causal-effect-of-lup

en:
Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China.
Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020.
Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020.
XZQDM2 is villages' unique administrative ID;
Area is the land area of village i;
Dis2water is the Euclidean distance from village i to the nearest waterbody;
Dis2coastl is the Euclidean distance from village i to the nearest coastline;
Dis2city is the the Euclidean distance from village i to the city center;
Dis2county is the the Euclidean distance from village i to the nearest county center;
Elevation is the the average elevation within village i;
Dis2road is the the Euclidean distance from village i to the nearest road;
Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t;
Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones;
Intensity is the percentage of land that was assigned to the development-permitted zones in village i;
Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020;
BuLE is the dependent variable, representing built-up land expansion in village i in year t;
Town is town' unique administrative ID.
Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend
assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect.

en: Causal effect of LUP

en: psm-did land-use-planning annual-effect plan-evaluation land-use-change

title_for_slug: causal-effect-of-lup

annual-effect

display_name: annual-effect

land-use-change

display_name: land-use-change

land-use-planning

display_name: land-use-planning

plan-evaluation

display_name: plan-evaluation

psm-did

display_name: psm-did


$oid: OBSOLETE

en: The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.

en: Land use/cover dynamics in Austin metropolitan area since 1992

en: land-use urban-morphology landscape austin land-dynamics

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

format: ZIP

austin

display_name: austin

land-dynamics

display_name: land-dynamics

land-use

display_name: land-use

landscape

display_name: landscape

urban-morphology

display_name: urban-morphology


en: The data results from a questionnaire survey conducted at 8 schools in the cantons Zurich, Aargau and St. Gallen. Respondents aged 13-22 years. The aim of the survey was to gain insight into teenagers' relationship to the forest, reasons for visiting or not visiting the forest and activities in the forest.

en: Factors influencing teenagers' forest visit frequency

en: recreational-activities childhood adolescents forest teenagers

en:
Resource Title: Dataset comments
    Size: 32680 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/04ecf3b8-76aa-4b45-af4e-e06da88a8fcd/resource/29308f48-7be2-4afb-9b4d-829c5618e50a/download/datensatz-kommentar.xlsx
    Description: The codebook to the dataset

format: XLS

en:
Resource Title: Dataset survey at schools
    Size: 844175 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/04ecf3b8-76aa-4b45-af4e-e06da88a8fcd/resource/9b9bee0b-a041-4870-b845-1115c5deed6d/download/datensatz-umfrage-schulen.xlsx
    Description: The dataset of the questionnaire survey at schools in excel format.

en:
Resource Title: Survey at schools 2017
    Size: 638125 bytes
    Format: application/x-spss-sav
    URL: https://www.envidat.ch/dataset/04ecf3b8-76aa-4b45-af4e-e06da88a8fcd/resource/fe6746a6-96c4-484e-99c4-8f8556d76d86/download/datensatz-umfrage-schulen.sav
    Description: The dataset of the questionnaire survey at schools in SPSS-format.

en:
Resource Title: Questionnaire school survey
    Size: 141278 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/04ecf3b8-76aa-4b45-af4e-e06da88a8fcd/resource/c7f5fdd3-361e-44bf-ba4c-fa97f6164bf0/download/fragebogenv2.pdf
    Description: The questionnaire used in the survey at 8 schools aimed at teenagers in German.

format: PDF

media_type: application/pdf

adolescents

display_name: adolescents

childhood

display_name: childhood

forest

display_name: forest

recreational-activities

display_name: recreational-activities

teenagers

display_name: teenagers


en: The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Zürich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban "green" and "grey" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity.

en: Amphibian and urban-rural landscape data Swiss Lowlands

en: human-dominated-landscapes biodiversity integrated-modelling urban-spatial-planning ecological-corridors circuit-theory

en:
Resource Title: Amphibian species specific landscape resistance maps.
    Size: 51864736 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/acaac64b-3ebe-4e97-ae24-a35736dfa1d5/download/resistance.zip
    Description: For each amphibian species we used the weighted average Ensemble model suitability map and converted it to a resistance map using an exponential decay function following (Duflot et al., 2018) using the species-specific mpa threshold previously computed (Preau et al., 2020). The function assigns a resistance value of 1,000 when habitat suitability equals zero, and a value of one when habitat suitability is greater than or equal to the binarization threshold. The exponential shape strengthens the barrier effect of less favorable areas.

media_type: application/zip

en:
Resource Title: Amphibian breeding sites of national importance - cantons of Aargau and Zürich
    Size: 39808424 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/fd5b1fe3-bdf9-4095-816a-edaf22178df2/download/breeding_sites_raster_30m.asc
    Description: Amphibian breeding sites of national importance (focal nodes) in the cantons of Aargau and Zürich, 30x30 m resolution, binarised ascii format.

format: pgp-keys

en:
Resource Title: Amphibian whole life cycle predictors - 10m resolution
    Size: 734915785 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/26ceef50-633c-469b-970d-cd6e453e9500/download/amphibian_predictors_sdms.zip
    Description: Amphibian whole life cycle predictors include 6 variable types:
(1)	Topographic – slope [°]: derived from the Swiss Topographical Landscape model (swissTLM 3D 2015) at 2 m resolution (elevation uncertainty ± 0.5m) and, subsequently, aggregated to a median value;
(2)	Hydrologic: two predictors including nearest distance to water [m] (all water-related objects and rasterized lotic, lentic and reedbeds from the regional cadastral maps – AV: Daten der Amtliche Vermessung) and runoff coefficient [C] (between 0 and 1 relating amount of runoff to amount of precipitation, mapped AV: Daten der Amtliche Vermessung data against literature values for different land covers);
(3)	Edaphic – soil moisture variability (not available): can gathered from the spatial modelling of Ecological Indicator Values in Switzerland (Descombes et al., 2020) and resampled from 100m to 10m;
(4)	Vegetation: we calculated the median and standard deviation of the Normalized Difference Vegetation Index (NDVI) from all available cloud free Sentinel-2 pixels available between April and October for the years 2016 to 2019 (Drusch et al., 2012) and the median vegetation height [m] (not available) can gathered from Ginzler and Hobi (2015);
(5)	Land-use derived: includes three predictors, grassland density [grassland/cell] (not available) - can be gathered from Pazur et al. (2021), total grasslands area evaluated using a spatial weight matrix and a 250m neighborhood radius around each cell; urbanization [buildings/cell] – average building density for a 250m neighborhood around each cell based on the AV: Daten der Amtliche Vermessung ; and traffic intensity [vehicles/day] – average daily vehicle count (Nationales Personenverkehrsmodells);
(6)	Movement-ecology related: includes three predictors of proximity – nearest forest distance [m]; nearest rock-gravel and sandy area (RGS) distance [m] and nearest road distance [m] (considering any road type) – all calculated based on the AV: Daten der Amtliche Vermessung maps.

