Freshwater fish zoogeography in west Africa

This data set contains informations about faunal similarities between river basins in West africa.

Usage

data(westafrica)

Format

westafrica is a list containing the following objects :

tab: : a data frame with absence/presence of 268 species (rows) at 33 embouchures (columns)
spe.names: : a vector of string of characters with the name of species
spe.binames: : a data frame with the genus and species (columns) of the 256 species (rows)
riv.names: : a vector of string of characters with the name of rivers
atlantic: : a data frame with the coordinates of a polygon that represents the limits of atlantic (see example)
riv.xy: : a data frame with the coordinates of embouchures
lines: : a data frame with the coordinates of lines to complete the representation (see example)
cadre: : a data frame with the coordinates of points used to make the representation (see example)

Source

Data provided by B. Hugueny hugueny@mnhn.fr.

Paugy, D., Traoré, K. and Diouf, P.F. (1994) Faune ichtyologique des eaux douces d'Afrique de l'Ouest. In Diversité biologique des poissons des eaux douces et saumâtres d'Afrique. Synthèses géographiques, Teugels, G.G., Guégan, J.F. and Albaret, J.J. (Editors). Annales du Musée Royal de l'Afrique Centrale, Zoologie, 275, Tervuren, Belgique, 35–66.

Hugueny, B. (1989) Biogéographie et structure des peuplements de Poissons d'eau douce de l'Afrique de l'ouest : approches quantitatives. Thèse de doctorat, Université Paris 7.

References

Hugueny, B., and Lévêque, C. (1994) Freshwater fish zoogeography in west Africa: faunal similarities between river basins. Environmental Biology of Fishes, 39, 365–380.

Examples

data(westafrica)

if(!adegraphicsLoaded()) {
  s.label(westafrica$cadre, xlim = c(30, 500), ylim = c(50, 290),
    cpoi = 0, clab = 0, grid = FALSE, addax = 0)
  old.par <- par(no.readonly = TRUE)
  par(mar = c(0.1, 0.1, 0.1, 0.1))
  rect(30, 0, 500, 290)
  polygon(westafrica$atlantic, col = "lightblue")
  points(westafrica$riv.xy, pch = 20, cex = 1.5)
  apply(westafrica$lines, 1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 1))
  apply(westafrica$riv.xy,1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 1))
  text(c(175, 260, 460, 420), c(275, 200, 250, 100), c("Senegal", "Niger", "Niger", "Volta"))
  par(srt = 270)
  text(westafrica$riv.xy$x2, westafrica$riv.xy$y2-10, westafrica$riv.names, adj = 0, cex = 0.75)
  par(old.par)
  rm(old.par)
}


# multivariate analysis
afri.w <- data.frame(t(westafrica$tab))
afri.dist <- dist.binary(afri.w,1)
afri.pco <- dudi.pco(afri.dist, scannf = FALSE, nf = 3)
if(adegraphicsLoaded()) {
  G1 <- s1d.barchart(afri.pco$li[, 1:3], p1d.horizontal = FALSE, plabels.cex = 0)
} else {
  par(mfrow = c(3, 1))
  barplot(afri.pco$li[, 1])
  barplot(afri.pco$li[, 2])
  barplot(afri.pco$li[, 3])
}


if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) {
  # multivariate spatial analysis
  afri.neig <- neig(n.line = 33)
  afri.nb <- neig2nb(afri.neig)
  afri.listw <- spdep::nb2listw(afri.nb)
  afri.ms <- adespatial::multispati(afri.pco, afri.listw, scannf = FALSE, nfposi = 6, nfnega = 0)
  
  if(adegraphicsLoaded()) {
    G2 <- s1d.barchart(afri.ms$li[, 1:3], p1d.horizontal = FALSE, plabels.cex = 0)
    
    g31 <- s.label(afri.ms$li, plabels.cex = 0.75, ppoints.cex = 0, nb = afri.nb, plot = FALSE)
    g32 <- s.value(afri.ms$li, afri.ms$li[, 3], plot = FALSE)
    g33 <- s.value(afri.ms$li, afri.ms$li[, 4], plot = FALSE)
    g34 <- s.value(afri.ms$li, afri.ms$li[, 5], plot = FALSE)
    G3 <- ADEgS(list(g31, g32, g33, g34), layout = c(2, 2))
    
  } else {
    par(mfrow = c(3, 1))
    barplot(afri.ms$li[, 1])
    barplot(afri.ms$li[, 2])
    barplot(afri.ms$li[, 3])
    
    par(mfrow = c(2, 2))
    s.label(afri.ms$li, clab = 0.75, cpoi = 0, neig = afri.neig, cneig = 1.5)
    s.value(afri.ms$li, afri.ms$li[, 3])
    s.value(afri.ms$li, afri.ms$li[, 4])
    s.value(afri.ms$li, afri.ms$li[, 5])
  }
  summary(afri.ms)
}


#> 
#> Multivariate Spatial Analysis
#> Call: adespatial::multispati(dudi = afri.pco, listw = afri.listw, scannf = FALSE, 
#>     nfposi = 6, nfnega = 0)
#> 
#> Scores from the initial duality diagram:
#>            var        cum     ratio     moran
#> RS1 0.05660710 0.05660710 0.1635437 0.9598444
#> RS2 0.03641095 0.09301805 0.2687386 0.7372155
#> RS3 0.02776793 0.12078598 0.3489629 0.6735561
#> 
#> Multispati eigenvalues decomposition:
#>            eig        var     moran
#> CS1 0.05455534 0.05641792 0.9669861
#> CS2 0.03084339 0.03406832 0.9053395
#> CS3 0.02052642 0.02544985 0.8065439
#> CS4 0.01671907 0.01891409 0.8839480
#> CS5 0.01342381 0.01539323 0.8720593
#> CS6 0.01043363 0.01371212 0.7609053

par(mfrow = c(1, 1))
plot(hclust(afri.dist, "ward.D"), h = -0.2)