rangemodelR (version 1.0.4)

range_shuffle: Range shuffle models for for range extents reccorded along gradients

Description

This function was used by Wang et. al.(2012) to test geometric constraints, on elevational gradients. The function randomizes the range extents and range location for each species and returns expected pattern in species richness under geometric constraints.

Usage

range_shuffle(x, boundary, var, interval, sites, reps, degen, lowest = NA,
  highest = NA)

Arguments

x

Input data for elevational extents of species.data.dataframe with names 'genus_species','min','max','range','mid', 'num_zones'. See Wang et.al (2012) for details. Species names column is optional.

boundary

nature of boundaries at the extremes of the gradien ie. either 'hard boundaries' that species cannot cross, or 'soft boundaries' that species can move across. Can be one of the following choises "hh", "sh", "hs", or "ss"

var

Predictor variable for constraining randomizations. dataframe with columns 'mid' and 'weights'. Where 'wights' provide relative chance of selecting a range location, typically based on environmental predictors

interval

Numeric. Interval between

sites

Numeric. Locations on domain for calculating species richness

reps

number of iterations

degen

logical. If TRUE save each randomized distribution.

lowest

Numeric. If analysis is only for a subset of the sampled gradient then the minimum point within the subset

highest

Numeric. If analysis is only for a subset of the sampled gradient then the maximum point within the subset

Value

If degen is FALSE, a data frame with four colums for mean, SD and confidence intervals of expected richness

• "mod.rich" mean richness of each site

• "mod.sd" standard deviation of species richness

• "q2.5" lower limit of the confidence interval

• "q97.5" upper limit of the confidence interval

If degen is TRUE, then a list containing above data frame and a list of all the randomized matrices

Details

range_shuffle impliments simulations described by Wang et.al (2012) to estimate effect of geometric and environmental constraints on pattern in species richness across spatial graidients. It calculates a vector of all possible range locations for each given range extent based on the conditions for geometric constraints given by 'boundary'. Range locations are randomized by sampling from this vector.

References

Wang, X., and J. Fang. 2012. Constraining null models with environmental gradients: a new method for evaluating the effects of environmental factors and geometric constraints on geographic diversity patterns. Ecography 35:1147-1159.

Examples

# NOT RUN {
data(rangedata)
range_shuffle_rnd <- range_shuffle(x=rangedata,boundary = 'hh',
                                   interval = 200, var = NULL,
                                   sites = seq(600,2400,200),
                                   reps =10,degen = FALSE)
range_shuffle_rnd
plot(range_shuffle_rnd$mean,ylab = "Species Richness",pch = 19)
# }