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2,445 results for “Genetics: population”
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. 3 in Weak Genetic Differentiation among Populations of the Andean Ground Beetle Pelmatellus columbianus (Reiche, 1843) (Coleoptera: Carabidae)
Fig. 3. TCS haplotype network for the COI and CAD gene fragments in Pelmatellus columbianus.
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).
Pathotype complexity and genetic characterization of Phytophthora sojae populations in Illinois, Indiana, Kentucky, and Ohio
<p></p><p> Phytophthora sojae, the causal agent of Phytophthora root and stem rot of soybean, has been managed with single Rps genes since the 1960's, but has subsequently adapted to many of these resistance genes, rendering them ineffective. The objective of this study was to examine the pathotype and genetic diversity of P. sojae from soil samples across Illinois, Indiana, Kentucky, and Ohio by assessing which Rps gene(s) were still effective and identifying possible population clusters. There were 218 pathotypes identified from 473 P. sojae isolates with an average of 6.7 out of 15 differential soybean lines exhibiting a susceptible response for each isolate. Genetic characterization of 103 P. sojae isolates from across Illinois, Indiana, Kentucky, and Ohio with 19 simple sequence repeat markers identified 92 multilocus genotypes. There was a moderate level of population differentiation among these four states, with pairwise F<sub>ST</sub> values ranging from 0.026 to 0.246. There was also moderate to high levels of differentiation between fields, with pairwise F<sub>ST</sub> values ranging from 0.071 to 0.537. Additionally, cluster analysis detected the presence of P. sojae population structure across neighboring states. The level of pathotype and genetic diversity, in addition to the identification of population clusters, supports the hypothesis of occasional outcrossing events that allow for an increase in diversity and the potential to select for a loss in avirulence to specific resistance genes within regions. The trend of suspected gene flow among neighboring fields is expected to be an ongoing issue with current agricultural practices. </p><p></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>
Genetic variation in sea otters (Enhydra lutris) from the North Pacific with relevance to the threatened Southwest Alaska distinct population segment
<p>For the sea otter (<i>Enhydra lutris</i>), genetic population structure is an area of research that has not received significant attention, especially in Southwest Alaska where that distinct population segment has been listed as threatened since 2005 pursuant to the U.S. Endangered Species Act. In this study, 501 samples from 14 locations from Prince William Sound, Alaska to the Commander Islands in Russia were analyzed for variation at 13 microsatellite loci. Our results indicate a high degree of genetic divergence among the 14 locations (<i>F</i><sub>ST</sub> = 0.12) with gene flow conforming to the isolation by distance (IBD) model (<i>r<sup>2</sup></i> = 0.491, <i>p</i> < 0.05). The 14 sampling locations formed six geographic associations in clustering and ordination analyses that likely correspond to remnant population lineages: 1) Southcentral Alaska, 2) Kodiak and North Alaska Peninsula, 3) South Alaska Peninsula and Bristol Bay, 4) Eastern Aleutian, 5) Western Aleutian, and 6) the Commander Islands. Except for South Alaska Peninsula and Bristol Bay, these clusters closely agree with previously defined stock and management unit boundaries. Our results reveal significant genetic population structure and are generally congruent with current management strategies for the threatened Southwest Alaska distinct population segment.</p>
Figure 3 from: Matern A, Drees C, Hardtle W, von Oheimb G, Assmann T (2011) Historical ecology meets conservation and evolutionary genetics: a secondary contact zone between Carabus violaceus (Coleoptera, Carabidae) populations inhabiting ancient and recent woodlands in north-western Germany. ZooKeys 100: 545-563. https://doi.org/10.3897/zookeys.100.1546
Figure 3 - Correlogram showing the result of spatial autocorrelation analysis at three allozyme loci. Genetic distances D (Nei 1972) are indicated for the population pairs of the respective distance classes (squares). Dashed lines show the 95% confidence interval (1000 permutations) under the null hypothesis of spatially random differentiation. Significant deviations from the mean are indicated by filled squares (p < 0.05).
