Due to its low cost and its thermal and acoustic insulation properties,clay masonry is widely used in construction.Its compressive strength is the main mechanical property and is critical for structural design.This st...Due to its low cost and its thermal and acoustic insulation properties,clay masonry is widely used in construction.Its compressive strength is the main mechanical property and is critical for structural design.This study aims to predict it using machine learning(ML)techniques.An experimental database was compiled from uniaxial compression tests on solid clay masonry specimens.First,the performance of 18 empirical models from the literature was evaluated.Then,several ML algorithms were developed,including least absolute shrinkage and selection operator regression,decision tree regression,support vector regression,bagging tree,gradient boosting,random forest regression,artificial neural networks,and Gaussian process regression(GPR).The models were trained on 80%of the data and tested on the remaining 20%,with hyperparameter optimisation and 10-fold cross-validation.The findings highlight the lower performance of traditional empirical models compared to ML methods.They also show the superior predictive ability of GPR over other ML algorithms for estimating the compressive strength of clay solid masonry.Sensitivity analysis confirms that masonry unit strength is the most influential factor,with notable nonlinear interactions involving mortar strength and geometric ratios,while joint thickness primarily acts as a regime modulator.展开更多
This study investigates the floristic diversity,structural attributes,and spatial organisation of woody communities in miombo woodlands within GiléNational Park(GNP)and Niassa Special Reserve(NSR),two protected a...This study investigates the floristic diversity,structural attributes,and spatial organisation of woody communities in miombo woodlands within GiléNational Park(GNP)and Niassa Special Reserve(NSR),two protected areas in Mozambique characterised by contrasting ecological conditions and disturbance regimes.Using seven 1-ha permanent sampling plots(PSPs)—three in GNP and four in NSR—we quantified horizontal and vertical forest structure,species diversity,and spatial patterns of trees with DBH≥5 cm.The objectives were to compare the structure of tree communities(adults and juveniles),assess alpha diversity using Shannon,Simpson,and Hill numbers,analyze spatial distribution through classical aggregation indices(Payandeh,Morisita and Hazen),and evaluate floristic similarity using Bray-Curtis clustering.A total of 1,753 adult individuals(DBH≥10 cm),representing 92 species across 23 families,were recorded.Based on observed values,NSR exhibited slightly higher adult species richness(58 vs.55)and greater tree density for both adults(982 vs.771 individuals·ha⁻¹)and juveniles(1,160 vs.540 individuals·ha⁻¹),reflecting active regeneration and structural maturity.In contrast,GNP showed greater species evenness(Pielou's J=0.77 vs.0.73)and higher localized floristic heterogeneity.Dominant species such as Brachystegia spiciformis,Julbernardia globiflora,and Pseudolachnostylis maprouneifoliastrongly influenced these structural patterns,shaping spatial organization and contributing nearly half of the total basal area.Most species displayed moderate intraspecific aggregation,with conspecific individuals often clustered locally,whereas overall tree spacing tended to be regular—indicating limited interspecific mixing and the coexistence of species-level aggregation with stand-level regularity.These findings highlight the ecological distinctiveness of both forest systems and reinforce the need to expand and establish more PSPs for long-term monitoring and adaptive forest management within the framework of REDD+and national biodiversity strategies.展开更多
This paper proposes a fast quality control strategy for P-wave receiver functions based on AlexNet and wiggle plots.Receiver functions are essential tools in seismology,particularly for analyzing seismic wave propagat...This paper proposes a fast quality control strategy for P-wave receiver functions based on AlexNet and wiggle plots.Receiver functions are essential tools in seismology,particularly for analyzing seismic wave propagation and subsurface structures,such as the crust and upper mantle.However,the quality control of receiver functions is often a tedious,time-consuming process.In this study,we transform the time series classification problem of receiver function quality control problem into an image classification task by plotting receiver functions as wiggle diagrams and using the deep learning model AlexNet for binary classification to distinguish between“good”and“bad”receiver functions.The model achieved an accuracy of 92.55%on the testing set and demonstrated strong generalization performance with an accuracy of 89.23%on receiver functions of another seismic network(Sichuan Provincial Permanent Seismic Network).While maintaining strong performance,the model is capable of processing approximately 32 receiver function wiggle plots per second on an NVIDIA GeForce RTX 4050.The results show that the proposed feature mapping strategy significantly improves the efficiency and accuracy of receiver function quality control,making it a valuable tool for practical applications.Future work will focus on expanding the dataset and optimizing model performance for broader seismic data applications.展开更多
