DOI: 10.5061/dryad.hdr7sqvtk
title: Understanding the long-term dynamics of vegetation since 1953 in high-mountain regions

description: Alpine ecosystems, highly sensitive to climate change, are experiencing
 shifts in species ranges and community structure. These changes are driven
 by a complex interplay of climatic and environmental factors, land use
 changes, geomorphological dynamics, and species interactions, which can
 often lead to contrasted and sometimes unexpected dynamics. Historical
 records provide a valuable opportunity to capture these complexities by
 revealing long-term changes, opening a gateway to hypothesise about the
 key underlying processes. We investigated changes in the floristic
 composition of subalpine to nival vegetation communities by resurveying a
 period of 70 years. To understand vegetation patterns, we (i) resampled
 vegetation at plot level and remapped the areas, (ii) analysed the role of
 driving climate, environmental, and land use factors on vegetation
 distribution and vascular plant species richness, and (iii) modelled
 plant-plant interactions from community data. The results reveal that
 vegetation cover patterns were strongly influenced by local climate and
 soil properties. The species richness is also influenced by the livestock
 density and the flat morphology. It should be noted that climate change
 caused wetland habitats to become drier and accelerated secondary
 succession through upward migration and range-infilling processes.
 Furthermore, a trend towards eutrophication was observed. The results
 suggested that certain plant communities, particularly those found in
 snowbeds, were more vulnerable to environmental changes that have occurred
 over the past 70 years. Synthesis: This study highlighted the complexity
 of vegetation dynamics. In addition to thermophilisation and aridisation,
 changes in land use affect species composition, species richness, and
 vegetation cover. Substrate conditions also play an important role.
description: <strong>Fieldwork</strong> The
 study area was located within the Stilfserjoch/Stelvio National Park
 (46°31'19" N, 10°25'2" E, Bormio, Italy) and extends
 from 2000 m to 3094 m above sea level (a.s.l.). Historical data from 1953
 documented plant communities in the study area using 219 plots (Giacomini
 and Pignatti, 1955). The plots were characterised based on elevation,
 inclination, aspect, and geographical location within this region. These
 informations facilitated the selection and repositioning of 42 original
 plots in 2023, using the topographical parameters derived from the 2015
 digital elevation model (DEM; Regione Lombardia©). For full comparison
 with the original data, the survey method was based on Giacomini and
 Pignatti (1953), using the same plot size along with the original cover
 scale. Vegetation surveys were performed in July 2023. Permit for the
 fieldwork by: "Ente regionale per i servizi all'agricoltura e
 alle foreste" (ERSAF), with the number -
 ERSAF.2023.0003633. The available map of the plant
 communities on a scale of 1:12,500 was scanned, georectified,
 georeferenced, and manually digitised in ArcGis Pro®. The remapping took
 place between July and October 2023 and the current mapping was adjusted
 to the scale and minimum mapping unit of Pignatti's map (Giacomini
 and Pignatti, 1953). Plant communities were derived
 from Two-Way-SPecies_INdicator-analysis (TWINSPAN) and an Nonmetrical
 MultiDimensional Scaling (NMDS) utilising the package vegan (Oksanen et
 al., 2022) in R (RStudio Team, 2023).  For further
 analyses a Principal Component Analysis (PCA) and a Spearman correlation
 were performed to avoid multicollinearity. Generalized Additive Models
 (GAM, mgcv package, Wood, 2022) were employed to examine the influence of
 potential environmental drivers on vascular species richness, total
 vegetation cover, and species composition stability, measured as
 Bray-Curtis dissimilarity between paired plots.
