Although numerous studies have proposed explanations for the specific and relative effects of stand structure,plant diversity,and environmental conditions on carbon(C)storage in forest ecosystems,understanding how the...Although numerous studies have proposed explanations for the specific and relative effects of stand structure,plant diversity,and environmental conditions on carbon(C)storage in forest ecosystems,understanding how these factors collectively affect C storage in different community layers(trees,shrubs,and herbs)and forest types(mixed,broad-leaved(E),broad-leaved(M),and coniferous forest)continues to pose challenges.To address this,we used structural equation models to quantify the influence of biotic factors(mean DBH,mean height,maximum height,stem density,and basal area)and abiotic factors(elevation and canopy openness),as well as metrics of species diversity(Shannon–Wiener index,Simpson index,and Pielou’s evenness)in various forest types.Our analysis revealed the critical roles of forest types and elevation in explaining a substantial portion of variability in C storage in the overstory layer,with a moderate influence of stand factors(mean DBH and basal area)and a slightly negative impact of tree species diversity(Shannon–Wiener index).Notably,forest height emerged as the primary predictor of C storage in the herb layer.Regression relationships further highlighted the significant contribution of tree species diversity to mean height,understory C storage,and branch biomass within the forest ecosystem.Our insights into tree species diversity,derived from structural equation modeling of C storage in the overstory,suggest that the effects of tree species diversity may be influenced by stem biomass in statistical reasoning within temperate forests.Further research should also integrate tree species diversity with tree components biomass,forest mean height,understory C,and canopy openness to understand complex relationships and maintain healthy and sustainable ecosystems in the face of global climate challenges.展开更多
Training generative adversarial networks is data-demanding,which limits the development of these models on target domains with inadequate training data.Recently,researchers have leveraged generative models pretrained ...Training generative adversarial networks is data-demanding,which limits the development of these models on target domains with inadequate training data.Recently,researchers have leveraged generative models pretrained on sufficient data and fine-tuned them using small training samples,thus reducing data requirements.However,due to the lack of explicit focus on target styles and disproportionately concentrating on generative consistency,these methods do not perform well in diversity preservation which represents the adaptation ability for few-shot generative models.To mitigate the diversity degradation,we propose a framework with two key strategies:1)To obtain more diverse styles from limited training data effectively,we propose a cross-modal module that explicitly obtains the target styles with a style prototype space and text-guided style instructions.2)To inherit the generation capability from the pretrained model,we aim to constrain the similarity between the generated and source images with a structural discrepancy alignment module by maintaining the structure correlation in multiscale areas.We demonstrate the effectiveness of our method,which outperforms state-of-the-art methods in mitigating diversity degradation through extensive experiments and analyses.展开更多
Biodiversity is found to have a significant promotion effect on ecosystem functions in manipulation experiments on grassland communities.However,its relative role compared with stand factors or functional identity is ...Biodiversity is found to have a significant promotion effect on ecosystem functions in manipulation experiments on grassland communities.However,its relative role compared with stand factors or functional identity is still controversial in natural forests.Here,we examined their relative effects on biomass and productivity during forest restoration.We investigated stand biomass and productivity for 24 plots(600 m2)across restoration stages in the subtropical forests of Mt.Shennongjia,Central China.We measured five key functional traits and calculated functional diversity(functional richness,evenness and dispersion)and community-weighted mean of traits.We used general linear models,variation partitioning methods to test the relative importance of stand factors(density,stand age,maximum height,etc.),functional identity,species and functional diversity on biomass and productivity.Our results illustrated that stand biomass and productivity increased significantly as forest restoration,and that community species richness increased,while functional dispersion decreased significantly.Variation partitioning analyses showed that diversity had no significant pure effects on biomass and productivity.However,diversity may affect biomass and productivity through the joint effect with stand factors and functional identity.Overall,we found that stand factors had the strongest effect on biomass and productivity,while functional identity significantly affects productivity but not biomass,suggesting that modulating stand structure and species identity are effective ways to enhance forest carbon storage and sequestrations potential in forest management.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(2021ZY89)the National Natural Science Foundation of China(32201258 and 32271652)+4 种基金Research Service Project on the Effects of Extreme Climate on Biodiversity and Conservation Strategies in Mentougou District(2024HXFWBH-XJL-02)the Fang Jingyun Ecological Study Studio of Yunnan Province(China)the State Scholarship Fund of China(2011811457)support to the Xingdian Scholar Fund of Yunnan Provincethe Double Top University Fund of Yunnan University.
