Plant environmental DNA extracted from lacustrine sediments(sedimentary DNA,sedDNA)has been increasingly used to investigate past vegetation changes and human impacts at a high taxonomic resolution.However,the represe...Plant environmental DNA extracted from lacustrine sediments(sedimentary DNA,sedDNA)has been increasingly used to investigate past vegetation changes and human impacts at a high taxonomic resolution.However,the representation of vegetation communities surrounding the lake is still unclear.In this study,we compared plant sedDNA metabarcoding and pollen assemblages from 27 lake surface-sediment samples collected from alpine meadow on the central-eastern Tibetan Plateau to investigate the representation of sedDNA data.In general,the identified components of sedDNA are consistent with the counted pollen taxa and local plant communities.Relative to pollen identification,sedDNA data have higher taxonomic resolution,thus providing a potential approach for reconstructing past plant diversity.The sedDNA signal is strongly influenced by local plants while rarely affected by exogenous plants.Because of the overrepresentation of local plants and PCR bias,the abundance of sedDNA sequence types is very variable among sites,and should be treated with caution when investigating past vegetation cover and climate based on sedDNA data.Our finding suggests that sedDNA analysis can be a complementary approach for investigating the presence/absence of past plants and history of human land-use with higher taxonomic resolution.展开更多
Benefiting from the rapid development of environmental DNA(eDNA) technologies, sedimentary DNA(sedDNA)emerges as a promising tool for monitoring plant compositions in remote regions. The Tibetan Plateau(TP), renowned ...Benefiting from the rapid development of environmental DNA(eDNA) technologies, sedimentary DNA(sedDNA)emerges as a promising tool for monitoring plant compositions in remote regions. The Tibetan Plateau(TP), renowned for its harsh environment and numerous ponds and lakes, presents a potentially demanding region for the application of sedDNA on vegetation investigations. Here, we used the g and h universal primers for the P6 loop region of the chloroplast trn L(UAA)intron to amplify plant DNA in surface sediments from 59 ponds and small lakes on the southwestern TP. The applicability and limitations of using plant DNA metabarcoding for modern vegetation monitoring and palaeo-vegetation reconstructions have been assessed by comparing sedDNA, pollen, and vegetation survey data. Our results showed that plant DNA metabarcoding recorded 186 terrestrial taxa, of which 30.1% can be identified at the species level. The plant sedDNA approach can effectively disclose the dominant plant taxa(including Asteraceae, Cyperaceae and Poaceae) and significant vegetation assemblages in the vicinity of the investigated sites. The number of taxa and taxonomic resolution of plant sedDNA exceeded that of pollen analysis(75 taxa detected, 5.3% can be identified at species level). Unlike pollen that retains a broad spectrum of regional plant signals(including Pinus and Artemisia), plant sedDNA mirrors very local plants, underscoring its utility in local vegetation monitoring and reconstructions. To conclude, plant DNA metabarcoding of(small) lake sediments warrant increased attention in the future for local vegetation monitoring and reconstructions on the TP.展开更多
There is no doubt that land cover and climate changes have consequences on landslide activity,but it is still an open issue to assess and quantify their impacts.Wanzhou County in southwest China was selected as the te...There is no doubt that land cover and climate changes have consequences on landslide activity,but it is still an open issue to assess and quantify their impacts.Wanzhou County in southwest China was selected as the test area to study rainfall-induced shallow landslide susceptibility under the future changes of land use and land cover(LULC)and climate.We used a high-resolution meteorological precipitation dataset and frequency distribution model to analyse the present extreme and antecedent rainfall conditions related to landslide activity.The future climate change factors were obtained from a 4-member multimodel ensemble that was derived from statistically downscaled regional climate simulations.The future LULC maps were simulated by the land change modeller(LCM)integrated into IDRISI Selva software.A total of six scenarios were defined by considering the rainfall(antecedent conditions and extreme events)and LULC changes towards two time periods(mid and late XXI century).A physically-based model was used to assess landslide susceptibility under these different scenarios.The results showed that the magnitude of both antecedent effective