Landslides influence the capacity for safe and sustainable development of mountainous environments.This study explores the spatial distribution of and the interactions between landslides that are mapped using global p...Landslides influence the capacity for safe and sustainable development of mountainous environments.This study explores the spatial distribution of and the interactions between landslides that are mapped using global positioning system(GPS) and extensive field surveys in Mazandaran Province,Iran.Point-pattern assessment is undertaken using several univariate summary statistical functions,including pair correlation,spherical-contact distribution,nearest-neighbor analysis,and O-ring analysis,as well as bivariate summary statistics,and a markcorrelation function.The maximum entropy method was applied to prioritize the factors controlling the incidence of landslides and the landslides susceptibility map.The validation processes were considered for separated 30%data applying the ROC curves,fourfold plot,and Cohen’s kappa index.The results show that pair correlation and O-ring analyses satisfactorily predicted landslides at scales from 1 to 150 m.At smaller scales,from 150 to 400 m,landslides were randomly distributed.The nearest-neighbor distribution function show that the highest distance to the nearest landslide occurred in the 355 m.The spherical-contact distribution revealed that the patterns were random up to a spatial scale of 80 m.The bivariate correlation functions revealed that landslides were positively linked to several linear features(including faults,roads,and rivers) at all spatial scales.The mark-correlation function showed that aggregated fields of landslides were positively correlated with measures of land use,lithology,drainage density,plan curvature,and aspect,when the numbers of landslides in the groups were greater than the overall average aggregation.The results of analysis of factor importance have showed that elevation(topography map scale:1:25,000),distance to roads,and distance to rivers are the most important factors in the occurrence of landslides.The susceptibility model of landslides indicates an excellent accuracy,i.e.,the AUC value of landslides was 0.860.The susceptibility map of landslides analyzed has shown that 35% of the area is low susceptible to landslides.展开更多
Analysis of spatial patterns to describe the spatial correlation between a tree location and marks(i.e.,structural variables),can reveal stand history,population dynamics,competition and symbiosis.However,most studies...Analysis of spatial patterns to describe the spatial correlation between a tree location and marks(i.e.,structural variables),can reveal stand history,population dynamics,competition and symbiosis.However,most studies of spatial patterns have concentrated on tree location and tree sizes rather than on crown asymmetry especially with direct analysis among marks characterizing facilitation and competition among of trees,and thus cannot reveal the cause of the distributions of tree locations and quantitative marks.To explore the spatial correlation among quantitative and vectorial marks and their implication on population dynamics,we extracted vertical and horizontal marks(tree height and crown projection area)characterizing tree size,and a vectorial mark(crown displacement vector characterizing the crown asymmetry)using an airborne laser scanning point cloud obtained from two forest stands in Oxfordshire,UK.Quantitatively and vectorially marked spatial patterns were developed,with corresponding null models established for a significance test.We analyzed eight types of univariate and bivariate spatial patterns,after first proposing four types.The accuracy of the pattern analysis based on an algorithm-segmented point cloud was compared with that of a truly segmented point cloud.The algorithm-segmented point cloud managed to detect 70–86%of patterns correctly.The eight types of spatial patterns analyzed the spatial distribution of trees,the spatial correlation between tree size and facilitated or competitive interactions of sycamore and other species.These four types of univariate patterns jointly showed that,at smaller scales,the trees tend to be clustered,and taller,with larger crowns due to the detected facilitations among trees in the study area.The four types of bivariate patterns found that at smaller scales there are taller trees and more facilitation among sycamore and other species,while crown size is mostly homogeneous across scales.These results indicate that interspecific facilitation and competition mainly affect tree height in the study area.This work further confirms the connection of tree size with individual facilitation and competition,revealing the potential spatial structure that previously was hard to detect.展开更多
