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Customized ResNatSeed training database

Source: R/trainingDB.R
trainingDB.Rd

This function creates a training database based on vegetation and topographical variables database provided by the user, which must be specified in the trainingDB argument of the function RestInd.

see vignettes for additional details and examples

Usage

trainingDB(data, spe.freq, min.spe.abundance = NULL)

Arguments

data

dataframe with vegetation and topographical variables with columns arranged as shown in @details and vignettes.

spe.freq

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

threshold of minimum abundance (greater than) of each species in each survey. When NULL the parameter is set to 0.

Value

a list containing two dataframes:

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.

  • "cep.names": a two-column dataframe reporting the full species names and their abbreviations in the CEP format (see make.cepnames for more details). These species are those that need to be indicated (in CEP names format) in the seed mixture or donor grassland composition (i.e. in the composition argument of RestInd).

  • "trainingDB.ResNatSeed": a seven-column dataframe reporting the following information: Survey ID, elevation, slope, southness, species full names, species CEP names, and species abundance. This dataframe must be used in the trainingDB argument of RestInd function.

Details

The format of the dataframe required in the data argument must strictly follow this column order:

SurveyID | Elevation | Slope | Aspect | Species1 | Species2 | Species3 | Species…

  • SurveyID should contain an alphanumeric coding to uniquely identify each survey

  • 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 statistical models through the RestInd function (Chang et al., 2004)

  • Species... columns containing each plant species abundances. Abundances must be numbers 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 or see vegetation_abundance and here

References

  • Chang, C., P. Lee, M. Bai, and T. Lin. 2004. Predicting the geographical distribution of plant communities in complex terrain–a case study in Fushian Experimental Forest, north- eastern Taiwan. Ecography. 27:577–588. doi:10.1111/j.0906- 7590.2004.03852.x

  • Pittarello, M., Probo, M., Lonati, M., Lombardi, G., 2016. Restoration of sub-alpine shrub-encroached grasslands through pastoral practices: effects on vegetation structure and botanical composition. Appl Veg Sci 19, 381–390. https://doi.org/10.1111/avsc.12222

  • Verdinelli, M., Pittarello, M., Caria, M.C., Piga, G., Roggero, P.P., Marrosu, G.M., Arrizza, S., Fadda, M.L., Lombardi, G., Lonati, M., Nota, G., Sitzia, M., Bagella, S., 2022. Congruent responses of vascular plant and ant communities to pastoral land-use abandonment in mountain areas throughout different biogeographic regions. Ecol Process 11, 35. https://doi.org/10.1186/s13717-022-00379-9

  • Pittarello M. (2022). iPastoralist: Management, conversion, and analyses of vegetation data derived from phytosociological and point-quadrat method surveys. R package version 0.0.0.9000. https://github.com/MarcoPittarello/iPastoralist.git