Understanding local variation in forest biomass allows for a better evaluation of broad-scale patterns and interpretation of forest ecosystems’role in carbon dynamics.This study focuses on patterns of aboveground tre...Understanding local variation in forest biomass allows for a better evaluation of broad-scale patterns and interpretation of forest ecosystems’role in carbon dynamics.This study focuses on patterns of aboveground tree biomass within a fully censused 20 ha forest plot in a temperate forest of northern Alabama,USA.We evaluated the relationship between biomass and topography using ridge and valley landforms along with digitally derived moisture and solar radiation indices.Every live woody stem over 1 cm diameter at breast height within this plot was mapped,measured,and identified to species in 2019-2022,and diameter data were used along with speciesspecific wood density to map the aboveground biomass at the scale of 20 m×20 m quadrats.The aboveground tree biomass was 211 Mg·ha-1.Other than small stream areas that experienced recent natural disturbances,the total stand biomass was not associated with landform or topographic indices.Dominant species,in contrast,had strong associations with topography.American beech(Fagus grandifolia)and yellow-poplar(Liriodendron tulipfera)dominated the valley landform,with 37% and 54% greater biomass in the valley than their plot average,respectively.Three other dominant species,white oak(Quercus alba),southern shagbark hickory(Carya carolinaeseptentrionalis),and white ash(Fraxinus americana),were more abundant on slopes and benches,thus partitioning the site.Of the six dominant species,only sugar maple(Acer saccharum)was not associated with landform.Moreover,both topographic wetness and potential radiation indices were significant predictors of dominant species biomass within each of the landforms.The study highlights the need to consider species when examining forest productivity in a range of site conditions.展开更多
Plant species diversity is one of the most widely used indicators in ecosystem management.The relation of species diversity with the size of the sample plot has not been fully determined for Oriental beech forests(Fag...Plant species diversity is one of the most widely used indicators in ecosystem management.The relation of species diversity with the size of the sample plot has not been fully determined for Oriental beech forests(Fagus orientalis Lipsky),a widespread species in the Hyrcanian region.Assessing the impacts of plot size on species diversity is fundamental for an ecosystem-based approach to forest management.This study determined the relation of species diversity and plot size by investigating species richness and abundance of both canopy and forest floor.Two hundred and fifty-six sample plots of 625 m2 each were layout in a grid pattern across 16 ha.Base plots(25 m×25 m)were integrated in different scales to investigate the effect of plot size on species diversity.The total included nine plots of 0.063,0.125,0.188,0.250,0.375,0.500,0.563,0.750 and 1 ha.Ten biodiversity indices were calculated.The results show that species richness in the different plot sizes was less than the actual value.The estimated value of the Simpson species diversity index was not significantly different from actual values for both canopy and forest floor diversity.The coefficient of variation of this index for the 1-ha sample plot showed the lowest amount across different plot sizes.Inverse Hill species diversity was insignificant difference across different plot sizes with an area greater than 0.500 ha.The modified Hill evenness index for the 1-ha sample size was a correct estimation of the 16-ha for both canopy and forest floor;however,the precision estimation was higher for the canopy layer.All plots greater than 0.250-ha provided an accurate estimation of the Camargo evenness index for forest floor species,but was inaccurate across different plot sizes for the canopy layer.The results indicate that the same plot size did not have the same effect across species diversity measurements.Our results show that correct estimation of species diversity measurements is related to the selection of appropriate indicators and plot size to increase the accuracy of the estimate so that the cost and time of biodiversity management may be reduced.展开更多
Understanding the degree to which the species diversity-productivity relationship(SDPR)is applicable to natural ecosystems-beyond modeling and experimental contexts-is of vital importance for comprehending the conse-q...Understanding the degree to which the species diversity-productivity relationship(SDPR)is applicable to natural ecosystems-beyond modeling and experimental contexts-is of vital importance for comprehending the conse-quences of global biodiversity loss on terrestrial ecosystems.Two essential features of natural forests that have not received adequate attention in the SDPR are seasonality and species evenness.Here,we monitor the intra-and inter-annual growths of 6,515 trees in a subtropical seasonal(temperature-and rainfall-seasonal)forest over a six-year period.We investigate whether evenness affects forest productivity independently or interacting with richness and how the underlying mechanisms shift with seasonality and soil properties,employing structural equation modeling.Our findings reveal a consistent decline in species diversity,functional diversity and forest productivity from the wet-warm season to the dry-cold season,with community traits shifting from acquisition to conservative strategies.Species richness increases but evenness decreases forest productivity-uneven commu-nities are more productive,and the attenuation effect of evenness on productivity varies slightly across different seasons.Species richness and evenness jointly affect productivity through community-weighted means(CWM)of functional traits in the wet-warm season and through both functional diversity and CWM of functional traits in the dry-cold season,indicating that the mass ratio effect is predominant during the wet-warm season,whereas both niche complementarity and mass ratio effects jointly drive productivity in the dry-cold season.Soil water availability directly affects forest productivity in the wet-warm season and indirectly through CWM of functional traits in the dry-cold season.Our study,the first to elucidate the seasonal dynamics of the SDPR in subtropical forests.Our results highlight species evenness as a key component of species diversity regulating seasonal pro-ductivity dynamics in heterogeneous,species-rich natural forests.To enhance forest resilience under climate change(e.g.,drought),management should prioritize maintaining moderate evenness,while strategically planting drought-tolerant species and acquisitive species of subtropical forest ecosystems.展开更多
Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of ...Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of this species,it is important to have accurate and precise projections over time to make efficient decisions for forest management and greenfield investments in afforestation projects,especially for permanent carbon forests.Future projections of any natural resource systems rely on modeling;however,the acceleration of climate change makes future projections of yield less certain.These challenges also impact national expectations of the contribution planted forests will provide to address climate change and meet international commitments under the Paris Agreement.Using a large national-scale set of contemporary ground-measured data(2013–2023),this study investigates the performance of two growth models developed over 30 years ago that are widely used by NZ plantation growers:1)the Pumice Plateau Model 1988(PPM88)and 2)the 300-index(including a model variant of regional drift).Model simulations were made using the FORECASTER modeling suite with geographic boundaries to adjust for drift in space and time.Basal area(BA,m2⋅ha-1)and volume(m3⋅ha-1)were simulated,and standard errors and goodness-of-fit metrics calculated up to a typical rotation age of 30 years.Model residuals were then separated and analysed for the main plantation growing regions.The models overpredicted observed growth by between 6.8%and 16.2%,but model predictions and errors varied significantly between regions.The results of this study provided clear evidence of divergence between the outputs of both models and the measured data.Finally,this study suggests future measures to address challenges posed by these discrepancies that will provide better information for forest management and investment decisions in a changing climate.展开更多
摘要Due to its low cost and its thermal and acoustic insulation properties,clay masonry is widely used in construction.Its compressive strength is the main mechanical property and is critical for structural design.This study aims to predict it using machine learning(ML)techniques.An experimental database was compiled from uniaxial compression tests on solid clay masonry specimens.First,the performance of 18 empirical models from the literature was evaluated.Then,several ML algorithms were developed,including least absolute shrinkage and selection operator regression,decision tree regression,support vector regression,bagging tree,gradient boosting,random forest regression,artificial neural networks,and Gaussian process regression(GPR).The models were trained on 80%of the data and tested on the remaining 20%,with hyperparameter optimisation and 10-fold cross-validation.The findings highlight the lower performance of traditional empirical models compared to ML methods.They also show the superior predictive ability of GPR over other ML algorithms for estimating the compressive strength of clay solid masonry.Sensitivity analysis confirms that masonry unit strength is the most influential factor,with notable nonlinear interactions involving mortar strength and geometric ratios,while joint thickness primarily acts as a regime modulator.
基金the National Sustainable Development Fund(FNDS)for financially supporting the activities through the project“Inves-timento Florestal de Moçambique”(MozFIP),which enabled the estab-lishment of PSPs。
摘要This study investigates the floristic diversity,structural attributes,and spatial organisation of woody communities in miombo woodlands within GiléNational Park(GNP)and Niassa Special Reserve(NSR),two protected areas in Mozambique characterised by contrasting ecological conditions and disturbance regimes.Using seven 1-ha permanent sampling plots(PSPs)—three in GNP and four in NSR—we quantified horizontal and vertical forest structure,species diversity,and spatial patterns of trees with DBH≥5 cm.The objectives were to compare the structure of tree communities(adults and juveniles),assess alpha diversity using Shannon,Simpson,and Hill numbers,analyze spatial distribution through classical aggregation indices(Payandeh,Morisita and Hazen),and evaluate floristic similarity using Bray-Curtis clustering.A total of 1,753 adult individuals(DBH≥10 cm),representing 92 species across 23 families,were recorded.Based on observed values,NSR exhibited slightly higher adult species richness(58 vs.55)and greater tree density for both adults(982 vs.771 individuals·ha⁻¹)and juveniles(1,160 vs.540 individuals·ha⁻¹),reflecting active regeneration and structural maturity.In contrast,GNP showed greater species evenness(Pielou's J=0.77 vs.0.73)and higher localized floristic heterogeneity.Dominant species such as Brachystegia spiciformis,Julbernardia globiflora,and Pseudolachnostylis maprouneifoliastrongly influenced these structural patterns,shaping spatial organization and contributing nearly half of the total basal area.Most species displayed moderate intraspecific aggregation,with conspecific individuals often clustered locally,whereas overall tree spacing tended to be regular—indicating limited interspecific mixing and the coexistence of species-level aggregation with stand-level regularity.These findings highlight the ecological distinctiveness of both forest systems and reinforce the need to expand and establish more PSPs for long-term monitoring and adaptive forest management within the framework of REDD+and national biodiversity strategies.
