The East African region hosts diverse forest ecosystems, such as woodlands, highland forests, and coastal mangrove forests. These ecosystems are crucial for biodiversity and support various plant and animal species. They provide essential resources, benefiting livelihoods directly and indirectly. Examining regional and global variations in forest ecosystem valuation, especially regarding livelihood benefits, is crucial for sustainable resource management aligned with forest-based climate solutions. The aim of this review is to assess how regional contextual factors and global trends impact the valuation of ecosystem services (ES) in East Africa, with two main objectives (1) to identify the most evaluated ES in East Africa and (2) to identify the preferred ES valuation methods and approaches applied in East Africa. Data from the Web of Science/Clarivate platform was used in the systematic review process using the Reporting standard for Systematic Evidence Syntheses to retrieve and analyse 222 articles. The ES were categorized using the Millennium Ecosystem Assessment framework and the Common International Classification of Ecosystem Services in order to create a review database. Provisioning services i.e., wood biomass and food production were the most assessed at 55%, followed by 30% regulating services i.e., carbon storage, 8.6% support services i.e., habitat and 6.4% cultural services i.e., ecotourism which reflected communities’ direct reliance on forest resources. The preferred direct ES valuation techniques included market price at 14.9% for assessing provisioning services and travel cost methods at 10.4% for cultural services. Amongst the indirect ES valuation techniques, remote sensing at 14.7% were preferred as they proved efficient for large and remote tropical forest areas. Participatory methods such as interviews at 11.8% were also preferred because they offer holistic perspectives on community priorities. This review provides spatial context on the regional priorities of ES valuation which is vital for safeguarding natural resources for future generations.

Kakamega National Forest Reserve is a tropical forest ecosystem at high risk of irreplaceable biodiversity loss due to persistent human-induced pressures. The aim of this paper is to assess the effect of fragmentation and forest cover loss on forest ecosystems in Kakamega National Forest Reserve, with the objectives: (1) to quantify the forest cover loss and analyse fragmentation in the Kakamega forest ecosystem and (2) to analyse the effect of forest cover loss on the spatial distribution of the Kakamega forest ecosystem at different timescales. Hansen global forest change data was used as an input training dataset on the Google Earth Engine platform (GEE) to estimate the area of forest cover loss by aggregating the sum of pixel values, and to provide a time series visualization of forest change by the extent of cover loss using Sentinel-2 and Landsat 7 false colour composites (RBG) in QGIS software. Fragmentation analysis was performed using reclassified forest loss and distribution data from the Hansen product as binary raster input in Guidos software. Total forest cover loss over 20 years was estimated at 826.60 ha. The first decade (2000–2010) accounted for 146.31 ha of forest cover loss, and the second decade (2010–2020) accounted for 680.29 ha of forest cover loss. Forest area density (FAD) analysis depicted an increase in the dominant layer by 8.5% and a 2.5% decrease in the interior layer. Morphological spatial pattern analysis (MSPA) illustrated a change in the core layer of 96% and a 14% increase in the openings class layer. Therefore, this study demonstrates that forest cover loss and landscape pattern alteration changed the dynamics of species interaction within ecological communities. Fragmented habitats adversely affected the ecosystem’s ability to recover the loss of endemic species, which are at risk of extinction in the backdrop of climate change. Anthropogenic drivers i.e., the clearing of natural forest and conversion of forest land for non-forest use, have contributed significantly to the loss of forest cover in the study area.

The potential for accelerate loss of ecosystem services exists when the livelihood of the rural poor is heavily reliant on local ecosystems. For an improvement in livelihood of the rural poor there is need for assessment of key elements of the livelihood framework. This is especially important to Kakamega forest, the last remaining tropical rainforest in Kenya. This paper is a comprehensive literature review aimed at identifyed the research gaps in the area of the relationships between local communities and Kakamega forest ecosystem services. We derived the 42 case studies using ROSES framework and used descriptive statistics to analysis the data set. 93% of the case studies analyse provisional ecosystem, 83% cultural, 55% regulating and 31% supporting ecosystem. We identified r research gaps in our interest area. Most used research methodology is the participatory rural methods which involved interviews, key informant interviews, and focus group discussion. The identified research gaps will enable us to develop tools that can be used to assess the livelihood improvement of local forest communities in Kakamega. The comprehensive review is also useful for planning the research in other parts of Kenya and beyond, in order to improve livelihoods of local forest communities.

Forest ecosystems deliver ecosystem services (ESs) that are essential for the well-being of local communities. These services necessitate a clear understanding of their perception among community members in order to facilitate informed and sustainable forest management decisions. This study aimed to explore the relationship between forest ESs and local communities by identifying community preferences for key ESs and examining how socio-economic factors influence this perception. Data was collected through participatory methods, including focus group discussions, key informant interviews, and a household survey involving 453 respondents. The data was analyzed using descriptive statistics and binary logistic regression. We identified firewood, rainfall attraction, air quality, and wildlife habitat as the most acknowledged services in their respective ES categories. Land ownership, education level, Community Forest Association membership, and labor migration significantly influenced service recognition. These findings highlight critical areas for intervention, including livelihood enhancement, cultural heritage preservation, and improved community outreach programs. The outcomes of this research are expected to contribute to the sustainable management of forest ecosystems.

Effective initiatives for forest-based mitigation of climate change rely on continuous efforts to improve the estimation of forest biomass. Allometric biomass models, which are nonlinear models that predict aboveground biomass (AGB) as a function of diameter at breast height (D) and tree height (H), are typically used in forest biomass estimations. A combined variable D²H may be used instead of two separate predictors. The Q-ratio (i.e., the ratio between the parameter estimates of D and parameter estimates of H, in a separate variable model) was proposed recently as a measure to guide the decision on whether D and H can be safely combined into D²H, being shown that the two model forms are similar when Q = 2.0. Here, using five European beech (Fagus sylvatica L.) biomass datasets (of different Q-ratios ranging from 1.50 to 5.05) and an inventory dataset for the same species, we investigated the effects of combining the variables in allometric models on biomass estimation over large forest areas. The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean AGB per hectare for Q > 2.0 (i.e., by 6.3% for Q = 5.05), underestimation for Q < 2.0 (i.e., by –3.9% for Q = 1.50), whereas for Q = 2.03, the differences were minimum (0.1%). The standard errors of mean AGB per hectare were similar for Q = 2.03 (differences up to 0.2%), and the differences increased with the Q-ratio, by up to 10.2% for Q = 5.05. Therefore, we demonstrated for European beech that combining the variables in allometric biomass models when Q ≠ 2.0 resulted in biased estimates of mean AGB per hectare and of uncertainty.