en: typoch schweiz trockenrasen distribution-models sentinel lebensraum remote-sensing flachmoore magerrasen dry-grasslands fens bogs milieux-naturels fettwiesen lebensraumkarte

en: Map for TypoCH 2.3.2/4 (Caricion firmae + Elynion)

en:
Probability of occurrence for *Caricion firmae* (TypoCH 4.3.2) and *Elynion* (TypoCH 4.3.4) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.5.2 (Polygono-Trisetion)

en:
Probability of occurrence for *Polygono-Trisetion* (TypoCH 4.5.2) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 2.3.1 (Molinion)

en:
Probability of occurrence for *Molinion* (TypoCH 2.3.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.5.3 (Cynosurion)

en:
Probability of occurrence for *Cynosurion* (TypoCH 4.5.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.3.6 (Festucion variae)

en:
Probability of occurrence for *Festucion variae* (TypoCH 4.3.6) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.3.5 (Nardion)

en:
Probability of occurrence for *Nardion* (TypoCH 4.3.5) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map of the most likely grassland habitat type

en:
Final combined map of the most likely grassland habitat type (i.e., after regional corrections) at 10x10 m resolution

Coordinate System: CH1903+_LV95

Value:	    TypoCH     Name
1:           2.2.1       *Magnocaricion* + *Cladetium*
2:           2.2.2       *Caricion fuscae*
3:	 2.2.3	*Caricion davallianae*
4:	 2.3.1	*Molinion	*
5:	 2.3.2/3	*Calthion* + *Filipendulion*
6:	 2.4.1	*Sphagnion magellanici *
7:	 4.0.2	Re-seeded and heavy fertilized grassland/Kunstrasen
8:	 4.2.1/2/3	*Stipo-Poion*, *Xerobromion*, *Diplachnion*
9:	 4.2.4	*Mesobromion *
10:	 4.3.1	*Seslerion*
11:	 4.3.2/4	*Caricion firmae* + *Elynion*
12:	 4.3.3	*Caricion ferrugineae*
13:	 4.3.5	*Nardion*
14:	 4.3.6	*Festucion variae*
15:	 4.3.7	*Caricion curvulae*
16:	 4.5.1	*Arrhenatherion*
17:	 4.5.2	*Polygono-Trisetion*
18:	 4.5.3	*Cynosurion*
19: 4.5.4	*Poion alpinae*
20: 	 4.6	Fallow grassland/Grasbrachen

en:
Countrywide ensemble maps of the 20 grassland habitat types

(1) Median predicted probability of occurrence

(2) Standard deviation of predicted probability of occurrence

(3) Presence/Absence map

en: Map for TypoCH 2.4.1 (Sphagnion magellanici)

en:
Probability of occurrence for *Sphagnion magellanici* (TypoCH 2.4.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.2.4 (Mesobromion)

en:
Probability of occurrence for *Mesobromion* (TypoCH 4.2.4) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.3.3 (Caricion ferrugineae)

en:
Probability of occurrence for *Caricion ferrugineae* (TypoCH 4.3.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en:
Map of the second most likely grassland habitat type at 10x10 m resolution; Coordinate System: CH1903+_LV95

Value:	 TypoCH  Name
1:  2.2.1  *Magnocaricion* + *Cladetium*
2:  2.2.2  *Caricion fuscae*
3:	 2.2.3	*Caricion davallianae*
4:	 2.3.1	*Molinion	*
5:	 2.3.2/3	*Calthion* + *Filipendulion*
6:	 2.4.1	*Sphagnion magellanici *
7:	 4.0.2	Re-seeded and heavy fertilized grassland/Kunstrasen
8:	 4.2.1/2/3	*Stipo-Poion*, *Xerobromion*, *Diplachnion*
9:	 4.2.4	*Mesobromion *
10:	 4.3.1	*Seslerion*
11:	 4.3.2/4	*Caricion firmae* + *Elynion*
12:	 4.3.3	*Caricion ferrugineae*
13:	 4.3.5	*Nardion*
14:	 4.3.6	*Festucion variae*
15:	 4.3.7	*Caricion curvulae*
16:	 4.5.1	*Arrhenatherion*
17:	 4.5.2	*Polygono-Trisetion*
18:	 4.5.3	*Cynosurion*
19: 4.5.4	*Poion alpinae*
20: 	 4.6	Fallow grassland/Grasbrachen

en: Overview: individual grassland habitats in the combined map

en:
Overview of the occurrence of the individual habitat types in the combined map (M1F):

For each grassland habitat type, its predicted occurrence is shown per elevation, slope, aspect and biogeographic region.

en: Map for TypoCH 4.3.1 (Seslerion)

en:
Probability of occurrence for *Seslerion* (TypoCH 4.3.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 2.3.2/3 (Calthion + Filipendulion)

en:
Probability of occurrence for *Calthion* (TypoCH 2.3.2) and *Filipendulion* (TypoCH 2.3.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.5.1 (Arrhenatherion)

en:
Probability of occurrence for *Arrhenatherion* (TypoCH 4.5.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 2.2.1 (Magnocaricion + Cladietum)

en:
Probability of occurrence for *Magnocaricion* and *Cladietum* (TypoCH 2.2.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 2.2.3 (Caricion davallianae)

en:
Probability of occurrence for *Caricion davallianae* (TypoCH 2.2.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: List of modelled grassland habitat types

en:
List of the 20 grassland habitat types modelled

Attributes: TypoCH, Modelled, Latin, Français, Deutsch, Italiano, English

en: Map for TypoCH 2.2.2 (Caricion fuscae)

en:
Probability of occurrence for *Caricion fuscae* (TypoCH 2.2.2) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.5.4 (Poion alpinae)

en:
Probability of occurrence for *Poion alpinae* (TypoCH 4.5.4) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.6 (Fallow/Abandoned grassland)

en:
Probability of occurrence for fallow/abandoned grasslands (TypoCH 4.6) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.3.7 (Caricion curvulae)

en:
Probability of occurrence for *Caricion curvulae* (TypoCH 4.3.7) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.2.1/2/3 (Stipo-Poion/Cirsio-Brachypodion/Xerobromion)

en:
Probability of occurrence for *Stipo Poion* (TypoCH 4.2.1.1), *Cirsio-Brachypodion* (TypoCH 4.2.1.2), *Xerobromion* (TypoCH 4.2.2) and *Diplachnion* (TypoCH 4.2.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en:
We modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances according to the Swiss habitat typology (TypoCH; Delarze et al. 2015) at 10x10 m resolution across Switzerland. The 20 grassland habitat types belong to the following habitat groups: fens, wet meadows, raised bogs, re-seeded and heavy fertilized grasslands, dry grasslands, nutrient-poor alpine and subalpine grasslands, nutrient-rich pastures and meadows as well as fallow grasslands.

We followed a two-step approach:

(1) Ensemble models provide **distribution maps of the 20 individual grassland habitat types**, using training data from various sources. Predictors were Copernicus Sentinel satellite imagery and variables describing climate, soil and topography. The performance of these maps was assessed with the True Skill Statistics and split‐sampling of the data.

Available maps for each grassland habitat: (1) *Map of the median of predicted probability of occurrence*; (2) *Map of the standard deviation of the predicted probability of occurrence* (available upon request); (3) *Binary presence/absence map* (available upon request). For an overview, see *Overview: Maps of the individual grassland habitats*.

(2) **Combined maps**: The individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert‐based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat‐type proportions with extrapolations from field surveys.

