Eastern Mau Forest Research Articles

Settlement Schemes and their Implication on Eastern Mau Water Catchment, Kenya

Settlement schemes are aimed at settling landless people and those displaced by disasters to support socio-economic and environmental development of a country. Eastern Mau Forest Reserve is an important water catchment that has settlement schemes established, which has led to encroachments and degradation of the catchment. This paper, therefore, assesses the implications of human settlements on Eastern Mau water catchment by examining the trends in land use/cover change and river flows for four decades. Primary data was collected from key informant interviews based on purposive sampling whereas secondary data was derived from Landsat satellite images over a 10-year period and analysed using Maximum Likelihood Function from ArcGIS. Data on river flows from River Njoro was obtained from Water Resources Authority Office in Nakuru. Rainfall and temperature data were obtained from Kenya Meteorological Station in Nakuru. Time series analysis is used to understand the trend in river flows over time while Pearson correlation is used to determine relationship between farmlands and river flows. The results indicate a sharp decline in forest cover by 42.7% and an increase in farmlands by 41%. Dense vegetation and farmlands have an inverse relationship as an increase in farmlands lead to a decrease in forest cover. People have settled beyond the established settlement schemes boundaries leading to encroachment and drying up of some rivers. There is also an increase in rainfall and river flows over the years, with monthly river flows increasing in peak flows and declining during low seasons. There is a positive correlation between farmlands and river flows between 1989 and 2020. There is need for regeneration of encroached areas and defining boundary of Eastern Mau to allow initiatives and interventions that help with sustainable management of the catchment area.

Late Pleistocene and Holocene Afromontane vegetation and headwater wetland dynamics within the Eastern Mau Forest, Kenya

ABSTRACTThe Mau Forest Complex is Kenya's largest fragment of Afromontane forest, providing critical ecosystem services, and has been subject to intense land use changes since colonial times. It forms the upper catchment of rivers that drain into major drainage networks, thus supporting the livelihoods of millions of Kenyans and providing important wildlife areas. We present the results of a sedimentological and palynological analysis of a Late Pleistocene–Holocene sediment record of Afromontane forest change from Nyabuiyabui wetland in the Eastern Mau Forest, a highland region that has received limited geological characterization and palaeoecological study. Sedimentology, pollen, charcoal, X‐ray fluorescence and radiocarbon data record environmental and ecosystem change over the last ~16 000 cal a bp. The pollen record suggests Afromontane forests characterized the end of the Late Pleistocene to the Holocene with dominant taxa changing from Apodytes, Celtis, Dracaena, Hagenia and Podocarpus to Cordia, Croton, Ficus, Juniperus and Olea. The Late Holocene is characterized by a more open Afromontane forest with increased grass and herbaceous cover. Continuous Poaceae, Cyperaceae and Juncaceae vegetation currently cover the wetland and the water level has been decreasing over the recent past. Intensive agroforestry since the 1920s has reduced Afromontane forest cover as introduced taxa have increased (Pinus, Cupressus and Eucalyptus).

Effect of Forest Management Types on Soil Carbon Stocks in Montane Forests: A Case Study of Eastern Mau Forest in Kenya

Mau Forest, a major forest reserve in Kenya, has experienced anthropogenic disturbances through encroachment and forest fires. This study aimed at comparing the soil carbon stocks in different forest management types as well as how seasonal climatic changes influence its dynamics. The study was undertaken in the Eastern Mau block (Sururu) which forms part of the greater Mau Forest Complex. The forest management interventions have been in place for over 15 years with disturbed (fire) natural forest experiencing fires in 2005, 2007, and 2014 while cypress plantations were established in 1994. A nested experimental design was used in data collection, where thirty-two sample plots were nested into four blocks each measuring 100 m2 delineated by forest management types (disturbed by fire, natural forest, undisturbed natural forest, plantation, and glades). In each plot, data on soil carbon stocks, soil bulk density, soil moisture, and temperature were collected for both dry and wet seasons. Data collection was carried out between November 2015 and December 2016. The results indicated that there were no significant differences in the carbon stocks among the forest management types (F4,16 = 0.61, P = 0.613 ). However, seasonal weather changes significantly affected the amount of carbon stocks among the forest management types (F4,16 = 0.61, P > 0.05 ). The undisturbed natural forest had the highest mean soil carbon stocks, while the plantation forest had the lowest as follows: undisturbed natural forest (135.17 ± 35.99.0 Mg·C−ha), disturbed natural forest by fire (134.52 ± 38.11 Mg·C−ha), glades (122.4 ± 64.9 Mg·C−ha), and plantation forest (116.51 ± 39.77 Mg·C−ha). Furthermore, the undisturbed natural forest management had the highest bulk density (0.66 g/cm3), while the disturbed (fire) natural forest had the lowest (0.59 g/cm3). These values were low compared to most normal mineral soils which have a bulk density of between 1.0 g/cm3 and 1.5 g/cm3. There was a significant ( P > 0.01 ) relationship between seasonal weather (temperature) changes and soil carbon stocks under different forest management types with the relationship being stronger in soils under glades (r2 = 0.62) and weak in the undisturbed natural forest (r2 = 0.26). In conclusion, forest disturbances have an impact on soil carbon stocks, and for effective management of forest towards climate stabilization, then disturbance should be minimized if not avoided.