Figure 4 from: Matern A, Drees C, Hardtle W, von Oheimb G, Assmann T (2011) Historical ecology meets conservation and evolutionary genetics: a secondary contact zone between Carabus violaceus (Coleoptera, Carabidae) populations inhabiting ancient and recent woodlands in north-western Germany. ZooKeys 100: 545-563. https://doi.org/10.3897/zookeys.100.1546
Figure 4 - Maximum width of the aedeagus tip A and the quotient of maximum and minimum width of the aedeagus tip B are plotted for each population. Boxes display 25–75%- quartiles and bars indicate medians. Whiskers show the total range of values without outliers. Outliers are indicated as circles and extreme outliers as diamonds. Numbers of measured individuals per population are shown in brackets. Pie charts show frequencies of elytral sculpture classes "0" (white), "1" (grey), and "2" (black) in each population. Significant differences between populations are indicated by the lines marked with asterisks.
Figure 1 from: Matern A, Drees C, Hardtle W, von Oheimb G, Assmann T (2011) Historical ecology meets conservation and evolutionary genetics: a secondary contact zone between Carabus violaceus (Coleoptera, Carabidae) populations inhabiting ancient and recent woodlands in north-western Germany. ZooKeys 100: 545-563. https://doi.org/10.3897/zookeys.100.1546
Figure 1 - Carabus violaceus populations studied and proportion of specimens with different elytron sculptures (pie charts). White sections indicate the frequencies of smooth elytra, black sections indicate the frequencies of more than three striae per elytron, and grey sections indicate the frequencies of intermediate phenotypes, i.e. class "1". Numbers next to the pie charts indicate population number followed by sample size in brackets. The location of the study area is indicated as a white square on the map of Germany. Woodlands in the study region northwest of the town of Bramsche according to TK 50 3512 Bramsche (Landesvermessungsamt Niedersachsen 1998) are presented as striped patches. Size and position of ancient woodlands (black patches) are taken from the map by LeCoq (1805). In this study, these are called "Börsteler Wald" (in the north) and "Gehn" (in the south). White patches within woodlands indicate openings. Hedges are not shown.
Figure 2 from: Matern A, Drees C, Hardtle W, von Oheimb G, Assmann T (2011) Historical ecology meets conservation and evolutionary genetics: a secondary contact zone between Carabus violaceus (Coleoptera, Carabidae) populations inhabiting ancient and recent woodlands in north-western Germany. ZooKeys 100: 545-563. https://doi.org/10.3897/zookeys.100.1546
Figure 2 - Aedeagus tip of Carabus violaceus. 1 Maximum aedeagus width (AedMax), 2 minimum aedeagus width (AedMin), and 3 preputial field.
Figure 3 from: Manni M, Lima KM, Guglielmino CR, Lanzavecchia SB, Juri M, Vera T, Cladera J, Scolari F, Gomulski L, Bonizzoni M, Gasperi G, Silva JG, Malacrida AR (2015) Relevant genetic differentiation among Brazilian populations of Anastrepha fraterculus (Diptera, Tephritidae). 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: 157-173. https://doi.org/10.3897/zookeys.540.6713
Figure 3 - Correlation of FST values with the geographic distances (upper plot) and altitude differences (bottom plot) among the 6 Brazilian samples of Anastrepha fraterculus.
Figure 2 from: Manni M, Lima KM, Guglielmino CR, Lanzavecchia SB, Juri M, Vera T, Cladera J, Scolari F, Gomulski L, Bonizzoni M, Gasperi G, Silva JG, Malacrida AR (2015) Relevant genetic differentiation among Brazilian populations of Anastrepha fraterculus (Diptera, Tephritidae). 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: 157-173. https://doi.org/10.3897/zookeys.540.6713
Figure 2 - Two-dimensional plot of Principal Coordinate Analysis (PCoA) based on similarity matrix derived from Anastrepha fraterculus microsatellites data. The Morphotype classification (Hernández-Ortiz et al. 2012), relative to each sample is also reported.
Figure 2 from: Virgilio M, Delatte H, Nzogela YB, Simiand C, Quilici S, De Meyer M, Mwatawala M (2015) Population structure and cryptic genetic variation in the mango fruit fly, Ceratitis cosyra (Diptera, Tephritidae). 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: 525-538. https://doi.org/10.3897/zookeys.540.9618
Figure 2 - Individual Bayesian assignments. STRUCTURE sequential individual assignments of 348 specimens of Ceratitis cosyra from 13 African countries.