Available combined maps: Map of the most likely habitat type (M1F; after regional corrections); Map of the second most likely habitat type (M2); Map of the most likely habitat type without regional corrections (available upon request); Map of the weighted median of the predicted probability of occurrence for the most/second most likely habitat type, respectively (available upon request); map of the ratio of the probabilities of occurrence of the most and second most likely habitat types (available upon request)

bogs

display_name: bogs

distribution-models

display_name: distribution-models

dry-grasslands

display_name: dry-grasslands

fens

display_name: fens

fettwiesen

display_name: fettwiesen

flachmoore

display_name: flachmoore

lebensraum

display_name: lebensraum

lebensraumkarte

display_name: lebensraumkarte

magerrasen

display_name: magerrasen

milieux-naturels

display_name: milieux-naturels

remote-sensing

display_name: remote-sensing

en: Distribution maps of permanent grassland habitats for Switzerland


maintainer: EnviDat Support

en: traits reef-fish dispersal biodiversity ecology

state: active

type: dataset

en: Coral reef grid cells (shape, dbf, etc. files)

media_type: application/zip

en:
Grid cells of 5 × 5°, corresponding to circa 555 × 555 km at the Equator (see Parravicini et al. 2013, Leprieur et al. 2016)

Parravicini, V. et al. 2013. Global patterns and predictors of tropi- cal reef sh species richness. – Ecography 36: 1254–1262.

Leprieur, F. et al. 2016. Plate tectonics drive tropical reef biodiver- sity dynamics. – Nat. Commun. 7: 11461.

format: ZIP

mimetype: application/zip

en: Simulated rates of speciation and extinction under a range of dispersal parameters. Net diversification rates are calculated from the difference between speciation and extinction rates. This data allows the creation of Figure A4 in Supporting information, Appendix 1.

format: SERVICE

en: Global species distribution of tropical reef fish species

media_type: text/csv

en: Each species corresponds to a "Gaspar_code" gathered from the General Approach to Species Abundance Relationships (GASPAR) database (Kulbicki et al. 2013, Parravicini et al. 2013) and is linked to a specific Fishned ID corresponding to a specific Latitude and Longitude.

format: CSV

mimetype: text/csv

en: Tropical reef fish trait data

en:
Species trait (i.e. adult body size, home range mobility behaviour, circadian activity and schooling) data gathered from the General Approach to Species Abundance Relationships (GASPAR) database (Kulbicki et al. 2013, Parravicini et al. 2013).

Kulbicki, M. et al. 2013. Global biogeography of reef shes: a hier- archical quantitative delineation of regions. – PLoS One 8: e81847.

Parravicini, V. et al. 2013. Global patterns and predictors of tropi- cal reef sh species richness. – Ecography 36: 1254–1262.

To complement the trait data with Pelagic Larval Duration information refer to the following publication:

Luiz, Osmar J., et al. "Adult and larval traits as determinants of geographic range size among tropical reef fishes." Proceedings of the National Academy of Sciences 110.41 (2013): 16498-16502.

In Donati et al. "A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography in August 2019, we used the PLD gathered from Luiz et al. 2013 as described in the methods - section "Species trait data".

en: Supplementary material Appendix 1 Figures illustrated in Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages". accepted in Ecography, August 2019

en: Input for spatial congruence test overlap between proportions of low and high dispersal and observed low- and high- dispersal-related traits in assemblages (30-50% threshold). see Table 2.

en: Functions used in R scripts in: Donati et al. 2019 "A process-based-model supports an association between  # dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019

en: Functions for Figures illustrated in Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019.

en: Global simulated distribution of species under a range of dispersal parameter values

media_type: text/plain

en:
Global distribution of simulated species richness under a range of dispersal parameter values d
d ∈ [1.75°: 5.45°].

format: TXT

mimetype: text/plain

en: Dispersal parameter value d per simulation starting with a minimum dispersal value, where each species is able to colonise each adjacent cell, and ending with the value of d for which all the extant habitats, including the most remote Pacific islands, have been colonised (d ∈ [1.75°: 5.45°]).

en: Donati et al. main script

en:
Main script used in : Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages"
accepted in Ecography, August 2019

en: Input for the Global distribution maps for proportions of species richness for (a) simulated dispersal parameter value (b) observed dispersal-related traits

en: Geographic distribution data (species richness and proportions of high/low dispersal-related traits) on predominantly low dispersing and high dispersing species expected based on the simulations, and those where low- and high- dispersal related traits values are observed for the five investigated traits.

en: Main text figures

en: R Script used for figures in main text in: Donati et al. 2019: "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019.

en: Global coast files from https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html used in the Figures

en:
This dataset contains all data and R codes (R Development Core Team, https://www.R-project.org) used in the following publication: Donati GFA, Parravicini V, Leprieur F, Hagen O, Gaboriau T, Heine C, Kulbicki M, Rolland J, Salamin N, Albouy C, Pellissier L. "A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted by Ecography in August 2019. When using this data and R scripts the above publication should be cited.

The interaction of habitat dynamics with species dispersal abilities could generate gradients
in species diversity and prevalence of life-history and ecological traits, when the latter are associated with dispersal potential. In this dataset, we use a spatial mechanistic model of speciation, extinction and dispersal, constrained by a dispersal parameter. This model allows to simulate the interplay between reef habitat dynamics over the past 140 million years and dispersal, shaping lineage diversification history and global assemblage composition of over 6000 tropical reef fish species. Global trait distribution data of tropical reef fish are used to evaluate the congruence between simulations and observations.

biodiversity

display_name: biodiversity

dispersal

display_name: dispersal

ecology

display_name: ecology

reef-fish

display_name: reef-fish

traits

display_name: traits

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

fr: EnviDat est le portail de données du WSL; il offre un accès uniformisé et réglementé aux données de surveillance et de recherche en matière d’environnement. Ce portail est capable d’héberger et de publier des ensembles de données précieuses et uniques, qui sont partagées aux quatre coins de la planète. Ce faisant, EnviDat favorise la recherche et contribue à l’avancement des sciences environnementales.

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

it: EnviDat è il portale dati del WSL che fornisce un accesso unificato e gestito ai dati di monitoraggio ambientale e di ricerca. Il portale ha la capacità di ospitare e pubblicare serie di dati preziosi e unici che vengono condivisi in tutto il mondo. Così facendo, EnviDat promuove la ricerca e contribuisce al progresso della scienza ambientale.

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Simulated and observed prevalence of dispersal-related traits in tropical reef fish assemblages worldwide


en: urbanization modeling landscape-services statistical-model land-use land-use-change switzerland

en: Scripts for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
Scripts ordered by their main purpose within sequential folders in this ReadMe marked with

Within the folders the capital letters refer to the parallel or sequential use of the scripts; scripts here in ReadMe are marked with

 00_Function Scripts
 Functions.r
Script containing several functions for analyzing statistical models: cohen.kappa, meva.table, D2, max-Tss, plot-TSS

 Service_functions.R
Script containing functions for calculating all services and returning sums (and median for bd and rec) as well as rasters

 01_Data Preparation
 A_AS_raster_preparation.R
Preparation of the land use rasters for the years 1985, 1997, and 2009 based on the original Arealstatistik (AS/ASCH); Assigning our categories and assining new values to all "construction sites" for calibrating the models
For model projection starting point (2009) and for service calculations (all ASCH years) the "other" categories were assigned to the land use categories based on dominant neighbor, so that there were no empty cells
Creation of water & mountainous area raster that stayed stable throughout the projections
Creation of MFH raster (2009), to have as base for all new projection years, as this category never becames any other

 B_Factors_preparation.R
Preparation of the factor rasters that were used to calculate service values per cell (see Table 2): LWfactor, Transportation, Vol, Rivers, Lakes, slope, TWW, Hazard_risk

 B_Predictors_preparation.R
Preparation of the raster of the predictors used for calibrating (ASCH years) and projecting (stable) the transition models that do not depend on the land use categories: Temperature (ASCH years), solar radiation (stable), Public transportation access (stable), Elevation (stable), Workers density (ASCH years), Population change (ASCH transitions)

