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1,467 results for “Genetic structure”
Genotype and individual data for genetic structure in Louisiana Iris species reveals patterns of recent and historical admixture
<p><b><span>Premise: </span></b><span>When divergent lineages come into secondary contact reproductive isolation may be incomplete, thus providing an opportunity to investigate how speciation is manifested in the genome. The Louisiana Irises (<i>Iris</i>, series <i>Hexagonae</i>) comprise a group of three or more ecologically and reproductively divergent lineages that can produce hybrids where they come into contact. In this study we sought to estimate standing genetic variation to understand the current distribution of population structure in the Louisiana Irises.</span></p> <p><b><span>Methods:</span></b><span> We used genotyping-by-sequencing techniques to sample the genomes of Louisiana Iris species across their ranges. Twenty populations were sampled (total n=632) across 11,249 loci. Population genetic data were assessed using ENTROPY and PCA models. </span></p> <p><b><span>Results: </span></b><span>We discovered evidence for interspecific gene flow in parts of the range and revealed patterns of population structure at odds with widely accepted nominal taxonomy. Undescribed hybrid populations were discovered that were designated as belonging to the <i>I. brevicaulis</i> lineage. <i>Iris nelsonii </i>shared significant ancestry with only one of the purported parent species, <i>I. fulva, </i>evidence inconsistent with a hybrid origin.</span></p> <p><b><span>Conclusions: </span></b><span>This study provides several key findings important to the investigation of standing genetic variation in the Louisiana Iris species complex. <i>Iris brevicaulis</i> has a large amount of genetic diversity within it relative to the other nominal species. In addition, this study has discovered a previously unknown hybrid zone between <i>I. brevicaulis </i>and <i>I. hexagona</i> along the Texas coast. Finally, <i>I. nelsonii</i> does not appear to have mixed ancestry from three parental taxa as has been the longstanding hypothesis. </span></p>
Figure 2 from: Liu D, Lan F, Xie S, Diao Y, Zheng Y, Gong J (2021) Dynamic genetic diversity and population structure of Coreius guichenoti. ZooKeys 1055: 135-148. https://doi.org/10.3897/zookeys.1055.70117
Figure 2 Phylogenetic trees of the mtDNA control region haplotypes in C. guichenoti reconstructed with Bayesian inference. Numbers at nodes represent Bayesian posterior probabilities and neighbor-joining tree. At the right side of the figure, the numbers represent the total of individuals from different sampling locations in each haplotype.
Figure 3 from: Liu D, Lan F, Xie S, Diao Y, Zheng Y, Gong J (2021) Dynamic genetic diversity and population structure of Coreius guichenoti. ZooKeys 1055: 135-148. https://doi.org/10.3897/zookeys.1055.70117
Figure 3 Median-joining network of the mtDNA control region haplotypes of C. guichenoti. The size of each circle indicates the relative frequency of the corresponding haplotype in the whole data set.
Supplementary material 1 from: Liu D, Lan F, Xie S, Diao Y, Zheng Y, Gong J (2021) Dynamic genetic diversity and population structure of Coreius guichenoti. ZooKeys 1055: 135-148. https://doi.org/10.3897/zookeys.1055.70117
Tables S1, S2, Figures S1, S2
Figure 4 from: Liu D, Lan F, Xie S, Diao Y, Zheng Y, Gong J (2021) Dynamic genetic diversity and population structure of Coreius guichenoti. ZooKeys 1055: 135-148. https://doi.org/10.3897/zookeys.1055.70117
Figure 4 Isolation by distance (IBD) relationship among C. guichenoti wild populations in five populations collected in 2009 a and seven populations collected in 2019 b.
Figure 1 from: Liu D, Lan F, Xie S, Diao Y, Zheng Y, Gong J (2021) Dynamic genetic diversity and population structure of Coreius guichenoti. ZooKeys 1055: 135-148. https://doi.org/10.3897/zookeys.1055.70117
Figure 1 Sampling localities of China (solid triangles indicate sites in 2009; solid circle indicate sites in 2019) of C. guichenoti. For full names of populations, see Table 1.
Figure 2 in Genetic diversity, population structure and demographic history of Dugesia japonica in Taihang Mountains
Figure 2. Mismatch distribution of Dugesia japonica from Taihang Mountains based on mitochondrial COI.
