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Objective To predict the potential suitable habitat of Haemaphysalis concinna in Heilongjiang Province under different climatic scenarios. Methods The geographic locations of ticks in Heilongjiang Province from 1980 to 2022 were captured from literature review and field ticks monitoring data from Harbin Center for Disease Control and Prevention in Heilongjiang Province, and the tick distribution sites with spatial correlations were removed using the software ArcGIS 10.2. The environment data under historical climatic scenarios from 1970 to 2000 and the climatic shared socioeconomic pathways (SSP) 126 scenario model from 2021 to 2040 and from 2041 to 2060 were downloaded from the WorldClim website, and the elevation (1 km, 2010), population (1 km grid population dataset of China, 2010) and annual vegetation index (1 km, 2010) data were downloaded from the Resource and Environmental Science and Data Center, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences. The contribution of environmental factors to H. concinna distribution was evaluated and environmental variables were screened using the software MaxEnt 3.4.1 and R package 4.1.0, and the areas of suitable habitats of H. concinna and changes in center of gravity were analyzed using the maximum entropy model in Heilongjiang Province under different climatic scenarios. In addition, the accuracy of the maximum entropy model for prediction of H. concinna distribution was assessed using the area under curve (AUC) of the receiver operating characteristic curve. Results A total of 79 H. concinna distribution sites and 24 environmental variables were collected, and 70 H. concinna distribution sites and 9 environmental factors that contributed to distribution of the potential suitable habitats of H. concinna in Heilongjiang Province were screened. The three most significant contributing factors included precipitation seasonality, annual precipitation, and mean temperature of the driest quarter, with cumulative contributions of 60.7%. The total area of suitable habitats of H. concinna was 29.05 × 104 km2 in Heilongjiang Province under historical climatic scenarios, with the center of gravity of suitable habitats located at (47.31° N, 129.16° E), while the total area of suitable habitats of H. concinna reduced by 0.97 × 104 km2 in Heilongjiang Province under the climatic SSP126 scenario from 2041 to 2060, with the center of gravity shifting to (47.70° N, 129.28° E). Conclusions The distribution of suitable habitats of H. concinna strongly correlates with temperature and humidity in Heilongjiang Province. The total area of potential suitable habitats of H. concinna may appear a tendency towards a decline with climatic changes in Heilongjiang Province, and high-, medium- and low-suitable habitats may shift.
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Objective To identify the environmental factors affecting the distribution of suitable habitats of Ixodes ovatus, and to examine the effects of environmental changes on the distribution of suitable habitats of I. ovatus. Methods Data pertaining to the distribution of I. ovatus in China were captured by literature review. The distribution of suitable habitats of I. ovatus in China was simulated using the maximum entropy model with the environmental variable data and the I. ovatus distribution data. In addition, the potential distribution of suitable habitats of I. ovatus was predicted based on the 2050 and 2070 environmental data. Results The current suitable habitats of I. ovatus cover 3.11 million km2, accounting for 32.28% of the total land area of China, and they are mainly concentrated in southwestern and northwestern China. Among the screened 16 environmental factors, the standard deviation of seasonal variation of temperature, average annual precipitation and altitude were predominant environmental factors affecting the distribution of suitable habitats of I. ovatus, and these three variables contributed 76.5% to the distribution of suitable habitats of I. ovatus in China. The suitable habitats of I. ovatus were predicted to showing a tendency towards expansion to northwestern and northeastern China in 2050 and 2070, and the proportion of highly suitable habitats of I. ovatus was predicted to increase. Conclusions Moderate climate, adequate precipitation and high altitude are favorable for the survival of I. ovatus. Future climate changes may cause expansion of suitable habitats of I. ovatus in China.
