Exploring zeta-diversity

References for zeta diversity

• R package publication 17 may 2018!
• R package manual
• Personal web of the author
• Original paper: Hui C. & McGeoch M. A. (2014) Zeta Diversity as a Concept and Metric That Unifies Incidence-Based Biodiversity Patterns. Am. Nat. 184, 684-94.
• McGeogch et al. 2017, The application of zeta diversity as a continuous measure of compositional change in ecology. bioRxiv.

Highlights by authors (Latombe et al. 2018)

• Zeta diversity is the mean number of species shared by any number of assemblages
• Zeta diversity captures all diversity components produced by assemblage partitioning biodiversity
• Analyses relate zeta diversity to space, environment and spatial scale
• Analyses differentiate the contribution of rare and common species to biodiversity

Our concerns (deCastro-Arrazola et al. 2018)

• it DOES NOT consider abundances!!! Can’t compare with Bray-Curtis dissimilarities.
• totally new framework, would require a complete change of point of view of the manuscript
• manuscript already includes too many analytical tools, adding would make the publication complicated.

Design a function to read datasets of four campaingns and converting abundance into incidence

(zeta-diversity can’t handle abundance data)

read.and.pa <- function(data.path) {
  raw <- read.table(data.path, header=T, sep=",")
  rownames(raw) <- raw$sites
  raw$sites <- NULL
  decostand(raw, "pa")
}

Read data

adry13 <- read.and.pa("adry13.csv")
adry14 <- read.and.pa("adry14.csv")
awet13 <- read.and.pa("awet13.csv")
awet14 <- read.and.pa("awet14.csv")

Calculate zeta diversity and visually explore patterns

Plot.zeta.decline(Zeta.decline.ex(adry13, orders = 1:10))
text(5, 5, "adry13", cex=3, font=2, col="red")

Plot.zeta.decline(Zeta.decline.ex(adry14, orders = 1:10))
text(5, 5, "adry14", cex=3, font=2, col="red")

Plot.zeta.decline(Zeta.decline.ex(awet13, orders = 1:10))
text(5, 5, "awet13", cex=3, font=2, col="blue")

Plot.zeta.decline(Zeta.decline.ex(awet14, orders = 1:10))
text(5, 5, "awet14", cex=3, font=2, col="blue")

Read coordinates data

points <- read.table("/home/indra/Documents/2013scarabs/publications/20160601.c3.sahara.tax/tables.and.figures/f1.map/dat.20140724.aridity.at.replicas.csv", header=T, dec=".")
replicate.coords <- points[, c("LONGITUD", "LATITUD")]
replicate.coords$point <- as.numeric(gsub("mor.|A|B", "", points$replicas))

library(plyr)
mor.sites <- ddply(replicate.coords, .(point), summarize,
      "x" = mean(LONGITUD),
      "y" = mean(LATITUD))
mor.coords <- mor.sites[,c("x","y")]

Distance decay at a all orders of zeta

par(mfrow=c(3,3), mar=c(4,4,1,1))
plot(mor.coords, main="Geografic distribution of sampling sites", asp=1)

orders <- 2:9
invisible(lapply(orders, function(order){
      Zeta.ddecay(mor.coords*100000, adry13, sam = 100, order = order, method.glm = "glm.fit2", confint.level = 0.95, trsf = "log", normalize = "Jaccard")
      }))

Distance decay slopes

zeta.ddecays.marion <- Zeta.ddecays(mor.coords*100000, adry13, sam = 100,
   orders = orders, plot = TRUE, confint.level = 0.95)

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