THE CONVERGENCE OF HABITAT STRUCTURE IN TUNDRA COLLEMBOLAN COMMUNITIES (INSECTA: ORDER COLLEMBOLA)

1983 ◽  
Vol 115 (9) ◽  
pp. 1129-1145 ◽  
Author(s):  
Masanori J. Toda ◽  
Kouzou Tanno
Keyword(s):  
Habitat Structure ◽  
Principal Component ◽  
The Other ◽  
Statistical Techniques ◽  
Mackenzie Delta ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Cluster Analyses ◽  
Ecological Changes ◽  
Principal Component Analyses

AbstractHabitat structure of two collembolan communities, one at Barrow, Alaska, U.S.A., the other at Tuktoyaktuk in the Mackenzie Delta, Canada, has been analyzed in relation to microtopographies characteristic of tundra regions. Multivariate statistical techniques, cluster analyses (UPGMA), and principal component analyses (PCA) reveal various ecological changes in component species. In spite of such local variations in component species, the two communities show similar patterns of habitat structure that are organized principally along a gradient of environmental moisture.

scholarly journals Evaluation of the anthropogenic impact in Suat Ugurlu Dam lake using multivariate statistical techniques

2018 ◽  
Vol 20 (1) ◽  
pp. 161-168 ◽  
Keyword(s):  
Anthropogenic Impact ◽  
Sediment Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Cluster Number ◽  
Multivariate Statistical ◽  
Oxidation Reduction ◽  
Oxidation Reduction Potential ◽  
And Cluster Analysis

Sediments play an important role in the quality of aquatic ecosystems in the Dam Lake where they can either be a sink or a source of contaminants, depending on the management. This purpose of this study is to identify the sediment quality in order to find out the causes for the malodor and the eutrophication that is causing a bad scenario. Solutions for improving the dam are proposed. Multivariate statistical techniques, such as a principal component analysis (PCA) and cluster analysis (CA), were applied to the data regarding sediment quality in relation to anthropogenic impact in Suat Ugurlu Dam Lake. This data was generated during 2014-2015, with monitoring at four sites for 11 parameters. A PCA and CA were used in the study of the samples. The total variance of 84.1%, 74.3%, 87.4% and 91.5% suggest 4, 3, 3 and 4 principle components (PCs) in the four locations: LC1, LC2, LC3 and LC4, respectively. Also, a CA was applied to both the variables and the observations. Some variables and observations showed a high similarity based on the results of variables in the CA. Also, the similarity ratio of temperature-mercury (Hg) and oxidation reduction potential (ORP) was high and generally, the cluster number of variables was 5, according to the selected similarity level.

scholarly journals Study of groundwater contamination and drinking suitability in basaltic terrain of Maharashtra, India through PIG and multivariate statistical techniques

2020 ◽  
Vol 69 (4) ◽  
pp. 398-414 ◽  
Author(s):  
Vasant Wagh ◽  
Shrikant Mukate ◽  
Aniket Muley ◽  
Ajaykumar Kadam ◽  
Dipak Panaskar ◽  
...  
Keyword(s):  
Major Ions ◽  
Principal Component ◽  
World Health ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Post Monsoon ◽  
Groundwater Samples

Abstract The integration of pollution index of groundwater (PIG), multivariate statistical techniques including correlation matrix (CM), principal component analysis (PCA), cluster analysis (CA) and various ionic plots was applied to elucidate the influence of natural and anthropogenic inputs on groundwater chemistry and quality of the Kadava river basin. A total of 80 groundwater samples were collected and analysed for major ions during pre- and post-monsoon seasons of 2012. Analytical results inferred that Ca, Mg, Cl, SO4 and NO3 surpass the desirable limit (DL) and permissible limit (PL) of Bureau of Indian Standards (BIS) and the World Health Organization (WHO) in both the seasons. The elevated content of total dissolved solids (TDS), Cl, SO4, Mg, Na and NO3 is influenced by precipitation and agricultural dominance. PIG results inferred that 52.5 and 35%, 30 and 37.5%, 12.5 and 20%, 2.5 and 5% groundwater samples fall in insignificant, low, moderate and high pollution category (PC) in pre- and post-monsoon seasons, respectively. PC 1 confirms salinity controlled process due to high inputs of TDS, Ca, Mg, Na, Cl and SO4. Also, PC 2 suggests alkalinity influence by pH, CO3, HCO3 and F content. PIG and statistical techniques help to interpret the water quality data in an easier way.

