Understanding how genetic variation within forest species influences growth responses under climate change is essential for improving the accuracy of forest models and guiding adaptive management strategies.This study...Understanding how genetic variation within forest species influences growth responses under climate change is essential for improving the accuracy of forest models and guiding adaptive management strategies.This study models the dynamics of Italian silver fir(Abies alba)forests under varying climate change scenarios using the forest gap model FORMIND.Focusing on three distinct silver fir provenances(Western Alps,Northern Apennines,and Southern Apennines),the study simulates forest growth in the Tuscan-Emilian Apennine National Park under different representative concentration pathways(RCPs).The individual-based model FORMIND was parameterized and validated with field data for each of the provenances,demonstrating its ability to accurately reproduce key forest metrics and dynamics.Our results reveal significant differences in expected growth patterns,productivity,metabolism,and carbon storage capacity among the silver fir provenances in pure and mixed stands.In the simulations,the Northern Apennines provenance showed higher biomass production(biomass>10%±1%)and carbon uptake(net primary productivity,NPP>8%±1%)at the end of the century compared to the Western Alps provenance in the pure provenance(PP)and no regeneration scenario.Conversely,the Southern Apennines provenance showed higher biomass(biomass>5%–10%)and NPP(>15%–18%)in mixed provenance(MP)and regeneration scenarios.These results show that genetic diversity strongly affects forest growth and resilience to environmental changes.Hence,it should be included as a predictor variable in forest models.The study also demonstrates the resilience of silver fir to climatic stressors,emphasizing its potential as a robust species in multiple forest contexts.The integration of forest provenance data into the FORMIND model represents a significant advancement in forest modelling,enabling more accurate and reliable predictions under climate change scenarios.The study's findings advocate for a greater understanding and consideration of genetic diversity in forest management and conservation strategies,in support of assisted migration strategies aiming to enhance the resilience of forest ecosystems in a changing climate.展开更多
Sparse finite impulse response(FIR)filters reduce computational cost on resource-constrained devices,but selecting the sparsification thresholdλis typically left to grid search or hand tuning.We propose a two-stage m...Sparse finite impulse response(FIR)filters reduce computational cost on resource-constrained devices,but selecting the sparsification thresholdλis typically left to grid search or hand tuning.We propose a two-stage method:a 67,331-parameter surrogate network predicts(Ap,As,S)(passband ripple in dB,stopband attenuation in dB,sparsity in%)from a filter specification and a candidate λ,and split conformal prediction(CP)calibrates±intervals around each prediction.We then select λ by minimizing a worst-case penalty computed on the conservative ends of the intervals(the upper bound on Ap and the lower bound on As).On 10,000 test specifications the method reaches 76.5%specification satisfaction,near-parity with grid search(78.4%)with a 1.9× speedup,while point-prediction surrogates reach only 39.4%.On feasible specifications(where any grid λ satisfies both constraints),the method reaches 97.6%.Stratified(Mondrian)conformal prediction lifts standard CP coverage from 67%-75%to 95.5%,and adaptive recalibration brings passband coverage to 91.3%.The procedure transfers without modification to iteratively reweighted least squares(IRLS)sparsification(76.6%)and to highpass(79.2%)and bandpass(52.4%)filters.The implementation runs on a central processing unit(CPU)and is suitable for edge deployment;code and data are public.展开更多
针对硬件开发过程中存在编程效率低、开发难度高及Vivado HLS资源使用率高等问题,利用Vivado及Vitis HLS平台设计并实现了基于127阶Hamming窗的流水线式直接型并行结构有限冲激响应滤波器,在Vivado Simulator环境下对比了HDL FIR IP、HL...针对硬件开发过程中存在编程效率低、开发难度高及Vivado HLS资源使用率高等问题,利用Vivado及Vitis HLS平台设计并实现了基于127阶Hamming窗的流水线式直接型并行结构有限冲激响应滤波器,在Vivado Simulator环境下对比了HDL FIR IP、HLS FIR IP与XILINX FIR IP的滤波表现,详细分析了不同实现方式在资源使用率、时序、功耗、执行时间等方面的差异。实验结果表明:在相同条件下HLS FIR IP相较HDL FIR IP及XILINX FIR IP的资源使用率降低了1%,执行时间分别降低了24.5%、808.2%,且代码量节省了98.5%。以本文实验方法为基础与前人工作进行对比,客观分析了在一定条件下不同开发平台及方式的效率差异,结果表明本文设计方法可显著降低逻辑单元和存储资源的使用率,并提升开发效率。展开更多