 B_Regions_preparation.R
Preparation of bioregions as raster to be used to mask asxxxx later to calculated services and land use area just for one region

 C_Pred_proj.R
Projecting the dynamic predictors into the future for projecting the land use transitions with the model: Linear extrapolations for workers densities and population; more detailed extrapolation of temperature (based on data for all stations that matched between the temp table we used for the calibration period and the projections from CS2M, and then making a spatial interpolation for all years based on the interpolation of temperature for all years per station)

 02_Model calibration
 A_Bioreg_pred_corr.R
Calculating correlation between the predictors for each bioregion and land use category, Output are those with too high correlation so that one predictor can be removed from transitions were both predictors occur

 A_trans_numbers_bioreg.R
Calculate transition numbers for each bioregion to decide which transitions to primarily consider

 B_Bioreg_ext_eval.R
Calculating TSS based on the external evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 85-97, evaluation data: 97-09)

 B_Bioreg_int_eval.R
Calculating TSS based on the internal evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 70%, evaluation data: 30%)

 C_Bioreg_TSS_analysis.R
Extracting TSS numbers from internal and external evaluation, calculating mean and median for each transition for each region for each evaluation method and saving as csv per region to be able to select only the transitions with high enough model performance

 D_Bioreg_Models_for_prediction.R
Calibration of the models that were then used to project land use transitions into the future; calibrated separately for each bioregion

 E_Residuals_trans_models.R
Calculation of Moran's I statistics to analyze the residuals of the land use transition models

 03_Projections
 A_Bioreg_Projections_run.R
Master script for: Extrapolaitng into the future with the regional models and then assembling for each time step to calculate new AS predictors; Several Outputs: txt file to test for NAs in Predictors, txt file with random seed number for sampling, txt file with times for calculation steps (check progress of script while running), csv sheet with projected transition frequencies, tif-rasters for every region and time step, pdf with raster plot for every region and time step

 04_Service calculation
 A_Bioreg_as_services.R
Calculate services per bioregion for years 1985, 1997, 2009

 A_Service_calculation_bioregions.R
Extracting service numbers(sums) per bioregions for all projection runs and storing them in a list of matrixes to be available for further plotting; can be run for swiss wide projections or regional ones

 05_Analysis
 A_Bioreg_ASCH_development.R
Extracting the occurrence of each category per region for all runs (first part), then for ASCH years, and then plotting them nicely; only works for regional projections

 A_Bioreg_Predictors_importanceD2.R
Calculating D2 for each transition and each considered predictor from bivariate models to get value of importance

 B_Bioreg_Pred_imp_analysis.R
Using calculated D2 to sort Predictors according to their importance for each transition

 A_Bioreg_Spatial_results_analysis.R
Plotting randomly sampled runs for ASCH and services to analyses their spatial development

 A_Bioreg_Transition_numbers_check_v1.R
Analyzing the frequency for all transitions for all regions and producing the point plots for each transition separately

 A_Bioreg_Transition_numbers_check_v2.R
Analyzing percentage and numbers of all transitions but plotting them per region and originating ASCH category

 B_Transition_Analysis.R
Further Analysis of transition numbers

 A_Service_analysis.R
Plotting of the service development for bioregions in several ways for analyzing

en: Data for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
The provided folder structure follows the set up of the scripts.
Data is only provided when it has been fully produced by us, otherwise dummies are given.
For data that will be produced by one of the scripts, no dummies are given.
The folders may contain sub-folders, which would be filled if the scripts were all run

 00_Analysis_Data
Data produced throughout the process of the calibration and projection that is later needed for analysis

 01_Raw_Data
Primary data from external sources that were used to compile the rasters and numbers used during the calibration and projection of landscape transitions as well as service calculation

 02_Prepared_Data
Data that will be prepared for further use by scripts are put in here
Additionally it contains the parameter sheets for the transition predictors that we compiled

 03_Model_Data
 Model outputs
Folder were the projection models statistics will be saved
 Models
Folder into which the actual transition models will be saved
 Residuals
Folder into which data on models residuals will be saved for future use

 04_Projected_Data
The output of the projections will be saved into this folder

 05_Service_Data
Any service calculations will be saved in here

 06_Results_Analysis
Any Analysis plots and tables as well as intermediate data will be saved here in the respective sub-folder

en:
Data and scripts of publication:

Madleina Gerecke, Oskar Hagen, Janine Bolliger, Anna M. Hersperger, Felix Kienast, Bronwyn Price, Loïc Pellissier (2019) Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave communications.

Contains land use projections for Switzerland and scripts and data for these projections as well as the calculation of landscape services.

Data Folder:
Contains sub-folder with the data necessary for this study (provided were no copyright issues, otherwise placeholders with descriptions), and folders where produced data may be stored

Scripts Folder:
Contains scripts organized into subfolders depending on their purpose

Note: Some abbreviations within the scripts and data are derived from German words and not English.

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

switzerland

display_name: switzerland

urbanization

display_name: urbanization

en: Land use projections and services for Switzerland


en: uav forest-snow climate albedo snow-modelling

en: Sodankylä (FIN) pine 2019

en: UAV-based LSA data collected in Sodankylae (FIN) between April and May 2019. The zip file contains one csv file per waypoint location, please refer to the following paper on further information on waypoint locations etc. (to be updated once published): Malle et al. (2021): Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements in Journal of Geophysical Research: Atmospheres (under review).

en: Davos Laret 2018

en: UAV-based LSA data collected in Davos Laret between January and May 2018. The zip file contains one csv file per waypoint location, please refer to the following paper on further information on waypoint locations etc. (to be updated once published): Malle et al. (2021): Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements in Journal of Geophysical Research: Atmospheres (under review).

en:
This data-set contains Land Surface Albedo (LSA) data obtained via a UAV sytem with up and downlooking shortwave radiation sensors, as described in the JGR-Atmospheres paper "Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements", by Malle et al. (2021, under review). This publication must be cited when using the data.

Data was collected across a large range of forest structures and solar angles in Switzerland (Davos Laret) and in Finland (Sodankylä). For each waypoint location at each site, data includes measured LSA, incoming SWR, reflected SWR and sunlit snow-view fraction alongside zenith angle, azimuth angle and measurement time (local time). Please refer to the abovementioned article for more detailed explanation.

albedo

display_name: albedo

climate

display_name: climate

forest-snow

display_name: forest-snow

snow-modelling

display_name: snow-modelling

uav

display_name: uav

en: Wintertime UAV-based land surface albedo data over forested environments


en: strategic-planning multifunctionality planning-principles green-infrastructure connectivity

en: Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure corresponding to items from 5 to 11 of the protocol. The data was discussed qualitatively in the research paper.

en:
The present dataset is part of the published scientific paper Grădinaru, S. R., & Hersperger, A. M. (2019). Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban forestry & urban greening, 40, 17-28.
The goal of this research was to conduct a comparative analysis of the integration of green infrastructure concept in strategic spatial plans of European Urban regions. Specifically, the paper has the following objectivs: 1) which principles of GI planning are followed in strategic plans of urban regions? 2) can we identify different approaches to GI integration into strategic planning?. The study focues on a sample consisting of 14 case studies spanning 11 countries. We retrieved the strategic plans from the websites of the planning authorities. The list of the reviewed planning documents can be found in Appendix A of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper.
The planning documents were read in order to address the protocol items. The answer to the protocol items in each of the first two categories (items 1–11) was documented as text, while the answer for the third category, namely items addressing the planning principles (items 12–36), was coded according to Table 1 of the article. As a result, we provide the folowing outputs:
•	GI_Dataset_1_Items_1-12.xlsx – available on request
o	Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure coresponding to items from 1 to 12 of the protocol. The data was discussed qualitativly in the research paper.
•	GI_Dataset_2_Items_12-36.csv – freely available
o	Results of the coding on principles of GI planning followed in strategic plans of urban regions– this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper.