Characterization of Balsamic Vinegar by Multivariate Statistical Analysis of Trace Element Content

1998 ◽  
Vol 81 (5) ◽  
pp. 1087-1095 ◽  
Author(s):  
Antonella Del Signore ◽  
Barbara Campisi ◽  
Franco Di Giacomo
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Trace Element Content ◽  
The Other ◽  
Statistical Techniques ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Balsamic Vinegar ◽  
And Cluster Analysis

Abstract To characterize vinegars according to the types prescribed by Italian regulations, 8 trace elements (Cr, Mn, Co, Ni, Cu, Zn, Cd, and Pb) were determined. The data collected were successively elaborated by 3 statistical techniques: linear principal component analysis (LPCA), linear discriminant analysis (LDA), and cluster analysis (CA). LDA and LPCA best classified and discriminated the 3 types of vinegar under study, separating traditional balsamic vinegars from the other 2 types, nontraditionally aged balsamic vinegars and common vinegars. The latter 2 types were appreciably distinguished only by LDA through bidimensional analysis of discriminant scores

Infrastrutture e sviluppo locale in Italia

2009 ◽  
pp. 101-165
Author(s):  
Roberto Zoboli ◽  
Anna Montini
Keyword(s):  
Comparative Analysis ◽  
The Other ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
North West ◽  
Empirical Results ◽  
North East ◽  
Complex Picture ◽  
The One

- This study suggests the possibility of developing an analysis of Italian infrastructures on the basis of municipal data analysed through the use of multivariate statistical techniques. The empirical results of this work confirm some well-known characteristics of the infrastructural Italian system and identify some less obvious features. In fact, while on the one hand there emerges the usual picture of a country characterised by a North-South dualism, on the other hand, various clusters are identified within each macro-region North-West, North-east, Centre and South which give a more complex picture. This article also provides a comparative analysis with earlier studies.

scholarly journals Seasonal Source Apportionment of Heavy Metals and Physicochemical Parameters: A Case Study of Sapanca Lake Watershed

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Asude Ateş ◽  
Hülya Demirel ◽  
Rabia Köklü ◽  
Şenay Çetin Doğruparmak ◽  
Hüseyin Altundağ ◽  
...  
Keyword(s):  
Toxic Metals ◽  
Physicochemical Parameters ◽  
Principal Component ◽  
Pollution Sources ◽  
Statistical Techniques ◽  
Natural Soil ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Pollutant Sources ◽  
Mlr Model

This study was aimed to evaluate the water quality and pollution sources in Sapanca Lake and its tributaries by applying multivariate statistical techniques to physicochemical parameters and toxic metals. For this purpose, the multivariate statistical methods such as principal component analysis (PCA) and absolute principal component score-multiple linear regression (APCS-MLR) model have been employed. It was tried to determine the seasonal pollution sources of physicochemical parameters and toxic metals obtained from 22 different sampling points between the years of 2015 and 2017. PCA was applied to the datasets, and 6 varimax factors describing 84%, 80%, 76%, and 79% of the total variance for each season were extracted. The obtained factors were analyzed using the APCS-MLR model for the apportionment of various pollution sources affecting physicochemical parameters and toxic metals. The results show that the natural soil structure, municipal-industrial wastewater, agricultural-atmospheric runoff, highways, and seasonal effects are the major pollution sources for toxic metals and physicochemical parameters. The material contribution of pollutant sources to toxic metals and physicochemical parameters was calculated and verified by the concentrations analyzed. Consequently, multivariate statistical techniques are useful to determine the physicochemical parameters and toxic metals through reciprocal correlation and assess the seasonal impact of pollutant sources in the basin. This study also provides a basis for the creation of measurement programs, determination of pollution sources, and provision of sustainable watershed management regarding other water resources.

scholarly journals Water Quality Assessment of Porsuk River, Turkey

2010 ◽  
Vol 7 (2) ◽  
pp. 593-599 ◽  
Author(s):  
Suheyla Yerel
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Water ◽  
Surface Water Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Porsuk River

The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management.

scholarly journals Spatio-Temporal Variation of Groundwater Quality and Source Apportionment Using Multivariate Statistical Techniques for the Hutuo River Alluvial-Pluvial Fan, China

2020 ◽  
Vol 17 (3) ◽  
pp. 1055 ◽  
Author(s):  
Qianqian Zhang ◽  
Long Wang ◽  
Huiwei Wang ◽  
Xi Zhu ◽  
Lijun Wang
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Principal Component ◽  
Domestic Sewage ◽  
Pollution Sources ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Polluted Sites ◽  
Industrial Sewage