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Objective To identify the environmental factors affecting the geographical distribution of Rhipicephalus microplus in China, and to examine the impact of climate changes on the distribution of R. microplus in China. Methods The national and international publications pertaining to the geographical distribution of R. microplus in China were retrieved, and the geographical location was extracted. The suitable habitats of R. microplus and the dominant environmental factors affecting the distribution of suitable habitats of R. microplus were predicted in China based on the geographical data and environmental variables using the ArcGIS 10.7 software and the maximum entropy model. Results Among the main climatic factors affecting the geographical distribution of R. microplus in China, the factors contributing more than 10% to the suitable habitats of R. microplus mainly include the annual mean precipitation (38.2%), the average temperature of the coldest quarter (28.4%) and the precipitation of the driest month (14.2%). The current suitable habitats of R. microplus were mainly found in southern China, and the high-, medium- and low-suitable areas accounted for 8.6%, 13.1% and 10.5% of the total land area of China, respectively. The suitable habitats of R. microplus were predicted to increase by 399 800 km2 in China using the maximum entropy model under the RCP 4.5 emissions scenario in 2070, and the emerging suitable habitats were mainly distributed in Gansu Province, Ningxia Hui Autonomous Region, Qinghai Province, Hebei Province, Shaanxi Province, Liaoning Province, Inner Mongolia Autonomous Region, Shandong Province, Sichuan Province and Tibeten Autonomous Region. In addition, the suitable habitats of R. microplus were predicted to show an overall expansion towards northward from present to 2070. Conclusions Climate changes affect the distribution of suitable habitats of R. microplus in China, and annual mean precipitation may be a key factor affecting the distribution of suitable habitats of R. microplus.
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Objective To evaluate the effects of environmental factors the distribution of Ixodes persulcatus, and to predict the future suitable habitats of I. persulcatus in China. Methods The known distribution sites of I. persulcatus in China were captured from national and international published literatures. The effects of 14 environmental factors on the distribution of I. persulcatus were examined using the Jackknife test, including mean annual temperature, mean monthly temperature range, isothermality, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the coldest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the wet-test quarter, mean temperature of the coldest quarter, annual mean precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, precipitation of the coldest quarter, elevation, slope, aspect and vegetation. The suitable habitats of I. persulcatus were predicted in China using the maximum entropy model and ArcGIS 10.7 software with the environmental factors. Results Currently, the highly suitable habitats of I. persulcatus covered an area of 886 600 km2 in China, which were predominantly located in northeastern China. The environmental factors that contributed more than 10% to the distribution of the suitable habitats of I. persulcatus in China included annual temperature variation range (39.1%), the coldest quarterly precipitation (23.2%), and the annual mean precipitation (11.9%). Based on the maximum entropy model, the suitable habitats of I. persulcatus were predicted to show a shrinking tendency towards northeastern China in 2070. Conclusions The suitable habitat of I. persulcatus strongly correlates with temperature and precipitation, and climate and environmental changes may lead to shrinking of the future suitable habitat of I. persulcatus in China.
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Objective To investigate the current distribution of ticks and predict the suitable habitats of ticks in the Yangtze River Delta urban agglomeration in 2017, so as to provide insights into tick control and management of tick-borne diseases in these areas. Methods All publications pertaining to tick and pathogen distribution in the Yangtze River Delta urban agglomeration were retrieved, and the geographical location of tick distribution was extracted. The effects of 19 climatic factors on the distribution of ticks were examined using the jackknife method, including the mean temperature of the wettest quarter, precipitation of the coldest quarter, mean temperature of the driest quarter, maximum temperature of the warmest month, precipitation of the driest month, minimal temperature of the coldest month, annual precipitation, mean daily temperature range, precipitation seasonality, annual temperature range, temperature seasonality, annual mean temperature, mean temperature of the warmest quarter, precipitation of the wettest quarter, isothermality, mean temperature of the coldest quarter, precipitation of the wettest month, precipitation of the driest quarter and precipitation of the warmest quarter. The distribution of ticks was analyzed in 2020 using the maximum entropy (MaxEnt) model, and the potential suitable habitats of ticks were predicted in 2070 using the MaxEnt model based on climatic data. Results A total of 380 Chinese and English literatures were retrieved, and 148 tick distribution sites were extracted, with 135 sites included in the subsequent analysis. There were 7 genera (Haemaphysalis, Rhipicephalus, Ixodes, Dermacentor, Boophilus, Hyalomma and Amblyomma) and 27 species of ticks detected in the Yangtze River Delta urban agglomeration. The climatic factors affecting the distribution of ticks in the Yangtze River Delta urban agglomeration mainly included the mean temperature of the wettest quarter and the precipitation of the coldest quarter, with 26.1% and 23.6% contributions to tick distributions. The high-, medium- and low-suitable habitats of ticks were 20 337.08, 40 017.38 km2 and 74 931.43 km2 in the Yangtze River Delta urban agglomeration in 2020, respectively. The climate changes led to south expansion of the suitable habitats of ticks in the Yangtze River Delta urban agglomeration in 2070, and the total areas of suitable habitats of ticks was predicted to increase by 18 100 km2. In addition, the high-, medium- and low-suitable habitats of ticks were predicted to increase to 24 317.84, 45 283.02 km2 and 83 766.38 km2 in the Yangtze River Delta urban agglomeration in 2070, respectively. Conclusions Multiple tick species are widespread in the Yangtze River Delta urban agglomeration, and the future climate changes may lead to expansion of tick distribution in these areas.
