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Welcome to fuzzySim project!

fuzzySim is an R package for calculating fuzzy similarity in species occurrence patterns.

It includes functions for data preparation, such as converting species lists (long format) to presence-absence tables (wide format), obtaining unique abbreviations of species names, or transposing (parts of) complex data frames; and sample data sets for providing practical examples.

It can convert binary presence-absence to fuzzy occurrence data, using e.g. trend surface analysis, inverse distance interpolation or prevalence-independent environmental favourability modelling, for multiple species simultaneously.

It then calculates fuzzy similarity among (fuzzy) species distributions and/or among (fuzzy) regional species compositions. Currently available similarity indices are Jaccard, Sørensen, Simpson, and Baroni-Urbani & Buser.

Some of the fuzzySim functions are also being implemented within a graphical-interface extension for the QGIS Processing Toolbox - you can download their current versions from here, place them in your ".qgis2/processing/rscripts" folder (search for it in your computer; you may need to toggle "show hidden files" to see it) and give them a try. You need to have installed QGIS > 2.0, R with the fuzzySim package, and tell QGIS (under Processing - Options and configuration - Providers) where your R instalation is. Feedback welcome!

Install and load

To install fuzzySim directly from R-Forge, paste the following command in the R console (when connected to the internet):

install.packages("fuzzySim", repos="http://R-Forge.R-project.org")

This should work if you have the latest version of R; otherwise, it may either fail (producing a message like "package 'fuzzySim' is not available for your R version") or install an older version of fuzzySim. To check the version that you have actually installed, type citation(package="fuzzySim"). To install the latest version of the package, you can either upgrade R or download the compressed fuzzySim package source files to your disk (.zip or .tar.gz available here or here) and then install the package from there, e.g. with R menu "Packages - Install packages from local zip files" (Windows), or "Packages & Data - Package installer, Packages repository - Local source package" (Mac), or "Tools - Install packages - Install from: Package Archive File" (RStudio).

You only need to install (each version of) the package once, but then every time you re-open R you need to load it by typing:


You can then check out the package help files and try some of the provided examples:



If you use fuzzySim in publications, please cite the following paper:

Barbosa A.M. (2015) fuzzySim: applying fuzzy logic to binary similarity indices in ecology. Methods in Ecology and Evolution, 6: 853-858 (DOI: 10.1111/2041-210X.12372)

To see how to cite the package itself, load it in R and type citation(package="fuzzySim").

Articles citing fuzzySim

A number of articles have already used or cited fuzzySim in diverse studies, from biogeography and evolutionary ecology to climatology or robotics -- here are just a few examples, excluding self-citations:

Broeckhoven C., El Adak Y., Hui C., Van Damme R. & Stankowich T. (2018) On dangerous ground: the evolution of body armour in cordyline lizards. Proceedings of the Royal Society B: Biological Sciences/ 285(1880): 20180513. DOI: 10.1098/rspb.2018.0513

Caravaggi A., Leach K., Santilli F., Rintala J., Helle P., Tiainen J., Bisi F., Martinoli A., Montgomery W.I. & Reid N. (2017) Niche overlap of mountain hare subspecies and the vulnerability of their ranges to invasion by the European hare; the (bad) luck of the Irish. Biological Invasions, 19: 655-674. DOI: 10.1007/s10530-016-1330-z

Caraveo C., Valdez F. & Castillo O. (2017) A new meta-heuristics of optimization with dynamic adaptation of parameters using type-2 fuzzy logic for trajectory control of a mobile robot. Algorithms, 10(3): 85. DOI: 10.3390/a10030085

Coelho L., Romero D., Queirolo D. & Guerrero J.C. (2018) Understanding factors affecting the distribution of the maned wolf (Chrysocyon brachyurus) in South America: Spatial dynamics and environmental drivers. Mammalian Biology, 92: 54-61. DOI: 10.1016/j.mambio.2018.04.006

Dick, DG & Laflamme, M (2018) Fuzzy ecospace modelling. Methods in Ecology and Evolution, 9(6): 1442-1452 (DOI: 10.1111/2041-210X.13010)

Herkt K.M.B., Skidmore A.K., Fahr J. (2017) Macroecological conclusions based on IUCN expert maps: A call for caution. Global Ecology and Biogeography, 26: 930-941. DOI: 10.1111/geb.12601

Title, P.O. & Bemmels, J. (2018) ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41: 291-307. DOI:10.1111/ecog.02880

Find out more

There's an illustrated beginners tutorial of fuzzySim (updated 22 Jul 2015), and a reference manual based on the package help files.

Here's a poster made to present fuzzySim at Rencontres R 2014.

There's also a beginners tutorial on species distribution modelling with fuzzySim (updated 29 Oct 2015), and a course manual on model building with fuzzySim and model evaluation with modEvA (in Spanish).

Recently, I've added a tutorial on rangemap comparison (updated 12 Feb 2016).

Go here for further info on the package and its origins.

The R-Forge project summary page you can find here.