The *sharedFav* function below implements the graphical analyses of Acevedo et al. (2010, 2012) on biogeographical interactions. It takes two vectors of favourability values at different localities for, respectively, a stronger and a weaker competing species (or two equally strong competitors), and plots their favourableness or** shared favourability **to assess **potential competitive interactions**.

sharedFav <- function(strong_F, weak_F, conf = 0.95, main = "Shared favourability") {

stopifnot(length(strong_F) == length(weak_F))

opar <- par(no.readonly = T)

par(mar = c(4, 4, 2, 4.5))

F_intersection <- fuzzyOverlay(cbind(strong_F, weak_F), op = "intersection")

F_union <- fuzzyOverlay(cbind(strong_F, weak_F), op = "union")

Fovl <- sum(F_intersection, na.rm = TRUE) / sum(F_union, na.rm = TRUE)

brks <- seq(0, 1, by = 0.1)

bins <- 1:10

bin <- cut(F_intersection, breaks = brks, labels = bins)

strong_mean <- tapply(strong_F, INDEX = bin, FUN = mean)

weak_mean <- tapply(weak_F, INDEX = bin, FUN = mean)

strong_ci <- tapply(strong_F, INDEX = bin, FUN = function(x) t.test(x, conf.level = conf, na.action = na.pass)$conf.int)

weak_ci <- tapply(weak_F, INDEX = bin, FUN = function(x) t.test(x, conf.level = conf)$conf.int)

strong_ci[names(Filter(is.null, strong_ci))] <- NA

weak_ci[names(Filter(is.null, weak_ci))] <- NA

strong_ci_up <- unlist(lapply(strong_ci, `[`, 2))

strong_ci_dn <- unlist(lapply(strong_ci, `[`, 1))

weak_ci_up <- unlist(lapply(weak_ci, `[`, 2))

weak_ci_dn <- unlist(lapply(weak_ci, `[`, 1))

bin_size <- table(bin)

names(bin_size) <- names(bins)

props <- bin_size / length(bin)

bin <- as.integer(bin)

bar_plot <- barplot(rep(NA, length(bins)), ylim = c(0, 1), xlab = "Favourability intersection", ylab = "Mean favourability", names.arg = brks[-1], main = main)

col_bar <- "grey50"

col_ci <- "grey"

poly_left <- mean(bar_plot[2:3])

poly_right <- mean(bar_plot[8:9])

polygon(x = c(poly_left, poly_left, poly_right, poly_right), y = c(0, 1, 1, 0), col = "lightgrey", border = NA, density = 25, angle = -45)

par(new = TRUE)

barplot(props, col = col_bar, border = FALSE, xaxt = "n", yaxt = "n", add = TRUE)

axis(side = 4, col = col_bar, col.axis = col_bar, col.ticks = col_bar, col.lab = col_bar)

mtext(side = 4, line = 3, "Proportion of localities", col = col_bar)

abline(h = 0.8, col = "grey", lty = 3)

strong_x <- bar_plot - 0.1

weak_x <- bar_plot + 0.1

arrows(strong_x, strong_ci_dn, strong_x, strong_ci_up, code = 3, length = 0.03, angle = 90, col = col_ci)

arrows(weak_x, weak_ci_dn, weak_x, weak_ci_up, code = 3, length = 0.03, angle = 90, col = col_ci)

lines(x = strong_x, y = strong_mean, lwd = 2, lty = 1)

lines(x = weak_x, y = weak_mean, lwd = 2, lty = 2)

points(x = strong_x, y = strong_mean, pch = 20)

points(x = weak_x, y = weak_mean, pch = 15, cex = 0.8)

par(opar)

return(Fovl)

}

The function returns the **fuzzy overlap index** and a **shared favourability plot** similar to those of Acevedo et al. (2010, 2012). For more details, examples of use and additional references, see the help files of these functions in package *fuzzySim* >= 2.2, currently available on R-Forge. Special thanks to Pelayo Acevedo for clarifications on how to build the plot!

**References**

Acevedo P., Ward A.I., Real R. & Smith G.C. (2010) Assessing biogeographical relationships of ecologically related species using favourability functions: a case study on British deer. *Diversity and Distributions*, 16: 515-528

Acevedo P., Jiménez-Valverde A., Melo-Ferreira J., Real R. & Alves, P.C. (2012) Parapatric species and the implications for climate change studies: a case study on hares in Europe. *Global Change Biology*, 18: 1509-1519

Richerson P.J. & Lum K. (1980) Patterns of plant species and diversity in California: relation to weather and topography. *American Naturalist* 116: 504-536