[Prediction of suitable habitats of Phlebotomus chinensis in Gansu Province based on the Biomod2 ensemble model].

Q3 Medicine
D Yu, Y Hou, A He, Y Feng, G Yang, C Yang, H Liang, H Zhang, F Li
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Twelve species distribution models were built using the biomod2 package in R project, including surface range envelope (SRE) model, generalized linear model (GLM), generalized additive model (GAM), multivariate adaptive regression splines (MARS) model, generalized boosted model (GBM), classification tree analysis (CTA) model, flexible discriminant analysis (FDA) model, maximum entropy (MaxEnt) model, optimized maximum entropy (MAXNET) model, artificial neural network (ANN) model, random forest (RF) model, and extreme gradient boosting (XGBOOST) model. The performance of 12 models was evaluated using the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS), and <i>Kappa</i> coefficient, and single models with high performance was selected to build the optimal ensemble models. Factors affecting the survival of <i>Ph. chinensis</i> were identified based on climatic, geographical, population and economic variables. In addition, the suitable distribution areas of <i>Ph. chinensis</i> were predicted in Gansu Province under shared socioeconomic pathway 126 (SSP126), SSP370 and SSP585 scenarios based on climatic data during the period from 1991 to 2020, from 2041 to 2060 (2050s), and from 2081 to 2100 (2090s) .</p><p><strong>Results: </strong>A total of 11 species distribution models were successfully built for prediction of potential distribution areas of <i>Ph. chinensis</i> in Gansu Province, and the RF model had the highest predictive accuracy (AUC = 0.998). The ensemble model built based on the RF model, XGBOOST model, GLM, and MARS model had an increased predictive accuracy (AUC = 0.999) relative to single models. Among the 26 environmental factors, precipitation of the wettest quarter (12.00%), maximum temperature of the warmest month (11.58%), and annual normalized difference vegetation index (11.29%) had the greatest contributions to suitable habitats distribution of <i>Ph. sinensis</i>. Under the climatic conditions from 1991 to 2020, the potential suitable habitat area for <i>Ph. chinensis</i> in Gansu Province was approximately 5.80 × 10<sup>4</sup> km<sup>2</sup>, of which the highly suitable area was 1.42 × 10<sup>4</sup> km<sup>2</sup>, and primarily concentrated in the southernmost region of Gansu Province. By the 2050s, the unsuitable and lowly suitable areas for <i>Ph. chinensis</i> in Gansu Province had decreased by varying degrees compared to that of 1991 to 2020 period, while the moderately and highly suitable areas exhibited expansion and migration. By the 2090s, under the SSP126 scenario, the suitable habitat area for <i>Ph. chinensis</i> increased significantly, and under the SSP585 scenario, the highly suitable areas transformed into extremely suitable areas, also showing substantial growth. Future global warming is conducive to the survival and reproduction of <i>Ph. chinensis</i>. From the 2050s to the 2090s, the highly suitable areas for <i>Ph. chinensis</i> in Gansu Province will be projected to expand northward. Under the SSP126 scenario, the suitable habitat area for <i>Ph. chinensis</i> in Gansu Province is expected to increase by 194.75% and 204.79% in the 2050s and 2090s, respectively, compared to that of the 1991 to 2020 period. Under the SSP370 scenario, the moderately and highly suitable areas will be projected to increase by 164.40% and 209.03% in the 2050s and 2090s, respectively, while under the SSP585 scenario, they are expected to increase by 195.98% and 211.66%, respectively.</p><p><strong>Conclusions: </strong>The distribution of potential suitable habitats of <i>Ph. sinensis</i> gradually shifts with climatic changes. 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引用次数: 0

Abstract

Objective: To investigate the suitable habitats of Phlebotomus chinensis in Gansu Province, so as provide insights into effective management of mountain-type zoonotic visceral leishmaniasis (MT-ZVL).