recharge and event rainfall in the region will evidently increase in the future.Under the scenario with a return period of 100 years,the antecedent rainfall in summer will increase by up to 63%whereas the event rainfall will increase by up to 54%for the late 21st century.The most considerable changes of LULC will be the increase of forest cover and the decrease of farming land.The magnitude of this change can reach+22.1%(forest)and–9.2%(farmland)from 2010 until 2100,respectively.We found that the negative impact of climate change on landslide susceptibility is greater than the stabilizing effect of LULC change,leading to an over decrease in stability over the study area.This is one of the first studies across Asia to assess and quantify changes of regional landslide susceptibility under scenarios driven by LULC and climate change.Our results aim to guide land use planning and climate change mitigation considerations to reduce landslide risk.展开更多
Accurate and credible identification of the drivers of algal growth is essential for sustainable utilization and scientific management of freshwater.In this study,we developed a deep learning-based Transformer model,n...Accurate and credible identification of the drivers of algal growth is essential for sustainable utilization and scientific management of freshwater.In this study,we developed a deep learning-based Transformer model,named Bloomformer-1,for end-to-end identification of the drivers of algal growth without the needing extensive a priori knowledge or prior experiments.The Middle Route of the South-to-North Water Diversion Project(MRP)was used as the study site to demonstrate that Bloomformer-1 exhibited more robust performance(with the highest R^(2),0.80 to 0.94,and the lowest RMSE,0.22–0.43μg/L)compared to four widely used traditional machine learning models,namely extra trees regression(ETR),gradient boosting regression tree(GBRT),support vector regression(SVR),and multiple linear regression(MLR).In addition,Bloomformer-1 had higher interpretability(including higher transferability and understandability)than the four traditional machine learning models,which meant that it was trustworthy and the results could be directly applied to real scenarios.Finally,it was determined that total phosphorus(TP)was the most important driver for the MRP,especially in Henan section of the canal,although total nitrogen(TN)had the highest effect on algal growth in the Hebei section.Based on these results,phosphorus loading controlling in the whole MRP was proposed as an algal control strategy.展开更多
Timing of the precipitation optimum in the Holocene for the semi-arid northern China affects our understanding of the temporal-patterns of the East Asian Summer Monsoon and its connection with precipitation in the mon...Timing of the precipitation optimum in the Holocene for the semi-arid northern China affects our understanding of the temporal-patterns of the East Asian Summer Monsoon and its connection with precipitation in the monsoon fringe area.Discrepancies about when this occurs(early Holocene or mid-Holocene)exist in paleoclimate records based on various proxies and models.展开更多
A growing focus is being placed on both individuals and communities to adapt to flooding as part of the Sendai Framework for Disaster Risk Reduction 2015–2030.Adaptation to flooding requires sufficient social capital...A growing focus is being placed on both individuals and communities to adapt to flooding as part of the Sendai Framework for Disaster Risk Reduction 2015–2030.Adaptation to flooding requires sufficient social capital(linkages between members of society),risk perceptions(understanding of risk),and self-efficacy(selfperceived ability to limit disaster impacts)to be effective.However,there is limited understanding of how social capital,risk perceptions,and self-efficacy interact.We seek to explore how social capital interacts with variables known to increase the likelihood of successful adaptation.To study these linkages we analyze survey data of 1010 respondents across two communities in Thua Tien-Hue Province in central Vietnam,using ordered probit models.We find positive correlations between social capital,risk perceptions,and self-efficacy overall.This is a partly contrary finding to what was found in previous studies linking these concepts in Europe,which may be a result from the difference in risk context.The absence of an overall negative exchange between these factors has positive implications for proactive flood risk adaptation.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42071107 and 41877459)the Mobility program of the Sino-German Center for Research Promotion(No.M-0359)+1 种基金the CAS Pioneer Hundred Talents Program(Xianyong Cao)the Russian Science Foundation(No.20-17-00110).
