Learn R Programming

disdat (version 1.0-1)

SWI: Swiss species distribution data

Description

Species occurrence data for 30 tree species in Switzerland (SWI, a country in Europe) and associated environmental data. Full details of the dataset are provided in the reference below. There are four data sets with training (po and bg) and test (pa, env) data:

po (training data) includes site names, species names, coordinates, occurrence ("1" for all, since all are presence records), group (tree), and site values for 13 environmental variables (below).

bg (training data) has 10000 sites selected at random across the study region. It is structured identically to po, with "0" for occurrence (not implying absence, but denoting background in a way suited to most modelling methods) and NA for group.

env (testing data) includes group, site names, coordinates, and site values for 13 environmental variables (below), at 10103 sites. This file is suited to making predictions.

pa (testing data) includes group, site names, coordinates, and presence-absence records, one column per species. The sites are identical to the sites in env. This file is suited to evaluating the predictions made to env.

Raster (gridded) data for all environmental variables are available - see the reference below for details.

The reference system of the x and y coordinates is Transverse, spheroid Bessel (EPSG:21781) (note all SWI data has a constant shift applied).

The vignette provided with this package provides an example of how to fit and evaluate a model with these data.

Environmental variables:

CodeDescriptionUnitsType
bccBroadleaved continuous cover (based on Landsat images)percentageContinuous
calcBedrock is strictly calcareous1 (yes) or 0 (no)Categorical
cccConiferous continuous cover (based on Landsat images)percentageContinuous
ddegGrowing degree-days above a threshold of 0 degrees Cdegrees C * daysContinuous
nutriSoil nutrients index between 0-45D mval/cm2Continuous
pdsumNumber of days with rainfall higher than 1 mmndaysContinuous
precyyAverage yearly precipitation summmContinuous
sfroSummer Frost FrequencydaysContinuous
slopeSlopedegrees x 10Continuous
sradyyPotential yearly global radiation (daily average)(kJ/m2)/dayContinuous
swbSite water balancemmContinuous
taveccAverage temperature of the coldest monthdegrees CContinuous
topoTopographic positiondimensionlessContinuous

Arguments

References

Elith, J., Graham, C.H., Valavi, R., Abegg, M., Bruce, C., Ferrier, S., Ford, A., Guisan, A., Hijmans, R.J., Huettmann, F., Lohmann, L.G., Loiselle, B.A., Moritz, C., Overton, J.McC., Peterson, A.T., Phillips, S., Richardson, K., Williams, S., Wiser, S.K., Wohlgemuth, T. & Zimmermann, N.E., (2020). Presence-only and presence-absence data for comparing species distribution modeling methods. Biodiversity Informatics 15:69-80.

Examples

Run this code
swi_po <- disPo("SWI")
swi_bg <- disBg("SWI")

swi_pa <- disPa("SWI")
swi_env <- disEnv("SWI")

x <- disData("SWI")
sapply(x, head)

disCRS("SWI")

Run the code above in your browser using DataLab