Fig. 1 in Microsatellite variation and population genetic structure of a neotropical endangered Bryconinae species Brycon insignis Steindachner, 1877: implications for its conservation and sustainable management
Fig. 1. Location of sampling sites of Brycon insignis in drainages in Southeastern Brazil. Power Company Hatchery (PCH), São João River (SJR), Paraíba do Sul River (PSR), Imbé River (IMR), Muriaé River (MUR) and Itabapoana River (ITR).
Hierarchical genetic structure and implications for conservation of the world's largest salmonid, Hucho taimen
<p>Population genetic analyses can evaluate how evolutionary processes shape diversity and inform conservation and management of imperiled species. Taimen (<i>Hucho taimen</i>), the world's largest freshwater salmonid, is threatened, endangered, or extirpated across much of its range due to anthropogenic activity including overfishing and habitat degradation. We generated genetic data using high throughput sequencing of reduced representation libraries for taimen from multiple drainages in Mongolia and Russia. Nucleotide diversity estimates were within the range documented in other salmonids, suggesting moderate diversity despite widespread population declines. Similar to other recent studies, our analyses revealed pronounced differentiation among the Arctic (Selenge) and Pacific (Amur and Tugur) drainages, suggesting historical isolation among these systems. However, we found evidence for finer-scale structure within the Pacific drainages, including unexpected differentiation between tributaries and the mainstem of the Tugur River. Differentiation across the Amur and Tugur basins together with coalescent-based demographic modeling suggests the ancestors of Tugur tributary taimen likely diverged in the eastern Amur basin, prior to eventual colonization of the Tugur basin. Our results suggest the potential for differentiation of<i> </i>taimen<i> </i>at different geographic scales, and suggest more thorough geographic and genomic sampling may be needed to inform conservation and management of this iconic salmonid.</p>
Population genetic structure Arctosa sanctaerosae
<p>The continued increase in the number of tourists visiting the Northern Gulf Coast (NGC), USA, in the last century, and the resulting sprawl of large cities along the coast, has degraded and fragmented the available habitat of Arctosa sanctaerosae, a wolf spider endemic to the secondary dunes of the white sandy beaches of the NGC. In addition to anthropogenic disturbance to this coastal region, hurricanes are an additional and natural perturbation to the ecosystem. The data presented here explore the status of populations of this species spanning the entire known range and the factors influencing population demography including anthropogenic disturbance and severe tropical storms. Using microsatellite markers, we were able to document the genetic structure of Arctosa sanctaerosae, including current and historical patterns of migration. These results combined with ecological and census data reveal the characteristics that have influenced population persistence: ecological variables affecting the recovery of the population clusters after severe tropical storms, genetic fragmentation due to anthropogenic disturbance, and their interaction. These findings demonstrate the significance that the high traffic beach communities of the NGC and their impact on the once intact contiguous dune ecosystem have on recovery after severe tropical storms. Contemporary modeling methods that compare current and historical levels of gene flow suggest Arctosa sanctaerosae has experienced a single, contiguous population subdivision, and the isolates reduced in size since the onset of commercial development of the NGC. These results point to the need for monitoring of the species and increased protection for this endangered habitat. </p>
The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical datasets
<p class="CxSpFirst">The software program STRUCTURE is one of the most cited tools for determining population structure. To infer the optimal number of clusters from STRUCTURE output, the Δ<i>K</i> method is often applied. However, a recent study relying on simulated microsatellite data suggested that this method has a downward bias in its estimation of <i>K</i> and is sensitive to uneven sampling. If this finding holds for empirical datasets, conclusions about the scale of gene flow may have to be revised for a large number of studies. To determine the impact of method choice, we applied recently described estimators of <i>K</i> to re-estimate genetic structure in 41 empirical microsatellite datasets; 15 from a broad range of taxa and 26 focused on a diverse phylogenetic group, coral. We compared alternative estimates of <i>K</i> (Puechmaille statistics) with traditional (Δ<i>K</i> and posterior probability) estimates and found widespread disagreement of estimators across datasets. Thus, one estimator alone is insufficient for determining the optimal number of clusters regardless of study organism or evenness of sampling scheme. Subsequent analysis of molecular variance (AMOVA) between clustering solutions did not necessarily clarify which solution was best. To better infer population structure, we suggest a combination of visual inspection of STRUCTURE plots and calculation of the alternative estimators at various thresholds in addition to Δ<i>K</i>. Differences between estimators could reveal patterns with important biological implications, such as the potential for more population structure than previously estimated, as was the case for many studies reanalyzed here.</p>
Figure 5 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 5 - Simplified network of Bactrocera carambolae and Bactrocera dorsalis groups, and the sequential disconnection of the network. The network was constructed using eight SSRs. Scanning was done for decreasing thresholds A is the fully connected network B is the percolation threshold (Dp = 0.20, with all links corresponding to distances superior to Dp excluded). DP, JK, and NT are connecting between Bactrocera carambolae and Bactrocera dorsalis groups. Red dashed lines with number are corresponded to the threshold values, revealing serial disconnection of the network C is the lowest threshold (thr = 0.15).