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Objective To evaluate the impact of environmental and climatic factors on the distribution of suitable habitats of Haemaphysalis longicornis, and to predict the potential distribution of H. longicornis under different climate patterns in China. Methods Data pertaining to the distribution of H. longicornis were retrieved from public literatures. The effects of 19 climatic factors (annual mean temperature, annual mean temperature difference between day and night, isothermality, standard deviation of seasonal variation of temperature, maximum temperature of the warmest month, minimum temperature of the coldest month, temperature annual range, mean temperature of the wettest season, mean temperature of the driest season, mean temperature of the warmest season, mean temperature of the coldest season, annual mean precipitation, precipitation of the wettest month, precipitation of the driest month, coefficient of variance of precipitation, precipitation of the wettest season, precipitation of the driest season, precipitation of the warmest season and precipitation of the coldest season) and 4 environmental factors (elevation, slope, slope aspect and vegetation coverage) on the potential distribution of H. longicornis were assessed using the maximum entropy (MaxEnt) model based on the H. longicornis distribution data and climatic and environmental data, and the potential distribution of H. longicornis was predicted under the RCP 2.6 and 8.5 emissions scenarios. Results Among the environmental and climatic factors affecting the geographical distribution of H. longicornis in China, the factors contributing more than 10% to the distribution of H. longicornis mainly included the precipitation of the driest month (26.0%), annual mean temperature (11.2%), annual mean precipitation (10.0%) and elevation (24.2%). Under the current climate pattern, the high-, medium- and low-suitable habitats of H. longicornis are 1 231 900, 1 696 200 km2 and 1 854 400 km2 in China, respectively. The distribution of H. longicornis increased by 336 100 km2 and 367 300 km2 in 2050 and 2070 under the RCP 2.6 emissions scenario, and increased by 381 000 km2 and 358 000 km2 in 2050 and 2070 under the RCP 8.5 emissions scenario in China, respectively. Conclusions Climatic and environmental factors, such as precipitation, temperature and elevation, greatly affect the distribution of H. longicornis in China, and the suitable habitats of H. longicornis may expand in China under different climate patterns in future.
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Objective:To analyze the main factors affecting the <italic>Ziziphus jujuba</italic> distribution and expand the understanding of its distribution and the corresponding influencing factors by comparing the distribution sites of <italic>Z. jujuba</italic> predicted by models with those recorded in the literature. Method:More than 200 distribution sites of <italic>Z. jujuba</italic> accompanied by 55 environmental factors were obtained from literature and specimen review. The environmental factors that affect the distribution of <italic>Z. jujuba</italic> were explored by maximum entropy （MaxEnt） model， and the potential distribution areas of <italic>Z. jujuba</italic> in China were analyzed by ArcGIS， followed by the verification of the main environmental factors using receiver-operating characteristic （ROC） curve and Jackknife method. Result:The area under the curve （AUC） values for the test data and training data were both greater than 0.9， which perfectly satisfied the standard， indicating that the research results were accurate and reliable. Conclusion:The annual average temperature， the average temperature in May， the average temperature in the warmest season， vegetation type， soil type， average temperature in June， average temperature in September， and average temperature in August are proved to be the main environmental factors affecting the distribution of <italic>Z. jujuba</italic>， which can be found almost all over China， except for Heilongjiang and Tibet. <italic>Z. jujuba</italic> is most suitable to be planted in southeastern Sichuan， Chongqing， southern Gansu， Ningxia， most areas of central Shaanxi， eastern and southwestern Shanxi， Henan， eastern and northern Hubei， northern and eastern Anhui， Shandong， Hebei， Beijing， Tianjin， western Liaoning， and Zhejiang. As revealed by literature review， the most suitable growing areas of <italic>Z. jujuba</italic> are southeastern Sichuan， central Shaanxi， southwestern Shanxi， western and northern Henan， Shandong， and southwestern and eastern Hebei.