Methods: The geographical coordinates of locations where MT-ZVL cases were reported were retrieved in Gansu Province from 2015 to 2023, and data pertaining to 26 environmental variables were captured, including 19 climatic variables (annual mean temperature, mean diurnal 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 warmest quarter, mean temperature of the coldest quarter, annual 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, and precipitation of the coldest quarter), five geographical variables (elevation, annual normalized difference vegetation index, vegetation type, landform type and land use type), and two population and economic variables (population distribution and gross domestic product). Twelve species distribution models were built using the biomod2 package in R project, including surface range envelope (SRE) model, generalized linear model (GLM), generalized additive model (GAM), multivariate adaptive regression splines (MARS) model, generalized boosted model (GBM), classification tree analysis (CTA) model, flexible discriminant analysis (FDA) model, maximum entropy (MaxEnt) model, optimized maximum entropy (MAXNET) model, artificial neural network (ANN) model, random forest (RF) model, and extreme gradient boosting (XGBOOST) model. The performance of 12 models was evaluated using the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS), and Kappa coefficient, and single models with high performance was selected to build the optimal ensemble models. Factors affecting the survival of Ph. chinensis were identified based on climatic, geographical, population and economic variables. In addition, the suitable distribution areas of Ph. chinensis were predicted in Gansu Province under shared socioeconomic pathway 126 (SSP126), SSP370 and SSP585 scenarios based on climatic data during the period from 1991 to 2020, from 2041 to 2060 (2050s), and from 2081 to 2100 (2090s) .

Results: A total of 11 species distribution models were successfully built for prediction of potential distribution areas of Ph. chinensis in Gansu Province, and the RF model had the highest predictive accuracy (AUC = 0.998). The ensemble model built based on the RF model, XGBOOST model, GLM, and MARS model had an increased predictive accuracy (AUC = 0.999) relative to single models. Among the 26 environmental factors, precipitation of the wettest quarter (12.00%), maximum temperature of the warmest month (11.58%), and annual normalized difference vegetation index (11.29%) had the greatest contributions to suitable habitats distribution of Ph. sinensis. Under the climatic conditions from 1991 to 2020, the potential suitable habitat area for Ph. chinensis in Gansu Province was approximately 5.80 × 104 km2, of which the highly suitable area was 1.42 × 104 km2, and primarily concentrated in the southernmost region of Gansu Province. By the 2050s, the unsuitable and lowly suitable areas for Ph. chinensis in Gansu Province had decreased by varying degrees compared to that of 1991 to 2020 period, while the moderately and highly suitable areas exhibited expansion and migration. By the 2090s, under the SSP126 scenario, the suitable habitat area for Ph. chinensis increased significantly, and under the SSP585 scenario, the highly suitable areas transformed into extremely suitable areas, also showing substantial growth. Future global warming is conducive to the survival and reproduction of Ph. chinensis. From the 2050s to the 2090s, the highly suitable areas for Ph. chinensis in Gansu Province will be projected to expand northward. Under the SSP126 scenario, the suitable habitat area for Ph. chinensis in Gansu Province is expected to increase by 194.75% and 204.79% in the 2050s and 2090s, respectively, compared to that of the 1991 to 2020 period. Under the SSP370 scenario, the moderately and highly suitable areas will be projected to increase by 164.40% and 209.03% in the 2050s and 2090s, respectively, while under the SSP585 scenario, they are expected to increase by 195.98% and 211.66%, respectively.

Conclusions: The distribution of potential suitable habitats of Ph. sinensis gradually shifts with climatic changes. Intensified surveillance and management of Ph. sinensis is recommended in central and eastern parts of Gansu Province to support early warning of MT-ZVL.