en:
Resource Title: Binarized "green" and "grey" landcover data (Cantons of Aargau and Zürich)
    Size: 531399 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/5752ef42-b6cc-4365-bb61-288dfa3d5d95/download/urban_green_grey.zip
    Description: "Green" landcover refers to lawns and vegetated urban surfaces such as, for example private gardens, public parks, cemeteries, green stripes along roadsides, etc. While "grey" landcover refers to impervious grounds such as roads, troittoirs, other impervious. Binarized maps, 30x30m resolution.

en:
Resource Title: Amphibian monitoring data
    Size: 310101 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/ce42d731-4b85-4da0-b356-35deb8f80607/download/amphibian_presence_2017_2019.csv
    Description: Processed amphibian occurrence data (2017-2019) in the cantons of Aargau and Zürich. The dataset includes information for the following species:  _Alytes obstetricans_,_Bombina variegata_, _Bufo bufo_, _Hyla arborea_, _Ichtyosaura alpestris_, _Lissotriton helveticus_, _Lissotriton vulgaris_, _Rana temporaria_, _Salamandra salamandra_, and _Triturus cristatus_.

format: CSV

media_type: text/csv

biodiversity

display_name: biodiversity

circuit-theory

display_name: circuit-theory

ecological-corridors

display_name: ecological-corridors

human-dominated-landscapes

display_name: human-dominated-landscapes

integrated-modelling

display_name: integrated-modelling

urban-spatial-planning

display_name: urban-spatial-planning


en: L'objectif de ce livre blanc est de fournir aux décideurs, aux administrations et aux parties prenantes les résultats de recherche les plus récents afin de promouvoir l'utilisation optimale de la bioénergie issue des engrais de ferme dans la transition énergétique suisse. A cette fin, les résultats du centre de compétence suisse pour la recherche en bioénergie - SCCER BIOSWEET - sont résumés et présentés dans un contexte plus large. Si rien d'autre n'est mentionné, les résultats se réfèrent à la Suisse et, dans le cas de la matière première, aux potentiels nationaux de biomasse.

en: Production de biogaz à partir d’engrais de ferme en Suisse

en: manure biogas switzerland review bioenergy

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: EnviDat: the Environmental Data Portal

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland


biogas-aus-hofdunger-in-der-schweiz

en: Ziel dieses Whitepapers ist es, Entscheidungsträgern, Verwaltungen und Stakeholdern die aktuellsten Forschungsergebnisse zur Verfügung zu stellen, um die optimale Nutzung von Bioenergie aus Hofdünger in der Schweizer Energiewende zu fördern. Zu diesem Zweck werden die Ergebnisse des Schweizer Kompetenzzentrums für Bioenergieforschung - SCCER BIOSWEET - zusammengefasst und in einem breiteren Kontext dargestellt. Wenn nichts anderes erwähnt wird, beziehen sich die Ergebnisse auf die Schweiz und im Falle der Ressourcen auf die heimischen Biomassepotenziale.

en: Biogas aus Hofdünger in der Schweiz

en: switzerland review bioenergy manure biogas

title_for_slug: biogas-aus-hofdunger-in-der-schweiz

envi

en: Environment


en: Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.

en: Biogas from animal manure in Switzerland

en: biogas switzerland review bioenergy manure


en:
The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation.
We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality.
1)	Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase
We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software.
Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015).
To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication).
The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of - 0.2 NDVI to represent an improvement in forest quality.
2)	Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population:
The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication).
3)	Topographic drivers
To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

en: Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil

en: urban-compactness built-up-classification land-use-and-cover-change topography forest-gain spatial-modelling urban-morphology nature-conservation forest-persistence planning-outcome basic-services urban-sprawl

en:
Resource Title: Axial
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/7dc6ab63-6a3b-4744-8b8e-b899427e95a5/download/axial.tif
    Description: New built-up pixels classified as axial

format: TIFF

media_type: image/tiff

en:
Resource Title: income_0010
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/d4800254-34c8-4699-ad14-78a32d90a8e8/download/income_0010.tif
    Description: Mean income in Reais (R$) per census sector (change per year)

en:
Resource Title: infill
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/5b598ade-cb86-4f8b-aadc-c18e12814d15/download/infill.tif
    Description: New built-up pixels classified as infill

en:
Resource Title: forest_persist_0515
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/a7d6f906-ee5d-4d64-a713-754764577b8a/download/forest_persist_0515.tif
    Description: Native forest cover persistence from 2005 to 2015

en:
Resource Title: EEZ_2005
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    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/df399923-2f14-45df-bc12-707385ea2fe7/download/eez_2005.7z
    Description: Ecological-Economic Zoning (EEZ)

en:
Resource Title: urban_persistence
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/09b0444f-8dbd-4a8a-b5ac-26ba6d463401/download/urban_persistence.tif
    Description: Urban persistence: Built-up 2005 = Built-up 2015

en:
Resource Title: pop_0010.
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/21d0b38e-7066-4ac1-9c43-4c6fb732f337/download/pop_0010.tif
    Description: Population density per pixel (change per year)

en:
Resource Title: house_inc_0010
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/309672b6-4fa2-411c-999a-5b119c72fbef/download/house_inc_0010.tif
    Description: Permanent housing density increase

en:
Resource Title: sanit_inc_0010
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/a9a2d0d1-2de5-4a14-9b82-eb22e9dd2a8f/download/sanit_inc_0010.tif
    Description: Increase in sanitation service

en:
Resource Title: Descricao dos dados em portiuguês
    Size: 93276 bytes
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    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3142ea4a-a5cf-41e9-b04e-3eea956e32af/download/tabela.pdf
    Description: Portugues description

en:
Resource Title: waste_inc_0010
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    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/e50c4311-9521-4ec1-a177-cc9952a5cff3/download/waste_inc_0010.tif
    Description: Increase in waste collection service

