Potential natural vegetation(PNV)is a valuable reference for ecosystem renovation and has garnered increasing attention worldwide.However,there is limited knowledge on the spatio-temporal distributions,transitional pr...Potential natural vegetation(PNV)is a valuable reference for ecosystem renovation and has garnered increasing attention worldwide.However,there is limited knowledge on the spatio-temporal distributions,transitional processes,and underlying mechanisms of global natural vegetation,particularly in the case of ongoing climate warming.In this study,we visualize the spatio-temporal pattern and inter-transition procedure of global PNV,analyse the shifting distances and directions of global PNV under the influence of climatic disturbance,and explore the mechanisms of global PNV in response to temperature and precipitation fluctuations.To achieve this,we utilize meteorological data,mainly temperature and precipitation,from six phases:the Last Inter-Glacial(LIG),the Last Glacial Maximum(LGM),the Mid Holocene(MH),the Present Day(PD),2030(20212040)and 2090(2081–2100),and employ a widely-accepted comprehensive and sequential classification sy–stem(CSCS)for global PNV classification.We find that the spatial patterns of five PNV groups(forest,shrubland,savanna,grassland and tundra)generally align with their respective ecotopes,although their distributions have shifted due to fluctuating temperature and precipitation.Notably,we observe an unexpected transition between tundra and savanna despite their geographical distance.The shifts in distance and direction of five PNV groups are mainly driven by temperature and precipitation,although there is heterogeneity among these shifts for each group.Indeed,the heterogeneity observed among different global PNV groups suggests that they may possess varying capacities to adjust to and withstand the impacts of changing climate.The spatio-temporal distributions,mutual transitions and shift tendencies of global PNV and its underlying mechanism in face of changing climate,as revealed in this study,can significantly contribute to the development of strategies for mitigating warming and promoting re-vegetation in degraded regions worldwide.展开更多
Potential Natural Vegetation(PNV)represents the climax of vegetation succession in a natural environment,free from significant disturbances.The reconstruction of PNV is widely used to study climate-vegetation relation...Potential Natural Vegetation(PNV)represents the climax of vegetation succession in a natural environment,free from significant disturbances.The reconstruction of PNV is widely used to study climate-vegetation relationships and predict future vegetation distributions.However,fine-scale PNV maps with high accuracy are still rare in biodiversity hotspots due to the complexity of ecosystems and limited field observations.In this study,we mapped the spatiotemporal distribution of 16 PNV types using adequate field and literature data,and an improved Comprehensive and Sequential Classification System(CSCS)approach under current(2005-2016)and future(2021-2080)climate scenarios in Yunnan province,Southwest China.We found that 1)from T0(2005-2016)to T3(2021-2080),regions with cold alpine PNV types,such as mid-mountain humid evergreen broad-leaved forests(EBLF),are projected to experience more significant temperature increases compared to regions with warm PNV types,like tropic rainforests and monsoon rainforests.High-emission scenarios(SSP585)are expected to result in temperature increases approximately 2°C higher than low-emission scenarios(SSP126).Precipitation is projected to increase for water-deficient PNV types(e.g.,monsoon rainforest and semi-humid EBLF)but decrease for humid PNV types(e.g.,rainforest and mountain mossy EBLF).The SSP370 scenario predicts a slightly smaller increase in precipitation compared to other scenarios.2)All PNV types are expected to shift to higher latitudes(by an average of 0.86°)and higher elevations(by an average of 454 m)by T3,based on their current niches.Alpine PNV types are more sensitive to climate change and are projected to shift more prominently than other types.For example,mountain mossy EBLF is expected to move 1.78°northward,while mid-mountain moist EBLF is projected to rise by 578 m.3)Cold PNV types are likely to be replaced by warm types both in latitude and altitude.Semi-humid EBLF is projected to shrink the most,by 57,984 km2(51.5%of its present range),while monsoon EBLF is expected to expand the most,by 44,881 km2(64.7%of its present range).The suitable habitat for cold-temperate sclerophyllous EBLF and temperate shrublands may disappear entirely in Yunnan.Given the over-estimate of the projected PNV shift without accounting for the lag effects,these findings are still useful in planning future conservation and management efforts,which should prioritize PNV types experiencing drastic changes in temperature(e.g.,mid-mountain moist EBLF),precipitation(e.g.,mountain mossy EBLF),and distribution area(e.g.,semi-humid EBLF and cold-temperate sclerophyllous EBLF).展开更多
Net Primary Productivity (NPP) is an important parameter, which is closely connected with global climate change, the global carbon balance and cycle. The study of climate- vegetation interaction is the basis for res...Net Primary Productivity (NPP) is an important parameter, which is closely connected with global climate change, the global carbon balance and cycle. The study of climate- vegetation interaction is the basis for research on the responses of terrestrial ecosystemto global change and mainly comprises two important components: climate vegetation classification and the NPP of the natural vegetation. Comparing NPP estimated from the classification indices-based model with NPP derived from measurements at 3767 sites in China indicated that the classification indices-based model was capable of estimating large scale NPP. Annual cumulative temperature above 0~C and a moisture index, two main factors affecting NPP, were spatially plotted with the ArcGIS grid tool based on measured data in 2348 meteorological stations from 1961 to 2006. The distribution of NPP for potential vegetation classes under present climate conditions was simulated by the classification indices-based model. The model estimated the total NPP of potential terrestrial vegetation of China to fluctuate between 1.93 and 4.54 Pg C year-1. It pro- vides a reliable means for scaling-up from site to regional scales, and the findings could potentially favor China's position in reducing global warming gases as outlined in the Kyoto Protocol in order to fulfill China's commitment of reducing greenhouse gases.展开更多
