Species’ geographical range, environmental range and traits lead to specimen collection preference of dominant plant species of grasslands in Northern China

Many different factors,such as species traits,socio-economic factors,geographical and environmental factors,can lead to specimen collection preference.This study aims to determine whether grassland specimen collection in China is preferred by species traits(i.e.,plant height,flowering and fruiting period),environmental range(i.e.,the temperature and precipitation range)and geographical range(i.e.,distribution range and altitudinal range).Ordinary least squares models and phylogenetic generalized linear mixed models were used to analyze the relationships between specimen number and the explanatory variables.Random Forest models were then used to find the most parsimonious multivariate model.The results showed that interannual variation in specimen number between 1900 and 2020 was considerable.Specimen number of these species in southeast China was notably lower than that in northwest China.Environmental range and geographical range of species had significant positive correlations with specimen number.In addition,there were relatively weak but significant associations between specimen number and species trait(i.e.,plant height and flowering and fruiting period).Random Forest models indicated that distribution range was the most important variable,followed by flowering and fruiting period,and altitudinal range.These findings suggest that future floristic surveys should pay more attention to species with small geographical range,narrow environmental range,short plant height,and short flowering and fruiting period.The correction of specimen collection preference will also make the results of species distribution model,species evolution and other works based on specimen data more accurate.

Geographical Variation in the Advertisement Calls of Leptobrachella ventripunctata(Anura:Megophryidae)in Southwestern China

In anurans,advertisement calls(ACs)are an essential form of intraspecific communication.This study evaluates geographical variation in the ACs of Leptobrachella ventripunctata in the Guizhou Plateau,southwestern China,and explores correlations between call characteristics,body size,and environmental factors.ACs are simple calls of L.ventripunctata,and apparent differences were observed in the ACs among different geographical populations of L.ventripunctata.The Call duration(CD)revealed a significant positive correlation with altitude and a significant negative correlation with temperature and humidity.Moreover,the Dominant frequency(DF)exhibited a significant negative correlation with altitude and the habitat closure degree and a significant positive correlation with temperature.These variations in ACs between different geographical populations of L.ventripunctata may critically impact the adaptive evolution of species,and the calls may also be relevant for environmental selection.

Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in Yunnan Border Regions

Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-temporal variability of these factors in border regions.Methods We conducted a descriptive analysis of dengue fever’s temporal-spatial distribution in Yunnan border areas.Utilizing annual data from 2013 to 2019,with each county in the Yunnan border serving as a spatial unit,we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.Results The GTWR model,proving more effective than Ordinary Least Squares(OLS)analysis,identified significant spatial and temporal heterogeneity in factors influencing dengue fever’s spread along the Yunnan border.Notably,the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence,meteorological variables,and imported cases across different counties.Conclusion In the Yunnan border areas,local dengue incidence is affected by temperature,humidity,precipitation,wind speed,and imported cases,with these factors’influence exhibiting notable spatial and temporal variation.

Estimation of the potential geographical distribution of a new potato pest(Schrankia costaestrigalis) in China under climate change

Global food security is threatened by the impacts of the spread of crop pests and changes in the complex interactions between crops and pests under climate change.Schrankia costaestrigalis is a newly-reported potato pest in southern China.Early-warning monitoring of this insect pest could protect domestic agriculture as it has already caused regional yield reduction and/or quality decline in potato production.Our research aimed to confirm the potential geographical distributions(PGDs)of S.costaestrigalis in China under different climate scenarios using an optimal MaxEnt model,and to provide baseline data for preventing agricultural damage by S.costaestrigalis.Our findings indicated that the accuracy of the optimal MaxEnt model was better than the default-setting model,and the minimum temperature of the coldest month,precipitation of the driest month,precipitation of the coldest quarter,and the human influence index were the variables significantly affecting the PGDs of S.costaestrigalis.The highly-and moderately-suitable habitats of S.costaestrigalis were mainly located in eastern and southern China.The PGDs of S.costaestrigalis in China will decrease under climate change.The conversion of the highly-to moderately-suitable habitat will also be significant under climate change.The centroid of the suitable habitat area of S.costaestrigalis under the current climate showed a general tendency to move northeast and to the middle-high latitudes in the 2030s.The agricultural practice of plastic film mulching in potato fields will provide a favorable microclimate for S.costaestrigalis in the suitable areas.More attention should be paid to the early warning and monitoring of S.costaestrigalis in order to prevent its further spread in the main areas in China’s winter potato planting regions.

