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Recent advances in the application of spectral bands from satellite observations and machine learning algorithms (MLA) in the Google Earth Engine (GEE) cloud-computing platform have been demonstrated to enhance the accuracy of mapping forest resources. This study presents a novel method for mapping the natural distribution of highland bamboo (Oldeania alpina) using spectral bands and three machine learning algorithms, namely random forest, gradient tree boosting, and classification and regression tree. First, spectral bands, vegetation indices and textural features including contrast, entropy and inverse difference moment were derived from the Sentinel-2 elevation data. Second, observations were categorized as the dry season (December-February), short rainy season (March-May), main (long) rainy season (June-August), wet season (September-November) and annual composite. Third, the machine learning algorithms were tested using 1882 ground control points collected from field and high spatial resolution images. Finally, the best-performing machine learning algorithm was used to classify and map bamboo forests. The five land cover categories identified were bamboo stands, natural forest, other vegetation, non-vegetated areas and water bodies. The result shows that the random forest classifier is a robust algorithm with an overall accuracy of 94% considering all the annual composite's spectral, vegetation and textural variables. The highland bamboo coverage (91 km2) in the Andracha district provided valuable insights. Bamboo stands were mostly distributed in the southern and southeastern parts of the district. Here, we show that image compositing and multiple input parameters using machine learning techniques can overcome challenges facing land cover classification, which can hinder accurate mapping of Afromontane forests. We conclude that the incorporation of vegetation indices and textural features in land cover classification increases accuracy of mapping natural highland bamboo coverage and distribution. The application of this new method is a promising prospect not only in Ethiopia but also in Afromontane forests elsewhere in Africa. [ABSTRACT FROM AUTHOR]

Climate change impacts are posing greater risks to biodiversity, food security, and livelihood in Africa, specifically in arid and semi‐arid environments. In Ethiopia, the genus Balanites Del., which belongs to the monogeneric family called Balanitaceae, has the multipurpose B. aegyptiaca and B. rotundifolia. However, these species are overlooked and endangered by climate change, and their species distributions are not well documented and understood in Ethiopia. Therefore, this study aimed to model the habitat suitability of these two species using current occurrence data, climate, and landscape data and predict their distribution under climate change. Occurrence points of B. aegyptiaca (n = 224) and B. rotundifolia (n = 80) were collected from field surveys and herbarium. Bioclimatic (WorldClim.v2), soil, and landscape variables were used in the ensemble species distribution models (SDMs) using six algorithms (GLM, GAM, BRT, RF, MARS, and SVM). The ensemble SDMs under the current climate showed that the Central Ethiopian Rift Valley is highly suitable habitats for B. aegyptiaca accounting for an area of 114,517 km2, and the Southern Ethiopian Rift Valley is highly suitable habitats for B. rotundifolia accounting for an area of 41,373 km2. The performance of ensemble SDM under the current climate for B. aegyptiaca showed 0.95 AUC, 0.80 TSS, 0.79 COR, and 0.87 deviance; and that of B. rotundifolia with 0.90 AUC, 0.80 TSS, 0.80 COR, and 0.50 deviance. Temperature annual range (Bio07) and precipitation seasonality (Bio15) for B. aegyptiaca; and precipitation of driest quarter (Bio17) and annual precipitation (Bio12) for B. rotundifolia are the most key bioclimatic variables that affect their distributions. The ensemble SDMs under SSP2‐45 and SSP5‐85 (HadGEM3‐GC31‐LL) climates showed that the highly suitable areas will remain suitable for both species (B. aegyptiaca with 116,934 km2 area cover (ΔA = +2.1%) and 125,757 km2 area cover (ΔA = +7.5%) and B. rotundifolia with 29,547 km2 area cover (ΔA = −28.6%) and 50, 894 km2 area cover (ΔA = +23%), respectively). The findings of this study implicated that the Ethiopian Rift Valley region in general for B. aegyptiaca and the Southern Rift Valley of Ethiopia for B. rotundifolia are suitable areas for conservation and sustainable use of the species. [ABSTRACT FROM AUTHOR]