en:
Resource Title: forest_gain_0515
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/cc2499a2-e179-473d-ba04-ba7502d0e1ca/download/forest_gain_0515.tif
    Description: Native forest cover gains from 2005 to 2015

en:
Resource Title: ndvi_inc
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3f14dfcc-668a-4321-9719-f0b58737deda/download/ndvi_inc.tif
    Description: Increase in Normalized Difference Vegetation Index

en:
Resource Title: Variables description
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/8a4afdc9-9af4-4922-8856-6c0e67a1bcca/download/table.pdf
    Description: Table of content. Variables description, source, and other important information

en:
Resource Title: isolated
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    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/08978ae8-8980-446a-af9b-28b6e18b8dcb/download/isolated.tif
    Description: New built-up pixels classified as isolated

en:
Resource Title: slope
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3ad576ef-181c-485a-b794-53381eed55d5/download/slope.tif
    Description: Slope °

en:
Resource Title: master_plan_Ubatuba
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    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3c1e0ad8-fb01-447a-b325-a9574bdaeb06/download/master_plan_ubatuba.7z
    Description: Master plan Ubatuba (zones)

en:
Resource Title: alfa_0010
    Size: 2405447 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/b0604e68-15bf-4c85-a4ca-9bfe519fd03a/download/alfa_0010.tif
    Description: Basic education in % per census sector (change per
year)

en:
Resource Title: master_plan_Ilhabela
    Size: 92087 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/b3f65b7e-8f44-4cb1-8f3b-21e45da6ecd4/download/master_plan_ilhabela.7z
    Description: Master plan Ihabela (zones)

en:
Resource Title: TPI300
    Size: 11941205 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3a51f451-aa57-4d4e-b4a8-a07786d7a18b/download/tpi300.tif
    Description: Topographic Index Position (TPI)

en:
Resource Title: hdi_0010
    Size: 1672974 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/4e9794cf-e518-4b03-abe9-8c797b3a0562/download/hdi_0010.tif
    Description: Human Development Index (HDI) per municipality (change per year)

en:
Resource Title: water_inc_0010
    Size: 1464652 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/5f52216a-8f44-472b-b70d-6bb003968d1d/download/water_inc_0010.tif
    Description: Increase in clean water provision service

basic-services

display_name: basic-services

built-up-classification

display_name: built-up-classification

forest-gain

display_name: forest-gain

forest-persistence

display_name: forest-persistence

land-use-and-cover-change

display_name: land-use-and-cover-change

nature-conservation

display_name: nature-conservation

planning-outcome

display_name: planning-outcome


en: This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS)  and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46°49’47’’ N, 9°48’34’’E), Laret (1515 m asl, . 46°50’2’’N, 9°52’17’’E), Klosters (1200 m asl, 46°51’49’’N, 9°53’17’’E), and Küblis (815 m asl, 46°54’48’’N, 9°46’54’’E).

en: Snow water equivalent measurements with low-cost GNSS receivers along a steep elevation gradient in the East-ern Swiss Alps

en: lwc snow-water-equivalent gps liquid-water-content snow gnss swe

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: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: Meteo_LAR
    Size: 2994700 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/e582db3a-a0ba-4527-9c2f-ca961562c317/download/lar_meteo.csv
    Description: Snow and meteo data from automatic sensors for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en:
Resource Title: day_Kueblis
    Size: 28335 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/df76c91e-b71e-4441-a905-31b32b8782fb/download/kub_day.csv
    Description: Daily data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en:
Resource Title: Profiles_Klosters
    Size: 2530 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/dc102dd3-9d9d-4757-9263-9306c6474b5c/download/klo_prof.csv
    Description: Snow profiles data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en:
Resource Title: Prof_Laret
    Size: 3424 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/d18ef060-e61f-4196-82ad-859e3faa1c9b/download/lar_prof.csv
    Description: Profile data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en:
Resource Title: Obs_WFJ
    Size: 23691 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/4b796a10-3ab7-42c9-ab1f-3d6218692326/download/wfj_obs.csv
    Description: Daily observer data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en:
Resource Title: Manual profiles
    Size: 4065671 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/f7ac1de9-3b24-47fa-9f67-cd6560674a18/download/profiles.zip
    Description: Data of the traditional manual profiles for the locations Weissfluhjoch, Klosters and Küblis. For the location of Küblis there are no traditional manual profile data.  We provided the data as both PDF and .CAAML format (See https://niviz.org/  or http://caaml.org/Schemas/V5.0/Profiles/SnowProfileIACS/CAAMLv5_SnowProfileIACS.pdf). Moreover, we added LWC and density profiles when available (all locations).

en:
Resource Title: GNSS_WFJ
    Size: 54679 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/f791ca79-0c9a-4055-9989-d5176e222935/download/wfj_gnss.csv
    Description: GNSS derived SWE, HS, and LWC for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en:
Resource Title: GNSS_laret
    Size: 15587 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/0a95d946-3fd0-4d83-bfb9-4fdcb210a0d3/download/lar_gnss.csv
    Description: GNSS derived data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en:
Resource Title: GNSS_kueblis
    Size: 17196 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/973a7cde-cf6e-484e-8da1-a2da1de4fc49/download/kub_gnss.csv
    Description: GNSS derived SWE, LWC and HS from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-20

en:
Resource Title: day_Laret
    Size: 27183 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/3af4ec59-d2ff-425e-ba49-996cd5a85247/download/lar_day.csv
    Description: Daily data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en:
Resource Title: Webcam images
    Size: 236611230 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/292ddd39-edd8-4df4-8dc7-f79137c6494d/download/pictures_webcam.zip
    Description: Webcam images showing some typical differences in the snow conditions at the four sites (Küblis, Klosters, Laret and Weissfluhjoch) for two time periods (14-18.3.2019 and 27.1-11.2.2020). Both time periods included a precipitation event with partial rain-on–snow occurring at the lower locations.