Figure 4 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 4 - Simplified network of seven Bactrocera carambolae populations, and the sequential forms of cluster. The network was constructed using eight SSRs. Scanning was done for decreasing thresholds A is the fully connected network B is the percolation threshold (Dp = 0.52, with all links corresponding to distances superior to Dp excluded). JK plays an important role connecting between native and introduced populations C–D are the lower thresholds chosen (thr = 0.40 and 0.15, respectively) to reveal sub-structured network.
Figure 3 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 3 - The individual admixture plot for K = 3. Each bar reveals a single individual. Each color of bars represents each genetic cluster. Samples of Bactrocera carambolae belong to clusters 2 and 3 (green and blue, respectively) while samples of Bactrocera dorsalis belong to cluster 1 (red). Potential hybrids have a proportion of genetic cluster (Q) between 0.100 to 0.900 (0.100 ≤ Q ≤ 0.900) as identified with asterisk (*).
Figure 1 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 1 - Sampling collections of Bactrocera carambolae and Bactrocera dorsalis in this study. Seven populations of Bactrocera carambolae (blue dots) were collected from Southeast Asia and Suriname. Three populations of Bactrocera dorsalis (red dots) were sampled from East and Southeast Asia. Two other unidentified populations (purple dots) were included. Information for each population is described in Table 1.
Figure 6 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 6 - Simplified network of the SY5 strain and wild populations, and the sequential disconnection of the network. The network was constructed using seven SSRs. Scanning was done for decreasing thresholds A is the fully connected network B is the percolation threshold (Dp = 0.23, with all links corresponding to distances superior to Dp excluded). DP, JK, and NT are connecting between Bactrocera carambolae and Bactrocera dorsalis groups C is the lowest threshold (thr = 0.15). Red dashed lines with number are corresponded to the threshold values, revealing serial disconnection of the network.
Figure 2 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 2 - Three-dimensional plot of Principal Coordinate Analysis (PCoA) and STRUCTURE analysis. A the planes of the first three principal coordinates explain 43.65%, 20.13%, and 16.91% of total genetic variation, respectively, for seven Bactrocera carambolae populations using eight SSRs B the planes of the first three principal coordinates explain 33.05%, 23.17%, and 15.87%, respectively, for Bactrocera carambolae and Bactrocera dorsalis groups using eight SSRs C the planes of the first three principal coordinates explain 30.50%, 22.14%, and 18.53%, respectively, for the SY5 strain and wild populations using seven SSRs. Pie graphs, consisting of different colored sections, represent co-ancestor distribution of 185, 289, and 321 individuals in A two, B three, and C two hypothetical clusters, respectively.
Figure 6 from: Osikowski A, Georgiev D, Hofman S, Falniowski A (2015) Does the genetic structure of spring snail Bythinella (Caenogastropoda, Truncatelloidea) in Bulgaria reflect geological history? ZooKeys 518: 67-86. https://doi.org/10.3897/zookeys.518.10035
Figure 6 - The maximum-likelihood phylogram for the ITS-1 gene. Haplotypes obtained in present work are shown in bold. The COI clades are also shown.
Figure 5 from: Osikowski A, Georgiev D, Hofman S, Falniowski A (2015) Does the genetic structure of spring snail Bythinella (Caenogastropoda, Truncatelloidea) in Bulgaria reflect geological history? ZooKeys 518: 67-86. https://doi.org/10.3897/zookeys.518.10035
Figure 5 - The median-joining haplotype network of COI haplotypes for clades I, II and III. Sequences from Falniowski et al. 2009a and Falniowski et al. 2012 are also included. Arrows and the letter C indicate cave haplotypes.
Figure 2 from: Osikowski A, Georgiev D, Hofman S, Falniowski A (2015) Does the genetic structure of spring snail Bythinella (Caenogastropoda, Truncatelloidea) in Bulgaria reflect geological history? ZooKeys 518: 67-86. https://doi.org/10.3897/zookeys.518.10035
Figure 2 - Shells of Bythinella. A locality B1 B–C locality B7 D–F locality B5 G–Q locality B10; bar equals 1 mm.
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