woangers {ade4}R Documentation

Plant assemblages in woodlands of the conurbation of Angers (France)

Description

This data set gives the presence of plant species in relev├ęs of woodlands in the conurbation of Angers; and their biological traits.

Usage

data(woangers)

Format

woangers is a list of 2 components.

  1. flois a data frame that contains the presence/absence of species in each sample site. In the codes for the sample sites (first column of the data frame), the first three letters provide the code of the woodland and the numbers represent the 5 quadrats sampled in each site. Codes for the woodlands are based on either their local name when they have one or on the name of the nearest locality.

  2. traitsis a data frame that contains the values of the 13 functional traits considered in the paper. One trait can be encoded by several columns. The codes are as follows:
    1. Column 1:Species names;
    2. Column 2:li, nominal variable that indicates the presence (y) or absence (n) of ligneous structures;
    3. Column 3:pr, nominal variable that indicates the presence (y) or absence (n) of prickly structures;
    4. Column 4:fo, circular variable that indicates the month when the flowering period starts (from 1 January to 9 September);
    5. Column 5:he, ordinal variable that indicates the maximum height of the leaf canopy;
    6. Column 6:ae, ordinal variable that indicates the degree of aerial vegetative multiplication;
    7. Column 7:un, ordinal variable that indicates the degree of underground vegetative multiplication;
    8. Column 8:lp, nominal variable that represents the leaf position by 3 levels (ros = rosette, semiros = semi-rosette and leafy = leafy stem);
    9. Column 9:le, nominal variable that represents the mode of leaf persistence by 5 levels (seasaes = seasonal aestival, seashib = seasonal hibernal, seasver = seasonal vernal, everalw = always evergreen, everparti = partially evergreen);
    10. Columns 10, 11 and 12:fuzzy variable that describes the modes of pollination with 3 levels (auto = autopollination, insects = pollination by insects, wind = pollination by wind); this fuzzy variable is expressed as proportions, i.e. for each row, the sum of the three columns equals 1;
    11. Columns 13, 14 and 15:fuzzy variable that describes the life cycle with 3 levels (annual, monocarpic and polycarpic); this fuzzy variable is expressed as proportions, i.e. for each row, the sum of the three column equals 1;
    12. Columns 16 to 20:fuzzy variable that describes the modes of dispersion with 5 levels (elaio = dispersion by ants, endozoo = injection by animals, epizoo = external transport by animals, wind = transport by wind, unsp = unspecialized transport); this fuzzy variable is expressed as proportions, i.e. for each row, the sum of the three columns equals 1;
    13. Column 21:lo, quantitative variable that provides the seed bank longevity index;
    14. Column 22:lf, quantitative variable that provides the length of the flowering period.

Source

Pavoine, S., Vallet, J., Dufour, A.-B., Gachet, S. and Daniel, H. (2009) On the challenge of treating various types of variables: Application for improving the measurement of functional diversity. Oikos, 118, 391–402.

Examples

# Loading the data
data(woangers)

# Preparating of the traits
traits <- woangers$traits
# Nominal variables 'li', 'pr', 'lp' and 'le'
# (see table 1 in the main text for the codes of the variables)
tabN <- traits[, c(1:2, 7, 8)]
# Circular variable 'fo'
tabC <- traits[3]
tabCp <- prep.circular(tabC, 1, 12)
# The levels of the variable lie between 1 (January) and 12 (December).
# Ordinal variables 'he', 'ae' and 'un'
tabO <- traits[, 4:6]
# Fuzzy variables 'mp', 'pe' and 'di'
tabF <- traits[, 9:19]
tabFp <- prep.fuzzy(tabF, c(3, 3, 5), labels = c("mp", "pe", "di"))
# 'mp' has 3 levels, 'pe' has 3 levels and 'di' has 5 levels.
# Quantitative variables 'lo' and 'lf'
tabQ <- traits[, 20:21]

# Combining the traits
ktab1 <- ktab.list.df(list(tabN, tabCp, tabO, tabFp, tabQ))
## Not run: 
# Calculating the distances for all traits combined
distrait <- dist.ktab(ktab1, c("N", "C", "O", "F", "Q"))
is.euclid(distrait)

# Calculating the contribution of each trait in the combined distances
contrib <- kdist.cor(ktab1, type = c("N", "C", "O", "F", "Q"))
contrib
dotchart(sort(contrib$glocor), labels = rownames(contrib$glocor)[order(contrib$glocor[, 1])])

