ecospat.testData: Test Data For The Ecospat package

Description

Data frame that contains vegetation plots data: presence records of 50 species, a set of environmental variables (topo-climatic) and SDM predictions for some species in the Western Swiss Alps (Canton de Vaud, Switzerland).

Usage

data("ecospat.testData")

Arguments

Format

A data frame with 300 observations on the following 96 variables.

numplots: Number of the vegetation plot.
long: Longitude, in Swiss plane coordinate system of the vegetation plot.
lat: Latitude, in Swiss plane coordinate system of the vegetation plot.
ddeg: Growing degree days (with a 0 degrees Celsius threshold).
mind: Moisture index over the growing season (average values for June to August in mm day-1).
srad: The annual sum of radiation (in kJ m-2 year-1).
slp: Slope (in degrees) calculated from the DEM25.
topo: Topographic position (an integrated and unitless measure of topographic exposure.
Achillea_atrata
Achillea_millefolium
Acinos_alpinus
Adenostyles_glabra
Aposeris_foetida
Arnica_montana
Aster_bellidiastrum
Bartsia_alpina
Bellis_perennis
Campanula_rotundifolia
Centaurea_montana
Cerastium_latifolium
Cruciata_laevipes
Doronicum_grandiflorum
Galium_album
Galium_anisophyllon
Galium_megalospermum
Gentiana_bavarica
Gentiana_lutea
Gentiana_purpurea
Gentiana_verna
Globularia_cordifolia
Globularia_nudicaulis
Gypsophila_repens
Hieracium_lactucella
Homogyne_alpina
Hypochaeris_radicata
Leontodon_autumnalis
Leontodon_helveticus
Myosotis_alpestris
Myosotis_arvensis
Phyteuma_orbiculare
Phyteuma_spicatum
Plantago_alpina
Plantago_lanceolata
Polygonum_bistorta
Polygonum_viviparum
Prunella_grandiflora
Rhinanthus_alectorolophus
Rumex_acetosa
Rumex_crispus
Vaccinium_gaultherioides
Veronica_alpina
Veronica_aphylla
Agrostis_capillaris
Bromus_erectus_sstr
Campanula_scheuchzeri
Carex_sempervirens
Cynosurus_cristatus
Dactylis_glomerata
Daucus_carota
Festuca_pratensis_sl
Geranium_sylvaticum
Leontodon_hispidus_sl
Potentilla_erecta
Pritzelago_alpina_sstr
Prunella_vulgaris
Ranunculus_acris_sl
Saxifraga_oppositifolia
Soldanella_alpina
Taraxacum_officinale_aggr
Trifolium_repens_sstr
Veronica_chamaedrys
Parnassia_palustris
glm_Agrostis_capillaris: GLM model for the species Agrostis_capillaris.
glm_Leontodon_hispidus_sl: GLM model for the species Leontodon_hispidus_sl.
glm_Dactylis_glomerata: GLM model for the species Dactylis_glomerata.
glm_Trifolium_repens_sstr: GLM model for the species Trifolium_repens_sstr.
glm_Geranium_sylvaticum: GLM model for the species Geranium_sylvaticum.
glm_Ranunculus_acris_sl: GLM model for the species Ranunculus_acris_sl.
glm_Prunella_vulgaris: GLM model for the species Prunella_vulgaris.
glm_Veronica_chamaedrys: GLM model for the species Veronica_chamaedrys.
glm_Taraxacum_officinale_aggr: GLM model for the species Taraxacum_officinale_aggr.
glm_Plantago_lanceolata: GLM model for the species Plantago_lanceolata.
glm_Potentilla_erecta: GLM model for the species Potentilla_erecta.
glm_Carex_sempervirens: GLM model for the species Carex_sempervirens.
glm_Soldanella_alpina: GLM model for the species Soldanella_alpina.
glm_Cynosurus_cristatus: GLM model for the species Cynosurus_cristatus.
glm_Campanula_scheuchzeri: GLM model for the species Campanula_scheuchzeri.
glm_Festuca_pratensis_sl: GLM model for the species Festuca_pratensis_sl.
gbm_Bromus_erectus_sstr: GBM model for the species Bromus_erectus_sstr.
glm_Saxifraga_oppositifolia: GLM model for the species Saxifraga_oppositifolia.
glm_Daucus_carota: GLM model for the species Daucus_carota.
glm_Pritzelago_alpina_sstr: GLM model for the species Pritzelago_alpina_sstr.
glm_Bromus_erectus_sstr: GLM model for the species Bromus_erectus_sstr.
gbm_Saxifraga_oppositifolia: GBM model for the species Saxifraga_oppositifolia.
gbm_Daucus_carota: GBM model for the species Daucus_carota.
gbm_Pritzelago_alpina_sstr: GBM model for the species Pritzelago_alpina_sstr.

Details

The study area is the Western Swiss Alps of Canton de Vaud, Switzerland.

Five topo-climatic explanatory variables to calibrate the SDMs: growing degree days (with a 0 degrees Celsius threshold); moisture index over the growing season (average values for June to August in mm day-1); slope (in degrees); topographic position (an integrated and unitless measure of topographic exposure; Zimmermann et al., 2007); and the annual sum of radiation (in kJ m-2 year-1). The spatial resolution of the predictor is 25 m x 25 m so that the models could capture most of the small-scale variations of the climatic factors in the mountainous areas.

Two modelling techniques were used to produce the SDMs: generalized linear models (GLM; McCullagh & Nelder, 1989; R library 'glm') and generalized boosted models (GBM; Friedman, 2001; R library 'gbm'). The SDMs correpond to 20 species: Agrostis_capillaris, Leontodon_hispidus_sl, Dactylis_glomerata, Trifolium_repens_sstr, Geranium_sylvaticum, Ranunculus_acris_sl, Prunella_vulgaris, Veronica_chamaedrys, Taraxacum_officinale_aggr, Plantago_lanceolata, Potentilla_erecta, Carex_sempervirens, Soldanella_alpina, Cynosurus_cristatus, Campanula_scheuchzeri, Festuca_pratensis_sl, Daucus_carota, Pritzelago_alpina_sstr, Bromus_erectus_sstr and Saxifraga_oppositifolia.

References

Guisan, A. 1997. Distribution de taxons vegetaux dans un environnement alpin: Application de modelisations statistiques dans un systeme d'information geographique. PhD Thesis, University of Geneva, Switzerland.

Guisan, A., J.P. Theurillat. and F. Kienast. 1998. Predicting the potential distribution of plant species in an alpine environment. Journal of Vegetation Science, 9, 65-74.

Guisan, A. and J.P. Theurillat. 2000. Assessing alpine plant vulnerability to climate change: A modeling perspective. Integrated Assessment, 1, 307-320.

Guisan, A. and J.P. Theurillat. 2000. Equilibrium modeling of alpine plant distribution and climate change : How far can we go? Phytocoenologia, 30(3-4), 353-384.

Dubuis A., J. Pottier, V. Rion, L. Pellissier, J.P. Theurillat and A. Guisan. 2011. Predicting spatial patterns of plant species richness: A comparison of direct macroecological and species stacking approaches. Diversity and Distributions, 17, 1122-1131.

Zimmermann, N.E., T.C. Edwards, G.G Moisen, T.S. Frescino and J.A. Blackard. 2007. Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. Journal of Applied Ecology 44, 1057-1067.

Examples

Run this code

data(ecospat.testData)
str(ecospat.testData)
dim(ecospat.testData)
names(ecospat.testData)

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