Geographical Indication Intellectual Property Protection and Regional Public Brand Construction of Rape Industry in China

As China s largest oil crop,rape occupies a central position in ensuring the safety of China s cooking oil supply.This paper introduced China s rapeseed industry from the rape type,rapeseed production,planting area,national rapeseed production protected area,rape national dominant characteristic industrial clusters,and rapeseed industry,etc.Besides,from the aspects of geographical indication products,geographical indication trademarks,and geographical indications of agricultural products,this paper discussed the intellectual property protection of geographical indications of rape,rapeseed,and rapeseed oil in China.It analyzed the main problems such as the lag in the formulation of relevant standards for geographical indications and the low use of special signs for geographical indications,and finally came up with recommendations including building a public brand of geographical indications and expanding foreign exchanges of geographical indications.

Monitoring the little fire ant,Wasmannia auropunctata(Roger 1863),in the early stage of its invasion in China:Predicting its geographical distribution pattern under climate change

Invasive alien ants(IAAs)are among the most aggressive,competitive,and widespread invasive alien species(IAS)worldwide.Wasmannia auropunctata,the greatest IAAs threat in the Pacific region and listed in“100 of the world’s worst IAS”,has established itself in many countries and on islands worldwide.Wild populations of W.auropunctata were recently reported in southeastern China,representing a tremendous potential threat to China’s agricultural,economic,environmental,public health,and social well-being.Estimating the potential geographical distribution(PGD)of W.auropunctata in China can illustrate areas that may potentially face invasion risk.Therefore,based on the global distribution records of W.auropunctata and bioclimatic variables,we predicted the geographical distribution pattern of W.auropunctata in China under the effects of climate change using an ensemble model(EM).Our findings showed that artificial neural network(ANN),flexible discriminant analysis(FDA),gradient boosting model(GBM),Random Forest(RF)were more accurate than categorical regression tree analysis(CTA),generalized linear model(GLM),maximum entropy model(MaxEnt)and surface distance envelope(SRE).The mean TSS values of ANN,FDA,GBM,and RF were 0.820,0.810,0.843,and 0.857,respectively,and the mean AUC values were 0.946,0.954,0.968,and 0.979,respectively.The mean TSS and AUC values of EM were 0.882 and 0.972,respectively,indicating that the prediction results with EM were more reliable than those with the single model.The PGD of W.auropunctata in China is mainly located in southern China under current and future climate change.Under climate change,the PGD of W.auropunctata in China will expand to higher-latitude areas.The annual temperature range(bio7)and mean temperature of the warmest quarter(bio10)were the most significant variables affecting the PGD of W.auropunctata in China.The PGD of W.auropunctata in China was mainly attributed to temperature variables,such as the annual temperature range(bio7)and the mean temperature of the warmest quarter(bio10).The populations of W.auropunctata in southern China have broad potential invasion areas.Developing strategies for the early warning,monitoring,prevention,and control of W.auropunctata in southern China requires more attention.

