Dendrobium speciosum (Dendrocoryne: Orchidaceae) complex in north Queensland

2006 ◽  
Vol 19 (3) ◽  
pp. 259 ◽  
Author(s):  
Peter B. Adams ◽  
Jacinta M. Burke ◽  
Sheryl D. Lawson

Dendrobium speciosum Sm. has received insufficient taxonomic study north of St Lawrence, Queensland, where plants display much morphological variation in diverse habitats. Two varieties have been described previously, variety pedunculatum, occurring north of Townsville, and variety curvicaule for plants between the Connors Range south of Mackay and Annan River, south of Cooktown. In this multivariate analysis of 107 representative plants sampled from areas between St Lawrence and Cooktown, cluster analysis and principal coordinates analysis, were used to categorise the variation. Three overlapping varieties are revealed. North of Townsville variety pedunculatum intergrades with a medium-to-tall rainforest form, which separates with a small overlap in analyses from variety curvicaule plants south of Townsville. We formally describe these rainforest forms, previously referred to as variety curvicaule, as a new variety, Dendrobium speciosum variety boreale, which occurs between Cooktown and Mt Elliot, south of Townsville. Variety boreale is characterised by the presence of a collum in most individuals, medium to long pseudobulbs, large, wide leaves, long pedicels, and fairly uniform off-white to cream flowers. Dendrobium speciosum variety curvicaule Bailey is shown to be a name of uncertain application, and is neotypified to apply to the southern group of north Queensland plants, which occur between St Lawrence and Mt Dryander and on the Whitsunday Islands. Variety curvicaule is characterised by pseudobulbs that are of medium length, wide base and have an inconspicuous collum. The flowers have relatively wide segments in relation to all other varieties, long wide petals and incurving lateral sepals.

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 97 ◽  
Author(s):  
Xiaofeng Chi ◽  
Faqi Zhang ◽  
Qingbo Gao ◽  
Rui Xing ◽  
Shilong Chen

The uplift of the Qinghai-Tibetan Plateau (QTP) had a profound impact on the plant speciation rate and genetic diversity. High genetic diversity ensures that species can survive and adapt in the face of geographical and environmental changes. The Tanggula Mountains, located in the central of the QTP, have unique geographical significance. The aim of this study was to investigate the effect of the Tanggula Mountains as a geographical barrier on plant genetic diversity and structure by using Lancea tibetica. A total of 456 individuals from 31 populations were analyzed using eight pairs of microsatellite makers. The total number of alleles was 55 and the number per locus ranged from 3 to 11 with an average of 6.875. The polymorphism information content (PIC) values ranged from 0.2693 to 0.7761 with an average of 0.4378 indicating that the eight microsatellite makers were efficient for distinguishing genotypes. Furthermore, the observed heterozygosity (Ho), the expected heterozygosity (He), and the Shannon information index (I) were 0.5277, 0.4949, and 0.9394, respectively, which indicated a high level of genetic diversity. We detected high genetic differentiation among all sampling sites and restricted gene flow among populations. Bayesian-based cluster analysis (STRUCTURE), principal coordinates analysis (PCoA), and Neighbor-Joining (NJ) cluster analysis based on microsatellite markers grouped the populations into two clusters: the southern branch and the northern branch. The analysis also detected genetic barriers and restricted gene flow between the two groups separated by the Tanggula Mountains. This study indicates that the geographical isolation of the Tanggula Mountains restricted the genetic connection and the distinct niches on the two sides of the mountains increased the intraspecific divergence of the plants.


2010 ◽  
Vol 90 (4) ◽  
pp. 443-452 ◽  
Author(s):  
T. Karuppanapandian ◽  
H W Wang ◽  
T. Karuppudurai ◽  
J. Rajendhran ◽  
M. Kwon ◽  
...  

The DNA fingerprinting methodologies, random amplified polymorphic DNA (RAPD) and inter-simple sequence repeat (ISSR), were used to estimate genetic diversity and relationships among 20 black gram (Vigna mungo L. Hepper) varieties. Thirty selected RAPD primers amplified 255 bands, 168 of which were polymorphic (66.5%). On average, these primers produced 8.5 bands, 5.6 of which were polymorphic. Polymorphic band number varied from 2 (A-05) to 10 (OPA-02), with sizes ranging from 100 to 2550 bp. Twenty-four selected ISSR primers produced 238 amplified products, 184 of which were polymorphic (77.8%). On average, these primers generated 9.8 bands, with 7.7 polymorphic bands ranging in number from 4 (ISSR-13) to 11 (ISSR-03), and size from 100-2650 bp. Genetic relationships were estimated using similarity coefficient (Jaccard’s) values between different accession pairs; these varied from 30.7 to 85.0 for RAPD, and from 37.2 to 88.4 with ISSR. UPGMA analysis indicated that the varieties ranged in similarity from 0.50 to 1.00 (mean of 0.75) for RAPD, and from 0.47 to 1.00 (mean of 0.76) with ISSR. Cluster analysis of RAPD and ISSR results identified three clusters with significant bootstrap values, which revealed greater homology between the varieties. Principal coordinates analysis also supported this conclusion. Among the black gram varieties, WBU-108 and RBU-38 were highly divergent, whereas LBG-648 and LBG-623 were genetically similar. The markers generated by RAPD and ISSR assays can provide practical information for the management of genetic resources and these results will also provide useful information for the molecular classification and breeding of new black gram varieties.Key words: Black gram, cluster analysis, genetic diversity, ISSR, molecular markers, RAPD


