该研究首次借助林冠塔吊调查了西双版纳国家级自然保护区龙脑香热带雨林样地内69棵树13个垂直高度上的附生苔藓植物,结果表明:目标样树上共记录到隶属于25科60属的90种附生苔藓,其中细鳞苔科物种数最多,占比达25.6%。13个垂直高度上共...该研究首次借助林冠塔吊调查了西双版纳国家级自然保护区龙脑香热带雨林样地内69棵树13个垂直高度上的附生苔藓植物,结果表明:目标样树上共记录到隶属于25科60属的90种附生苔藓,其中细鳞苔科物种数最多,占比达25.6%。13个垂直高度上共划分出三种生态类型:喜阳苔藓(散生巨树上>45 m 的区域),喜阴苔藓(乔木树干上<15 m的区域),广布苔藓(广泛分布于宿主各个垂直高度上,生态位宽),并筛选出对微生境有特殊偏好的17种苔藓指示种(IndVal≥0.7, P <0.05)。随宿主垂直高度的升高,扇型和交织型的苔藓占比降低,悬垂型苔藓占比先升高后降低,细平铺型和粗平铺型的苔藓占比升高。大气湿度、水汽压、胸径以及树皮粗糙度对附生苔藓生活型的分布偏好具有显著影响。总之,沿宿主垂直高度上的附生苔藓对微环境变化在生活型和形态结构上有着不同的响应方式,而同一种生态型的苔藓群落有相似的适应机制。因此,在森林林冠生境变化的监测和管理中,对微生境具有明显偏好的附生苔藓物种可作为有效的指示材料。展开更多
Geogenic lead (Pb) is considered to be less bioavailable than anthropogenic Pb and exerts less effect on the soil fauna. However,Pb contamination in vegetables has been reported in the case of geogenic anomalies, even...Geogenic lead (Pb) is considered to be less bioavailable than anthropogenic Pb and exerts less effect on the soil fauna. However,Pb contamination in vegetables has been reported in the case of geogenic anomalies, even at moderate concentrations (around 170 mgkg^(-1)). In this study, we investigated collembolan communities using both taxonomic- and trait-based approaches and observed fungal communities to assess the effects of a moderate geogenic Pb anomaly on collembolans and fungi in an urban vegetable garden soil.Results indicated that geogenic Pb indeed modified fungi communities and altered the functional structure of collembolan communities in garden soils. Although geogenic Pb presented low bioavailability, it affected soil fauna and vegetables similar to anthropogenic Pb.展开更多
Anthropogenic ammonia emissions primarily originate from agriculture,especially field fertilization.These emissions represent nitrogen loss for farmers and contribute to air pollution,posing risks to human health and ...Anthropogenic ammonia emissions primarily originate from agriculture,especially field fertilization.These emissions represent nitrogen loss for farmers and contribute to air pollution,posing risks to human health and the environment.Estimating ammonia emissions is crucial for national inventories and policy-making.Various models exist for predicting emissions,including mechanistic,empirical,and semi-empirical approaches.While machine learning(ML)is widely used in environmental science,its application to ammonia emissions remains limited.In this study,we used 5939 ammonia emission data from 538 trials,extracted from the ALFAM2 database,to train three machine learning methods-random forest,gradient boosting,and lasso-for predicting cumulative ammonia emissions 72 h after manure application.These methods were compared to the semi-empirical ALFAM2 model using an independent test dataset.Random forest(RMSE=4.51,r=0.94,MAE=3.28,Bias=0.92)and gradient boosting(RMSE=6.19,r=0.89,MAE=4.10,Bias=0.51)showed the best performance,while the lasso log-linear model(RMSE=7.30,r=0.84,MAE=5.57,Bias=-1.38)performed worst.Both random forest and gradient boosting outperformed the semi-empirical ALFAM2 model,which showed performance comparable to the lasso model.We then used these models and the ALFAM2 model to compare five slurry management techniques,varying in application method(trailing hoses,trailing shoes,and open slot)and post-application incorporation,across 128 scenarios with different manure types and weather conditions.Compared to broadcast application,alternative techniques reduced emissions by a median of-13.6%to-61.7%.This study highlights the promise of ML models in assessing ammonia emission reduction methods,while emphasizing the importance of evaluating model sensitivity to algorithm choice.展开更多
