The availability of nitrogen(N)is crucial for both the productivity of terrestrial and aquatic ecosystems globally.However,the overuse of artificial fertilizers and the energy required to fix nitrogen have pushed the ...The availability of nitrogen(N)is crucial for both the productivity of terrestrial and aquatic ecosystems globally.However,the overuse of artificial fertilizers and the energy required to fix nitrogen have pushed the global nitrogen cycle(N-cycle)past its safe operating limits,leading to severe nitrogen pollution and the production of significant amounts of greenhouse gas nitrous oxide(N2O).The anaerobic ammonium oxidation(anammox)mechanism can counteract the release of ammonium and N2O in many oxygenlimited situations,assisting in the restoration of the homeostasis of the Earth’s N biogeochemistry.In this work,we looked into the characteristics of the anammox hotspots’distribution across various types of ecosystems worldwide.Anammox hotspots are present at diverse oxic-anoxic interfaces in terrestrial systems,and they are most prevalent at the oxic-anoxic transition zone in aquatic ecosystems.Based on the discovery of an anammox hotspot capable of oxidizing ammonium anoxically into N2 without N2O by-product,we then designed an innovative concept and technical routes of nature-based anammox hotspot geoengineering for climate change,biodiversity loss,and efficient utilization of water resources.After 15 years of actual use,anammox hotspot geoengineering has proven to be effective in ensuring clean drinking water,regulating the climate,fostering plant and animal diversity,and enhancing longterm environmental quality.The sustainable biogeoengineering of anammox could be a workable natural remedy to resolve the conflicts between environmental pollution and food security connected to N management.展开更多
Those familiar with Gastrodia elata Blume(Tianma,天麻)in China will most certainly recognise the name of Xuan Zhou(周铉),the first postgraduate student of Professor Zheng-Yi Wu(吴征镒),widely respected professor of bo...Those familiar with Gastrodia elata Blume(Tianma,天麻)in China will most certainly recognise the name of Xuan Zhou(周铉),the first postgraduate student of Professor Zheng-Yi Wu(吴征镒),widely respected professor of botany and an academician of the Chinese Academy of Sciences(Peng et al.,2013).Xuan Zhou(abbreviated S.Chow in taxonomic citations,also Chou,Hsüan in Wade-Giles transliterations)(Fig.1)was born in Xinzheng,in the central Chinese Province of Henan on 4 May 1926.He graduated from Tongji University in 1951.He took part in and successfully passed the first national post-graduate examination held by the Chinese Academy of Sciences in 1956 and became a candidate for an Associate Doctor degree(equivalent to MSc)under the tutelage of Prof.Wu.展开更多
Elevational patterns of tree diversity are well studied worldwide.However,few studies have examined how seedlings respond to elevational gradients and whether their responses vary across climatic zones.In this study,w...Elevational patterns of tree diversity are well studied worldwide.However,few studies have examined how seedlings respond to elevational gradients and whether their responses vary across climatic zones.In this study,we established three elevational transects in tropical,subtropical and subalpine mountain forests in Yunnan Province,southern China,to examine the responses of tree species and their seedlings to elevational gradients.Within each transect,we calculated species diversity indices and composition of both adult trees and seedlings at different elevations.For both adult trees and seedlings,we found that species diversity decreased with increasing elevation in both tropical and subalpine transects.Species composition showed significant elevational separation within all three transects.Many species had specific elevational preferences,but abundant tree species that occurred at specific elevations tended to have very limited recruitment in the understory.Our results highlight that the major factors that determine elevational distributions of tree species vary across climatic zones.Specifically,we found that the contribution of air temperature to tree species composition increased from tropical to subalpine transects,whereas the contribution of soil moisture decreased across these transects.展开更多
Trend analysis and change point detection in a time series are frequent analysis tools.Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes,whe...Trend analysis and change point detection in a time series are frequent analysis tools.Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes,whereas trend can be defined as estimation of gradual departure from past norms.We analyze the time series data in the presence of trend,using Cox-Stuart methods together with the change point algorithms.We applied the methods to the nearsurface wind speed time series for Australia as an example.The trends in near-surface wind speeds for Australia have been investigated based upon our newly developed wind speed datasets,which were constructed by blending observational data collected at various heights using local surface roughness information.The trend in wind speed at 10 m is generally increasing while at 2 m it tends to be decreasing.Significance testing,change point analysis and manual inspection of records indicate several factors may be contributing to the discrepancy,such as systematic biases accompanying instrument changes,random data errors(e.g.accumulation day error)and data sampling issues.Homogenization technique and multiple-period trend analysis based upon change point detections have thus been employed to clarify the source of the inconsistencies in wind speed trends.展开更多
