Data from: Long-term and year-to-year stability and its drivers in a Mediterranean grassland
- Valerio, Mercedes 1
- Ibáñez, Ricardo 1
- Gazol, Antonio 2
- Götzenberger, Lars 3
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1
Universidad de Navarra
info
- 2 Instituto Pirenaico de Ecología (CSIC)
- 3 University of South Bohemia in České Budějovice; Institute of Botany of the Czech Academy of Sciences
Argitaratzaile: Dryad
Argitalpen urtea: 2022
Mota: Dataset
Laburpena
Understanding the mechanisms underlying community stability has become an urgent need in order to protect ecosystems from global change and resulting biodiversity loss. While community stability can be influenced by richness, synchrony in annual fluctuations of species, species stability and functional traits, the relative contributions of these drivers to stability are still unclear. In semi-natural grasslands, land-use changes such as fertilization might affect stability by decreasing richness and influencing year-to-year fluctuations. In addition, they can promote long-term directional trends, shifting community composition and influencing grassland maintenance. Thus, it is important to consider how species and community stability vary year-to-year but also in the long term. Using a 14-year vegetation time series of a species-rich semi-natural Mediterranean grassland, we studied the relative importance of richness, synchrony, species stability and functional traits on community stability. To assess land-use change effects on stability, we applied a fertilization treatment. To distinguish stability patterns produced by year-to-year fluctuations from those caused by long-term trends, we compared the results obtained using a detrending approach from those without detrending. Stability is influenced by richness, synchrony and functional traits. Fertilization decreases species and community stability by promoting long-term trends in species composition, favouring competitive species and decreasing richness. Studying stability at the community and species level, and accounting for the effect of trends is essential to understand stability and its drivers more comprehensively.