High frequency spatio-temporal dynamics of seagrass meadows in a mediterranean lagoon

Seagrass meadows are a famously productive habitat found globally in intertidal and shallow subtidal shelves, hosting a rich biodiversity and efficiently sequestering carbon. They also exert an influence on coastal morphodynamics by modifying tidal flat roughness and erodibility, affecting sediment dynamics in coastal environments in a way that is not yet fully documented.

Due to their sensitivity to environmental change and anthropogenic pressure, seagrass is prone to widespread die-off, which can be worsened by punctual degradation events such as dredging. Conversely, high primary productivity allows degraded meadows to recover rapidly, as well as expand through clonal and sexual reproduction. This potential for rapid change, however, is not currently matched by the frequency of observations of seagrass meadows, making it difficult to assess the spatial and temporal dynamics of seagrass meadows and their impact on intertidal zones.

Using a novel method combining machine learning and time-series analysis, we extract seasonal maps of seagrass cover in the Southern Venice Lagoon from over 150 Landsat images over the 1999-2021 period and over 100 Sentinel images over the 2017-2021 period. By analysing changes in seagrass distribution over time, we identify seasonal extrema in seagrass surface area and observe their decadal evolution. Furthermore, we record the frequency and magnitude of sudden seagrass die-off events as well as recovery times. From these data we identify regions of preserved root mat, and by coupling seagrass dynamics with additional environmental data such as water temperature, sediment concentration and wave height, propose a set of possible degradation drivers and conditions for seagrass meadow recovery.

With this contribution, we show how high-frequency mapping of seagrass distribution can reveal spatio-temporal dynamics of a highly productive coastal ecosystem, as well as offer keys to their response to environmental change.