The nexus between land cover changes, politics and conflict in Eastern Mau forest complex, Kenya

There is growing literature that links land cover changes to resource governance regimes. Whereas natural resource degradation has been successfully linked to weak governance, the reciprocal relationship between degradation and conflict has not been clearly established especially in sub-Saharan region where natural resource conflicts are common. This paper utilizes remote sensing and spatial techniques to examine land cover changes and conflict in light of the changing Kenyan policy and political contexts. The paper draws evidence from data collected through time series of satellite imagery for Eastern Mau forest complex between 1976 and 2014 and qualitative data including key informant interviews and observation through geo-coded transect walks. The changes in land cover and conflict intractability were analyzed in light of post-independence land use policy history of Kenya and related to conflict occurrences among Eastern Mau forest adjacent communities. The study results show that between 1976 and 2014 over 40% of forest land was converted to other uses. The study also documents both spatio-temporal drivers of conflict (e.g. forest degradation) and drivers related to political practice and competition among ethnic groupings. This paper concludes that to stem land cover changes there is need to pay greater attention to the underlying factors to land cover changes such as conflict, policy and politics.

Spatial and Political Factors in Forest Resource Conflicts: The Eastern Mau Forest Case 1992–2014

This paper examines conflicts and forest resource politics in Eastern Mau, which is part of the Mau forest complex in Kenya. Conflict hotspots are mapped based on geo-coded transect walks, observation points derived from a combination of qualitative interviews, and informal conversations with local residents. Conflicts are categorized in terms of their temporal, spatial and substantial dimensions; and connections between conflict hotspots and forest resource politics are discussed. The study documents drivers of conflict such as forest and land resources access, political patronage and competition among ethnic groupings. Political patronage related to the voter sourcing seems to be a key underlying driver of forest and land use conflict. There is need to improve forest resources governance in order to water down patronage and reduce conflicts.

Inventory, reward value and diversity of Apis mellifera nectariferous and polleniferous forage in Eastern Mau forest, Kenya

Inventory, reward value and diversity of Apis mellifera nectariferous and polleniferous forage in Eastern Mau forest, Kenya

Melissopalynological determination of the Pollen density, Botanical origin and Ogiek Honey types in Eastern Mau Forest, Kenya

The current international market demands for the set up of honey quality control protocols according to the botanical origin based on pollen content. The objective of the present study was to determine the pollen density, honey types, and botanical origin of honey from the Eastern Mau Ogiek beekeepers as a reference to commercial strategies for sellers and buyers. 27 honey samples, three from each of the strata (Mariashoni, Kapkembu, and Nessuit) were collected at the end of April, 2016; August 2016; December, 2016 from the hives of Beekeeping Ogieks in Eastern Mau forest region. 10g of the honey samples were processed through approved melissopalynological procedures. Absolute pollen counts ranged from 47,898-160,200/10g. 59.3% and 40.7% of honey samples fell into classes II and III respectively with no representations of Classes I, IV and V. 29.63% of the honey samples were unifloral, while the rest constituted heterofloral honey. Unifloral honey samples were observed from the three mesoregions of Eastern Mau forest. 50% of the unifloral honey were collected in April (2016). Botanical origin from predominant pollen types were Acacia type, Eucalyptus type, Croton spp. type, Albizia coriaria type, Cordia abyssinica type, and Vernonia auriculifera type. The extent of predominance ranged from (47.1%-66.40%), there was bifloral honey observed in MA-S1-DE sample (Vernonia auriculifera type and Croton spp. type, 46.0% and 47.50% respectively). Secondary pollen ranged from (16.4%-43.4%). All honey samples were floral honey.

