Zooplancton limnétique de 46 lacs et 17 rivières du territoire de la baie de James

1979 ◽  
Vol 57 (8) ◽  
pp. 1693-1709 ◽  
Author(s):  
Bernadette Pinel-Alloul ◽  
Pierre Legendre ◽  
Etienne Magnin
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Statistical Analyses ◽  
Environmental Data ◽  
James Bay ◽  
Single Linkage ◽  
Widespread Species ◽  
Bay Area ◽  
Components Analysis ◽  
Western Portion

From June through October 1973, 335 samples of limnetic plankton were collected from 46 lakes and 17 rivers of the James Bay area. Sixty zooplanktonic species were identified (20 Copepoda, 27 Cladocera, and 13 Rotifera). The most common and widespread species are cold stenotherms (Leptodiaptomus minutus, Diacyclops bicuspidatus thomasi, Epischura lacustris, Holopedium gibberum, Bosmina longirostris, Daphnia longiremis, and Kellicottia longispina). In order to study the typology of the lake samples, the data were subjected to three types of statistical analyses: principal components analysis, single linkage, and complete linkage clustering. Five groups of lakes emerged from these analyses: types IV and V are located in the northeastern portion of the studied area, whereas types I and II were identified in the western portion, corresponding with the area occupied by the Tyrrell glacial sea. Type III fills an intermediate position. Types II, III, and V are smalt lakes. The characteristic zooplanktonic communities of each group are described, whereas the principal components and the components of the diversity are correlated with the environmental data.

scholarly journals Principal components analysis of employment in Eastern Europe

2006 ◽  
Vol 53 (4) ◽  
pp. 427-437 ◽  
Author(s):  
Mirko Savic
Keyword(s):  
Eastern Europe ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Statistical Analyses ◽  
Multivariate Methods ◽  
Eastern European ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Full Picture ◽  
Components Analysis

For the last decade, the employment structure is one of the fastest changing areas of Eastern Europe. This paper explores the best methodology to compare the employment situations in the countries of this region. Multivariate statistical analyses are very reliable in portraying the full picture of the problem. Principal components analysis is one of the simplest multivariate methods. It can produce very useful information about Eastern European employment in a very easy and understandable way.

scholarly journals Multivariate Statistical Analysis Applied to Water Quality of a Tropical Coastal Lagoon, Cartagena, Colombian Caribbean

2019 ◽  
Vol 20 (1) ◽  
pp. 141
Author(s):  
Ildefonso Baldiris-Navarro ◽  
Juan Carlos Acosta-Jimenez ◽  
Angel Dario Gonzalez-Delgado ◽  
Alvaro Realpe-Jimenez ◽  
Juan Gabriel Fajardo-Cuadro
Keyword(s):  
Water Quality ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Coastal Lagoon ◽  
Coastal Lagoons ◽  
Oxygen Demand ◽  
Habitat Destruction ◽  
Environmental Data ◽  
Fecal Coliforms ◽  
Components Analysis

Coastal lagoons are one of the most threatened ecosystems in the world, because of population growth, habitat destruction, pollution, wastewater, overexploitation and invasive species which are the main causes of their degradation. The objective of this paper was to evaluate the water quality behavior in a stressed coastal lagoon in Cartagena, Colombian Caribbean. Environmental data was analyzed using hypothesis testing, confidence intervals, and also Principal components analysis (PCA). The study was focused on water parameters such as dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (COD), salinity, pH, total dissolved solids, total coliforms (TC), Fecal coliforms (FC), ammonium (NH4+) and total phosphorus (TP). The analysis was conducted in line with the Colombian national water standard. Results showed that BOD5, COD, phosphorus, and coliforms are out of the limits for these variables in Colombia and are reaching levels that may be a threat to human health. Principal components analysis detected five components that explained 79.4% of the variance of data and showed that anthropogenic and temporal factors might be affecting the variation of the parameters.

Vegetation studies at Polar Bear Pass, Bathurst Island, N.W.T. II. Vegetation–environment relationships

1983 ◽  
Vol 61 (6) ◽  
pp. 1637-1646 ◽  
Author(s):  
J. W. Sheard ◽  
Dorothy W. Geale
Keyword(s):  
Correlation Analysis ◽  
Principal Components Analysis ◽  
Canonical Correlation Analysis ◽  
Principal Components ◽  
Canonical Correlation ◽  
Environmental Gradient ◽  
Environmental Influence ◽  
Environmental Data ◽  
Components Analysis ◽  
Soil Moisture Status

Vegetation–environment relationships are defined with the aid of principal-components analysis and canonical correlation analysis. In both the uplands and lowlands a moisture gradient, determined by measuring gravimetric moisture and indicated by organic carbon, is the most important environmental influence on the vegetation. In the uplands this gradient is also associated with snow depth (drifting) and in the lowlands with conductivity. The second environmental gradient in the uplands is associated with depth to permafrost and its soil textural correlates. Thus soil texture, independent of its effect on soil moisture status, influences the distribution of plant communities. In the lowlands the second environmental gradient is less clear but is also associated with depth to permafrost and, in addition, elevation and CaCO3 equivalent. Canonical correlation analysis shows that the components extracted by principal-components analysis of the vegetation data did not conform to the important trends of variation in the environmental data. Principal-components analysis is nevertheless an essential means of data reduction prior to the application of canonical correlation. The statistical model used in the study has potential advantages over the independent use of either principal-components analysis or canonical correlation.

Phenotypic plasticity and contrasting habitat colonization in Festuca pallescens

1993 ◽  
Vol 71 (7) ◽  
pp. 970-977 ◽  
Author(s):  
Gabriel Oliva ◽  
Arturo Martínez ◽  
Marta Collantes ◽  
Jorge Dubcovsky
Keyword(s):  
Phenotypic Plasticity ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Widespread Species ◽  
Southern Patagonia ◽  
Spikelets Per Panicle ◽  
Habitat Colonization ◽  
Components Analysis ◽  
Festuca Pallescens ◽  
Plastic Components

Eight populations of Festuca pallescens (Poaceae) from southern Patagonia (Argentina) were chosen to test whether their morphology and anatomy were associated with climatic and edaphic characteristics of their habitats and to discriminate genetic and plastic components of the variation. Fourteen environmental variables were measured to characterize eight localities. Cluster analysis classified them into four habitats. Principal components analysis based on 22 morphological and anatomical characters of five individuals from each population was performed. This ordination grouped them by habitat. Small plants with a low number of spikelets were found in the xeric habitat, whereas plants of the saline habitat showed a significantly higher number of spikelets. Large plants were characteristic of the humid habitats, but at higher elevations of foothill humid valleys the plants produced few spikelets per panicle with larger lemmas and heavier propagules than those in the coastal humid plains. A larger sample of 10 populations drawn from across the distribution of F. pallescens showed that seed collected at high altitudes were significantly heavier then those collected near sea level. The same characters were measured again 1 year after transplanting to uniform conditions. Significant differences between habitats disappeared. These data showed that phenotypic plasticity allows for diverse habitat colonization of this widespread species. Key words: Gramineae, phenotypical plasticity, seed weight, principal components analysis, ecotypes.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

Sign in / Sign up

Export Citation Format

Share Document