文摘Plant environmental DNA extracted from lacustrine sediments(sedimentary DNA,sedDNA)has been increasingly used to investigate past vegetation changes and human impacts at a high taxonomic resolution.However,the representation of vegetation communities surrounding the lake is still unclear.In this study,we compared plant sedDNA metabarcoding and pollen assemblages from 27 lake surface-sediment samples collected from alpine meadow on the central-eastern Tibetan Plateau to investigate the representation of sedDNA data.In general,the identified components of sedDNA are consistent with the counted pollen taxa and local plant communities.Relative to pollen identification,sedDNA data have higher taxonomic resolution,thus providing a potential approach for reconstructing past plant diversity.The sedDNA signal is strongly influenced by local plants while rarely affected by exogenous plants.Because of the overrepresentation of local plants and PCR bias,the abundance of sedDNA sequence types is very variable among sites,and should be treated with caution when investigating past vegetation cover and climate based on sedDNA data.Our finding suggests that sedDNA analysis can be a complementary approach for investigating the presence/absence of past plants and history of human land-use with higher taxonomic resolution.
基金supported by the National Natural Science Foundation of China (Grant Nos. 42071107, 42177433)the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA2009000003)the Zhejiang Natural Science Foundation (Grant Nos. LY20D010002 and LY20D010003)。
文摘Benefiting from the rapid development of environmental DNA(eDNA) technologies, sedimentary DNA(sedDNA)emerges as a promising tool for monitoring plant compositions in remote regions. The Tibetan Plateau(TP), renowned for its harsh environment and numerous ponds and lakes, presents a potentially demanding region for the application of sedDNA on vegetation investigations. Here, we used the g and h universal primers for the P6 loop region of the chloroplast trn L(UAA)intron to amplify plant DNA in surface sediments from 59 ponds and small lakes on the southwestern TP. The applicability and limitations of using plant DNA metabarcoding for modern vegetation monitoring and palaeo-vegetation reconstructions have been assessed by comparing sedDNA, pollen, and vegetation survey data. Our results showed that plant DNA metabarcoding recorded 186 terrestrial taxa, of which 30.1% can be identified at the species level. The plant sedDNA approach can effectively disclose the dominant plant taxa(including Asteraceae, Cyperaceae and Poaceae) and significant vegetation assemblages in the vicinity of the investigated sites. The number of taxa and taxonomic resolution of plant sedDNA exceeded that of pollen analysis(75 taxa detected, 5.3% can be identified at species level). Unlike pollen that retains a broad spectrum of regional plant signals(including Pinus and Artemisia), plant sedDNA mirrors very local plants, underscoring its utility in local vegetation monitoring and reconstructions. To conclude, plant DNA metabarcoding of(small) lake sediments warrant increased attention in the future for local vegetation monitoring and reconstructions on the TP.
基金This study was funded by the National Natural Science Foundation of China(Grant No.41972297)Talents in Hebei Provincial Education Office(Grant No.SLRC2019027)+1 种基金Natural Science Foundation of Hebei Province(Grant No.D2022202005)the Spanish national project EROSLOP(Grant No.PID2019-104266RB-I00/AEI/10.13039/501100011033).