Impacts of forest management type and season on soil carbon fluxes in Eastern Mau Forest, Kenya

AbstractThe value of ecosystems functions performed by forests in the climate change era has prompted increasing attention towards assessment of carbon stocks and fluxes in tropical forests. The aim of this study was to understand how forest management approaches and environmental controls impacted on soil CO2 efflux in a tropical Eastern Mau forest which is one of the blocks of the greater Mau complex in Kenya. Nested experimental design approach was employed where 32 plots were nested into four blocks (disturbed natural, undisturbed natural, plantation and glades). In 10 m2 plots, data were collected on soil CO2 efflux, soil temperature and soil moisture using soda lime methods, direct measurement and proxy techniques, respectively. There was significant forest management type effect (F3,127 = 3.01, p = 0.033) and seasonality effect (t test = 3.31, df = 1, p < 0.05) on mean soil CO2 efflux. The recorded mean soil CO2 efflux levels were as follows: plantation forest (9.219 ± 3.067 g C M−2 day−1), undisturbed natural forest (8.665 ± 4.818 g C M−2 day−1), glades (8.592 ± 3.253 g C M−2 day−1) and disturbed natural forest (7.198 ± 3.457 g C M−2 day−1). The study concludes that managing a forest in plantation form is primarily responsible for forest soil CO2 efflux levels due to aspects such as increased microbial activity and root respiration. However, further studies are required to understand the role and impact of soil CO2 efflux on the greater forest carbon budget.

Using Land Cover Change to Predict Forest Degradation Pressure Points, Eastern Mau Forest, Kenya

Land cover change in any ecosystem vary in space and time. The study analyzed spatial-temporal land cover change to predict forest degradation pressure points in Eastern Mau Forest Reserve. The study objectives were to determine types and amount of spatial-temporal land cover change; land cover change drivers and; forest resources use sustainability. The study used mixed sample survey design involving purposive sampling of spatial data and cluster sampling of forest resource use data. Primary data included ground control points, field validation data and forest resource use data. Secondary data included Landsat imagery, forest and administration boundaries and settlements data. Analysis was done for 1986-2014 period using Remote Sensing and Geographical Information System. The analysis techniques used included object based image segmentation and classification, accuracy assessment and land cover change detection. Land cover types in Eastern Mau Forest consisted of indigenous forest, shrubland, grassland, plantation forest, cultivated fields, bare ground and built-up area. The analysis results depicted that cultivated fields coverage increased from 1% to 47%. Indigenous and plantation forests decreased from 43% to 36% and 34% to 7% respectively. Grassland and shrubland decreased from 16% to 8% and 6% to 2% respectively. Bare ground and built up area had a change of less than 1% each. Causes of pressure that lead to forest degradation included crop cultivation, settlement construction, livestock grazing, charcoal burning, firewood collection, logging, bee keeping and medicinal herbs extraction. Land cover change was more on the eastern side than on the western side. Indigenous and plantation forests were likely to disappear if cropland and built up area expansions were to remain unchecked. The study recommendations were: resettlement activities be eliminated in the Eastern Mau Forest; excision of forest land for crop cultivation should be discouraged; and scientific research should be carried out on sustainable plantation forest activities.

Using Land Cover Change to Predict Forest Degradation Pressure Points, Eastern Mau Forest, Kenya

Land cover change in any ecosystem vary in space and time. The study analyzed spatial-temporal land cover change to predict forest degradation pressure points in Eastern Mau Forest Reserve. The study objectives were to determine types and amount of spatial-temporal land cover change; land cover change drivers and; forest resources use sustainability. The study used mixed sample survey design involving purposive sampling of spatial data and cluster sampling of forest resource use data. Primary data included ground control points, field validation data and forest resource use data. Secondary data included Landsat imagery, forest and administration boundaries and settlements data. Analysis was done for 1986-2014 period using Remote Sensing and Geographical Information System. The analysis techniques used included object based image segmentation and classification, accuracy assessment and land cover change detection. Land cover types in Eastern Mau Forest consisted of indigenous forest, shrubland, grassland, plantation forest, cultivated fields, bare ground and built-up area. The analysis results depicted that cultivated fields coverage increased from 1% to 47%. Indigenous and plantation forests decreased from 43% to 36% and 34% to 7% respectively. Grassland and shrubland decreased from 16% to 8% and 6% to 2% respectively. Bare ground and built up area had a change of less than 1% each. Causes of pressure that lead to forest degradation included crop cultivation, settlement construction, livestock grazing, charcoal burning, firewood collection, logging, bee keeping and medicinal herbs extraction. Land cover change was more on the eastern side than on the western side. Indigenous and plantation forests were likely to disappear if cropland and built up area expansions were to remain unchecked. The study recommendations were: resettlement activities be eliminated in the Eastern Mau Forest; excision of forest land for crop cultivation should be discouraged; and scientific research should be carried out on sustainable plantation forest activities.

