scape  R Documentation 
scape
simulates communities that are phylogenetically structured
scape( tree, scape.size = 10, g.center = 1, g.range = 1, g.repulse = 1, wd.all = 150, signal.center = TRUE, signal.range = TRUE, same.range = TRUE, repulse = TRUE, center.scale = 1, range.scale = 1, repulse.scale = 1, site.stoch.scale = 0.5, sd.center = 1, sd.range = 1, rho = NULL, th = 8 )
tree 

scape.size 
edge dimension of square landscape 
g.center 
strength of phylogenetic signal in species range centers 
g.range 
strength of phylogenetic signal in species range sizes 
g.repulse 
strength of phylogenetic repulsion 
wd.all 
niche width, larger values simulate broader range sizes 
signal.center 
simulate with phylosignal in range centers 
signal.range 
simulate with phylosignal in range size 
same.range 
make all range sizes equal 
repulse 
include phylogenetic repulsion in range centers 
center.scale 
adjust strength of phylogenetic attraction in
range centers independent of 
range.scale 
adjust strength of phylogenetic signal in range
size independent of 
repulse.scale 
adjust strength of phylogenetic repulsion
independent of 
site.stoch.scale 
adjust strength of random variation in species richness across sites 
sd.center 
sd in 
sd.range 
sd 
rho 
Grafen branch adjustment of phylogenetic tree see

th 
probability threshold 10^th above which species are considered present at a site 
Simulates a landscape with species (i.e., tree tips)
distributions dependent on a supplied phylogenetic tree. The
amount and type of structure is determened by the signal parameters
g.center
, g.range
and g.repulse
. Parameters
are based on an OrnsteinUhlenbeck model of evolution with
stabilizing selection. Values of g=1 indicate no stabilizing
selection and correspond to the Brownian motion model of evolution;
0<g<1 represents stabilizing selection; and g>1 corresponds to
disruptive selection where phylogenetic signal for the supplied
tree is amplified. See corBlomberg
. Communities are
simulated along two gradients where the positions along those
gradients, g.center
and range sizes g.range
, can
exhibit phylogenetic signal. Phylogenetic attraction is simulated
in the g.center
paramter, while repulsion in
g.repulse
. Both can be exhibited such that closly related
species are generally found with similar range centers
(phylogenetic attraction) but just not at the same site
(phylogenetic repulsion). The function then returns probabilities
of of each species across sites and the presence and absences of
species based a supplied threshold, th
, which can be
increased to obtain more species at sites and thus increase average
site species richness.
cc 

X.joint 
full probabilities of species at sites, used
to construct 
X1 
probabilities of species along gradient 1 
X2 
probabilities of species along gradient 2 
sppXs 
full probabilities of each species as an array
arranged in a 
V.phylo 
initial phylogenetic covariance matrix from tree 
V.phylo.rho 
phylogenetic covariance matrix from tree scaled by Grafen if rho is provided 
V.center 
scaled by 
V.range 
scaled by 
V.repulse 
scaled by 
bspp1 
species optima for gradient 1 
bspp2 
species optima for gradient 2 
u 
the env gradients values for the two gradients 
wd 
the denominator for species ranges 
M.R. Helmus, cosmetic changes by Will Pearse
Helmus M.R. & Ives A.R. (2012). Phylogenetic diversity area curves. Ecology, 93, S31S43.
eco.scape
sim.phy
sim.meta
#Create balanced tree with equal branchlengths (signal in centers) tree < stree(8,type="balanced") tree$edge.length < rep(1, nrow(tree$edge)) tree$root < 1 kk < scape(tree, scape.size=100, g.center=100, g.range=1, g.repulse=1, wd.all=150, signal.center=TRUE, signal.range=FALSE, same.range=FALSE, repulse=FALSE,center.scale = 1, range.scale = 1, repulse.scale = 1, site.stoch.scale = 0, sd.center=3, sd.range=1, rho=NULL, th=20) #Make some plots par(mfrow=c(1,Ntip(tree)),mar=c(.1,.1,.1,.1)) for(j in seq_along(tree$tip.label)) image(t(1  kk$sppXs[,,j]/max(kk$sppXs[,,j])), xlab = "", ylab = "",main = "",axes=FALSE, col=grey.colors(10)) par(mfrow=c(2,1)) matplot((kk$X1), type = "l", xlab="gradient",ylab = "probability", main = "Gradient 1",col=rainbow(dim(kk$X1)[2]),lty=1) matplot((kk$X2), type = "l", xlab="gradient",ylab = "probability", main = "Gradient 2",col=rainbow(dim(kk$X2)[2]),lty=1) plot(x=seq_along(sites(kk$cc)),y = rowSums(comm(kk$cc)), main = "SR",type = "l") cor(kk$X1)
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