maintainer: Sebastian Schlögl

en: atmosphere turbulent-sensible-heat-fluxes snow atmospheric-turbulence stable-boundary-layer

en: Turbulence data AA00

en: sensor in 1 m height

en: Turbulence data PM07

en: NWW

en: Turbulence data WFJ07

en: upper sensor

en: Turbulence data WFJ11

en: sensor in 25 m height

en: SEE

en: Turbulence data GR00

en: sensor in 2 m height

en: lower sensor

en: sensor in 0.2 m height

en:
We used five different atmospheric turbulence datasets from four test sites, with these sites showing differences in their topographical characteristics. We chose one typical alpine test site with high topographical complexity (Weissfluhjoch, Davos, Switzerland) and three test sites consisting of one glacier site (Plaine Morte, Crans-Montana, Switzerland) and two polar sites (Greenland and Antarctica) representing a quasi-ideal site with homogeneous surface and quasi infinite fetch in all directions.
The turbulent sensible heat flux was calculated using the eddy-covariance method. Note that the sonic temperature fluctuations have been converted into virtual temperature fluctuations.
Three-dimensional wind velocity and air temperature were processed using a linear detrending (Rannik and Vesala, 1999) and a planar fit approach (Massmann and Lee, 2002) to rotate the coordinate system. Air temperature, relative humidity and air pressure from weather stations were used to calculate air properties, which are required for the data processing. The weather stations are located in the immediate vicinity of the turbulence tower and are affected by the same air masses. Turbulence data were averaged to 30-min intervals, whilst changing to a 15-min time interval marginally affects the heat fluxes at the Weissfluhjoch test site (Mott et al., 2011). Note that we define a negative sensible heat flux as being directed towards the snow surface and a positive sensible heat flux as being directed upwards.
The selected datasets and corresponding test sites are briefly introduced below:

Weissfluhjoch 2007 (WFJ07): A vertical set-up of two three-dimensional ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) was used at the traditional field site Weissfluhjoch (2540 m asl.) to measure three-dimensional wind velocity and air temperature at a frequency of 20 Hz. The sensors were mounted 3 m and 5 m above the ground and provided reliable data for 50 days between 11 February 2007 and 24 April 2007. Further information on the field campaign can be found in Stössel et al. (2010) and Mott et al. (2011).

Weissfluhjoch 2011-13 (WFJ11): Three-dimensional wind velocity and air temperature were recorded at 5 m above the ground at a frequency of 10 Hz with a three-dimensional ultrasonic anemometer (CSAT3). The analysis was conducted for data obtained between February and March in the years 2011-13.

Plaine Morte 2007 (PM07): Two three-dimensional ultrasonic anemometers (CSAT3) were installed on a horizontal boom facing opposite directions (west-north-west vs. east-south-east) at 3.75 m above the ground to measure air temperature and three-dimensional wind velocity at 20 Hz. The data were collected at the almost flat field site on the Plaine Morte glacier (2750 m asl.) near Crans-Montana, Switzerland from February to April 2007. High quality meteorological data were additionally recorded and used to force the model. A detailed description about the set-up at the Plaine Morte glacier can be found in Huwald et al. (2009) and Bou-Zeid et al. (2010).

Greenland 2000 (GR00): High-frequency three-dimensional ultrasonic anemometer measurements (CSAT3) were recorded at 50 Hz at the Summit Camp (72.3 °N, 38.8 °W, 3208 m asl.) located on the northern dome of the Greenland ice sheet. Data were collected at 1 m and 2 m above the snow surface during summer in 2000 and 2001. Additionally, meteorological measurements were obtained for the post processing and used to force the model. More information about the field campaign can be found in Cullen et al. (2007, 2014).

Antarctica 2000 (AA00): A set-up of three vertical three-dimensional ultrasonic anemometers (DA-600, Kaijo Denki) were installed at Mizuho Station (70°42' S, 44°20' E, 2230 m asl.) in Eastern Antarctica at 0.2, 1 and 25 m and recorded turbulence data at a frequency of 100 Hz from October to November 2000. Longwave and shortwave radiation, relative humidity, air and snow surface temperature were additionally measured and used to force the model. More information about the field campaign can be found in Nishimura and Nemoto (2005).

atmosphere

display_name: atmosphere

atmospheric-turbulence

display_name: atmospheric-turbulence

snow

display_name: snow

stable-boundary-layer

display_name: stable-boundary-layer

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

educ

en: Education, culture and sport

envidat

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: How do stability corrections perform in the stable boundary layer over snow?


state: active

type: dataset

format: SERVICE

en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were felled between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.

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

avalanches-silvaprotect

display_name: avalanches-silvaprotect

biogeographical-region

display_name: biogeographical-region

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

net-increment

display_name: net-increment

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

rockfall-silvaprotect

display_name: rockfall-silvaprotect

stage-of-stand-development

display_name: stage-of-stand-development

tree-species-woody-species

display_name: tree-species-woody-species

yield

display_name: yield

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

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.

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: Yield


maintainer: Markus Schlegel

en: metabarcoding spore-dispersal air-sampling monitoring edna

en: Data files and analysis scripts (Version 1)

media_type: application/zip

en: This ZIP archive contains the data and source code necessary to reproduce the analyses for the manuscript "A case study on the application of spore sampling for the monitoring of fungi". Please refer to README.pdf inside the archive for more information.

format: ZIP

mimetype: application/zip

en:
Resources associated with the manuscript *A case study on the application of spore sampling for the monitoring of fungi* (Schlegel et al. 2023, submitted).
Airborne environmental DNA (eDNA) monitoring of fungi was evaluated on a species-rich grassland site. Extensive fruiting body surveys yielded 29 waxcap and clavarioid species, 19 of which were also detectable in air eDNA. The analysis indicates that spores of locally occurring fruiting bodies were detected, in addition to a large diversity of common fungal species, while rare and threatened species were underrepresented in the air.
The contents of the dataset (ZIP archive):
* All analysis scripts in [R-Markdown](https://rmarkdown.rstudio.com) format, including the rendered PDF output with figures and tables (*analysis* directory)
* eDNA sample metadata (*amplicon_meta*) and results of the amplicon clustering (*amplicon_data*) and the source code of the pipeline (*amplicon_pipeline*)
* positions of all fruiting body groups found in the surveys (3838 GPS points) (*surveys*)
* positions of the spore traps, taxonomy of studied grassland fungi and red-list species, and many more resources
Please refer to `README.pdf` inside the archive for a complete description of the file structure and information on how to reproduce the analyses.

air-sampling

display_name: air-sampling

edna

display_name: edna

metabarcoding

display_name: metabarcoding

monitoring

display_name: monitoring

spore-dispersal

display_name: spore-dispersal

en: Supporting information for case study on the application of spore sampling for the monitoring of fungi


amount-of-dead-wood

title_for_slug: amount-of-dead-wood

en:
Wood volume of all deadwood recorded according to the NFI3 method. For standing trees and shrubs starting at 12 cm dbh, the volume of stemwood reduced due to stem breakage is recorded, and for lying deadwood the merchantable wood ( starting at 7 cm in diameter). Heaps of branches are not included.  The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.

