Functions representing the effect of a competitor on a subject plant, depending on distance and plant marks. For use in pairwise
.
1 2 3 4 5 6 7 8 |
imarks |
Marks for the subject plant, a 1-row data frame. |
jmarks |
Data frame with marks for competitors |
dists |
Vector of distances between the subject plant and the competitors. |
dranks |
Distance ranks. |
par |
List of parameters. |
The values of par
must be given in the argument kerpar
of pairwise
, they are shown here as examples.
smark
in par
indicates the location of the plant size variable in marks
. It can be a data frame column number, or a string id like "dbh".
Competition kernels seem to be limited only by the researchers imagination.
powers.ker
is a general form that includes many examples from the literature. If Si is the size of the subject plant, Sj the size of the competitor, and R is the distance between them, this kernel is (Sj^pj / Si^pi) / R^pr. For instance, the popular Hegyi's index corresponds to pi=1, pj=1, pr=1
.
This and other examples could be coded directly if computational efficiency is important.
staebler.ker
is the width of the overlap of zones of influence (ZOI), used by Staebler in 1951. Assumes that the ZOI radius is related to size S by k S^p + c.
spurr.ker
is an example of an index that depends on distance ranks: equations (9.5a), (9.5b) of Burkhart and Tomé (2012).
Vector of length equal to the length of dists
.
Oscar García.
http://forestgrowth.unbc.ca/siplab
Burkhart, H. E. and Tomé, M. (2012) Modeling Forest Trees and Stands. Springer.
García, O. “Siplab, a spatial individual-based plant modelling system”. Computational Ecology and Software 4(4), 215-222. 2014.
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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