Background： Australia＇s energy future is at the crossroads and the role of renewable sources is in focus. Biomass from sustainably managed forests provide a significant opportunity for electricity and heat generatio...Background： Australia＇s energy future is at the crossroads and the role of renewable sources is in focus. Biomass from sustainably managed forests provide a significant opportunity for electricity and heat generation and production of liquid fuels. Australia has extensive native forests of which a significant proportion are on private land. However, there is limited knowledge on the potential capacity of this resource to contribute to the expansion of a biomass for bioenergy industry. In addition, there are concerns on how to reconcile biomass harvesting with environmental protection. Methods： We used regional ecosystem vegetation mapping for Queensland to stratify harvestable forests within the 1.8 m hectares of private native forests present in the Southeast Queensland bioregion in 2014. We used a dataset of 52,620 individual tree measurements from 541 forest inventory plots collected over the last 10 years. Tree biomass was estimated using current biomass allometric equations for Australia. Biomass potentially available from selective sawlog harvesting and silvicultural treatment across the bioregion was calculated and mapped. Results： Current sawlog harvesting extracts 41.4% of the standing tree biomass and a biomass for bioenergy harvest would retain on average 36% of felled tree biomass on site for the protection of environmental and fauna habitat values. The estimated area extent of harvestable private native forests in the bioregion in 2013 was 888,000 ha and estimated available biomass for bioenergy in living trees was 13.6 million tonnes （t）. The spotted gum （Corymbio citriodora subsp, variegata） forests were the most extensive, covering an area of 379,823 ha and with a biomass for bioenergy yield of 14.2 t-ha-1 （with approximately 11.2 t.ha-1 of the biomass harvested from silvicultural thinning and 3 t.ha-1 recovered from sawlog harvest residual）. Conclusions： Silvicultural treatment of private native forests in the Southeast Queensland bioregion, has the capacity to supply a large quantity of biomass for bioenergy. The availability of a biomass for bioenergy market, and integration of sawlog harvesting and silvicultural treatment operations, could provide land owners with additional commercial incentive to improve the management of private native forests. This could potentially promote restoration of degraded forests, ecological sustainability and continued provision of wood products.展开更多
Rangelands worldwide have been subject to broadscale modification,such as widespread predator control,intro-duction of permanent livestock water and altered vegetation to improve grazing.In Australia,these landscape ch...Rangelands worldwide have been subject to broadscale modification,such as widespread predator control,intro-duction of permanent livestock water and altered vegetation to improve grazing.In Australia,these landscape changes have resulted in kangaroos(i.e.large macropods)populations increasing over the past 200 years.Kan-garoos are a key contributor to total grazing pressure and in conjunction with livestock and feral herbivores have been linked to land degradation.We used 22 years of aerial survey data to investigate whether the density of 3 macropod species in the southern rangelands of Western Australia was associated with:(i)land use,including type of livestock,total livestock,density of feral goats,type of land tenure,and kangaroo commercial harvest effort;(ii)predator management,including permitted dingo control effort,estimated dingo abundance,and presence of the State Barrier Fence(a dingo exclusion fence);and(iii)environmental variables:ruggedness,rainfall,fractional cover,and total standing dry matter.Red kangaroos(Osphranter rufus)were most abundant inflat,open vegetation,on pastoral land,where area permitted for dingo control was high,and numbers were positively associated with antecedent rainfall with a 12-month delay.Western grey kangaroos(Macropus fuliginosus)were most abundant onflat,agricultural land,but less abundant in areas with high permitted dingo control.Euros(Osphranter robustus)were most abundant in rugged pastoral land with open vegetation,where permitted dingo control was high.While environmental variables are key drivers of landscape productivity and kangaroo populations,anthropogenic factors such as land use and permitted dingo control are strongly associated with kangaroo abundance.展开更多
