Competition Kernel Functions

Description

Functions representing the effect of a competitor on a subject plant, depending on distance and plant marks. For use in pairwise.

Usage

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powers.ker(imarks, jmarks, dists, dranks, par = list(pi=1, pj=1,
    pr=1, smark = 1))

staebler.ker(imarks, jmarks, dists, dranks, par = list(k=0.1, p=1,
    smark=1))

spurr.ker(imarks, jmarks, dists, dranks, par = list(type=1,
    smark=1))

Arguments

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.

Details

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).

Value

Vector of length equal to the length of dists.

Author(s)

Oscar García.

References

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.

See Also

pairwise