Synchronized dynamics reduces ecosystem stability, as local variations in biomass or richness are directly propagated to variations in metacommunity dynamics. Synchronization of biodiversity dynamics can occur due to dispersal among communities and similar responses of different communities to correlated environmental variations, the Moran effect. This congruent response of different communities to environmental dynamics depends on their similar functional composition, which is determined by the similarity in local conditions and the spatial distance between them. In a metacommunity of 51 temporary ponds that were surveyed for 14 years, we evaluated the existence of synchronized dynamics in plant richness and biomass among communities, and their association with temporal stability. A wide range of dynamics was observed, from asynchronous to synchronous rhythms. Path analysis based on Mantel tests supported the decoupling of richness dynamics by the geographic, environmental, and functional distances between pairs of communities. However, only the functional distance between communities weakly affected biomass synchrony. Synchrony in both richness and biomass between communities reduced the stability of the biomass dynamic. However, while synchrony in richness reduced its stability, synchrony in biomass enhanced the stability in richness dynamic. The role of rare species in richness dynamics and of dominant species in biomass dynamics may explain the observed discrepancies. Consequently, the size of metacommunities (the spatial extent and number of local communities), spatial heterogeneity, and functional diversity promote ecosystem stabilization by the mechanisms evidenced here. Climate change, environmental homogenization, and landscape fragmentation may drive the synchronization and destabilization of biodiversity dynamics.

Note of caution: The last version of this database is available in https://doi.org/10.5061/dryad.12jm63z32, Arim and Pinelli, 2023. This database is continuously updated, and the taxonomy of some rare species was corrected in relation with the database used in the present manuscript. For reproductivity of Sosa-Panzera et al., 2024 results consider this database. In the case of performing novel analyses, we recommend using data available in Arim and Pinelli, 2023.

Methods

1. Every spring since 2005, ponds have been sampled equidistantly using 20x20 cm quadrants on the major axis. On average, five quadrants were sampled in most ponds. However, to account for the range of pond areas, which exhibits differences in several orders of magnitude (from 6.6 m2 to 24,673 m²) when the quadrants were closer than two meters, the number of sampled units was reduced, and if the quadrants were more than 10 meters apart, then the number of sampled units was increased.
2. A species-by-trait matrix was constructed from a review of the literature and direct observations. The list of traits considered is presented, introducing their description and functional roles. Different traits related to dispersal strategy, competitive ability, drought resistance strategy, life history, and tolerance to stress were considered.
3. Environmental data. The environmental variables recorded for each pond included area, shape (major versus minor diameter ratio), average depth, and the coefficient of variation for the depth of the pond.The area of the pond was estimated as the area of an oval using the length of the major and minor axes of the ponds. Heterogeneity was estimated as the number of 'islands', emergent mounds above water level, per meter of the main and minor axes of the ponds. The average environmental values during the sampling time were used.