Genetika ◽  
2014 ◽  
Vol 46 (2) ◽  
pp. 331-342 ◽  
Author(s):  
Dragana Miladinovic ◽  
Ksenija Taski-Ajdukovic ◽  
Nevena Nagl ◽  
Branislav Kovacevic ◽  
Aleksandra Dimitrijevic ◽  
...  

Random amplified polymorphic DNA (RAPD) markers were used to detect polymorphism among accessions of wild sunflower species H?lianthus maximiliani, Helianthus tuberosus, Helianthus mollis and Helianthus rigidus with different tolerance to mid-stalk white rot and selection of potential markers for different levels of tolerance to this disease. Estimates of genetic variation showed that genetic diversity was equally distributed between Helianthus species and within them. Cluster analysis corresponded to the phylogenetic relations within the genus Helianthus. The results obtained by principal coordinates analysis (PCoA), where the first two principal coordinates accounted for 83.7% of total variation, perfectly coincided with the results of cluster analysis. Contingency coefficient significance test showed that most of the used primers generated bands associated with some level of tolerance or susceptibility to mid- stalk white rot. Furthermore, contingency analysis showed that primer C12 generated bands associated with resistance (100%) to mid-stalk white rot both in H. mollis and in all accessions, while primer X18 generated bands significantly associated with high tolerance (75%) in H. rigidus, H. mollis as well as in all tested accessions. The C15-600 bp locus was found to be significantly associated with high tolerance (75%) in all accessions, and medium tolerance (50%) in H. mollis.


1991 ◽  
Vol 28 (4) ◽  
pp. 643-648 ◽  
Author(s):  
K. Gajewski

Modern pollen spectra from a series of lakes in northwestern Quebec reflect the major vegetation zones of the forest–tundra transition from latitude 55°N to 59°N. Shrub tundra samples are dominated by Betula and herb pollen, whereas Picea percentages are between 10 and 20%. Lichen woodland samples can contain over 60% Picea, with Betula and Alnus crispa each less than 20%. Pollen assemblages from the shrub subzone of the forest–tundra resemble those of the shrub tundra, while those from the forest subzone resemble lichen woodland samples. Maximum percentages of Alnus crispa are found in the forest–tundra. Classification of the samples using cluster analysis and an ordination by principal coordinates analysis suggest that densely and sparsely forested regions can be discriminated.


2018 ◽  
Vol 50 (1) ◽  
pp. 25-32
Author(s):  
Samaila Samaila Yaradua ◽  
Dhafer Ahmed Alzahrani ◽  
Abubakar Bello

Abstract Numerical taxonomic study of the genus Crotalaria L. in Nigeria was conducted to identify and differentiate some of the species of the genus Crotalaria using numerical taxonomy based on quantitative and qualitative characters. Field work was conducted, where different species were collected and analyzed using multivariate analysis. The results showed that all the collected species are distinct at Euclidian distance of 0.41 in the cluster analysis with Cophenetic correlation (r)=0.964. The ordination analysis based on the results of the PCA, separated the specimens into 7 groups corresponding to the result of cluster analysis. The first two components of the PCA account for 81.5%. The length of petiole, width of leaflet and length of fruit contributed more to showing delimitation among the species.


F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1299
Author(s):  
Johny Pambabay-Calero ◽  
Sergio Bauz-Olvera ◽  
Rubén Flores-González ◽  
Carlos Piña-García

Background: Multivariate analysis is fast becoming a key instrument that can be used to address crimes or incidents. It may be helpful to assess government policies on crime prevention. Methods: To distinguish between the 25 official crime categories reported in Mexico City (Jan 2019 - Jun 2019), principal coordinates analysis was used to determine the quality of a characteristic in this context. This study used cluster analysis via K-means and Biplot based on time and location in terms of crime occurrence. Results: The results obtained from this preliminary analysis indicates that around 70% of crime occurrence is shown in the following boroughs: Cuauhtémoc, Iztapalapa, Gustavo A. Madero, Benito Juárez, Álvaro Obregón, Coyoacán, and Miguel Hidalgo. Conclusion: There are two factors that contribute to the difficulties in crime analysis in Mexico City, namely, the lack of people’s trust in authorities and the insufficiency of tools for data analysis. The latter is an integral part in achieving justice for the victims of crimes because it impedes the process of observing patterns and predicting the perpetrators’ next actions, which may help in solving a number of types of crimes. It is then imperative for law enforcement to utilize data analysis tools that aid in identifying crime patterns and trends, such that the occurrences of crime show a downward trend and consequentially increase the people’s trust in law enforcement agencies.