Environmental transformations are the results of land-use and land-cover (LULC) changes. This study aims to investigate the LULC changes and associated factors in the Este District in northwestern Ethiopia, for the last four decades (the 1980s to 2020s). The land-use and land-cover classes were analyzed using supervised classification techniques in ERDASS IMAGINE 2015 and ArcGIS 10.3.1, categorizing the Landsat satellite images for 1984, 2000, and 2018 into six cover classes: settlement, forests-shrublands, cropland, grassland, bare land, and water body. We used a historical Google map, topo-sheets, and ancillary data to verify the classification accuracies for 1984, 2000, and 2018, respectively. The climate and demographic data were obtained from the Ethiopian Meteorological Station and Ethiopian Statistical Agency, respectively. In addition, data from key informant interviews and focus group discussions were also used to understand the local community experiences and perceptions toward LULC changes. The values of each LULC class were correlated with the demographic and climatic variables, using simple correlation analysis to evaluate the impact of demographic and climatic conditions on LULC changes. The analysis indicated that the least and largest classes of LULC in Este District were water bodies (mean cover = 1.9 km2) and croplands (mean cover = 791.7 km2), respectively. Cropland coverage increased by 2% in 2000, then decreased by 11% in 2018. Between 1984 and 2018, the grasslands and settlements increased by 22% and 0.5%, respectively. Half of the bare land and one-tenth of the forests-shrublands also decreased over the last four decades (the 1980s to 2020s). As a result, the original croplands and forests–shrublands classes (11% each) before 2000 were converted into new grasslands and croplands after 2018, respectively. The study indicated that precipitation, solar radiation, and population growth are the potential drivers, and the perceptions of local communities are nearly in line with the statistical analysis results. Alternative income sources, such as tourism and carbon trading and the participation in afforestation programs, could reverse the situation in the study area. [ABSTRACT FROM AUTHOR]

This study aimed to understand the impact of shrub encroachment on native species in the Guassa Community Conservation Area in Ethiopia. We assessed the soil seed bank composition and density across different elevations and aspects, and management systems within the area. The vegetation was stratified and eight blocks were selected across a range of elevation (<3350 m and >3350 m) and aspect (northeast, northwest, southeast, southwest). Within each block we established twenty 5m x 5m plots for a total of 160. We then collected soil samples from five subplots (1 m x 1 m) at three depths (0–3 cm, 3–6 cm and 6–9 cm) for a total of 480 samples, which were established in pots in greenhouse. We calculated species abundance by totaling the number of seedlings that emerged from each sample. To determine the variability in the abundance of Festuca macrophylla and Helichrysum splendidum in the soil seed bank along altitudinal gradient, we used two-way ANOVA using SAS statistical software version 9.0.1. Shannon diversity index was used to determine species diversity in the soil seedbank. After counting all the seeds, we identified 74 plant species represented in the soil seedbank which belong to 55 genera and 23 families. Eleven species are endemic to Ethiopia. At the lower elevation range, the effects of aspect (P <0.0088) and soil depth (P <0.005) are not significant to determine the abundance of seeds of H. splendidum and F. macrophylla. But when the factors are segregated, both aspect and soil depth play a significant role (p<0.0001) regarding the abundance of the seeds of the competing species at lower elevation. At higher elevation, only the effect of soil depth is significant (P<0.0001) for determining the abundance of H. splendidum. Soil depth and aspect have no significant effects on soil seed bank abundance at this elevation. [ABSTRACT FROM AUTHOR]

