Masters Theses:Department of Biological Sciences

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Browsing Masters Theses:Department of Biological Sciences by Subject "Mangrove Forests"

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    Cover Change and Vegetation Carbon Stocks of Mangrove Forests in Lamu County, Kenya
    (UoEm, 2023-04) Mbatha, Anthony Mutua
    Mangroves around the world are being threatened by a combination of natural and human factors. Losses of mangroves leads to reduced forest cover and enhanced carbon emission. This study assessed cover change, forest structure, natural regeneration, and carbon stocks of mangroves in Lamu County, Kenya. Landsat images were used to assess cover change from 1990 to 2019, and structural data were obtained in the field using the plot method. Using stratified random design, mangroves were sampled in 152 square plots of 400 m2 along belt transects established perpendicular to the waterline. Within each plot, all trees with stem diameters ≥ 2.5 cm were identified, counted and position marked, while those < 2.5 cm were counted and classified as juveniles. The following parameters were recorded: tree height (m), stem diameter (cm), and canopy cover (%); from which stem density (stems ha-1), basal area (m2 ha-1), volume (m3 ha-1), and biomass (t ha-1) were enumerated. Six mangrove species were encountered during this study. Based on importance value index, the dominant mangrove species in Lamu were Rhizophora mucronata (Lam.) and Ceriops tagal (perr.) C.B. Rob., that accounted for more than 70% of the mangrove formations. Mean standing density of the mangroves was estimated at 2,339±241 stems ha-1 (range:1,607-3,092 stems ha-1), with a basal area of 24.26±3.18 m2 ha-1, and volume of 157.97±15.22 m3 ha-1. At least 42% of the forest was stocked with low-quality poles, indicating prolonged human pressure. However, natural regeneration rate of 7,342±450 juveniles ha-1 observed in the forest was considered adequate to support forest recovery following disturbance. The mean biomass was estimated at 354.98±49.81 Mg ha-1. This translates to vegetation carbon storage of 166.56±23.41 Mg C ha-1. Mangroves in Lamu were estimated at 35,678 ha, representing 62% of the country’s total. Approximately, 1,739 ha of mangroves were lost between 1990 and 2019, mainly due to anthropogenic activities, representing a decline of 60 ha yr-1. Total emission from loss and degradation of mangrove vegetation in Lamu was estimated at 41.64 Mg C ha-1; which translates to 9,169.13 Mg CO2e yr-1. Assuming an offset price of US$10/Mg CO2e, the estimated cost of avoided emissions in Lamu County is US$91,691.3 yr-1 plus other co-benefits such as fishery support and shoreline protection. Mainstreaming mangroves and associated blue carbon ecosystems into national development and climate change agendas could accelerate Kenya’s achievements to the Paris Agreement and other processes.
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    Structural Variability Of Mangrove Forests along the Coast of Kenya
    (UoEm, 2023-08) Njiru, Derrick Muthomi
    Mangrove forests occur across a diversity of coastal landforms with different geomorphological, climatic and oceanographic influences. These factors influence mangrove structural development and productivity and as a result, the structural development of mangroves varies with the coastal geomorphology. Earlier inventory studies in Kenya suggest that mangroves growing in north of the Tana River have different structural attributes from those growing south of the river. The current study characterised the structure and floristic composition of mangroves in Kenya by describing species composition, basal area (m2 ha-1), stem density (trees ha-1), importance value index complexity index and above ground biomass (Mg ha-1) across 14 sites spread across the coastline of Kenya. Variability in mangrove floristic composition was tested using analysis of similarities (ANOSIM) and the differences illustrated using non-metric multidimensional scaling (nMDS). Mangrove structural variability was tested using analysis of variance (ANOVA) and comparisons made by performing a post-hoc Tukey pairwise test. A hierarchical cluster analysis was then performed to determine the degree of similarity in mangrove species across the sites based on complexity index, biomass, tree diameter and tree height. To investigate the relationship between mangrove structure and possible drivers of variability, a regression fit model was used. The model described associations between mangrove standing biomass, environmental settings, precipitation, population density, and riverine influence across the sampled sites. Rhizophora mucronata was the most important species in most of the sites while Avicennia marina was the most important species in the estuarine area of Ungwana Bay. High values of structural complexity were observed in the estuarine and deltaic settings of Ngomeni and Kipini while relatively low levels of structural complexity were observed for the periurban mangroves of Mombasa and Mtwapa. Mangrove forest species composition differed significantly across the sampled sites (ANOSIM R: 0.24, p = 0.001). The mangroves of Kipini were significantly different from the rest of the sites. The study revealed significant differences in structural attributes of mangroves growing along the coast of Kenya, specifically, tree diameter [F (13, 34050) =163.01, p=0.000], tree height [F (13, 34050) =1827.28, p=0.000], basal area [F (13, 358) =5.45, p=0.000)], stand density [F (13, 358) =8.68, p=0.000], and standing biomass [F (13, 358) =15.36, p=0.000] across the sampled sites. Environmental settings and population density best explained the variability in mangrove standing biomass. The study suggests that the patterns of mangrove structural variability in Kenya closely follows the patterns of geomorphic variability along the coast. The study concluded that mangroves in Kenya are highly influenced by geomorphological and climatic variability along the coast as well as human influences. These findings are useful for mangrove managers and policy makers and have the potential to guide strategies and actions aimed towards sustainable management of mangrove forests in Kenya.