To enhance phenotypic plasticity,it is vital to maximize the genetic growth potential of trees and understand their adaptive responses to environmental conditions.Tree species adapt to dynamic environmental conditions...To enhance phenotypic plasticity,it is vital to maximize the genetic growth potential of trees and understand their adaptive responses to environmental conditions.Tree species adapt to dynamic environmental conditions by leveraging the interactions among the environment,genotype,and genotype-byenvironment.A total of 25 improved varieties of Chinese fir were transplanted and developed through multi-generational breeding into four types of artificial forest soils.Through a quantitative analysis of genotypic,soil environmental conditions,and genotype-by-environment interaction effects on variations in growth,biomass,and root functional traits,the key drivers of phenotypic plasticity were identified.The results indicate that soil environmental conditions and genotype-by-environment interactions are the primary factors influencing trait variation,explaining 55.89% to 93.94% of the observed variation,while the family effect is relatively minor.Notably,pronounced phenotypic plasticity drives divergent selection in both aboveground and belowground growth strategies.Critical traits influencing root dry weight include root average diameter,total root volume,and root-to-shoot ratio.Although root dry weight does not directly affect plant height,it has a substantial impact on aboveground dry weight.These findings highlight that the changes in the aboveground and belowground growth strategies of Chinese fir during the seedling stage are closely linked to the plasticity of root functional traits.For multi-generational genetically improved varieties,this study examined how the integration of genetic effects,soil environmental conditions,and genotype-by-environment interactions in the selection of aboveground growth and root functional traits influences the mechanisms driving biomass accumulation.The results provide actionable insights for selecting soil-specific families in subtropical plantations.展开更多
基金the University of Milan for funding the“ProForesta”project through the 2020 Research Support Planthe“Ente Parco Nazionale dell'Appennino Tosco-Emiliano”for having financed the project“First urgent measures to promote the adaptation of the silver fir forests of the Tuscan-Emilian Apennine National Park to the effects of climate change”。
摘要Understanding how genetic variation within forest species influences growth responses under climate change is essential for improving the accuracy of forest models and guiding adaptive management strategies.This study models the dynamics of Italian silver fir(Abies alba)forests under varying climate change scenarios using the forest gap model FORMIND.Focusing on three distinct silver fir provenances(Western Alps,Northern Apennines,and Southern Apennines),the study simulates forest growth in the Tuscan-Emilian Apennine National Park under different representative concentration pathways(RCPs).The individual-based model FORMIND was parameterized and validated with field data for each of the provenances,demonstrating its ability to accurately reproduce key forest metrics and dynamics.Our results reveal significant differences in expected growth patterns,productivity,metabolism,and carbon storage capacity among the silver fir provenances in pure and mixed stands.In the simulations,the Northern Apennines provenance showed higher biomass production(biomass>10%±1%)and carbon uptake(net primary productivity,NPP>8%±1%)at the end of the century compared to the Western Alps provenance in the pure provenance(PP)and no regeneration scenario.Conversely,the Southern Apennines provenance showed higher biomass(biomass>5%–10%)and NPP(>15%–18%)in mixed provenance(MP)and regeneration scenarios.These results show that genetic diversity strongly affects forest growth and resilience to environmental changes.Hence,it should be included as a predictor variable in forest models.The study also demonstrates the resilience of silver fir to climatic stressors,emphasizing its potential as a robust species in multiple forest contexts.The integration of forest provenance data into the FORMIND model represents a significant advancement in forest modelling,enabling more accurate and reliable predictions under climate change scenarios.The study's findings advocate for a greater understanding and consideration of genetic diversity in forest management and conservation strategies,in support of assisted migration strategies aiming to enhance the resilience of forest ecosystems in a changing climate.
