Variation in thallus morphology in response to climatic and geographical distribution in an Usnea complex

1992 ◽  
Vol 24 (3) ◽  
pp. 229-248 ◽  
Author(s):  
G. Nell Stevens

AbstractThe morphological, anatomical and chemical characteristics of five taxa of Usnea (e.g.U. scabrida, U. elegans, U. molliuscula, U. queenslandica and U.ramulosissima), which belong to the U. scabrida- U. molliuscula complex are described.Usnea scabrida subsp. tayloriana is described as new. Usnea elegans and U.queenslandica are reduced to subspecies: U. scabrida subsp.elegans and U. molliuscula subsp. queenslandica. The species U. consimilis Stirton is considered synonymous with U. scabrida. A canonical discriminant analysis of geographic and climatic data was undertaken, which demonstrated that the species under investigation generally occupied discrete non-overlapping zones, indicating that the differences in thallus morphology correlated with particular climatic regions.

Author(s):  
Qiao Dong ◽  
Xueqin Chen ◽  
Shi Dong ◽  
Jun Zhang

AbstractThis study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snowfall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Around one third of the 800 weather stations record variation of freeze and precipitation classifications and a few of them show significant change of classifications over time based on the results of logistic regression analyses. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.


2021 ◽  
Author(s):  
Qiao Dong ◽  
Xueqin Chen ◽  
Shi Dong ◽  
Jun Zhang

Abstract This study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snow fall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.


Author(s):  
Valentina P. Vetrova ◽  
◽  
Alexey P. Barchenkov ◽  
Nadezhda V. Sinelnikova ◽  
◽  
...  

Geometric morphometric analysis of shape variation in the cone scales of two closely related larch species, Larix dahurica Laws. (=Larix gmelinii (Rupr.) Rupr) and L. cajanderi Mayr, was carried out. The data on the taxonomy and distribution of L. dahurica and L. cajanderi are contradictory. The taxonomic status of L. cajanderi has been confirmed by the genetic and morphological studies performed in Russia and based on considerable evidence, but the species has not been recognized internationally, being considered as a synonym of Larix gmelinii var. gmelinii. In the systematics of larch, morphological characters of the generative organs are mainly used as diagnostic markers, among the most important being the shape variation of the cone scales. The aim of this study was to test geometric morphometrics as a tool for analyzing differentiation of L. dahurica and L. cajanderi in the shape of their cone scales. Characterization of shape variations in cone scales using geometric morphometric methods consists in digitizing points along an outline of scales followed by analysis of partial warps, describing individual differences in coordinates of the outline points. We studied the populations of L. dahurica from Evenkia and the Trans-Baikal region and six L. cajanderi populations from Yakutia and Magadan Oblast. In each population, we analyzed samples of 100-150 cones collected from 20-30 trees. Scales taken from the middle part of the cones were scanned using an Epson Perfection V500 Photo. On the scanned images, outline points were placed with a TPSDig program (Rolf, 2010), using angular algorithm (Oreshkova et al., 2015). The data were processed and analyzed using Integrated Morphometrics Programs (IMP) software (http://www.canisius.edu/~sheets/ morphsoft.html, Sheets, 2001), following the guidelines on geometric morphometrics in biology (Pavlinov, Mikeshina, 2002; Zelditch et al., 2004). Initial coordinates of the scale landmarks were aligned with the mean structure for L. dahurica and L. cajanderi cone scales using Procrustes superimposition in the CoordGen6 program. PCA based on covariances of partial warp scores was applied to reveal directions of variation in the shape of the cone scales. The relative deformations of the cone scales (PCA scores) were used as shape variables for statistical comparisons of these two larch species with canonical discriminant analysis. Morphotypes of the cone scales were distinguished in L. dahurica populations by pairwise comparison of samples from trees in the TwoGroup6h program using Bootstrap resampling-based Goodall’s F-test (Sheets, 2001). Samples from the trees in which the cone scales differed significantly (p < 0.01) were considered to belong to different morphotypes. Morphotypes distinguished in L. dahurica populations were compared with the morphotypes that we had previously determined in L. cajanderi populations. The composition and the frequency of occurrence of morphotypes were used to determine phenotypic distances between populations (Zhivotovskii, 1991). Multidimensional scaling matrix of the phenotypic distances was applied for ordination of larch populations. In this research, we revealed differentiation of L. dahurica and L. cajanderi using geometric morphometric analysis of the shape variation of cone scales. The results of PCA of partial warp scores exposed four principal components, which account for 90% of total explained variance in the shape of the cone scales in the two larch species. Graphical representations of these shape transformations in the vector form characterized directions of shape variability in scales corresponding to the maximum and minimum values of four principal components (See Fig. 2). PCA-ordination of the larch populations revealed some difference in the shape variation of the cone scales in L. dahurica and L. cajanderi (See Fig. 3). The results of canonical discriminant analysis of relative deformations of scales showed differentiation of the populations of the two larch species (See Fig. 4). Eleven morphotypes were identified in L. dahurica cones from Evenkia and nine morphotypes in the Ingoda population, three of the morphotypes being common for both populations (See Fig. 5). The shape of L. dahurica cone scales varied from spatulate to oval and their apical margins from weakly sinuate to distinctly sinuate. The Trans-Baikal population was dominated by scales with obtuse (truncate) and rounded apexes. The obtained morphotypes were compared with 25 cone scale morphotypes previously distinguished in the Yakut and the Magadan L. cajanderi populations (See Fig. 3). Four similar morphotypes of cone scales were revealed in the North-Yeniseisk population of L. dahurica and the Yakut populations of L. cajanderi. The differences between them in the populations of the two larch species were nonsignificant (p > 0.01). All morphotypes of cone scales from the Ingoda population of L. dahurica differed significantly from L. cajanderi cone scale morphotypes. The results of multidimensional scaling phenotypic distance matrix calculated based on the similarity of morphotypes of L. dahurica and L. cajanderi populations were consistent with the results of their differentiation based on relative deformations of scales obtained using canonical discriminant analysis (See Fig. 4 and Fig. 7). In spite of the differences in the shape of the cone scales between the North-Yeniseisk and the Trans-Baikal populations of L. dahurica, they both differed from L. cajanderi populations. Thus, phenotypic analysis confirmed differentiation of these two larch species. Despite the similarities between a number of morphotypes, the Yakut L. cajanderi populations were differentiated from L. dahurica populations. Significant differences were noted between intraspecific groups: between L. cajanderi populations from Okhotsk-Kolyma Upland and Yakutia and between L. dahurica populations from Evenkia and the Trans-Baikal region (See Fig. 4). The similarities between species and intraspecific differences may be attributed to the ongoing processes of hybridization and species formation in the region where the ranges of the larches overlap with the ranges of L. czekanowskii Szafer and L. dahurica×L. cajanderi hybrids. Geometric morphometrics can be used as an effective tool for analyzing differentiation of L. dahurica and L. cajanderi in the shape of their cone scales.


