polar ordination
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1993 ◽  
Vol 92 (3) ◽  
pp. 129-138
Author(s):  
George P. Malanson ◽  
Claire Pavlik ◽  
Dan Ceilley

1991 ◽  
Vol 48 (3) ◽  
pp. 307-332 ◽  
Author(s):  
A. T. Oliveira-Filho ◽  
F. R. Martins

Quantitative descriptions of the woody flora and soil analyses are given for five areas of cerrado (savanna woodland) near Cuiabá, Mato Grosso state, Brazil. Comparisons are made of the floristic composition and community structure of these areas using polar ordination and hierarchical classification techniques.Descrições quantitativas da flora lenhosa e anâlises de solos são apresentadas para cinco áreas de cerrado nas proximidades de Cuiabá, Mato Grosso. São realizadas comparações entre estas areas em composição florística e estrutura comunitária utilizando técnicas de ordenação polar e classifiçãcao hierárquica.


1980 ◽  
Vol 37 (9) ◽  
pp. 1358-1364 ◽  
Author(s):  
Joseph M. Culp ◽  
Ronald W. Davies

Reciprocal averaging and polar ordination techniques were applied to lotic macroinvertebrate field data to determine the relative performance of these techniques in lotic benthic community analyses. It was found that, because of inherent problems with endpoint determinations, polar ordination should only be used where a well-defined environmental gradient exists. Reciprocal averaging ordination produced interpretable axes and was preferred over polar ordination because endpoint determination was objective and simultaneous species–site ordinations were produced. Reciprocal averaging ordination allowed groupings of sites similar to those determined through cluster analysis to be recognized, while providing a visual representation of between-group relationships superior to that of cluster dendrograms. Combined with subsequent analyses of environmental data, reciprocal averaging ordination can be an excellent technique for summarizing spatial and temporal patterns of lotic macroinvertebrate communities.Key words: ordination, reciprocal averaging, benthic, lotic, communities, clustering


Vegetatio ◽  
1980 ◽  
Vol 40 (3) ◽  
Author(s):  
H.G. Gauch ◽  
W.M. Scruggs
Keyword(s):  

1979 ◽  
Vol 45 ◽  
Author(s):  
P. Van Hecke ◽  
I. Impens ◽  
T. Van Tilborgh

From  115 quadrats, laid out in the same way and on the same coordinates as  described in a first paper, cover-abundance and height data were collected in  april 1976 on 58 taxa, belonging to the upper and lower herb layer, on the  moss layer as a whole and on the litter. They were submitted to four  classification methods, namely Unweighted pair-group centroid technique  (UPGC), normal Association analysis (NAA) , agglomerative normal and inverse  Information analysis (NIA and IIA), Minimum variance clustering (MVC), and to  four ordination methods, namely Beals' Polar ordination (POS), Simple  ordination (SO), Optimized polar ordination (OPO) and Position vectors  ordination (PVO).     The only divisive method (NAA) produces more than sufficient results: they  are slightly better with the 1%-stopping rule especially when no species  reduction is involved. NAA and NIA give quite similar results. From the three  agglomerative techniques, the greatest number of vegetation- clusters (7),  recognizable in the field, has been obtained with MVC, the poorest picture  however by UPGC. With regard to the forest-structure, the better results are  with NIA, followed by MVC. Moreover. NIA applied in a quantitative way, is  less appropriate. The outcome of species classification is not  interpretable.    The ordination results obtained by OPO and PVO are better as compared to  those from POB and SO, as well in discovering gradients as clusters, the  gradients particularly reflect changes in cover-abundance and height. In the  ordination of vegetation quadrats, the total of the efficiency ratios  extracted for the first three axes in PVO and GPO are respectively four and  three times higher than with SO. Concerning the structure quadrats, the  percentage extraction values are very high and very alike.    Comparing the spring with the summer forest, UPGC distinguishes more  identifiable vegetation types in the summer data, NAA on the contrary in the  spring data.


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