Summary taxonomy for each plot

Import the library and the data set

library(vegtable)
releves <- readRDS ("sanmartin1998.rds")

Use the function count_taxa for counting species, genus or family per plot.

releves <- count_taxa (
  object = species ~ ReleveID,
  data = releves,
  suffix = "_count"
)
releves <- count_taxa (
  object = genus ~ ReleveID,
  data = releves,
  suffix = "_count2",
  include_lower = TRUE
)
releves <- count_taxa (
  object = family ~ ReleveID,
  data = releves,
  suffix = "_count",
  include_lower = TRUE
)

Use summary to summarize the results of maximum, minimum, and mean number of species, genera, and families per plot.

summary(releves$species_count)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   6.000   8.000   8.088  10.000  17.000

summary(releves$genus_count2)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   6.000   8.000   8.312  10.000  17.000

summary(releves$family_count)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   5.000   6.500   6.625   8.000  15.000

Also you can create histogram to show frequency of taxa number per plot. This is an example for species number.

hist(releves$species_count)
abline(v = mean(releves$species_count),
col = "red", lty = "dashed", lwd = 2)

Species attributes

In this case, the different life forms for all releves are shown.

summary(as.factor  (releves@species@taxonTraits$life_form))

##    annual   climber perennial     woody      NA's 
##        17         1        51         4        12

Use the function trait_proportion to calculate the proportion of life forms per plot

releves <- trait_proportion(
  object = releves,
  trait = "life_form",
  head_var = "ReleveID",
  include_nas = FALSE,
  weight = "cover_percentage",
  suffix = "_prop",
  in_header = TRUE)

Sumarize the results of maximum, minimum, and mean porportion of the different life forms.

summary(releves$annual_prop)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## 0.000000 0.000000 0.005076 0.147616 0.197087 0.983607        1

summary(releves$climber_prop)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
## 0.0000000 0.0000000 0.0000000 0.0001257 0.0000000 0.0051020         1

summary(releves$perennial_prop)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## 0.01639 0.80291 0.99048 0.85150 1.00000 1.00000       1

summary(releves$woody_prop)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
## 0.0000000 0.0000000 0.0000000 0.0007566 0.0000000 0.0084034         1

Describe different plant communities

See the number of plots per community type

releves <- relation2header(vegtable = releves,
    relation = "community_type")
summary(as.factor(releves$community_name))

##                    Anthoxanthum utriculatum 
##                                           4 
##                 Eleocharietum macrostachyae 
##                                           7 
##                   Eleocharietum pachycarpae 
##                                          11 
##       Eleocharis pachycarpa-Lythrum portula 
##                                           5 
##                     Glycerietum multiflorae 
##                                           2 
## Gnaphalio cymatoidis-Polygonetum piperoidis 
##                                           7 
##                            Juncetum proceri 
##                                          15 
##            Junco proceri-Caricetum ripariae 
##                                           1 
##  Ludwigia peploides-Sagittaria montevidense 
##                                           7 
##      Mentho pulegium-Agrostietum capillaris 
##                                          16 
##                             Phyla nodiflora 
##                                           4 
##                         Potamogeton pusilus 
##                                           1

Calculate the mean number of species per community type

releves <- count_taxa(species ~ ReleveID, data = releves,include_lower = TRUE)
aggregate(species_count ~ community_name, data = releves@header, FUN = mean)

##                                 community_name species_count
## 1                     Anthoxanthum utriculatum     14.000000
## 2                  Eleocharietum macrostachyae      7.142857
## 3                    Eleocharietum pachycarpae      8.545455
## 4        Eleocharis pachycarpa-Lythrum portula      7.400000
## 5                      Glycerietum multiflorae      4.500000
## 6  Gnaphalio cymatoidis-Polygonetum piperoidis      9.428571
## 7                             Juncetum proceri     10.066667
## 8             Junco proceri-Caricetum ripariae      6.000000
## 9   Ludwigia peploides-Sagittaria montevidense      5.142857
## 10      Mentho pulegium-Agrostietum capillaris     10.187500
## 11                             Phyla nodiflora      4.750000
## 12                         Potamogeton pusilus      1.000000