摘要Sparse finite impulse response(FIR)filters reduce computational cost on resource-constrained devices,but selecting the sparsification thresholdλis typically left to grid search or hand tuning.We propose a two-stage method:a 67,331-parameter surrogate network predicts(Ap,As,S)(passband ripple in dB,stopband attenuation in dB,sparsity in%)from a filter specification and a candidate λ,and split conformal prediction(CP)calibrates±intervals around each prediction.We then select λ by minimizing a worst-case penalty computed on the conservative ends of the intervals(the upper bound on Ap and the lower bound on As).On 10,000 test specifications the method reaches 76.5%specification satisfaction,near-parity with grid search(78.4%)with a 1.9× speedup,while point-prediction surrogates reach only 39.4%.On feasible specifications(where any grid λ satisfies both constraints),the method reaches 97.6%.Stratified(Mondrian)conformal prediction lifts standard CP coverage from 67%-75%to 95.5%,and adaptive recalibration brings passband coverage to 91.3%.The procedure transfers without modification to iteratively reweighted least squares(IRLS)sparsification(76.6%)and to highpass(79.2%)and bandpass(52.4%)filters.The implementation runs on a central processing unit(CPU)and is suitable for edge deployment;code and data are public.
摘要针对硬件开发过程中存在编程效率低、开发难度高及Vivado HLS资源使用率高等问题,利用Vivado及Vitis HLS平台设计并实现了基于127阶Hamming窗的流水线式直接型并行结构有限冲激响应滤波器,在Vivado Simulator环境下对比了HDL FIR IP、HLS FIR IP与XILINX FIR IP的滤波表现,详细分析了不同实现方式在资源使用率、时序、功耗、执行时间等方面的差异。实验结果表明:在相同条件下HLS FIR IP相较HDL FIR IP及XILINX FIR IP的资源使用率降低了1%,执行时间分别降低了24.5%、808.2%,且代码量节省了98.5%。以本文实验方法为基础与前人工作进行对比,客观分析了在一定条件下不同开发平台及方式的效率差异,结果表明本文设计方法可显著降低逻辑单元和存储资源的使用率,并提升开发效率。
基金funded by the project‘Breeding of New Varieties of Fast-growing Forest Trees in Southern China’of the National Key R&D Program during the 14th Five-Year Plan period(Grant No.2022YFD2200201)the topic of‘Breeding of New Varieties of High Carbon Sink and High-quality Timber Tree Species’of the 14th Five-Year Plan for Forest Tree New Variety Breeding in Zhejiang Province(Grant No.2021C02070-8).
摘要To enhance phenotypic plasticity,it is vital to maximize the genetic growth potential of trees and understand their adaptive responses to environmental conditions.Tree species adapt to dynamic environmental conditions by leveraging the interactions among the environment,genotype,and genotype-byenvironment.A total of 25 improved varieties of Chinese fir were transplanted and developed through multi-generational breeding into four types of artificial forest soils.Through a quantitative analysis of genotypic,soil environmental conditions,and genotype-by-environment interaction effects on variations in growth,biomass,and root functional traits,the key drivers of phenotypic plasticity were identified.The results indicate that soil environmental conditions and genotype-by-environment interactions are the primary factors influencing trait variation,explaining 55.89% to 93.94% of the observed variation,while the family effect is relatively minor.Notably,pronounced phenotypic plasticity drives divergent selection in both aboveground and belowground growth strategies.Critical traits influencing root dry weight include root average diameter,total root volume,and root-to-shoot ratio.Although root dry weight does not directly affect plant height,it has a substantial impact on aboveground dry weight.These findings highlight that the changes in the aboveground and belowground growth strategies of Chinese fir during the seedling stage are closely linked to the plasticity of root functional traits.For multi-generational genetically improved varieties,this study examined how the integration of genetic effects,soil environmental conditions,and genotype-by-environment interactions in the selection of aboveground growth and root functional traits influences the mechanisms driving biomass accumulation.The results provide actionable insights for selecting soil-specific families in subtropical plantations.