Soil has garnered global attention for its role in food security and climate change.Fine-scale soil-mapping techniques are urgently needed to support food,water,and biodiversity services.A global soil dataset integrat...Soil has garnered global attention for its role in food security and climate change.Fine-scale soil-mapping techniques are urgently needed to support food,water,and biodiversity services.A global soil dataset integrated into an Earth observation system and supported by cloud computing enabled the development of the first global soil grid of six key properties at a 90-m spatial resolution.Assessing them from environmental and socioeconomic perspectives,we demonstrated that 64%of the world’s topsoils are primarily sandy,with low fertility and high susceptibility to degradation.These conditions limit crop productivity and highlight potential risks to food security.Results reveal that approximately 900 Gt of soil organic carbon(SOC)is stored up to 20 cm deep.Arid biomes store three times more SOC than mangroves based on total areas.SOC content in agricultural soils is reduced by at least 60%compared to soils under natural vegetation.Most agricultural areas are being fertilized while simultaneously experiencing a depletion of the carbon pool.By integrating soil capacity with economic and social factors,we highlight the critical role of soil in supporting societal prosperity.The top 10 largest countries in area per continent store 75%of the global SOC stock.However,the poorest countries face rapid organic matter degradation.We indicate an interconnection between societal growth and spatially explicit mapping of soil properties.This soil-human nexus establishes a geographically based link between soil health and human development.It underscores the importance of soil management in enhancing agricultural productivity and promotes sustainable-land-use planning.展开更多
In a recent article in this journal,Cai et al.(2019)argue that,until now,“the importance of soil biofilms has largely been ignored”,and that this oversight needs to be corrected because,from their perspective,biofil...In a recent article in this journal,Cai et al.(2019)argue that,until now,“the importance of soil biofilms has largely been ignored”,and that this oversight needs to be corrected because,from their perspective,biofilms are a“dominant growth form”,central to many processes occurring in soils.These authors make a fervent plea that research take place in different fields“to lay the foundation for researching soil biofilms and to drive this field into a new era.”展开更多
文摘该研究首次借助林冠塔吊调查了西双版纳国家级自然保护区龙脑香热带雨林样地内69棵树13个垂直高度上的附生苔藓植物,结果表明:目标样树上共记录到隶属于25科60属的90种附生苔藓,其中细鳞苔科物种数最多,占比达25.6%。13个垂直高度上共划分出三种生态类型:喜阳苔藓(散生巨树上>45 m 的区域),喜阴苔藓(乔木树干上<15 m的区域),广布苔藓(广泛分布于宿主各个垂直高度上,生态位宽),并筛选出对微生境有特殊偏好的17种苔藓指示种(IndVal≥0.7, P <0.05)。随宿主垂直高度的升高,扇型和交织型的苔藓占比降低,悬垂型苔藓占比先升高后降低,细平铺型和粗平铺型的苔藓占比升高。大气湿度、水汽压、胸径以及树皮粗糙度对附生苔藓生活型的分布偏好具有显著影响。总之,沿宿主垂直高度上的附生苔藓对微环境变化在生活型和形态结构上有着不同的响应方式,而同一种生态型的苔藓群落有相似的适应机制。因此,在森林林冠生境变化的监测和管理中,对微生境具有明显偏好的附生苔藓物种可作为有效的指示材料。
基金supported by the ANR (French National Agency of Research, JASSUR research project ANR-12-VBDU-0011)
文摘Geogenic lead (Pb) is considered to be less bioavailable than anthropogenic Pb and exerts less effect on the soil fauna. However,Pb contamination in vegetables has been reported in the case of geogenic anomalies, even at moderate concentrations (around 170 mgkg^(-1)). In this study, we investigated collembolan communities using both taxonomic- and trait-based approaches and observed fungal communities to assess the effects of a moderate geogenic Pb anomaly on collembolans and fungi in an urban vegetable garden soil.Results indicated that geogenic Pb indeed modified fungi communities and altered the functional structure of collembolan communities in garden soils. Although geogenic Pb presented low bioavailability, it affected soil fauna and vegetables similar to anthropogenic Pb.