Background:Ecological processes such as seedling establishment,biotic interactions,and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis(SPPA).Be...Background:Ecological processes such as seedling establishment,biotic interactions,and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis(SPPA).Being widely used in plant ecology,SPPA is increasingly carried out to describe biotic interactions and interpret patternprocess relationships.However,some aspects are still subjected to a non-negligible debate such as required sample size(in terms of the number of points and plot area),the link between the low number of points and frequently observed random(or independent)patterns,and relating patterns to processes.In this paper,an overview of SPPA is given based on rich and updated literature providing guidance for ecologists(especially beginners)on summary statistics,uni-/bi-/multivariate analysis,unmarked/marked analysis,types of marks,etc.Some ambiguities in SPPA are also discussed.Results:SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns.Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species.Natural processes,plant environmental conditions,and human intervention are interrelated and are key drivers of plant spatial distribution.In spite of being not recommended,small sample sizes are more common in SPPA.In some areas,periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring.Conclusion:The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures.Despite the continuous progress of SPPA,some ambiguities require further clarifications.展开更多
Background:Mendelian randomization(MR)analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies.Many statistical approache...Background:Mendelian randomization(MR)analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies.Many statistical approaches have been developed and each of these methods require specific assumptions.Results:In this article,we review the pros and cons of these methods.We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation.Conclusion:The current available MR approaches allow us to study causality among risk factors and outcomes.However,novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.展开更多
Background:Polygenic risk score(PRS)derived from summary statistics of genome-wide association studies(GWAS)is a useful tool to infer an individuaPs genetic risk for health outcomes and has gained increasing popularit...Background:Polygenic risk score(PRS)derived from summary statistics of genome-wide association studies(GWAS)is a useful tool to infer an individuaPs genetic risk for health outcomes and has gained increasing popularity in human genetics research.PRS in its simplest form enjoys both computational efficiency and easy accessibility,yet the predictive performance of PRS remains moderate for diseases and traits.Results:We provide an overview of recent advances in statistical methods to improve PRS's performance by incorporating information from linkage disequilibrium,functional annotation,and pleiotropy.We also introduce model validation methods that fine-tune PRS using GWAS summary statistics.Conclusion:In this review,we showcase methodological advances and current limitations of PRS,and discuss several emerging issues in risk prediction research.展开更多
基金We would like to thank from Shiraz University for supporting us on this studyThe study was supported by College of Agriculture,Shiraz University(Grant No.96GRD1M271143).
文摘Landslides influence the capacity for safe and sustainable development of mountainous environments.This study explores the spatial distribution of and the interactions between landslides that are mapped using global positioning system(GPS) and extensive field surveys in Mazandaran Province,Iran.Point-pattern assessment is undertaken using several univariate summary statistical functions,including pair correlation,spherical-contact distribution,nearest-neighbor analysis,and O-ring analysis,as well as bivariate summary statistics,and a markcorrelation function.The maximum entropy method was applied to prioritize the factors controlling the incidence of landslides and the landslides susceptibility map.The validation processes were considered for separated 30%data applying the ROC curves,fourfold plot,and Cohen’s kappa index.The results show that pair correlation and O-ring analyses satisfactorily predicted landslides at scales from 1 to 150 m.At smaller scales,from 150 to 400 m,landslides were randomly distributed.The nearest-neighbor distribution function show that the highest distance to the nearest landslide occurred in the 355 m.The spherical-contact distribution revealed that the patterns were random up to a spatial scale of 80 m.The bivariate correlation functions revealed that landslides were positively linked to several linear features(including faults,roads,and rivers) at all spatial scales.The mark-correlation function showed that aggregated fields of landslides were positively correlated with measures of land use,lithology,drainage density,plan curvature,and aspect,when the numbers of landslides in the groups were greater than the overall average aggregation.The results of analysis of factor importance have showed that elevation(topography map scale:1:25,000),distance to roads,and distance to rivers are the most important factors in the occurrence of landslides.The susceptibility model of landslides indicates an excellent accuracy,i.e.,the AUC value of landslides was 0.860.The susceptibility map of landslides analyzed has shown that 35% of the area is low susceptible to landslides.
