Biological collections are critical for the understanding of species distributions and for formulating biodiversity conservation strategies.However,biological collections are susceptible to various biases,including th...Biological collections are critical for the understanding of species distributions and for formulating biodiversity conservation strategies.However,biological collections are susceptible to various biases,including the“road-map effect”,meaning that the geography of biological collections can be influenced by road networks.Here,using species occurrence records derived from 921,233 plant specimens,we quantified the intensity of the“road-map effect”on floristic collections of China,and investigated its relationships with various environmental and socio-economic variables.Species occurrence records mainly distributed in major mountain ranges,while lowlands were underrepresented.The distance of species occurrence records to the nearest road decreased from 19.54 km in 1960s to 3.58 km in 2010s.These records showed significant clustering within 5 km and 10 km buffer zones of roads.The road density surrounding these records was significantly higher than that in random patterns.Collectively,our results confirmed a significant“road-map effect”in the floristic collections of China,and this effect has substantially intensified from the 1960s to the 2010s,even after controlling for the impact of road network expansion.Topographic,climatic and socio-economic variables that determine regional species diversity,vegetation cover and human impact on vegetation played crucial roles in predicting the intensity of the“road-map effect”.Our findings indicate that biological surveys have become increasingly dependent on road networks,a trend rarely reported in published studies.Future floristic surveys in China should prioritize the lowland areas that have experienced stronger human disturbances,as well as remote areas that may harbor more unique and rare species.展开更多
With increasing renewable energy utilization,the industry needs an accurate tool to select and size renewable energy equipment and evaluate the corresponding renewable energy plans.This study aims to bring new insight...With increasing renewable energy utilization,the industry needs an accurate tool to select and size renewable energy equipment and evaluate the corresponding renewable energy plans.This study aims to bring new insights into sustainable and energy-efficient urban planning by developing a practical method for optimizing the production of renewable energy and carbon emission in urban areas.First,we provide a detailed formulation to calculate the renewable energy demand based on total energy demand.Second,we construct a dual-objective optimization model that represents the life cycle cost and carbon emission of renewable energy systems,after which we apply the differential evolution algorithmto solve the optimization result.Finally,we conduct a case study in Qingdao,China,to demonstrate the effectiveness of this optimizationmodel.Compared to the baseline design,the proposedmodel reduced annual costs and annual carbon emissions by 14.39%and 72.65%,respectively.These results revealed that dual-objective optimization is an effective method to optimize economic benefits and reduce carbon emissions.Overall,this study will assist energy planners in evaluating the impacts of urban renewable energy projects on the economy and carbon emissions during the planning stage.展开更多
Taxonomic bias is a well-known shortcoming of species occurrence databases.Understanding the causes of taxonomic bias facilitates future biological surveys and addresses current knowledge gaps.Here,we investigate the ...Taxonomic bias is a well-known shortcoming of species occurrence databases.Understanding the causes of taxonomic bias facilitates future biological surveys and addresses current knowledge gaps.Here,we investigate the main drivers of taxonomic bias in occurrence data of angiosperm species in China.We used a database including 5,936,768 records for 28,968 angiosperm species derived from herbarium specimens and literature sources.Generalized additive models(GAMs)were applied to investigate explanatory powers of 17 variables on the variation in record numbers of species.Five explanatory variables were selected for a multi-predictor GAM that explained 69%of the variation in record numbers:plant height,range size,elevational range,numbers of scientific publications and web pages.Range size was the most important predictor in the model and positively correlated with number of records.Morphological and phenological traits and social-economic factors including economic values and conservation status had weak explanatory powers on record numbers of plant species,which differs from the findings in animals,suggesting that causes of taxonomic bias in occurrence databases may vary between taxonomic groups.Our results suggest that future floristic surveys in China should more focus on range-restricted and socially or scientifically less"interesting"species.展开更多
基金funded by National Natural Science Foundation of China(32460276,32060275)Jiangxi Provincial Natural Science Foundation(20232BAB203058,20242BAB27001)。
文摘Biological collections are critical for the understanding of species distributions and for formulating biodiversity conservation strategies.However,biological collections are susceptible to various biases,including the“road-map effect”,meaning that the geography of biological collections can be influenced by road networks.Here,using species occurrence records derived from 921,233 plant specimens,we quantified the intensity of the“road-map effect”on floristic collections of China,and investigated its relationships with various environmental and socio-economic variables.Species occurrence records mainly distributed in major mountain ranges,while lowlands were underrepresented.The distance of species occurrence records to the nearest road decreased from 19.54 km in 1960s to 3.58 km in 2010s.These records showed significant clustering within 5 km and 10 km buffer zones of roads.The road density surrounding these records was significantly higher than that in random patterns.Collectively,our results confirmed a significant“road-map effect”in the floristic collections of China,and this effect has substantially intensified from the 1960s to the 2010s,even after controlling for the impact of road network expansion.Topographic,climatic and socio-economic variables that determine regional species diversity,vegetation cover and human impact on vegetation played crucial roles in predicting the intensity of the“road-map effect”.Our findings indicate that biological surveys have become increasingly dependent on road networks,a trend rarely reported in published studies.Future floristic surveys in China should prioritize the lowland areas that have experienced stronger human disturbances,as well as remote areas that may harbor more unique and rare species.
基金supported financially by the National Natural Science Foundation of China(No.62276080)National Key R&D Program of China(No.2018YFD1100703-06).
文摘With increasing renewable energy utilization,the industry needs an accurate tool to select and size renewable energy equipment and evaluate the corresponding renewable energy plans.This study aims to bring new insights into sustainable and energy-efficient urban planning by developing a practical method for optimizing the production of renewable energy and carbon emission in urban areas.First,we provide a detailed formulation to calculate the renewable energy demand based on total energy demand.Second,we construct a dual-objective optimization model that represents the life cycle cost and carbon emission of renewable energy systems,after which we apply the differential evolution algorithmto solve the optimization result.Finally,we conduct a case study in Qingdao,China,to demonstrate the effectiveness of this optimizationmodel.Compared to the baseline design,the proposedmodel reduced annual costs and annual carbon emissions by 14.39%and 72.65%,respectively.These results revealed that dual-objective optimization is an effective method to optimize economic benefits and reduce carbon emissions.Overall,this study will assist energy planners in evaluating the impacts of urban renewable energy projects on the economy and carbon emissions during the planning stage.
基金supported by the National Natural Science Foundation of China(41967055,41561097)。
文摘Taxonomic bias is a well-known shortcoming of species occurrence databases.Understanding the causes of taxonomic bias facilitates future biological surveys and addresses current knowledge gaps.Here,we investigate the main drivers of taxonomic bias in occurrence data of angiosperm species in China.We used a database including 5,936,768 records for 28,968 angiosperm species derived from herbarium specimens and literature sources.Generalized additive models(GAMs)were applied to investigate explanatory powers of 17 variables on the variation in record numbers of species.Five explanatory variables were selected for a multi-predictor GAM that explained 69%of the variation in record numbers:plant height,range size,elevational range,numbers of scientific publications and web pages.Range size was the most important predictor in the model and positively correlated with number of records.Morphological and phenological traits and social-economic factors including economic values and conservation status had weak explanatory powers on record numbers of plant species,which differs from the findings in animals,suggesting that causes of taxonomic bias in occurrence databases may vary between taxonomic groups.Our results suggest that future floristic surveys in China should more focus on range-restricted and socially or scientifically less"interesting"species.