Early crop yield prediction provides critical information for Precision Agriculture(PA)procedures,policymaking,and food security.The availability of Remote Sensing(RS)datasets and Machine Learning(ML)approaches improv...Early crop yield prediction provides critical information for Precision Agriculture(PA)procedures,policymaking,and food security.The availability of Remote Sensing(RS)datasets and Machine Learning(ML)approaches improved the prediction of sugarcane crop yield on the local and global scales,but an additional effort on the plot scale prediction is required.Challenges for plot-level prediction include a high ratooning capacity of the sugarcane crop,the lack of high spatial resolution data during the critical growth stages,and the non-linear complexation of yield data.The principal objective of the study is to analyse the potential of a time series of high-resolution multispectral Unmanned Aerial Vehicle(UAV)imagery along with three advanced ML techniques,namely Random Forest Regression(RFR),Support Vector Regression(SVR),and Nonlinear Autoregressive Exogenous Artificial Neural Network(NARX ANN)as a solution to the plot-level sugarcane yield prediction.An experimental sugarcane field containing 48 plots was selected,and UAV imagery was collected during the three consecutive cropping seasons’early and middle crop growth stages.Each dataset per growth stage was analyzed separately to predict the sugarcane crop yield in an attempt to discover how early the prediction of pre-harvest yield can be achieved.The datasets of the first two cropping seasons were trained and tested using the three ML techniques,utilizing 10-fold cross-validation to avoid overfitting.The third cropping season dataset was then used to evaluate the reliability of the developed prediction models.The results show that the correlation of Vegetation Indices(VIs)with crop yield in the middle stage outperforms the early stage in all three ML models.Moreover,comparing these models indicates that the NARX ANN method outperformed the others in the middle stage with the highest correlation coefficient(R^(2))of 0.96 and the lowest Root Mean Square Error(RMSE)of 4.92 t/ha.It was followed by the SVR(R^(2)=0.52,RMSE of 14.85 t/ha),which performed similarly to the RFR method(R^(2)=0.48,RMSE=11.20 t/ha).In conclusion,the best-suited model for predicting sugarcane yields during the middle growth stage is a NARX ANN model employing the Normalized Difference RedEdge(NDRE),which demonstrates the feasibility of the ML approaches to predict the plot level sugarcane yield at a specific period of growth as they are less sensitive to the inconsistency of data collection times.展开更多
Due to the worldwide population growth and the increasing needs for sugar-based products,accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth.This research aims to find th...Due to the worldwide population growth and the increasing needs for sugar-based products,accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth.This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles(UAVs).The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments.Individual spectral bands and different combinations of the plots,growth stages,and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling.A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution.The results showed that utilizing Green,Blue,and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates.Additionally,the combination of plots and growth stages outperformed all the candidates of random effects.The proposed model outperformed the Multiple Linear Regression(MLR),Generalized Linear Model(GLM),and Generalized Additive Model(GAM)for wet and dry sugarcane biomass,with coefficients of determination(R2)of 0.93 and 0.97,and Root Mean Square Error(RMSE)of 12.78 and 2.57 t/ha,respectively.This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices(VIs)in mature growth stages.展开更多
基金supported by the National Natural Science Foundation of China(91851204 and 42021005)the Special project of eco-environmental technology for peak carbon dioxide emissions and carbon neutrality(RCEES-TDZ-2021-20).
文摘The availability of nitrogen(N)is crucial for both the productivity of terrestrial and aquatic ecosystems globally.However,the overuse of artificial fertilizers and the energy required to fix nitrogen have pushed the global nitrogen cycle(N-cycle)past its safe operating limits,leading to severe nitrogen pollution and the production of significant amounts of greenhouse gas nitrous oxide(N2O).The anaerobic ammonium oxidation(anammox)mechanism can counteract the release of ammonium and N2O in many oxygenlimited situations,assisting in the restoration of the homeostasis of the Earth’s N biogeochemistry.In this work,we looked into the characteristics of the anammox hotspots’distribution across various types of ecosystems worldwide.Anammox hotspots are present at diverse oxic-anoxic interfaces in terrestrial systems,and they are most prevalent at the oxic-anoxic transition zone in aquatic ecosystems.Based on the discovery of an anammox hotspot capable of oxidizing ammonium anoxically into N2 without N2O by-product,we then designed an innovative concept and technical routes of nature-based anammox hotspot geoengineering for climate change,biodiversity loss,and efficient utilization of water resources.After 15 years of actual use,anammox hotspot geoengineering has proven to be effective in ensuring clean drinking water,regulating the climate,fostering plant and animal diversity,and enhancing longterm environmental quality.The sustainable biogeoengineering of anammox could be a workable natural remedy to resolve the conflicts between environmental pollution and food security connected to N management.
