Description Usage Arguments Details Value Note Author(s) References Examples
This function computes coefficients of dispersal direction between geographically connected areas, as defined by Legendre and Legendre (1984), and also described in Legendre and Legendre (2012, section 13.3.4).
1  bgdispersal(mat, PAonly = FALSE, abc = FALSE)

mat 
Data frame or matrix containing a community composition data table (species presenceabsence or abundance data). 
PAonly 

abc 
If 
The signs of the DD coefficients indicate the direction of dispersal, provided that the asymmetry is significant. A positive sign indicates dispersal from the first (row in DD tables) to the second region (column); a negative sign indicates the opposite. A McNemar test of asymmetry is computed from the presenceabsence data to test the hypothesis of a significant asymmetry between the two areas under comparison.
In the input data table, the rows are sites or
areas, the columns are taxa. Most often, the taxa
are species, but the coefficients can be computed
from genera or families as well. DD1 and DD2 only
are computed for presenceabsence data. The four
types of coefficients are computed for
quantitative data, which are converted to
presenceabsence for the computation of DD1 and
DD2. PAonly = FALSE
indicates that the four types
of coefficients are requested. PAonly = TRUE
if DD1
and DD2 only are sought.
Function bgdispersal
returns a list containing the following matrices:
DD1 
DD1[j,k] = (a * (b  c))/((a + b + c)^2) 
DD2 
DD2[j,k] = (2*a * (b  c))/((2*a + b + c) * (a + b + c)) where a, b, and c have the same meaning as in the computation of binary similarity coefficients. 
DD3 
DD3[j,k] = W*(AB) / (A+BW)^2 
DD4 
DD4[j,k] = 2*W*(AB) / ((A+B)*(A+BW))
where 
McNemar 
McNemar chisquare statistic of asymmetry (Sokal and Rohlf 1995): 2*(b*log(b) + c*log(c)  (b+c)*log((b+c)/2)) / q, where q = 1 + 1/(2*(b+c)) (Williams correction for continuity) 
prob.McNemar 
probabilities associated with McNemar statistics, chisquare test. H0: no asymmetry in (bc). 
The function uses a more powerful alternative for the McNemar test
than the classical formula. The classical formula was constructed in
the spirit of Pearson's Chisquare, but the formula in this function
was constructed in the spirit of Wilks Chisquare or the G
statistic. Function mcnemar.test
uses the classical
formula. The new formula was introduced in vegan version
1.1011, and the older implementations of bgdispersal
used the
classical formula.
Pierre Legendre, Departement de Sciences Biologiques, Universite de Montreal
Legendre, P. and V. Legendre. 1984. Postglacial dispersal of freshwater fishes in the Québec peninsula. Can. J. Fish. Aquat. Sci. 41: 17811802.
Legendre, P. and L. Legendre. 2012. Numerical ecology, 3rd English edition. Elsevier Science BV, Amsterdam.
Sokal, R. R. and F. J. Rohlf. 1995. Biometry. The principles and practice of statistics in biological research. 3rd edn. W. H. Freeman, New York.
1 2 3 4  mat < matrix(c(32,15,14,10,70,30,100,4,10,30,25,0,18,0,40,
0,0,20,0,0,0,0,4,0,30,20,0,0,0,0,25,74,42,1,45,89,5,16,16,20),
4, 10, byrow=TRUE)
bgdispersal(mat)

Loading required package: permute
Loading required package: lattice
This is vegan 2.54
$DD1
[,1] [,2] [,3] [,4]
[1,] 0.00 0.24 0.21 0.00
[2,] 0.24 0.00 0.08 0.24
[3,] 0.21 0.08 0.00 0.21
[4,] 0.00 0.24 0.21 0.00
$DD2
[,1] [,2] [,3] [,4]
[1,] 0.0000000 0.3428571 0.3230769 0.0000000
[2,] 0.3428571 0.0000000 0.1142857 0.3428571
[3,] 0.3230769 0.1142857 0.0000000 0.3230769
[4,] 0.0000000 0.3428571 0.3230769 0.0000000
$DD3
[,1] [,2] [,3] [,4]
[1,] 0.00000000 0.1567922 0.1420408 0.01325831
[2,] 0.15679216 0.0000000 0.1101196 0.20049485
[3,] 0.14204082 0.1101196 0.0000000 0.13586560
[4,] 0.01325831 0.2004949 0.1358656 0.00000000
$DD4
[,1] [,2] [,3] [,4]
[1,] 0.00000000 0.2513176 0.2425087 0.01960102
[2,] 0.25131757 0.0000000 0.1725441 0.30993929
[3,] 0.24250871 0.1725441 0.0000000 0.23381521
[4,] 0.01960102 0.3099393 0.2338152 0.00000000
$McNemar
[,1] [,2] [,3] [,4]
[1,] NA 7.677938 9.0571232 0.000000
[2,] NA NA 0.2912555 7.677938
[3,] NA NA NA 9.057123
[4,] NA NA NA NA
$prob.McNemar
[,1] [,2] [,3] [,4]
[1,] NA 0.005590001 0.002616734 1.000000000
[2,] NA NA 0.589417103 0.005590001
[3,] NA NA NA 0.002616734
[4,] NA NA NA NA
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