Natural variations and geographical distributions of seed carotenoids and chlorophylls in 1167 Chinese soybean accessions

Understanding the composition and contents of carotenoids in various soybean seed accessions is important for their nutritional assessment.This study investigated the variability in the concentrations of carotenoids and chlorophylls and revealed their associations with other nutritional quality traits in a genetically diverse set of Chinese soybean accessions comprised of cultivars and landraces.Genotype,planting year,accession type,seed cotyledon color,and ecoregion of origin significantly influenced the accumulation of carotenoids and chlorophylls.The mean total carotenoid content was in the range of 8.15–14.72μg g–1 across the ecoregions.The total carotenoid content was 1.2-fold higher in the landraces than in the cultivars.Soybeans with green cotyledons had higher contents of carotenoids and chlorophylls than those with yellow cotyledons.Remarkably,lutein was the most abundant carotenoid in all the germplasms,ranging from 1.35–37.44μg g–1.Carotenoids and chlorophylls showed significant correlations with other quality traits,which will help to set breeding strategies for enhancing soybean carotenoids without affecting the other components.Collectively,our results demonstrate that carotenoids are adequately accumulated in soybean seeds,however,they are strongly influenced by genetic factors,accession type,and germplasm origin.We identified novel germplasms with the highest total carotenoid contents across the various ecoregions of China that could serve as the genetic materials for soybean carotenoid breeding programs,and thereby as the raw materials for food sectors,pharmaceuticals,and the cosmetic industry.

A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

Testing Geographical Methodology and Tools for the Study of Territories Damaged by Earthquakes. The Case of L’Aquila and Other Localities Three Years after the April 6th 2009 Event

In this paper, we discuss a geographical methodology supported by specific geo-technologies which we are testing for the study of territories damaged by the L’Aquila earthquake of 6 April 2009 and which can be used in similar situations. Subsequently, we provide an overview of the current situation and make a comparison between some aerial photographs obtained from an overflight in March 2012 and some photos made during our first field study in February 2010, in order to show the work undertaken or not during this period and to substantiate any considerations regarding the choices adopted and the necessary future planning. Moreover, we provide an example of the added value provided by the analysis of aerial photographs in both visible and thermal light for recognizing the provisional non-painted metal roofing of buildings in a post-earthquake urban area. In fact this technique can be useful for the rapid identification of damaged buildings and zones with provisional covering. In the present paper, we focus attention on L’Aquila town centre which provides a significant example of a “City of Stone” almost “minus” the presence of people.

Elemental Assessments in Paddy Soil for Geographical Traceability of Rice from Peninsular Malaysia

This investigation aimed to establish the geographical traceability of Malaysian rice by assessing the elemental composition in paddy soil.Multi-element determination in combination with a chemometric approach was applied to evaluate the elemental concentrations of paddy soil from granaries cultivated with the same rice variety and to assess the relationship between elements in the soil and rice(SAR) system.A total of 29 elements(aluminum,arsenic,barium,bromine,calcium,chlorine,cobalt,chromium,cesium,europium,iron,gallium,hafnium,potassium,lanthanum,lutetium,magnesium,manganese,sodium,rubidium,antimony,scandium,samarium,thorium,titanium,uranium,vanadium,ytterbium and zinc) were successfully determined in paddy soil from Kedah,Selangor and Langkawi by neutron activation analysis.A significant difference(P < 0.05) between 18 elements in the soil samples was obtained.The chemometric approaches of principal component and linear discriminant analyses demonstrated clear discrimination and highly corrected classification(100%) of the soil samples.A high classification(98.1%) was also achieved by assessing 10 elements(aluminum,arsenic,bromine,chlorine,potassium,magnesium,manganese,sodium,rubidium and zinc),which similarly applied to rice geographical origin determination.Similar elements in SAR were also observed for differences in the pattern of correlation and bioaccumulation factor between the granaries.Furthermore,the generalized Procrustes analysis showed a 98% consensus between SAR and clear differences between the studied regions.The canonical correlation analysis demonstrated a significant correlation between the chemical profile of SAR(r~2 = 0.88,P < 0.001).Therefore,the current work model provides a reliable assessment to establish rice provenance.

