here for additional data file.Table S2ST based on COI  and associated P values  among the 21 populations .(DOC)Click here for additional data file.Table S3ST based on ITS-1  and associated P values  among the 12 populations .(DOC)Click here for additional data file.


Dunaliella. Glycerol-3-phosphate dehydrogenase (G3PDH) is a key enzyme in the pathway of glycerol synthesis, which converts dihydroxyacetone phosphate (DHAP) to glycerol-3-phosphate. Generally, the glycerol-DHAP cycle pathway, which is driven by G3PDH, is considered as the rate-limiting enzyme to regulate the glycerol level under osmotic shocks. Considering the peculiarity in osmoregulation, the cDNA of a NAD+-dependent G3PDH was isolated from D. salina using RACE and RT-PCR approaches in this study. Results indicated that the length of the cDNA sequence of G3PDH was 2,100 bp encoding a 699 amino acid deduced polypeptide whose computational molecular weight was 76.6 kDa. Conserved domain analysis revealed that the G3PDH protein has two independent functional domains, SerB and G3PDH domains. It was predicted that the G3PDH was a nonsecretory protein and may be located in the chloroplast of D. salina. Phylogenetic analysis demonstrated that the D. salina G3PDH had a closer relationship with the G3PDHs from the Dunaliella genus than with those from other species. In addition, the cDNA was subsequently subcloned in the pET-32a(+) vector and was transformed into E. coli strain BL21 (DE3), a expression protein with 100 kDa was identified, which was consistent with the theoretical value.Glycerol is an important osmotically compatible solute in  Dunaliella salina, one member of the genus Dunaliella , is an extremely halotolerant, unicellular, green, and motile algae. The genus Dunaliella is unique in its remarkable ability to survive in the media with a wide range of NaCl concentrations, from about 0.05 M to saturation (around 5.5 M), while maintaining a relatively low intracellular sodium concentration de novo synthesis of the compatible solute, the glycerol, following salt stress D. salina an obvious applicable value as a model organism in studying the mechanism of osmoregulation under salt environment conditions. In addition, under high salt stress, D. salina could accumulate large amounts of β-carotene in cells, which makes it one of the best sources of natural β-carotene Dunaliella under salt stress ella by changing intracellular glycerol content. The glycerol is synthesized rapidly when the concentration of saline increases, and the glycerol transforms to starch when the concentration of saline drops D. salina accumulates massive amounts of glycerol and the level of intracellular glycerol is proportional and osmotically equivalent to the external NaCl concentration, reaching about 8 M or 55% (w/v) of the cell weight at saturated NaCl concentrations Dunaliella tertiolecta could also adapt the different concentration of saline by synthesizing or eliminating the intracellular glycerol to balance the osmotic potential of intracellular and extracellular +)-dependent glycerol-3-phosphate dehydrogenase (G3PDH) plays a major role in the osmoregulation process in DunaliellaGlycerol is an important osmotically compatible solute in D. salina, and these isozymes respectively take effects in different salinities and play important roles in glycerol metabolism +-dependent G3PDH from D. salina, which is one isozyme with highly homology of previously isolated G3PDH in this alga Dunaliella.It was found that there are five isozymes of G3PDH in D. salina strain 435 (UTEX 200) conserved in our laboratory were cultivated in the culture medium according to Chen et al Cells of D. salina cells grown at the late log phase with using E.Z.N.A. Total RNA Kit II (OMEGA) according to the manufacture’s instruction. Subsequently, the total RNA was treated by DNase I (RNase Free) (TaKaRa) and was dissolved in 0.1% (v/v) diethyl pyrocarbonate solution (TaKaRa) The total RNA was prepared from 10 mL of D. salina G3PDH cDNA by using Premix Ex Taq (TaKaRa) following the manufacturer’s instructions. The PCR procedure is as the following: 1 cycle of 94°C, 5 min; 30 cycles of 94°C, 30 s, 51°C, 30 s, and 72°C, 1 min; and 1 cycle of 72°C, 10 min; used primers listed in The first strand of cDNA was synthesized from the total RNA using PrimeScript ™ RT-PCR kit (TAKARA) according to the manufacturer’s instructions Based on the obtained conserved cDNA fragment sequence, gene specific primer Dsgpdh3’F was designed and 3′ RACE was conducted with oligo dT-Adaptor primers using RNA PCR Kit (AMV) Ver.3.0 (TaKaRa). The first-strand cDNA was amplified by LA Taq (TaKaRa) with the parameters set as follows: 94°C, 4 min; 30 cycles of 94°C, 30 s, 46°C, 30 s, and 72°C, 1 min with a final extension at 72°C for 10 min.The 5′ RACE operation was accomplished with SMART™ MMLV Reverse Transcriptase (Clontech) and synthesized primers SMARTAO and 5′-RACE CDS. The second 5′RACE was conducted using the Dsgpdh5’F2primer designed according to the fragment obtained from the first 5′RACE reaction with Dsgpdh5’F1 primer. Other handlings including touchdown PCR were employed according to the SMARTer™ RACE cDNA Amplifcation Kit User Manual, with the exception of LA Taq DNA polymerase (TaKaRa) for touchdown PCR, rather than Advantage 2 Polymerase Mix.The full-length G3PDH cDNA was obtained with specific primes  agar gels, cloned in the pMD18-T vector (TaKaRa) and sequenced before the further experiments. Plasmid preparations, transformations, and other standard molecular biology techniques were carried out as described previously http://blast.ncbi.nlm.nih.gov/). Component analysis of G3PDH was calculated using DNAStar software 7.1.0 (Lasergene). Physical and chemical characteristics of G3PDH were analyzed by ProtParam tool (http://www.expasy.ch/tools/protparam.html). Multiple alignments among similar enzymes were conducted using Clustal X 1.83 . Phylogenetic and molecular evolutionary analysis of the amino acid sequences of different G3PDHs were conducted using the Neighbor Joining method and the molecular evolution genetics analysis (MEGA) software, version 4.0.2. The conserved structural domain of G3PDH was predicted via National Center for Biotechnology Information conserved domain database (CDD) in NCBI (http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi), and the protein conserved module of G3PDH was analyzed via the Pfam database (http://pfam.sanger.ac.uk/) to search its domain combinations. The amino acid sequence was subjected to TMHMM server (http://www.cbs.dtu.dk/services/TMHMM/) for transmembrane analysis and SignalP 3.0 Server  for the prediction of protein signal sequence. Subcellular localization presumption was performed using WoLF PSORT (http://wolfpsort.org/). Secondary structure was predicted via the NPS@ service (http://npsa-pbil.ibcp.fr/) and PredictProtein (https://www.predictprotein.org/); 3D structure was constructed using 3D-JIGSAW (http://bmm.cancerresearchuk.org/~3djigsaw/).Sequence analysis was performed using Blast Software  into the BamH I and Xho I  restriction sites of expression vector pET-32a(+) . The G3PDH cDNA fragment was used as templates to synthesize the G3PDH ORF by using PrimSTAR HS DNA Taq  with forward primer ORF-F and reverse primer ORF-R, which will