## End(Not run)

Worked out examples


> library(ade4)
> ### Name: woangers
> ### Title: Plant assemblages in woodlands of the conurbation of Angers
> ###   (France)
> ### Aliases: woangers
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # Loading the data
> data(woangers)
> 
> # Preparating of the traits
> traits <- woangers$traits
> # Nominal variables 'li', 'pr', 'lp' and 'le'
> # (see table 1 in the main text for the codes of the variables)
> tabN <- traits[, c(1:2, 7, 8)]
> # Circular variable 'fo'
> tabC <- traits[3]
> tabCp <- prep.circular(tabC, 1, 12)
> # The levels of the variable lie between 1 (January) and 12 (December).
> # Ordinal variables 'he', 'ae' and 'un'
> tabO <- traits[, 4:6]
> # Fuzzy variables 'mp', 'pe' and 'di'
> tabF <- traits[, 9:19]
> tabFp <- prep.fuzzy(tabF, c(3, 3, 5), labels = c("mp", "pe", "di"))
> # 'mp' has 3 levels, 'pe' has 3 levels and 'di' has 5 levels.
> # Quantitative variables 'lo' and 'lf'
> tabQ <- traits[, 20:21]
> 
> # Combining the traits
> ktab1 <- ktab.list.df(list(tabN, tabCp, tabO, tabFp, tabQ))
>  
> # Calculating the distances for all traits combined
> distrait <- dist.ktab(ktab1, c("N", "C", "O", "F", "Q"))
> is.euclid(distrait)
[1] FALSE
> 
> # Calculating the contribution of each trait in the combined distances
> contrib <- kdist.cor(ktab1, type = c("N", "C", "O", "F", "Q"))
> contrib
$paircov
              li            pr           lp            le            fo
li  0.2500534939  2.471981e-02  0.038851022  0.0200445124  1.447475e-03
pr  0.0247198080  1.997226e-01 -0.030237798 -0.0018367708 -4.197371e-05
lp  0.0388510224 -3.023780e-02  0.244163852  0.0367008519 -5.703983e-03
le  0.0200445124 -1.836771e-03  0.036700852  0.2154214983 -1.559745e-03
fo  0.0014474751 -4.197371e-05 -0.005703983 -0.0015597448  4.528208e-02
he  0.0680421313  8.466469e-04  0.025604692  0.0212553246 -1.418924e-03
ae -0.0018812563 -1.601963e-02  0.008221023  0.0057194746  1.306038e-02
un  0.0036063382  1.516450e-02 -0.010585786  0.0143104555  7.271514e-03
mp  0.0024114632 -1.555895e-02  0.013304589 -0.0069409683 -2.963572e-03
pe -0.0003519038 -1.142511e-02  0.004390976  0.0097460052 -6.007740e-03
di  0.0592153472 -1.385170e-02  0.044325306  0.0145622107 -2.335351e-03
lo  0.0091396503  9.856243e-03 -0.001868482  0.0121343465  3.625808e-03
lf  0.0002981235  4.051590e-03  0.003811518  0.0003994357 -1.510210e-03
              he            ae            un            mp            pe
li  0.0680421313 -0.0018812563  0.0036063382  0.0024114632 -0.0003519038
pr  0.0008466469 -0.0160196292  0.0151645015 -0.0155589542 -0.0114251127
lp  0.0256046923  0.0082210230 -0.0105857858  0.0133045886  0.0043909764
le  0.0212553246  0.0057194746  0.0143104555 -0.0069409683  0.0097460052
fo -0.0014189236  0.0130603753  0.0072715136 -0.0029635716 -0.0060077398
he  0.0574719777  0.0070992310 -0.0010677103  0.0041920534 -0.0082371855
ae  0.0070992310  0.1326613899 -0.0055169804 -0.0119644203 -0.0081662316
un -0.0010677103 -0.0055169804  0.1728843882 -0.0058348243 -0.0072676018
mp  0.0041920534 -0.0119644203 -0.0058348243  0.1950443384  0.0008347567
pe -0.0082371855 -0.0081662316 -0.0072676018  0.0008347567  0.0877808785
di  0.0241651036 -0.0009142773  0.0066962660  0.0278340279  0.0095001949
lo  0.0033306640 -0.0030305774  0.0078721307 -0.0089371746 -0.0077967582
lf -0.0026447598 -0.0002323546  0.0001973648  0.0034482577  0.0004945621
              di           lo            lf
li  0.0592153472  0.009139650  0.0002981235
pr -0.0138516994  0.009856243  0.0040515896
lp  0.0443253062 -0.001868482  0.0038115180
le  0.0145622107  0.012134346  0.0003994357
fo -0.0023353509  0.003625808 -0.0015102103
he  0.0241651036  0.003330664 -0.0026447598
ae -0.0009142773 -0.003030577 -0.0002323546
un  0.0066962660  0.007872131  0.0001973648
mp  0.0278340279 -0.008937175  0.0034482577
pe  0.0095001949 -0.007796758  0.0004945621
di  0.1841159926  0.014572402  0.0024682582
lo  0.0145724018  0.061043909 -0.0014773201
lf  0.0024682582 -0.001477320  0.0209363204

