Modeling effects of abiotic factors on the abundances of eight woody species in the Harana forest using artificial networks,random forest,and generalized linear models

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 R^(2)=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 nega-tive 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.

Sequence-Based Predicting Bacterial Essential ncRNAs Algorithm by Machine Learning

Essential ncRNA is a type of ncRNAwhich is indispensable for the sur-vival of organisms.Although essential ncRNAs cannot encode proteins,they are as important as essential coding genes in biology.They have got wide variety of applications such as antimicrobial target discovery,minimal genome construction and evolution analysis.At present,the number of species required for the deter-mination of essential ncRNAs in the whole genome scale is still very few due to the traditional methods are time-consuming,laborious and costly.In addition,tra-ditional experimental methods are limited by the organisms as less than 1%of bacteria can be cultured in the laboratory.Therefore,it is important and necessary to develop theories and methods for the recognition of essential non-coding RNA.In this paper,we present a novel method for predicting essential ncRNA by using both compositional and derivative features calculated by information theory of ncRNA sequences.The method was developed with Support Vector Machine(SVM).The accuracy of the method was evaluated through cross-species cross-vali-dation and found to be between 0.69 and 0.81.It shows that the features we selected have good performance for the prediction of essential ncRNA using SVM.Thus,the method can be applied for discovering essential ncRNAs in bacteria.

Spatial distribution of above ground carbondensity in Harana Forest,Ethiopia

Background:The need for understanding spatial distribution of forest aboveground carbon density(ACD)hasincreased to improve management practices of forest ecosystems.This study examined spatial distribution of theACD in the Harana Forest.A grid sampling technique was employed and three nested circular plots were establishedat each point where grids intersected.Forest-related data were collected from 1122 plots while the ACD of each plotwas estimated using the established allometric equation.Environmental variables in raster format were downloadedfrom 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 thatthe high class of the ACD was confined to eastern and southwest parts of the Harana Forest.The Moran’s statisticsdepicted similar observations showing the higher clustering of ACD in the eastern and southern parts of the studyarea.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 soilcation exchange capacity,silt content,and precipitation.Conclusions:The spatial distribution of ACD in this study area was mainly influenced by attributes of the forest standand edaphic factors in comparison to topographic and climatic factors.Our findings could provide basis for bettermanagement and conservation of aboveground carbon storage in the Harana Forest,which may contribute to Ethiopia’s strategy of reducing carbon emission.