data("ecospat.testData")
numplots
long
lat
ddeg
mind
srad
slp
topo
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_Leontodon_hispidus_sl
glm_Dactylis_glomerata
glm_Trifolium_repens_sstr
glm_Geranium_sylvaticum
glm_Ranunculus_acris_sl
glm_Prunella_vulgaris
glm_Veronica_chamaedrys
glm_Taraxacum_officinale_aggr
glm_Plantago_lanceolata
glm_Potentilla_erecta
glm_Carex_sempervirens
glm_Soldanella_alpina
glm_Cynosurus_cristatus
glm_Campanula_scheuchzeri
glm_Festuca_pratensis_sl
gbm_Bromus_erectus_sstr
glm_Saxifraga_oppositifolia
glm_Daucus_carota
glm_Pritzelago_alpina_sstr
glm_Bromus_erectus_sstr
gbm_Saxifraga_oppositifolia
gbm_Daucus_carota
gbm_Pritzelago_alpina_sstr
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.
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.
data(ecospat.testData)
str(ecospat.testData)
dim(ecospat.testData)
names(ecospat.testData)
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