Groundwater quality deterioration has become an environmental problem of widespread concern. In this study, we used a water quality index (WQI) and multivariate statistical techniques to assess groundwater quality and to trace pollution sources in the Hutuo River alluvial-pluvial fan, China. Measurement data of 17 variables in 27 monitoring sites from three field surveys were obtained and pretreated. Results showed that there were 53.09% of NO3−, 18.52% of SO42− and 83.95% of total hardness (TH) in samples that exceeded the Grade III standard for groundwater quality in China (GB/T 14848-2017). Based on WQI results, sampling sites were divided into three types: high-polluted sites, medium-polluted sites and low-polluted sites. The spatial variation in groundwater quality revealed that concentrations of total dissolved solids (TDS), Cl−, TH and NO3− were the highest in high-polluted sites, followed by medium-polluted and low-polluted sites. The temporal variation in groundwater quality was controlled by the dilution of rainwater. A principal component analysis (PCA) revealed that the primary pollution sources of groundwater were domestic sewage, industrial sewage and water–rock interactions in the dry season. However, in the rainy and transition seasons, the main pollution sources shifted to domestic sewage and water–rock interactions, nonpoint pollution and industrial sewage. According to the absolute principal component scores-multivariate linear regression (APCS-MLR), most water quality parameters were primarily influenced by domestic sewage. Therefore, in order to prevent the continuous deterioration of groundwater quality, the discharge of domestic sewage in the Hutuo River alluvial-pluvial fan region should be controlled.

Unboxing organisational complexity: how does it affect business performance during the COVID-19 pandemic?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jorgina Pereira ◽  
Vitor Braga ◽  
Aldina Correia ◽  
Aidin Salamzadeh
Keyword(s):  
Business Performance ◽  
Design Methodology ◽  
The Other ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Content Type ◽  
Independent Variables ◽  
Five Factors ◽  
Business Complexity

PurposeThis study aims to distinguish businesses by their degree of complexity and to analyse the influence of complexity on the performance of firms during the coronavirus disease 2019 (COVID-19) pandemic.Design/methodology/approachData were collected from 468 businesses, and various multivariate statistical techniques were used. Initially a factor analysis was conducted, organising variables into five factors. A discriminant analysis, performed with the five factors, allowed discriminating firms based on whether they internationalise or not. A linear regression was performed in order to estimate the contribution of each factor in the business performance.FindingsThe results suggest the existence of additional variables for measuring the complexity. From the factorial analysis it is possible to conclude that business complexity can be explained by size, indebtedness and profitability, internationalisation, number of employees, and age and leverage. Total assets, indebtedness and age are the variables that contribute the most to business performance. On the other hand, indebtedness, internationalisation, age and leverage are the independent variables that most contribute to explain business performance.Originality/valueThis paper presents advances in two ways. First, it proposes measures of complexity (highly debatable in the literature). It also proposes internationalisation as an explanation of complexity. Second, this paper sheds light on businesses decisions to grow, taking into account how complexity may affect performance.

scholarly journals Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245525
Author(s):  
Junzhao Liu ◽  
Dong Zhang ◽  
Qiuju Tang ◽  
Hongbin Xu ◽  
Shanheng Huang ◽  
...  
Keyword(s):  
Water Quality ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Temporal Analysis ◽  
Statistical Techniques ◽  
Complex Data ◽  
Monitoring Networks ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

scholarly journals Assessment of Seasonal and Spatial Variations of Coastal Water Quality Using Multivariate Statistical Techniques

2021 ◽  
Vol 9 (11) ◽  
pp. 1292
Author(s):  
Mohamad Alkhalidi ◽  
Abdalrahman Alsulaili ◽  
Badreyah Almarshed ◽  
Majed Bouresly ◽  
Sarah Alshawish
Keyword(s):  
Water Quality ◽  
Coastal Water ◽  
Pearson Correlation ◽  
Chlorophyll Concentration ◽  
Principal Component ◽  
Quality Parameters ◽  
Statistical Techniques ◽  
Principal Component Factor Analysis ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

This study investigates the seasonal and spatial trends in Kuwait’s coastal water’s physical, chemical, and biological parameters by applying multivariate statistical techniques, including cluster analysis (CA), principal component/factor analysis (PCA/FA), and the Pearson correlation (PC) method to the average daily reading of water quality parameters from fifteen stations over one year. The investigated parameters are pH, turbidity, chlorophyll-a, conductivity, dissolved oxygen (DO), phycoerythrin, salinity, and temperature. The results show that the coastal water of Kuwait is subjected to high environmental pressure due to natural and human interferences. During 2017, the DO levels were below the threshold limit, and at the same time, the water temperature and salinity were very high, causing a series of fish death events. CA resulted in three different regions based on the turbidity, including high, moderate, and low regions, and three seasons (winter, summer, and autumn). Spring is very short and overlaps with winter and summer. PCA/FA applied on the datasets assisted in extracting and identifying parameters responsible for the variations in the seasons and regions obtained from CA. Additionally, Pearson’s correlation resulted in a strong positive relation between chlorophyll and phycoerythrin in 7 out of the 15 stations. However, at high turbidity regions (stations 1 and 2), chlorophyll concentration was low. Additionally, the negative correlation between DO and temperature was observed at stations with rare human activities.

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