In MarcoPittarello/ResNatSeed: A tool for the assessment of the REStoration potential of NATive SEEDs based on topographic factors

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
#system.file("extdata", package = "ResNatSeed")
load("~/Pacchetti R/ResNatSeed/inst/extdata/veg.composition.example.RData")
library(tidyr)

library(ResNatSeed)

If the study area is beyond the Piedmont Region (North-Western Italy), it is advisable to provide a customized training database (see "Introduction to ResNatSeed" vignette for definitions) through the trainingDB function before using the RestInd function.

In order to be processed with the trainingDB function, the vegetation and topographical variables database (see "Introduction to ResNatSeed" vignette for definitions) must be structured as follows (see Table 1 for an example):

• Rows of the dataframe are the surveys
• The first column reports the survey codes
• The second, third and fourth columns report the topographical variables, respectively:
  • Elevation: expressed in meters above sea level (m a.s.l.)
  • Slope: expressed in degrees (°)
  • Aspect: expressed in degrees from North (°N) as it will be converted to "southness" to avoid circular variable issues in the models run with RestInd [@chang2004]
• From the fifth column onwards, the plant species names. Plant species names do not necessarily have to be coded, they can be left in full. Afterwards, abbreviation codes will be automatically created in CEP names format: an abbreviation of species names according to the Cornell Ecology Programs (CEP), which uses eight-letter abbreviations for species. The CEP names code will be used to formulate the seed mixture or donor grassland composition.

Table 1. Example of the structure of the vegetation and topographical variables database to provide in the function trainingDB. This database includes 703 vegetation surveys and 532 plant species

veg.composition.example

At this point, the customized training database can be generated by specifying the name of the vegetation and topographical variables database in the data argument of the trainingDB function. Through this function it is possible to select the species eligible for the statistical modeling through two arguments:

• spe.freq: this argument allows to set a threshold of minimum frequency of each species in the surveys. It is advisable to set values greater than or equal to 30 to allow appropriate statistical modeling.

• min.spe.abundance: this argument allows to set a threshold of minimum abundance (greater than) of each species in each survey. When NULL the parameter is set to 0.

training.custom<-trainingDB(data=veg.composition.example,
           spe.freq = 30,# only species occurring at least in 30 surveys will be retained
           min.spe.abundance = 1# only species with at least 1% of relative abundance will be retained
           )

Through trainingDB function, two dataframes are created and stored in a list, named in this example as 'training.custom'. The two dataframes are:

• cep.names: dataframe with the list of species suitable for modeling and their species codes (CEP names) to be used for the seed mixture or donor grassland composition. In this example 65 species (out of 532) are eligible for modeling

training.custom$cep.names

• trainingDB.ResNatSeed: training database to use in the RestInd function

head(training.custom$trainingDB.ResNatSeed)

From the list of species in training.custom$cep.names, it is necessary to create a database containing the seed mixture or donor grassland composition. The seed mixture or donor grassland composition must be a dataframe with 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 @pittarello2016; @verdinelli2022].

In this example, the donor grassland composition is characterized by four species:

• Dactylis glomerata (CEP name: Dactglom), abundance: 25%
• Festuca ovina aggr. (CEP name: Festaggr), abundance: 35%
• Thymus serpyllum aggr. (CEP name: Thymaggr), abundance: 15%
• Lotus corniculatus (CEP name: Lotucorn), abundance: 8%

Total abundances amount to 83%. 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("Dactglom","Festaggr","Thymaggr","Lotucorn"),
  abundance=c(25,35,15,8)
)

donor.composition

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: 1600 m a.s.l.
• slope: 10°
• aspect: 110° N

Remember that in the trainingDB argument it must be specified the one created above, i.e. training.custom$trainingDB.ResNatSeed

RestInd(trainingDB = training.custom$trainingDB.ResNatSeed, 
        composition=donor.composition,  
        elevation=1600, 
        slope=10, 
        aspect=110
        )

References

MarcoPittarello/ResNatSeed documentation built on April 14, 2023, 8:53 p.m.