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

amount-of-dead-wood

display_name: amount-of-dead-wood

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

economic-region

display_name: economic-region

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

en: Amount of dead wood


en:
Number of tree and shrub species starting at 12 cm dbh (diameter at breast height) within the 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)_

production-region

display_name: production-region

time-of-last-treatment

display_name: time-of-last-treatment

en: Number of woody species (>= 12 cm DBH)


en: atree biodiversity-offsetting forest-policy forest-clearances forest-management nature-conservation

en: Questionnaires für ATREE forest clearance offsetting survey

en: These are the "paper and pencil" questionnaires that were distributed among the forest owners invited to participate in the survey. There are 10 versions of this questionnaire as the conjoint survey experiment was based on 10 different versions of the 9 choice situations that are presented in the survey, which were assigned randomly to the invited participants.

en: ATREE clearance offsetting survey data in "stacked" format

en: The data contains information from 607 respondents. The information of each respondent is represented by 27 rows, one row for each of the three options that were presented in 9 choice situations. This is the "stacked" data-format resulting from a conjoint analysis survey experiment. The data has to be rearranged depending on the software used for analysis.

en: In April 2020, about 1700 forest owners of the plateau region of the Canton of Berne were invited to participate in a survey (virtually all of them received a conventional paper-pencil questionnaire) about their willingness to provide forest nature conservation measures in their forest to compensate forest clearances that cannot be compensated by afforestation. The questionnaire contained a survey experiment (conjoint analysis) that offered a choice between two options and the status quo in 9 decision-making situations. Of the 607 completed questionnaires that were returned the survey experiment was completed by about 400.

atree

display_name: atree

biodiversity-offsetting

display_name: biodiversity-offsetting

forest-clearances

display_name: forest-clearances

forest-management

display_name: forest-management

forest-policy

display_name: forest-policy

nature-conservation

display_name: nature-conservation

en: ATREE forest owners survey about forest clearances offsetting in the forest


forest-area-by-forest-function

title_for_slug: forest-area-by-forest-function

en: economic-region ownership forest-function terrestrial-grid-nfi3 forest-area-by-forest-function forest-area protection-forest-region biogeographical-region nfi3 production-region accessible-forest-without-shrub-forest canton grid-nfi4-2009-2013 forest nfi4b

en:
The forest area refers to all areas classified as forest according to NFI’s forest definition. The forest definition includes shrub forest.  For each forest function (including no special forest function) identified in the survey of the forestry services, the size of the associated forest area is displayed. One forest region may fulfil several different forest functions and may thus contribute to the forest area for several forest functions.

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)_

forest

display_name: forest

forest-area

display_name: forest-area

forest-area-by-forest-function

display_name: forest-area-by-forest-function

forest-function

display_name: forest-function

ownership

display_name: ownership

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: Forest area by forest function


maintainer: Yohann Chauvier

en: random-stratified-sampling background-data target-group survey-effort covariate-correction cluster independent-dataset environmental-stratification point-process-model plant-species

en: Non-copyright data, scripts, diversity outputs

en:
Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.

Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.

Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group

en: Novel methods to correct for observer and sampling bias in presence-only species distribution models


en: icp-forests swiss-forest-lab long-term-forest-ecosystem-research forest-vitality forest-condition damage-cause-assessment

en: Data Request

en:
Assessment of damges, i.e. symptoms, extent and causes on trees.

 Purpose:

Occurrence and extent of diseases

 Manual Citation:

 * Matthias Dobbertin, Christian Hug, Andreas Schwyzer, Serge Borer, Hanna Schmalz, 2016: Aufnahmeanleitung Kronenansprachen auf den Sanasilva- und den LWF-Flächen (Version 10). Sanasilva Inventur und Langfristige Waldökosystem-Forschung, Birmensdorf, 86 S. [>>>](https://www.wsl.ch/fileadmin/user_upload/WSL/Wald/Waldentwicklung_Monitoring/LWF/Sanasilva/ssi_anleitung_v10_extern.pdf)

 * Eichhorn J, Roskams P, Potocic N, Timmermann V, Ferretti M, Mues V, Szepesi A, Durrant D, Seletkovic  I,  Schröck  H-W,  Nevalainen  S,  Bussotti  F,  Garcia  P,  Wulff  S,  2016:  Part  IV:  Visual  Assessment of Crown Condition and Damaging Agents. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 49 p. + Annex [>>>](https://www.icp-forests.org/pdf/manual/2016/ICP_Manual_2017_02_part04.pdf )

 Paper Citation:

 * Michel A, Seidling W, Prescher A K (2018) Forest Condition in Europe. 2018 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW-Dokumentation 25/2018, BFW Austrian Research Centre for Forests, Vienna, 92 p. [>>>](http://icp-forests.net/page/icp-forests-technical-report)

 * Köhl M, San-Miguel-Ayanz J, Cools N, de Vos B, Fischer R, Camia A, Granke O, Hiederer R, Lorenz M, Montanarella L, Mues V, Nagel H-D, Poker J, Scheuschner T, Schlutow A (2011) Maintenance of Forest Ecosystem Health and Vitality. State of Europe s forests: status and trends in sustainable forest management in Europe 29-49. [>>>](http://www.foresteurope.org/documentos/State_of_Europes_Forests_2011_Report_Revised_November_2011.pdf)

 * Brang P., 1998: Sanasilva-Bericht 1997. Zustand und Gefährdung des Schweizer Waldes – eine Zwischenbilanz nach 15 Jahren Waldschadenforschung. Berichte der Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, Vol. 345. Eidg. Forschungsanstalt WSL, Birmensdorf, 102 S. [>>>](https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14555)

damage-cause-assessment

display_name: damage-cause-assessment

forest-condition

display_name: forest-condition

forest-vitality

display_name: forest-vitality

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

swiss-forest-lab

display_name: swiss-forest-lab

en: Crown Condition Assessment and Damage Cause Assessment LWF


en: plot lwf infrastructure irrigation forest swiss-forest-lab experiment

en: Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland

en: LWF Irrigation Plot Pfynwald

en: Photo: Maria Schmitt, 2013

en:
![alt text](https://www.envidat.ch/dataset/39a232b5-c50e-490c-9bee-f04c2f697e14/resource/6d38da33-adc3-498e-aa48-7faf60a50a02/download/lwf_irrigation_experiment-pfynwald_2013.jpg "LWF experimental irrigation site Pfynwald")