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Objective:To explore the potential suitable distribution area and the high-quality distribution area of <italic>Sabia parviflora</italic>. Method:Combined with the distribution information and environmental factors，the maximum entropy （MaxEnt） model and ArcGIS software were used to predict the potential suitable distribution area of <italic>S. parviflora</italic>. Based on the correlation between environmental factors and total saponins，total flavonoids，quercetin-3-<italic>O</italic>-gentiobioside，camellianoside，tsubakioside A，kaempferol-3-<italic>O</italic>-rutinoside and isobariclisin-3-<italic>O</italic>-rutinoside，the quality regionalization was conducted by using spatial interpolation method and fuzzy superposition function in ArcGIS software. Result:<italic>S. parviflora</italic> is mainly distributed in Yunnan，Guizhou，Guangxi province in China. The medium and high suitable areas accounts for about 2.88% of the national area. The precipitation in October and November，the precipitation in the warmest and driest seasons，the standard deviation of seasonal changes in temperature and altitude are the main environmental factors that affect the distribution of <italic>S. parviflora</italic>. Slope，precipitation，solar radiation and temperature change had great influence on the accumulation of secondary metabolites. Based on the results of potential suitable distribution and spatial interpolation of each component，the high-quality areas of <italic>S. parviflora</italic> are mainly concentrated in the southwest of Guizhou，with Qinglong，Guanling，Zhenning，Pu'an，Xingren county and other areas as the core. Conclusion:This study provides a scientific guidance for the site selection of artificial planting and the procurement of medicinal materials for <italic>S. parviflora</italic>.
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Using the 260 geographical distribution records of Polygonatum cyrtonema in China, combined with 53 environmental factors, the maximum entropy modeling(MaxEnt) was used to study the ecological factors affecting the suitability distribution of P. cyrtonema. The ArcGIS software was used to predict the potential distribution of the population of P. cyrtonema. The dominant factors were chosen by using the Jackknife test and the Receiver Operating Characteristic(ROC) curve was used to evaluate the simulation. The results showed that high value of area under curve(AUC) denoted good results, which significantly differed from random predictions. Based on the evaluation criterion, the accuracies of the predictions of P. cyrtonema potential distribution in the current periods were excellent. The main environmental factors affecting the suitable growth of P. cyrtonema were the monthly precipitation, the wettest monthly precipitation, the annual average temperature range and the precipitation of November, March, February, April, May and October. There are 9 environmental factors in soil type. The potential fitness of P. cyrtonema in China is high, mainly concentra-ted in Hunan, western Hubei, Guangdong, northeastern Guangxi, southeastern Guizhou, Jiangxi, southwestern Anhui, Fujian, Zhejiang, Shaanxi, southwestern Henan and Chongqing. The growth distribution of the potential distribution area of P. cyrtonema was divided, and the zoning map of the growth suitability of P. cyrtonema was formed. Through the comparative analysis of the potential distribution range based on MaxEnt and the distribution range of literature records, the understanding of the distribution range of P. cyrtonema was expanded.
Subject(s)
China , Ecology , Entropy , Polygonatum , Research Design , SoilABSTRACT
Retinal vascular function is complex, morphological structure varies from person to person, and is susceptible to vascular diseases and systemic vascular diseases. Its accurate segmentation is of great significance for disease diagnosis and identification. In this paper, a multi-scale matching filtering algorithm is proposed for the uneven size of retinal blood vessels. On the basis of the traditional singlescale Gaussian matching filter, multiscale Gaussian matched filters with two sizes are used to enhance grayscale images. Enhancement is performed, and the superimposed image is binarized using a twodimensional maximum entropy threshold segmentation algorithm. The algorithm is tested in the DRIVE database with sensitivity, specificity and accuracy of 0.803, 0.959, 0.981, respectively. Comparing with the traditional algorithm, the algorithm has high sensitivity, fast running speed and rich details of segmentation results.
