Background The need for understanding spatial distribution of forest aboveground carbon density (ACD) has increased to improve management practices of forest ecosystems. This study examined spatial distribution of the ACD in the Harana Forest. A grid sampling technique was employed and three nested circular plots were established at each point where grids intersected. Forest-related data were collected from 1122 plots while the ACD of each plot was estimated using the established allometric equation. Environmental variables in raster format were downloaded from open sources and resampled into a spatial resolution of 30 m. Descriptive statistics were computed to summarize the ACD. A Random Forest classification model in the R-software package was used to select strong predictors, and to predict the spatial distribution of ACD. Results The mean ACD was estimated at 131.505 ton per ha in this study area. The spatial prediction showed that the high class of the ACD was confined to eastern and southwest parts of the Harana Forest. The Moran’s statistics depicted similar observations showing the higher clustering of ACD in the eastern and southern parts of the study area. The higher ACD clustering was linked with the higher species richness, species diversity, tree density, tree height, clay content, and SOC. Conversely, the lower ACD clustering in the Harana Forest was associated with higher soil cation exchange capacity, silt content, and precipitation. Conclusions The spatial distribution of ACD in this study area was mainly influenced by attributes of the forest stand and edaphic factors in comparison to topographic and climatic factors. Our findings could provide basis for better management and conservation of aboveground carbon storage in the Harana Forest, which may contribute to Ethiopia’s strategy of reducing carbon emission.

Background Abiotic factors exert different impacts on the abundance of individual tree species in the forest but little has been known about the impact of abiotic factors on the individual plant, particularly, in a tropical forest. This study identified the impact of abiotic factors on the abundances of Podocarpus falcatus, Croton macrostachyus, Celtis africana, Syzygium guineense, Olea capensis, Diospyros abyssinica, Feliucium decipenses, and Coffea arabica. A systematic sample design was used in the Harana forest, where 1122 plots were established to collect the abundance of species. Random forest (RF), artificial neural network (ANN), and generalized linear model (GLM) models were used to examine the impacts of topographic, climatic, and edaphic factors on the log abundances of woody species. The RF model was used to predict the spatial distribution maps of the log abundances of each species. Results The RF model achieved a better prediction accuracy with R2 = 71% and a mean squared error (MSE) of 0.28 for Feliucium decipenses. The RF model differentiated elevation, temperature, precipitation, clay, and potassium were the top variables that influenced the abundance of species. The ANN model showed that elevation induced a negative impact on the log abundances of all woody species. The GLM model reaffirmed the negative impact of elevation on all woody species except the log abundances of Syzygium guineense and Olea capensis. The ANN model indicated that soil organic matter (SOM) could positively affect the log abundances of all woody species. The GLM showed a similar positive impact of SOM, except for a negative impact on the log abundance of Celtis africana at p < 0.05. The spatial distributions of the log abundances of Coffee arabica, Filicium decipenses, and Celtis africana were confined to the eastern parts, while the log abundance of Olea capensis was limited to the western parts. Conclusions The impacts of abiotic factors on the abundance of woody species may vary with species. This ecological understanding could guide the restoration activity of individual species. The prediction maps in this study provide spatially explicit information which can enhance the successful implementation of species conservation.