connectivity

display_name: connectivity

green-infrastructure

display_name: green-infrastructure

multifunctionality

display_name: multifunctionality

planning-principles

display_name: planning-principles

strategic-planning

display_name: strategic-planning

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal


en: magnitude illgraben monitoring-station debris-flow force-plate

en: Illgraben debris-flow characteristics 2019-2022

en:
List of key debris flow variables from the WSL Illgraben monitoring station (2019-2022) such as occurrence date and time, peak flow depth, peak flow velocity, total volume and bulk density.
This table contains values based on our current analysis methods. The list will be updated annually after each debris flow season, and as our methods continue to improve, individual values may change slightly in the future.

debris-flow

display_name: debris-flow

force-plate

display_name: force-plate

illgraben

display_name: illgraben

magnitude

display_name: magnitude

monitoring-station

display_name: monitoring-station

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.


maintainer: Joan Casanelles Abella

en: antwerp urban-ecology osmia-bicornis plant-pollinator-interactions paris osmia-cornuta trap-nest zurich tartu cavity-nesting-bees chelostoma-florisomne dna-metabarcoding poznan hylaeus-communis

en: Larval food composition

en: Explanation data variables

en: Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition.

antwerp

display_name: antwerp

cavity-nesting-bees

display_name: cavity-nesting-bees

chelostoma-florisomne

display_name: chelostoma-florisomne

dna-metabarcoding

display_name: dna-metabarcoding

hylaeus-communis

display_name: hylaeus-communis

osmia-bicornis

display_name: osmia-bicornis

osmia-cornuta

display_name: osmia-cornuta

paris

display_name: paris

plant-pollinator-interactions

display_name: plant-pollinator-interactions

poznan

display_name: poznan

tartu

display_name: tartu

trap-nest

display_name: trap-nest

urban-ecology

display_name: urban-ecology

zurich

display_name: zurich

en: Larval food composition of four wild bee species in five European cities


en: meteorology par wind-speed relative-humidity meteo temperature precipitation swiss-forest-lab climate-change long-term

en: Site aerial photo (Schänis)

en: Site aerial photo from both Schänis stations. Source: Google. 2016. Site aerial photo from both Schänis stations. Retrieved November 11, 2016, from https://www.google.ch/maps

en: Data plot of the forest station (SCB)

media_type: application/pdf

en: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

format: PDF

en: Data plot of the field station (SCF)

en: Further details on the data repository

en: Meta data documentation.

en: Site photo of the forest station (SCB)

en: Site photo of the forest station from below (viewing upwards the slope).

en: Field station data (SCF)

en: Data file.

en: Site photo of the field station (SCF)

en: Site photo of the field station from above (viewing downwards the slope).

en: Forest station data (SCB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Schänis, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1998 onwards


biomass-of-lying-dead-wood-lis

title_for_slug: biomass-of-lying-dead-wood-lis

en:
Dry weight (mass) of lying deadwood starting at 7 cm in diameter that does not fulfil the criteria for a tally tree (measurement location of dbh not identifiable or the dbh is less than 12cm).

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

biomass-of-lying-dead-wood-lis

display_name: biomass-of-lying-dead-wood-lis

canton

display_name: canton

economic-region

display_name: economic-region

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Biomass of lying dead wood (LIS)


en: sirex ips-acuminatus pinus pissodes wood-wasps phaenops bark-beetles tomicus decline defoliation

en: see ReadMe

en: After a major dieback of Scots pines in the Valais, an inner Alpine valley in Switzerland, the colonization of differently vigorous pines by stem and branch insects was investigated to assess their role in tree mortality. At 2 locations, the needle loss (defoliation) of some 500 pine trees was assessed twice a year. Of these trees, 34-36 trees were cut each year between 2001-2005 across all defoliation classes. From each tree, two 75-cm bolts were cut from both the stem and thick branches. They were incubated in photo-eclectors (metal cabinets) set up in a greenhouse where the insects could develop under the bark. The emerged adults were collected in water-filled eclector boxes and identified to species level by specialists. Attack time was estimated from the development time of each insect species emerged. The colonisation densities of the trees were related to the transparency level of each host tree at the time of attack.

bark-beetles

display_name: bark-beetles

decline

display_name: decline

defoliation

display_name: defoliation

ips-acuminatus

display_name: ips-acuminatus

phaenops

display_name: phaenops

pinus

display_name: pinus

pissodes

display_name: pissodes

sirex

display_name: sirex

tomicus

display_name: tomicus

wood-wasps

display_name: wood-wasps

en: Infestation of Scots pines with different vitalities by bark and wood boring insects


length-of-forest-edge

title_for_slug: length-of-forest-edge

en:
Length of the forest edge calculated on the basis of the forest boundary lines determined in the aerial photo.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

length-of-forest-edge

display_name: length-of-forest-edge

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

total-area

display_name: total-area

en: Length of forest edge


en:
Sum of stem cross-section areas at a height of 1.3 m (dbh measurement height) of all living and dead trees and shrubs starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

canton

display_name: canton

economic-region

display_name: economic-region

main-tree-species

display_name: main-tree-species

production-region

display_name: production-region

en: Total basal area NFI1


radiosondes

maintainer: Jörg Wieder

title_for_slug: radiosondes

en: holoballoon temperature wind relative-humidity radiosonde wind-direction pressure raclets

en: Radiosond Template Files

en:
The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosond Sounding Files

en:
The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction

en: Radiosondes


root-traits

maintainer: Ivano Brunner

title_for_slug: root-traits

en: belowground-biomass ingrowth-cores drought fine-root-traits soil-coring

en: Root-traits

en:
Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald

Data_Fig.1.xlsx
Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring

Data_Tab1+2_2005.xlsx
Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest

Data_Tab1+2_2016.xlsx
Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest

belowground-biomass

display_name: belowground-biomass

drought

display_name: drought

fine-root-traits

display_name: fine-root-traits

ingrowth-cores

display_name: ingrowth-cores

soil-coring

display_name: soil-coring

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.


vineyard-plots-in-southern-switzerland

title_for_slug: vineyard-plots-in-southern-switzerland

en: urban-sprawl vineyards agricultural-land-abandonment land-use-change urbanisation

en: Vineyard plots in southern Switzerland between 1989 and 2020

en: Compressed folder (.zip) containing a shapefile (.shp) with 25123 polygons features representing vineyard plots and 146 variables in the attribute table, as well as an Excel file (.xlsx) which provide a short and long description of the same variables.

en: Geospatial vector data (shapefile) representing the cadastral plots in the Canton Ticino and the Moesa region (southern Switzerland) having a part of the surface occupied by vineyards in the years 1989 and/or 2020 according to the corresponding edition of the Swiss national topographic maps in the scale 1:25,000 and to the topographic landscape model of Switzerland swissTLM3D (Federal office of topography Swisstopo). In the attribute table there is many variables which describe the topography of the site, the characteristics of the plots and the evolution of the wine growing area inside the plot between 1989 and 2020. Coordinate system: EPSG:2056 - Swiss CH1903+ / LV95.

agricultural-land-abandonment

display_name: agricultural-land-abandonment

land-use-change

display_name: land-use-change

urban-sprawl

display_name: urban-sprawl

urbanisation

display_name: urbanisation

vineyards

display_name: vineyards

en: Vineyard plots in southern Switzerland


en: antarctica drifting-snow snowmicropen mass-flux meteorological-data terrestrial-laser-scanning

en: Dataset

en: Links to the dataset

en: README

en: Describes the dataset in detail

en:
This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data.

Please refer to the README for more information about the data.

