Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract usef...Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.展开更多
Aims The prediction that facilitation is the dominant interaction in physically stressful conditions has been supported by many but not all field studies.In the present paper,we tested the effects of the identity of s...Aims The prediction that facilitation is the dominant interaction in physically stressful conditions has been supported by many but not all field studies.In the present paper,we tested the effects of the identity of species,the local environmental conditions and the currencies of performance measurement on such variation.Methods Using contrasting two plots,six species,and up to five multiple traits,we comprehensively explored the effects of the above factors on the assessment of plant interactions in an alpine meadow of the QingHai Tibetan Plateau.Additionally,we attempted to figure out the possible mechanisms underlying the responses observed.The data were analysed by both standard ANOVAs and multivariate statistics.Important findings Our results demonstrated that the response to the removal of neighbours was both species and trait specific,and the effect of the ocal environmental conditions was dependent on the species involved.The contrast between plots had crucial influence on the net interactions of Kobresia macrantha,but little effect on Elymus nutans.Regarding the abiotic conditions,neighbours had significant impact on soil temperature,moist and solar radiation.The results contribute to advance our knowledge on the potential underlying factors influencing the assessment of facilitation.展开更多
The influences of technical factors on the catching efficiencies of gillnets are well understood,but less is known about the importance of environmental factors and how these might concurrently affect target-species c...The influences of technical factors on the catching efficiencies of gillnets are well understood,but less is known about the importance of environmental factors and how these might concurrently affect target-species condition.Here we address this deficit for two economically important southeast Asian cyprinids(Labiobarbus festivus and Osteochilus hasseltii)during a one-year study at a key fishing location in Lake Kenyir,Malaysia.Three gillnets(each 200 m long,2 m deep and comprising either 38-,51-,or 76-mm mesh)were deployed each month concurrent with sampling of benthic macroinvertebrates and various environmental parameters.Various multivariate analyses(detrended correspondence analysis,redundancy analysis and permutational multivariate analysis of variance)were used to explore potential relationships between the extrinsic variables(mesh size,sampling season,water quality and,based on known prey items,benthic macroinvertebrates)and gillnet catches and the biological condition(growth co-efficient and hepatosomatic index)of the target species.Catches of L.festivus(the most abundant species)were positively influenced by water turbidity without seasonal effects,while their biological condition was positively influenced by benthic macroinvertebrates(mostly Trichoptera)and the concentrations of DO and phosphate,and negatively influenced by water temperature.By comparison,seasonal influences on the catches and biology of O.hasseltii were somewhat size specific with positive and negative effects of the monsoon on small and large fish,respectively.The abundance of phytoplankton also positively affected the catches of O.hasseltii,while their biological condition was positively influenced by water temperature,but negatively influenced by water turbidity.Such species-specific differences were attributed to life histories,and although the more abundant L.festivus might be best sought during any period of turbidity,effort should be focused during the monsoon when individuals have optimal condition(and therefore better flesh quality).In contrast,larger O.hasseltii might be best sought during non-monsoon months concurrent with greater catches and optimal condition.Collecting similar data for other freshwater species both nationally and internationally might facilitate future efforts at fine-tuning gillnet fishing effort.展开更多
文摘Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.
基金National Natural Science Foundation of China(30770360)Research Fund for the Doctoral Program of Higher Education of China(20070730051 to S.X.).
文摘Aims The prediction that facilitation is the dominant interaction in physically stressful conditions has been supported by many but not all field studies.In the present paper,we tested the effects of the identity of species,the local environmental conditions and the currencies of performance measurement on such variation.Methods Using contrasting two plots,six species,and up to five multiple traits,we comprehensively explored the effects of the above factors on the assessment of plant interactions in an alpine meadow of the QingHai Tibetan Plateau.Additionally,we attempted to figure out the possible mechanisms underlying the responses observed.The data were analysed by both standard ANOVAs and multivariate statistics.Important findings Our results demonstrated that the response to the removal of neighbours was both species and trait specific,and the effect of the ocal environmental conditions was dependent on the species involved.The contrast between plots had crucial influence on the net interactions of Kobresia macrantha,but little effect on Elymus nutans.Regarding the abiotic conditions,neighbours had significant impact on soil temperature,moist and solar radiation.The results contribute to advance our knowledge on the potential underlying factors influencing the assessment of facilitation.
基金the International Islamic University Malaysia,Malaysia for providing financial support through P-RIGS18-032-0032 project.
文摘The influences of technical factors on the catching efficiencies of gillnets are well understood,but less is known about the importance of environmental factors and how these might concurrently affect target-species condition.Here we address this deficit for two economically important southeast Asian cyprinids(Labiobarbus festivus and Osteochilus hasseltii)during a one-year study at a key fishing location in Lake Kenyir,Malaysia.Three gillnets(each 200 m long,2 m deep and comprising either 38-,51-,or 76-mm mesh)were deployed each month concurrent with sampling of benthic macroinvertebrates and various environmental parameters.Various multivariate analyses(detrended correspondence analysis,redundancy analysis and permutational multivariate analysis of variance)were used to explore potential relationships between the extrinsic variables(mesh size,sampling season,water quality and,based on known prey items,benthic macroinvertebrates)and gillnet catches and the biological condition(growth co-efficient and hepatosomatic index)of the target species.Catches of L.festivus(the most abundant species)were positively influenced by water turbidity without seasonal effects,while their biological condition was positively influenced by benthic macroinvertebrates(mostly Trichoptera)and the concentrations of DO and phosphate,and negatively influenced by water temperature.By comparison,seasonal influences on the catches and biology of O.hasseltii were somewhat size specific with positive and negative effects of the monsoon on small and large fish,respectively.The abundance of phytoplankton also positively affected the catches of O.hasseltii,while their biological condition was positively influenced by water temperature,but negatively influenced by water turbidity.Such species-specific differences were attributed to life histories,and although the more abundant L.festivus might be best sought during any period of turbidity,effort should be focused during the monsoon when individuals have optimal condition(and therefore better flesh quality).In contrast,larger O.hasseltii might be best sought during non-monsoon months concurrent with greater catches and optimal condition.Collecting similar data for other freshwater species both nationally and internationally might facilitate future efforts at fine-tuning gillnet fishing effort.
