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LOTVS: A global collection of permanent vegetation plots

Authors :
Royal Society of New Zealand
Czech Science Foundation
Academy of Sciences of the Czech Republic
Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
Institut National de la Recherche Agronomique (France)
Federal Ministry of Education and Research (Germany)
Oberfranken Stiftung
Agence Nationale de la Recherche (France)
Scottish Government's Rural and Environment Science and Analytical Services
Estonian Research Council
European Commission
Fundación Ramón Areces
Generalitat de Catalunya
European Research Council
Dresden University of Technology
Ministry of Business, Innovation, and Employment (New Zealand)
Natural Environment Research Council (UK)
Biotechnology and Biological Sciences Research Council (UK)
Comunidad de Madrid
Universidad Rey Juan Carlos
National Science Foundation (US)
Gaia Sperandii, Marta
de Bello, Francesco
Valencia, Enrique
Götzenberger, Lars
Bazzichetto, Manuele
Galland, Thomas
E-Vojtkó, Anna
Conti, Luisa
Adler, Peter B.
Buckley, Hannah
Danihelka, Jiří
Day, Nicola J.
Dengler, Jürgen
Eldridge, David J.
Estiarte, Marc
García-González, Ricardo
Garnier, Eric
Gómez García, Daniel
Hallett, Lauren
Harrison, Susan P.
Herben, Tomas
Ibáñez, Ricardo
Jentsch, Anke
Juergens, Norbert
Kertész, Miklós
Kimuyu, Duncan M.
Klumpp, Katja
Le Duc, Mike
Louault, Frédérique
Marrs, Rob H.
Ónodi, Gábor
Pakeman, Robin J.
Pärtel, Meelis
Peco, Begoña
Peñuelas, Josep
Rueda, Marta
Schmidt, Wolfgang
Schmiedel, Ute
Schuetz, Martin
Skalova, Hana
Šmilauer, Petr
Šmilauerová, Marie
Smit, Christian
Song, Ming‐Hua
Stock, Martin
Val, James
Vandvik, Vigdis
Wesche, Karsten
Wiser, Susan K.
Woodcock, Ben A.
Young, Truman P.
Yu, Fei‐Hai
Wolf, Amelia A.
Zobel, Martin
Lepš, J.
Royal Society of New Zealand
Czech Science Foundation
Academy of Sciences of the Czech Republic
Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
Institut National de la Recherche Agronomique (France)
Federal Ministry of Education and Research (Germany)
Oberfranken Stiftung
Agence Nationale de la Recherche (France)
Scottish Government's Rural and Environment Science and Analytical Services
Estonian Research Council
European Commission
Fundación Ramón Areces
Generalitat de Catalunya
European Research Council
Dresden University of Technology
Ministry of Business, Innovation, and Employment (New Zealand)
Natural Environment Research Council (UK)
Biotechnology and Biological Sciences Research Council (UK)
Comunidad de Madrid
Universidad Rey Juan Carlos
National Science Foundation (US)
Gaia Sperandii, Marta
de Bello, Francesco
Valencia, Enrique
Götzenberger, Lars
Bazzichetto, Manuele
Galland, Thomas
E-Vojtkó, Anna
Conti, Luisa
Adler, Peter B.
Buckley, Hannah
Danihelka, Jiří
Day, Nicola J.
Dengler, Jürgen
Eldridge, David J.
Estiarte, Marc
García-González, Ricardo
Garnier, Eric
Gómez García, Daniel
Hallett, Lauren
Harrison, Susan P.
Herben, Tomas
Ibáñez, Ricardo
Jentsch, Anke
Juergens, Norbert
Kertész, Miklós
Kimuyu, Duncan M.
Klumpp, Katja
Le Duc, Mike
Louault, Frédérique
Marrs, Rob H.
Ónodi, Gábor
Pakeman, Robin J.
Pärtel, Meelis
Peco, Begoña
Peñuelas, Josep
Rueda, Marta
Schmidt, Wolfgang
Schmiedel, Ute
Schuetz, Martin
Skalova, Hana
Šmilauer, Petr
Šmilauerová, Marie
Smit, Christian
Song, Ming‐Hua
Stock, Martin
Val, James
Vandvik, Vigdis
Wesche, Karsten
Wiser, Susan K.
Woodcock, Ben A.
Young, Truman P.
Yu, Fei‐Hai
Wolf, Amelia A.
Zobel, Martin
Lepš, J.
Publication Year :
2022

Abstract

Analysing temporal patterns in plant communities is extremely important to quantify the extent and the consequences of ecological changes, especially considering the current biodiversity crisis. Long-term data collected through the regular sampling of permanent plots represent the most accurate resource to study ecological succession, analyse the stability of a community over time and understand the mechanisms driving vegetation change. We hereby present the LOng-Term Vegetation Sampling (LOTVS) initiative, a global collection of vegetation time-series derived from the regular monitoring of plant species in permanent plots. With 79 data sets from five continents and 7,789 vegetation time-series monitored for at least 6 years and mostly on an annual basis, LOTVS possibly represents the largest collection of temporally fine-grained vegetation time-series derived from permanent plots and made accessible to the research community. As such, it has an outstanding potential to support innovative research in the fields of vegetation science, plant ecology and temporal ecology.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1306020063
Document Type :
Electronic Resource