This dataset is the basis of the following publication:

Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.

antarctica

display_name: antarctica

drifting-snow

display_name: drifting-snow

mass-flux

display_name: mass-flux

meteorological-data

display_name: meteorological-data

snowmicropen

display_name: snowmicropen

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

en: Expedition to Princess Elisabeth Antarctica Station, 2016/2017


en: branchwood nfi allometric whole-tree-volume stemwood model

en: linked data set with stem and branchwood data

en: Dataset comprising all measured variables in order to easily derive complete above-ground tree wood volume based on 38’841 trees including 14’038 trees with measured branchwood data.

en: Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory

en: Documentation of the data and review in 2022.

en:
In the Swiss National Forest Inventory (NFI) the volume of the stem and of large (≥ 7cm in diameter) and small branches is estimated based on allometric functions. These functions were developed based on data collected within the permanent plot network of the Experimental Forest Management (EFM) sites at WSL (David I. Forrester; Hubert Schmid; Jens Nitzsche (2021). The Experimental Forest Management network. EnviDat. doi: 10.16904/envidat.213). The data were converted to digital format in two separate steps in the mid-1970s for stemwood and the mid-1980s for branchwood.
The dataset on stemwood volume contains 38’864 single tree data for the mean crosswise diameter at two meter sections along the stem plus an additional measurements at 1.3 m (i.e. DBH) where the diameter is greater than or equal to 7 cm (i.e. threshold of merchantable wood) and the lengths of the stem from the base to the threshold of merchantable wood and to the tree top. The measurements were collected on 768 EFM sites in the period 1888 to 1974. The dataset on branchwood is based on a subset of the stemwood data and contains in the raw format information on 14'712 single trees. It includes aggregated data from the stemwood dataset, i.e. the DBH, the stem-diameter at 7 m from the base, and the tree height from the base to the top, as well as the measured volume of large and small branches.
In 2022, the metadata of both datasets were checked, values were examined for plausibility and duplicated entries. Duplicates were removed as far as possible and the branchwood volume data were appended to the stemwood dataset to obtain a final, single file with matching single tree data. Following this evaluation the final dataset consisted of a total of 38’841 trees including 14’038 trees with measured branchwood data.

allometric

display_name: allometric

branchwood

display_name: branchwood

model

display_name: model

nfi

display_name: nfi

stemwood

display_name: stemwood

whole-tree-volume

display_name: whole-tree-volume


en:
Volume of stemwood with bark of all dead trees and shrubs (standing and lying) starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. In addition, lying green trees were classified in NFI1 as deadwood.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

tree-state-standing-lying

display_name: tree-state-standing-lying

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Volume of dead wood NFI1


en: software toposcale rcm cryosphere downscaling cordex

en:
Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps.

A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md.

cordex

display_name: cordex

cryosphere

display_name: cryosphere

downscaling

display_name: downscaling

rcm

display_name: rcm

software

display_name: software

toposcale

display_name: toposcale

en: TopoCLIM v1.1 code


en: wrf cryowrf topography landuse antarctica

en: Scripts

en: Scripts to run the preprocessing to produce WRF binary tiles from REMA and AntarcticaLC2000 for topography and landuse (see Documentation for procedure).

en: Documentation

en: Documentation about how the provided dataset was produced including details to reproduce the dataset.

mimetype: application/pdf

en: WPSinput

en: Input files to run the Weather Research and Forecasting model (WRF) preprocessing system WPS with REMA topography and AntarcticaLC2000 landuse data (see Documentation for details).

en:
Reference Elevation Model of Antarctica (REMA) topography and AntarcticaLC2000 landuse data
are now available as static data input for the Weather Research and Forecasting model (WRF). Topography
and landuse are made available at a spatial resolution of 1 km. This documentation describes the methods
applied to convert REMA and AntarcticaLC2000 to WRF readable format and shows how this improves
the representation of the Antarctic topography and landuse categories over coastal Antarctic regions.

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf

en: REMA topography and AntarcticaLC2000 for WRF


en: insects pests gradient pinus parasitoids predators bark-beetles elevation

en: Gradients

en:
The column headers refer to:

- SamplingCode: WSL fauna database code referring to year and trap type (not relevant)
- SamplingDate: Date when the emergence traps were emptied, corresponds roughly to the date of emergence
- TreeNumber: Tree ID
- Defoliation: percentage of needle loss or crown transparency, according to international standards, of the trees prior to felling
- BarkSurface: bark surface area of stem parts exposed in emergence traps
- YearOfAttack: Year of colonization. Technically, the colonization window opened on 1st of January and closed in June, when sections of the tree stems were transported to the institute
- YearOfEmergence: Emergence year of a given cohort of insects in the greenhouse
- YearAfter Attack: Temporal difference between year of colonization and year of emergence of a given cohort of insects, corresponding to total development time
- Region: AO Aosta (ITA), GR Grisons (CH), VS Valais (CH)
- Location: locality nearest to the gradient
- Elevation: Site where trees were felled and exposed to colonization
- Aspect: north (N) or south (S) facing slope
- Species: taxonomic species according to Fauna Europaea
- Family: taxonomic family
- Order: taxonomic order: Col. Coleoptera, Hym. Hymenoptera, Dipt Diptera, Raph Raphidioptera
- Guild: Phyto phytophagous, Pred predatory, para parasitoid
- Number: number of sampled individuals of a given species at a given date.
- Density: number of emerged insects per m2 bark surface
- Identifyer: specialist who identified the insects

en:
The colonization of cut pine stems by wood-inhabiting insects was investigated at various elevations. The study sites were located in the regions of Aosta Valley (Italy), Valais (Switzerland), and Grisons (Switzerland). In each region, there were two gradients in pine (Pinus sylvestris) forests, with three study sites at 900 m, 1200 m, and 1600 m a.s.l. each. Vital trees were felled in late autumn and the stems were colonized by pioneering xylophagous insects and their natural enemies next spring. Pieces of these stems were cut and exposed in emergence traps in a greenhouse. In each region the survey was done in two consecutive years.

Please contact author for terms of use.

bark-beetles

display_name: bark-beetles

elevation

display_name: elevation

gradient

display_name: gradient

insects

display_name: insects

parasitoids

display_name: parasitoids

pests

display_name: pests

pinus

display_name: pinus

predators

display_name: predators

en: Pine insects along elevational gradients


maintainer: Milojevic

en: extreme-values alpine-precipitation low-flow return-periods reservoir-inflow

en: Daily Luzzone Reservoir Inflow 01-01-2000 to 31-12-2019

en: Daily total water inflow to Luzzone reservoir. Natural inflow (LUZ_inflow_raw) is back-calculated based on daily water level in reservoir (removing hydropower inputs and spills). Linear interpolation between daily values is used to bring all inflow values into positive space (LUZ_inflow_intrp).

en: R codes for Extreme Values Analysis

en: Zip file containing R codes for running extreme value analyses for high precipitation and inflow events as well as code for determining return levels of low inflow and low precipitation events

en:
 Summary

* Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland

* R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis

 Data
The dataset included here is the "natural" reservoir inflow for the Luzzone reservoir.
Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).