introduce the BamH I and Xho I site into the 5′ and 3′ end of the ORF, respectively. The PCR procedure is as the following: 1 cycle of 94°C, 30 s; 30 cycles of 94°C, 30 s, 60°C, 30 s, and 72°C, 2 min; and 1 cycle of 72°C, 10 min. PCR product was separated by electrophoresis in 1.5% (w/v) agar gels, cloned in the pMD18-T vector and transformed into E. coli JM109, which was verified by double digestion of BamH I and Xho I. Both plasmids pET-32a and pMD18-T-JM109-G3pdh were prepared from E. coli BL21 and JM109, then digested by BamH I and Xho I at 30°C for 45 min or 2 h. The digested products were separated by electrophoresis in 1% (w/v) and 1.5% (w/v) agar gels, and the ORF and pET-32a fragments were recycled using E.Z.N.A. kit . T4 DNA ligase  was then added, and samples were incubated at 16°C for 12–16 h. 5 µL of the ligation mixture was used to transform electronically competent E. coli (100 µL of BL21(DE3)). The correct strain of E. coli BL21 (DE3)/pET-32a-G3pdh was verified by double digestion of BamH I and Xho I.Plasmid pET-32a-G3pdh was constructed by insertion of the E. coli strains BL21 (DE3) were grown in LB medium at 37°C in darkness on a platform shaker at 230 cycles min−1. Ampicillin (100 µg mL−1) was used for selection or maintenance of plasmids. To induce the expression of the D. salina G3PDH, a final concentration of 1.0 mmol L−1 isopropyl-β-D-thiogalactopyranoside (IPTG) was added to the E. coli culture when the optical density (OD) value reached 0.4–0.6, and the culture was allowed to continue growing for 3–4 h before harvesting by centrifugation.−1 phenylmethysulfonyl fluoride (PMSF). The supernatants were collected by centrifugation at 12,000 g for 30 min at 4°C. Protein concentration was estimated as described The transformed cells with plasmid pET-32a-G3pdh or pET-32a was disrupted by ultrasonication in PBS buffer containing 1 mmol LD. salina on the basis of the G3PDH gene sequences of D. salina, Chlamydomonas reinhardii, Arabidopsis thaliana, Pichia stipitis, zygosaccha and four predicted G3PDH gene sequences. When using total RNA from D. salina cells as RT substrate, an expected 583 bp fragment amplified with primers Dsgdph1-F/R was cloned and sequenced , 83 acidic amino acids , 266 hydrophobic amino acids  and 144 polar amino acids . Analysis by ProtParam tool revealed that the molecular weight of this peptide was 76.6 kDa, the isoelectric point was 6.49.Nucleotide sequence analysis showed that the D. salina G3PDH with NAD+ as coenzyme (AY845323.1), 83% and 78% with D. viridis G3PDH1 (EU624406.1), 76% and 72% with D. viridis G3PDH2 (EU624407.1), which indicated that the protein encoded by the obtained cDNA in this study might belong to the G3PDH family with NAD+ as coenzyme.A complete homologous search by BLAST demonstrated that the nucleotide and putative protein sequence had, respectively, sequence identities of 91% and 95% with the published D. salina contained three conserved regions including phosphoserine phosphatase (SerB) domain, N- and C- terminals binding to NAD+ (+-dependent G3PDH (EC 1.1.1.8). Eukaryotic neural network (NN) search by SignalP 3.0 showed that this polypeptide had no signal peptide. It was also predicted to be non-secretory protein by Markov models (HMM) of SignalP 3.0. TMHMM Server v. 2.0 predicted that the G3PDH had no transmembrane domain. Presumption of subcellular localization performed using WoLF PSORT showed that D. salina G3PDH may be situated in the chloroplast.The Conserved Domain Database (CDD) provided by NCBI was employed to predict the structural and functional region , which m to NAD+ . Using PNPS@service PHD, GOR1, SOPMA and PREDATOR methods. On account of the distinct emphases of various methods, the predicted results were also different. So these methods were made a comparison and PredictProtein was adopted to analyze online , D. viridis G3PDH (GI 189187651), D. viridis G3PDH (GI 189187649), Chlamydomonas reinhardtii predicted G3PDH (GI 159463132), Chlorella variabilis hypothetical G3PDH (GI 307107298), Volvox carteri hypothetical G3PDH (GI 302833661), Hordeum vulgare predicted G3PDH (GI 326525148), Sorghum bicolor hypothetical G3PDH (GI 242060059), Zea mays G3PDH (GI 226494897), Oryza sativa Japonica Group G3PDH (GI 115442511), Cuphea lanceolata G3PDH (GI 840731), Vitis vinifera hypothetical G3PDH (GI 147796339), Arabidopsis thaliana putative G3PDH (GI 19424089), Populus trichocarpa predicted G3PDH (GI 224140865), Ricinus communis putative G3PDH (GI 255572030), Selaginella moellendorffii hypothetical G3PDH (GI 302820075), Physcomitrella patens subsp. patens predicted G3PDH (GI 168031121). The results indicated the general conservatism of botanical and algal G3PDH genes  expression vector in the BamH I/Xho I sites. The constructed prokaryotic expression vector pET-32a-G3pdh was transformed into E. coli strain BL21 (DE3) and IPTG was used for induction. The G3PDH in transgenic strains were analyzed by SDS-PAGE. As shown by The G3PDH ORF sequence of 2100 bp was amplified by PCR with Dunaliella  functioned by varying the intracellular concentration of a compatible solute glycerol to balance the osmotic pressure inside and outside of cells. When subjected to hyperosmotic shock D. salina cells rapidly shrink followed by synthesis of glycerol to increases the internal osmolarity for resuming original volume of cells. Under hypoosmotic shock, it was found rapid swells followed by a decrease in internal glycerol and volume resumption D. salina involved the key enzymes G3PDH, which convert DHAP to glycerol-3-phosphate. Some studies have been performed to investigate the G3PDH for elucidating the mechanism of osmotic stress tolerance by glycerol. In a study, a novel G3PDH (NAD+) (EC1.1.1.8) gene (PfGPD) was cloned from halotolerant yeast Pichia farinosa, and the PfGPD gene was induced by salt stress +-dependent G3PDH from D. salina, and the cDNA may encode an osmoregulated isoform primarily involved in glycerol synthesis DvGPDH1 and DvGPDH2) from Dunaliella viridis, which encode two polypeptides of 695 and 701 amino acids, respectively As is known, the osmotic adjustment response of D. salina acts with NAD+ as coenzyme, and the comparative study of conservative regions discovered NAD+ and 3-phosphoglycerate binding sites in the G3PDH protein, theoretically testifying the cloned cDNA encodes G3PDH of osmosis-adjusting type. The G3PDH protein deduced from G3PDH cDNA contains 699 amino acids, of which the molecular weight is 76.6 kDa and the isoelectric point is 6.49, with −3.33 charge in pH 7.0. This G3PDH gene and protein had 91% and 95% identity with the putative G3PDH gene sequence (AY845323.1) and protein (AAX56341.1) + as coenzyme. It was reported that three isoforms of G3PDH have been separated from D. tertiolectaD. salina FAD-G3PDH, which situated in the mitochondrial. The expression of FAD-G3PDH was enhanced by salt treatment. Its catalytic site facing toward the cytosol, combined action of this enzyme with the cytosolic NAD+-dependent G3PDH forms the glycerol-3-phosphate shuttle +-dependent G3PDH oxidizes cytosolic NADH to NAD+, and catalyzes the reduction of DHAP to glycerol-3-phosphate. Subsequently glycerol-3-phosphate passes the outer mitochondrial membrane and is oxidized to DHAP by FAD-G3PDH, simultaneously delivers its electrons