As the largest contiguous pine forest in Switzerland, the Pfyn forest in Canton Valais (46° 18' N, 7° 36' E, 615 m ASL) offers the best conditions for such measurements. In light of this, a WSL research team installed a long-term experiment of 20 years duration in the Pfyn forest. The average temperature here is 9.2°C, the yearly accumulated precipitation is 657 mm (average 1961-1990). The pines in the middle of the forest are about 100 years old and 10.8 m high. The test area has 876 trees covering 1.2 ha divided into 8 plots of 1'000 m2 each. Between the months of April and October, four of these plots are irrigated by a sprinkler system providing an additional 700 mm of water, annually. In the other four plots, the trees grow under natural, hence relatively dry conditions.

experiment

display_name: experiment

forest

display_name: forest

infrastructure

display_name: infrastructure

irrigation

display_name: irrigation

lwf

display_name: lwf

plot

display_name: plot

en: LWF Pfynwald long-term experimental irrigation site


en: strategic-planning planning-efficacy land-use-planning analytical-hierarchy-process governance

en: The present content is part of the published paper Palka, G., Oliveira, E., Pagliarin, S., & Hersperger, A. M. (2021). Strategic spatial planning and efficacy: an analytic hierarchy process (AHP) approach in Lyon and Copenhagen. European planning studies, 29(6), 1174-1192. It contains the jupyter notebook and sample data to compute Analytical Hierarchy Process, and a report on its use.

analytical-hierarchy-process

display_name: analytical-hierarchy-process

governance

display_name: governance

land-use-planning

display_name: land-use-planning

planning-efficacy

display_name: planning-efficacy

strategic-planning

display_name: strategic-planning

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Planning efficacy computation based on AHP


en: 3-pg climate-change vpd drought tree-rings growth-limitation

en: Limiting factors

en: Spatially resolved simulated environmental constraints during the 1961-1990 and 1991-2018 period.

en: Seasonal variation in environmental constraints (vapor pressure deficit – VPD, air temperature, and soil moisture) on tree growth for the potential distribution range of seven widespread Central European tree species. We simulated environmental constraints on growth fusing 3-PG model or the species’ potential distribution range within the forested area of Switzerland on a 1×1 km grid for seven dominant tree species: _Larix decidua_, _Picea abies_, _Abies alba_, _Fagus sylvatica_, _Acer pseudoplatanus_, _Pinus sylvestris_, and _Quercus robur_. For this purpose, we simulated the growth of these tree species in monocultures with the average climate observed during 1961–1990 or 1991-2018. The stands were initialized as 2-year-old plantations with an initial density of 2,500 trees ha-1 and simulated until the age of 30 years. For each simulated month, we obtained the relative contribution of environmental constraints (VPD, temperature, and soil water) on tree growth.

climate-change

display_name: climate-change

drought

display_name: drought

growth-limitation

display_name: growth-limitation

tree-rings

display_name: tree-rings

vpd

display_name: vpd

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: Environmental constraints on tree growth


hymenoptera

title_for_slug: hymenoptera

en: urban agricultural hymenoptera forests arthropoda hautfluger

en: Data query used on Fauna Database to retrieve the submitted data.

media_type: text/plain

en: Data extract in TAB-delimted format. First row is header, first column is counter.

format: TXT

en:
Hymenopteran data from all historic up to the recent projects (29.10.2019) of WSL, collected with various standardized methods in landscapes of different types.

Data are provided on request to contact person against bilateral agreement.

agricultural

display_name: agricultural

arthropoda

display_name: arthropoda

forests

display_name: forests

hautfluger

display_name: hautfluger

hymenoptera

display_name: hymenoptera

urban

display_name: urban

en: Hymenoptera


en: temperature ground borehole permafrost permos

en: Map of PERMOS borehole network

en: DATASET & PLOTS

en: PERMOS DATA POLICY

media_type: application/pdf

format: PDF

en: Processed ground temperature measurements at the Fluelapass permafrost borehole A (FLU_0102) in canton Graubunden, Switzerland. The borehole is located at 2394 m asl on a moderate (26°) North-east slope (45°). The surface material is talus and borehole depth is 23 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.

borehole

display_name: borehole

ground

display_name: ground

permafrost

display_name: permafrost

permos

display_name: permos

temperature

display_name: temperature

en: Processed permafrost borehole data (2394 m asl), Fluelapass A, Switzerland


en: consistency bucharest strategic-planning land-use-planning plan-evaluation

en: Plan statements for external consistency analysis: Evidence from Bucharest’s spatial plans

en: In total, 24 files are available with plan statements extracted from 10 spatial plans. Each file is extracted based on the items of the framework for plan external consistency analysis. Data is in Romanian.

en:
The present dataset is part of the published scientific paper Bacău, S., Grădinaru, S. R., & Hersperger, A. M. (2020). Spatial plans as relational data: Using social network analysis to assess consistency among Bucharest’s planning instruments. Land Use Policy, 92.
The goal of this paper was to first develop a theoretical framework for external consistency assessment in spatial plans and then to test the framework with ten spatial plans of the Bucharest (Romania) region. Specifically, the paper has the following workflow: (i) to develop a framework for consistency assessment covering four categories of external consistency; (ii) to extract relevant plan statements from all plans on the four categories; (iii) to assign one-way, symmetrical and contradictory relationships between the extracted plan statements; and (iv) to assess consistencies, inconsistencies and contradictions between plans using directed and valued network analyses. All results were then validated by applying questionnaires to local experts.
The study focuses on a sample consisting of 10 spatial plans of Bucharest that: (1) are currently in force, (2) have spatial implications, (3) involve different administrative levels and (4) come from different planning sectors. The list of the reviewed planning documents can be found in Table 2 of the paper. The framework of consistency assessment contains 24 items, which can be found in Table 1 of the paper. All planning documents were read in respect to all items of the framework in order to extract plan statements used in the analysis. As a result, we provide the plan statement extracted from 10 plans on the 24 items of the framework. All data is in Romanian. The data was discussed qualitatively in the research paper.

bucharest

display_name: bucharest

consistency

display_name: consistency

land-use-planning

display_name: land-use-planning

plan-evaluation

display_name: plan-evaluation

strategic-planning

display_name: strategic-planning

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal


lwf-alptal-long-term-research-site

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

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

en: LWF plot Alptal Photo: P.Waldner

en: LWF plot Alptal

en: Photo: Flurin Sutter, 2013

en: Alpthal Meteorological Data LWF

en:
![alt text](https://www.envidat.ch/dataset/49729a45-f5bf-4bc0-afdd-77123894d3bb/resource/aa505753-198c-49dc-a33e-c4c8e4fcb611/download/lwf_alptal.jpg "LWF Alptal")

LWF plot Alptal

- Community: Alpthal / canton SZ
- Date of installation: 31 May 1995
- Size of the plot: 0.6 ha
- Altitude: 1149-1170 m
- Mean slope: 23%
- Geology (in German): Nordpenninikum; obere Kreide-unteres Eozän, Wägitaler Flysch
- Soil types (WSL) : Mollic Gleysols, Gleyic Cambisols
- Woodland association after EK72: 49: Equiseto-Abietetum
- Main tree species: Picea abies
- Management system: high forest
- Silvicultural system: selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 39.3 cm
- Number of trees BHD >= 12 cm (2011): 321
- Maximum tree age: Picea abies 180-230 yr

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/alptal.html

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-forest-lab

display_name: swiss-forest-lab

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: LWF Alptal long-term research site


salvage-logging

title_for_slug: salvage-logging

en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were removed from the forest as a result of damage occurring (e.g. windthrow, avalanches, insects, rockfall), and not because of management planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester). *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.

