Article Sidebar

PDF
Date Published: 10/10/2021
Online Published: 10/10/2021
Abstract Views: 66
Views PDF: 45
DOI: https://doi.org/10.52714/dthu.10.5.2021.902
Issue
Vol. 10 No. 5 (2021): Natural Sciences Issue (English)
Section
Natural Sciences Issue (English)
How to Cite
Nguyen, T. H. L., Lu, N. T. A., & Nguyen, H. (2021). Application of multivariate statistical analysis in ecological environment research. Dong Thap University Journal of Science, 10(5), 115-120. https://doi.org/10.52714/dthu.10.5.2021.902

Application of multivariate statistical analysis in ecological environment research

Thi Hai Ly Nguyen1, , Ngoc Tram Anh Lu2, Ho Nguyen1
1 Department of Agriculture and Environmental Resources, Dong Thap University, Vietnam
2 Department of Natural Sciences Teacher Education, Dong Thap University, Vietnam

Main Article Content

Abstract

Multivariate statistics has proven many outstanding advantages and has been used extensively in various studies in the ecological environment field. They supported ecologists to discover the structure and previous relatively objective summary of the primary features of the data. In this paper, some important statistical techniques, including principal component analysis (PCA), canonical correspondence analysis (CCA) and cluster analysis, are explained briefly. Each of them is also examined by a corresponding case-study. The PCA is applied to identify and analyze the relationship between mangrove plant communities and soil factors. Meanwhile, the CCA is put in an application to analyze the relationship between the two sets of species and soil data, from which to determine the effect of soil on the distribution of dominant species. Finally, cluster analysis is examined to analyze the similarities among species in the studied area.

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Keywords

Canonical correlation analysis, cluster analysis, data analysis, ecology, environment, principal component analysis

References

Bui, M. H. (2018). Multivariate analysis methods for forestry research data, using SAS. Journal of Forestry Science and Technology, 1(2018), 43-52.
Clarke, K. R., & Gorley, R. N. (2006). Primer V6: User Manual/Tutorial. UK: Primer-E Ltd.
Jan Lepˇs, & PetrˇSmilauer. (2003). Multivariate analysis of ecological data using CANOCO. UK: Cambridge University Press.
Kevin, D. (2020). Principal Component Analysis (PCA). Process improvement using data (325 – 370). Ontario: McMaster University. Retrieved from https://learnche.org/pid/.
Anh, L. N. T., Nam, V. N., Thao, N. T. P., & Ly, N. T. H. (2018). The effects of soil characteristics on mangrove species distribution at Con Trong, Ong Trang estuary, Ngoc Hien district, Ca Mau province. Can Tho University Journal of Science, (54), 75-80.
Michael, W. P. (2020). Ordination methods - An overview. http://ordination.okstate.edu.
Nguyễn Thị Hải Lý. (2020). Nghiên cứu sự phân bố và đa dạng thực vật bậc cao trên các vùng sinh thái khác nhau tại tỉnh An Giang. Trường Đại học Cần Thơ, Việt Nam.
Pausas, J. G., & Austin, M. P. (2001). Patterns of plant species richness in relation to different environments: an appraisal. Journal of Vegetation Science, 12(2), 153-166.
Steven, M. H. (2019). Principal component analysis (PCA). Athens: University of Georgia.

Most read articles by the same author(s)

1 2 > >>