文摘Those familiar with Gastrodia elata Blume(Tianma,天麻)in China will most certainly recognise the name of Xuan Zhou(周铉),the first postgraduate student of Professor Zheng-Yi Wu(吴征镒),widely respected professor of botany and an academician of the Chinese Academy of Sciences(Peng et al.,2013).Xuan Zhou(abbreviated S.Chow in taxonomic citations,also Chou,Hsüan in Wade-Giles transliterations)(Fig.1)was born in Xinzheng,in the central Chinese Province of Henan on 4 May 1926.He graduated from Tongji University in 1951.He took part in and successfully passed the first national post-graduate examination held by the Chinese Academy of Sciences in 1956 and became a candidate for an Associate Doctor degree(equivalent to MSc)under the tutelage of Prof.Wu.
基金supported by the National Natural Science Foundation of China(31800353 and 32061123003)the West Light Foundation of the Chinese Academy of Sciences,the Applied Fundamental Research Foundation of Yunnan Province(2019FB038,2014GA003 and 2013FB079)the National Key Basic Research Program of China(2014CB954100)and the QueenslandeChinese Academy of Sciences Biotechnology Fund(GJHZ1130).
文摘Elevational patterns of tree diversity are well studied worldwide.However,few studies have examined how seedlings respond to elevational gradients and whether their responses vary across climatic zones.In this study,we established three elevational transects in tropical,subtropical and subalpine mountain forests in Yunnan Province,southern China,to examine the responses of tree species and their seedlings to elevational gradients.Within each transect,we calculated species diversity indices and composition of both adult trees and seedlings at different elevations.For both adult trees and seedlings,we found that species diversity decreased with increasing elevation in both tropical and subalpine transects.Species composition showed significant elevational separation within all three transects.Many species had specific elevational preferences,but abundant tree species that occurred at specific elevations tended to have very limited recruitment in the understory.Our results highlight that the major factors that determine elevational distributions of tree species vary across climatic zones.Specifically,we found that the contribution of air temperature to tree species composition increased from tropical to subalpine transects,whereas the contribution of soil moisture decreased across these transects.
文摘Trend analysis and change point detection in a time series are frequent analysis tools.Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes,whereas trend can be defined as estimation of gradual departure from past norms.We analyze the time series data in the presence of trend,using Cox-Stuart methods together with the change point algorithms.We applied the methods to the nearsurface wind speed time series for Australia as an example.The trends in near-surface wind speeds for Australia have been investigated based upon our newly developed wind speed datasets,which were constructed by blending observational data collected at various heights using local surface roughness information.The trend in wind speed at 10 m is generally increasing while at 2 m it tends to be decreasing.Significance testing,change point analysis and manual inspection of records indicate several factors may be contributing to the discrepancy,such as systematic biases accompanying instrument changes,random data errors(e.g.accumulation day error)and data sampling issues.Homogenization technique and multiple-period trend analysis based upon change point detections have thus been employed to clarify the source of the inconsistencies in wind speed trends.
基金supported by the Australian Biomass for Bioenergy Assessment(ABBA)Project,Queensland Government
文摘Background： Australia＇s energy future is at the crossroads and the role of renewable sources is in focus. Biomass from sustainably managed forests provide a significant opportunity for electricity and heat generation and production of liquid fuels. Australia has extensive native forests of which a significant proportion are on private land. However, there is limited knowledge on the potential capacity of this resource to contribute to the expansion of a biomass for bioenergy industry. In addition, there are concerns on how to reconcile biomass harvesting with environmental protection. Methods： We used regional ecosystem vegetation mapping for Queensland to stratify harvestable forests within the 1.8 m hectares of private native forests present in the Southeast Queensland bioregion in 2014. We used a dataset of 52,620 individual tree measurements from 541 forest inventory plots collected over the last 10 years. Tree biomass was estimated using current biomass allometric equations for Australia. Biomass potentially available from selective sawlog harvesting and silvicultural treatment across the bioregion was calculated and mapped. Results： Current sawlog harvesting extracts 41.4% of the standing tree biomass and a biomass for bioenergy harvest would retain on average 36% of felled tree biomass on site for the protection of environmental and fauna habitat values. The estimated area extent of harvestable private native forests in the bioregion in 2013 was 888,000 ha and estimated available biomass for bioenergy in living trees was 13.6 million tonnes （t）. The spotted gum （Corymbio citriodora subsp, variegata） forests were the most extensive, covering an area of 379,823 ha and with a biomass for bioenergy yield of 14.2 t-ha-1 （with approximately 11.2 t.ha-1 of the biomass harvested from silvicultural thinning and 3 t.ha-1 recovered from sawlog harvest residual）. Conclusions： Silvicultural treatment of private native forests in the Southeast Queensland bioregion, has the capacity to supply a large quantity of biomass for bioenergy. The availability of a biomass for bioenergy market, and integration of sawlog harvesting and silvicultural treatment operations, could provide land owners with additional commercial incentive to improve the management of private native forests. This could potentially promote restoration of degraded forests, ecological sustainability and continued provision of wood products.