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)_

avalanches-silvaprotect

display_name: avalanches-silvaprotect

canton

display_name: canton

cause-of-salvage-logging

display_name: cause-of-salvage-logging

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

main-type-of-harvest

display_name: main-type-of-harvest

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

rockfall-silvaprotect

display_name: rockfall-silvaprotect

salvage-logging-star

display_name: salvage-logging-star

en: Salvage logging*


en: long-term-forest-ecosystem-research swiss-forest-lab dendrochronology icp-forests tree-ring-width dominant-trees

en: Data Request

en:
There were 2 cores taken at 1.3 m height from each of 10 trees outside the LWF plot

 Purpose:

influence of drought and nutrient availabiliy on tree growth

 Paper Citation:

 * Lévesque, M., Walthert, L., Weber, P., 2016. Soil nutrients influence growth response of temperate tree species to drought. J. Ecol. 104, 377-387.

dendrochronology

display_name: dendrochronology

dominant-trees

display_name: dominant-trees

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

tree-ring-width

display_name: tree-ring-width

en: Dendrochronological analyses of tree core samples (CATS) adjacent to LWF sites


maintainer: Matthias Haeni

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

en: Data plot of the forest station (CLB)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Site photo of the field station (CLF)

en: Site phote of the field station from the side.

mimetype: image/jpeg

en: Site aerial photo (Celerina)

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

en: Data plot of the field station (CLF)

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

mimetype: application/pdf

en: Site photo of the forest station (CLB)

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

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 Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina 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

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Celerina, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


en: ambient-air-quality swiss-forest-lab long-term-forest-ecosystem-research icp-forests ozone

en:
Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.

 Purpose:

Ozone risk assessment: Continuous measurements of ozone concentrations

 Manual Citation:

 * Schaub M, Calatayud V, Ferretti M, Brunialti G, Lövblad G, Krause G, Sanz MJ, 2016: Part XV: Monitoring of Air Quality. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 11 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)

 * Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth – towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](https://doi.org/10.1111/1365-2745.12941)

 * Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](https://doi.org/10.1016/B978-0-08-098222-9.00019-4)

ambient-air-quality

display_name: ambient-air-quality

ozone

display_name: ozone

swiss-forest-lab

display_name: swiss-forest-lab

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: Continuous measurement of O3 LWF


swiss-canopy-crane-ii-research-site

title_for_slug: swiss-canopy-crane-ii-research-site

en: drought forest infrastructure ecosystem-functions swiss-forest-lab damage-of-tree climate-change

en: Research site metadata

format: XLS

en: Link to website

en: Swiss Canopy Crane II

en:
![alt text](https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg "Swiss Canopy Crane II")

This research site is located near Hölstein in Canton Basel-Landschaft in a mature temperate forest that harbours more than 400 trees from 14 different species. The 1.6 ha site is equipped with the latest infrastructure, including 60 automated point dendrometers, automated soil respiration chambers, 72 ceramic suction cups at various locations and depths across the site, and a range of automated environmental sensors in the soil, the forest floor and in the canopy. A key piece of infrastructure is the new Swiss Canopy Crane II (SCC II), a 50 m tall crane with a 50 m jib that provides canopy access to 250 trees from 12 different species.

damage-of-tree

display_name: damage-of-tree

drought

display_name: drought

ecosystem-functions

display_name: ecosystem-functions

forest

display_name: forest

infrastructure

display_name: infrastructure

en: Swiss Canopy Crane II research site


en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest as a result of damage occurring between two inventories, in this case insects, and not because of management planning. This feature is derived on the level of a sample plot from the cutting of the sample trees and the salvage cut proportion (according to information from the forester). *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.

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

canton

display_name: canton

salvage-logging-due-to-insects-star

display_name: salvage-logging-due-to-insects-star

en: Salvage logging due to insects*


maintainer: Francesco Comola

en: cohesion sand snow saltation sediment wind

en: Output of the model simulations

en: Model results on granular splash and saltation process of sand and snow particles

en:
The wind-driven saltation of sand and snow shapes dunes and ripples, generates dust emission, and erodes the surface of the Antarctic ice sheet. Here, we use a model based on the discrete element method to simulate grain-flow interactions and study the effect of particle cohesion on saltation dynamics.

The data contains the model output of granular splash simulations and saltation simulations. Granular splash, the main particle entrainment process in saltation, occurs upon impact of saltating particles with the granular bed. We performed Monte Carlo simulations of granular splash for loose sand grains and for cohesive ice grains. The analysis indicate that different values of cohesion have significant effects not on the number of splashed grains, on the ejection velocity, and the rebound velocity.

In our saltation simulations, we trigger particle movement with a single splash event at the inlet section section and let the system evolve until steady state. Our results show that saltation over cohesive surfaces is difficult to initiate but easy to sustain at low wind speed. The occurrence of transport thus depends on the history of the wind speed, a phenomenon known as hysteresis. We also show that saltation over cohesive surfaces presents higher mass fluxes but requires longer distances to saturate, which increases the size of the smallest stable surface ripples. Our model results have implications for large-scale aeolian processes on Earth and Titan, where sand grains are thought to be very cohesive.

cohesion

display_name: cohesion

saltation

display_name: saltation

sand

display_name: sand

sediment

display_name: sediment

snow

display_name: snow

wind

display_name: wind

en: Saltation of cohesive granular materials


mass-of-needles-or-leaves-of-live-trees

title_for_slug: mass-of-needles-or-leaves-of-live-trees

en:
Dry weight (mass) of the needles and leaves of the living trees and shrubs starting at 12 cm dbh.

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

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

mass-of-needles-or-leaves-of-live-trees

display_name: mass-of-needles-or-leaves-of-live-trees

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

educ

en: Education, culture and sport

en: Mass of needles or leaves of live trees


en: snow-depth snow-measurements break-detection homogenisation climate

en: Station relocations

en: Metadata compiled from various sources

en: Climatol HSavg

en: Breakpoints (R data file)

en: Break points detected by all three methods and draft metadata table

en: Station list

en: List of stations used in this study. This file also serves as the station input file for HOMER

en: HOMER dHS1

en: Input files for HOMER

en: ACMANT HSavg

en: ACMANT dHS1

en: HOMER HSavg

en: Climatol dHS1

en:
Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included.