to the respiratory chain In the present study, the cloned 2100 bp G3PDH cDNA from DsGPDH2 in D. salina and DvGPDH1 and DvGPDH2 in D. viridis all encoded unique bi-domain proteins with C-terminal G3PDH domains and additional N-terminal SerB domains Debaryomyces hansenii, Candida glycerinogenes and CandidamagnoliaeAccording to the analysis of conservative region of the G3PDH in the present study, it could be speculated that the G3PDH functional domain originate at the 331 amino acid of the amino sequence and the first 330 amino acids are potentially correlated with other properties of the protein. Namely, G3PDH protein has two independent functional domains, SerB and G3PDH domains. SerB (EC 3.1.3.3) and glycerol-3-phosphate phosphatase (EC 3.1.3.21) are attributed to the same type of hydrolase, and two enzymes have similar functions due to their active centers of similar size. Therefore, it was speculated the G3PDH might also have glycerol-3-phosphate phosphatase activity and can catalyze DHAP to glycerol directly without glycerol-3-phosphate phosphatase. Similarly, in the studies by He et al and He et al, protein domain analysis revealed that +-G3PDH are cloned from D. salina. These genes have been transferred into bacteria or plants to increase the salt-tolerance of these species. Zhang et al transferred the DsALDP gene into E. coli cultured in media with different NaCl concentration to analyze its expression Agrobacterium tumefaciens, and DsALDP gene was expressed effectively in transgenic tobacco, which exhibited a higher salt tolerance D. salina has been transferred into led discs cells of tobacco. RT-PCR analysis showed that G3PDH gene integrated into tobacco genome has produced mRNA E. coli strain BL21 (DE3). The analysis by SDS-PAGE showed that the G3PDH protein was expressed successfully in transgenic strains, and the further work would emphasize on transforming this G3PDH gene into other higher plants to improve their salt tolerance.Some key enzyme genes related to glycerol metabolism, such as the cDNA of fructose-1, 6-diphosphate aldolase (DsALDP) and NAD+-G3PDH was successfully isolated from D. salina. The cDNA was 2100 bp long, which encoded a deduced protein sequence of 699 amino acids with an estimated molecular weight of 76.6 kDa. Protein domain analysis revealed that G3PDH protein has two independent functional domains, SerB and G3PDH domains. The D. salina G3PDH was a nonsecretory protein that may be located in the chloroplast. The D. salina G3PDH had a closer relationship with Dunaliella G3PDHs than with those of other species in the phylogenetic analysis. The secondary and three-dimensional structure of the D. salina G3PDH is predicted. In addition, the prokaryotic expression vector pET-32a-G3pdh was constructed and transferred into E. coli, in which G3PDH protein was expressed successfully. To fully understand glycerol metabolism and osmotic adjustment based on glycerol in Dunaliella, future investigation should focus on the gene clone of some enzymes related to glycerol metabolism and the application of transgenic technology to increase salt-tolerance of other plants by transferring these cloned genes.In conclusion, in the present research the cDNA of a NAD


Saimiri oerstedii) that lives in the fragmented, human-modified habitats of the Central Pacific region of Costa Rica. We analyzed non-invasively collected fecal samples from 244 individuals from 14 groups for 16 microsatellite markers. We found two geographically separate genetic clusters in the Central Pacific region with evidence of recent gene flow among them. We also found significant differentiation among groups of S. o. citrinellus using pairwise FST comparisons. These groups are in fragments of secondary forest separated by unsuitable “matrix” habitats such as cattle pasture, commercial African oil palm plantations, and human residential areas. We used an individual-based landscape genetic approach to measure spatial patterns of genetic variance while taking into account landscape heterogeneity. We found that large, commercial oil palm plantations represent moderate barriers to gene flow between populations, but cattle pastures, rivers, and residential areas do not. However, the influence of oil palm plantations on genetic variance was diminished when we restricted analyses to within population pairs, suggesting that their effect is scale-dependent and manifests during longer dispersal events among populations. We show that when landscape genetic methods are applied rigorously and at the right scale, they are sensitive enough to track population processes even in species with long, overlapping generations such as primates. Thus landscape genetic approaches are extremely valuable for the conservation management of a diverse array of endangered species in heterogeneous, human-modified habitats. Our results also stress the importance of explicitly considering the heterogeneity of matrix habitats in landscape genetic studies, instead of assuming that all matrix habitats have a uniform effect on population genetic processes.Landscape genetic studies offer a fine-scale understanding of how habitat heterogeneity influences population genetic structure. We examined population genetic structure and conducted a landscape genetic analysis for the endangered Central American Squirrel Monkey ( Many species exist in spatially structured populations linked by dispersal and gene flow, which can influence evolutionary, demographic, and ecological processes. Studies of population genetic structure are important for species living in complex, heterogeneous landscapes, which may affect that structure Landscape genetic approaches are increasingly used to understand the influence of landscape characteristics on population genetic structure and dispersal patterns One particular challenge in landscape genetics has been to quantify the relative effects of various landscape parameters on gene flow Saimiri oerstedii, Primates: Cebidae) provides an ideal opportunity to investigate population genetic structure in a human modified, heterogeneous landscape. S. oerstedii live in groups of 18 or more individuals, which have home ranges of approximately 200 ha. Their diet includes arthropods, flowers, fruits, and small vertebrates S. o. citrinellus inhabits a particularly heterogeneous landscape in the Central Pacific region of Costa Rica, where fruit and rice plantations and cattle pasture replaced approximately 80% of the natural forest in the early 1900s The endangered Central American Squirrel Monkey  from Manuel Antonio National Park (MANP) S. o. citrinellus. The study concluded that S. oerstedii have moderate to high genetic diversity compared to the three other primate species in Costa Rica . A much larger sample collected across a broader spatial scale and wider range of habitats is necessary to determine the effects of landscape heterogeneity on S. o. citrinellus population genetic structure. In this study, we analyzed a large number of non-invasively collected molecular samples of S. o. citrinellus and characterized overall population genetic structure using Bayesian clustering algorithms and F-statistics. We also used fine-scale landscape data within a least-cost distance framework to determine whether there is a relationship