TUTORIAL 1 - Default training database
Marco Pittarello and Davide Barberis
Source:vignettes/TUTORIAL_1_-_Default_training_database.Rmd
TUTORIAL_1_-_Default_training_database.Rmd
When the user uses default settings, the training database refers to
the vegetation surveys from the Piedmont Region - North-Western Italy,
which includes 248 plant species eligible to modeling. Such a list can
be extracted through the command data("cep.piem")
. Species
names follow the Flora Alpina nomenclature (Aeschimann et al. 2004) and they are associated
with the ‘cep.names’ code, an eight-letter abbreviation of species names
according to the Cornell Ecology Programs (CEP).
data("cep.piem")
head(cep.piem)
#> species cep.names
#> 1 Achillea millefolium aggr. Achiaggr
#> 2 Acinos alpinus Acinalpi
#> 3 Agrostis alpina Agroalpi
#> 4 Agrostis capillaris Agrocapi
#> 5 Agrostis rupestris Agrorupe
#> 6 Agrostis schraderiana Agroschr
From this list is possible to create the mixture or donor grassland composition database, which is characterized by two columns:
- First column: species code abbreviated in CEP names format
- Second column: abundance of each species. Abundance must be a number bounded between 0 and 100, which can be either a species relative abundance or a species cover (sensu (Pittarello et al. 2016); (Verdinelli et al. 2022)).
In this example, the donor grassland composition is characterized by five species:
- Bromus erectus (CEP name: Bromere), relative abundance: 25%
- Brachypodium rupestre (CEP name: Bracrupe), relative abundance: 18%
- Festuca ovina aggr. (CEP name: Festaggr), relative abundance: 22%
- Knautia arvensis (CEP name: Knauarve), relative abundance: 8%
- Silene vulgaris (CEP name: Silevulg), relative abundance: 5%
Total abundances amount to 78%. The total abundance of the seed mixture or donor grassland composition should not necessarily amount 100%.
We can generate the dataframe of the donor grassland composition:
donor.composition<-data.frame(
species=c("Bromere","Bracrupe","Festaggr","Knauarve","Silevulg"),
abundance=c(25,18,22,8,5)
)
donor.composition
#> species abundance
#> 1 Bromere 25
#> 2 Bracrupe 18
#> 3 Festaggr 22
#> 4 Knauarve 8
#> 5 Silevulg 5
It is now possible to use the RestInd
function
to calculate the Suitability Index (SI) and
Reliability Index (RI) by means of the donor grassland
composition (dataframe ‘donor.composition’) and of the elevation, slope,
and aspect of restoration site:
- elevation: 2300 m a.s.l
- slope: 15°
- aspect: 135° N
RestInd(trainingDB = NULL,
composition=donor.composition,
elevation=2300,
slope=15,
aspect=135
)
#> $DESCRIPTIVES
#> cep.names species n.obs min.ele max.ele min.slope
#> Bracrupe Bracrupe Brachypodium rupestre 50 494 2367 3.5
#> Festaggr Festaggr Festuca ovina aggr. 67 489 2756 1.7
#> Knauarve Knauarve Knautia arvensis 49 494 2368 0.86
#> Silevulg Silevulg Silene vulgaris 62 204 2436 0.18
#> max.slope min.south max.south
#> Bracrupe 42 7.1 179
#> Festaggr 56 2.1 177
#> Knauarve 45 9.4 177
#> Silevulg 51 5.1 173
#>
#> $SPECIES_ABUNDANCES
#> cep.names species PMA POA ratio R2.adj RMSE SmDgA EA
#> 1 Bracrupe Brachypodium rupestre 40.9 90.5 0.45 0.52 19.6 18 8.10
#> 2 Festaggr Festuca ovina aggr. 80.7 92.2 0.88 0.77 12.2 22 19.36
#> 3 Knauarve Knautia arvensis 8.6 36.7 0.23 0.30 6.2 8 1.84
#> 4 Silevulg Silene vulgaris 6.7 15.6 0.43 0.10 7.5 5 2.15
#>
#> $INDEXES
#> SI RI
#> 1 0.59 0.68