基金funded by the National Natural Science Foundation of China(grants No.30960264,31160475 and 42071258)Open Research Fund of TPESER(grant No.TPESER202208)+2 种基金Special Fund for Basic Scientific Research of Central Colleges,Chang’an University,China(grant No.300102353501)Natural Science Foundation of Gansu Province,China(grant No.22JR5RA857)Higher Education Novel Foundation of Gansu Province,China(grant No.2021B-130)。
文摘Potential natural vegetation(PNV)is a valuable reference for ecosystem renovation and has garnered increasing attention worldwide.However,there is limited knowledge on the spatio-temporal distributions,transitional processes,and underlying mechanisms of global natural vegetation,particularly in the case of ongoing climate warming.In this study,we visualize the spatio-temporal pattern and inter-transition procedure of global PNV,analyse the shifting distances and directions of global PNV under the influence of climatic disturbance,and explore the mechanisms of global PNV in response to temperature and precipitation fluctuations.To achieve this,we utilize meteorological data,mainly temperature and precipitation,from six phases:the Last Inter-Glacial(LIG),the Last Glacial Maximum(LGM),the Mid Holocene(MH),the Present Day(PD),2030(20212040)and 2090(2081–2100),and employ a widely-accepted comprehensive and sequential classification sy–stem(CSCS)for global PNV classification.We find that the spatial patterns of five PNV groups(forest,shrubland,savanna,grassland and tundra)generally align with their respective ecotopes,although their distributions have shifted due to fluctuating temperature and precipitation.Notably,we observe an unexpected transition between tundra and savanna despite their geographical distance.The shifts in distance and direction of five PNV groups are mainly driven by temperature and precipitation,although there is heterogeneity among these shifts for each group.Indeed,the heterogeneity observed among different global PNV groups suggests that they may possess varying capacities to adjust to and withstand the impacts of changing climate.The spatio-temporal distributions,mutual transitions and shift tendencies of global PNV and its underlying mechanism in face of changing climate,as revealed in this study,can significantly contribute to the development of strategies for mitigating warming and promoting re-vegetation in degraded regions worldwide.
基金The Major Program for Basic Research Project of Yunnan Province,No.202101B C070002The Second Comprehensive Scientific Expedition of the Qinghai-Tibet Plateau,No.2019QZKK 04020101。
文摘Potential Natural Vegetation(PNV)represents the climax of vegetation succession in a natural environment,free from significant disturbances.The reconstruction of PNV is widely used to study climate-vegetation relationships and predict future vegetation distributions.However,fine-scale PNV maps with high accuracy are still rare in biodiversity hotspots due to the complexity of ecosystems and limited field observations.In this study,we mapped the spatiotemporal distribution of 16 PNV types using adequate field and literature data,and an improved Comprehensive and Sequential Classification System(CSCS)approach under current(2005-2016)and future(2021-2080)climate scenarios in Yunnan province,Southwest China.We found that 1)from T0(2005-2016)to T3(2021-2080),regions with cold alpine PNV types,such as mid-mountain humid evergreen broad-leaved forests(EBLF),are projected to experience more significant temperature increases compared to regions with warm PNV types,like tropic rainforests and monsoon rainforests.High-emission scenarios(SSP585)are expected to result in temperature increases approximately 2°C higher than low-emission scenarios(SSP126).Precipitation is projected to increase for water-deficient PNV types(e.g.,monsoon rainforest and semi-humid EBLF)but decrease for humid PNV types(e.g.,rainforest and mountain mossy EBLF).The SSP370 scenario predicts a slightly smaller increase in precipitation compared to other scenarios.2)All PNV types are expected to shift to higher latitudes(by an average of 0.86°)and higher elevations(by an average of 454 m)by T3,based on their current niches.Alpine PNV types are more sensitive to climate change and are projected to shift more prominently than other types.For example,mountain mossy EBLF is expected to move 1.78°northward,while mid-mountain moist EBLF is projected to rise by 578 m.3)Cold PNV types are likely to be replaced by warm types both in latitude and altitude.Semi-humid EBLF is projected to shrink the most,by 57,984 km2(51.5%of its present range),while monsoon EBLF is expected to expand the most,by 44,881 km2(64.7%of its present range).The suitable habitat for cold-temperate sclerophyllous EBLF and temperate shrublands may disappear entirely in Yunnan.Given the over-estimate of the projected PNV shift without accounting for the lag effects,these findings are still useful in planning future conservation and management efforts,which should prioritize PNV types experiencing drastic changes in temperature(e.g.,mid-mountain moist EBLF),precipitation(e.g.,mountain mossy EBLF),and distribution area(e.g.,semi-humid EBLF and cold-temperate sclerophyllous EBLF).
文摘Net Primary Productivity (NPP) is an important parameter, which is closely connected with global climate change, the global carbon balance and cycle. The study of climate- vegetation interaction is the basis for research on the responses of terrestrial ecosystemto global change and mainly comprises two important components: climate vegetation classification and the NPP of the natural vegetation. Comparing NPP estimated from the classification indices-based model with NPP derived from measurements at 3767 sites in China indicated that the classification indices-based model was capable of estimating large scale NPP. Annual cumulative temperature above 0~C and a moisture index, two main factors affecting NPP, were spatially plotted with the ArcGIS grid tool based on measured data in 2348 meteorological stations from 1961 to 2006. The distribution of NPP for potential vegetation classes under present climate conditions was simulated by the classification indices-based model. The model estimated the total NPP of potential terrestrial vegetation of China to fluctuate between 1.93 and 4.54 Pg C year-1. It pro- vides a reliable means for scaling-up from site to regional scales, and the findings could potentially favor China's position in reducing global warming gases as outlined in the Kyoto Protocol in order to fulfill China's commitment of reducing greenhouse gases.