fuzzySim (version 2.0)

fuzzySim-package: Fuzzy Similarity in Species Distributions

Description

Functions to calculate fuzzy versions of species' occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis and prevalence-independent favourability GLM), and pair-wise fuzzy similarity (based on fuzzy versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for data preparation, such as obtaining unique abbreviations of species names, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, assessing the false discovery rate, or analysing and dealing with multicollinearity among variables. Includes also sample datasets for providing practical examples. A step-by-step illustrated tutorial is available from the package homepage (http://fuzzysim.r-forge.r-project.org).

Details

Package:	fuzzySim
Type:	Package
Version:	2.0
Date:	2018-12-05
License:	GPL-3

References

Barbosa A.M. (2015) fuzzySim: applying fuzzy logic to binary similarity indices in ecology. Methods in Ecology and Evolution, 6: 853-858.

Examples

# NOT RUN {
data(rotifers)

head(rotifers)


# add column with species name abbreviations:

rotifers$spcode <- spCodes(rotifers$species, sep.species = "_", 
nchar.gen = 1, nchar.sp = 5, nchar.ssp = 0)

head(rotifers)


# convert species list (long format) to presence-absence table 
# (wide format):

rotifers.presabs <- splist2presabs(rotifers, sites.col = "TDWG4",
sp.col = "spcode", keep.n = FALSE)

head(rotifers.presabs)


# get 3rd-degree spatial trend surface for some species distributions:

data(rotif.env)

names(rotif.env)

rotifers.tsa <- multTSA(rotif.env, sp.cols = 18:20,
coord.cols = c("Longitude", "Latitude"), id.col = 1)

head(rotifers.tsa)


# get inverse squared distance to presence for each species:

rotifers.isqd <- distPres(rotif.env, sp.cols = 18:20,
coord.cols = c("Longitude", "Latitude"), id.col = 1, p = 2, inv = TRUE)

head(rotifers.isqd)


# get prevalence-independent environmental favourability models 
# for each species:

data(rotif.env)

names(rotif.env)

rotifers.fav <- multGLM(data = rotif.env, sp.cols = 18:20, 
var.cols = 5:17, id.col = 1, step = FALSE, trim = TRUE, 
Favourability = TRUE)


# get matrix of fuzzy similarity between species distributions:

# either based on inverse squared distance to presence:
rot.fuz.sim.mat <- simMat(rotifers.isqd[ , -1], method = "Jaccard")

# or on environmental favourability for presence:
rot.fuz.sim.mat <- simMat(rotifers.fav$predictions[ , 5:7], 
method = "Jaccard")

head(rot.fuz.sim.mat)


# transpose fuzzy rotifer distribution data to compare
# regional species composition rather than species' distributions:

names(rotifers.isqd)

rot.fuz.reg <- transpose(rotifers.fav$predictions, sp.cols = 5:7,
reg.names = 1)

head(rot.fuz.reg)


# get matrix of fuzzy similarity between (some) regions' 
# species compositions:

reg.fuz.sim.mat <- simMat(rot.fuz.reg[ , 1:10], method = "Jaccard")

head(reg.fuz.sim.mat)
# }