 Codes
R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:

* GEV (Generalized Extreme Value)

* GPD and GPDd (Generalized Pareto Distribution including declustered version)

* eGPD (extended Generalized Pareto Distribution)

* MEV (Metastatistical Extreme Value)

alpine-precipitation

display_name: alpine-precipitation

extreme-values

display_name: extreme-values

low-flow

display_name: low-flow

reservoir-inflow

display_name: reservoir-inflow

return-periods

display_name: return-periods

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs


lwf-lageren-long-term-research-site

title_for_slug: lwf-lageren-long-term-research-site

en: swiss-fluxnet meteorological-data forest plot meteorological-measurements lwf swiss-forest-lab infrastructure

en: LWF plot Lägeren. Photo: Peter Waldner, 2019

en:
![alt text](https://www.envidat.ch/dataset/b763c4e1-2de3-4e8f-9bb7-2ca533624060/resource/b6e747c6-7a85-43e0-958b-3522f370bbad/download/lwf_laegeren.jpg "LWF Lägeren")

This research site is located on the southern slope of the Lägern, which forms the eastern most part of the Jura mountains, within a managed mixed deciduous forest. The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and currently part of the following national networks:

* National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel))
* [TreeNet](https://treenet.info/switzerland/laegeren): The biological drought and growth indicator network
* Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/laegeren.html))
* [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)

The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the groups [Grassland Sciences](https://www.gl.ethz.ch) and [Land-Climate Dynamics](https://iac.ethz.ch/group/land-climate-dynamics.html) from the Swiss Federal Institute of Technology Zurich, the unit [Soil Science & Biogeochemistry](https://www.geo.uzh.ch/en/units/2b.html) from the University of Zurich, and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)).

LWF Plot Lägeren

- Community: Wettingen / canton AG
- Date of installation: 1.05.2012
- Size of the plot: 1.34 ha
- Altitude: 643 - 718 m
- Mean slope: 37 %
- Geology (in German): Kettenjura; Jura: Malm, Molassehangschutt
- Soil types (WSL) : calcareous brown soil, chromic luvisol, mixed rendzina
- Woodland association after Ellenberg and Klötzli's classification (1972): Galio odoratio-Fagetum typicum bis - Pulmonario-Fagetum typicum
- Main tree species: fagus sylvatica
- Management system: high forest
- Silvicultural system: forest reserve
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 72.18 cm
- Number of trees BHD >= 12 cm (2011): 503
- Maximum tree age: picea abies: 120-170 years, fagus sylvatica: ca. 150 years

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-fluxnet

display_name: swiss-fluxnet

swiss-forest-lab

display_name: swiss-forest-lab

en: LWF Lägeren long-term research site


maintainer: Loïc Pellissier Pellissier

en: herbivores-communities elevation alps functional-traits community-indices environmental-gradients plant-herbivore-interactions

en: Nematode surveys

en: Surveys of soil nematode along elevation

en: Study sites

en: Coordinates and elevation of the study sites

en: Plant traits

en: Plant functional traits (SLA, LDMC, carbon and nitrogen content, punch strenght; averaged by species)

en: Orthoptera traits

en: Orthoptera functional traits (incisive mandibular strength averaged by species)

en: Orthoptera surveys

en: Species inventories of Orthoptera

en: Vegetion surveys

en: Species inventories of plants

en: Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017.

alps

display_name: alps

community-indices

display_name: community-indices

elevation

display_name: elevation

environmental-gradients

display_name: environmental-gradients

functional-traits

display_name: functional-traits

herbivores-communities

display_name: herbivores-communities

plant-herbivore-interactions

display_name: plant-herbivore-interactions

en: Above- and below-ground herbivore communities along elevation


en: generation gui semantic xml numerical-model ini

en: Release 2.0.4 from https://models.slf.ch/p/inishell-ng/source/tree/v2.0.4/

en: This is the source code of the Inishell-2.0.4 flexible Graphical User Interface. It is configured through an XML file for applications that themselves need to be configured via ini-files. It allows to set constraints regarding the sections, keys and values that may be present in the ini-files that are produced by the end user. It is released under the GPL-v3 or later license. Precompiled binaries are available at https://models.slf.ch/p/inishell-ng/downloads/ while the development takes place at https://code.wsl.ch/snow-models/inishell (gitlab forge).

generation

display_name: generation

gui

display_name: gui

ini

display_name: ini

numerical-model

display_name: numerical-model

semantic

display_name: semantic

xml

display_name: xml

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.


forest-area

title_for_slug: forest-area

en:
The forest area is the total sum of all areas classified as forest according to NFI’s forest definition. The forest definition includes shrub forest. This theme is also used to assess the total area when forest and non-forest need to be distinguished.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

basal-area-of-conifers-classified

display_name: basal-area-of-conifers-classified

basal-area-standing-live-trees

display_name: basal-area-standing-live-trees

basis-for-local-planning

display_name: basis-for-local-planning

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

cause-of-salvage-logging

display_name: cause-of-salvage-logging

certification-label

display_name: certification-label

classified

display_name: classified

concept-of-transportation-system

display_name: concept-of-transportation-system

crown-closure

display_name: crown-closure

degree-of-cover-aerial-photo

display_name: degree-of-cover-aerial-photo

degree-of-cover-of-assured-regeneration

display_name: degree-of-cover-of-assured-regeneration

degree-of-damage-of-the-stand

display_name: degree-of-damage-of-the-stand

degree-of-mixture

display_name: degree-of-mixture

dominance-by-exotic-species-neophytes

display_name: dominance-by-exotic-species-neophytes

economic-region

display_name: economic-region

forest

display_name: forest

forest-area

display_name: forest-area

forest-function-agricultural-use

display_name: forest-function-agricultural-use

forest-function-drinking-water

display_name: forest-function-drinking-water

forest-function-game-reserve

display_name: forest-function-game-reserve

forest-function-landscape-protection

display_name: forest-function-landscape-protection

forest-function-military

display_name: forest-function-military

forest-function-nature-protection

display_name: forest-function-nature-protection

forest-function-recreation

display_name: forest-function-recreation

forest-function-shelterbelt

display_name: forest-function-shelterbelt

forest-function-wood-production

display_name: forest-function-wood-production

forest-type-and-stage-of-development

display_name: forest-type-and-stage-of-development


en: illgraben volume monitoring-station force-plate magnitude debris-flow

en: Illgraben debris-flow bulk volumes from 2000 to 2017.

en:
Debris-flow bulk volumes from the WSL monitoring station.
More information can be found in McArdell et al. (2007) and Schlunegger et al. (2009).

debris-flow

display_name: debris-flow

force-plate

display_name: force-plate

illgraben

display_name: illgraben

magnitude

display_name: magnitude

monitoring-station

display_name: monitoring-station

volume

display_name: volume

en: Debris-flow volumes at the Illgraben 2000-2017


en: ticino flavescence-doree phytoplasma 16srv switzerland alnus-glutinosa scaphoideus-titanus orientus-ishidae map-genotype alternative-vector

en: Leafhopper captures in vineyards.

en: Leafhoppers captured on yellow sticky traps (YST) placed in vineyards and sampled for molecular analyses. Counts may differ if the quality of the individual specimens was deemed as insufficient for the molecular analysis. Specimens of Scaphoideus titanus caught in Camorino were sampled but not analyzed.

en: Analyzed and infected specimens.

en: Number of analyzed and 16SrV-infected specimens collected on common alder trees and on yellow sticky traps (YST) placed inside the vineyard and corresponding infection rate.

en: Features of the phytoplasma isolates

en: Features of the phytoplasma isolates analyzed in this work and reference strains, together with relative GenBank accession numbers and host.

en: Results of the molecular analyses conducted on the sampled common alder trees in Origlio, Camorino, and Montalbano.

en: Real-time PCR (conducted on all samples), and nested PCR and RFLP digestion (Origlio only). Cq; cycle quantification value; +: positive response to real-time PCR test; -: negative response to real-time PCR test.

en: Plots description

en: Characteristics of the monitored vineyards, including the number of sampled common alders and the number of yellow sticky traps (YST) placed in the canopy.

en: Orientus ishidae (development stages L1-2; L3-5; and imago) caught with sweeping net on common alders per plot.

en: Number of sampling common alder trees (NAlders) and data regarding captures of O. ishidae (mean, standard deviation (SD), number of observations (NObservations) and total seasonal captures) are reported.

en: Results of molecular analyses on a selection of the sampled symptomatic grapevines in Origlio.

en: Real-time PCR for 16SrV and 16SrXII phytoplasmas, and nested PCR and RFLP digestion (16SrV phytoplasma only). Cq; cycle quantification value; BDL: below detection limit; FDp: “Flavescence dorée” phytoplasma; BNp: “Bois noir” phytoplasma; +: positive response to real-time PCR test; -: negative response to real-time PCR test.

en:
Flavescence dorée (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported.