Figure 1 from: Virgilio M, Delatte H, Nzogela YB, Simiand C, Quilici S, De Meyer M, Mwatawala M (2015) Population structure and cryptic genetic variation in the mango fruit fly, Ceratitis cosyra (Diptera, Tephritidae). 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: 525-538. https://doi.org/10.3897/zookeys.540.9618
Figure 1 - Unconstrained and constrained ordination. Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC) of 348 Ceratitis cosyra microsatellite genotypes. Specimen groups are labelled inside their 95% inertia ellipses and genotypes are connected to the corresponding group centroids.
Figure 3 from: Bu Y, Ma Y, Luan Y-X (2016) Paracerella Imadaté in China: the description of a new species and the analysis of genetic differences between populations (Protura, Acerentomata, Nipponentomidae). ZooKeys 604: 1-11. https://doi.org/10.3897/zookeys.604.8737
Figure 3 - Paracerella sinensis sp. n. holotype. A Tergite VII (psm= posterosubmedial) B tergite VIII C sternite I D sternite II E sternite IV F sternite VI (spm= sternal posteromedial) G sternite VII H striate band of abdominal VIII I Tergite VIII–XII J sternites VIII–XII. Arrows indicate pores. Scale bars: 20 μm.
Figure 2 from: Bu Y, Ma Y, Luan Y-X (2016) Paracerella Imadaté in China: the description of a new species and the analysis of genetic differences between populations (Protura, Acerentomata, Nipponentomidae). ZooKeys 604: 1-11. https://doi.org/10.3897/zookeys.604.8737
Figure 2 - Paracerella sinensis sp. n. holotype. A Habitus B ventral side of head (s=sensillum) C pronotum D mesonotum E metanotum F prosternum G mesosternum H metasternum I tergite I, right side J tergite III, right side. Arrows indicate pores. Scale bars: (A)100 μm, others, 20 μm.
Figure 1 from: Bu Y, Ma Y, Luan Y-X (2016) Paracerella Imadaté in China: the description of a new species and the analysis of genetic differences between populations (Protura, Acerentomata, Nipponentomidae). ZooKeys 604: 1-11. https://doi.org/10.3897/zookeys.604.8737
Figure 1 - Paracerella sinensis sp. n. holotype. A Head, dorsal view (cp = clypeal pore, fp = frontal pore) B pseudoculus C canal of maxillary gland D labial palpus E maxillary palpus (d = dorsal sensillum, v = ventral sensillum) F foretarsus, exterior view G foretarsus, interior view H foretarsus, interolateral view (paratype No. LM6-14D) I comb J female quama genitalis. Scale bars: (A, F–H) 50 μm; others, 20 μm.
Supplementary material 15 from: Gloria-Soria A, Shragai T, Ciota AT, Duval TB, Alto BW, Martins AJ, Westby KM, Medley KA, Unlu I, Campbell SR, Kawalkowski M, Tsuda Y, Higa Y, Indelicato N, Leisnham PT, Caccone A, Armstrong PM (2022) Population genetics of an invasive mosquito vector, Aedes albopictus in the Northeastern USA. NeoBiota 78: 99-127. https://doi.org/10.3897/neobiota.78.84986
Inferred genetic clusters from Aedes albopictus of the Connecticut temporal series
Supplementary material 13 from: Gloria-Soria A, Shragai T, Ciota AT, Duval TB, Alto BW, Martins AJ, Westby KM, Medley KA, Unlu I, Campbell SR, Kawalkowski M, Tsuda Y, Higa Y, Indelicato N, Leisnham PT, Caccone A, Armstrong PM (2022) Population genetics of an invasive mosquito vector, Aedes albopictus in the Northeastern USA. NeoBiota 78: 99-127. https://doi.org/10.3897/neobiota.78.84986
Geographic genetic differentiation (IBD: isolation by distance) across the Northeast USA
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Annotated Behaviour and Observability Dataset (ABODe)
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