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Vegetation ecology meets ecosystem science: Permanent grasslands as a functional biogeography case study
Cyrille Violle1, Philippe Choler2, Benjamin Borgy1
1CNRS, Centre d'Ecologie Fonctionnelle et Evolutive (UMR 5175), 1919 route de Mende, 34293 Montpellier Cedex 5, France; CESAB/FRB, Domaine du Petit Arbois, Avenue Louis Philibert, 13545 Aix-en-Provence, France.
Functional biogeography integrates species traits and diversity to understand ecosystem processes at large scales. This study uses Big Data for country-wide grassland assessments, predicting forage digestibility and improving climate models.
Area of Science:
- Ecology
- Biogeography
- Environmental Science
Background:
- Biodiversity significantly impacts ecosystem functioning, with species' functional traits playing a key role.
- Bridging biodiversity and ecosystem science at biogeographic scales is crucial but remains underdeveloped.
- Functional biogeography emerges as a field to address this gap, leveraging large datasets.
Purpose of the Study:
- To establish a framework for functional biogeography research at a country-wide scale.
- To integrate diverse datasets for assessing ecosystem properties and services in C3 permanent grasslands.
- To improve regional models for climate and land use change impacts.
Main Methods:
- Collation and integration of large databases: vegetation relevés, plant traits, and environmental layers.
- Development of ecoinformatics approaches to handle Big Data challenges.
- Upscaling of ecosystem-level properties, demonstrated with leaf dry matter content.
Main Results:
- Country-wide assessment of ecosystem properties in C3 permanent grasslands.
- Prediction of forage digestibility across the country.
- Demonstration of framework feasibility for large-scale functional biogeography.
Conclusions:
- The developed framework enables hypothesis testing in functional biogeography and earth system modeling.
- Large-scale data integration is essential for understanding biodiversity-ecosystem functioning relationships.
- This initiative provides a foundation for improved regional climate and land-use change modeling.
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