Compute species association between each pair of species.
sp.pair(matr)
Chi Square matrix
V
positive or negative association
Ochiai's index
Dice's index
Jaccard's index
Pearson's correlation coefficient
Spearman's rank correlation coefficient
Point correlation coefficient
Association coefficient
Standard community matrix, with rows representing sites and columns representing species.
Jinlong Zhang jinlongzhang01@gmail.com
If a
, b
, c
, d
denote the co-occurrence the two species A and B, where:
a
= number of plots occupied both by A and B.
b
= number of plots only have A.
c
= number of plots only have B.
d
= number of plots without A or B.
N
= a+b+c+d
Then, it is possible to compute:
Chi square (Yate's correction): chi^{2}=((((a*d-b*c)-0.5*N)^2)*N)/(a+b)*(a+c)*(b+d)*(c+d)
V ratio: V = ((a+d)-(b+c))/(a + b + c + d)
Jaccard index: Jaccard =a/(a + b + c)
Ochiai index: Ochiai = a/sqrt((a+b)*(a+c))
Dice index: Dice = 2*a/(2*a + b + c)
The Association Coefficient(AC
):
if a*d >= b*c
:
AC = (a*d - b*c)/((a+b)*(b+d))
if a*d < b*c and a <= d
:
AC = (a*d - b*c)/((a+b)*(a+c))
if a*d < b*c and a > d
:
AC = (a*d - b*c)/((b+d)*(c+d))
Point correlation coefficient
(PCC
):
PCC = {a*d-b*c}/{(a+b)*(a+c)*(c+d)*(b+d)}
HURLBERT, S. H. (1969). A coefficient of interspecific association. Ecology, 50(1), 1-9.
WANG, B. S., & PENG S. L. (1985). Studies on the Measuring Techniques of Interspecific Association of Lower-Subtropical Evergreen-Broadleaved Forests. I. The Exploration and the Revision on the Measuring Formulas of Interspecific Association. Chinese Journal of Plant Ecology, 9(4), 274-285.
JIAN, M. F., LIU, Q. J., ZHU, D., & YOU, H. (2009). Inter-specific correlations among dominant populations of tree layer species in evergreen broad-leaved forest in Jiulianshan Mountain of subtropical China. Chinese Journal of Plant Ecology, 33(4), 672-680.
ZHOU, X. Y., WANG, B. S., LI, M. G., & ZAN, Q. J. (2000). An analysis of interspecific associations in secondary succession forest communities in Heishiding Natural Reserve, Guangdong Province. Chinese Journal of Plant Ecology, 24(3), 332-339
See Also as sp.assoc
for computing association for all the species.
data(testdata)
spmatrix <- data2mat(testdata)
result <- sp.pair(spmatrix)
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