description: # Understanding the long-term dynamics of vegetation since 1953 in
 high-mountain regions [https://doi.org/10.5061/dryad.hdr7sqvtk
 ](https://doi.org/10.5061/dryad.hdr7sqvtk) ## Description of the data and
 file structure Species data for 1953 were derived from Giacomini and
 Pignatti, 1955. The vegetation sampling was performed in July 2023. ###
 Files and variables #### File: Environmental\_variables.xlsx
 **Description:**  The data set with the environmental variables include
 all environmental variables used. The columns with the Landolt indicator
 values (LIV) from Landolt et al., 2010 contain the community weighted
 means for each plot. Climatic variables were derived from: Downscaling of
 bioclimatic variables (Climate EU software, Marchi et al., 2020) for 1953
 and the recent period. ##### Variables * plot_ID, year of survey,
 elevation [m a.s.l.]m inclination [°], eastness, northness, log stream
 power index, morphology, livestock units, potential incomin solar
 radiation [Wh m-2], mean annual air temperature [°C], mean annual
 precipitation [mm], mean summer air temperature [°C], mean summer
 precipitation [mm], summer heat:moisture index, LIV temperature, LIV igt,
 growing degree days &gt; 5°C [n], snow cover days [n], mineralogy (ca =
 calcareous, si = siliceous), LIV soil pH, LIV soil nutrients, LIV soil
 humus,LIV soil moisture, Shannon diversity, Shannon eveness, relative
 cover of trees - shrubs - dwarf shrubs - graminoids - legumes - lichens -
 mosses [%], proportion of competitive species - CRS-species - ruderal
 species - stresstolerant species, component 1 - 'thermal
 gradient', component 2 - 'soil maturity', vascular species
 richness [n], total vegetatio cover, cover vascular species [%], cover
 lichens [%], cover mosses [%], GPS-coordinates from corner 1 latitude -
 longitude, proposed association, association, habitat type * abbrevations
 association: PR = *Papaveretum rhaetici*, OD = *Sieversio-Oxyrietum
 digynae*, LS = *Luzuletum spadicae*, Sha = *Salicetum herbaceae*, CF_D =
 *Caricetum goodenowii *degraded, CF = *Caricetum goodenowii*, ES =
 *Eriophoretum scheuchzeri*, CFi = *Caricetum firmae*, SCS =
 *Seslerio-Caricetum sempervirentis, *DP* = Deschampsio cespitosae-Poetum
 alpinae, *FH* = Festucetum halleri, *LC/CC *= Loiseleurio-Caricetum
 cu*rvulae, CC = *Caricetum curvulae, *NS* = Sieversio-Nardetum strictae,
 *NGS *= Nardo-Gnaphalietum supini, *SCS_PM = *Seslerio-Caricetum
 sempervirentis* in transition to *Mugo-Rhododentreum hirsuti*, RF =
 *Rhododendretum ferruginei* #### File: Species\_data\_final.xlsx
 ***Description:** In the species data set is the cover of each species in
 percent given. We used the original cover scale, to estimate species’
 cover. These cover values were transformed into mean cover valu*es (r =
 0.02 %, + = 0.1 %, 1 = 10 %, 2 = 30 %, 3 = 50 %, 4 = 70 %, 5 = 90 %). 
 ##### Variables * The first column shows the plot_ID, the other columns
 contain the species cover values in percent. ## Access information Other
 publicly accessible locations of the data: * Downscaling of bioclimatic
 variables (Climate EU software, Marchi et al., 2020) for 1953 and the
 recent period (Marchi, M., Castellanos-Acuña, D., Hamann, A., Wang, T.,
 Ray, D., Menzel, A., 2020. ClimateEU, scale-free climate normals,
 historical time series, and future projections for Europe. Scientific Data
 7. [https://doi.org/10.1038/s41597-020-00763-0
 ](https://doi.org/10.1038/s41597-020-00763-0)). Data was derived from the
 following sources: * Species data from 1953 were derived from Giacomini
 and Pignatti, 1955 (Giacomini, V., Pignatti, S., 1955. Flora e vegetazione
 dell’alta valle del Braulio con speciale riferimento ai pascoli di
 altitudine, Supplemento Agli Atti. Istituto Botanico della Universita
 Laboratorio Crittogamico Pavia).