en:
Resource Title: day_WFJ
    Size: 71919 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/01d5ba5c-4734-43b0-b1cd-68e97e0fd711/download/wfj_day.csv
    Description: Daily data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en:
Resource Title: Obs_Klosters
    Size: 13499 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/9bb74e8a-d19b-49ca-aecf-feed84c3e324/download/klo_obs.csv
    Description: Daily observer data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en:
Resource Title: Meteo_Kueblis
    Size: 674638 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/398c7a10-d1bf-4fd3-b7a9-8175843077d7/download/kub_meteo.csv
    Description: Snow and meteo data from automatic sensors from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en:
Resource Title: GNSS_klosters
    Size: 19682 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/bc65a02c-f311-49c7-9007-a8df51a87610/download/klo_gnss.csv
    Description: GNSS derived SWE, LWC and HS from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-20

en:
Resource Title: Meteo_Klosters
    Size: 697178 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/0ba3714e-3caa-47d3-8b8c-c235fc57760e/download/klo_meteo.csv
    Description: Snow and meteo data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en:
Resource Title: Obs_Kueblis
    Size: 10744 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/ba8b38d2-fcc3-4345-b763-4b618e49836f/download/kub_obs.csv
    Description: Daily observer data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en:
Resource Title: Meteo_WFJ
    Size: 2779069 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/be4ca553-12f8-4a2a-94bc-1672dfbac5b9/download/wfj_meteo.csv
    Description: Snow and meteo data from automatic sensors for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en:
Resource Title: Profiles_WFJ
    Size: 2365 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/8a7318b9-571d-4da9-9686-2330bf0f373a/download/wfj_prof.csv
    Description: Snow profiles data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en:
Resource Title: day_Klosters
    Size: 28691 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/40726e6f-f175-4033-8df7-526e42cdc266/download/klo_day.csv
    Description: Daily data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en:
Resource Title: Profiles_Kueblis
    Size: 1151 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/e6d6f714-5f1a-4cc9-9ab1-3cf5417073a7/download/kub_prof.csv
    Description: Snow profiles data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

snow

display_name: snow

snow-water-equivalent

display_name: snow-water-equivalent


en:
Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016.
Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.

en: Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch

en: avalanche-activity crocus snowpack avalanche-danger avalanche-problem-type weissfluhjoch snow-instability snow-cover-modeling avalanche snow

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: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: 2015-2016 Seismic avalanche acitivity catalogue
    Size: 322 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/aee18298-2eea-4876-ba1c-15cd7e69a031/download/2015_16_ava_seismic.csv
    Description: Avalanches were indentified in seismic data recorded by two seismic arrays, one deployed at Wannengrat (2,500 m) and another one deployed in the Dischma valley (2,000 m).
The data cover the period from 01.01.2016 - 30.04.2016 and are presented in Figure 7 in Reuter et al. (2021).

en:
Resource Title: 1999-2017 Avalanche problem types from Crocus
    Size: 507 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/3b8f8b29-2be6-473e-8e33-0b598670531d/download/wfj_1999-2017_crocus_avalanche_problem_types.csv
    Description: Avalanche problem types were derived from snow cover simulations with the model Crocus at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are presented in Figure 8 in Reuter et al. (2021).

en:
Resource Title: 1999-2017 Avalanche problem types from SNOWPACK
    Size: 493 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/fd62a04d-b072-46d9-b860-0415656b3e38/download/wfj_1999-2017_snowpack_avalanche_problem_types.csv
    Description: Avalanche problem types were derived from snow cover simulations with the model SNOWPACK at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are shown in Figure 8 in Reuter et al. (2021).

en:
Resource Title: 2015-2016 Avalanche problem types from SNOWPACK
    Size: 2749 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/b76405da-ff97-4720-8a1e-323d6c4b6da9/download/2015-2016_snowpack_ava_prob_types.csv
    Description: Avalanche problem types were derived from a snow cover simulation with the model SNOWAPCK for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en:
Resource Title: 2015-2016 Avalanche problem types from Crocus
    Size: 2749 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/07bdeaa3-c5c4-4c04-bca2-66a8d84310ab/download/2015-2016_crocus_ava_prob_types.csv
    Description: Avalanche problem types were derived from a snow cover simulation with the model Crocus for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

avalanche-danger

display_name: avalanche-danger

avalanche-problem-type

display_name: avalanche-problem-type

crocus

display_name: crocus

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

weissfluhjoch

display_name: weissfluhjoch


en:
This dataset includes the parallel application and the main results supporting the research article "Modeling snow saltation: the effect of grain size and interparticle cohesion" published at the Journal of Geophysical Research: Atmospheres.
The code is a flow solver based on the Large Eddy Simulation (LES) technique coupled with a Lagrangian Stochastic Model (LSM). The interaction of snow particles with the bed is modeled with statistical and physically-based models for aerodynamic entrainment, rebound and splash, following the works of Groot Zwaaftink et al. (2014), Comola and Lehning (2017) and Sharma et al. (2018). This algorithm was also used by Sigmund et al. (2021) to model snow sublimation.

en: large-eddy-simulations drifting-snow saltation snow-cohesion mixed-grain-transport

en:
Resource Title: LES-LSM code
    Size: 405936 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/c4543b3d-d898-4364-8194-bb56f2fe41f9/resource/aa04cdac-a8cc-446d-84ef-b9f69b767d83/download/les-lsm.tar.xz
    Description: Source code used to perform the analysis presented at the research article "Modeling snow saltation: the effect of grain size and interparticle cohesion". The source code is also available at the WSL/SLF GitLab Repository (https://gitlabext.wsl.ch/atmospheric-models/les-lsm).

en:
Resource Title: Data in Figures
    Size: 312666 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c4543b3d-d898-4364-8194-bb56f2fe41f9/resource/9824aead-cf4d-4499-ac43-d392daca5417/download/figures_data.zip
    Description: Simulation results presented in Figures 1 to 11 of the research article "Modeling snow saltattion: the effect of grain size and interparticle cohesion" in csv format.

drifting-snow

display_name: drifting-snow

large-eddy-simulations

display_name: large-eddy-simulations

mixed-grain-transport

display_name: mixed-grain-transport

saltation

display_name: saltation

snow-cohesion

display_name: snow-cohesion


en:
One individual per species, vitality class (low and high) and height class (eight classes: 0–10, 11–20, 21–35, 36–60, 61–90, 91–130, 131–200 and 201–500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively.
 Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015–2018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected.
 Variables:
* species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica
* species: as above
* dummy: 0 - living individual, 1 - dead individual
* LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g]
* tree_age: in years
* avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring
* dry_mass_g: aboveground and belowground biomass
* DLI: direct light index (measured only under living individuals)
* BLI: diffuse light index (measured only under living individuals)
* GLI: global light index

en: Mortality of regeneration: Acer spp. and Fagus sylvatica

en: acer lar mortality-rate fagus dominance shade-tolerance regeneration

en:
Resource Title: Mortality of regeneration
    Size: 60209 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8d9df0cd-f20c-423f-a4ab-71fc213871de/resource/37fb69d9-c553-4931-89ba-31857ac15c04/download/imputed.csv
    Description: Acer platanoides, Acer pseudoplatanus and Fagus sylvatica living and dead juveniles (0-500 cm in height) grown in the deep shade of a prime beech-dominated forest.

