Analysis of spatial association between two species based on the interspecies mean crowding |
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Authors: | Syun’iti Iwao |
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Institution: | (1) Laboratory of Applied Entomology and Nematology, Faculty of Agriculture, Nagoya University, 464 Nagoya, Japan |
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Abstract: | Summary and Conclusion The measurement of spatial association between two species is considered on the basis of interspecies mean crowding. Two indices
of overlapping, γ andC
μ, are derived as geometric and weighted arithmetic means of the same component ratios related to inter- and intraspecies mean
crowdings. Both indices behave in a similar way, ranging from 1 when the distributions of two species are completely overlapped
to 0 when they are completely exclusive with each other. The former is essentially identical with indices proposed byKuno (1968) andPianka (1973), and the latter is a modified form ofMorisita’s (1959)C
δ index. Indices to measure the degree of spatial correlation between species, ω andR
μ, are then derived for both kinds of overlapping indices, which vary from 1 in complete overlapping, through 0 in independent
occurrence, to −1 in complete exclusion.
Various kinds of interspecies association are analyzed using these indices and an extended form of the
regression graph which provides a convenient way of indicating the spatial interrelation between two species as well as distribution
patterns of respective species.
The method presented in this paper may also be applicable to compare temporal distribution patterns between species, similarity
between communities, etc. For such a wider application which includes continuous as well as discrete distributions, the interpretation
of intra- and interspecies mean crowdings is not necessarily appropriate, and hence the concept of mean concentration with
the symbols
and
for intraspecies relation and
and
for interspecies relation is suggested.
This study was supported by Science Research Fund (No. 148041) from the Ministry of Education. |
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Keywords: | Spatial Association Stem Borer Quadrat Size Independent Distribution Weighted Arithmetic |
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