Geographical distribution of Batrachospermaceae genera in Asia and its environmental factors

Batrachospermaceae is an important group of freshwater red algae.Available data of the latitude,longitude,and environmental factors on Batrachospermaceae distribution in Asia were analyzed to understand the geographical distribution of Batrachospermaceae genera in Asia.Statistical analyses,including one-way ANOVA,correlation analysis,stepwise regression analysis,principal component analysis,and linear discriminant analysis were conducted to characterize variation in geographical distribution and growth environment.Results reveal high variation in geographical distribution and growth environment among different Batrachospermaceae genera in Asia.Specifically,correlations between latitude and all environmental factors exclusive of altitude are significant,and longitude is significantly correlated with all environmental factors except for average relative humidity.The geographical distribution and growth environment of different Batrachospermaceae genera significantly differed.Altitude,maximum temperature,average temperature,minimum temperature,average relative humidity,average wind speed,maximum wind speed,and atmospheric pressure all contributed to explaining differences in the geographical distribution of Batrachospermaceae genera.Combining the results of correlation analysis,stepwise regression analysis,and principal component analysis,all environmental factors contributed to the different geographical distribution of Batrachospermum,Paludicola,Sheathia,Sirodotia,and Remainder(the rest),all environmental factors but atmospheric pressure contributed to the different geographical distribution of Kumanoa,and all environmental factors but average wind speed and maximum wind speed contributed to the different geographical distribution of Virescentia.However,the correlation between these significantly related environmental factors and taxa is not necessarily causative,and many other environmental factors,such as temperature,pH,conductivity,shading,current velocity,dissolved oxygen,hardness,substrata types,and nutrients etc.,are likely to have an important impact on the geographical distribution of taxa,which is an important topic for future research.

Exploring spatial non-stationarity of near-miss ship collisions from AIS data under the influence of sea fog using geographically weighted regression:A case study in the Bohai Sea,China

Sea fog is a disastrous weather phenomenon,posing a risk to the safety of maritime transportation.Dense sea fogs reduce visibility at sea and have frequently caused ship collisions.This study used a geographically weighted regression(GWR)model to explore the spatial non-stationarity of near-miss collision risk,as detected by a vessel conflict ranking operator(VCRO)model from automatic identification system(AIS)data under the influence of sea fog in the Bohai Sea.Sea fog was identified by a machine learning method that was derived from Himawari-8 satellite data.The spatial distributions of near-miss collision risk,sea fog,and the parameters of GWR were mapped.The results showed that sea fog and near-miss collision risk have specific spatial distribution patterns in the Bohai Sea,in which near-miss collision risk in the fog season is significantly higher than that outside the fog season,especially in the northeast(the sea area near Yingkou Port and Bayuquan Port)and the southeast(the sea area near Yantai Port).GWR outputs further indicated a significant correlation between near-miss collision risk and sea fog in fog season,with higher R-squared(0.890 in fog season,2018),than outside the fog season(0.723 in non-fog season,2018).GWR results revealed spatial non-stationarity in the relationships between-near miss collision risk and sea fog and that the significance of these relationships varied locally.Dividing the specific navigation area made it possible to verify that sea fog has a positive impact on near-miss collision risk.

Exploring the drivers of urban expansion in a medium-class urban agglomeration in India using the remote sensing techniques and geographically weighted models

Rapid urbanization urges the immediate attention of policymakers to ensure sustainable city development.Under-standing the urban growth drivers is essential to address effective strategies for urbanization-related challenges.This work aims to study Raiganj’s urban development and the factors associated with this expansion.This study employed global logistic regression(LR)and geographical weighted logistic regression(GWLR)to explore the role of different factors.The results showed that the role of the central business district(covariate>-1),commercial market(covariate>-3),and police station(covariate>-4)were significant to the development of new built-up areas.In the second period,major roads(covariate>-2)and new infrastructures(covariate>-4)became more relevant,particularly in the eastern and southern areas.GWLR was more accurate in assessing the different fac-tors’impact than LR.The results obtained are essential to understanding urban expansion in India’s medium-class cities,which is critical to effective policies for sustainable urbanization.