en:
Resource Title: Survival_AFT_R_script
    Size: 18015 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/8d9df0cd-f20c-423f-a4ab-71fc213871de/resource/6ff5b7a2-6662-41ab-8c2d-68f688cde189/download/survival_script.r
    Description: The R-script of survival analysis for accelerated failure time model

acer

display_name: acer

dominance

display_name: dominance

fagus

display_name: fagus

lar

display_name: lar

mortality-rate

display_name: mortality-rate

regeneration

display_name: regeneration

shade-tolerance

display_name: shade-tolerance


en:
Table of content: 1. Frequency of early concepts; 2. Frequency of additional concepts; 3. Use of any early concept; 4. Use of any additional concept, 5. Planning steps; 6. Protocol.
The present dataset is part of the published scientific paper entitled “Landscape ecological concepts in planning: review of recent developments” (Hersperger et al., 2021). The goal of this research was to review recent publications to assess the use of landscape ecological concepts in planning. Specifically, we address the following research questions: Q1. Landscape ecological concepts: What are they? How frequently are they mentioned in current research? Q2. How are landscape ecological concepts integrated in landscape planning?
We analysed all empirical and overview papers that have been published in four key academic journals in the field of landscape ecology and landscape planning in the years 2015–2019 (n = 1918). Four key journals in the field of landscape ecology were selected to conduct the analysis, respectively Landscape Ecology (LE), Landscape Online (LO), Current Landscape Ecology Reports (CLER), and Landscape and Urban Planning (LUP). The title, abstract and keywords of all papers were read in order to identify landscape ecological concepts. Then, all 1918 papers went through a keyword search to identify the use of early and additional concepts. We used the “pdfsearch” package in R programming language and searched for singular and plural forms and different variations of the concepts (see Supplementary material 1, Table A). As a result, we provided four outputs:
  1. Frequency of early concepts. This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.
  2. Frequency of additional concepts. This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.
  3. Use of any early concept. This data provides the total number of times each article used any early concept (Q1).
This data was used to produce the Figure 3a at the original publication.
  4. Use of any additional concept. This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication.
To address the second question (Q2), the title, abstract and keywords of the papers included in our sample (n=1918 articles) were screened to identify papers that might show how landscape ecological concepts are integrated into planning. We selected 52 empirical papers (see Supplementary material – 4 Integration of landscape ecological concepts into planning), and we provided two outputs:
  5. Planning steps.  This data provides the number of times landscape ecological concepts were addressed in each
planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the
original publication.
  6. Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the
data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the
paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning
steps described above?

en: The usage of landscape ecological concepts in the planning literature

en: green-infrastructure resilience planning ecosystem-services multifunctionality pdfsearch literature-review sense-of-place landscape-perception socio-ecological-system landscape-planning landscape-services landscape-ecology decision-making

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:
Resource Title: 5. Planning steps.
    Size: 3898 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/f456f712-f67c-40d0-9f6a-7e18f2746ce4/download/5.-planning_steps.csv
    Description: This data provides the number of times landscape ecological concepts were addressed in each planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the original publication.

en:
Resource Title: 4. Use of any additional concept.
    Size: 146949 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/070af529-0b2d-4ead-b0d8-86aaf15afd04/download/4.-use_of_any_additional_concept.csv
    Description: This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication.

en:
Resource Title: 2. Frequency of additional concepts.
    Size: 1187545 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/7ae0ec68-0675-4d2b-a5d2-51d08e153f99/download/2-frequency_of_additional_concept.csv
    Description: This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.

en:
Resource Title: 3. Use of any early concept.
    Size: 157296 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/21b7a6f8-9f37-4ddb-b8fa-b17f25428996/download/3.-use_of_any_early_concept.csv
    Description: This data provides the total number of times each article used any early concept (Q1). This data was used to produce the Figure 3a at the original publication.

en:
Resource Title: 6. Protocol
    Size: 91483 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/8675307f-37dc-4062-b990-3202634a5dbc/download/6_protocol.xlsx
    Description: Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning steps described above?

en:
Resource Title: 1. Frequency of early concepts.
    Size: 1002570 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/3b4f848b-5dc5-4f24-96b3-09778c0a8655/download/1.-frequency_of_early_concepts.csv
    Description:  This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.

decision-making

display_name: decision-making

ecosystem-services

display_name: ecosystem-services

green-infrastructure

display_name: green-infrastructure

landscape-ecology

display_name: landscape-ecology

landscape-perception

display_name: landscape-perception

landscape-planning

display_name: landscape-planning

landscape-services

display_name: landscape-services

literature-review

display_name: literature-review

multifunctionality

display_name: multifunctionality

pdfsearch

display_name: pdfsearch

planning

display_name: planning

resilience

display_name: resilience

sense-of-place

display_name: sense-of-place

socio-ecological-system

display_name: socio-ecological-system


en:
This dataset contains all data, on which the following publication below is based.
Paper Citation:
_Resch, M.C., Schütz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_
Please cite this paper together with the citation for the datafile.
Study area and experimental design
The study was conducted in and around two nature reserves, Eigental and Altläufe der Glatt, which were located approximately 5 km apart (47°27´ to 47°29´ N, 8°37´ to 8°32´ E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 ± 0.6 °C and 990 ± 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).
Collection of plants and selected soil biota data
Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 °C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 μL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 °C for 3 h). Thereafter, 180 μL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 °C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 °C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 μL of genomic DNA template (ca. 1 ng/μL) in 25 μL reactions containing 5 μL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 μM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 μL BSA, and 0.25 μL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 °C for 2 min, followed by 35 cycles at 94 °C for 40 sec, 58 °C for 40 sec, 72 °C for 1 min, and a final elongation step at 72 °C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.
We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 °C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer´s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings ≥ 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.
Collection of soil physical and chemical properties
We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 °C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (≤ 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.

