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Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning.

Fons van der PlasThomas Schröder-GeorgiAlexandra WeigeltKathryn E BarrySebastian T MeyerAdriana AlzateRomain L BarnardNina BuchmannHans de KroonAnne EbelingHéctor J AguadoChristof EngelsMarkus FischerGerd GleixnerAnke HildebrandtEva Koller-FranceSophia LeimerAlexandru MilcuLiesje MommerPascal Alex NiklausYvonne OelmannChristiane RoscherChristoph ScherberMichael Scherer-LorenzenStefan ScheuBernhard SchmidErnst-Detlef SchulzeVicky TempertonTeja TscharntkeWinfried VoigtWolfgang W WeisserWolfgang WilckeChristian Wirth
Published in: Nature ecology & evolution (2020)
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.
Keyphrases
  • climate change
  • human health
  • genome wide
  • healthcare
  • gene expression
  • electronic health record
  • transcription factor
  • deep learning
  • microbial community