Understanding the long-term dynamics of vegetation since 1953 in high-mountain regions
Data files
Dec 09, 2024 version files 152.56 KB
-
Environmental_variables.xlsx
47.85 KB
-
README.md
3.80 KB
-
Species_data_final.xlsx
100.90 KB
Abstract
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.
README: Understanding the long-term dynamics of vegetation since 1953 in high-mountain regions
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 > 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 ).
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).
Methods
Fieldwork
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.