Complementarity of lacustrine pollen and sedimentary DNA in representing vegetation on the central-eastern Tibetan Plateau

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.

Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China

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.

Identification of driving factors of algal growth in the South-to-North Water Diversion Project by Transformer-based deep learning

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.

Potential Linkages Between Social Capital,Flood Risk Perceptions,and Self-Efficacy

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.

湖泊沉积植物古DNA的现代过程

对植被历史变化过程的研究是理解现代植被组成、分布及其对全球变化响应的基础。近年来,随着分子古生态学的发展,分析沉积介质中的陆生植物古DNA信号,以研究植被及植物多样性演变的历史过程正在成为研究热点,湖泊沉积植物古DNA已成为古植被和古生态学研究的成熟代用指标。然而与第四纪孢粉分析相比较,湖泊沉积植物古DNA的现代过程依然不明确,成为其进一步发展和应用的限制因素。基于此,该文综述了湖泊沉积植物古DNA技术研究进展,尝试阐明湖泊沉积植物古DNA的现代过程,包括植物DNA的来源、沉积和保存过程及其影响因素,以及植物DNA与现代植被的关系等。已有研究表明,湖泊沉积植物古DNA主要来自湖泊周边或流域范围,其丰度和组成除受到源植物生物量的影响外,同样受到沉积物的搬运和沉积过程中DNA降解作用、土壤以及沉积物中颗粒的吸附过程和稀释作用等因素的影响。湖泊沉积物中植物DNA的保存则主要受到微生物活动、湖水的化学性质(电导率和pH值)、湖泊深度、沉积物组成等一系列生物与非生物因素的共同影响。湖泊沉积植物古DNA可以揭示其沉积时代的植物群落类型以及气候环境信息,但目前并不能够用来定量重建古植被变化过程。鉴于湖泊沉积植物古DNA现代过程的复杂性,对研究结果的解释要格外小心。与孢粉分析相比,湖泊沉积植物古DNA研究仍处于起步阶段,但随着分子生物技术的进步、实验设计的优化、物种条形码的扩充及参考数据库的完善等,以DNA宏条形码和宏基因组学为主要技术手段的植物古DNA技术,必将推动我国植物古生态研究的进一步发展。

地层孢粉数据的远距离现代类比影响降水定量重建可靠性

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.