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 m^(2) 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
Moldy core is a serious internal defect in pears.Since there is no significant difference in appearance between the healthy pears and those with mild moldy core,it is still a great challenge for the early detection of...Moldy core is a serious internal defect in pears.Since there is no significant difference in appearance between the healthy pears and those with mild moldy core,it is still a great challenge for the early detection of moldy pear core.This study transformed the vibration acoustic signals(VA signal)of pears into recurrence plots and Markov transition field to enable image-based classification of moldy cores.In addition to traditional machine-learning baselines(Random Forest and k-Nearest Neighbors)trained on LBP-extracted texture features from RP/MTF,the deep models were constructed and compared,which include ResNet-101,DenseNet-121,SqueezeNet,Vision Transformer(ViT),and an improved SqueezeNet(ISqueezeNet).Hyperparameters were tuned via Bayesian optimization over optimizer type,learning rate,batch size,and L2 weight decay,yielding model-specific optimal settings.Under these configurations,the ISqueezeNet achieved the highest test accuracy of 93.05%,with class-wise accuracies of 89.28%(healthy),96.15%(slight),and 94.44%(moderate and severe).Comparisons with lightweight networks(MobileNetV1 and ShuffleNetV2)further showed that ISqueezeNet attains superior accuracy with favorable parameter efficiency and inference speed.Grad-CAM visualizations confirmed that the model focuses on lesionrelevant regions,supporting interpretability and practical reliability.These results indicate that the proposed approach is promising for early,nondestructive detection of moldy pear cores.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
基金funded by Gorgan University of Agricultural Sciences and Natural Resources(grant number 9318124503).
文摘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 m^(2) 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Key R&D Projects in Shandong Province(Grant No.2022TZXD007)PhD Start-up Fund of University of Jinan(Grant No.XBS2494).
文摘Moldy core is a serious internal defect in pears.Since there is no significant difference in appearance between the healthy pears and those with mild moldy core,it is still a great challenge for the early detection of moldy pear core.This study transformed the vibration acoustic signals(VA signal)of pears into recurrence plots and Markov transition field to enable image-based classification of moldy cores.In addition to traditional machine-learning baselines(Random Forest and k-Nearest Neighbors)trained on LBP-extracted texture features from RP/MTF,the deep models were constructed and compared,which include ResNet-101,DenseNet-121,SqueezeNet,Vision Transformer(ViT),and an improved SqueezeNet(ISqueezeNet).Hyperparameters were tuned via Bayesian optimization over optimizer type,learning rate,batch size,and L2 weight decay,yielding model-specific optimal settings.Under these configurations,the ISqueezeNet achieved the highest test accuracy of 93.05%,with class-wise accuracies of 89.28%(healthy),96.15%(slight),and 94.44%(moderate and severe).Comparisons with lightweight networks(MobileNetV1 and ShuffleNetV2)further showed that ISqueezeNet attains superior accuracy with favorable parameter efficiency and inference speed.Grad-CAM visualizations confirmed that the model focuses on lesionrelevant regions,supporting interpretability and practical reliability.These results indicate that the proposed approach is promising for early,nondestructive detection of moldy pear cores.
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.
文摘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.