 Columns
Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero

break-detection

display_name: break-detection

climate

display_name: climate

homogenisation

display_name: homogenisation

snow-depth

display_name: snow-depth

snow-measurements

display_name: snow-measurements

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: Input data for break point detection of Swiss snow depth time series


maintainer: Anita C Risch

en: soil-properties herbivore-exclusion nutrient-addition climate microbial-biomass-carbon microbial-respiration anthropogenic-management

en: Risch et al. GEB_data.xlsx

en:
Study sites and experimental design
We collected data from 23 sites that are part of the Nutrient Network Global Research Cooperative (NutNet, https://nutnet.umn.edu/). The mean annual air temperature (MAT) across these sites ranged from -4 to 22°C, mean annual precipitation (MAP) from 252 to 1592 mm, and elevations from 6 to 4261 m above sea level (Fig 1a, Supplementary Table S1), hence cover a wide range of climatic conditions under which grasslands occur (Fig 1b). Soil organic C concentrations ranged between 0.8 to 7.8%, soil total N concentrations between 0.1 and 0.6%, and the soil C:N ratio between 9.1 and 21.5. Soil clay content spanned from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2).
At each site, the effects of nutrient addition and herbivore exclusion were tested via a randomized-block design (Borer et al., 2014). Three blocks with 10 treatment plots each were established at each site, except for the site at bldr.us (only two blocks). Each of these 10 plots was randomly assigned to a nutrient or fencing treatment. An individual plot was 5 x 5 m, divided into four 2.5 x 2.5 m subplots. Each subplot was further divided into four 1 x 1 m square sampling plots, one of which was set aside for soil sampling (Borer et al., 2014). Plots were separated by at least 1 m wide walkways. We collected soil samples from four different treatments for this study: (i) untreated control plots (Control), (ii) herbivore exclusion plots (Fence), (iii) plots fertilized with N, P, K, plus nine essential macro and micronutrients (NPK), and (iv) plots with simultaneous fertilizer addition and herbivore exclusion (NPK+Fence). The experiments were established at different times in the past, with years of treatment different among sites (2 – 9 years since start of treatment; Supplementary Table S1). For the nutrient additions, all sites applied 10 g N m-2 each year as time-release urea; 10 g P m-2 yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at 100 g m-2 together with K in the first year of treatments but not thereafter.
We excluded large vertebrate herbivores (Fence) by fencing two plots, one with and one without NPK additions, within each block. The fences excluded all aboveground mammalian herbivores with a body mass of over 50 g (Borer et al., 2014). At most sites, the fences were 180 cm high, and the fence contained a wire mesh (1 cm holes) for the bottom 90 cm with a 30 cm outward-facing flange stapled to the ground to exclude burrowing animals. Climbing and subterranean animals may potentially still access these plots (Borer et al., 2014). For slight modifications in fence design at a few sites see Supplementary Table S3. Most sites only had wild herbivores, although four sites were also grazed by domestic animals (Supplementary Table S1).