Data used for the publication.
Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses.
Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dorée phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees.

alnus-glutinosa

display_name: alnus-glutinosa

alternative-vector

display_name: alternative-vector

flavescence-doree

display_name: flavescence-doree

map-genotype

display_name: map-genotype

orientus-ishidae

display_name: orientus-ishidae

phytoplasma

display_name: phytoplasma

scaphoideus-titanus

display_name: scaphoideus-titanus

switzerland

display_name: switzerland

ticino

display_name: ticino

en: Alnus glutinosa (L.) Gaertn. and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the “Flavescence dorée” epidemics


en: amphibians detectability occupancy-models monitoring breeding conservation

en: This dataset includes data from 15 native pond breeding species in Switzerland in addition to observations of any species within the Pelophylax genus of water frogs. 233 sites (obnr) sampled during the 2011-2016 round of the WBS survey, which are listed as the "first" round of surveys. Data are also provided at 73 sites which were resurveyed in 2017 or 2018 ("second" surveyround). The data are filtered as described in Cruickshank et al. (2021) to remove data from surveys carried out after the final sighting of a species within a year, and before the first observation of the species within a year. Observational data are provided as one of 3 observation types; 1 denotes a survey where the species was not detected, 2 denotes surveys where the species was detected but no life stages indicating successful breeding (e.g. the presence of eggs or larvae) were observed. Observation type 3 denotes a survey where evidence of successful breeding was observed (i.e. eggs or larvae). Survey protocols and full descriptions of the data are provided in Cruickshank et al (2021).

amphibians

display_name: amphibians

breeding

display_name: breeding

conservation

display_name: conservation

detectability

display_name: detectability

monitoring

display_name: monitoring

occupancy-models

display_name: occupancy-models

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Data from: Estimation of breeding probbability can make monitoring data more revealing: a case study of amphibians


bryophytes-of-europe-traits-bet-dataset

title_for_slug: bryophytes-of-europe-traits-bet-dataset

en: indicator-values iucn liverworts bioclimatic-variables life-history-traits hornworts mosses red-list

en: Text file including the BET dataset.

en: PDF including the (trait) explanations.

en: Excel file including the BET dataset and (trait) explanations.

format: XLS

en: The Bryophytes of Europe Traits (BET) dataset includes values for 65 biological and ecological traits and 25 bioclimatic variables for all 1816 bryophytes included in the European Red List (Hodgetts et al. 2019). The traits are compiled from several regional trait datasets and manually complemented using Floras, species-specific literature and expert knowledge. The bioclimatic variables are calculated using the European range of each species. Details regarding the trait compilation and extraction of bioclimatic variables can be found in the corresponding data paper (Van Zuijlen et al. 2023).

bioclimatic-variables

display_name: bioclimatic-variables

hornworts

display_name: hornworts

indicator-values

display_name: indicator-values

iucn

display_name: iucn

life-history-traits

display_name: life-history-traits

liverworts

display_name: liverworts

mosses

display_name: mosses

red-list

display_name: red-list


en: avalanche-forcasting machine-learning avalanche wet-snow-avalanche numerical-avalanche-forecasting

en: This dataset contains input and target data for winter 2020-2021. Dataset2 was used for training model RF12.

en: This dataset contains nowcast input and target data for winter 2021-2022. This dataset is the nowcast test set for RF12. The column RF12_AvD_proba is the predicted probability for wet-snow avalanche day provided by the RF12 model.

en: This dataset contains forecast input and target data for winter 2021-2022. This dataset is the forecast test set for RF12. The column RF12_AvD_proba is the predicted probability for wet-snow avalanche day provided by the RF12 model.

en: This dataset contains input and target data for winters before September 2020. Dataset1 was used for training model RF12.

en: Features description

en:
Datasets used to implement the wet-snow avalanche activity model presented in the article:

Hendrick, M., Techel, F., Volpi, M., Olevski, T., Pérez-Guillén, C., van Herwijnen, A., Schweizer, J. (2023). Automated prediction of wet-snow avalanche activity in the Swiss Alps. Journal of Glaciology, under review

Each dataset includes the input variables (weather and snowpack features) and the target variable (wet-snow avalanche day or not) used to build the model. Additionally, Dataset3_nowcast and Dataset3_forecast contain the predictions provided by the RF12 model.

All input variables are described in the Appendix of the article and also in the read_me file. Further information on SNOWPACK variables is also available at https://models.slf.ch/p/snowpack/ .

avalanche

display_name: avalanche

avalanche-forcasting

display_name: avalanche-forcasting

machine-learning

display_name: machine-learning

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

wet-snow-avalanche

display_name: wet-snow-avalanche

en: Weather, snowpack and avalanche occurrence data for automated prediction of wet-snow avalanche activity


en: climate-change reservoirs water-demand water-scarcity water-supply

en: Water demand version 2 (corrected version)

en: Water supply

en: Simulated discharge series for a reference period 1981-2010 and till the end of the century. Data are provided for 39 climate model chains and a control run based on the observed meteorology. Data are provided at daily resolution (mm/d).

en: Reservoir volumes

en: Reservoir volume for the 307 medium-sized catchments for different storage categories: natural lakes, artificial reservoirs, reservoirs for artificial snow production, drinking water reservoirs, and usable volumes of natural lakes. Unit: Mio m³. One column per catchment (see shapefile).

en: Water demand (deprecated)

en:
Warning: please use the corrected version 2, since this file contains errors for certain categories such as Livestock, Industry or Houshold.

Water demand estimates for the 307 medium-sized catchments for different categories for current and future conditions. One text file is provided per demand category. One column corresponds to one catchment (IDs compare to shapefile), The data are provided at monthly resolution in mm/d and can be converted to volumes by using the catchment area. E.g.
Q[mm/d] * catchment area [m²] /1000 = Q[m³/d]
or to obtain discharge in m³/s
Q[mm/d] * catchment area [m²]/1000/24/3600 = Q [m³/s].
Future irrigation and hydropower water demand are provided per climate model chain.

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of 307 catchments for which estimates of water supply, demand, and reservoir volumes are provided. The OBJECTID entry corresponds to the catchment ID which is used as a reference in the other datasets provided.

en:
The dataset Hydro-CH2018 reservoirs provides estimates of current and future water supply, water demand, and storage volumes for 307 medium-sized catchments in Switzerland.
Water supply for current (1981-2010) and future (2070-2099) climate conditions was simulated using the hydrological model PREVAH. For modeling current water supply, observed meteorological time series were used as input, while simulated meteorological time series derived from 39 model chains of the CH2018 initiative were used as an input for simulating future climate conditions.

Water demand was estimated for six categories:

- 1) Drinking water (households and tourism),
- 2) industry (second and third sector),
- 3) artificial snow production,
- 4) agriculture (irrigation and livestock feeding),
- 5) ecology (residual flows), and
- 6) hydropower.

Future estimates consider changes in demand related to population growth and changes in the hydrological conditions.
Storage volumes are provided for natural lakes (storage capacities and usable volumes), artificial reservoirs, reservoirs for artificial snow production, and drinking reservoirs.

A detailed description of the simulation and estimation procedures can be found in

*  Brunner, M.I., Björnsen Gurung, A., Zappa, M., Zekollari, H., Farinotti, D., Stähli, M., 2019. Present and future water scarcity in Switzerland: Potential for alleviation through reservoirs and lakes. Sci. Total Environ. 666, 1033–1047. https://doi.org/10.1016/j.scitotenv.2019.02.169.