文摘There is no doubt that land cover and climate changes have consequences on landslide activity,but it is still an open issue to assess and quantify their impacts.Wanzhou County in southwest China was selected as the test area to study rainfall-induced shallow landslide susceptibility under the future changes of land use and land cover(LULC)and climate.We used a high-resolution meteorological precipitation dataset and frequency distribution model to analyse the present extreme and antecedent rainfall conditions related to landslide activity.The future climate change factors were obtained from a 4-member multimodel ensemble that was derived from statistically downscaled regional climate simulations.The future LULC maps were simulated by the land change modeller(LCM)integrated into IDRISI Selva software.A total of six scenarios were defined by considering the rainfall(antecedent conditions and extreme events)and LULC changes towards two time periods(mid and late XXI century).A physically-based model was used to assess landslide susceptibility under these different scenarios.The results showed that the magnitude of both antecedent effective recharge and event rainfall in the region will evidently increase in the future.Under the scenario with a return period of 100 years,the antecedent rainfall in summer will increase by up to 63%whereas the event rainfall will increase by up to 54%for the late 21st century.The most considerable changes of LULC will be the increase of forest cover and the decrease of farming land.The magnitude of this change can reach+22.1%(forest)and–9.2%(farmland)from 2010 until 2100,respectively.We found that the negative impact of climate change on landslide susceptibility is greater than the stabilizing effect of LULC change,leading to an over decrease in stability over the study area.This is one of the first studies across Asia to assess and quantify changes of regional landslide susceptibility under scenarios driven by LULC and climate change.Our results aim to guide land use planning and climate change mitigation considerations to reduce landslide risk.
基金This research was Jointly funded by National Key R&D plan(No.2021YFC3200900)National Natural Science Foundation of China(No.31971477).
文摘Accurate and credible identification of the drivers of algal growth is essential for sustainable utilization and scientific management of freshwater.In this study,we developed a deep learning-based Transformer model,named Bloomformer-1,for end-to-end identification of the drivers of algal growth without the needing extensive a priori knowledge or prior experiments.The Middle Route of the South-to-North Water Diversion Project(MRP)was used as the study site to demonstrate that Bloomformer-1 exhibited more robust performance(with the highest R^(2),0.80 to 0.94,and the lowest RMSE,0.22–0.43μg/L)compared to four widely used traditional machine learning models,namely extra trees regression(ETR),gradient boosting regression tree(GBRT),support vector regression(SVR),and multiple linear regression(MLR).In addition,Bloomformer-1 had higher interpretability(including higher transferability and understandability)than the four traditional machine learning models,which meant that it was trustworthy and the results could be directly applied to real scenarios.Finally,it was determined that total phosphorus(TP)was the most important driver for the MRP,especially in Henan section of the canal,although total nitrogen(TN)had the highest effect on algal growth in the Hebei section.Based on these results,phosphorus loading controlling in the whole MRP was proposed as an algal control strategy.
基金supported by the National Natural Science Foundation of China(41988101 and 41877459)the Pioneer Hundred Talents Program of Chinese Academy of Sciences。
文摘Timing of the precipitation optimum in the Holocene for the semi-arid northern China affects our understanding of the temporal-patterns of the East Asian Summer Monsoon and its connection with precipitation in the monsoon fringe area.Discrepancies about when this occurs(early Holocene or mid-Holocene)exist in paleoclimate records based on various proxies and models.
基金funding received from the Global Resilience Partnership(GRP)through the Water Window’s Resil Nam CoastalResil Nam Urban projects as funded by the Z Zurich Foundation。
文摘A growing focus is being placed on both individuals and communities to adapt to flooding as part of the Sendai Framework for Disaster Risk Reduction 2015–2030.Adaptation to flooding requires sufficient social capital(linkages between members of society),risk perceptions(understanding of risk),and self-efficacy(selfperceived ability to limit disaster impacts)to be effective.However,there is limited understanding of how social capital,risk perceptions,and self-efficacy interact.We seek to explore how social capital interacts with variables known to increase the likelihood of successful adaptation.To study these linkages we analyze survey data of 1010 respondents across two communities in Thua Tien-Hue Province in central Vietnam,using ordered probit models.We find positive correlations between social capital,risk perceptions,and self-efficacy overall.This is a partly contrary finding to what was found in previous studies linking these concepts in Europe,which may be a result from the difference in risk context.The absence of an overall negative exchange between these factors has positive implications for proactive flood risk adaptation.