A Novel Evolutionary Genetic Optimization-Based Adaptive Neuro-Fuzzy Inference System and Geographical Information Systems Predict and Map Soil Organic Carbon Stocks Across an Afromontane Landscape

AbstractSoil organic carbon (SOC) pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide (CO2) and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth. As such, the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system (ANFIS-EG) in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve, Kenya. Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model, which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest Reserve. This was achieved with a reasonable level of uncertainty (i.e., root mean square error of 15.07 Mg C ha−1), hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies. There is potential for improving the model performance, as indicated by the current ratio of performance to deviation (1.6). The mapping also revealed marginally higher SOC stocks in the forested ecosystems (i.e., an average of 109.78 Mg C ha−1) than in the agro-ecosystems (i.e., an average of 95.9 Mg C ha−1).

What rights and benefits? The implementation of participatory forest management in Kenya: The case of Eastern Mau Forest Reserve

ABSTRACTThe study espoused the access analytical framework to investigate how introduction of Participatory Forest Management (PFM) in Kenya has changed the various actors’ ability to benefit from the forest resources of Eastern Mau Forest Reserve. Data collected through key informant interviews, and a household survey showed that implementation of PFM has triggered new income opportunities for forest adjacent communities in seedling production and beekeeping. However, PFM bestowed no real decision-making powers to the established Community Forest Associations (CFAs) over important forest resources such as timber and firewood. Members of the local communities and other actors have continued to access these resources through various structural and relational means, in the same way as before the introduction of PFM. Further, it is documented that PFM has introduced additional burdens on the local communities, especially the poorest households, as a result of increased enforcement of rules. Based on these findings, it is suggested that the PFM policy in Kenya, in its current form, is unlikely to realize its dual objectives of forest conservation and livelihood enhancement. To attain them would require a further devolution of rights to the CFAs.

Subfossil statoblasts of Lophopodella capensis (Sollas, 1908) (Bryozoa, Phylactolaemata, Lophopodidae) in the Upper Pleistocene and Holocene sediments of a montane wetland, Eastern Mau Forest, Kenya

Lophopodella capensis (Sollas, 1908) is only known from a limited number of palustrine and lacustrine sites in southern Africa and single sites in both Kenya and Israel. Statoblasts of L. capensis were found preserved in the Upper Pleistocene and Holocene aged sediments of Enapuiyapui wetland, Eastern Mau Forest, western Kenya. The wetland is a headwater microcatchment of tributaries that feed into the Mara River and the Lake Victoria Basin. Bryozoan taxa were not surveyed in a 2007 macroinvertebrate biodiversity assessment. The presence of L. capensis at this site marks the second observation of this taxon in Kenya, 65 km from Lake Naivasha, where observed prior, and in a location some 1000 meters higher. The results suggest Bryozoa should be included in aquatic biodiversity surveys that target these wetlands and that bryozoan remains should be incorporated into palaeoecological studies as useful palaeoenvironmental indicators.

Spatially distributed modelling and mapping of soil organic carbon and total nitrogen stocks in the Eastern Mau Forest Reserve, Kenya

Detailed knowledge about the estimates and spatial patterns of soil organic carbon (SOC) and total nitrogen (TN) stocks is fundamental for sustainable land management and climate change mitigation. This study aimed at: (1) mapping the spatial patterns, and (2) quantifying SOC and TN stocks to 30 cm depth in the Eastern Mau Forest Reserve using field, remote sensing, geographical information systems (GIS), and statistical modelling approaches. This is a critical ecosystem offering essential services, but its sustainability is threatened by deforestation and degradation. Results revealed that elevation, silt content, TN concentration, and Landsat 8 Operational Land Imager band 11 explained 72% of the variability in SOC stocks, while the same factors (except silt content) explained 71% of the variability in TN stocks. The results further showed that soil properties, particularly TN and SOC concentrations, were more important than that other environmental factors in controlling the observed patterns of SOC and TN stocks, respectively. Forests stored the highest amounts of SOC and TN (3.78 Tg C and 0.38 Tg N) followed by croplands (2.46 Tg C and 0.25 Tg N) and grasslands (0.57 Tg C and 0.06 Tg N). Overall, the Eastern Mau Forest Reserve stored approximately 6.81 Tg C and 0.69 Tg N. The highest estimates of SOC and TN stocks (hotspots) occurred on the western and northwestern parts where forests dominated, while the lowest estimates (coldspots) occurred on the eastern side where croplands had been established. Therefore, the hotspots need policies that promote conservation, while the coldspots need those that support accumulation of SOC and TN stocks.