基金supported by the National Natural Science Foundation of China(No.42174071)the National Key Research and Development Program of China(No.2022YFF0800601)Sichuan Key Research and Development Program(No.2023YFS0433)。
摘要This paper proposes a fast quality control strategy for P-wave receiver functions based on AlexNet and wiggle plots.Receiver functions are essential tools in seismology,particularly for analyzing seismic wave propagation and subsurface structures,such as the crust and upper mantle.However,the quality control of receiver functions is often a tedious,time-consuming process.In this study,we transform the time series classification problem of receiver function quality control problem into an image classification task by plotting receiver functions as wiggle diagrams and using the deep learning model AlexNet for binary classification to distinguish between“good”and“bad”receiver functions.The model achieved an accuracy of 92.55%on the testing set and demonstrated strong generalization performance with an accuracy of 89.23%on receiver functions of another seismic network(Sichuan Provincial Permanent Seismic Network).While maintaining strong performance,the model is capable of processing approximately 32 receiver function wiggle plots per second on an NVIDIA GeForce RTX 4050.The results show that the proposed feature mapping strategy significantly improves the efficiency and accuracy of receiver function quality control,making it a valuable tool for practical applications.Future work will focus on expanding the dataset and optimizing model performance for broader seismic data applications.
基金supported in part by the intramural research program of the US Department of Agriculture,National Institute of Food and Agriculture,Evans-Allen#1024525,and Capacity Building Grant#006531supported in part by the US National Science Foundation RII Track 2 FEC:Leveraging Intelligent Informatics and Smart Data for Improved Understanding of Northern Forest Ecosystem Resiliency(INSPIRES)#1920908by The Lyndhurst Foundation.
摘要Understanding local variation in forest biomass allows for a better evaluation of broad-scale patterns and interpretation of forest ecosystems’role in carbon dynamics.This study focuses on patterns of aboveground tree biomass within a fully censused 20 ha forest plot in a temperate forest of northern Alabama,USA.We evaluated the relationship between biomass and topography using ridge and valley landforms along with digitally derived moisture and solar radiation indices.Every live woody stem over 1 cm diameter at breast height within this plot was mapped,measured,and identified to species in 2019-2022,and diameter data were used along with speciesspecific wood density to map the aboveground biomass at the scale of 20 m×20 m quadrats.The aboveground tree biomass was 211 Mg·ha-1.Other than small stream areas that experienced recent natural disturbances,the total stand biomass was not associated with landform or topographic indices.Dominant species,in contrast,had strong associations with topography.American beech(Fagus grandifolia)and yellow-poplar(Liriodendron tulipfera)dominated the valley landform,with 37% and 54% greater biomass in the valley than their plot average,respectively.Three other dominant species,white oak(Quercus alba),southern shagbark hickory(Carya carolinaeseptentrionalis),and white ash(Fraxinus americana),were more abundant on slopes and benches,thus partitioning the site.Of the six dominant species,only sugar maple(Acer saccharum)was not associated with landform.Moreover,both topographic wetness and potential radiation indices were significant predictors of dominant species biomass within each of the landforms.The study highlights the need to consider species when examining forest productivity in a range of site conditions.
基金funded by Gorgan University of Agricultural Sciences and Natural Resources(grant number 9318124503).