en: Long-term recovery of above-and belowground interactions in restored grasslands

en: grassland bacteria soil-chemistry plants soil-biodiversity topsoil-removal fungi soil-biology biodiversity nematodes soil restoration

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: Nematode data
    Size: 256130 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/3f820b86-ee6b-4466-bc40-4346eac4cbcd/download/nematoda_sample.data_taxonomy_abundance.xlsx
    Description: Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
ASV.ID	unit number.letter code per amplicon sequence variant (in total 904)
Taxonomic rank	Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	ASV.ID (see Taxonomy)

en:
Resource Title: Fungi data
    Size: 1175564 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/64d9bac3-d7ce-4ae9-8922-f0ee496292a9/download/fungi_sample.data_taxonomy_abundance.xlsx
    Description: Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
OTU.ID	consecutive numbers for operational taxonomic units (OTUe 1-7794)
Taxonomic rank	Domain, Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	OTU.ID (see Taxonomy)

en:
Resource Title: Vegetation data
    Size: 58554 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/9940b2c1-35a6-46e8-9604-4fb866f1ddd4/download/vegetation_sample.data_coverage.xlsx
    Description: Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
column I-LW	Plant abundance per species (in total 327 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature  according to Lauber & Wagner (1996)

en:
Resource Title: Soil properties
    Size: 23266 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/143c6e0e-1aa5-4510-b333-9c00e99c59fd/download/soil.properties_pcoas_h.values.xlsx
    Description: Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
N.pool	Soil nitrogen pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
Corg.pool	Soil organic carbon pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
pH	soil pH [CaCl2]
BD	Bulk density [g cm-3]
FC	Field capacity [%]
Veg.1	principle coordinate axis 1 (vegetation)
Veg.1	principle coordinate axis 2  (vegetation)
Nem.1	principle coordinate axis 1 (nematoda)
Nem.2	principle coordinate axis 2  (nematoda)
Fun.1	principle coordinate axis 1 (fungi)
Fun.2	principle coordinate axis 2  (fungi)
Prok.1	principle coordinate axis 1 (prokaryota)
Prok.2	principle coordinate axis 2  (prokaryota)
Veg.H	Shannon diversity (vegetation)
Nem.H	Shannon diversity (nematoda)
Fun.H	Shannon diversity (fungi)
Prok.H	Shannon diversity (prokaryota)

en:
Resource Title: Distance to target results
    Size: 13678 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/ed2658c5-c2c2-4b13-b29a-187f199f9f47/download/distance.to.target.xlsx
    Description: Community	biotic community (vegetation, nematoda, fungi, prokaryota)
Treatment	Treatment groups; Y.4-30= restored grasslands (time since restoration); Initial=intensively managed grasslands (I)
Comparison	within- or between-community Bray-Curtis dissimilarity distance comparison
Label	within-community distances of initial/restored grasslands: treatment abbreviation; between-communities distances of initial/restored-to-target grasslands: treatment abbreviation plus Target (T; species-rich grasslands)
mean	Mean distance to Target  (6 plots per treatment group)
sd	Standard deviation of distances to Target (6 plots per treatment group)

en:
Resource Title: Networks
    Size: 21006 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/3f35aa3b-5f64-4d55-907f-ac6c1f2d490c/download/network.correlations.xlsx
    Description: Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Correlation	non-parametric Spearman rank correlations
Vertex.1+2	Comparison between network components; N.pool = total soil nitrogen pool [kg ha-1 12 cm-1]; Corg.pool = soil organic carbon pool [kg ha-1 12 cm-1], pH = soil pH [CaCl2]; BD = bulk density [g cm-3]; FC = field capacity [%]
	PCoA 1 + 2 biotic communities [Veg = vegetation; Nem = nematoda; Fun = fungi; Prok = prokaryota]
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic

en:
Resource Title: Ecosystem coupling_interactions
    Size: 18890 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/046d129c-3bc2-4214-8d9c-ffbf8659b245/download/ecosystem.coupling_ecological.interactions.xlsx
    Description: Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic
Correlation	non-parametric Spearman rank correlations between biotic and abiotic parameters (ecological interactions)