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Humans , Algorithms , Entropy , Image Processing, Computer-Assisted , Retinal Vessels/diagnostic imagingABSTRACT
Objective To forecast the risk distribution of inter-animal plague in Meriones unguiculatus effectively and provide scientific evidence for prevention and control of inter-animal plague,through studying the correlation between meteorological and environmental factors and inter-animal plague in Meriones unguiculatus.Methods Positive data of plague bacterial culture in 30 epidemic source areas of Merioncs unguiculatus in the Inner Mongolia plateau from 2005 to 2018,including detecting time,number of bacteria,latitude and longitude or detailed location,host type,were from the Chinese Disease Prevention and Control System Plague Prevention Management Information System and related professional institutions for plague prevention and treatment.Logistic regression was used to explore the relationship between the inter-animal plague and climate-related risk factors.The Maximum Entropy (Maxent) model was used to predict the habitat distributions of inter-animal plague,and the receiver operating characteristic (ROC) curve was used to vzlidate the model.Results There were 11 climatic factors including annual mean temperature,isothermality,temperature seasonality,mean temperature of driest quarter,mean temperature of warmest quarter,mean temperature of coldest quarter,annual precipitation,precipitation seasonality,globcover,normalized difference vegetation index and slope,were related to the outbreak of plague among Meriones unguiculatus and included in the model (OR =1.302,0.455,0.957,0.9130,4.864,0.179,0.986,1.126,0.992,0.981,0.721,P ＜ 0.01).The increase of annual mean temperature,mean temperature of warmest quarter and precipitation seasonality will increase the risk of animal plague in the plague foci of Meriones unguiculatus;the increase of isothermality,temperature seasonality,mean temperature of driest quarter,mean temperature of coldest quarter,annual precipitation,globcover,normalized difference vegetation index,and slope will reduce the risk of animal plague in the plague foci of Meriones unguiculatus.The areas under the curve (AUCs) of the Maxent model training data and test data were 0.988 and 0.985,the prediction effect of the model was better.The habitat distribution of Meriones unguiculatus plague mainly concentrated in the central and northem Ulanqabplateau,Ordos plateau,and eastern Hetao plain.Conclusions The use of Maxent model and climate data can predict the potential risks and spatial distribution of animal plague in Meriones unguiculatus;the results are accurate and reliable.
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Objective@#To forecast the risk distribution of inter-animal plague in Meriones unguiculatus effectively and provide scientific evidence for prevention and control of inter-animal plague, through studying the correlation between meteorological and environmental factors and inter-animal plague in Meriones unguiculatus.@*Methods@#Positive data of plague bacterial culture in 30 epidemic source areas of Meriones unguiculatus in the Inner Mongolia plateau from 2005 to 2018, including detecting time, number of bacteria, latitude and longitude or detailed location, host type, were from the Chinese Disease Prevention and Control System Plague Prevention Management Information System and related professional institutions for plague prevention and treatment. Logistic regression was used to explore the relationship between the inter-animal plague and climate-related risk factors. The Maximum Entropy (Maxent) model was used to predict the habitat distributions of inter-animal plague, and the receiver operating characteristic (ROC) curve was used to validate the model.@*Results@#There were 11 climatic factors including annual mean temperature, isothermality, temperature seasonality, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual precipitation, precipitation seasonality, globcover, normalized difference vegetation index and slope, were related to the outbreak of plague among Meriones unguiculatus and included in the model (OR = 1.302, 0.455, 0.957, 0.930, 4.864, 0.179, 0.986, 1.126, 0.992, 0.981, 0.721, P < 0.01). The increase of annual mean temperature, mean temperature of warmest quarter and precipitation seasonality will increase the risk of animal plague in the plague foci of Meriones unguiculatus; the increase of isothermality, temperature seasonality, mean temperature of driest quarter, mean temperature of coldest quarter, annual precipitation, globcover, normalized difference vegetation index, and slope will reduce the risk of animal plague in the plague foci of Meriones unguiculatus. The areas under the curve (AUCs) of the Maxent model training data and test data were 0.988 and 0.985, the prediction effect of the model was better. The habitat distribution of Meriones unguiculatus plague mainly concentrated in the central and northern Ulanqab plateau, Ordos plateau, and eastern Hetao plain.@*Conclusions@#The use of Maxent model and climate data can predict the potential risks and spatial distribution of animal plague in Meriones unguiculatus; the results are accurate and reliable.