摘要Plant species diversity is one of the most widely used indicators in ecosystem management.The relation of species diversity with the size of the sample plot has not been fully determined for Oriental beech forests(Fagus orientalis Lipsky),a widespread species in the Hyrcanian region.Assessing the impacts of plot size on species diversity is fundamental for an ecosystem-based approach to forest management.This study determined the relation of species diversity and plot size by investigating species richness and abundance of both canopy and forest floor.Two hundred and fifty-six sample plots of 625 m2 each were layout in a grid pattern across 16 ha.Base plots(25 m×25 m)were integrated in different scales to investigate the effect of plot size on species diversity.The total included nine plots of 0.063,0.125,0.188,0.250,0.375,0.500,0.563,0.750 and 1 ha.Ten biodiversity indices were calculated.The results show that species richness in the different plot sizes was less than the actual value.The estimated value of the Simpson species diversity index was not significantly different from actual values for both canopy and forest floor diversity.The coefficient of variation of this index for the 1-ha sample plot showed the lowest amount across different plot sizes.Inverse Hill species diversity was insignificant difference across different plot sizes with an area greater than 0.500 ha.The modified Hill evenness index for the 1-ha sample size was a correct estimation of the 16-ha for both canopy and forest floor;however,the precision estimation was higher for the canopy layer.All plots greater than 0.250-ha provided an accurate estimation of the Camargo evenness index for forest floor species,but was inaccurate across different plot sizes for the canopy layer.The results indicate that the same plot size did not have the same effect across species diversity measurements.Our results show that correct estimation of species diversity measurements is related to the selection of appropriate indicators and plot size to increase the accuracy of the estimate so that the cost and time of biodiversity management may be reduced.
基金funded by the National Natural Science Foundation of China(Nos.32471622 and 32171536).
摘要Understanding the degree to which the species diversity-productivity relationship(SDPR)is applicable to natural ecosystems-beyond modeling and experimental contexts-is of vital importance for comprehending the conse-quences of global biodiversity loss on terrestrial ecosystems.Two essential features of natural forests that have not received adequate attention in the SDPR are seasonality and species evenness.Here,we monitor the intra-and inter-annual growths of 6,515 trees in a subtropical seasonal(temperature-and rainfall-seasonal)forest over a six-year period.We investigate whether evenness affects forest productivity independently or interacting with richness and how the underlying mechanisms shift with seasonality and soil properties,employing structural equation modeling.Our findings reveal a consistent decline in species diversity,functional diversity and forest productivity from the wet-warm season to the dry-cold season,with community traits shifting from acquisition to conservative strategies.Species richness increases but evenness decreases forest productivity-uneven commu-nities are more productive,and the attenuation effect of evenness on productivity varies slightly across different seasons.Species richness and evenness jointly affect productivity through community-weighted means(CWM)of functional traits in the wet-warm season and through both functional diversity and CWM of functional traits in the dry-cold season,indicating that the mass ratio effect is predominant during the wet-warm season,whereas both niche complementarity and mass ratio effects jointly drive productivity in the dry-cold season.Soil water availability directly affects forest productivity in the wet-warm season and indirectly through CWM of functional traits in the dry-cold season.Our study,the first to elucidate the seasonal dynamics of the SDPR in subtropical forests.Our results highlight species evenness as a key component of species diversity regulating seasonal pro-ductivity dynamics in heterogeneous,species-rich natural forests.To enhance forest resilience under climate change(e.g.,drought),management should prioritize maintaining moderate evenness,while strategically planting drought-tolerant species and acquisitive species of subtropical forest ecosystems.
基金funded by Scion's Strategic Science Investment Fund(SSIF)the Forest Growers Levy Trust(FGLT)through the Resilient Forests Programme(Task No.A89220)。
摘要Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of this species,it is important to have accurate and precise projections over time to make efficient decisions for forest management and greenfield investments in afforestation projects,especially for permanent carbon forests.Future projections of any natural resource systems rely on modeling;however,the acceleration of climate change makes future projections of yield less certain.These challenges also impact national expectations of the contribution planted forests will provide to address climate change and meet international commitments under the Paris Agreement.Using a large national-scale set of contemporary ground-measured data(2013–2023),this study investigates the performance of two growth models developed over 30 years ago that are widely used by NZ plantation growers:1)the Pumice Plateau Model 1988(PPM88)and 2)the 300-index(including a model variant of regional drift).Model simulations were made using the FORECASTER modeling suite with geographic boundaries to adjust for drift in space and time.Basal area(BA,m2⋅ha-1)and volume(m3⋅ha-1)were simulated,and standard errors and goodness-of-fit metrics calculated up to a typical rotation age of 30 years.Model residuals were then separated and analysed for the main plantation growing regions.The models overpredicted observed growth by between 6.8%and 16.2%,but model predictions and errors varied significantly between regions.The results of this study provided clear evidence of divergence between the outputs of both models and the measured data.Finally,this study suggests future measures to address challenges posed by these discrepancies that will provide better information for forest management and investment decisions in a changing climate.