between landscape heterogeneity and population genetic structure and which, if any, matrix habitats might be related to genetic structure in S. o. citrinellus. Several aspects of S. oerstedii behavioral ecology suggest that some types of matrix habitat will affect patterns of gene flow more than others. For example, S. oerstedii are known to traverse small fruit plantations and live fences around residential areas, while they likely do not traverse large commercial oil palm plantations and rice plantations Despite this heterogeneity, little work has been done to determine the effects of such drastic natural and anthropogenic landscape change on the genetic structure of S. o. citrinellus fecal samples included Costa Rican Ministry of Energy and the Environment permit ACOPAC-INVN-14-08 and Centers for Disease Control and Prevention permit 2008-08-151. Also, an IACUC animal care protocol was approved by Columbia University for this research (AC-AAAA5583).Permits to collect and import S. o. citrinellus in the Central Pacific region of Costa Rica from September 2008 – April 2009 . Whenever possible (N = 13 groups), more than 10 individuals per group were sampled to increase the precision of genetic analyses in detecting dispersal and migration RNAlater buffer (Ambion) at −4°C in the field and −20°C in the laboratory. Eleven additional DNA samples from individuals of the southern subspecies S. o. oerstedii, used as an outgroup, were contributed by G. Gutierrez (University of Costa Rica) for a total of 244 samples.Fecal samples were collected from ril 2009 . S. o. cWe extracted DNA using QIAamp DNA Stool Minikits (Qiagen) with small modifications to the “Isolation of DNA from Stool for Human DNA Analysis” protocol see . We usedWe PCR amplified 17 autosomal microsatellite markers on multiplex panels of three or four markers CJ7  using MuP(X | K) and the ΔK method We ran our multilocus genotypes in STRUCTURE v 2.2 S. o. citrinellus microsatellite data with pairwise tests for differentiation among groups and populations using F-statistics We also examined genetic structure in et al.et al.S. o. citrinellus found using the software BAYESASS et al.h and Lh/Lmax, which represent, respectively, the most appropriate statistic when all potential source populations have not been sampled and when they have We detected first generation migrants and admixed individuals using STRUCTURE and GENECLASS v 2.0 We tested for genetic signatures of a recent population bottleneck using BOTTLENECK 2 study area. We defined residential areas as clusters of human residences less than 100 m apart and consisting of a total area greater than 3 hectares. Our classification also included an “other non-forest” category, which included shrimp farms, rice plantations, abandoned lots, and residences that were not concentrated enough to fit our definition of a residential area. Ninety-eight percent of 131 ground reference points were accurately reflected in the classification.Using 32 geo-referenced aerial photographs , a 90×7 km multispectral MASTER line image taken in the year 2005 obtained from the Centro Nacional de Alta Technología (CENAT) in Costa Rica, and a forest cover dataset generated by EOSL, CCT and FONAFIFO âIâ) and similarity (I) We estimated pairwise genetic relationships between individuals using Rousset’s We performed Mantel tests of matrix correspondence S. o. citrinellus as inferred by analyses of population genetic structure, excluding between population pairs.Recent studies have suggested that the spatial scale of landscape genetic analyses can have important effects on results, especially inferences about which landscape features affect gene flow S. o. citrinellus, implemented in the software CIRCUITSCAPE 3.5 We used the results of our least-cost distance analyses to generate a resistance surface characterizing the cumulative effects of landscape heterogeneity on gene flow in S. o. citrinellus in the Central Pacific of Costa Rica. The most likely number of clusters across the whole sample, which included outgroup S. o. oerstedii individuals, was four in both STRUCTURE and BAPS . Microsatellite allelic diversity in populations of S. o. citrinellus ranged from 3 to 15 alleles (mean = 8.1) in the western population and from 5 to 27 (mean = 10.6) in the eastern population. Pairwise FST values among groups of S. o. citrinellus ranged from 0.016–0.19, with a mean of 0.103  were less than pairwise FST values among groups from different populations , while GENECLASS estimated the same individual as a migrant in addition to 6 others (P<0.01) using both likelihood methods (Lh and Lh/Lmax). These 7 migrants were assigned to a cluster where they were not geographically sampled in GENECLASS and also had lower probabilities of belonging to their geographic origin cluster compared to other individuals in STRUCTURE . We interpreted this individual as potentially admixed, or a migrant from an unsampled ‘ghost’ population.In STRUCTURE, we found breaks in mean  and 0.8 , and 10  and 0.8 . In one We found no evidence of a bottleneck from the microsatellite data. None of the sign or Wilcoxon tests across any models suggested heterozygosity excess. Similarly, all mode-shift tests showed normal L-shaped distributions.â: r = 0.14, P = 0.0021; I: r = −0.10, P<0.0001; western population, â: r = 0.17, P<0.0001; I: r = −0.32, P<0.0001) and when the entire sample was considered . Across the entire sample, oil palm plantations at a cost of 10 were the only habitat class for which Mantel’s r-values between least-cost and genetic distance matrices were consistently larger than Mantel’s r-values between genetic and Euclidean distance matrices; these results held for both Moran’s I and Rousset’s â and in both simple and partial Mantel tests . Relationships were not as strong when only within population pairs were considered . When controlling for the effect of one another, resistance distances and least-cost distances for oil palm plantations at a cost of 10 both showed significant relationships with genetic distances  using pairwise FST comparisons. These groups are in fragments of secondary forest separated by varying types of unsuitable habitats such as cattle pasture, African oil palm plantations, and rice plantations. While F-statistics effectively measure spatial variance in gene frequencies, we used an individual-based landscape genetic approach to instead measure aspects of spatial patterns of gene frequencies while taking into account landscape heterogeneity to better understand the forces behind the patterns of population genetic structure shown here.In addition to significant differentiation among populations, we also found significant genetic differentiation among several groups of S. o. citrinellus and that different matrix habitat classes have different effects on dispersal in the studied landscape. Least-cost distances for oil palm plantations at a cost of 10 had stronger relationships with genetic distances than Euclidean distances in both simple and, more importantly, partial Mantel tests, suggesting that these least-cost distances contributed different information from Euclidean distances and that oil palm plantations represent moderate barriers to gene flow. When we restricted the analyses to within population pairs, none of the