A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape

Soil organic carbon (SOC) is a key indicator of ecosystem health, with a great potential to affect climate change. This study aimed to develop, evaluate, and compare the performance of support vector regression (SVR), artificial neural network (ANN), and random forest (RF) models in predicting and mapping SOC stocks in the Eastern Mau Forest Reserve, Kenya. Auxiliary data, including soil sampling, climatic, topographic, and remotely-sensed data were used for model calibration. The calibrated models were applied to create prediction maps of SOC stocks that were validated using independent testing data. The results showed that the models overestimated SOC stocks. Random forest model with a mean error (ME) of −6.5MgCha−1 had the highest tendency for overestimation, while SVR model with an ME of −4.4MgCha−1 had the lowest tendency. Support vector regression model also had the lowest root mean squared error (RMSE) and the highest R2 values (14.9MgCha−1 and 0.6, respectively); hence, it was the best method to predict SOC stocks. Artificial neural network predictions followed closely with RMSE, ME, and R2 values of 15.5, −4.7, and 0.6, respectively. The three prediction maps broadly depicted similar spatial patterns of SOC stocks, with an increasing gradient of SOC stocks from east to west. The highest stocks were on the forest-dominated western and north-western parts, while the lowest stocks were on the cropland-dominated eastern part. The most important variable for explaining the observed spatial patterns of SOC stocks was total nitrogen concentration. Based on the close performance of SVR and ANN models, we proposed that both models should be calibrated, and then the best result applied for spatial prediction of target soil properties in other contexts.

Exploring the geophysical and socio-economic determinants of land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage basin, Kenya

Understanding the linkages between the biogeophysical and socio-economic processes that operate at different spatial and temporal scales is important for land cover change mitigation. This study analysed several factors that explained the forest-shrubland conversions, grassland conversions and cropland expansions in Lake Nakuru drainage basin and Eastern Mau forest reserve in Kenya from 1985 to 2011. Logistic regression models were developed using a combination of remote sensing-based land cover data, and geographical information systems-based geophysical and socio-economic data (i.e., temperature, rainfall, elevation, slope, aspect, topographic wetness, curvature, soil pH, soil cation exchange capacity (CEC), population density and distance to road, river and town). The results were varied; for example, in the period 1985–2000, forest-shrubland conversions were linked to distance to road), distance to town, soil pH, soil CEC, rainfall, topographic wetness, curvature and aspect. The same factors, in addition to slope and distance to river, also determined the likelihood of forest-shrubland conversions in the period 2000–2011. Overall, significance of the determining factors varied depending on time and nature of land cover change. For example, topographical factors influenced grassland conversions in the period 1985–2000, while soil-related factors did not. But in the period 2000–2011, the converse was true. Therefore, policies for restoration, conservation and sustainable management of critical ecosystems (e.g., forests) should be spatially targeted and time-specific. These results broaden our knowledge of land cover dynamics in this locality, and provide a base for effective environmental policy formulation, planning and management.

Remotely sensing the spatial and temporal land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage basin, Kenya

This study aimed at characterizing land cover dynamics for four decades in Eastern Mau forest and Lake Nakuru basin, Kenya. The specific objectives were to: (i) identify and map the major land cover types in 1973, 1985, 2000 and 2011; (ii) detect and determine the magnitude, rates and nature of the land cover changes that had occurred between these dates, and; (iii) establish the spatial and temporal distribution of these changes. Land cover types were discriminated through partitioning, hybrid classification and spatial reclassification of multi-temporal Landsat imagery. The land cover products were then validated and overlaid in post-classification comparison to detect the changes between 1973 and 2011. The accuracies of the land cover maps for 1973, 1985, 2000 and 2011 were 88%, 95%, 80% and 89% respectively. Six land cover classes, namely forests-shrublands, grasslands, croplands, built-up lands, bare lands and water bodies, were mapped. Forests-shrublands dominated in 1973, 1985 and 2000 covering about 1067 km2, 893 km2 and 797 km2 respectively, but were surpassed by croplands (953 km2) in 2011. Bare lands occupied the least area that varied between 2 km2 and 7 km2 during this period. Overall, forests-shrublands and grasslands decreased by 428 km2 and 258 km2 at the annual average rates of 1% each, whereas croplands and built-up lands expanded by 660 km2 and 24 km2 at the annual rates of 6% and 16% respectively. The key hotspots of these changes were distributed in all directions of the study area, but at different times. Therefore, policies that integrate restoration and conservation of natural ecosystems with enhancement of agricultural productivity are strongly recommended. This will ensure environmental sustainability and socio-economic well-being in the area. Future research needs to assess the impacts of the land cover changes on ecosystem services and to project the future patterns of land cover changes.