Conventional wisdom states that genetic variation reduces disease levels in plant populations. Nevertheless, crop species have been subject to a gradual loss of genetic variation through selection for specific traits during breeding, thereby increasing their vulnerability to biotic stresses such as pathogens. We explored how genetic variation in Arabica coffee sites in southwestern Ethiopia was related to the incidence of four major fungal diseases. Sixty sites were selected along a gradient of management intensity, ranging from nearly wild to intensively managed coffee stands. We used genotyping‐by‐sequencing of pooled leaf samples (pool‐GBS) derived from 16 individual coffee shrubs in each of the 60 sites to assess the variation in genetic composition (multivariate: reference allele frequency) and genetic diversity (univariate: mean expected heterozygosity) between sites. We found that genetic composition had a clear spatial pattern and that genetic diversity was higher in less managed sites. The incidence of the four fungal diseases was related to the genetic composition of the coffee stands, but in a specific way for each disease. In contrast, genetic diversity was only related to the within‐site variation of coffee berry disease, but not to the mean incidence of any of the four diseases across sites. Given that fungal diseases are major challenges of Arabica coffee in its native range, our findings that genetic composition of coffee sites impacted the major fungal diseases may serve as baseline information to study the molecular basis of disease resistance in coffee. Overall, our study illustrates the need to consider both host genetic composition and genetic diversity when investigating the genetic basis for variation in disease levels. [ABSTRACT FROM AUTHOR]

Echinops kebericho is a narrow‐range multipurpose medicinal plant confined to Ethiopia. Intense land use change and overharvesting for traditional medicine have resulted in narrow distributions of its populations. It is a threatened species with a decreasing population trend. This study aims to map its potential distribution, which is key to guide conservation efforts and sustainable use. We modeled the potential distribution of E. kebercho using the maximum entropy model (MaxEnt) employing 11 less correlated predictor variables by calibrating the model at two complexity levels and replicating each model 10 times using a cross validation technique. We projected the models into the whole of Ethiopia and produced binary presence–absence maps by classifying the average map from both complexity levels applying three threshold criteria and ensembling the resulting maps into one for the final result. We mapped suitable habitat predicted with high certainty and identified local districts where E. kebericho can be cultivated or introduced to enhance its conservation. We estimated that E.kebercho has about 137,925 km2 of suitable habitat, mainly concentrated in the western highlands of the Ethiopian mountains. Our models at both complexity levels had high average performances, AUC values of 0.925 for the complex model and 0.907 for the simpler model. The variations in performance among the 10 model replicates were not remarkable, an AUC standard deviation of 0.040 for complex and 0.046 for simple model. Although E. kebericho is locally confined, our models predicted that it has a remarkably wider potential distribution area. We recommend introducing E. kebericho to these areas to improve its conservation status and tap its multiple benefits on a sustainable basis. Locally confined threatened plants and animals likely have wider potential distributions than their actual distributions and thus similar methodology can be applied for their conservation. [ABSTRACT FROM AUTHOR]

Distributions of potential ranges of plant species are not yet fully known in Ethiopia where high climatic variability and vegetation types are found. This study was undertaken to predict distributions of suitable habitats of Pouteria adolfi‐friederici and Prunus africana under current and two future climate scenarios (RCP 4.5 and RCP 8.5 in 2050 and 2070) in Ethiopia. Eleven environmental variables with less correlation coefficients (r < 0.7) were used to make the prediction. Shifting in extents of habitat suitability and effects of elevation, solar radiation and topographic position in relation to the current and future climatic scenarios were statistically analysed using independent t‐test and linear model. We found decreasing area of highly suitable habitat from 0.51% to 0.46%, 0.36% and 0.33%, 0.24% for Prunus africana and 1.13% to 1.02%, 0.77% and 0.76%, 0.60% for Pouteria adolfi‐friederici, under RCP 4.5 and RCP 8.5 by 2050 and 2070 respectively. Moist and dry afromontane forests are identified as the most suitable habitat for both species. Overall, our results suggest that climate change can promote dynamic suitable habitat niches under different future climate scenarios. Therefore, biodiversity conservation strategies should take into account habitat suitability dynamics issues and identify where to conserve species before implementing conservation practices. [ABSTRACT FROM AUTHOR]