This dataset contains the following information:

1.	Shapefile with the 307 medium-sized Swiss catchments.
2.   Textfiles with the water supply simulations for the control run and the 39 climate model chains (one file per chain) at daily resolution for the 307 catchments.
3.	Textfiles with the current and future demand estimates per category at monthly resolution for the 307 catchments.
4.	Textfiles with the storage volumes per category and catchment.

climate-change

display_name: climate-change

reservoirs

display_name: reservoirs

water-demand

display_name: water-demand

water-scarcity

display_name: water-scarcity

water-supply

display_name: water-supply

educ

en: Education, culture and sport

en: Hydro-CH2018 reservoirs


en:
Number of species of living trees and shrubs starting at 40 cm plant height that occur within a 200 m2 sample plot.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Number of woody species (from 40 cm height)


en: wind-velocity anemometer wind-speed wind-direction raclets wind

en: Sonic time

en: The data set contains the time in YYYY-mm-dd HH:MM:SS.sss for the anemometer data. The time is given in UTC+1.

en: Sonic wind component v

en:
The data contains values for v direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: Sonic temperature

en: The data set contains values of temperature (°C) measured with the anemometer at a sampling frequency of 20 Hz.

en: Sonic wind component w

en:
The data contains values for w direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: Sonic wind component u

en:
The data contains values for u direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: A Young 81000 sonic anemomenter was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849) to record three components of the wind velocity (u, v, w in [m s‾ ¹]) and air temperature (Ts in [°C]). The anemomenter was mounted in direction North at a height of 1.5 m above snow surface at the beginning. The time within each data set is given in UTC+1. Instrument specifications can be found [here](http://www.youngusa.com/Manuals/81000-90(I).pdf) .

anemometer

display_name: anemometer

raclets

display_name: raclets

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity

en: Snow Drift Station - 3D Ultrasonic


maintainer: Ionuț Iosifescu Enescu

en: map 0.5GB

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: map 1GB

en: large map 10GB

en: huge map 100GB

en: map 0.2GB

en: map preview 0.5MB

mimetype: image/jpeg

en: Bulk Data Access

en: Link to the S3 bucket where all the data resources can be downloaded in bulk

en: map 2GB

en:
The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

educ

en: Education, culture and sport

en: Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)


en: infrared-thermography patchy-snow-cover surface-heat-exchange snow-melt 2d-wind-field near-surface-atmospheric-layer 2d-temperature-field snow-atmosphere-interactions

en: kaijo

en: Data measured with a 3D short-path ultrasonic anemometer in close vicinity to the screens. Note that the time stamps are in local time (GMT+2).

en: IRdata

en: Sequence of 2700 frames recorded with the thermal IR cam. The data is stored as the variable 'irdata' in the .nc file.

en: folder containing the source code for the WEIRD method. Written in JULIA.

en:
The lateral transport of heat above abrupt (sub-)metre-scale steps in land surface temperature influences the local surface energy balance. We present a novel experimental method to investigate the stratification and dynamics of the near-surface atmospheric layer over a heterogeneous land surface. Using a high resolution thermal infrared camera pointing at synthetic screens, a 30Hz sequence of frames is recorded. The screens are deployed upright and horizontally aligned with the prevailing wind direction. The screen’s surface temperature serves as a proxy for the local air temperature. We developed a method to estimate near-surface two-dimensional wind fields at centimetre resolution from tracking the air temperature pattern on the screens. Wind field estimations are validated with near-surface three-dimensional short-path ultrasonic data. To demonstrate the capabilities of the screen method, we present results from a comprehensive field campaign at an alpine research site during patchy snow cover conditions. The measurements reveal an extremely heterogeneous near-surface atmospheric layer. Vertical profiles of horizontal and vertical wind speed reflect multiple layers of different static stability within 2m above the surface. A dynamic, thin stable internal boundary layer (SIBL) develops above the leading edge of snow patches protecting the snow surface from warmer air above. During pronounced gusts the warm air from aloft entrains into the SIBL and reaches down to the snow surface adding energy to the snow pack. Measured vertical turbulent sensible heat fluxes are shown to be consistent with air temperature and wind speed profiles obtained using the screen method and confirm its capabilities to investigate complex in situ near-surface heat exchange processes.
Here you find the data and the documented code used to create the plots in the publication.

infrared-thermography

display_name: infrared-thermography

near-surface-atmospheric-layer

display_name: near-surface-atmospheric-layer

patchy-snow-cover

display_name: patchy-snow-cover

snow-atmosphere-interactions

display_name: snow-atmosphere-interactions

snow-melt

display_name: snow-melt

surface-heat-exchange

display_name: surface-heat-exchange

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Quantifying Surface Heat Exchange over Heterogeneous Land Surfaces at Ultra-High Spatio-Temporal Resolution


maintainer: Peter Bebi

en: vegetation soil-temperature carbon-dioxide stillberg climate-change experiment

en: Metadata FACE Stillberg Experimental Design

en: Schematic illustration and tabular listing of the FACE experimental design.

en: Metadata FACE Stillberg Data File Description

en: List of descriptions for each of the .csv FACE data files.

en: Project Description FACE Stillberg

media_type: text/html

en: Link to FACE project description on the WSL website.

format: HTML

en: Data FACE Stillberg

en: Folder in .zip format containing the FACE datasets.

en:
 Background information

High elevation ecosystems are important in research about environmental change because shifts in climate associated with anthropogenic greenhouse gas emissions are predicted to be more pronounced in these areas compared to most other regions of the world. This project involved a Free Air CO2 Enrichment (FACE) and soil warming experiment located in a natural treeline environment near Davos, Switzerland (Stillberg, 2200 m a.s.l.). Elevated atmospheric CO2 concentrations (+200 ppm) were applied from 2001 until 2009, and a soil warming treatment (+4 °C) was applied from 2007 until 2012. The combined CO2 enrichment and warming treatment reflects conditions expected to occur in this region in approximately 2050. A broad range of ecological and biogeochemical research was carried out as part of this environmental change project.

 Experimental design

The experiment consisted of 40 hexagonal 1.1 m² plots, 20 with a *Pinus mugo* ssp. *uncinata* (mountain pine, evergreen) individual in the centre and 20 with a *Larix decidua* (European larch, deciduous) individual in the centre. A dense cover of understorey vegetation surrounded the tree in each plot, including the dominant dwarf shrub species *Vaccinium myrtillus* (bilberry), *Vaccinium gaultherioides* (group *V. uliginosum agg.*, northern bilberry) and *Empetrum nigrum* ssp. *hermaphroditum* (crowberry) plus several herbaceous and non-vascular species.
At the beginning of the experimental period, the 40 plots were assigned to ten groups of four neighbouring plots (two larch and two pine trees per group) in order to facilitate the logistics of CO2 distribution and regulation. Half of these groups were randomly assigned to an elevated CO2 treatment, while the remaining groups served as controls and received no additional CO2. In spring 2007, one plot of each tree species identity was randomly selected from each of the 10 CO2 treatment groups and assigned a soil warming treatment, yielding a balanced design with a replication of five individual plots for each combination of CO2 level, warming treatment and tree species.

 Data description

Soil and air conditions have been monitored closely throughout the study period, with most measurements made during the combined CO2 x warming experiment (2007-2009). The data comprise of air temperature, soil temperature, soil moisture, sapflow, tree diameter and CO2 measurements.

carbon-dioxide

display_name: carbon-dioxide

climate-change

display_name: climate-change

experiment

display_name: experiment

soil-temperature

display_name: soil-temperature

stillberg

display_name: stillberg

vegetation

display_name: vegetation

en: FACE: Stillberg CO2 enrichment and soil warming study