Back

Seagrass Extent Expansion in the Gulf of Mexico and Northwestern Caribbean (1987-2021)

Lizcano-Sandoval, L.; Bell, S.; Cerdeira-Estrada, S.; Martinez-Daranas, B.; Montes, E.; van Tussenbroek, B.; Muller-Karger, F.

2025-11-12 ecology
10.1101/2025.11.10.687714 bioRxiv
Show abstract

Historically, the areal extent of seagrass beds in the Gulf of Mexico, Florida peninsula, and Northwestern Caribbean (Mexico and Cuba) have been estimated to cover approximately 35,510 km2. However, the absence of regular monitoring limits our ability to detect interannual variability and up-to-date health conditions, and to respond to stressors, such as degraded water quality. Seagrass areal extent is one of the core Essential Ocean variables (EOV) of the Global Ocean Observing System (GOOS). Resource managers and researchers are interested in how this seagrass extent and the distribution of this habitat changes over time to focus possible action to mitigate impacts of various stressors, such as degraded water quality. In this study, we mapped 19,405 km2 of seagrasses for the region for the period 2019-2021, using satellite imagery from Landsat 5, Landsat 7, Landsat 8, and Sentinel-2. Interannual changes in seagrass areal extent at 17 representative sites indicated that seagrass habitats increased by 12.3% between 1987 and 2021. Sites off the West Florida coast showed the highest increasing rates in seagrass extent (up to 3.1% yr-1) and lowest rate (-0.43% yr-1) was observed in Biscayne Bay, South Florida. Seagrasses in the Mexican Caribbean showed positive extent expansion rates (1.3-1.8% yr-1), while in Texas were stable over the period of study. Continuous observations of seagrasses like these help managers to understand spatial and temporal patterns of change and encourage further studies on seagrass ecology and carbon stocks.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.