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

fungi

display_name: fungi

grassland

display_name: grassland

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

topsoil-removal

display_name: topsoil-removal


en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
 - Resch, M.C., Schütz, M., Graf, U., Wagenaar, R., van der Putten, W.H., Risch, A.C. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56: 1782-1793.
Please cite this paper together with the citation for the datafile.
 Methods
 Study area and experimental settings
The study was conducted in a nature reserve (Eigental: 47° 27’ to 47° 29’ N, 8° 37’ E, 461 to 507 m a.s.l.) that is located on the Swiss Central plateau close to Zurich airport (Canton Zurich, Switzerland). The mean annual temperature in this area ranges from 8.9 to 10.6 °C, mean annual precipitation from 910 to 1260 mm [10-year average (2007-2017); MeteoSchweiz, 2018]. The main soil types are calcaric to gleyic Cambisol and Gleysols. The reserve was established in 1967 to protect small remnants of oligotrophic semi-natural grasslands (roughly 12 ha). The plant community can be characterized as Molinion and Mesobromion (semi-wet to semi-dry), depending on the site-specific groundwater level and slope inclination (Delarze, Gonseth, Eggenberg, & Vust, 2015). These remnants represent species-rich islands in an otherwise intensively managed agricultural landscape. Semi-natural grasslands covered an area of 60,000 ha in the Canton Zurich in 1939, however, by 2005 only roughly 600 ha remained (Baudirektion Kanton Zürich, 2007). In 1990, the government of Canton Zurich decided to enlarge the nature reserve Eigental. The goal was to incorporate eleven patches of 20 ha adjacent intensively farmed land and transform these patches into semi-natural grasslands. The patches had a different agricultural history, ranging from permanent (no tillage for -50 years) to temporary grassland (as part of crop rotation; last tillage -5 years). On all freshly integrated patches fertilization was stopped in 1992 and from then on biomass was harvested three times a year and removed. After 5 years without noticeable effects on vegetation composition, the Nature Conservation Agency of Canton Zurich decided to increase the restoration efforts. In 1995, a large-scale experiment was initialized to evaluate if certain treatments can facilitate restoration within a reasonable timeframe of 5 to 10 years after treatment implementation.
The three restoration treatments used were:
i. “Harvest only”: Plots are being mowed two to three times a year and the biomass is removed.
ii. “Topsoil”: Topsoil was removed to a depth of 10 to 20 cm, depending on the depth of the O and A horizon, in four randomly selected areas within each of the eleven patches in late autumn 1995. The size of each topsoil removal area depended on individual patch size and was between 2700 and 7000 m2.
iii. “Topsoil+Propagules”: Propagules from target vegetation were added on half of the area where topsoil was removed, using fresh, seed-containing hay originating from a mixture of semi-dry to semi-wet species-rich grasslands of local provenance (within a radius of 7 km). Hay applications were conducted twice in 1995 and 1996. Repeated applications were chosen to account for the low quantity of available plant material per transfer, since area ratio between receptor and donor sites was roughly 1:1. In addition, hand-collected propagules from 15 selected target species of regional provenance (within a radius of 30 km) were equally applied in 1996 and 1997.
“Topsoil” and “Topsoil+Propagules” plots are mowed once a year, and the biomass is removed. Mowing on these plots started five years after the treatment was implemented.
Eleven permanent plots of 5 m x 5 m were randomly established in each treatment to monitor the vegetation development. The experiment was complemented with 11 control plots that represent the initial state of intensively managed grasslands, further referred to as “Initial”, and 11 control plots that represent the targeted state of donor sites for “Topsoil+Propagules”, further referred to as “Target”. Consequently, the experiment consists of 55 plots (5 treatments x 11 replicates). Management of intensively used grasslands includes mowing and fertilizing (manure) between two to five times a year, as well as different tillage regimes (no tillage for -50 years; last time of tillage -5 years).
 Nematode and plant sampling
Soil nematodes were sampled in 2 m x 2 m plots, randomly established at least 2 m away from the vegetation plots. We collected eight soil cores with a 2.2 cm diameter soil core sampler (Giddings Machine Company, Windsor, CO, USA) to a depth of 12 cm (representing the majority of the plant rooting system) in each plot at the beginning of July 2017. The eight cores within each replicate plot were combined, gently homogenized, placed in coolers and transported to the laboratory of NIOO in Wageningen, the Netherlands, within one week. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink, 1960) and concentrated, resulting in 6 mL nematode solution. The nematode solution was subdivided into three subsamples, two for morphological identification and quantification, and one for molecular work (not used in this study). For morphological identification and quantification, nematodes were heat-killed at 90 °C and fixed in 4 % formaldehyde solution (final volume 10 mL per subsample). All nematodes in 1 mL of formaldehyde solution were counted, and a minimum of 150 individuals per 1 mL sample (or all if less nematodes were present) were identified to family level using Bongers (1988). We then extrapolated the numbers of each nematode taxa identified to the entire sample and expressed them per 100 g dry soil for further analyses.
We calculated number of nematode taxa and Shannon diversity and assessed nematode community composition. In addition, we classified the nematode taxa into feeding types (herbivores, bacterivores, fungivores, omni-carnivores), structural and functional guilds (Table S4). Structural guilds assign nematode taxa according to life-history traits into five colonizer-persister (C-P) classes, ranging from one (early colonizers of new resources) to five (persisters in undisturbed habitats; Bongers 1990). C-P classes can be categorized as indicators for nutrient-enriched (C-P1), stressed (C-P2) and structured (C-P3 + C-P4 + C-P5) soil conditions (Ferris, Bongers, & de Goede, 2001). Functional guilds assign nematode taxa according to their C-P classification combined with their feeding habits (Ferris, Bongers, & de Goede, 2001). Based on the structural and functional guild classification we calculated five additional indices to assess soil nutrient status, disturbance and food web characteristics using NINJA (Sieriebriennikov, Ferris, & de Goede, 2014). 1) The Maturity index indicates the degree of different environmental perturbations (e.g., tillage, nutrient enrichment, pollution) and is used to monitor colonization and subsequent succession after disturbances (Bongers, 1990). 2) The ratio between the Plant Parasite (C-P of herbivorous nematodes only) to Maturity index is used to monitor the recovery of disturbed habitats incorporating information of life-history traits for all feeding types (Bongers, van der Meulen, & Korthals, 1997). 3) The Enrichment index indicates nutrient-enriched soils and agricultural management practices (Ferris, Bongers, & de Goede, 2001). 4) The Structure index provides information about the succession stage of the soil food web and therefore correlates with the degree of maturity of an ecosystem (Ferris, Bongers, & de Goede, 2001). 5) The Channel index provides information about the predominant decomposition pathways, where higher values stand for a higher proportion of energy transformed through the slow fungal decomposition channel (Ferris, Bongers, & de Goede, 2001). In addition, the Structure and Enrichment indices can be displayed in a biplot where nematode assemblages are plotted along a structure (x-axis) and enrichment (y-axis) trajectory (increasing index values). Each biplot quadrat reflects different levels of disturbance, soil nutrient pools and decomposition pathways (Ferris, Bongers, & de Goede, 2001).
The plant surveys were conducted on the 25 m2 permanent plots in June 2017. Plant species cover was visually assessed according to the semi-quantitative cover-abundance scale of Braun-Blanquet (1964; nomenclature: Lauber & Wagner, 1996). We calculated number of species and Shannon diversity, and assessed plant community composition. We also counted the number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species) and categorized plant species into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002). Furthermore, we calculated indicator values for soil moisture and soil nutrients for each species according to Landolt et al. (2010).
 References
Baudirektion Kanton Zürich (2007). 10 Jahre Naturschutz-Gesamtkonzept für den Kanton Zürich 1995-2005 – Stand der Umsetzung. Zürich: Baudirektion Kanton Zürich.
Bongers, T. (1988). De nematoden van Nederland. Utrecht: Stichting Uitgeverij Koninklijke Nederlandse Natuurhistorische Vereniging.
Bongers, T. (1990). The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia, 83, 14-19. doi:10.1007/BF00324627
Bongers, T., van der Meulen, H., & Korthals, G. (1997). Inverse relationship between the nematode maturity index and plant parasite index under enriched nutrient conditions. Applied Soil Ecology, 6, 195-199. doi:10.1016/S0929-1393(96)00136-9
Braun-Blanquet, J. (1964). Pflanzensoziologie, Grundzüge der Vegetationskunde (3rd ed.). Wien: Springer.
Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten (3rd ed.). Bern: Ott.
Ferris, H., Bongers, T., & de Goede, R.G.M. (2001). A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Applied Soil Ecology, 18, 13-29. doi:10.1016/S0929-1393(01)00152-4
Landolt, E., Bäumler, B., Erhardt, A., Hegg, O., Klötzli, F., Lämmler, W., … Wohlgemuth, T. (2010). Flora indicativa. Ecological indicator values and biological attributes of the Flora of Switzerland and the Alps (2nd ed.). Bern: Haupt.
Lauber, K., & Wagner, G. (1996). Flora Helvetica. Flora der Schweiz. Bern: Haupt.
MeteoSchweiz (2018). Klimabulletin Jahr 2017, Zürich: MeteoSchweiz.
Moser, D., Gygax, A., Bäumler, B., Wyler, N., & Palese, R. (2002) Rote Liste der gefährteten Farn- und Blütenpflanzen der Schweiz. Bern: BUWAL.
Oostenbrink, M. (1960). Estimating nematode populations by some selected methods. In N.J. Sasser & W.R. Jenkins (Eds.), Nematology (pp. 85-101). Chapel Hill: University of North Carolina Press.
Sieriebriennikov, B., Ferris, H., & de Goede, R.G.M (2014). NINJA: An automated calculation system for nematode-based biological monitoring. European Journal of Soil Biology, 61, 90-93. doi:10.1016/j.ejsobi.2014.02.004