基金the French state aid managed by the ANR under the“Investissements d’avenir”programme with the reference ANR-16-CONV-0003from the AgroEcoSystem department of INRAE.We are grateful to the INRAE MIGALE bioinformatics facility(MIGALE,INRAE,2020.Migale bioinformatics Facility,doi:10.15454/1.5572390655343293E12)for providing help and/or computing and/or storage resources.We are also grateful to Sasha Hafner for his help in reproducing some of the results of Hafner et al.(2019).
文摘Anthropogenic ammonia emissions primarily originate from agriculture,especially field fertilization.These emissions represent nitrogen loss for farmers and contribute to air pollution,posing risks to human health and the environment.Estimating ammonia emissions is crucial for national inventories and policy-making.Various models exist for predicting emissions,including mechanistic,empirical,and semi-empirical approaches.While machine learning(ML)is widely used in environmental science,its application to ammonia emissions remains limited.In this study,we used 5939 ammonia emission data from 538 trials,extracted from the ALFAM2 database,to train three machine learning methods-random forest,gradient boosting,and lasso-for predicting cumulative ammonia emissions 72 h after manure application.These methods were compared to the semi-empirical ALFAM2 model using an independent test dataset.Random forest(RMSE=4.51,r=0.94,MAE=3.28,Bias=0.92)and gradient boosting(RMSE=6.19,r=0.89,MAE=4.10,Bias=0.51)showed the best performance,while the lasso log-linear model(RMSE=7.30,r=0.84,MAE=5.57,Bias=-1.38)performed worst.Both random forest and gradient boosting outperformed the semi-empirical ALFAM2 model,which showed performance comparable to the lasso model.We then used these models and the ALFAM2 model to compare five slurry management techniques,varying in application method(trailing hoses,trailing shoes,and open slot)and post-application incorporation,across 128 scenarios with different manure types and weather conditions.Compared to broadcast application,alternative techniques reduced emissions by a median of-13.6%to-61.7%.This study highlights the promise of ML models in assessing ammonia emission reduction methods,while emphasizing the importance of evaluating model sensitivity to algorithm choice.
基金supported by the São Paulo Research Foundation(FAPESP)under grants 2014-22262-0 and 2021/05129-8the from the Center for Carbon Research in Tropical Agriculture(CCARBON)at the University of São Paulo,under grant 2021/10573-4+1 种基金the support of the MII Project of the Russian Federation(reg.123030300031-6)CNPq for research scholarship(307190-2021-8).
文摘Soil has garnered global attention for its role in food security and climate change.Fine-scale soil-mapping techniques are urgently needed to support food,water,and biodiversity services.A global soil dataset integrated into an Earth observation system and supported by cloud computing enabled the development of the first global soil grid of six key properties at a 90-m spatial resolution.Assessing them from environmental and socioeconomic perspectives,we demonstrated that 64%of the world’s topsoils are primarily sandy,with low fertility and high susceptibility to degradation.These conditions limit crop productivity and highlight potential risks to food security.Results reveal that approximately 900 Gt of soil organic carbon(SOC)is stored up to 20 cm deep.Arid biomes store three times more SOC than mangroves based on total areas.SOC content in agricultural soils is reduced by at least 60%compared to soils under natural vegetation.Most agricultural areas are being fertilized while simultaneously experiencing a depletion of the carbon pool.By integrating soil capacity with economic and social factors,we highlight the critical role of soil in supporting societal prosperity.The top 10 largest countries in area per continent store 75%of the global SOC stock.However,the poorest countries face rapid organic matter degradation.We indicate an interconnection between societal growth and spatially explicit mapping of soil properties.This soil-human nexus establishes a geographically based link between soil health and human development.It underscores the importance of soil management in enhancing agricultural productivity and promotes sustainable-land-use planning.
文摘In a recent article in this journal,Cai et al.(2019)argue that,until now,“the importance of soil biofilms has largely been ignored”,and that this oversight needs to be corrected because,from their perspective,biofilms are a“dominant growth form”,central to many processes occurring in soils.These authors make a fervent plea that research take place in different fields“to lay the foundation for researching soil biofilms and to drive this field into a new era.”
