PhD-ThesisThis study aimed to investigate and document human activities disturbances on the ecosystems structure and impacts on ecosystem services values (ESV) along the coastal areas of Tanzania. The study provides initial understanding about spatial characteristics ofsoils and vegetation on human disturbed sites in comparison with the intact sites, and establishment of temporal coastal ESV to provide information for sustainable management of tropical ecosystems in Tanzania. Specifically, this study aimed 1. To establish spatial variation on soil physical properties (electrical conductivity, soil texture and bulk density), and soil chemical properties (nitrogen (N), carbon (C), phosphorus (P), calcium (Ca), magnesium (Mg), potassium (K) and sodium (Na) across disturbed and intact sites of the coastal ecosystems; 2. To investigate the impacts of human activities disturbances on the vegetation and regeneration potential of naturally occurring tropical coastal forests; 3. To establish the correlations between vegetation and soil properties of the human disturbed and intact coastal forest ecosystems; 4. To explore how socioeconomic activities contribute to temporal change of coastal ecosystems and their impacts of changes on the coastal ESV; 5. To identify and gauge the current restoration interventions and factors influencing restoration of the disturbed coastal ecosystems in Tanzania. This study was conducted by collecting data from the Coastal Zone sites in Tanzania. The zone stretches within 850km from the boarder of Tanzania and Kenya in the north, and Tanzania and Mozambique in the south. The zone represents the coastal ecosystems, which are affected by human disturbances especially between 2000 and 2016. The classification of land cover and land uses was firstly computed from the areas that cover S37 00, S37 50 and S37 50, S37 10 geographical coordinates. Samples were collected from Uzigua Forest Reserve (UFR). Certainly, this forest is within 100 km from the coastline of Indian Ocean, thus the reserve is considered among the true representative of the Tropical Coastal Ecosystems in Tanzania. The study collected bio-physical and socioeconomic data to understand the interplays between human activities, structure and services of the coastal sites. Soil samples were collected from ADS, DGS and CFS in forty-seven (25m x 25m) sampling plots. Each site provided 47 samplings plots, which resulted into 141 plots in total. These plots were laid randomly because agriculture and livestock grazing are not uniformly distributed within the ecosystems. In each of the plots, 10 soil samples were collected from 1- 30cm depth because agriculture and livestock grazing activities affect the surface and near surface layers ofthe soils. Representative soil samples were put into tightened double plastic bags, labeled and stored at 4°C to reduce further microbial degradation during transportation and storage in the laboratory. Fresh air-dried and oven-dried weights were determined before subjecting soil samples into further laboratory analysis. Soil physical and chemical properties were analyzed as follows: Electric conductivity was determined by electrical conductivity meter, soil texture by pipette method and bulk density calculated by dividing dry weight of soils by volume. Determination of total nitrogen followed the Kjeldahl acid digestion procedures while total carbon were analysed by the Walkley-Black procedures. Potassium Dichromate (KoCriCh) and concentrated Sulphuric Acid (H2SO4) were used to produce the reaction and products. Available phosphorus was determined by the Bray-II method. The Ammonium Acetate was used to extract exchangeable Ca, K, Mg and Na. Then K content was determined by using flame photometer while ethylenediaminetetraacetic acid (EDTA) titration was done to measure Ca, Mg and Na. A combined glass calomel electrode used to determine the pH of aqueous suspensions. Tree inventories were prepared on each sites to determine forest structure in the human disturbed areas compared to intact sites. From live trees (i) number oflive trees per unit area (N/ha), (ii) basal area of live trees (m2 /ha), and (iii) volume of live tree per unit area (m3 /ha). Basal area and volume were compared between and across CFS, ADS and DGS. The mean values and t-test were used to compare and judge the variations, which exist across the sites. The results were considered significant atp < .050. To analyze the differences in vegetation the following parameters were computed (i) Shannon diversity index, (ii) Simpson diversity index, iii) species evenness and (iv) the importance value index (IVI). Each of the computed diversity indices were subjected into the Microsoft Excel 10 and Statistical Package for Social Sciences Software (SPSS) for production of means, standard deviation and t-tests.The Detrended Canonical Correspondence Analysis (DCCA)) was used to obtain multiple linear regressions and optimal linear combination between tree parameters and soil variables for comparisons across CFS, ADS and DGS. To analyze the impacts of human activities on changes of ecosystems, land cover and land use (LCLU) and estimation of ESV, area changes were detected based on differences between imagery identification ofthe changed areas. The LCLU information were used to compare land changes in relationships with the socioeconomic activities, and the dynamics of ESV in the coastal zone. The LCLU data for 2000 and 2010 were from the Globe Land 30 mapping products at 30-meter spatial resolution developed by National Geomatics Center of China, while 2016 images were produced from Landsat 8. Classification of images was done from Landsat TM/ETM+ for 2000, 2010 and 2016 years complemented with MODIS and Normalized Difference Vegetation Index time series, and Chinese HJ imagery. The LCLU categories and ecosystem service coefficients used to compute ESV on each LCLU categories. There were significant variations in spatial soil properties as well as forest structural parameters and temporal ecosystems service values. 