Collection of soil samples, soil microbial respiration, microbial biomass, and other soil properties
Each of the 23 sites received a package containing identical material from the Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland to be used for sampling (Risch et al., 2015; Risch et al., 2019). We collected two soil cores of 5 cm diameter and 12 cm depth in each sampling plot and composited them to measure MR, MBC, and soil chemical properties (see below). An additional sample (5 x 12 cm) was collected to assess soil physical properties. This sample remained within a steel sampling core after collection and both ends were tightly closed with plastic caps to avoid disturbance. All soils were shipped cooled to the laboratory at (Location will be disclosed after manuscript acceptance) within a few days after collection. Soils were sampled roughly 6 weeks prior to peak biomass at each site during 2015 and 2016.
To assess MR (CO2 production) in a laboratory incubation experiment we weighed duplicate soil samples (8 g dry soil equivalent) into 50-ml Falcon tubes. No additional substrate (glucose, sugar) was added to these samples. We adjusted the soil moisture of each sample to 60% field capacity. We then placed a 15 ml plastic test tube (Semadeni 1701A) containing 7.25 ml 0.05 M NaOH over each soil sample. The test tube was fixed with a plastic rod so that it was not in contact with the soil sample. The Falcon tubes were then sealed with a screw cap and placed in an incubator under completely dark conditions at 20°C. The CO2 produced by microbial respiration was absorbed by the 0.05 M NaOH. For five weeks we measured the decrease in conductivity within the 0.05 M NaOH solution on a weekly basis with a Multimeter WTW Multi 3410 (WTW GmbH, Germany) and replaced the 0.05 M NaOH with fresh solution. We included Falcon tubes without soil samples in each incubation run as blanks to test if tubes were tight and no CO2 could enter or escape. We calibrated the relationship between conductivity reduction and NaOH absorbed as follows: 400 ml 0.05 M NaOH was placed in a beaker and its conductivity was measured with the multimeter. While stirring, air containing CO2 was blown into the solution for approximately one minute, which reacts with NaOH to form Na2CO3. After this process, conductivity was measured again. We then transferred 7.25 ml of the solution into a smaller beaker and added 1 ml of 0.1 M BaCl2 to precipitate Na2CO3 and then titrated the solution with 0.05 M HCl to determine the remaining NaOH. We then repeated these steps with the remaining solution a total of nine times and plotted the conductivities (y-axis) against the NaOH consumed (x-axis, Supplementary Fig S1). This regression line was used to infer the consumption of NaOH from the conductivity reduction in the incubation experiments and to calculate CO2 evolution during incubation. In addition, we determined the optimum concentration for the NaOH solution in series of preliminary experiments, so that the concentration was not too high to become insensitive, but also not too low so that not all NaOH reacts during incubation. We then calculated MR (mg CO2-C kg dry soil-1 h-1) as total amount of CO2 released over the 5 weeks divided by the duration of the entire incubation in hrs.
Soil microbial biomass carbon (MBC; mg C kg soil-1 ) was measured at the beginning of the experiment by measuring the maximal respiratory response to the addition of glucose solution (4 mg glucose per g soil dry weight dissolved in distilled water; substrate-induced respiration method) on approximately 5.5 g of soil (J. P. E. Anderson & Domsch, 1978; Nico Eisenhauer et al., 2018; Scheu, 1992). For this purpose we used an O2-micro-compensation apparatus (Scheu, 1992). More specifically, substrate-induced respiration was calculated from the respiratory response to D-glucose for 10 hr at 20°C. Glucose was added according to preliminary studies to saturate the catabolic enzymes of microorganisms (4 mg g soil-1 dissolved in 400 ml deionized water). The mean of the lowest three readings within the first 10 hrs (between the initial peak caused by disturbing the soil and the peak caused by microbial growth) was taken as maximum initial respiratory response (MIRR; ml O2 kg soil-1 h-1) and microbial biomass (mg C kg soil-1) was calculated as 38 x MIRR (Beck et al., 1997; Cesarz et al., 2022; Thakur et al., 2015).
The rest of the composited sample was dried at 65°C for 48 h, ground and sieved (2 mm mesh) to assess the soil pH, mineral soil total C and N and C:N ratio, and mineral soil organic C (Risch et al., 2019). The undisturbed sample was used to assess water holding capacity (WHC), bulk density (BD), and soil texture [sand, silt, clay; methods in (Risch et al., 2019)]. We used the percentage of sand and clay as an indicator of soil texture in this study. MAT (°C), MAP (mm) and temperature of the wettest quarter (°C) were obtained from www.worldclim.com (Fick & Hijmans, 2017; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). These variables were selected as they were found to be drivers of soil nutrient processes across these sites in earlier studies (Risch et al., 2020; Risch et al., 2019). Mean annual soil temperatures (MAST; °C) for the 0 to 5 cm soil layer were obtained for each site from the SoilTemp maps (J. Lembrechts et al., 2021; J. J. Lembrechts et al., 2022), global gridded modelled products of soil bioclimatic variables for the 1979-2013 period at a 1-km² resolution, based on CHELSA, ERA5 and in-situ soil temperature measurements.
Numerical calculations and statistical analyses
We calculated MMQ as MR/MBC. We corrected this measure using the average soil temperature of each site (MMQsoil). This temperature correction is necessary as incubation temperatures are usually much higher than site mean annual soil temperatures (see Xu et al. 2017). MMQsoil = MMQ x Q10(MAST – 20)/10, where Q10 was assumed to be 2 (Xu et al. 2017). See Supplementary Fig S2 for comparison of air and soil temperatures across the 23 sites as well as the incubation temperature.
Some of the explanatory variables (clay, soil organic C, C:N ratio) were skewed and were thus log-transformed prior to analyses. All continuous explanatory variables were centred and scaled to have a mean of zero and variance of one. To avoid collinearity between them we filtered them using correlation analysis (Supplementary Fig S3). From the variables that were strongly correlated (Pearson’s |r| > 0.70) (Dormann et al., 2013), we selected the ones that allowed us to minimize the number of variables (Supplementary Fig S3). Specifically, soil total N concentration, soil total C concentration, soil sand content and soil bulk density were dropped from the dataset. We then assessed how these edaphoclimatic variables are related to MMQ across our global grasslands.
For this, we used linear mixed effects models (LMMs) fitted by maximum likelihood with the lme function in the nlme package (version 3.1-153) (Pinheiro, Bates, DebRoy, & Sarkar, 2021) in R version 3.6.3. (R Core Team, 2019). We used treatment as a fixed effect and plot nested in site as random effects to assess treatment differences in MMQsoil, as well as MR, and MBC. The number of years since the treatment started was included as a fixed effect in all the initial models but was not significant and therefore not retained in the models. To assess how differences in MMQsoil were affected by environmental factors (soil, climatic properties) we again used LMMs. Soil and climatic properties were included as fixed effects and plot nested in site as random effects. We did not include interactions between environmental variables. We then used the MuMin package (Barton, 2018) (version 1.42.1) to select the best models that explained the most variation based on Akaike’s information criterion (AIC; model.avg function). We used the corrected AIC (AICc) to account for our small sample size and selected the top models that fell within 4 AICc units (delta AICc < 4) (Burnham & Anderson, 2002; Johnson & Omland, 2004). We present all our top models rather than model averages. Conditional averages are provided in the Supplementary material.
Based on findings from analyses described above and the literature, we developed a conceptual model of direct and indirect relationships between both edaphoclimatic variables and experimental treatments (Supplement Figure S4) to obtain a more holistic approach in understanding how these properties affect MMQsoil. We had data from 23 sites with 272 observations. We tested this model using structural equation modelling based on a d-sep approach (Lefcheck, 2016; Shipley, 2009). We considered those environmental drivers that were included in our top LMMs, namely temperature of the wettest quarter (T.q.wet), soil pH, water holding capacity (WHC) and soil organic C (organic C; Supplementary Figure S4). These factors were allowed to directly affect MMQsoil, and via their interactions with treatments. In addition, treatments were allowed to directly affect MMQsoil. Treatments were included as dummy variables in the model. We tested our conceptual model (Supplementary Fig S4) using the piecewiseSEM package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a structured set of linear models are fitted individually. This approach allowed us to account for the nested experimental design, and overcome some of the limitations of standard structural equation models, such as small sample sizes (Lefcheck, 2016; Shipley, 2009). We used the lme function of the nlme package to model response variables, including site as a random factor. Good fit of the SEM was assumed when Fisher’s C values were non-significant (p > 0.05). For all significant interactions between soil or climate variables and treatments detected in the SEMs, we calculated treatment effect sizes, i.e., the differences in MMQsoil between Control and treatments as log response ratios (LRR) and plotted these values against the climate or soil factors. The LRR were defined as log(Control/Treatment), where treatment was either Fence, NPK or NPK+Fence. To assess which of the LRR-climate or soil property relationships were significant we again used LMMs, in which soil and climatic properties were included as fixed effects and plot nested in site as random effects.

en:
This dataset contains all data on which the following publication below is based.

Paper Citation:

Risch Anita C., Zimmermann, Stefan, Schütz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography

Please cite this paper together with the citation for the datafile.

The microbial metabolic quotient (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C (MBC; mg C kg soil-1), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ across 23 sites (5 continents). Our sites encompassed a wide range of edaphoclimatic conditions, thus we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Soils were collected in plots with established experimental treatments. MR was assessed in a five-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using LMMs and SEMs, we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity, and soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (<1.7%). Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies.

anthropogenic-management

display_name: anthropogenic-management

herbivore-exclusion

display_name: herbivore-exclusion

microbial-biomass-carbon

display_name: microbial-biomass-carbon

microbial-respiration

display_name: microbial-respiration

nutrient-addition

display_name: nutrient-addition

soil-properties

display_name: soil-properties

educ

en: Education, culture and sport


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

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.

format: GeoTIFF

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 4.3.1 (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 2.2.3 (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: 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 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: 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 4.2.1/2/3 (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: 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 4.5.3 (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 4.3.7 (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 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 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 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 4.3.5 (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 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 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: 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:
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 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 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: 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 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:
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


yield

title_for_slug: yield

en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh cut between two inventories. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.