Industrial Carbon Emission Distribution and Regional Joint Emission Reduction:A Case Study of Cities in the Pearl River Basin,China

China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exists an imbalance in the distribution of car-bon emissions.Therefore,regional cooperation serves as an effective means to attain low-carbon development.This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intens-ity(ICEI)as a crucial factor.We utilized social network analysis and Local Indicators of Spatial Association(LISA)space-time trans-ition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin(PRB),China from 2010 to 2020.The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression.The results were as follows:1)the overall ICEI in the Pearl River Basin is showing a downward trend,and there is a significant spatial imbalance.2)There are numerous network connections between cities regarding the ICEI,but the network structure is relatively fragile and unstable.3)Economically developed cities such as Guangzhou,Foshan,and Dongguan are in the center of the network while playing an intermediary role.4)Energy consumption,industrialization,per capita GDP,urbanization,science and techno-logy,and productivity are found to be the most influential variables in the spatial differentiation of ICEI,and their combination in-creased the explanatory power of the geographic variation of ICEI.Finally,through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI,the study suggests joint carbon reduction strategies,which are centered on carbon transfer,financial support,and technological assistance among cities.

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Rural settlement is the basic spatial unit for compact communities in rural area. Scientific exploration of spatial-temporal differentiation and its influencing factors is the premise of spatial layout rationalization. Based on land use data of Liangshan Yi Autonomous Prefecture(hereinafter referred to as Liangshan Prefecture) in Sichuan Province, China from 1980 to 2020, compactness index, fractal dimension, imbalance index, location entropy and the optimal parameters-based geographical detector(OPGD) model are used to analyze the spatial-temporal evolution of the morphological characteristics of rural settlements, and to explore the influence of natural geographical factors, socioeconomic factors, and policy factors on the spatial differentiation of rural settlements. The results show that:(1) From 1980 to 2020, the rural settlements area in Liangshan Prefecture increased by 15.96 km^(2). In space, the rural settlements are generally distributed in a local aggregation, dense in the middle and sparse around the periphery. In 2015, the spatial density and expansion index of rural settlements reached the peak.(2) From 1980 to 2020, the compactness index decreased from 0.7636 to 0.7496, the fractal dimension increased from 1.0283 to 1.0314, and the fragmentation index decreased from 0.1183 to 0.1047. The spatial morphological structure of rural settlements tended to be loose, the shape contour tended to be complex, the degree of fragmentation decreased, and the spatial distribution was significantly imbalanced.(3) The results of OPGD detection in 2015 show that the influence of each factor is slope(0.2371) > traffic accessibility(0.2098) > population(0.1403) > regional GDP(0.1325) > elevation(0.0987) > poverty alleviation(0). The results of OPGD detection in 2020 show that the influence of each factor is slope(0.2339) > traffic accessibility(0.2198) > population(0.1432) > regional GDP(0.1219) > poverty alleviation(0.0992) > elevation(0.093). Natural geographical factors(slope and elevation) are the basic factors affecting the spatial distribution of rural settlements, and rural settlements are widely distributed in the river valley plain and the second half mountain area. Socioeconomic factors(traffic accessibility, population, and regional GDP) have a greater impact on the spatial distribution of rural settlements, which is an important factor affecting the spatial distribution of rural settlements. Policy factors such as poverty alleviation relocation have an indispensable impact on the spatial distribution of rural settlements. The research results can provide decisionmaking basis for the spatial arrangement of rural settlements in Liangshan Prefecture, and optimize the implementation of rural revitalization policies.