基金supported by the China Scholarship Council(Grant No.201906010036)。
文摘Analysis of spatial patterns to describe the spatial correlation between a tree location and marks(i.e.,structural variables),can reveal stand history,population dynamics,competition and symbiosis.However,most studies of spatial patterns have concentrated on tree location and tree sizes rather than on crown asymmetry especially with direct analysis among marks characterizing facilitation and competition among of trees,and thus cannot reveal the cause of the distributions of tree locations and quantitative marks.To explore the spatial correlation among quantitative and vectorial marks and their implication on population dynamics,we extracted vertical and horizontal marks(tree height and crown projection area)characterizing tree size,and a vectorial mark(crown displacement vector characterizing the crown asymmetry)using an airborne laser scanning point cloud obtained from two forest stands in Oxfordshire,UK.Quantitatively and vectorially marked spatial patterns were developed,with corresponding null models established for a significance test.We analyzed eight types of univariate and bivariate spatial patterns,after first proposing four types.The accuracy of the pattern analysis based on an algorithm-segmented point cloud was compared with that of a truly segmented point cloud.The algorithm-segmented point cloud managed to detect 70–86%of patterns correctly.The eight types of spatial patterns analyzed the spatial distribution of trees,the spatial correlation between tree size and facilitated or competitive interactions of sycamore and other species.These four types of univariate patterns jointly showed that,at smaller scales,the trees tend to be clustered,and taller,with larger crowns due to the detected facilitations among trees in the study area.The four types of bivariate patterns found that at smaller scales there are taller trees and more facilitation among sycamore and other species,while crown size is mostly homogeneous across scales.These results indicate that interspecific facilitation and competition mainly affect tree height in the study area.This work further confirms the connection of tree size with individual facilitation and competition,revealing the potential spatial structure that previously was hard to detect.
文摘Background:Ecological processes such as seedling establishment,biotic interactions,and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis(SPPA).Being widely used in plant ecology,SPPA is increasingly carried out to describe biotic interactions and interpret patternprocess relationships.However,some aspects are still subjected to a non-negligible debate such as required sample size(in terms of the number of points and plot area),the link between the low number of points and frequently observed random(or independent)patterns,and relating patterns to processes.In this paper,an overview of SPPA is given based on rich and updated literature providing guidance for ecologists(especially beginners)on summary statistics,uni-/bi-/multivariate analysis,unmarked/marked analysis,types of marks,etc.Some ambiguities in SPPA are also discussed.Results:SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns.Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species.Natural processes,plant environmental conditions,and human intervention are interrelated and are key drivers of plant spatial distribution.In spite of being not recommended,small sample sizes are more common in SPPA.In some areas,periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring.Conclusion:The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures.Despite the continuous progress of SPPA,some ambiguities require further clarifications.
基金grants HG003054 and HGO11052(to X.Z.)from the National Human Genome Research Institute(NHGRI),USA.
文摘Background:Mendelian randomization(MR)analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies.Many statistical approaches have been developed and each of these methods require specific assumptions.Results:In this article,we review the pros and cons of these methods.We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation.Conclusion:The current available MR approaches allow us to study causality among risk factors and outcomes.However,novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.
文摘Background:Polygenic risk score(PRS)derived from summary statistics of genome-wide association studies(GWAS)is a useful tool to infer an individuaPs genetic risk for health outcomes and has gained increasing popularity in human genetics research.PRS in its simplest form enjoys both computational efficiency and easy accessibility,yet the predictive performance of PRS remains moderate for diseases and traits.Results:We provide an overview of recent advances in statistical methods to improve PRS's performance by incorporating information from linkage disequilibrium,functional annotation,and pleiotropy.We also introduce model validation methods that fine-tune PRS using GWAS summary statistics.Conclusion:In this review,we showcase methodological advances and current limitations of PRS,and discuss several emerging issues in risk prediction research.