2016 ◽  
Vol 46 (9) ◽  
pp. 1535-1541 ◽  
Author(s):  
Rodolfo Schmit ◽  
Rita Carolina de Melo ◽  
Thayse Cristine Vieira Pereira ◽  
Mattheus Beck ◽  
Altamir Frederico Guidolin ◽  
...  

ABSTRACT: The objective of this study was to apply multivariate techniques, canonical discriminant analysis, and multivariate contrasts, indicating the most favorable inferences in the evaluation of pure lines of beans. The study was conducted at the experimental field of the Institute for Breeding and Molecular Genetics, in Lages, SC, Brazil. The experiment was composed of 24 pure lines of beans from the Santa Catarina test of cultivars. Plant height, numbers of pods and grains per plant, and stem diameter were the variables measured. The complete randomized block design was used with four replications. The data were subjected to multivariate analysis of variance, canonical discriminant analysis, multivariate contrasts and univariate contrasts. The first canonical discriminant function has captured 81% of the total variation in the data. The Scott-Knott test showed two groups of inbred lines at the average -of scores of the first canonical discriminant function. It was considered that testing hypotheses with the canonical scores may result in loss of information obtained from the original data. Multivariate contrasts indicated differences within the group formed by the Scott-Knott test. The canonical discriminant analysis and multivariate contrasts are excellent techniques to be combined in the multivariate assessment, being used to explore and test hypotheses, respectively.


2015 ◽  
Vol 3 (2) ◽  
pp. 258
Author(s):  
Abram Jared Bicksler ◽  
John B Masiunas

Phenotypes of sorghum species (Sorghum sp.) have characteristics making them valuable summer annual cover crops and/or biofuel feedstocks for temperate climates. In field studies conducted at Urbana, IL, USA, fourteen USDA sorghum landrace accessions and three commercial sorghum accessions were evaluated for their growth habits and regrowth potential. In Canonical Discriminant Analysis (CDA) analysis, the first two canonical variates were significant and accounted for 86% of the among-accession variability. Unmown tiller number, regrowth tiller number, and regrowth biomass best discriminated between accessions in CDA and scattergrams. The accessions clustered into three subgroups. Three multi-stemmed accessions (two commercial varieties and one USDA accession) with an ability to regrow clustered away from the bulk of the USDA sorghums. Multi-stemmed accessions are useful for breeding improved summer annual cover crops that are tall, produce copious amounts of biomass, and rapidly regrow after defoliation; although propensity to lodging and poor germination of accessions will need attention. Additionally, landrace sorghum accessions in the USDA germplasm collection are useful for breeding cover crop and biofuel feedstocks, due to their great height and biomass production, although it will be necessary to select for improved regrowth potential. Crosses between USDA landraces and the commercially available multi-stemmed accessions could lead to a sorghum cover crop and biofuel plant with great biomass and height and ability to regrow following defoliation.


