Background:Ecological niche modeling of the main forest-forming species within the same geographic range contributes significantly to understanding the coexistence of species and the regularities of formation of their...Background:Ecological niche modeling of the main forest-forming species within the same geographic range contributes significantly to understanding the coexistence of species and the regularities of formation of their current spatial distribution.The main abiotic and biotic environmental variables,as well as species dispersal capability,affecting the spatial distribution of the main forest-forming species in the Caucasus,have not been sufficiently studied.Methods:We conducted studies within the physiographic boundaries of the Caucasus,including the Russian Federation,Georgia,Armenia,and Azerbaijan.Our studies focused on ecological niche modeling of pure fir,spruce,pine,beech,hornbeam,and birch forests through species distribution modeling and the concept of BAM(Biotic-Abiotic-Movement)diagram.We selected 648 geographic records of pure forests occurrence.ENVIREM and SoilGrids databases,statistical tools in R,Maxent were used to assess the influence of abiotic,biotic,and movement factors on the spatial distribution of the forest-forming species.Results:Geographic expression of fundamental ecological niches of the main forest-forming species depended mainly on topographic conditions and water regime.Competitor influence reduced the potential ranges of the studied species by 1.2–1.7 times to the geographic expression of their realized niches and led to differences in the distribution of species with similar requirements for abiotic conditions.Movement factor significantly limited the areas suitable for pure forests(by 1.2–1.8 times compared with geographic expression of realized ecological niches),except for birch forests.Conclusion:Distribution maps,constructed by abiotic,biotic,and movement factors,were the models of the occupied distributional area of the forest-forming species in the Caucasus.Biotic and movement factors should be considered in modeling studies of forest ecosystems if models are to have biological meaning and reality.展开更多
Mountainous rangelands play a pivotal role in providing forage resources for livestock, particularly in summer, and maintaining ecological balance. This study aimed to identify environmental variables affecting range ...Mountainous rangelands play a pivotal role in providing forage resources for livestock, particularly in summer, and maintaining ecological balance. This study aimed to identify environmental variables affecting range plant species distribution, ecological analysis of the relationship between these variables and the distribution of plants, and to model and map the plant habitats suitability by the Random Forest Method(RFM) in rangelands of the Taftan Mountain, Sistan and Baluchestan Province, southeastern Iran. In order to determine the environmental variables and estimate the potential distribution of plant species, the presence points of plants were recorded by using systematic random sampling method(90 points of presence) and soils were sampled in 5 habitats by random method in 0–30 and 30–60 cm depths. The layers of environmental variables were prepared using the Kriging interpolation method and Geographic Information System facilities. The distribution of the plant habitats was finally modelled and mapped by the RFM. Continuous maps of the habitat suitability were converted to binary maps using Youden Index(?) in order to evaluate the accuracy of the RFM in estimation of the distribution of species potentialhabitat. Based on the values of the area under curve(AUC) statistics, accuracy of predictive models of all habitats was in good level. Investigating the agreement between the predicted map, generated by each model, and actual maps, generated from fieldmeasured data, of the plant habitats, was at a high level for all habitats, except for Amygdalus scoparia habitat. This study concluded that the RFM is a robust model to analyze the relationships between the distribution of plant species and environmental variables as well as to prepare potential distribution maps of plant habitats that are of higher priority for conservation on the local scale in arid mountainous rangelands.展开更多
Background Childhood infectious diseases and related symptoms,such as fever,cough,and diarrhea among children constitute the leading cause of death in low-and middle-income countries.We examined the environmental pred...Background Childhood infectious diseases and related symptoms,such as fever,cough,and diarrhea among children constitute the leading cause of death in low-and middle-income countries.We examined the environmental predictors of double and triple burden(D/TB)of infection symptoms among under-five children using multilevel machine learning(ML)methods.Methods We used Demographic and Health Surveys(DHS)data from 58 LMICs between 2000 and 2023.These data were merged with cluster-level particulate matter and nitrogen dioxide from the National Aeronautics and Space Administration and country-level data on political,social,and economic globalization from the World Bank report.We applied multilevel models to screen out the most important predictors of D/TB symptoms and applied machine learning algorithms to predict these symptoms among children across LMICs.We trained and validated ML algorithms on(80,70,and 60%)of the data and tested on the remaining(20,30,and 40%)with 2,5 and 10 cross-validations.Results Of 1,546,243 children,19.2%,20.5%and 12.6%had fever,cough,and diarrhea,respectively;while the overall D/TB prevalence was 11.9%and 3.7%,respectively.The result revealed D/TB were associated with the location of a child,survey years,wealth index,family size,air pollutants,and environmental covariates.The estimated prevalence of both D/TB symptoms substantially varies across districts[intraclass correlation(intraclass correlation,ICC=13.3%)]and countries(ICC=8.8%).We found that the Random Forest gave the maximum area Under the curve of 94%and 99%for D/TBs for the K10 protocol and 80:20 training and testing dataset splits.Conclusions The study found substantial variation in the prevalences of D/TB of illness among children under five and identified several environmental and sociodemographic predictors of these health outcomes.The Random Forest algorithm performed best in predicting these burdens.The study emphasized how integrating environmental and sociodemographic data with machine learning can enhance targeted interventions to reduce childhood infectious disease burdens in low-and middle-income countries.展开更多
基金This work was supported by the state assignment“Patterns of the Spatiotemporal Dynamics of Meadow and Forest Ecosystems in Moun-tainous Areas(Russian Western and Central Caucasus)”,No.075-00347-19-00.