2010 ◽  
Vol 10 (5) ◽  
pp. 710-720 ◽  
Author(s):  
J. L. Solanas ◽  
M. R. Cussó

Multivariate Consumption Profiling (MCP) is a methodology to analyse the readings made by Intelligent Meter (IM) systems. Even in advanced water companies with well supported IM, full statistical analyses are not performed, since no efficient methods are available to deal with all the data items. Multivariate Analysis has been proposed as a convenient way to synthesise all IM information. MCP uses Factor Analysis, Cluster Analysis and Discriminant Analysis to analyse data variability by categories and levels, in a cyclical improvement process. MCP obtains a conceptual schema of a reference population on a set of classifying tables, one for each category. These tables are quantitative concepts to evaluate consumption, meter sizing, leakage and undermetering for populations and groupings and individual cases. They give structuring items to enhance “traditional” statistics. All the relevant data from each new meter reading can be matched to the classifying tables. A set of indexes is computed and thresholds are used to select those cases with the desired profiles. The paper gives an example of a MCP conceptual schema for five categories, three variables, and five levels, and obtains its classifying tables. It shows the use of case profiles to implement actions in accordance with the operative objectives.


Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1803
Author(s):  
Valentino Palombo ◽  
Elena De Zio ◽  
Giovanna Salvatore ◽  
Stefano Esposito ◽  
Nicolaia Iaffaldano ◽  
...  

Mediterranean trout is a freshwater fish of particular interest with economic significance for fishery management, aquaculture and conservation biology. Unfortunately, native trout populations’ abundance is significantly threatened by anthropogenic disturbance. The introduction of commercial hatchery strains for recreation activities has compromised the genetic integrity status of native populations. This work assessed the fine-scale genetic structure of Mediterranean trout in the two main rivers of Molise region (Italy) to support conservation actions. In total, 288 specimens were caught in 28 different sites (14 per basins) and genotyped using the Affymetrix 57 K rainbow-trout-derived SNP array. Population differentiation was analyzed using pairwise weighted FST and overall F-statistic estimated by locus-by-locus analysis of molecular variance. Furthermore, an SNP data set was processed through principal coordinates analysis, discriminant analysis of principal components and admixture Bayesian clustering analysis. Firstly, our results demonstrated that rainbow trout SNP array can be successfully used for Mediterranean trout genotyping. In fact, despite an overwhelming number of loci that resulted as monomorphic in our populations, it must be emphasized that the resulted number of polymorphic loci (i.e., ~900 SNPs) has been sufficient to reveal a fine-scale genetic structure in the investigated populations, which is useful in supporting conservation and management actions. In particular, our findings allowed us to select candidate sites for the collection of adults, needed for the production of genetically pure juvenile trout, and sites to carry out the eradication of alien trout and successive re-introduction of native trout.


2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Francisco M. P. Gonçalves ◽  
Rasmus Revermann ◽  
Amândio L. Gomes ◽  
Marcos P. M. Aidar ◽  
Manfred Finckh ◽  
...  

The study was carried out in the Cusseque area of the Municipality of Chitembo in south-central Angola. Our objectives were to assess the floristic diversity, the species composition, and stand structure of Miombo woodlands during regeneration after shifting cultivation. A total of 40 plots of 1000 m2were surveyed and analyzed, corresponding to mature forests/woodlands and three fallow types of different age. The analyses were based on plot inventories of all trees with DBH ≥ 5 cm. A total of 51 woody species, 38 genera, and 19 families were recorded. The dominant family was Fabaceae, with subfamily Caesalpinioideae being very abundant. Shannon Diversity and Evenness were highest in mature forests and young fallows, while the mature forest stands showed the highest species richness. A Principal Coordinates Analysis (PCoA) showed many species shared between the intermediate fallow types, but only few species were shared with young fallows. Mature forests formed a clearly distinct group. This study shows potential pathways of forest recovery in terms of faster regeneration after agricultural abandonment and, thus, the results presented here can be used in future conservation and management plans in order to reduce the pressure on mature forests.


2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Runzhi Zhang ◽  
Alejandro R. Walker ◽  
Susmita Datta

Abstract Background Composition of microbial communities can be location-specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selecting, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets. Results Features selecting, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.93 and 30.37% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively. Conclusions The results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which could be used to identify the sample origins. This was also supported by the results from ANCOM and importance score from the RF. In addition, the accuracy of the prediction could be improved by more samples and better sequencing depth.


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