blend: Calculate biotic homogenization using Jaccard's index and an...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/blend.R

Description

blend finds native landscape similarity and exotic species' contributions to landscape homogeneity using average pairwise Jaccard similarity (J.Bar) and an analytical approximation (J.Star) described in Harris et al. (2011, "Occupancy is nine-tenths of the law," The American Naturalist) and in blender.basics.

blend can be called on a list of data.frames or on a character vector pointing to a directory containing data as .csv files.

If blend is called multiple landscapes, it will analyze each one individually and then combine the results together in a blended.landscape.bundle, which has its own method for plotting.

Usage

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  blend(x, warn = FALSE)

Arguments

x

Either the file path to your landscapes as .csv files (character vector) or a list of data.frames. The files or data.frames must be named and structured as described below. On Windows, the directory must use either double backslashes or single forward slashes to separate directories (e.g., R cannot read "c:\Users\Dave\Data").

warn

Logical. Should blender warn you if it encounters problems when smoothing your data? Defaults to FALSE.

Details

J.Bar, J.Star, and P.Star are defined in Harris et al. (2011) and in the documentation for blender.basics.

blend expects a character vector pointing to .csv files on your hard drive or a list of data.frames.

The files or data.frames must be named to include a landscape ID (e.g. "Iowa" or "Region 7") before the word "native" or "exotic", separated by a space, as in the included PLANTS data set. blend needs these names to match for the native and exotic landscape in order to compare them. Any landscapes that do not have a counterpart will not be included in the output. If blend cannot find any matching native-exotic landscape pairs, it will return an error.

blend expects sites as columns and species as rows. In .csv files, the first row must be site names and the first column must be species names. If you input data as data.frames, these attributes should be included as dimnames instead. The column names, corresponding to site names, must match between the native and exotic landscapes.

The body of your files or data.frames should be 1s indicating species presence at a given site, or 0s for absences.

Value

blend returns a blended.landscape object if called on a single landscape or a blended.landscape.bundle if called on more than one. A bundle includes all of the below for each landscape, plus a summary data.frame.

blended.landscape objects contain:

name

The name of the landscape analyzed (e.g. "Nebraska" if the contents of x included "Nebraska native table" and "Nebraska exotic table")

J.Bar, J.Star

J.Bar is the average Jaccard similarity among sites in the native landscape (i.e. the average ratio of shared species to total species among pairs of sites).

J.Star is the approximation from Harris et al: average number of species shared between each pair of sites divided by the average number of species present at least once among pairs in the native landscape.

delta.J.Bar, delta.J.Star

delta.J.Bar is the increase or decrease in average Jaccard similarity observed when incorporating the full complement of exotic species

delta.J.Star is the corresponding increase or decrease in J.Star.

R2

The proportion of variance in single-species changes in J.Bar explained by variance in single-species changes in J.Star.

threshold

The proportion of sites that must be occupied by an exotic species for it to have no net effect on J.Bar, Calculated by smoothing the observed delta.J.Bars in species.delta.table using the loess function. Will return NA if data cannot be smoothed near this point.

p.Star

The proportion of sites that must be occupied by an exotic species for it to have no effect on mean similarity, according to the effective occupancy equation in Harris et al.(which is based on J.Star)

nadir

The level of exotic occupancy for which mean similarity is minimized. Calculated by smoothing the observed delta.J.Bars in species.delta.table using the loess function. Will return NA if data cannot be smoothed near this point.

results.table

A summary data.frame containing all the above information (except name).

species.delta.table

A data.frame containing delta.J.Bars and delta.J.Stars attributed to each of the exotic species in the exotic data table individually.

scoop

A set of points used for plotting the "scoop"-shaped model predictions

native, exotic

The original imported landscapes

If called on more than one landscape, blend produces a blended.landscape.bundle, which includes one blended.landscape for each landscape included, as well as a data.frame called summary that includes all the information from each landscape's resuls.table.

Author(s)

David Jay Harris <[email protected]>

References

Harris, D. J., K. G. Smith, and P. J. Hanly. 2011. "Occupancy is nine-tenths of the law: Occupancy rates determine the homogenizing and differentiating effects of exotic species" The American Naturalist.

See Also

blender.basics

Examples

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data(PLANTS)

wy.results = blend(PLANTS[c("WY native table", "WY exotic table")])

# print a summary of the results 
wy.results

# plot contributions of individual exotic species to mean similarity
plot(wy.results)

# blend a set of five landscapes simultaneously
five.results = blend(PLANTS[1:(5 * 2)])

## Not run: 
  # Alternative method of calling blend using a directory
  five.results = blend("Users/Dave/Documents/similarity stuff/state matrices")

## End(Not run)

# print a summary of the results across all landscapes
five.results

# plot predictions vs. observations across all landscapes
plot(five.results)

# plot contributions of individual exotic species to mean similarity in 
# the first landscape
plot(five.results[[1]])

blender documentation built on May 30, 2017, 4:10 a.m.