文摘Background:Ecological niche modeling of the main forest-forming species within the same geographic range contributes significantly to understanding the coexistence of species and the regularities of formation of their current spatial distribution.The main abiotic and biotic environmental variables,as well as species dispersal capability,affecting the spatial distribution of the main forest-forming species in the Caucasus,have not been sufficiently studied.Methods:We conducted studies within the physiographic boundaries of the Caucasus,including the Russian Federation,Georgia,Armenia,and Azerbaijan.Our studies focused on ecological niche modeling of pure fir,spruce,pine,beech,hornbeam,and birch forests through species distribution modeling and the concept of BAM(Biotic-Abiotic-Movement)diagram.We selected 648 geographic records of pure forests occurrence.ENVIREM and SoilGrids databases,statistical tools in R,Maxent were used to assess the influence of abiotic,biotic,and movement factors on the spatial distribution of the forest-forming species.Results:Geographic expression of fundamental ecological niches of the main forest-forming species depended mainly on topographic conditions and water regime.Competitor influence reduced the potential ranges of the studied species by 1.2–1.7 times to the geographic expression of their realized niches and led to differences in the distribution of species with similar requirements for abiotic conditions.Movement factor significantly limited the areas suitable for pure forests(by 1.2–1.8 times compared with geographic expression of realized ecological niches),except for birch forests.Conclusion:Distribution maps,constructed by abiotic,biotic,and movement factors,were the models of the occupied distributional area of the forest-forming species in the Caucasus.Biotic and movement factors should be considered in modeling studies of forest ecosystems if models are to have biological meaning and reality.
基金funded by University of Zabol,Iran(Grant No.UOZ-GR-9517-24)the Vice Chancellery for Research and Technology,University of Zabol,for funding this study
文摘Mountainous rangelands play a pivotal role in providing forage resources for livestock, particularly in summer, and maintaining ecological balance. This study aimed to identify environmental variables affecting range plant species distribution, ecological analysis of the relationship between these variables and the distribution of plants, and to model and map the plant habitats suitability by the Random Forest Method(RFM) in rangelands of the Taftan Mountain, Sistan and Baluchestan Province, southeastern Iran. In order to determine the environmental variables and estimate the potential distribution of plant species, the presence points of plants were recorded by using systematic random sampling method(90 points of presence) and soils were sampled in 5 habitats by random method in 0–30 and 30–60 cm depths. The layers of environmental variables were prepared using the Kriging interpolation method and Geographic Information System facilities. The distribution of the plant habitats was finally modelled and mapped by the RFM. Continuous maps of the habitat suitability were converted to binary maps using Youden Index(?) in order to evaluate the accuracy of the RFM in estimation of the distribution of species potentialhabitat. Based on the values of the area under curve(AUC) statistics, accuracy of predictive models of all habitats was in good level. Investigating the agreement between the predicted map, generated by each model, and actual maps, generated from fieldmeasured data, of the plant habitats, was at a high level for all habitats, except for Amygdalus scoparia habitat. This study concluded that the RFM is a robust model to analyze the relationships between the distribution of plant species and environmental variables as well as to prepare potential distribution maps of plant habitats that are of higher priority for conservation on the local scale in arid mountainous rangelands.
基金the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Actions grant agreement No.101126602(Data4Healthcare),and the University of Oulu.
文摘Background Childhood infectious diseases and related symptoms,such as fever,cough,and diarrhea among children constitute the leading cause of death in low-and middle-income countries.We examined the environmental predictors of double and triple burden(D/TB)of infection symptoms among under-five children using multilevel machine learning(ML)methods.Methods We used Demographic and Health Surveys(DHS)data from 58 LMICs between 2000 and 2023.These data were merged with cluster-level particulate matter and nitrogen dioxide from the National Aeronautics and Space Administration and country-level data on political,social,and economic globalization from the World Bank report.We applied multilevel models to screen out the most important predictors of D/TB symptoms and applied machine learning algorithms to predict these symptoms among children across LMICs.We trained and validated ML algorithms on(80,70,and 60%)of the data and tested on the remaining(20,30,and 40%)with 2,5 and 10 cross-validations.Results Of 1,546,243 children,19.2%,20.5%and 12.6%had fever,cough,and diarrhea,respectively;while the overall D/TB prevalence was 11.9%and 3.7%,respectively.The result revealed D/TB were associated with the location of a child,survey years,wealth index,family size,air pollutants,and environmental covariates.The estimated prevalence of both D/TB symptoms substantially varies across districts[intraclass correlation(intraclass correlation,ICC=13.3%)]and countries(ICC=8.8%).We found that the Random Forest gave the maximum area Under the curve of 94%and 99%for D/TBs for the K10 protocol and 80:20 training and testing dataset splits.Conclusions The study found substantial variation in the prevalences of D/TB of illness among children under five and identified several environmental and sociodemographic predictors of these health outcomes.The Random Forest algorithm performed best in predicting these burdens.The study emphasized how integrating environmental and sociodemographic data with machine learning can enhance targeted interventions to reduce childhood infectious disease burdens in low-and middle-income countries.