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

avalanches-silvaprotect

display_name: avalanches-silvaprotect

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

net-increment-star

display_name: net-increment-star

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

rockfall-silvaprotect

display_name: rockfall-silvaprotect

stage-of-stand-development

display_name: stage-of-stand-development

tree-species-woody-species

display_name: tree-species-woody-species

yield-star

display_name: yield-star

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: Yield*


maintainer: Anita C. Risch

en: weed-management herbivory grassland non-native-plant native-plant

en: Correction: Data file native and non-native plant interactions in Australian grasslands

en:
taxa tab:	list and overview of all the recorded species

* family:                 family of the plant

* genus:                genus of the plant

* species:                plant species name

* origin:                indicates whether the plant is native to Australia or not (non-native)

* common name:	        common name of the plant species

* functional group:	        functional group of the plant species: forb, graminoid, fern, tree

* functional group detailed:	gives more detail about the type of graminoid and forb: legume, forb (non-leguminous),
                        grass, sedge, rush, fern, tree

* lifeform:                annual or perennial lifeform (species which can have both of these lifeforms where
                        considered as annuals)

plot tab:	list of species found in each plot and their cover (%)

* sampling.session:        provides information about the sampling time:
                        May2020 = data that was collected in May 2020
                        Nov2020 = data that was collected in November 2020

* farm.name:	            name of the farms where the sampling took place:
                        SF = farm of the Southern group
                        NF = farm of the Northern group
                        numbers 1-3 = randomly allocated to the Southern group of farms
                        numbers 4-6 = randomly allocated to the Northern group of farms

* site.type:	                KG: Kangaroo grass dominated site
                        ALG: ALG+KG site (Kangarooo grass and African lovegrass co-dominated site)

* plot.no:                plot number: 9-12

* scientific.name:	        scientific name of the plant species found in a certain plot

* functional group:	        indicates whether the species found is a tree, forb, graminoid or fern

* functional group detailed:	indicates whether the species found is a tree, legume, forb (non-leguminous),
                        grass, sedge, rush, fern

* lifeform:                specifies the lifeform of the species: annual or perennial lifeform
                        (species which can have both of these lifeforms where considered as annuals)

corrections tab:	Detailed description of corrections made in plot.data tab

en:
This dataset contains all data, on which the following publication below is based.

Paper Citation:

_Schlierenzauer, C., Risch, A.C., Schütz, M., Firn, J. 2021. Non-native Eragrostis curvula reduces plant species diversity in pastures of South-eastern Australia even when native Themeda triandra remains co-dominant. Plants 10, 596._

Please cite this paper together with the citation for the datafile.

Study area
The study was conducted in the lowland grassy woodlands of the Bega Valley Region, which is located in the south-east corner of New South Wales, Australia. Embedded between the Pacific Ocean and the Australian Alps, the lowland grassy woodlands are mostly located on granitic substrates and reach elevations of roughly 500 m above sea level. Typically, these grassy woodlands receive less precipitation (mean annual precipitation between 700-1100 mm) compared to the more elevated areas that surround them (NSW Government - Office of Environment and Heritage 2017).
The vegetation is dominated by an open tree canopy layer consisting of Eucalyptus tereticornis Sm, Angophora floribunda Sm. (Sweet) and a range of other eucalypt species. Sometimes shrub or small trees are also present, whereas grasses and forbs form the ground-cover. In areas without intensive agricultural history, this layer is dominated by perennial, tussock grasses such as Themeda triandra Forssk, Microlaena stipoides R.Br (Weeping Grass), Eragrostis leptostachya Steud. (Paddock Lovegrass) and Echinopogon ovatus P.Beauv (Forest Hedgehog Grass). The remaining inter-tussock spaces are occupied by a diversity of growth-restricted grasses and herbaceous forbs (NSW - Department of Planing, Industries and Environment 2019; NSW Government - Office of Environment and Heritage 2017).
Clearing, pasture sowing, fertilizer application and livestock grazing resulted in a dramatic decrease in the extent of these natural woodlands, with less than five percent within conservation reserves and overall, with only about 20% of their original extent in New South Wales still existing (Tozer et al. 2010). The remaining areas outside of reserves are threatened by altered fire frequencies, habitat clearing, livestock grazing and especially by non-native plant invasion, particularly Eragrostis curvula (Schrad.) Nees. For this reason, the grassy woodlands are listed as an endangered ecological community in the NSW state legislation. Additionally, they are considered as critically endangered by the Commonwealth of Australia (Threatened Species Scientific Committee (TSSC) 2013).

Experimental design and sampling
The study was conducted on six farms and in each of them two sites were chosen, representing a paired design. One of the sites at each farm is dominated by native Themeda triandra, the other one co-dominated by non-native Eragrostis curvula and Themeda triandra. All farms are within a radius of approximately 10 km from the town Candelo. Three of the farms are located North (36°40’ to 36°42’ S and 149°38’ to 149°42’ E) and three of them are located South (36°51’ to 36°49’ S and 149°38’ to 149°42’ E) of Candelo.
Non-native herbivores (mainly cattle, sheep and rabbits) and native herbivorous marsupials (mainly kangaroos, wallabies and wombats) are present in the area of these sites. On each site, data was collected within four plots (each 1 x 1 m) in May and November 2020. All plant species found within a plot were recorded and their relative abundance was estimated.

References
NSW - Department of Planing, Industries and Environment. 2019. “Lowland Grassy Woodland in the South East Corner Bioregion - Endangered Ecological Community Listing.” https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2004-2007/lowland-grassy-woodland-south-east-corner-bioregion-endangered-ecological-community-l (February 18, 2021).
NSW Government - Office of Environment and Heritage. 2017. “Lowland Grassy Woodland in the South East Corner Bioregion - Profile.” https://www.environment.nsw.gov.au/threatenedSpeciesApp/profile.aspx?id=20070 (January 31, 2021).
Threatened Species Scientific Committee (TSSC). 2013. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) Conservation Advice for Lowland Grassy Woodland in the South East Corner Bioregion. http://www.environment.gov.au/biodiversity/threatened/communities/pubs/82-conservation-advice.pdf.
Tozer, Mark et al. 2010. “Native Vegetation of Southeast NSW: A Revised Classification and Map for the Coast and Eastern Tablelands.” Cunninghamia : a journal of plant ecology for eastern Australia 11(3): 359–406.

grassland

display_name: grassland

herbivory

display_name: herbivory

native-plant

display_name: native-plant

non-native-plant

display_name: non-native-plant

weed-management

display_name: weed-management

en: Native and no-native plant interactions in Australian grasslands


maintainer: Lisa Hülsmann

en: forest-reserves mortality-models tree-mortality fagus-sylvatica primeval-forests forest-inventory

en: Ukrainian data

en:
READ ME for

Hülsmann L, Bugmann H, Commarmot B, Meyer P, Zimmermann S, Brang P
Data from: Does one model fit all? Patterns of beech mortality in natural forests of three European regions.
Ecological Applications http://dx.doi.org/10.5061/dryad.h4s6t

Forest inventory data of beech for the development of individual-tree mortality models from strict forests reserves in Switzerland, Germany and Ukraine.