least-cost distances had stronger effects than Eucidean distances, suggesting that the effect of oil palm plantations on gene flow is largely scale-dependent and manifests during longer dispersal events between populations.Our results suggest that landscape heterogeneity affects genetic relationships in Similarly, the resistance surface showed how oil palm plantations, even when given only moderate resistance values, impede current flow because they dominate the landscape and the cost of crossing them accumulates over large distances. The strongest current flow in the resistance surface was in the eastern population near Manuel Antonio National Park (MANP), where there is not only the highest density of natural forest and monkey groups in the region, but also a break in the oil palm plantations due to complex topography. We found weaker isolation by distance in the eastern population as compared to the western population, also likely due to this break in the oil palm plantations.S. o. citrinellus gene flow in this landscape. Alternatively, the landscape composition and configuration of these habitat types is quite different from that of oil palm plantations, which occur as large, contiguous expanses. Cattle pastures, rivers, and residential areas, by contrast, are smaller and more isolated from one another. In this particular landscape, these features did not influence gene flow, but if we tested multiple landscapes with a range of variability in the composition and configuration of landscape features, we might find different results By contrast, we found that cattle pastures, rivers, and residential areas do not differ greatly from Euclidean distance in their effects on genetic distance. Cattle pastures, rivers, and residential areas in this region are often surrounded by live fences of fruiting trees, which might explain why they did not show strong negative effects on r-values between least-cost and genetic distances, but in the eastern population, residential areas showed a non-barrier pattern, with increasing costs resulting in decreasing Mantel’s r-values. It is likely that residential areas are non-barriers in both populations, but this analysis was not sensitive enough to pick up the signal in the western population, where residential areas are smaller in number and more spread out in comparison to the eastern population.We can lend further support to this idea by examining differences in landscape configuration between the eastern and western populations in our dataset. For example, in the western population, increasing cost for residential areas did not change Mantel’s r-values r-values to least-cost distances for oil palm plantations at a cost of 10. Ours is one of a small number of empirical studies that has used CIRCUITSCAPE to characterize landscape connectivity in human-dominated environments, and the other studies have found that resistance distances outperform least-cost distances in how well they characterize gene flow r-values were within a reasonable margin of error of one another. Both distances assigned moderate costs to oil palm plantations and had stronger relationships with genetic distances than Euclidean distance matrices when between population pairs were included in the analysis, showing a consistent trend.A disadvantage to the Mantel test framework is that it is difficult to choose among closely related models or models with only slightly different Mantel’s rSaimiri have an average generation time of 3–6 years â are based on FST and as such may reflect processes that are more likely to be apparent in historical landscape data It is possible that the temporal scale of our landscape data has influenced our results Although we tested for a population contraction and found no evidence for a bottleneck, sequencing additional loci, in particular nuclear introns and coding mtDNA loci, would allow for a more robust analysis of alternative hypotheses through Approximate Bayesian Computation (ABC) Another issue that is common in many landscape genetic studies is that the different focal matrix habitats used in our analysis are likely of different ages. Rivers are older than the other four habitat classes we considered, and some residential areas are likely younger than the cattle pastures and oil palm plantations, most of which were established in the early 1900s. Future analyses might address this issue by including different sets of models and molecular markers that pinpoint different temporal scales in order to distinguish between the effects of historical and recent landscape changes on population genetic structure S. o. citrinellus gene flow in the Central Pacific Costa Rican landscape. We are also able to make more detailed recommendations to conservation managers regarding the types of matrix habitat that S. o. citrinellus may or may not use to disperse among patches of forest in the Central Pacific. In a concurrent study, we used resistance surfaces to test different biological corridor configurations for their potential ability to augment gene flow through oil palm plantations This study exemplifies how important it is to conduct landscape genetic analyses in species that show evidence of population genetic structure in heterogeneous landscapes. Furthermore, we highlight the importance of quantifying the relative effects of different matrix habitat classes in landscape genetic analyses S. oerstedii. For example, in a landscape where cattle pastures dominate instead of oil palm plantations, a separate landscape genetic study would be necessary to measure the relative effects of each matrix habitat to determine whether it might be more important for conservation managers to augment gene flow through cattle pastures rather than oil palm plantations.However, we must recognize that our results are specific to the Central Pacific landscape, and conservation managers should be careful not to apply our results in other landscapes or for other populations of When attempting to translate the results of any landscape genetic analysis to patterns of functional connectivity, we must also acknowledge that measures of genetic distance do not equate to animal movement patterns. Simulations that model the sums of individual behavioral decisions are likely necessary to best inform conservation management of taxa in heterogeneous landscapes Figure S1Inference of the number of genetic clusters (K) estimated using STRUCTURE.(DOC)Click here for additional data file.Figure S2Ranked mean Q  for each individual.(DOC)Click here for additional data file.Table S1Microsatellite markers amplified in 244 Saimiri oerstedii samples.(DOC)Click here for additional data file.Table S2Results of simple and partial Mantel tests between genetic distances (Moran’s I and Rousset’s â) and cost distances among all individuals.(DOC)Click here for additional data file.Table S3Results of simple and partial Mantel tests between genetic distances (Moran’s I and Rousset’s â) and cost distances, including only sample pairs within the eastern population.(DOC)Click here for additional data file.Table S4Results of simple and partial Mantel tests between genetic distances (Moran’s I and Rousset’s â) and cost distances, including only sample pairs within the western population.(DOC)Click here for additional data file.


