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).
data("ecospat.testData")A data frame with 300 observations on the following 96 variables.
numplotsNumber of the vegetation plot.
longLongitude, in Swiss plane coordinate system of the vegetation plot.
latLatitude, in Swiss plane coordinate system of the vegetation plot.
ddegGrowing degree days (with a 0 degrees Celsius threshold).
mindMoisture index over the growing season (average values for June to August in mm day-1).
sradThe annual sum of radiation (in kJ m-2 year-1).
slpSlope (in degrees) calculated from the DEM25.
topoTopographic position (an integrated and unitless measure of topographic exposure.
Achillea_atrataAchillea_millefoliumAcinos_alpinusAdenostyles_glabraAposeris_foetidaArnica_montanaAster_bellidiastrumBartsia_alpinaBellis_perennisCampanula_rotundifoliaCentaurea_montanaCerastium_latifoliumCruciata_laevipesDoronicum_grandiflorumGalium_albumGalium_anisophyllonGalium_megalospermumGentiana_bavaricaGentiana_luteaGentiana_purpureaGentiana_vernaGlobularia_cordifoliaGlobularia_nudicaulisGypsophila_repensHieracium_lactucellaHomogyne_alpinaHypochaeris_radicataLeontodon_autumnalisLeontodon_helveticusMyosotis_alpestrisMyosotis_arvensisPhyteuma_orbicularePhyteuma_spicatumPlantago_alpinaPlantago_lanceolataPolygonum_bistortaPolygonum_viviparumPrunella_grandifloraRhinanthus_alectorolophusRumex_acetosaRumex_crispusVaccinium_gaultherioidesVeronica_alpinaVeronica_aphyllaAgrostis_capillarisBromus_erectus_sstrCampanula_scheuchzeriCarex_sempervirensCynosurus_cristatusDactylis_glomerataDaucus_carotaFestuca_pratensis_slGeranium_sylvaticumLeontodon_hispidus_slPotentilla_erectaPritzelago_alpina_sstrPrunella_vulgarisRanunculus_acris_slSaxifraga_oppositifoliaSoldanella_alpinaTaraxacum_officinale_aggrTrifolium_repens_sstrVeronica_chamaedrysParnassia_palustrisglm_Agrostis_capillarisGLM model for the species Agrostis_capillaris.
glm_Leontodon_hispidus_slGLM model for the species Leontodon_hispidus_sl.
glm_Dactylis_glomerataGLM model for the species Dactylis_glomerata.
glm_Trifolium_repens_sstrGLM model for the species Trifolium_repens_sstr.
glm_Geranium_sylvaticumGLM model for the species Geranium_sylvaticum.
glm_Ranunculus_acris_slGLM model for the species Ranunculus_acris_sl.
glm_Prunella_vulgarisGLM model for the species Prunella_vulgaris.
glm_Veronica_chamaedrysGLM model for the species Veronica_chamaedrys.
glm_Taraxacum_officinale_aggrGLM model for the species Taraxacum_officinale_aggr.
glm_Plantago_lanceolataGLM model for the species Plantago_lanceolata.
glm_Potentilla_erectaGLM model for the species Potentilla_erecta.
glm_Carex_sempervirensGLM model for the species Carex_sempervirens.
glm_Soldanella_alpinaGLM model for the species Soldanella_alpina.
glm_Cynosurus_cristatusGLM model for the species Cynosurus_cristatus.
glm_Campanula_scheuchzeriGLM model for the species Campanula_scheuchzeri.
glm_Festuca_pratensis_slGLM model for the species Festuca_pratensis_sl.
gbm_Bromus_erectus_sstrGBM model for the species Bromus_erectus_sstr.
glm_Saxifraga_oppositifoliaGLM model for the species Saxifraga_oppositifolia.
glm_Daucus_carotaGLM model for the species Daucus_carota.
glm_Pritzelago_alpina_sstrGLM model for the species Pritzelago_alpina_sstr.
glm_Bromus_erectus_sstrGLM model for the species Bromus_erectus_sstr.
gbm_Saxifraga_oppositifoliaGBM model for the species Saxifraga_oppositifolia.
gbm_Daucus_carotaGBM model for the species Daucus_carota.
gbm_Pritzelago_alpina_sstrGBM model for the species Pritzelago_alpina_sstr.
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.
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.
# NOT RUN {
data(ecospat.testData)
str(ecospat.testData)
dim(ecospat.testData)
names(ecospat.testData)
# }
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