killSome: Remove most closely related individuals
In metricTester: Test Metric and Null Model Statistical Performance

Description Usage Arguments Details Value References Examples

Given a phylogenetic tree, a spatial arena of individuals with species identities, and arguments for the desired distance and percent removed, removes some of the most closely related individuals in the arena.

1	killSome(tree, arena.output, max.distance, proportion.killed)

`tree`	Phylo object
`arena.output`	A spatial arena with three columns: individuals (the species ID), X (the x axis location of that individual), and Y (the y axis location). The arena.output actually needs a number of other elements in order for later functions to work properly, so any modifications to the code should take note of this.
`max.distance`	The geographic distance within which geographically neighboring individuals should be considered to influence the individual in question.
`proportion.killed`	The percent of individuals in the total arena that should be considered (as a proportion, e.g. 0.5 = half).

This function identifies individuals in the most genetically clustered geographic neighborhoods, continues on to identify the most closely related individual to a focal individual, and randomly chooses whether to remove that individual or the focal individual. It expects a list with a number of additional elements beyond the arena (currently, the mean genetic relatedness of geographic neighborhoods, a vector of regional abundance [where each element is a species name, repeated as many times as is present in pool], and the dimensions of the arena).

A list of 5 elements: the average relatedness in the geographic neighbordhood of consideration (appended to any previous values that were fed into the function), the number of individuals killed, the original input regional abundance vector, the new spatial arena, and the dimensions of that arena.

Miller, E. T., D. R. Farine, and C. H. Trisos. 2016. Phylogenetic community structure metrics and null models: a review with new methods and software. Ecography DOI: 10.1111/ecog.02070

#simulate tree with birth-death process
tree <- geiger::sim.bdtree(b=0.1, d=0, stop="taxa", n=50)

#prep the data for the simulation
prepped <- prepSimulations(tree, arena.length=300, mean.log.individuals=2, 
	length.parameter=5000, sd.parameter=50, max.distance=20, proportion.killed=0.2,
	competition.iterations=5)

#use the competition simulation
positions <- competitionArena(prepped)

#in normal use, these parameters will be carried down from the simulations.input object
new.arena <- killSome(tree, arena.output=positions, max.distance=50, 
	proportion.killed=0.2)

#look at how number of individuals in arena changes
dim(positions$arena)
dim(new.arena$arena)