en: Grassland restoration: nematodes and plant communities

en: grassland vegetation long-term-experiment diversity nematodes topsoil-removal restoration

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:
Resource Title: Correction_Nematodes_vegetation_20200131.xlsx
    Size: 39601 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/f13545be-edc5-41fd-86db-0d8a3ea9c16b/download/correction_nematodes_vegetation_20200131.xlsx
    Description: Coordinates of one research plot were wrong. This was corrected in the current version of the file.

en:
Resource Title: Nemadotes and vegetation
    Size: 34575 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/f6b57e7e-0d1c-4701-bd0a-695af2744f75/download/nematodes_vegetation.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

X: X-Coordinate

Y: Y-Coordinate

Ri.veg: Total number of plant species

Div.veg: Shannon diversity of plant species

Ri.nem: Total number of nematode families

Div.nem: Shannon diversity of nematode families

abund: Total nematode abundance (individuals per 100g dry soil)

MI: Maturity index according to Bongers 1990; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

PPI: Plant parasite index according to Bongers 1990; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

PPI.MI: Plant parasite to Maturity index according to Bongers, van der Meulen, & Korthals 1997; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

CI: Channel index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

EI: Enrichment index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

SI: Structure index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

He.a: Herbivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.ecto.a: Herbivorous ectoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.endo.a: Herbivorous endoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ba.a:  Bacterivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Fu.a: Fungivorous  nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.a: Omnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ca.a: Carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.Ca.a	Sum of omnivorous and carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014
CP1.a: C-P (colonizer-persister) class 1 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to enrichment indicators according to Ferris, Bongers, & de Goede 2001

CP2.a: C-P (colonizer-persister) class 2 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to stress indicators according to Ferris, Bongers, & de Goede 2001

CP3.a: C-P (colonizer-persister) class 3 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP4.a: C-P (colonizer-persister) class 4 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP5.a: C-P (colonizer-persister) class 5 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP3-5.a: Sum of C-P3, C-P4 and C-P5 nematode abundance (individuals per 100g dry soil) equivalent to structure indicators according to Ferris, Bongers, & de Goede 2001

protect.sp.pro: plant species categorized into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002)

tar.sp: Number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species)

Indi.moist: Plant indicator values for soil moisture according to Landolt et al. 2010

Indi.nutri: Plant indicator values for soil nutrients according to Landolt et al. 2010

Management: level of mowing and manuring frequency (35-40 kg N ha-1 application-1); intensity1 = mowing once and no manure; intensity2 = 2-3 times mowing and no manure; intensity3 = 2-3 times mowing and manuring; intensity4 = 4-5 times mowing and manuring

Tillage: last time of tillage

Land.use.history: for Initial plots = recent agricultural use; for all restored sites = agricultural use before restoration
Mowing	earliest date of biomass harvesting (excluding “Initial”) based on contracts between farmers and the Nature Protection Agency of Zurich

Soil.type: based on soil mapping survey 1991 (before restoration)

Soil.texture: determined by the sediment method (Gee & Bauder 1986) and classified into soil textural classes according to FAO (1990)

pH: soil pH (CaCl2)

Slope: slope gradient classes according to FAO (1990; %)

en:
Resource Title: Nematode colonizer-persister classes
    Size: 14565 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/b7005b87-699b-4ff1-8c59-82c49ae96d5e/download/nematodes_community.composition_colonizer.persister.level.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

CP1.a: C-P (colonizer-persister) class 1 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to enrichment indicators according to Ferris, Bongers, & de Goede 2001
CP2.a: C-P (colonizer-persister) class 2 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to stress indicators according to Ferris, Bongers, & de Goede 2001
CP3.a: C-P (colonizer-persister) class 3 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990
CP4.a: C-P (colonizer-persister) class 4 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990
CP5.a: C-P (colonizer-persister) class 5 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

en:
Resource Title: Nematode feeding types
    Size: 20512 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/f782653d-25d4-45c6-bf9a-64d921bba0cc/download/nematodes_community.composition_feedingtype.level.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

He.ecto.a: Herbivorous ectoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.endo.a: Herbivorous endoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ba.a: Bacterivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Fu.a: Fungivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.a: Omnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ca.a: Carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

en:
Resource Title: Nematode families
    Size: 28500 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/82bd6601-d124-4e6d-9508-696b79afe53a/download/nematodes_community.composition_family.level.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

column 3-41: Nematode abundance (individuals per 100g dry soil) per family; nomenclature according to Bongers 1988

en:
Resource Title: Plant community composition (plant species)
    Size: 64984 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c0d0f316-cf15-405f-aca8-bf5167724fbe/resource/783ee905-958a-4e63-b1eb-6de2dc5b44e1/download/plant_community.composition_species.level.xlsx
    Description: Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

column 3-252: Plant abundance per species (in total 252 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature  according to Lauber & Wagner (1996)

diversity

display_name: diversity

long-term-experiment

display_name: long-term-experiment

vegetation

display_name: vegetation