Intra-annual climatic variability plays a critical role in regulating wood formation dynamics during the growing season,particularly in seasonally arid regions—such as the Qinling Mountains,China,and Mediterranean fo...Intra-annual climatic variability plays a critical role in regulating wood formation dynamics during the growing season,particularly in seasonally arid regions—such as the Qinling Mountains,China,and Mediterranean forests—where trees exhibit bimodal radial growth patterns as an adaptive response to water stress.While these growth patterns reflect immediate climatic conditions,the role of ecological memory,specifically vegetation growth carryover(VGC)and lagged climate effects(LCEs),remains poorly quantified.We employed the Vaganov–Shashkin(VS)model to analyze intra-annual bimodal growth patterns in two regions and used a vector autoregressive model with impulse response functions to assess the duration and intensity of VGC and LCE on tree-ring growth and remote sensing vegetation indices(leaf area index(LAI)and gross primary productivity(GPP)).Our results revealed bimodal growth patterns with spring and autumn peaks,but the autumn peak occurred earlier in the Qinling Mountains(August–October)than in Mediterranean forests(late September–October).VGC exerted the strongest influence on tree-ring growth in the first year,diminishing significantly after eight years in both regions(p<0.01).Tree-ring growth exhibited positive LCE responses to precipitation and soil moisture but negative responses to temperature(p<0.05).Remote sensing indices(LAI and GPP)displayed stronger VGC effects in the Qinling Mountains than in Mediterranean forests.While both LAI and GPP responded positively to soil moisture,temperature-induced LCE was positive in the Qinling Mountains but negative in the Mediterranean forests(p<0.05).Overall,VGC was the dominant ecological memory effect in both regions.Our results suggest that coupling the VGC and LCE of multiple vegetation growth indicators at multiple scales has the potential to improve the accuracy of global dynamic vegetation models.展开更多
基金supported by the National Natural Science Foundation of China(Nos.42277448,42330501,41971104,and 41807431)。
文摘Intra-annual climatic variability plays a critical role in regulating wood formation dynamics during the growing season,particularly in seasonally arid regions—such as the Qinling Mountains,China,and Mediterranean forests—where trees exhibit bimodal radial growth patterns as an adaptive response to water stress.While these growth patterns reflect immediate climatic conditions,the role of ecological memory,specifically vegetation growth carryover(VGC)and lagged climate effects(LCEs),remains poorly quantified.We employed the Vaganov–Shashkin(VS)model to analyze intra-annual bimodal growth patterns in two regions and used a vector autoregressive model with impulse response functions to assess the duration and intensity of VGC and LCE on tree-ring growth and remote sensing vegetation indices(leaf area index(LAI)and gross primary productivity(GPP)).Our results revealed bimodal growth patterns with spring and autumn peaks,but the autumn peak occurred earlier in the Qinling Mountains(August–October)than in Mediterranean forests(late September–October).VGC exerted the strongest influence on tree-ring growth in the first year,diminishing significantly after eight years in both regions(p<0.01).Tree-ring growth exhibited positive LCE responses to precipitation and soil moisture but negative responses to temperature(p<0.05).Remote sensing indices(LAI and GPP)displayed stronger VGC effects in the Qinling Mountains than in Mediterranean forests.While both LAI and GPP responded positively to soil moisture,temperature-induced LCE was positive in the Qinling Mountains but negative in the Mediterranean forests(p<0.05).Overall,VGC was the dominant ecological memory effect in both regions.Our results suggest that coupling the VGC and LCE of multiple vegetation growth indicators at multiple scales has the potential to improve the accuracy of global dynamic vegetation models.