基于Biomod2集合模型的中国白蛉适宜生境预测[j]。
目的:探讨甘肃省中国白蛉的适宜生境,为山区型人畜共患内脏利什曼病(MT-ZVL)的有效防治提供依据。方法:利用2015 - 2023年甘肃省MT-ZVL病例报告地点地理坐标,获取26个环境变量数据,包括19个气候变量(年平均气温、平均日差、等温线、温度季节性、最暖月最高气温、最冷月最低气温、年温差、最湿季平均气温、最湿季平均气温、最湿季平均气温、最湿季平均气温、最湿季平均气温、最湿季平均气温、最湿季平均气温、最湿季平均气温、最湿季平均气温、最湿季平均气温和最湿季平均气温)。最干季平均气温、最暖季平均气温、最冷季平均气温、年降水量、最湿月降水量、最干月降水量、降水季节性、最湿季降水量、最干季降水量、最暖季降水量、最冷季降水量)、5个地理变量(海拔、年归一化植被指数、植被类型、地貌类型和土地利用类型),以及两个人口和经济变量(人口分布和国内生产总值)。利用R项目中的biomod2软件包建立了12个物种分布模型,包括表面范围包络(SRE)模型、广义线性模型(GLM)、广义加性模型(GAM)、多变量自适应回归样条(MARS)模型、广义提升模型(GBM)、分类树分析(CTA)模型、柔性判别分析(FDA)模型、最大熵(MaxEnt)模型、优化最大熵(MAXNET)模型、人工神经网络(ANN)模型、生物多样性模型、生物多样性模型、生物多样性模型、生物多样性模型、生物多样性模型、生物多样性模型、生物多样性模型。随机森林(RF)模型和极端梯度增强(XGBOOST)模型。采用受者工作特征曲线下面积(AUC)、真技能统计量(TSS)和Kappa系数对12个模型的性能进行评价,选择性能较高的单个模型构建最优集成模型。从气候、地理、人口和经济等方面分析了影响中华棉铃虫生存的因素。此外,基于1991 - 2020年、2041 - 2060年和2081 - 2100年的气候数据,在共享社会经济路径126 (SSP126)、SSP370和SSP585情景下,预测了甘肃省柽柽树的适宜分布区。共建立了11个物种分布模型用于预测甘肃省中华按蚊潜在分布区域,其中RF模型预测精度最高(AUC = 0.998)。基于RF模型、XGBOOST模型、GLM和MARS模型构建的集成模型的预测精度(AUC = 0.999)高于单一模型。26个环境因子中,最湿季降水(12.00%)、最暖月最高气温(11.58%)和年归一化植被指数(11.29%)对柽柳适宜生境分布的贡献最大。在1991 - 2020年气候条件下,甘肃省柽柽树潜在适宜生境面积约为5.80 × 104 km2,其中高度适宜生境面积为1.42 × 104 km2,主要集中在甘肃省最南部地区。到2050年代,甘肃省不适宜和低适宜区较1991 ~ 2020年有不同程度的减少,而中等和高度适宜区则呈现扩张和迁移的趋势。到20世纪90年代,在SSP126情景下,中国剑齿虎适宜生境面积显著增加,在SSP585情景下,高度适宜生境向极适宜生境转变,也出现了大幅增长。未来全球变暖有利于中华Ph. chinensis的生存和繁殖。20世纪50年代至90年代,甘肃省柽柳高适区将向北扩展。在SSP126情景下,2050年代和204.79%的适宜生境面积将比1991 ~ 2020年分别增加194.75%和204.79%。在SSP370情景下,中等和高度适宜地区预计在2050年代和209.03%分别增加164.40%和209.03%,而在SSP585情景下,预计分别增加195.98%和211.66%。结论:随着气候的变化,中华棉铃虫潜在适宜生境的分布逐渐发生变化。建议在甘肃省中东部地区加强对中华按蚊的监测和管理,以支持MT-ZVL的早期预警。
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来源期刊
中国血吸虫病防治杂志
中国血吸虫病防治杂志 Medicine-Medicine (all)
CiteScore
1.30
自引率
0.00%
发文量
7021
期刊介绍: Chinese Journal of Schistosomiasis Control (ISSN: 1005-6661, CN: 32-1374/R), founded in 1989, is a technical and scientific journal under the supervision of Jiangsu Provincial Health Commission and organised by Jiangsu Institute of Schistosomiasis Control. It is a scientific and technical journal under the supervision of Jiangsu Provincial Health Commission and sponsored by Jiangsu Institute of Schistosomiasis Prevention and Control. The journal carries out the policy of prevention-oriented, control-oriented, nationwide and grassroots, adheres to the tenet of scientific research service for the prevention and treatment of schistosomiasis and other parasitic diseases, and mainly publishes academic papers reflecting the latest achievements and dynamics of prevention and treatment of schistosomiasis and other parasitic diseases, scientific research and management, etc. The main columns are Guest Contributions, Experts‘ Commentary, Experts’ Perspectives, Experts' Forums, Theses, Prevention and Treatment Research, Experimental Research, The main columns include Guest Contributions, Expert Commentaries, Expert Perspectives, Expert Forums, Treatises, Prevention and Control Studies, Experimental Studies, Clinical Studies, Prevention and Control Experiences, Prevention and Control Management, Reviews, Case Reports, and Information, etc. The journal is a useful reference material for the professional and technical personnel of schistosomiasis and parasitic disease prevention and control research, management workers, and teachers and students of medical schools.    The journal is now included in important domestic databases, such as Chinese Core List (8th edition), China Science Citation Database (Core Edition), China Science and Technology Core Journals (Statistical Source Journals), and is also included in MEDLINE/PubMed, Scopus, EBSCO, Chemical Abstract, Embase, Zoological Record, JSTChina, Ulrichsweb, Western Pacific Region Index Medicus, CABI and other international authoritative databases.
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