| Literature DB >> 33020598 |
Fons van der Plas1, Thomas Schröder-Georgi2, Alexandra Weigelt2,3, Kathryn Barry2,3, Sebastian Meyer4, Adriana Alzate3, Romain L Barnard5, Nina Buchmann6, Hans de Kroon7, Anne Ebeling8, Nico Eisenhauer3,9, Christof Engels10, Markus Fischer11, Gerd Gleixner12, Anke Hildebrandt3,13,14, Eva Koller-France15, Sophia Leimer16, Alexandru Milcu17,18, Liesje Mommer19, Pascal A Niklaus20, Yvonne Oelmann15, Christiane Roscher3,21, Christoph Scherber22,23, Michael Scherer-Lorenzen24, Stefan Scheu25,26, Bernhard Schmid27,28, Ernst-Detlef Schulze12, Vicky Temperton29, Teja Tscharntke30, Winfried Voigt8, Wolfgang Weisser4, Wolfgang Wilcke16, Christian Wirth2,3,12.
Abstract
Earth is home to over 350,000 vascular plant species that differ in their traits in innumerable ways. A key challenge is to predict how natural or anthropogenically driven changes in the identity, abundance and diversity of co-occurring plant species drive important ecosystem-level properties such as biomass production or carbon storage. Here, we analyse the extent to which 42 different ecosystem properties can be predicted by 41 plant traits in 78 experimentally manipulated grassland plots over 10 years. Despite the unprecedented number of traits analysed, the average percentage of variation in ecosystem properties jointly explained was only moderate (32.6%) within individual years, and even much lower (12.7%) across years. Most other studies linking ecosystem properties to plant traits analysed no more than six traits and, when including only six traits in our analysis, the average percentage of variation explained in across-year levels of ecosystem properties dropped to 4.8%. Furthermore, we found on average only 12.2% overlap in significant predictors among ecosystem properties, indicating that a small set of key traits able to explain multiple ecosystem properties does not exist. Our results therefore suggest that there are specific limits to the extent to which traits per se can predict the long-term functional consequences of biodiversity change, so that data on additional drivers, such as interacting abiotic factors, may be required to improve predictions of ecosystem property levels.Entities:
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Year: 2020 PMID: 33020598 DOI: 10.1038/s41559-020-01316-9
Source DB: PubMed Journal: Nat Ecol Evol ISSN: 2397-334X Impact factor: 15.460