Ecomorphological Convergence

Description

This data set gives ecomorphological informations about 129 bird species.

Usage

data(ecomor)

Format

ecomor is a list of 7 components.

forsub

is a data frame with 129 species, 6 variables (the feeding place classes): foliage, ground , twig , bush, trunk and aerial feeders. These dummy variables indicate the use (1) or no use (0) of a given feeding place by a species.

diet

is a data frame with 129 species and 8 variables (diet types): Gr (granivorous: seeds), Fr (frugivorous: berries, acorns, drupes), Ne (frugivorous: nectar), Fo (folivorous: leaves), In (invertebrate feeder: insects, spiders, myriapods, isopods, snails, worms), Ca (carnivorous: flesh of small vertebrates), Li (limnivorous: invertebrates in fresh water), and Ch (carrion feeder). These dummy variables indicate the use (1) or no use (0) of a given diet type by a species.

habitat

is a data frame with 129 species, 16 dummy variables (the habitats). These variables indicate the species presence (1) or the species absence (0) in a given habitat.

morpho

is a data frame with 129 species abd 8 morphological variables: wingl (Wing length, mm), taill (Tail length, mm), culml (Culmen length, mm), bilh (Bill height, mm), bilw (Bill width, mm), tarsl (Tarsus length, mm), midtl (Middle toe length, mm) and weig (Weight, g).

taxo

is a data frame with 129 species and 3 factors: Genus, Family and Order. It is a data frame of class 'taxo': the variables are factors giving nested classifications.

labels

is a data frame with vectors of the names of species (complete and in abbreviated form.

categ

is a data frame with 129 species, 2 factors : 'forsub' summarizing the feeding place and 'diet' the diet type.

Source

Blondel, J., Vuilleumier, F., Marcus, L.F., and Terouanne, E. (1984). Is there ecomorphological convergence among mediterranean bird communities of Chile, California, and France. In Evolutionary Biology (eds M.K. Hecht, B. Wallace and R.J. MacIntyre), 141–213, 18. Plenum Press, New York.

References

See a data description at http://pbil.univ-lyon1.fr/R/pps/pps023.pdf (in French).

Examples

data(ecomor)
ric <- apply(ecomor$habitat, 2, sum)
s.corcircle(dudi.pca(log(ecomor$morpho), scan = FALSE)$co)

forsub <- data.frame(t(apply(ecomor$forsub, 1,
     function (x) x/sum(x))))
pca1 <- dudi.pca(forsub, scan = FALSE, scale = FALSE)
s.arrow(pca1$c1)
w <- as.matrix(forsub)
s.label(w, clab = 0, add.p = TRUE, cpoi = 2)

diet <- data.frame(t(apply(ecomor$diet, 1,
     function (x) x/sum(x))))
pca2 <- dudi.pca(diet, scan = FALSE, scale = FALSE)
s.arrow(pca2$c1)
w <- as.matrix(diet)
s.label(w, clab = 0, add.p = TRUE, cpoi = 2)
## Not run: 
dmorpho <- dist.quant(log(ecomor$morpho), 3)
dhabitat <- dist.binary(ecomor$habitat, 1)
dtaxo <- dist.taxo(ecomor$taxo)

mantel.randtest(dmorpho, dhabitat)
RV.rtest(pcoscaled(dmorpho), pcoscaled(dhabitat), 999)
procuste.randtest(pcoscaled(dmorpho), pcoscaled(dhabitat))

ecophy <- taxo2phylog(ecomor$taxo, add.tools=TRUE)
table.phylog(ecomor$habitat, ecophy, clabel.n = 0.5, f = 0.6,
     clabel.c = 0.75, clabel.r = 0.5, csi = 0.75, cleg = 0)
plot.phylog(ecophy, clabel.n = 0.75, clabel.l = 0.75,
     labels.l = ecomor$labels[,"latin"])
mantel.randtest(dmorpho, dtaxo)
mantel.randtest(dhabitat, dtaxo)

## End(Not run)

Worked out examples

> library(ade4)
> ### Name: ecomor
> ### Title: Ecomorphological Convergence
> ### Aliases: ecomor
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(ecomor)
> ric <- apply(ecomor$habitat, 2, sum)
> s.corcircle(dudi.pca(log(ecomor$morpho), scan = FALSE)$co)

> 
> forsub <- data.frame(t(apply(ecomor$forsub, 1,
+      function (x) x/sum(x))))
> pca1 <- dudi.pca(forsub, scan = FALSE, scale = FALSE)
> s.arrow(pca1$c1)

> w <- as.matrix(forsub)
> s.label(w, clab = 0, add.p = TRUE, cpoi = 2)

> 
> diet <- data.frame(t(apply(ecomor$diet, 1,
+      function (x) x/sum(x))))
> pca2 <- dudi.pca(diet, scan = FALSE, scale = FALSE)
> s.arrow(pca2$c1)

> w <- as.matrix(diet)
> s.label(w, clab = 0, add.p = TRUE, cpoi = 2)
> 
> dmorpho <- dist.quant(log(ecomor$morpho), 3)

> dhabitat <- dist.binary(ecomor$habitat, 1)
> dtaxo <- dist.taxo(ecomor$taxo)
> 
> mantel.randtest(dmorpho, dhabitat)
Monte-Carlo test
Call: mantel.randtest(m1 = dmorpho, m2 = dhabitat)

Observation: 0.06043084 

Based on 999 replicates
Simulated p-value: 0.001 
Alternative hypothesis: greater 

     Std.Obs  Expectation     Variance 
3.2300379963 0.0000333508 0.0003496412 
> RV.rtest(pcoscaled(dmorpho), pcoscaled(dhabitat), 999)
Monte-Carlo test
Observation: 0.1269869 
Call: RV.rtest(df1 = pcoscaled(dmorpho), df2 = pcoscaled(dhabitat), 
    nrepet = 999)
Based on 999 replicates
Simulated p-value: 0.001 
> procuste.randtest(pcoscaled(dmorpho), pcoscaled(dhabitat))
Monte-Carlo test
Call: procuste.randtest(df1 = pcoscaled(dmorpho), df2 = pcoscaled(dhabitat))

Observation: 0.2959761 

Based on 999 replicates
Simulated p-value: 0.001 
Alternative hypothesis: greater 

     Std.Obs  Expectation     Variance 
4.6397769185 0.2301967320 0.0002009951 
> 
> ecophy <- taxo2phylog(ecomor$taxo, add.tools=TRUE)
> table.phylog(ecomor$habitat, ecophy, clabel.n = 0.5, f = 0.6,
+      clabel.c = 0.75, clabel.r = 0.5, csi = 0.75, cleg = 0)

> plot.phylog(ecophy, clabel.n = 0.75, clabel.l = 0.75,
+      labels.l = ecomor$labels[,"latin"])

> mantel.randtest(dmorpho, dtaxo)
Monte-Carlo test
Call: mantel.randtest(m1 = dmorpho, m2 = dtaxo)

Observation: 0.4350921 

Based on 999 replicates
Simulated p-value: 0.001 
Alternative hypothesis: greater 

      Std.Obs   Expectation      Variance 
 8.1344355854 -0.0007739536  0.0028711194 
> mantel.randtest(dhabitat, dtaxo)
Monte-Carlo test
Call: mantel.randtest(m1 = dhabitat, m2 = dtaxo)

Observation: 0.03270164 

Based on 999 replicates
Simulated p-value: 0.028 
Alternative hypothesis: greater 

      Std.Obs   Expectation      Variance 
 2.0002711863 -0.0000635870  0.0002683172 
> 
> 
> 
>