Skip to contents

Converting Frequency of Occurrence and computation of Ecological Indexes

Marco Pittarello

Source: vignettes/TUTORIAL_1.Rmd
TUTORIAL_1.Rmd
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
library(iPastoralist)
#> Registered S3 method overwritten by 'vegan':
#>   method     from      
#>   rev.hclust dendextend

Example 1 - use of ‘vegetation_abundance’ function

Suppose we want to convert Frequency of occurrence (FO) to Species percentage cover (%SC), considering also occasional species. As the %SC for each survey will (likely) be greater than 100, we want to rescale %SC of each species per each survey to obtain a sum of 100 (i.e. a proportion of %SC). The dataset used as example is shown in “Data input format” vignette, where the total measurements per transect is 25. Therefore, to obtain $SC FO should be multiplied by 4 (so that they refer to 100 measurements.)

library(iPastoralist)
vegetation.sc <- vegetation_abundance(
  database = vegetation,
  species.cover.coefficient = 4,
  method = "SRA_SC.fo.occ"
)
head(vegetation.sc)
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15
Achmacr 0.0000000 0.0000000 0 0 0 0 0.0000000 0 0.000000 0.000000 0.0000000 0.000000 0 2.643754 0
Achmill 0.0000000 0.0000000 0 0 0 0 0.0918555 0 6.118079 0.103484 0.0927357 11.289867 0 0.000000 0
Achmosc 0.0000000 0.0000000 0 0 0 0 0.0000000 0 0.000000 0.103484 0.0000000 0.000000 0 0.000000 0
Acialpi 0.0000000 0.0000000 0 0 0 0 0.0000000 0 0.000000 0.103484 2.4729521 2.257973 0 0.000000 0
Acolama 0.0971817 0.0000000 0 0 0 0 0.0000000 0 0.000000 0.000000 0.0000000 0.000000 0 2.643754 0
Adealli 0.0000000 0.1612903 0 0 0 0 0.0000000 0 0.000000 0.000000 0.0000000 0.000000 0 21.150033 0

we can check that the sum of %SC for each survey is 100

colSums(vegetation.sc)
#>  R1  R2  R3  R4  R5  R6  R7  R8  R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 
#> 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 
#> R21 R22 R23 R24 
#> 100 100 100 100

Example 2 - computation of ecological indexes

In this case we want to compute the average Landolt indicator values for each survey, weighted with species abundance.

If the occasional species are not considered, the SRA will be used. Conversely, if we would like to keep into account also occasional species, the SRA will be calculated with the %SC rescaled to 100 (more detail in “vegetation_abundance” function).

In this case we will consider also occasional species.

The input database is the one with the Frequency of occurrences, i.e. the dataframe used in this tutorial named “vegetation”.

ec.index <- ecological_indexes(
  database.vegetation = vegetation,
  database.indexes = data[, c("F_Landolt", "R_Landolt", "N_Landolt")],
  occasional.species = TRUE,
  species.cover.coefficient = 4,
  weight = TRUE
)
#> [1] "INDEX WEIGHTED WITH OCCASIONAL SPECIES"

Notes about the “ecological_indexes” function:

  • database.indexes = database with Ecological indicators, without the column of species names. NA values must indicated as 999
  • occasional.species = Logical. TRUE if you want to take into account occasional species.
  • species.cover.coefficient = only if “occasional.species=TRUE”. Coefficient that multiplies FO so that the number of total touches refer to 100
  • weight: Logical. TRUE if you want to weight Ecological indicators with abundance.

the output will be as follow:

ec.index
#>    survey F_Landolt R_Landolt N_Landolt
#> 1      R1  2.971600  2.253535  2.649474
#> 2      R2  3.716373  3.265993  2.867099
#> 3      R3  3.215537  2.981710  3.898948
#> 4      R4  3.537678  2.765013  3.392741
#> 5      R5  2.943190  2.734974  3.177504
#> 6      R6  2.926874  2.671846  2.978367
#> 7      R7  2.970695  2.635527  3.145236
#> 8      R8  3.362342  3.126754  4.306474
#> 9      R9  3.071611  2.824006  3.469644
#> 10    R10  2.612803  2.806719  2.820766
#> 11    R11  2.519994  2.808848  2.687676
#> 12    R12  2.812606  2.856213  3.017767
#> 13    R13  3.386566  2.210897  2.851104
#> 14    R14  3.417881  2.941060  4.184106
#> 15    R15  2.996712  2.227660  2.680000
#> 16    R16  3.073070  1.668550  2.091525
#> 17    R17  3.597692  2.285484  3.584541
#> 18    R18  2.464270  2.142214  2.375730
#> 19    R19  3.519828  2.043754  3.045977
#> 20    R20  2.802508  2.937901  3.024198
#> 21    R21  2.739275  2.523727  2.727625
#> 22    R22  2.895071  2.384567  2.791045
#> 23    R23  2.695533  2.274914  2.006186
#> 24    R24  2.683857  1.880419  2.004484