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An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-28
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Functional diversity in plant communities is a key driver of ecosystem processes.
The effective methods for measuring functional diversity are important in ecological studies.
A new method based on neural network, self-organizing feature map (SOFM index), was put forward and described.
A case application to the study of functional diversity of Phellodendron amurense communities in Xiaolongmen Forest Park of Beijing was carried out in this paper.
The results showed that SOFM index was an effective method in the evaluation of functional diversity and its change in plant communities.
Significant nonlinear correlations of SOFM index with the common used methods, FAD, MFAD, FDp, FDc, FRic, and FDiv indices, also proved that SOFM index is useful in the studies of functional diversity.
American Psychological Association (APA)
Song, Naiqi& Zhang, Jin-Tun. 2013. An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory. Journal of Applied Mathematics،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-463312
Modern Language Association (MLA)
Song, Naiqi& Zhang, Jin-Tun. An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory. Journal of Applied Mathematics No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-463312
American Medical Association (AMA)
Song, Naiqi& Zhang, Jin-Tun. An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory. Journal of Applied Mathematics. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-463312
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-463312