文摘Although numerous studies have proposed explanations for the specific and relative effects of stand structure,plant diversity,and environmental conditions on carbon(C)storage in forest ecosystems,understanding how these factors collectively affect C storage in different community layers(trees,shrubs,and herbs)and forest types(mixed,broad-leaved(E),broad-leaved(M),and coniferous forest)continues to pose challenges.To address this,we used structural equation models to quantify the influence of biotic factors(mean DBH,mean height,maximum height,stem density,and basal area)and abiotic factors(elevation and canopy openness),as well as metrics of species diversity(Shannon–Wiener index,Simpson index,and Pielou’s evenness)in various forest types.Our analysis revealed the critical roles of forest types and elevation in explaining a substantial portion of variability in C storage in the overstory layer,with a moderate influence of stand factors(mean DBH and basal area)and a slightly negative impact of tree species diversity(Shannon–Wiener index).Notably,forest height emerged as the primary predictor of C storage in the herb layer.Regression relationships further highlighted the significant contribution of tree species diversity to mean height,understory C storage,and branch biomass within the forest ecosystem.Our insights into tree species diversity,derived from structural equation modeling of C storage in the overstory,suggest that the effects of tree species diversity may be influenced by stem biomass in statistical reasoning within temperate forests.Further research should also integrate tree species diversity with tree components biomass,forest mean height,understory C,and canopy openness to understand complex relationships and maintain healthy and sustainable ecosystems in the face of global climate challenges.
基金supported by the National Key Research and Development Program of China,China(No.2021YFC3320103)the National Natural Science Foundation of China,China(NSFC)(Nos.62372452 and 62272460)+1 种基金the Open Research Project of the State Key Laboratory of Media Convergence and Communication,Communication University of China,China(No.SKLM CC2022KF002)Youth Innovation Promotion Association CAS,China.
文摘Training generative adversarial networks is data-demanding,which limits the development of these models on target domains with inadequate training data.Recently,researchers have leveraged generative models pretrained on sufficient data and fine-tuned them using small training samples,thus reducing data requirements.However,due to the lack of explicit focus on target styles and disproportionately concentrating on generative consistency,these methods do not perform well in diversity preservation which represents the adaptation ability for few-shot generative models.To mitigate the diversity degradation,we propose a framework with two key strategies:1)To obtain more diverse styles from limited training data effectively,we propose a cross-modal module that explicitly obtains the target styles with a style prototype space and text-guided style instructions.2)To inherit the generation capability from the pretrained model,we aim to constrain the similarity between the generated and source images with a structural discrepancy alignment module by maintaining the structure correlation in multiscale areas.We demonstrate the effectiveness of our method,which outperforms state-of-the-art methods in mitigating diversity degradation through extensive experiments and analyses.
基金supported by the National Natural Science Foundation of China(31870430)the National Key Research and Development Program of China(2017YFC0503901,2016YFC0502104).
文摘Biodiversity is found to have a significant promotion effect on ecosystem functions in manipulation experiments on grassland communities.However,its relative role compared with stand factors or functional identity is still controversial in natural forests.Here,we examined their relative effects on biomass and productivity during forest restoration.We investigated stand biomass and productivity for 24 plots(600 m2)across restoration stages in the subtropical forests of Mt.Shennongjia,Central China.We measured five key functional traits and calculated functional diversity(functional richness,evenness and dispersion)and community-weighted mean of traits.We used general linear models,variation partitioning methods to test the relative importance of stand factors(density,stand age,maximum height,etc.),functional identity,species and functional diversity on biomass and productivity.Our results illustrated that stand biomass and productivity increased significantly as forest restoration,and that community species richness increased,while functional dispersion decreased significantly.Variation partitioning analyses showed that diversity had no significant pure effects on biomass and productivity.However,diversity may affect biomass and productivity through the joint effect with stand factors and functional identity.Overall,we found that stand factors had the strongest effect on biomass and productivity,while functional identity significantly affects productivity but not biomass,suggesting that modulating stand structure and species identity are effective ways to enhance forest carbon storage and sequestrations potential in forest management.
