Leaf trait networks(LTNs)visualize the intricate linkages reflecting plant trait-functional coordination.Typical karst vegetation,developed from lithological dolomite and limestone,generally exhibits differential comm...Leaf trait networks(LTNs)visualize the intricate linkages reflecting plant trait-functional coordination.Typical karst vegetation,developed from lithological dolomite and limestone,generally exhibits differential communities,possibly due to habitat rock exposure,soil depth,and soil physicochemical properties variations,leading to a shift from plant trait variation to functional linkages.However,how soil and habitat quality affect the differentiation of leaf trait networks remains unclear.LTNs were constructed for typical dolomite and limestone habitats by analyzing twenty-one woody plant leaf traits across fifty-six forest subplots in karst mountains.The differences between dolomite and limestone LTNs were compared using network parameters.The network association of soil and habitat quality was analyzed using redundancy analysis(RDA),Mantle's test,and a random forest model.The limestone LTN exhibited significantly higher edge density with lower diameter and average path length when compared to the dolomite LTN.It indicates LTN differentiation,with the limestone network displaying a more compact architecture and higher connectivity than the dolomite network.The specific leaf phosphorus and leaf nitrogen contents of dolomite LTN,as well as the leaf mass and leaf carbon contents of limestone LTN,significantly contributed to network degree and closeness,serving as crucial node traits regulating LTN connectedness.Additionally,both habitat LTNs significantly correlated with soil nitrogen and phosphorus,stoichiometric ratios,pH,and organic carbon,as well as soil depth and rock exposure rates,with soil depth and rock exposure showing greater relative importance.Soil depth and rock exposure dominate trait network differentiation,with the limestone habitat exhibiting a more compact network architecture than the dolomite habitat.展开更多
Background:With the expansion of urban areas,the remnants of forested areas play a crucial role in preserving biodiversity in urban environments.This study aimed to explore the impact of spatiotemporal urban expansion...Background:With the expansion of urban areas,the remnants of forested areas play a crucial role in preserving biodiversity in urban environments.This study aimed to explore the impact of spatiotemporal urban expansion on the networks of leaf traits in woody plants within remnant forest patches,thereby enhancing our understanding of plant adaptive strategies and contributing to the conservation of urban biodiversity.Methods:Our study examined woody plants within 120 sample plots across 15 remnant forest patches in Guiyang,China.We constructed leaf trait networks (LTNs) based on 26 anatomical,structural,and compositional leaf traits and assessed the effects of the spatiotemporal dynamics of urban expansion on these LTNs.Results and conclusions:Our results indicate that shrubs within these patches have greater average path lengths and diameters than trees.With increasing urban expansion intensity,we observed a rise in the edge density of the LTN-shrubs.Additionally,modularity within the networks of shrubs decreased as road density and urban expansion intensity increased,and increases in the average path length and average clustering coefficient for shrubs were observed with a rise in the composite terrain complexity index.Notably,patches subjected to‘leapfrog’expansion exhibited greater average patch length and diameter than those experiencing edge growth.Stomatal traits were found to have high degree centrality within these networks,signifying their substantial contribution to multiple functions.In urban remnant forests,shrubs bolster their resilience to variable environmental pressures by augmenting the complexity of their leaf trait networks.展开更多
Traits and their correlation networks can reflect plant adaptive strategies. However, variations in traits and trait correlation networks across heteromorphic leaves within species remain largely unexplored. In this s...Traits and their correlation networks can reflect plant adaptive strategies. However, variations in traits and trait correlation networks across heteromorphic leaves within species remain largely unexplored. In this study, we systematically quantified a diverse array of leaf traits—spanning morphology, anatomy, physiology and biochemistry—among the striped, lanceolate, ovate, and broadly ovate leaves of Populus euphratica, aiming to elucidate the adaptive differences across these various leaf types. We found that the four heteromorphic leaves showed significant differences in leaf traits. From striped leaves to broadly ovate leaves, leaf size, leaf thickness, water use efficiency and catalase content significantly increased, while specific leaf area showed the opposite pattern. Principal component analysis and cluster analysis revealed distinct aggregation and clear demarcation of the four leaf types, indicating substantial variations in trait compositions and their distinct ecological adaptations. Plant trait networks varied significantly across the four leaf types, with the broadly ovate leaves exhibiting a fragmented network structure that enhances their modularity. This suggests strong resilience to disturbances and is consistent with the characteristic foliage on mature trees. Regardless of leaf type, nitrogen and phosphorus consistently emerged as hub traits within plant trait networks, underscoring their fundamental role in driving physiological processes and influencing phenotypic expression. This study meticulously delineates the variations in both individual leaf traits and trait correlation networks across the heteromorphic leaves of P. euphratica, significantly deepening our understanding of plant adaptive strategies.展开更多
Evaluating individuals＇ personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possibl...Evaluating individuals＇ personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possible correlations between personality traits （also measured intelligence） and face images, we first construct a dataset consisting of face photographs, personality measurements, and intelligence measurements. Then, we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image. To our knowledge, it is the first work where deep learning is applied to this problem. Experimental results show the following three points： 1） ＂Rule-consciousness＂ and ＂Tension＂ can be reliably predicted from face images. 2） It is difficult, if not impossible, to predict intelligence from face images, a finding in accord with previous studies. 3） Convolutional neural network （CNN） features outperform traditional handcrafted features in predicting traits.展开更多
基金supported by the National Natural Science Foundation of China(NSFC:32260268)the Science and Technology Project of Guizhou Province[(2021)General-455]the Guizhou Hundred-level Innovative Talents Project[Qian-ke-he platform talents(2020)6004-2].