Life forms affect beta-diversity patterns of larch forests in China

Beta-diversity reflects the spatial changes in community species composition which helps to understand how communities are assembled and biodiversity is formed and maintained. Larch(Larix) forests, which are coniferous forests widely distributed in the mountainous and plateau areas in North and Southwest China, are critical for maintaining the environmental conditions and species diversity. Few studies of larch forests have examined the beta-diversity and its constituent components(species turnover and nestedness-resultant components). Here, we used 483 larch forest plots to determine the total betadiversity and its components in different life forms(i.e., tree, shrub, and herb) of larch forests in China and to evaluate the main drivers that underlie this beta-diversity. We found that total betadiversity of larch forests was mainly dependent on the species turnover component. In all life forms,total beta-diversity and the species turnover component increased with increasing geographic, elevational, current climatic, and paleoclimatic distances. In contrast, the nestedness-resultant component decreased across these same distances. Geographic and environmental factors explained 20%-25% of total beta-diversity, 18%-27% of species turnover component, and 4%-16% of nestedness-resultant component. Larch forest types significantly affected total beta-diversity and species turnover component. Taken together, our results indicate that life forms affect beta-diversity patterns of larch forests in China, and that beta-diversity is driven by both niche differentiation and dispersal limitation. Our findings help to greatly understand the mechanisms of community assemblies of larch forests in China.

Cluster Analysis of Morphologic Characteristic of Eight Geographical Populations of Rana Dybowskii

[ObJective] The research aimed to determine the geographic distribution map of system of Rana dybowskii. [Method] Four morphologic indices （body length, body weight, forelimb length, hindlimb length） of eight geographical populations of R.dybowskii which naturally distribute in Changhai Mountain and Xiaoxing＇an Mountain were measured. Measure results were variance analyzed and cluster analyzed. [Result] Variance analysis showed： the genetic branching among the Dongfanghong male population（ belongs to Wandashan） and Xiaoxing＇an Mountain male population and Changbai Mountain male population were significantly different （P〈0.05） ; the genetic branching between the Hebei female population （belongs to Xiaoxing＇an Mountain） and Changbai Mountain female population was significantly different （P〈0.05 ）. Cluster analysis showed ： male R.dybowskii can be divided into three groups ： the first group included Quanyang, Tianbei, Chaoyang and Ddkouqin, the second group included Tieli and Anshan, the third group included Dongfanghong; and the female R. dybowskii can be divided into three groups ： the first group included Quanyang and Chaoyang, the second group included Tianbei and Dakouqin, the third group included Hebei. [Condusion] The paper deduced that the Sanjiang Plain was the geographical origin center ofR. dybowskii which radiated to Changbai Mountain and Xiaoxing＇an Mountain along the adverse current of Songhua River basin, therefore, the current distribution pattern of R. dybowskii was formed.

Research on Geographical Indications and Cultural Heritage of Famous Tea in Hubei

In the study, analysis was made on present situation and development measures of geographical indications and cultural heritage protection of famous teas in Hubei Province. In addition, 8 related suggestions were proposed as well.

Flood Velocity Prediction Using Deep Learning Approach

Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.

Research on Geographical Environment and Culture Symbiosis of Chishui River Basin

This paper took Chishui River Basin as the research object,and pointed out that its culture,which reflected local life and production and was featured with regional characteristics as well as abundant cultural connotation,was constituted by native natural and cultural geography,folk customs and historical feature.The relation between Chishui salt path and salt business culture under the typical closed and half-closed geographical environment was analyzed to indicate that the realignment of Chishui River had brought prosperity of Chishui River,booming of commercial towns,and development of ancient architectures and guild hall culture.The relation between rich natural resources and production and life culture of Chishui River was explained to show that Danxia landform nurtured stone culture,Chishui River resources cultivated fishing culture,special hydrogeological environment fostered liquor culture represented by Maotai,and Bamboo culture accumulated for hundreds of years.Finally,it introduced military culture and the spirit of the Long March forming based on the special location of Chishui River.It emphasized that geological environment was the important basis for human's survival and played an immeasurable role in the cultural development of a region.