Project description
Characterising a unified plant functional space and its relationship to ecosystem functioning
Trait-based ecology focuses on how organisms’ functional traits affect their responses to the environment, their interactions and their effects on ecosystem processes. An important goal, particularly in the context of global climate change and biodiversity decline, is the ability to predict community composition and ecosystem functioning from traits. The ERC-funded PLECTRUM project aims to make this possible. Using unprecedented data of aboveground and belowground traits, the team will quantify functional dimensionality and estimate the position of thousands of species in the unified plant functional space. Researchers will then use deep learning to link the functional structure of communities to ecosystem functioning and environmental change.
Objective
Functional traits of organisms determine their responses to the environment, disturbances, biotic interactions, but also their effects on ecosystem processes. Therefore trait-based approaches can potentially advance our understanding of complex ecological questions. However, straightforward approaches for accurate predictions of community composition and ecosystem functioning from traits are not yet available. Studies have considered different traits, which hampers synthesis, and analytical tools have been limited. Trait-based predictions need a holistic approach incorporating all aspects of the functional structure of plant communities within a unified plant functional space (UPFS) that considers the independent information provided by above- and belowground traits. PLECTRUM takes advantage of the UPFS and provides solutions for three of the most intractable problems for trait-based ecology: (i) the dimensionality of functional variation across ecosystems, (ii) predicting functional structure from environmental variables, and (iii) using this knowledge to forecast the effects of global change on functional structure, ecosystem functioning, and species’ extinction risk. I will combine the information from massive datasets of vegetation plots and plant traits with the first global standardized sampling of key above- and belowground traits. I will use this data to quantify functional dimensionality across ecosystems, and estimate the position of thousands of species in the UPFS. Then, I will use species distributions in the UPFS analogous to images and apply deep learning methods to link the functional structure of communities to ecosystem functioning and environmental change. The methodological toolbox developed in the project, combined with the synergy of aboveground and root traits, will allow us to forecast the effects of different global change scenarios on plant communities and their functioning across scales.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
51005 Tartu
Estonia