文摘Leaf trait networks(LTNs)visualize the intricate linkages reflecting plant trait-functional coordination.Typical karst vegetation,developed from lithological dolomite and limestone,generally exhibits differential communities,possibly due to habitat rock exposure,soil depth,and soil physicochemical properties variations,leading to a shift from plant trait variation to functional linkages.However,how soil and habitat quality affect the differentiation of leaf trait networks remains unclear.LTNs were constructed for typical dolomite and limestone habitats by analyzing twenty-one woody plant leaf traits across fifty-six forest subplots in karst mountains.The differences between dolomite and limestone LTNs were compared using network parameters.The network association of soil and habitat quality was analyzed using redundancy analysis(RDA),Mantle's test,and a random forest model.The limestone LTN exhibited significantly higher edge density with lower diameter and average path length when compared to the dolomite LTN.It indicates LTN differentiation,with the limestone network displaying a more compact architecture and higher connectivity than the dolomite network.The specific leaf phosphorus and leaf nitrogen contents of dolomite LTN,as well as the leaf mass and leaf carbon contents of limestone LTN,significantly contributed to network degree and closeness,serving as crucial node traits regulating LTN connectedness.Additionally,both habitat LTNs significantly correlated with soil nitrogen and phosphorus,stoichiometric ratios,pH,and organic carbon,as well as soil depth and rock exposure rates,with soil depth and rock exposure showing greater relative importance.Soil depth and rock exposure dominate trait network differentiation,with the limestone habitat exhibiting a more compact network architecture than the dolomite habitat.
基金funded by the National Natural Science Foundation of China (No.32360418)the Guizhou Provincial Basic Research Program (Natural Science)(No.QianKeHeJiChu-ZK[2024]YiBan022)。
文摘Background:With the expansion of urban areas,the remnants of forested areas play a crucial role in preserving biodiversity in urban environments.This study aimed to explore the impact of spatiotemporal urban expansion on the networks of leaf traits in woody plants within remnant forest patches,thereby enhancing our understanding of plant adaptive strategies and contributing to the conservation of urban biodiversity.Methods:Our study examined woody plants within 120 sample plots across 15 remnant forest patches in Guiyang,China.We constructed leaf trait networks (LTNs) based on 26 anatomical,structural,and compositional leaf traits and assessed the effects of the spatiotemporal dynamics of urban expansion on these LTNs.Results and conclusions:Our results indicate that shrubs within these patches have greater average path lengths and diameters than trees.With increasing urban expansion intensity,we observed a rise in the edge density of the LTN-shrubs.Additionally,modularity within the networks of shrubs decreased as road density and urban expansion intensity increased,and increases in the average path length and average clustering coefficient for shrubs were observed with a rise in the composite terrain complexity index.Notably,patches subjected to‘leapfrog’expansion exhibited greater average patch length and diameter than those experiencing edge growth.Stomatal traits were found to have high degree centrality within these networks,signifying their substantial contribution to multiple functions.In urban remnant forests,shrubs bolster their resilience to variable environmental pressures by augmenting the complexity of their leaf trait networks.
基金supported by the National Natural Science Foundation of China (Grant number 31570407)。
文摘Traits and their correlation networks can reflect plant adaptive strategies. However, variations in traits and trait correlation networks across heteromorphic leaves within species remain largely unexplored. In this study, we systematically quantified a diverse array of leaf traits—spanning morphology, anatomy, physiology and biochemistry—among the striped, lanceolate, ovate, and broadly ovate leaves of Populus euphratica, aiming to elucidate the adaptive differences across these various leaf types. We found that the four heteromorphic leaves showed significant differences in leaf traits. From striped leaves to broadly ovate leaves, leaf size, leaf thickness, water use efficiency and catalase content significantly increased, while specific leaf area showed the opposite pattern. Principal component analysis and cluster analysis revealed distinct aggregation and clear demarcation of the four leaf types, indicating substantial variations in trait compositions and their distinct ecological adaptations. Plant trait networks varied significantly across the four leaf types, with the broadly ovate leaves exhibiting a fragmented network structure that enhances their modularity. This suggests strong resilience to disturbances and is consistent with the characteristic foliage on mature trees. Regardless of leaf type, nitrogen and phosphorus consistently emerged as hub traits within plant trait networks, underscoring their fundamental role in driving physiological processes and influencing phenotypic expression. This study meticulously delineates the variations in both individual leaf traits and trait correlation networks across the heteromorphic leaves of P. euphratica, significantly deepening our understanding of plant adaptive strategies.
基金supported by National Natural Science Foundation of China(Nos.61333015,61421004 and 61375042)Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB02070002)
文摘Evaluating individuals＇ personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possible correlations between personality traits （also measured intelligence） and face images, we first construct a dataset consisting of face photographs, personality measurements, and intelligence measurements. Then, we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image. To our knowledge, it is the first work where deep learning is applied to this problem. Experimental results show the following three points： 1） ＂Rule-consciousness＂ and ＂Tension＂ can be reliably predicted from face images. 2） It is difficult, if not impossible, to predict intelligence from face images, a finding in accord with previous studies. 3） Convolutional neural network （CNN） features outperform traditional handcrafted features in predicting traits.
