Phenological modeling is not only important for the projection of future changes of certain phenophases but also crucial for systematically studying the spatiotemporal patterns of plant phenology.Based on ground pheno...Phenological modeling is not only important for the projection of future changes of certain phenophases but also crucial for systematically studying the spatiotemporal patterns of plant phenology.Based on ground phenological observations,we used two existing temperature-based models and 12 modified models with consideration of precipitation or soil moisture to simulate the bud-burst date(BBD)of four common herbaceous plants-Xanthium sibiricum,Plantago asiatica,Iris lactea and Taraxacum mongolicum-in temperate grasslands in Inner Mongolia.The results showed that(1)increase in temperature promoted the BBD of all species.However,effects of precipitation and soil moisture on BBD varied among species.(2)The modified models predicted the BBD of herbaceous plants with R^2 ranging from 0.17 to 0.41 and RMSE ranging from 9.03 to 11.97 days,better than classical thermal models.(3)The spatiotemporal pattern of BBD during 1980–2015 showed that species with later BBD,e.g.X.sibiricum(mean:day of year 135.30)exhibited an evidently larger spatial difference in BBD(standard deviation:13.88 days)than the other species.Our findings suggest that influences of temperature and water conditions need to be considered simultaneously in predicting the phenological response of herbaceous plants to climate change.展开更多
Aboveground biomass in grasslands of the Qinghai-Tibet Plateau has displayed an overall increasing trend during 2003–2016, which is profoundly influenced by climate change. However, the responses of different biomes ...Aboveground biomass in grasslands of the Qinghai-Tibet Plateau has displayed an overall increasing trend during 2003–2016, which is profoundly influenced by climate change. However, the responses of different biomes show large discrepancies, in both size and magnitude. By applying partial least squares regression, we calculated the correlation between peak aboveground biomass and mean monthly temperature and monthly total precipitation in the preceding 12 months for three different grassland types(alpine steppe, alpine meadow, and temperate steppe) on the central and eastern Qinghai-Tibet Plateau. The results showed that mean temperature in most preceding months was positively correlated with peak aboveground biomass of alpine meadow and alpine steppe, while mean temperature in the preceding October and February to June was significantly negatively correlated with peak aboveground biomass of temperate steppe. Precipitation in all months had a promoting effect on biomass of alpine meadow, but its correlations with biomass of alpine steppe and temperate steppe were inconsistent. It is worth noting that, in a warmer, wetter climate, peak aboveground biomass of alpine meadow would increase more than that of alpine steppe, while that of temperate steppe would decrease significantly, providing support for the hypothesis of conservative growth strategies by vegetation in stressed ecosystems.展开更多
基金National Key R&D Program of China,No.2018YFA0606102National Natural Science Foundation of China,No.41771056,No.41901014
文摘Phenological modeling is not only important for the projection of future changes of certain phenophases but also crucial for systematically studying the spatiotemporal patterns of plant phenology.Based on ground phenological observations,we used two existing temperature-based models and 12 modified models with consideration of precipitation or soil moisture to simulate the bud-burst date(BBD)of four common herbaceous plants-Xanthium sibiricum,Plantago asiatica,Iris lactea and Taraxacum mongolicum-in temperate grasslands in Inner Mongolia.The results showed that(1)increase in temperature promoted the BBD of all species.However,effects of precipitation and soil moisture on BBD varied among species.(2)The modified models predicted the BBD of herbaceous plants with R^2 ranging from 0.17 to 0.41 and RMSE ranging from 9.03 to 11.97 days,better than classical thermal models.(3)The spatiotemporal pattern of BBD during 1980–2015 showed that species with later BBD,e.g.X.sibiricum(mean:day of year 135.30)exhibited an evidently larger spatial difference in BBD(standard deviation:13.88 days)than the other species.Our findings suggest that influences of temperature and water conditions need to be considered simultaneously in predicting the phenological response of herbaceous plants to climate change.
基金National Key R&D Program of China,No.2018YFA0606102National Natural Science Foundation of China,No.41771056National Key Technology Support Program,No.2012BAH31B02
文摘Aboveground biomass in grasslands of the Qinghai-Tibet Plateau has displayed an overall increasing trend during 2003–2016, which is profoundly influenced by climate change. However, the responses of different biomes show large discrepancies, in both size and magnitude. By applying partial least squares regression, we calculated the correlation between peak aboveground biomass and mean monthly temperature and monthly total precipitation in the preceding 12 months for three different grassland types(alpine steppe, alpine meadow, and temperate steppe) on the central and eastern Qinghai-Tibet Plateau. The results showed that mean temperature in most preceding months was positively correlated with peak aboveground biomass of alpine meadow and alpine steppe, while mean temperature in the preceding October and February to June was significantly negatively correlated with peak aboveground biomass of temperate steppe. Precipitation in all months had a promoting effect on biomass of alpine meadow, but its correlations with biomass of alpine steppe and temperate steppe were inconsistent. It is worth noting that, in a warmer, wetter climate, peak aboveground biomass of alpine meadow would increase more than that of alpine steppe, while that of temperate steppe would decrease significantly, providing support for the hypothesis of conservative growth strategies by vegetation in stressed ecosystems.