文摘Rangelands worldwide have been subject to broadscale modification,such as widespread predator control,intro-duction of permanent livestock water and altered vegetation to improve grazing.In Australia,these landscape changes have resulted in kangaroos(i.e.large macropods)populations increasing over the past 200 years.Kan-garoos are a key contributor to total grazing pressure and in conjunction with livestock and feral herbivores have been linked to land degradation.We used 22 years of aerial survey data to investigate whether the density of 3 macropod species in the southern rangelands of Western Australia was associated with:(i)land use,including type of livestock,total livestock,density of feral goats,type of land tenure,and kangaroo commercial harvest effort;(ii)predator management,including permitted dingo control effort,estimated dingo abundance,and presence of the State Barrier Fence(a dingo exclusion fence);and(iii)environmental variables:ruggedness,rainfall,fractional cover,and total standing dry matter.Red kangaroos(Osphranter rufus)were most abundant inflat,open vegetation,on pastoral land,where area permitted for dingo control was high,and numbers were positively associated with antecedent rainfall with a 12-month delay.Western grey kangaroos(Macropus fuliginosus)were most abundant onflat,agricultural land,but less abundant in areas with high permitted dingo control.Euros(Osphranter robustus)were most abundant in rugged pastoral land with open vegetation,where permitted dingo control was high.While environmental variables are key drivers of landscape productivity and kangaroo populations,anthropogenic factors such as land use and permitted dingo control are strongly associated with kangaroo abundance.
文摘Early crop yield prediction provides critical information for Precision Agriculture(PA)procedures,policymaking,and food security.The availability of Remote Sensing(RS)datasets and Machine Learning(ML)approaches improved the prediction of sugarcane crop yield on the local and global scales,but an additional effort on the plot scale prediction is required.Challenges for plot-level prediction include a high ratooning capacity of the sugarcane crop,the lack of high spatial resolution data during the critical growth stages,and the non-linear complexation of yield data.The principal objective of the study is to analyse the potential of a time series of high-resolution multispectral Unmanned Aerial Vehicle(UAV)imagery along with three advanced ML techniques,namely Random Forest Regression(RFR),Support Vector Regression(SVR),and Nonlinear Autoregressive Exogenous Artificial Neural Network(NARX ANN)as a solution to the plot-level sugarcane yield prediction.An experimental sugarcane field containing 48 plots was selected,and UAV imagery was collected during the three consecutive cropping seasons’early and middle crop growth stages.Each dataset per growth stage was analyzed separately to predict the sugarcane crop yield in an attempt to discover how early the prediction of pre-harvest yield can be achieved.The datasets of the first two cropping seasons were trained and tested using the three ML techniques,utilizing 10-fold cross-validation to avoid overfitting.The third cropping season dataset was then used to evaluate the reliability of the developed prediction models.The results show that the correlation of Vegetation Indices(VIs)with crop yield in the middle stage outperforms the early stage in all three ML models.Moreover,comparing these models indicates that the NARX ANN method outperformed the others in the middle stage with the highest correlation coefficient(R^(2))of 0.96 and the lowest Root Mean Square Error(RMSE)of 4.92 t/ha.It was followed by the SVR(R^(2)=0.52,RMSE of 14.85 t/ha),which performed similarly to the RFR method(R^(2)=0.48,RMSE=11.20 t/ha).In conclusion,the best-suited model for predicting sugarcane yields during the middle growth stage is a NARX ANN model employing the Normalized Difference RedEdge(NDRE),which demonstrates the feasibility of the ML approaches to predict the plot level sugarcane yield at a specific period of growth as they are less sensitive to the inconsistency of data collection times.
文摘Due to the worldwide population growth and the increasing needs for sugar-based products,accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth.This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles(UAVs).The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments.Individual spectral bands and different combinations of the plots,growth stages,and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling.A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution.The results showed that utilizing Green,Blue,and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates.Additionally,the combination of plots and growth stages outperformed all the candidates of random effects.The proposed model outperformed the Multiple Linear Regression(MLR),Generalized Linear Model(GLM),and Generalized Additive Model(GAM)for wet and dry sugarcane biomass,with coefficients of determination(R2)of 0.93 and 0.97,and Root Mean Square Error(RMSE)of 12.78 and 2.57 t/ha,respectively.This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices(VIs)in mature growth stages.