$paircor
             li            pr          lp           le            fo
li  1.000000000  0.1106152512  0.15723358  0.086362888  0.0136028981
pr  0.110615251  1.0000000000 -0.13692913 -0.009009945 -0.0004413677
lp  0.157233581 -0.1369291310  1.00000000  0.159827877 -0.0542468263
le  0.086362888 -0.0090099446  0.15982788  1.000000000 -0.0157493640
fo  0.013602898 -0.0004413677 -0.05424683 -0.015749364  1.0000000000
he  0.567588221  0.0079024292  0.21614779  0.189447455 -0.0278142752
ae -0.010328660 -0.0997944149  0.04556777  0.033606280  0.1681535156
un  0.017344303  0.0827515807 -0.05139838  0.073910180  0.0820105076
mp  0.010919468 -0.0785817255  0.06123574 -0.033911383 -0.0321943061
pe -0.002375240 -0.0862873064  0.02999304  0.070507551 -0.0952901382
di  0.275979010 -0.0720054108  0.20879827  0.073540182 -0.0255916725
lo  0.073965943  0.0922461136 -0.01520356  0.107350984  0.0690026317
lf  0.004120307  0.0626558533  0.05330981  0.005937895 -0.0490483097
            he           ae          un           mp           pe           di
li  0.56758822 -0.010328660  0.01734430  0.010919468 -0.002375240  0.275979010
pr  0.00790243 -0.099794415  0.08275158 -0.078581726 -0.086287306 -0.072005411
lp  0.21614779  0.045567771 -0.05139838  0.061235742  0.029993038  0.208798270
le  0.18944745  0.033606280  0.07391018 -0.033911383  0.070507551  0.073540182
fo -0.02781428  0.168153516  0.08201051 -0.032194306 -0.095290138 -0.025591673
he  1.00000000  0.081759418 -0.01077145  0.039266816 -0.115971376  0.236949219
ae  0.08175942  1.000000000 -0.03642934 -0.074427901 -0.074891813 -0.006193944
un -0.01077145 -0.036429338  1.00000000 -0.032340431 -0.058384579  0.037654676
mp  0.03926682 -0.074427901 -0.03234043  1.000000000  0.006346682  0.146273813
pe -0.11597138 -0.074891813 -0.05838458  0.006346682  1.000000000  0.074343032
di  0.23694922 -0.006193944  0.03765468  0.146273813  0.074343032  1.000000000
lo  0.05630599 -0.034236757  0.07586800 -0.083443578 -0.096809073  0.144618408
lf -0.07624441 -0.004360944  0.00324484  0.053466747  0.011536411  0.039689434
            lo           lf
li  0.07396594  0.004120307
pr  0.09224611  0.062655853
lp -0.01520356  0.053309808
le  0.10735098  0.005937895
fo  0.06900263 -0.049048310
he  0.05630599 -0.076244407
ae -0.03423676 -0.004360944
un  0.07586800  0.003244840
mp -0.08344358  0.053466747
pe -0.09680907  0.011536411
di  0.14461841  0.039689434
lo  1.00000000 -0.040074265
lf -0.04007426  1.000000000

$glocor
   global distance
li       0.5859411
pr       0.2309621
lp       0.4632443
le       0.4484787
fo       0.1331704
he       0.5098172
ae       0.1979766
un       0.2926322
mp       0.2867519
pe       0.1356886
di       0.5223928
lo       0.2492998
lf       0.1290551

> dotchart(sort(contrib$glocor), labels = rownames(contrib$glocor)[order(contrib$glocor[, 1])])
> 
> 
> 
> 
> 

[Package ade4 Index]