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.

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 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.

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.

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.

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.

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.

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.

Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.

Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysis.In this study,speech samples are categorized for both training and testing purposes based on their geographical origin.Category 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian speakers.Testing speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the speakers.Significantly,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas.Indian speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers.The gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of India.In the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech data.The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.

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 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.

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.

Exploration of the spatial pattern of urban residential land use with geographically weighted regression technique: a case study of Nanjing,China

As the traditional methods and technical means cannot meet the quantitative research needs of the urban land use patterns, quantitative research methods for the urban land use pattern are established via the GIS （geographic information system ） technique combined with the related theories and models. Taking the city of Nanjing as an example, a spatial database of urban land use and other environmental and socio-economic data is constructed. A multiple linear regression model is developed to determine the statistically significant factors affecting the residential land use distributions. To explain the spatial variations of urban land use patterns, the geographically weighted regression （GWR） is employed to establish spatial associations between these significant factors and the distribution of urban residential land use. The results demonstrate that the GWR can provide an effective approach to the exploration of the urban land use spatial patterns and also provide useful spatial information for planning residential development and other types of urban land use.

Analysis of the Correlation between the Geographical Distribution of Populus euphratica Oliv. and the Climatic Environment

[Objective] This research was to study the correlation regional climatic characteristics and changing geographic distribution of Populus euphratica Oliv. (Salicaceae), as well as the adaption of Populus euphratica Oliv. to the climatic environment. [Method] The climatic characteristics, water source, groundwater and soil type in the distribution regions of Populus euphratica Oliv. and the effect of long-term human activities were comprehensively analyzed based an overview of Populus euphratica Oliv. and its distribution. [Result] Specific regional climatic characteristics and long term human activities are the principle determinants for the growth of Populus euphratica Oliv. The change of leaf shape is a distinct feature of Populus euphratica Oliv. in adapting to the climatic environment. Populus euphratica Oliv. withstands various environmental stresses by means of in vivo synthesis, transport and conversion of secondary phenolic metabolites. Effective protection and rehabilitation measures, and ecological water transport have obvious effect on the restoration and reconstruction of damaged ecological environment of Populus euphratica oasis. [Conclusion] This study is of great significance for the restoration of ecological environment in the arid inland regions, north-west China.

Geographical history的概念和汉译——兼论Alan R.H.Baker地理历史学思想

针对国内将贝克的geographical history一词译为"地理学史"或"地理史"的问题,结合他的新作对geographical history这一术语的论述及学科合法地位的诉求,认为应译为"地理历史学"。同时结合国内学者对"地理历史学"的术语混乱和学科诉求做了论述,分析了"地理历史学"术语混乱的部分原因。认为对国内外"地理历史学"思想应给予注意,给予"地理历史学"一个合法的学科地位。