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Objective: American ginseng is a medicinal plant with large market demands, however, its producing areas are shrinking because of the continuous cropping obstacles in China. Therefore, it is urgent to establish a suitable model to determine the new producing areas. Here we evaluated and predicted the suitable areas of American ginseng using the maximum entropy model (MaxEnt). Methods: Based on the 37 environmental variables over thirty years from 1970 to 2000 and 226 global distribution points of American ginseng, MaxEnt was used to determine the global ecological suitable areas for American ginseng. The Receiver Operating Curve (ROC) was used to evaluate the model prediction accuracy. Meanwhile, an innovative ecological variable, the precipitation–temperature ratio, was established to indicate the climate characteristic in the American ginseng suitable areas based on the monthly precipitation and temperature. Results: The potential ecological suitable areas of American ginseng were primarily in Appalachian Mountain in America and Changbai Mountain in China, about in the range of 35°N–50°N, 60°W–120°W and 35°N–50°N, 110°E–145°E, respectively, including the United States, Canada, China, North Korea, South Korea, Russia and Japan. South Korea and Japan were the potential producing regions. The precipitation–temperature ratios were stable at (0.22, 0.56) of the vigorous growth period (April–October) in the best suitable areas of American ginseng, serving as characteristic parameters to optimize the prediction model. The model showed that the common soil parameters were pH 4.5–7.2, Base Saturation (BS) above 80%, Cation Exchange Capacity (CEC) 10–20 cmol/kg, organic carbon (OC) < 1.4%, and the soil types were sandy loam or loam. Conclusion: An optimized MaxEnt model was established to predict the producing area for American ginseng that needed to be validated by a field test.
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In this paper,the potential climate factors affecting the Pairs polyphylla var. yunnanensis distribution in China at rational scales were selected from related literatures, using the sampling point geographic information from of P. polyphylla var. yunnanensis, combine the maximum entropy model (MaxEnt) with spatial analyst function of ArcGIS software, to study the climate suitability of P. polyphylla var. yunnanensis cultivating region in China and the leading climate factors. The results showed that, average rainfall in August, average rainfall in October, coefficient of variation of seasonal precipitation, the average temperature of the dry season, isothermal characteristic, average temperature in July were the leading climate factors affecting the potential distribution of P. polyphylla var. yunnanensis cultivating region in China, with their cumulative contribution rate reached 97.2% of all candidate climate factors. Existence probability of the region to be predicted of P. polyphylla var. yunnanensis through the constructed model, the climate unsuitable region, low, medium and high region of P. polyphylla var. yunnanensis in China were clarified and the threshold of climatic factors were gave and clarified the climate characteristics of the cultivating region in each climatic suitability division. The results of research can provide reference for production layout and introduction of P. polyphylla var. yunnanensis.
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At the urgent practical issue of resource protection and artificial cultivation area selection of Dioscorea nipponica, the dominant environmental factors affecting the distribution of D. nipponicain Jilin province were selected by field investigation and using the maximum information entropy model and geographic information technology. MaxEnt model study found that the standard deviation of seasonal variation of temperature, precipitation in October and other six environmental factors on the growth of D. nipponica are the greatest impacting factors. The range of suitability for the growth of D. nipponica was 4.612 08×10-6-0.544 31, and the regionalization study was divided into four parts: high fitness area, middle fitness area, low fitness area and unfavorable area. The high fitness area is concentrated in the central and southern areas of Jilin Province, using ArcGIS statistical environment factors in the appropriate area of the numerical situation. The results showed that the regionalization study of D. nipponica was basically the same as the actual situation. It is clear that the natural environment suitable for the growth of D. nipponica is also the basis for the protection of the resources and the selection of cultivated area.
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Objective To explore the technique of maximum entropy model for extracting Oncomelania hupensis snail habi?tats in Poyang Lake zone. Methods The information of snail habitats and related environment factors collected in Poyang Lake zone were integrated to set up the maximum entropy based species model and generate snail habitats distribution map. Two Land?sat 7 ETM+remote sensing images of both wet and drought seasons in Poyang Lake zone were obtained,where the two indices of modified normalized difference water index(MNDWI)and normalized difference vegetation index(NDVI)were applied to ex?tract snail habitats. The ROC curve,sensitivities and specificities were applied to assess their results. Furthermore,the impor?tance of the variables for snail habitats was analyzed by using Jackknife approach. Results The evaluation results showed that the area under receiver operating characteristic curve(AUC)of testing data by the remote sensing?based method was only 0.56, and the sensitivity and specificity were 0.23 and 0.89 respectively. Nevertheless,those indices above?mentioned of maximum en?tropy model were 0.876,0.89 and 0.74 respectively. The main concentration of snail habitats in Poyang Lake zone covered the northeast part of Yongxiu County,northwest of Yugan County,southwest of Poyang County and middle of Xinjian County,and the elevation was the most important environment variable affecting the distribution of snails,and the next was land surface tem?perature(LST). Conclusions The maximum entropy model is more reliable and accurate than the remote sensing?based meth?od for the sake of extracting snail habitats,which has certain guiding significance for the relevant departments to carry out mea?sures to prevent and control high?risk snail habitats.