Ursus arctos) in Alberta, Canada. We detected distinct genetic and habitat use (ecotype) clusters, with individual cluster assignments, or genotype/ecotype, being correlated . Related individuals showed evidence of similar habitat use patterns, irrespective of geographic distance and sex. Fitness proxies were influenced by sex, age, and habitat use, and homozygosity had a positive effect on these proxies that could be indicative of outbreeding depression. We further documented over 300 translocations occurring in the province since the 1970s, often to areas with significantly different habitat. We argue this could be unintentionally causing the pattern of outbreeding, although the heterozygosity correlation may instead be explained by the energetic costs associated with larger body size. The observed patterns, together with the unprecedented human-mediated migrations, make understanding the link between genotype, ecotype, and phenotype and mechanisms behind the negative heterozygosity-fitness correlations critical for management and conservation of this species.Numerous factors influence fitness of free-ranging animals, yet often these are uncharacterized. We integrated GPS habitat use data and genetic profiling to determine their influence on fitness proxies  in a threatened population of grizzly bears ( Finding a correlation between genetics and fitness-related traits in free-ranging vertebrates is challenging at best. Heterozygosity-fitness correlations (HFCs) have proven ambiguous  is an interesting species for studying genetic and ecological relationships to phenotypes and testing for local adaptation. Genetic data suggest large subpopulations, but with fine-scale differentiation often corresponding to landscape features . Using genetic and GPS data from individual grizzly bears monitored since 1999, we tested for distinct genetic and habitat use clusters (that we refer to as ecotypes), and whether genetic relatedness was correlated with ecological distance after accounting for the confounding effects of geography: both of these can be viewed as signatures of local adaptation. In a mixed-model framework, we quantified the relationship among genetic diversity and differentiation, habitat use and the three fitness proxies. Finally, in an effort to shed some mechanistic insight into the purported patterns, we examined differences in habitat availability among management units and documented the translocation history of bears in the province using records dating back to the 1970s.Our study area consisted of the eastern slope of the Canadian Rocky Mountains in Alberta, Canada and contained six of the seven provincially recognized grizzly bear management units . Albertaarcgis , Redlands, CA, USA) in accordance with previous grizzly bears studies , referred to as habitat (see Table S1). The proportion of use for each habitat was calculated by comparing the number of bear observations (GPS radiotelemetry fixes) in each habitat attribute with the total number of observations across all habitat types  and pseudo-F were used to select the number of k (referred to as ecotype). Second, for the genotype data, we used structure 2.3.4  to account for overdispersion  were used: the Queller and Goodnight  between individual capture locations was included to account for the confounding effects of eco-spatial autocorrelation and isolation-by-distance (Shafer and Wolf E) was based on the absolute difference between individual PC scores on the first three axes of habitat use (denoted EPC1-3). The relationships among matrices and 95% confidence intervals were estimated from 10 000 permutations (‘ecodist’ R library).The relationship between gene flow and ecological distance, known as isolation-by-ecology , and individual PC1 and 2 axis scores. Mass and SLL were log-transformed to ensure proper support. We fit a set of four models to each dependent variable starting with a base model of the five covariates predicted to influence fitness measures, and adding in genetic or habitat use variables (see Table S2). Models were compared using the deviance information criteria , age , capture location (body size is expected to increase with latitude), and capture season . In addition, we included covariates relevant to the current analysis, including individual homozygosity, Finally, we obtained the translocation history for grizzly bears moved by management agencies in the province between 1974 and 2012 and determined the rate and direction of translocation among management units. We determined the amount of each habitat available within management unit by random sampling (by 30 × 30-meter raster cell) the multi-annual home ranges of each individual bear. A total of 150 000 random points were selected across our study system. Each random point was scored according to habitat class and averaged across the management unit. The absolute difference between each management unit was calculated, and a neighbor-joining tree was constructed using the population-habitat availability distance matrix (‘ape’ library in R).n = 64) individuals had complete morphometric data and were used in the mixed-model analysis. Correlations between the three phenotypic variables ranged from 0.40 to 0.87.A total of 88 grizzly bears had GPS radio collar and genetic data . Over thk of 2 for habitat use (k = 20). structure supported a k of 3 . The three main genetic axes of principal components (PCs) accounted for 7% 6%, and 5% of the variance, respectively . The main habitat use axes accounted for 49%, 22%, and 13% of the individual variance . The IBE correlations with interpretations are presented in EPC2) and genetic coancestry  with the exception of capture location. Capture latitude and longitude were highly correlated due to grizzly bear distribution .In the mixed-effects models, correlations among predictor variables were low  were translocations to different management units, of which 204 (~90%) were to a more northern management unit. The overall trends are visualized in As expected, factors such as sex and age influenced fitness-associated traits in Alberta's grizzly bears, as did habitat use and genetic diversity. This is the first attempt to explicitly link genotype, ecotype, and phenotype in a wild carnivore. We also observed correlated genetic and habitat (ecotypes) clusters, and individual-based evidence for isolation-by-ecology (IBE), where ecological divergence is correlated with reduced gene flow. These patterns are consistent with ecologically mediated divergence or local adaptation. Surprisingly, we documented a positive effect of homozygosity on fitness traits , most noThe link that exists between ecological and genetic divergence can have important implications for population demographics and adaptive divergence Nosil , 2012. IHabitat use also influenced grizzly bear phenotype, with both the ecotype and principal components influencing body mass and condition . While ir2 of 0.70: Väli et al. A major question remains: is there outbreeding depression in Alberta's grizzly bears, and if so, what is causing it? The impetus behind this study was to explore how differences in habitat use influenced fitness-associated traits. Given the literature on HFCs it was reasonable to include homozygosity as a covariate: however, we did not anticipate a negative HFC, let alone the magnitude of the effect, leading us to question the potential for outbreeding depression. Doubt may arise as to whether the findings we present are really evidence for such depression given sparse empirical evidence (Frankham et al. The second relevant question that must be answered is what processes could be causing this negative HFC? For this, we focused on the role of management-related translocation of bears, for which we were interested in quantifying the direction of translocations and difference in habitat availability among management units. The sheer number of translocations, particularly those being moved to areas with different habitat availability, is staggering, and a negative consequence on fitness would not be surprising (Robbins et al. An alternative explanation to the negative HFC involves trade-offs in life-history where a larger body size may not be optimal. For example, in lemon sharks, a negative relationship between heterozygosity and survival was observed (DiBattista et al. Given the threatened conservation status of Alberta's grizzly bears (Festa-Bianchet and Kansas meaningful environmental differences is vague at best (Frankham et al. Considering these factors together, we find it plausible that current management practices are having negative long-term fitness consequences on the population. While short-term effects of captures are known (Cattet et al.


