In boreal forest ecosystems, permafrost and forest types are mutually interdependent;permafrost degradation impacts forest ecosystem structure and functions. The Xing’an permafrost in Northeast China is on the southe...In boreal forest ecosystems, permafrost and forest types are mutually interdependent;permafrost degradation impacts forest ecosystem structure and functions. The Xing’an permafrost in Northeast China is on the southern margin of the Eastern Asia latitudinal permafrost body. Under a warming climate, permafrost undergoes rapid and extensive degradation. In this study, the frost-number (Fn) model based on air temperatures and ground surface temperatures was used to predict the distribution of the Xing’an permafrost, and, temporal and spatial changes in air and ground-surface temperatures from 1961 to 2019 are analyzed. The results show that Northeast China has experienced a rapid and substantial climate warming over the past 60 years. The rises in mean annual air and mean annual ground-surface temperatures were higher in permafrost zones than those in the seasonal frost zone. The frost numbers of air and ground-surface temperatures were calculated for determining the southern limit of latitudinal permafrost and for permafrost zonation. The southern limits of discontinuous permafrost, sporadic permafrost, and latitudinal permafrost moved northward significantly. According to the air-temperature frost-number criteria for permafrost zoning, compared with that in the 1960s, the extent of Xing’an permafrost in Northeast China had decreased by 40.6% by the 2010s. With an average rate of increase in mean annual air temperatures at 0.03 ℃ a^(−1), the extent of permafrost in Northeast China will decrease to 26.42 × 10^(4) by 2020, 14.69 × 10^(4) by 2040 and to 11.24 × 10^(4) km^(2) by 2050. According to the ground-surface temperature frost-number criteria, the southern limit of latitudinal permafrost was at the 0.463. From the 1960s to the 2010s, the extent of latitudinal permafrost declined significantly. Due to the nature of the ecosystem-protected Xing’an-Baikal permafrost, management and protection (e.g., more prudent and effective forest fire management and proper logging of forests) of the Xing’an permafrost eco-environment should be strengthened.展开更多
Background:Forecasts of climate change impacts on biodiversity often assume that the current geographical distributions of species match their niche optima.However,empirical evidence has challenged this assumption,sug...Background:Forecasts of climate change impacts on biodiversity often assume that the current geographical distributions of species match their niche optima.However,empirical evidence has challenged this assumption,suggesting a mismatch.We examine whether the mismatch is related to functional traits along temperature or precipitation gradients.Methods:The observed distributions of 32 tree species in northeast China were evaluated to test this mismatch.Bayesian models were used to estimate the climatic niche optima,i.e.the habitats where the highest species growth and density can be expected.The mismatch is defined as the difference between the actual species occurrence in an assumed niche optimum and the habitat with the highest probability of species occurrence.Species’functional traits were used to explore the mechanisms that may have caused the mismatches.Results:Contrasting these climatic niche optima with the observed species distributions,we found that the distribution-niche optima mismatch had high variability among species based on temperature and precipitation gradients.However,these mismatches depended on functional traits associated with competition and migration lags only in temperature gradients.Conclusions:We conclude that more relevant research is needed in the future to quantify the mismatch between species distribution and climatic niche optima,which may be crucial for future designs of forested landscapes,species conservation and dynamic forecasting of biodiversity under expected climate change.展开更多
基金The project is fully funded by the Natural Science Foundation of China Program(Grant Nos.42001052 and 41871052)Startup Research Funding of Northeast Forestry University for Chengdong Outstanding Youth Scholarship(YQ2020-10)+1 种基金Chengdong Leadership(LJ2020-01)the State Key Laboratory of Frozen Soils Engineering Open Fund Project(Grant No.SKLFSE202008).
文摘In boreal forest ecosystems, permafrost and forest types are mutually interdependent;permafrost degradation impacts forest ecosystem structure and functions. The Xing’an permafrost in Northeast China is on the southern margin of the Eastern Asia latitudinal permafrost body. Under a warming climate, permafrost undergoes rapid and extensive degradation. In this study, the frost-number (Fn) model based on air temperatures and ground surface temperatures was used to predict the distribution of the Xing’an permafrost, and, temporal and spatial changes in air and ground-surface temperatures from 1961 to 2019 are analyzed. The results show that Northeast China has experienced a rapid and substantial climate warming over the past 60 years. The rises in mean annual air and mean annual ground-surface temperatures were higher in permafrost zones than those in the seasonal frost zone. The frost numbers of air and ground-surface temperatures were calculated for determining the southern limit of latitudinal permafrost and for permafrost zonation. The southern limits of discontinuous permafrost, sporadic permafrost, and latitudinal permafrost moved northward significantly. According to the air-temperature frost-number criteria for permafrost zoning, compared with that in the 1960s, the extent of Xing’an permafrost in Northeast China had decreased by 40.6% by the 2010s. With an average rate of increase in mean annual air temperatures at 0.03 ℃ a^(−1), the extent of permafrost in Northeast China will decrease to 26.42 × 10^(4) by 2020, 14.69 × 10^(4) by 2040 and to 11.24 × 10^(4) km^(2) by 2050. According to the ground-surface temperature frost-number criteria, the southern limit of latitudinal permafrost was at the 0.463. From the 1960s to the 2010s, the extent of latitudinal permafrost declined significantly. Due to the nature of the ecosystem-protected Xing’an-Baikal permafrost, management and protection (e.g., more prudent and effective forest fire management and proper logging of forests) of the Xing’an permafrost eco-environment should be strengthened.
基金supported by the Key Project of National Key Research and Development Plan(No.2022YFD2201004)Beijing Forestry University Outstanding Young Talent Cultivation Project(No.2019JQ03001)。
文摘Background:Forecasts of climate change impacts on biodiversity often assume that the current geographical distributions of species match their niche optima.However,empirical evidence has challenged this assumption,suggesting a mismatch.We examine whether the mismatch is related to functional traits along temperature or precipitation gradients.Methods:The observed distributions of 32 tree species in northeast China were evaluated to test this mismatch.Bayesian models were used to estimate the climatic niche optima,i.e.the habitats where the highest species growth and density can be expected.The mismatch is defined as the difference between the actual species occurrence in an assumed niche optimum and the habitat with the highest probability of species occurrence.Species’functional traits were used to explore the mechanisms that may have caused the mismatches.Results:Contrasting these climatic niche optima with the observed species distributions,we found that the distribution-niche optima mismatch had high variability among species based on temperature and precipitation gradients.However,these mismatches depended on functional traits associated with competition and migration lags only in temperature gradients.Conclusions:We conclude that more relevant research is needed in the future to quantify the mismatch between species distribution and climatic niche optima,which may be crucial for future designs of forested landscapes,species conservation and dynamic forecasting of biodiversity under expected climate change.