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ResumenLos especímenes silvestres de Vanilla planifolia G. Jack forman parte del acervo genético primario, los cuales solo se han reportado en Oaxaca, México. Por ello se evaluó la distribución de esta especie con el objetivo de ubicar y describir características ecológicas en zonas potenciales de distribución. La metodología empleada consistió de cuatro etapas: 1) Elaboración de una base de datos con registros de herbario;2) Construcción de la distribución potencial basado en los registros históricos de herbario para la especie, mediante el modelo de máxima entropía (Maxent), con el uso de 22 variables bioclimáticas como predictoras; 3) Realización de búsquedas sistemáticas de individuos in situ con base en los registros de herbario y las áreas de distribución potencial en 24 municipios, para conocer la situación y la distribución del hábitat actual, y 4) Descripción mediante factores ambientales de los nichos ecológicos potenciales generados por MaxEnt. La revisión de las colecciones de herbarios reportó un total de 18 registros de V. planifolia, comprendidos entre 1939 y 1998.La búsqueda sistemática de individuos en campo ubicó 28 plantas distribuidas en 12 sitios sobre 95 364 Km2. Las variables que contribuyeron con mayor valor porcentual para determinar la estimación del modelo de distribución potencial en vainilla son precipitación del periodo más lluvioso (61.9 %), régimen de humedad del suelo (23.4 %) y precipitación del cuatrimestre más lluvioso (8.1 %). El hábitat potencial de la especie se distribuyó en cuatro zonas; trópico húmedo del golfo de México, templado húmedo, trópico húmedo y templado húmedo del pacifico. La precipitación anual osciló de 2 500 a 4 000 mm, con lluvias en verano y porcentaje de precipitación invernal de 5 a 10 %. El régimen de humedad y clima predominantes fueron údico tipo I (330 a 365 días de humedad) y cálido húmedo (Am/A(C) m). Las plantas se ubicaron en altitudes de 200 a 1 190 msnm, en laderas accidentadas, que por lo general están al pie de sistemas montañosos de 1 300 a 2 500 metros de altitud. En condiciones naturales la distribución de la especie no se limita a selva alta perennifolia, dado que se ubicó en bosque mesófilo de montaña y bosque tropical perennifolio. La ubicación de nuevos especímenes de V. planifolia en condiciones silvestres reduce un 66 % del área potencial de distribución, y la fragmenta, al pasar de ser una zona continua a convertirse en tres zonas geográficamente separadas. La reducción del hábitat se debió a un aumento en el número de plantas ubicadas, lo que define las condiciones ambientales a un nivel más exacto. Por lo anterior, se pueden emprender o diseñar acciones de conservación enfocadas a áreas más específicas dentro del estado de Oaxaca, México.
AbstractWild specimens of Vanilla planifolia represent a vital part of this resource primary gene pool, and some plants have only been reported in Oaxaca, Mexico. For this reason, we studied its geographical distribution within the state, to locate and describe the ecological characteristics of the areas where they have been found, in order to identify potential areas of establishment. The method comprised four stages: 1) the creation of a database with herbarium records, 2) the construction of the potential distribution based on historical herbarium records for the species, using the model of maximum entropy (MaxEnt) and 22 bioclimatic variables as predictors; 3) an in situ systematic search of individuals, based on herbarium records and areas of potential distribution in 24 municipalities, to determine the habitat current situation and distribution; 4) the description of the environmental factors of potential ecological niches generated by MaxEnt. A review of herbarium collections revealed a total of 18 records of V. planifolia between 1939 and 1998. The systematic search located 28 plants distributed in 12 sites in 95 364 Km2. The most important variables that determined the model of vanilla potential distribution were: precipitation in the rainy season (61.9 %), soil moisture regime (23.4 %) and precipitation during the four months of highest rainfall (8.1 %). The species potential habitat was found to be distributed in four zones: wet tropics of the Gulf of Mexico, humid temperate, humid tropical, and humid temperate in the Pacific. Precipitation oscillated within the annual ranges of 2 500 to 4 000 mm, with summer rains, and winter precipitation as 5 to 10 % of the total. The moisture regime and predominating climate were udic type I (330 to 365 days of moisture) and hot humid (Am/A(C) m). The plants were located at altitudes of 200 to 1 190 masl, on rough hillsides that generally make up the foothills of mountain systems, with altitudes of 1 300 to 2 500 masl. In natural conditions, distribution of the species is not limited to high evergreen forests, since it was also found in mountain mesophyll and tropical evergreen forests. The location of new specimens of V. planifolia in its wild condition reduces the potential distribution area by 66 %. This area is fragmented into three geographically separated areas. Habitat reduction was due to the increased number of located plants that define the environmental conditions into a more accurate level. Conservation actions can thus be designed and implemented, focusing on more specific areas within the state of Oaxaca, Mexico.