1. The mean variation in electrical conductivity (pS/cm) between CFS and ADS was 26.197 ± 8.42; CFS and DGS was 5.55 ± 7.45; ADS and DGS was 20.65 ± 3.97. The soil particles ranged between 86.06% to 86.79% for sandy, 11.40% to 14.98% for clay and 1.81% to 2.57% for silt across land uses. The bulk density in CFS and ADS was 0.05 ± 0.23, in CFS and DGS was 0.13 ± 0.02 and, in ADS and DGS was 0.08 ± 0.02. The mean values (percentage) for nitrogen = 16.07 ± 0.34, 1.75 ± 0.25, 6.5 ± 0.20; carbon =14.48 ± 0.23, 11.81 ± 0.13, 12.24 ± 0.30; phosphorus =14.12 ± 6.57, 17.74 ± 3.96, and 13.31± 2.86 for CFS, ADS and DGS respectively. There were slightly lower amount oftotal carbon on ADS than DGS. Carbon-nitrogen ratio was higher in CFS than in the disturbed sites. The mean values for soluble bases were 3.75, 3.11 and 0.63 for Ca2+; 0.80, 5.87 and 6.67 for Mg2+; 0.03, 0.55, and 0.52 for K+; 0.01, 0.31 and 0.31 for Na+; 2.61, 13.74 and 16.36 (cmol(+)/kg) for cation exchange capacity and 10.29, 5.86 and 4.42 (% volume ) for base saturation in three areas: CFS, ADS and DGS. 2. There were significant variation across the vegetation stand parameters and as well as for diversity indices. Plots in the CFS had higher mean values of adult trees, basal area and volume than the ADS and DGS. Plots in ADS had the highest Shannon-Wiener index of seedlings and saplings, followed by CFS and DGS. The CFS and DGS had higher Simpson’s index for seedlings and saplings than DGS. Plots in ADS and DGS had higher adult tree Simpson than closed sites. The equitability of seedlings and saplings was highest in CFS, followed by ADS and DGS. Plots in ADS had higher equitability of adult trees than DGS and CFS. Moreover, plots in ADS and DGS had higher importance value index for seedlings than CFS. 3. The multivariate canonical correlation showed significant variation across CFS, ADS and DGS. The canonical correlation was F = 2.400, p < .012 for tree stand parameters (TSP) and soil physical parameters (SPP). In ADS, the F- test was 0.529, p = .938. In DGS, the significance of all canonical axes was F = 1.207, p = .242. Correlation of soluble bases and TSP was F = 2.448, p = .018 in CFS, F = 0.687, p = .790 in ADS and F = 0.743, p = .808 in DGS. The values of non-soluble bases and TSP were F = 0.816, p = .572 in CFS, F = 0.687, p = .790 and F = .070, p = .020 in DGS. The SPP and Shannon index was that F = 1.103, p < .388 in CFS, F = 0.520, p = .714 in ADS and F = 0.932, p = .444 in DGS. The SPP and IVI was F = 0.042, p = .996 in CFS, F = 0.819, p = 620 in ADS and F= 0.633, p = .724 in DGS. Soluble bases and equitability was F = 0.119, p = .968 in CFS, F = 0.001, p = .001 in ADS and F = 0.011, p = .001 in DGS. There were almost no established correlation between CNP and equitability across CFS, ADS and DGS. 4. Between 2000 and 2016, ecosystems along coast changed significantly. The decline (ha) was in forest by -36 441 (-10%), grazing land by -6 347 (- 2%) and wetland by -112 (-2%). The expansion on LCLU areas was in shrub land by 11 751 (37%), farm land by 30 506 (43%), waterbody by 279 (14%) and artificial surface by 365 (14%). The ESV declined in forest cover by 12%, grazing land by 2% and wetlands by 2% while farmland increased by 30%, shrub land by 27% and water body by 12%. The ESV and the total population ratios declined from $80.4, 63.8 and $46.0 million in 2000, 2010 and 2016 respectively. Perfect positive correlation was on LCLU change and ESV, population and households in crop farming, livestock keeping and bioenergy use. 5. The study found that afforestation, reforestation and retaining natural growing trees are the major restoration interventions used along the coastal zone of Tanzania. In some cases, eviction of forest invaders are executed to allow disturbed forests to regenerate naturally. Socioeconomic and climatic factors significantly have negatively affected artificially planted and natural regenerating trees (p<.050). Indeed, there were limited understanding on the ecological values of restoration interventions. Lack of improved planting materials and local community overdependence on forests resources significantly impede restoration interventions. The implications of the findings is that human activities disturbances affect the structure of ecosystems and services at large. Population and socioeconomic activities are the main drivers and have increased demand, as a result the coastal ecosystems is losing ESV largely. If not abetted, there is a danger of further impairments on these ecosystems. Therefore, this study advise 1. To regulate population and socioeconomic activities to avoid further negative impacts of coastal LCLU change. 2. To improve the current restoration interventions by ensuring that the socioeconomic and ecological interplays along the coastal zones are fully addressed. 3. To investigate and establish minimum allowed crop-agriculture and livestock grazing integration equilibrium point in the coastal sites is needed to make socioeconomic activities a solution with no harm on coastal ecosystems.