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Integrating models of relative abundance of species with the dry-weight-rank method for the botanical analysis of forest understorey vegetation

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Title Integrating models of relative abundance of species with the dry-weight-rank method for the botanical analysis of forest understorey vegetation
 
Creator González-Estrada, E.
Fawcett, R.H.
Herrero, M.
 
Subject FORAGE
FORESTS
 
Description The objective of this work was to determine the applicability of the dry-weight-rank (DWR) method for evaluating the botanical composition of forest understorey vegetation. An analysis of plant species abundance was carried out, and instead of ranking the three most abundant species, as is commonly used, up to twelve ranks were scored. Concurrently, four models of relative abundance distribution (RAD) of species were compared for their ability to explain the abundance of species in the study area. The Power-fraction model resulted in the best goodness-of-fit and it was subsequently used to produce the coefficients for the DWR method. Lin's concordance correlation coefficient, the adjusted coefficient of determination, the residual standard deviation and Spearman's rank-order correlation coefficient indicated a good performance of the DWR method. Biomass data and the Shannon index for diversity were also considered. Further analyses showed that there was a trade-off between the number of ranks scored and the accuracy of the botanical composition produced by the DWR method. It is concluded that, so long as the RAD model that explains the distribution of plant species is known, the DWR method can be applied to forest understorey vegetation.
 
Date 2010-08-07T12:05:26Z
2010-08-07T12:05:26Z
2002
 
Type Journal Article
 
Identifier González-Estrada, E.; Fawcett, R.H.; Herrero, M. 2002. Integrating models of relative abundance of species with the dry-weight-rank method for the botanical analysis of forest understorey vegetation. Grass & Forage Science 57(2):171-183.
http://hdl.handle.net/10568/2199
 
Language en
 
Source Grass and Forage Science