Subject(s)
Vanilla/classification , Seasons , Biodiversity , Geography , MexicoABSTRACT
Objective To predict the potential geographic distribution of Lyme disease in Qinghai by using Maximum Entropy model (MaxEnt).Methods The sero-diagnosis data of Lyme disease in 6 counties (Huzhu,Zeku,Tongde,Datong,Qilian and Xunhua) and the environmental and anthropogenic data including altitude,human footprint,normalized difference vegetation index (NDVI) and temperature in Qinghai province since 1990 were collected.By using the data of Huzhu Zeku and Tongde,the prediction of potential distribution of Lyme disease in Qinghai was conducted with MaxEnt.The prediction results were compared with the human sero-prevalence of Lyme disease in Datong,Qilian and Xunhua counties in Qinghai.Results Three hot spots of Lyme disease were predicted in Qinghai,which were all in the east forest areas.Furthermore,the NDVI showed the most important role in the model prediction,followed by human footprint.Datong,Qilian and Xunhua counties were all in eastern Qinghai.Xunhua was in hot spot area Ⅱ,Datong was close to the north of hot spot area Ⅲ,while Qilian with lowest sero-prevalence of Lyme disease was not in the hot spot areas.The data were well modeled in MaxEnt (Area Under Curve=0.980).Conclusions The actual distribution of Lyme disease in Qinghai was in consistent with the results of the model prediction.MaxEnt could be used in predicting the potential distribution patterns of Lyme disease.The distribution of vegetation and the range and intensity of human activity might be related with Lyme disease distribution.
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The distribution information of Glycyrrhiza uralensis was collected by interview investigation and field survey, and 46 related environmental factors were collected, some kinds of functional chemical constituents of G.uralensis were analyzed. Integrated climate, topography and other related ecological factors, the habitat suitability study was conducted based on Arc geographic information system(ArcGIS),and maximum entropy model. The AUC of ROC curve was both above 0.95, indicating that the predictive results with the maximum model were highly precise. The results showed that 5 major ecological factors have obvious influence on ecology suitability distributions of G. uralensis, including July average temperature, soil sub category, Dec precipitation, vegetation types and standard deviation of seasonal variation in temperature, et al. It is suitable for the living habits of the G. uralensis, adequate light, low rainfall, summer heat and large temperature difference between day and night, which is suitable for distribution in the northern temperate plains and mountains. In addition, the ecological suitability regionalization based on the chemical constituents of G.uralensis also provides a new suitable distribution area other than the traditional distribution area, which provides a scientific basis for the reasonable introduction of G.uralensis.
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The distribution information of Lycii Fructus was collected by interview investigation and field survey, and 46 related environmental factors were collected, some kinds of functional chemical constituents the of Lycii Fructus were analyzed. Integrated climate, topography and other related ecological factors, the habitat suitability study was conducted based on Arc geographic information system(ArcGIS),and maximum entropy model. The AUC of ROC curve was both above 0.95, indicating that the predictive results with the maximum model were highly precise. The results showed that 5 major ecological factors had obvious influence on ecology suitability distributions of Lycii Fructus, including soil pH, soil subclass, vegetation type and in August the average temperature et al. It is suitable for the living habits of the Lycii Fructus, dry, cool weather, more hardy, drought-resistant, alkali soil, which is suitable for distribution in the northern temperate plains. In addition, the ecological suitability regionalization based on the chemical constituents of Lycii Fructus also provides a new suitable distribution area other than the traditional distribution area, which provides a scientific basis for the reasonable introduction of Lycii Fructus.