2017 ◽  
Vol 3 (1) ◽  
pp. 30-35
Author(s):  
Ganis Lukmandaru

Ethanol-benzene soluble extracts from the heartwood collected from 87 individual teak trees grown in the island of Java were analyzed using GC and GC-MS. The variations of quinones (tectoquinone, deoxylapachol, isodeoxylapachol, lapachol, tectol) and other components (palmitic acid, squalene, and two unknown compounds) were investigated for a chemotaxonomical study. There were wide variations in the contents of the constituents among individuals from three habitats, Purwakarta (plantation forest), Randublatung (plantation forest)  and  Gunungkidul (community forest)  regions. Cluster  and  discriminant analysis  results  showed  that  teak  trees  can  be  classified  into  three  clusters  based  on  the composition of quinones and squalene. Cluster I was distinguished by relatively high amount of squalene and low amount of quinones. In contrast, high amount of tectoquinone and low amount of squalene was observed in cluster III. Further, comparatively high amount of naphtaquinones (lapachol, deoxylapachol and its isomer) and tectol was found in cluster II. Based on geographical distribution, Purwakarta, Randublatung, and Gunungkidul regions mostly produce cluster II, I, and III type individuals, respectively.


2015 ◽  
Vol 29 (1) ◽  
Author(s):  
Amal Arfan

The study was conducted to determine whether the vegetation in the mangrove ecosystem, can be contrasted with another objectt, using Spectroradiometer HR-1024. The data used is data visible spectrum(400-700 nm)  which resulted in 204 bands. The analysis used is the integrated analysis with three levels. First, using ANOVA to determine significant differences in spectral reflectance between vegetation with water, wet soil and dry soil. Second, using Step wise Canonical Discriminant Analysis to identify the most sensitive band for discrimination reflection spectrum. This analysis which resulted in six bands are considered practical to distinguish vegetation with another object namely  401.5 nm, 416.9 nm, 508.2 nm, 599.3 nm, 660.3nm and 689.2 nm. Third using the Jeffries-Matusita separability index which resulted in the separation index of mangrove vegetation, water, wet soil and dry soil is 1.414.


animal ◽  
2008 ◽  
Vol 2 (3) ◽  
pp. 419-424 ◽  
Author(s):  
M.F. Rosário ◽  
M.A.N. Silva ◽  
A.A.D. Coelho ◽  
V.J.M. Savino ◽  
C.T.S. Dias

2010 ◽  
Vol 11 (2) ◽  
pp. 388-404 ◽  
Author(s):  
Xiaoming Sun ◽  
Ana P. Barros

Abstract Confidence in the estimation of variations in the frequency of extreme events, and specifically extreme precipitation, in response to climate variability and change is key to the development of adaptation strategies. One challenge to establishing a statistical baseline of rainfall extremes is the disparity among the types of datasets (observations versus model simulations) and their specific spatial and temporal resolutions. In this context, a multifractal framework was applied to three distinct types of rainfall data to assess the statistical differences among time series corresponding to individual rain gauge measurements alone—National Climatic Data Center (NCDC), model-based reanalysis [North America Regional Reanalysis (NARR) grid points], and satellite-based precipitation products [Global Precipitation Climatology Project (GPCP) pixels]—for the western United States (west of 105°W). Multifractal analysis provides general objective metrics that are especially adept at describing the statistics of extremes of time series. This study shows that, as expected, multifractal parameters estimated from the NCDC rain gauge dataset map the geography of known hydrometeorological phenomena in the major climatic regions, including the strong orographic gradients from west to east; whereas the NARR parameters reproduce the spatial patterns of NCDC parameters, but the frequency of large rainfall events, the magnitude of maximum rainfall, and the mean intermittency are underestimated. That is, the statistics of the NARR climatology suggest milder extremes than those derived from rain gauge measurements. The spatial distributions of GPCP parameters closely match the NCDC parameters over arid and semiarid regions (i.e., the Southwest), but there are large discrepancies in all parameters in the midlatitudes above 40°N because of reduced sampling. This study provides an alternative independent backdrop to benchmark the use of reanalysis products and satellite datasets to assess the effect of climate change on extreme rainfall.


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