An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory
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 applic...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/320905 |
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| _version_ | 1832568309046837248 |
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| author | Naiqi Song Jin-Tun Zhang |
| author_facet | Naiqi Song Jin-Tun Zhang |
| author_sort | Naiqi Song |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ad7cadbc5ec34ff59809f168c4c6776f |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-ad7cadbc5ec34ff59809f168c4c6776f2025-02-03T00:59:18ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/320905320905An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network TheoryNaiqi Song0Jin-Tun Zhang1School of Mathematical Sciences, Beijing Normal University, Beijing 100875, ChinaCollege of Life Sciences, Beijing Normal University, Beijing 100875, ChinaFunctional 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.http://dx.doi.org/10.1155/2013/320905 |
| spellingShingle | Naiqi Song Jin-Tun Zhang An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory Journal of Applied Mathematics |
| title | An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory |
| title_full | An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory |
| title_fullStr | An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory |
| title_full_unstemmed | An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory |
| title_short | An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory |
| title_sort | index for measuring functional diversity in plant communities based on neural network theory |
| url | http://dx.doi.org/10.1155/2013/320905 |
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