The data package provides the files
CH_reduced: inventory data of the reduced dataset from Switzerland that was used to assess the influence of additional environmental and stand characteristics on beech mortality
CH: inventory data from strict forest reserves in Switzerland
DE: inventory data from strict forest reserves in Germany
UA: inventory data from a primeval beech forest in Ukraine

A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.
Diameter at breast height: dbh (mm)
Annual relative basal area increment: relBAI (), calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area (Bigler and Bugmann 2004)
Status of the tree: y, 0 = alive, 1 = dead
Length of the mortality period: dt (years)

Additional environmental and stand characteristics were derived for the reduced Swiss dataset.
Note the varying seasonal level of the covariates considering the entire year, the growing season from April to September (G) and the winter month January to March (W).
All climatic covariates were calculated for both the ‘growth’ (2) and the ‘mortality’ period (3). The stand covariates were derived for the second inventory of each record (2).

Mean diameter at breast height: dbh.mean.2 (mm)
Interquartile range of diameter at breast height: dbh.IQR.2 (mm)
Median diameter at breast height: dbh.median.2 (mm)
Basal area per hectare: BA.2 (m2 ha-1)
Number of trees per hectare: N.2 (ha-1)
pH-value: pH ()
Available water capacity in the soil: AWC (mm)
Mean annual precipitation sum: P.2, P.3 (mm)
Mean precipitation sum during growing season: PG.2, PG.3 (mm)
Mean annual air temperature: mT.2, mT.3 (°C)
Mean air temperature during growing season: mTG.2, mTG.3 (°C)
Mean air temperature during winter months: mTW.2, mTW.3 (°C)
Mean growing degree-days: GDD.2, GDD.3 ()
Mean number of months during growing season with water deficit: mDEFdurG.2, mDEFdurG.3 ()
Mean annual maximum water deficit: mDEFmax.2, mDEFmax.3 (mm)
Overall maximum of the annual maximum water deficit: maxDEFmax.2, maxDEFmax.3 (mm)
Mean of cumulative water deficit during growing season: mDEFcumG.2, mDEFcumG.3 (mm)
Overall maximum of cumulative water deficitduring growing season: maxDEFcumG.2, maxDEFcumG.3 (mm)
Percentage of years with at least one month water deficit: percDEF.2, percDEF.3 (%)

en: Swiss data full

en: Swiss data reduced

en: German data

en:
The datasets comprise nearly 19’000 trees of European beech (_Fagus sylvatica_ L.) from unmanaged forests in Switzerland, Germany / Lower Saxony and Ukraine. Tree death was modelled as a function of size and growth, i.e., stem diameter (DBH) and relative basal area increment (relBAI). To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics.

 Inventory data
The strict forest reserves in Switzerland and Germany had been established in the period of 1961-1975 and 1971-1974, respectively. Every reserve included up to 10 permanent plots ranging from 0.09 to 1.8 ha in size, with slightly irregular re-measurement intervals. Permanent plots with pure or mixed beech stands were selected from the reserves of both networks. Reserves with considerable wind disturbance during the monitored intervals were excluded from the analysis. In addition to data from the Swiss and German reserves, data from a 10 ha plot in the primeval beech forest Uholka in Western Ukraine including three remeasurements were used. The inventory data provide diameter measurements at breast height (dbh) for revisited trees with a diameter of more than 4, 7 and 6 cm for Switzerland, Germany and Ukraine, respectively.

 Mortality predictors
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory. As an explanatory variable, the annual relative basal area increment (relBAI) was calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area. Tree dbh in the second inventory was used in addition to relBAI to model tree status (alive or dead) of the third inventory.

To increase the generality of the mortality models, we selected environmental variables that are known to have a considerable influence on growth and mortality of beech. We emphasized the effects of water availability using a large set of drought characteristics that were calculated based on the local site water balance. We also related beech mortality to soil pH, temperature, precipitation and growing degree-days. Additionally, we considered stand characteristics that reflect the development stage, competition and structure of the forests.

 Further information
For further information, refer to Hülsmann _et al_. (2016) Does one model fit all? patterns of beech mortality in natural forests of three European regions. _Ecological Applications_.

fagus-sylvatica

display_name: fagus-sylvatica

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

mortality-models

display_name: mortality-models

primeval-forests

display_name: primeval-forests

tree-mortality

display_name: tree-mortality

en: Data from: Does one model fit all? patterns of beech mortality in natural forests of three European regions


en: soil-solution-chemistry long-term-monitoring wdb long-term-forest-ecosystem-research swiss-forest-lab

en: Data request

en:
Since 1997 soil solution samplers are installed in certain plots of the Intercantonal Forest Observation Program *[Intercantonal Forest Observation Program](https://www.envidat.ch/dataset/interkantonale-walddauerbeobachtung-wdb)*. There solution is analysed monthly at the moment (2023) in 46 Swiss forest sites. The sites cover a uniquely broad range of Swiss forest soils, with gradients in elevation (290-1870 m), precipitation (915-2211 mm), base saturation averaged over the top 40 cm (6-100%) and total N deposition (10-81 kg ha⁻¹a⁻¹).
For each site and soil depth, eight soil solution samplers (ceramic suction cup) are installed in the topsoil and five in the subsoil. Actual depths varied according to soil condition but a frequent sampling design is 20, 50 and 80 cm. Following the monthly sampling of the soil solution, the samples from the same location and from the same depth are pooled. Monthly data about following chemical soil solution indicators are measured:

 * pH and conductivity
 * Anions: Cl⁻, NO₃⁻, SO₂⁻
 * Kations: Ca²⁺, Mg²⁺, K⁺, Na⁺, Mn²⁺, NH4⁺, Altot, Alorg
 * DOC

More information about the analytical methods and results can be found in *[Braun & Tresch (2020)](https://doi.org/10.1371/journal.pone.0227530)*.
The long-term measurements of the soil solution provide the basis for revealing changes in forest soil quality. The chemical composition of the soil solution allows comparison and assessment of internationally agreed critical values and criteria on acid-base status (indicators: BC/Al ratio, pH value, N concentrations in the soil solution) as well as the leaching of N, Al and basic cations.

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

long-term-monitoring

display_name: long-term-monitoring

soil-solution-chemistry

display_name: soil-solution-chemistry

swiss-forest-lab

display_name: swiss-forest-lab

wdb

display_name: wdb

educ

en: Education, culture and sport

en: Soil solution monitoring of the Intercantonal Forest Observation Program