This function will simulate 3-dimensional landscapes of varying complexity.
varLandscape(cells, seeds = 1, exponent = 1, cutoff = 0)The number of cells to divide each side of the arena into. Larger values provide smoother looking surfaces, but values larger than 100 can require too much RAM to run.
The number of "peaks" or trait "optima" that will be chosen in the landscape. Default is 1.
The exponent to which the distances will be raised. Default is 1. Values larger than 1 have the effect of making distance decay slowly at first, then drop off more quickly at the end, while values smaller than 1 have the effect of dropping off quickly and then decreasing slowly.
Values below which distances from the focal cell will be converted to zero. This operates after the exponent is applied to the distance matrix, and after the distances specific to a given focal cell have been scaled to min 0 max 1. The default cutoff is zero, meaning that all but the most distinct cells are still influenced by the new optimum of the focal cell. Increasing this number towards 1 has the effect of minimizing the distance over which the focal cell influences neighboring cells.
A square matrix of dimensions cells x cells.
This function forms the guts of a new habitat filtering spatial simulation. The output from the function is a square matrix with values corresponding, in my mind, to optimum trait values for a location in 2d space. Alternatively, this might be useful for simulations of elevational gradients. A good sequence to show how landscapes can be varied might be (all with cells = 100 and exponent = 1) to change seeds from 1 to 2 to 10 while holding cutoff at 0. Then change cutoff from 0.01 to 0.1 to 0.9 while holding seeds at 10.
Miller, E. T. 2016. A new dispersal-informed null model for community ecology shows strong performance. biorxiv.
# NOT RUN {
plotrix::color2D.matplot(varLandscape(10, seeds=1, exponent=1, cutoff=0),
cs1=c(0.2,0.4,0.8), cs2=c(0,0.5,0.8), cs3=c(1,0.5,0), border=NA)
# }
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