The conservation of unique populations of animals is critical in order to preserve valuable genetic diversity and, where populations are free-living, maintain their irreplaceable influence upon habitat ecology. An accurate assessment of genetic diversity and structure within and between populations is crucial in order to design and implement conservation strategies in natural and domesticated species. Moreover, where it is possible to identify relic populations that are related to a structured breed an ideal opportunity presents itself to model processes that reveal historical factors that have shaped genetic diversity. The origins of native UK mountain and moorland ponies are uncertain, but they may have directly descended from prehistoric populations and potentially harbour specific adaptations to the uplands of Britain and Ireland. To date, there have been no studies of population structure and genetic diversity present within a free-living group of ponies in the Carneddau mountain range of North Wales. Herein, we describe the use of microsatellites and SNPs together with analysis of the mitochondrial control region to quantify the extent and magnitude of genetic diversity present in the feral Carneddau pony and relate this to several recognised British and Irish pony breeds. Our results establish that the feral Carneddau ponies represent a unique and distinctive population that merits recognition as a defined population and conservation priority. We discuss the implications for conservation of this population as a unique pool of genetic diversity adapted to the British uplands and potentially of particular value in maintaining the biodiversity of these habitats. An understanding of the genetic characteristics of a population is paramount in order to inform and implement conservation strategies that preserve and maintain genetic diversity  that have been suggested to be direct descendants of the ancestral Welsh Mountain Pony Fig. . The CarNo previous studies have examined Carneddau ponies in terms of effective population size, degree of inbreeding or their molecular relationship to Welsh and other UK and Ireland native pony breeds. The topography of Wales creates natural environmental barriers to gene flow and population migration in the form of mountain ranges and expanses of water. More specifically, the Carneddau ponies are limited to the Carneddau mountain range and are believed to have maintained a relatively stable population of around 300 breeding animals. Thus, the Carneddau may have evolved or maintained adaptations specific to their upland habitat that have been lost from the related ponies during breed formation. We present a comprehensive analysis of the genetic diversity within the Carneddau ponies relative to other UK native pony breeds from Wales, Ireland and Scotland. The aim is to establish to what extent they represent a genetically unique subpopulation formed as a result of long-term geographical isolation within Wales or whether this population is a more recent separation from the existing Section A pony breed.Studies were conducted using animals representative of different bloodlines for each of five British-Irish native pony populations. Samples were collected from Welsh Section A and Section D animals, Connemara ponies (native to Ireland) and Highland ponies (native to Scotland) from individuals registered with their respective breed society stud book  was calculated using STRUCTURE Harvester v0.6.7 (http://taylor0.biology.ucla.edu/struct_harvest/) as per Evanno et al.  amplification by PCR was performed using the StockMarks® equine genotyping kit  comprising 17 multiplexed fluorescent dye-labelled primers for the microsatellite markers, recommended by the International Society of Animal Genetics. PCR amplification used 10 ng of template genomic DNA with reagents at concentrations as provided; 2.5 μL Stockmarks PCR Buffer, 4 μL dNTP mix, 0.5 μL AmpliTaq Gold® DNA Polymerase, 4 μL multiplexed primer mix and 3 μL water. DNA was amplified in a G-Storm GS-1 thermal cycler  and reactions incubated at 95°C for 10 min followed by 40 cycles of 95°C for 30 sec, 60°C for 30 sec and 72°C for 60 sec, followed by final extension at 72°C for 60 min. PCR products were diluted 1:20 with molecular grade water and subjected to capillary electrophoresis using an ABI 3730 DNA Analyzer (Applied Biosystems). Alleles were sized using the internal size standard GeneScan™ -500LIZ (Applied Biosystems). Size analyses of separated DNA fragments were performed with the GeneMapper software version 3.7 (Applied Biosystems). Alleles were scored based on PCR product size and recorded manually. Allele frequencies were examined for significant deviation from Hardy-Weinberg expectations using the exact test in Arlequin 3.5  (determined using PLINK http://pngu.mgh.harvard.edu/purcell/plink/). After filtering observed heterozygosity (Ho), expected heterozygosity (He) and FIS values were calculated using PLINK, using between 48,703 and 48,710 SNPs per population (see Table K ranging from 2 to 10 (five iterations per K). As Alpha values stabilised rapidly, burnin and MCMC values were set at 25,000 each to improve computational speed. Data from each subsample analysis were combined and ΔK statistics calculated using STRUCTURE Harvester as for the SSR analysis. Results from replicate runs at the optimal K were combined in CLUMPP  for genotyping using the Illumina Equine SNP50 assay. Data were returned as binary allele calls and reformatted for STRUCTURE. Data were filtered for quality control purposes by removing any loci with >10% missing data and/or loci with extreme deviation from HW expectations (http://www.fluxus-engineering.com) was employed to produce a median-joining network based on maximum parsimony to reconstruct the phylogenetic relationships among haplotypes. Individuals showing unusually long branch lengths as part of the network were examined more closely for poor sequence quality. Sequence traces were checked for nucleotide call errors and either manually rectified or removed in the case of overall low-quality sequence. Population allelic and molecular diversities were compared using Arlequin 3.5 (Excoffier and Lischer Using a Welsh Cob sequence (GenBank AF072983), the primer pair (Forward - 5′-ATT TCT TCC CCT AAA CGA CAA C-3′) and (Reverse - 5′-CGT TCA ATT TAA GTC CAG CTT C-3′) was designed to a 606 bp amplicon of the mitochondrial control region (MTCR). PCR amplifications consisted of 10 ng template DNA, 7.5 μL of ImmoMix , 1 μmol/L forward and reverse primer and water to a final volume of 15 μL. Amplifications were performed in a G-Storm GS-1 thermal cycler at 95°C for 10 min, followed by 35 cycles of 95°C for 30 sec, 55°C for 30 sec and 72°C for 60 sec, with final extension at 72°C for 10 min. Reaction products were purified using a Multiscreen® 96-well filter plate  to remove primers and dNTPs then diluted 1:20 with molecular grade water in preparation for sequencing using an ABI 3730 DNA Analyzer (Applied Biosystems) and 1 μmol/L of the forward primer described above. To place the current data into a wider context against the major worldwide horse mtDNA haplogroups, 83 reference