Competition Kernel Functions
Functions representing the effect of a competitor on a subject plant, depending on distance and plant marks. For use in
1 2 3 4 5 6 7 8
Marks for the subject plant, a 1-row data frame.
Data frame with marks for competitors
Vector of distances between the subject plant and the competitors.
List of parameters.
The values of
par must be given in the argument
pairwise, they are shown here as examples.
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
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.
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