Bamboos provide a number of ecosystem services, including the provision of a permanent carbon (C) sink. The present study was undertaken in the Sheka forest, currently recognized as a UNESCO Biosphere Reserve. The objectives of this study were to: (1) develop species- and site-specific allometric models for biomass estimation and (2) quantify the carbon storage capacity of highland bamboo stands. A total of 12 plots each measuring 10 m x 10 m were established at a distance of 200 m in the forest. 96 culms were harvested for the development of biomass estimation equations. Litter samples were collected in 1 m x 1 m subplots, while soil samples were collected at 0–10, 10–20, 20–40, and 40–60 cm soil depths for determination of soil organic carbon (SOC) contents. The estimated mean DBH, basal area and plant height were 7.0 cm, 53.2 m2 ha−1 and 12.9 m, respectively. The study has established allometric scaling of plant height with stem diameter in highland bamboo. It has also established that the allometric model is superior to commonly used non-linear H-DBH models. Aboveground biomass was estimated at 93 Mg ha−1 with mean C stocks of 43.7 Mg ha−1. The estimated below-ground biomass was 18.6 Mg ha−1 with C stocks of 8.7 Mg ha−1. The C stocks in the litter layer were estimated at 1.57 Mg ha−1. Average soil C storage was estimated at 388.12 Mg ha−1 within the 0–60 cm soil depth. In total, the natural highland bamboo stands store approximately 442.1 Mg C ha−1. The estimated C stocks were comparable with values reported for bamboos in Ethiopia and elsewhere. It is concluded that natural highland bamboo stands play a significant role as carbon sinks. The insights gained in this study are expected to be applicable to Afromontane ecosystems where highland bamboo occurs in Africa. Highland bamboo (Oldeania alpina) is a conspicuous element of Afromontane vegetation in Africa We developed models for estimating biomass carbon (C) stocks in highland bamboo stands We estimated biomass C stock of highland bamboo stands at 52.5 Mg ha−1 and litter C at 1.6 Mg ha−1 Soil organic C stock of highland bamboo stands was 388.1 Mg ha−1 Total system C in highland bamboo stands was over 442 Mg ha−1 [ABSTRACT FROM AUTHOR]

Climate warming threatens the future sustainability of mountains, and tropical mountains are particularly threatened with loss of biodiversity and associated ecosystem services. Conservation biologists increasingly turn to habitat suitability models to guide the establishment and assessment of protected area networks to protect the highest number of species, yet this focus often neglects the values, attitudes, and beliefs of the people living around protected areas. If we consider protected areas as dynamic social–ecological systems, habitat suitability modeling for conservation planning must pay greater attention to the role of biocultural diversity, rather than biodiversity alone. Here, we describe a conservation assessment of the Afroalpine grassland ecosystem in the northern highlands of Ethiopia. We use a cultural keystone species known as guassa grass (Festuca macrophyhlla) to focus our modeling efforts and evaluate the potential distribution of this endemic species in relation to current national and community-based protected areas. Our model performed highly according to the area under the curve (AUC = 0.96), yet nearly 80% of highly suitable guassa habitat falls outside the range of our training data and thus must be interpreted conservatively. We found that guassa grass distribution is primarily limited by low mean temperature in the warmest quarter (BIO10, 36.7%), high precipitation in the wettest quarter (BIO16, 21%), and low precipitation in the driest quarter (BIO17, 15.6%). As climate change causes rapid warming at high elevations and alters rainfall patterns in the Ethiopian highlands, we urge managers to carefully monitor the changing populations of guassa grass to evaluate whether the species is experiencing an extinction debt. We also recommend four additional areas as potential community-based conservation areas, with government and NGO support for peer-to-peer communication networks. Spatially explicit modeling may be a valuable tool to determine whether these existing and proposed protected areas can sustain future populations of guassa grass. [ABSTRACT FROM AUTHOR]