horse and pony samples were downloaded from GenBank based upon a recent whole mitochondrial genome study   (Table HE) and observed (HO) heterozygosity values of 0.778 and 0.659 respectively. In contrast to the MTCR results, the Carneddau showed higher expected and observed heterozygosity than both the Highland and the Section D. The Section D samples possessed the lowest number of alleles, the lowest HE and HO and also the highest inbreeding coefficient (FIS), demonstrating a clear reduction in heterozygosity probably due to non-random mating. Section A showed the highest MNA although Connemara had equal HO and displayed higher HE. SNP results, based on 12 individuals per population, show roughly similar patterns of diversity with no significant differences in heterozygosity between the 5 groups, but with the Connemara ponies again showing the highest expected heterozygosity (He = 0.342) and the Carneddau the highest observed (Ho = 0.338). Global exact tests of HWE performed across all loci revealed all five groups deviated significantly (P < 0.01) from HWE indicating a significant level of structure between the groups. Molecular analysis of variation demonstrated highly significant levels of between group variation relative to within group for both SSR and MTCR results, with 9% and 85% of total variation between groups respectively. Individual HW tests indicate that consistently significant deviations were found across several of the populations for a few of the SSR markers. These loci were removed from the data set for later STRUCTURE analysis below, to exclude the possibility of allele drop out. Similar results were obtained with SNP data again indicating that the Carneddau had relatively high heterozygosity levels and no evidence of significant inbreeding.Summary statistics for MTCR, SSR and SNP data are presented in Table u) Table  identifiu) Table . The meaX/K)] of the posterior probability of the SSR data for a given K suggested the optimal value of K was 5. The five clusters that were assigned match closely to the populations from which individuals had been sampled, though all populations contained individual animals that could not be unambiguously assigned to a single cluster . The partial assignment of most Section B/C animals to both Section A and Section D clusters confirmed the package was effectively detecting cases of known admixture. The Section B and Section C animals were excluded from the main analysis as the sample size available were much fewer than in the samples from the 5 populations whose results are presented here. STRUCTURE analysis of the log likelihood [Ln Pr, although the Section Ds also had a moderate disturbance .Analyses of potential population expansion based upon Harpending's raggedness score . SSR/SNP analysis suggested that heterozygosity levels, although lower than some existing reports  stallions were imported and used to breed with small mountain mares Davies , 1999. TAlthough clearly related to the Section A pony, the Carneddau represent a genetically distinct population. For many populations of domestic livestock the definition of uniqueness is relatively subjective and differences between two populations may only be a function of the relative frequencies of a few genes, often associated with a single physical character or small group of specific characters Henson . Any popThe Carneddau population is believed to be ≤300 individuals and at risk due to economic pressures on the land they graze. A derogation has been granted so that only ponies sold at auction have to be passported and microchipped, rather than requiring that all new-born Carneddaus are issued with a passport (in accordance with UK law for other equids). However, because the ponies are unregistered their sales value is very low , and so the expense of microchipping and passporting is unviable for their owners. Animals which are sold have limited use; they have no pedigree and are not registered as a recognised breed, preventing them from competing in many show classes. From the perspective of population conservation, the removal of young fillies for auction may compound the issue of reduced mitochondrial haplotype numbers. Other human selection practices are minimal in these animals, making them a particularly important group. In the New Forest, Dartmoor and the Brecon Beacons, specially selected individual stallions are released every year into known, limited geographical areas frequented by free-living mares and re-captured at the end of the breeding season. With the exception of three stallions released (but only one since removed) in 2007–2008, this does not occur with the Carneddau population.relative levels of admixture, the Carneddaus appear to show similar degrees to that found between the recognised breeds included in this analysis. In addition to this, the distinct genetic signatures  suggest that the Carneddau ponies are a defined, long-established population worthy of conservation. However, whilst the WPCS register Section A ponies that are free-living on the mountains of south Wales, this semi-feral grouping does not include the Carneddau. Consequently, the Carneddau are excluded from current Rare Breeds Survival Trust conservation management schemes and are not considered for use within nationwide conservation grazing projects utilising ponies such as Dartmoor, Exmoor and imported Polish Koniks. The current lack of recognition surrounding the Carneddau ponies contributes to the risk of losing a potentially useful, and distinct, native British breed.It is widely acknowledged that domestic pony and horse breeds have undergone varying degrees of admixture and gene flow prior to establishment of defined, “closed” pedigrees (Thirstrup et al. The genetic relationship of Carneddau ponies in relation to other native breeds of the UK and Ireland was revealed by the integrated results of each genetic analysis. The Carneddau ponies have a shared ancestry with the recognized Section A pony yet have remained in relative isolation, subjected to natural selective pressures for a significant period of time. This has been followed by more recent genetic introgression by Section A stallions and round up practices. The genetic impact of human activities, particularly the selective use of stallion crossing upon the pony breeds, are evident in the comparison of the different genetic systems; both more recently in the effect upon SSR frequencies within the Carneddaus and historically in the SNP profile of the Section D cobs. Despite the recent management practices, the Carneddau ponies maintain a distinct genetic signature that should be conserved. Given the restricted geographic distribution and the low numbers of the Carneddau, in order to provide some conservation protection measures, these animals should be classified as a rare pony population at critical extinction status. This study illustrates the relevance and potential power of combining autosomal and maternally transmitted polymorphic assays to unravel genetic structure and relatedness in feral and intensively managed populations. Herein we provide new information and knowledge to support conservation strategies of endangered breeds.