Variation in Seagrass Carbon Stocks Between Tropical Estuarine and Marine Mangrove-Fringed Creeks - Juma, Gabriel A.; Magana, Adiel M.; Michael, Githaiga N.; Kairo, James G. (Frontiers in Marine Science, 2020)
Seagrass and associated blue carbon ecosystems are important carbon sinks, and hence understanding their spatial and temporal variability is vital in appreciating their potential roles in climate change mitigation and adaptation. The Indo-Pacific region has the highest seagrass biodiversity, yet little focus has been made to compare seagrass habitat extent and carbon dynamics with their temperate counterparts. The present study assessed habitat characteristics and seagrass species distribution, diversity, and carbon storage in Eastern (marine) and Western (estuarine) mangrove-fringed creeks of Gazi Bay, Kenya. Data on species composition, canopy cover, biomass, and sediment organic carbon were collected in 80 plots of 0.25 × 0.25 m laid along transects established perpendicular to the waterline. Five species formation, viz., Thalassia hemprichii, Cymodocea rotundata, Cymodocea serrulata, Enhalus acoroides, and Thalassidendron ciliatum, were encountered as either single or mixed stands. There was a significant difference in total seagrass biomass between creeks (p < 0.01), with the Eastern creek recording a mean of 10.2 ± 0.6 Mg C ha–1 while the Western creek recording 4.3 ± 0.3 Mg C ha–1. In addition, sediment carbon to 1-m depth varied significantly (p < 0.01) between species in the two creeks and ranged from 98 to 302 Mg C ha–1, with the Eastern and Western creeks recording means of 258 ± 90 and 107 ± 21 Mg C ha–1, respectively. The total carbon stock from 50 ha of seagrasses in the Eastern creek was 13,420 Mg C, whereas in the 70 ha of the Western creek it was 7,769 Mg C. The study shows that seagrass community attributes such as species composition and productivity can vary dramatically over a small spatial extent due to differences in biophysical conditions and caution estimations of site-specific carbon stocks using generalized global values.