Prediction of suitable habitats of <i>Phlebotomus chinensis</i> in Gansu Province based on the Biomod2 ensemble model

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d2987514e148"> <b>Objective</b> To investigate the suitable habitats of <i>Phlebotomus chinensis</i> in Gansu Province, so as provide insights into effective management of mountain-type zoonotic visceral leishmaniasis (MT-ZVL). </p><p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d2987514e156"> <b>Methods</b> 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 <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 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d2987514e170"> <b>Results</b> 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 ⁴ km ², of which the highly suitable area was 1.42 × 10 ⁴ km ², 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 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d2987514e213"> <b>Conclusions</b> The distribution of potential suitable habitats of <i>Ph. sinensis</i> gradually shifts with climatic changes. Intensified surveillance and management of <i>Ph. sinensis</i> is recommended in central and eastern parts of Gansu Province to support early warning of MT-ZVL. </p><p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d2987514e225"> <b>［摘要］ 目的</b> 识别甘肃省中华白蛉适生区, 为有效防控山丘型内脏利什曼病 (mountain-type zoonotic visceral leishmaniasis, MT-ZVL) 提供参考。 <b>方法</b> 收集 2015—2023 年甘肃省MT-ZVL报告病例信息, 获取病例分布点地理坐标。收集 26 个环境变量数据, 包括年平均温度、平均气温日较差、等温性、气温季节性变化标准差、最暖月最高气温、最冷月最低气 温、温度年较差、最湿季度平均气温、最干季度平均气温、最暖季度平均气温、最冷季度平均气温、年降水量、最湿月降水 量、最干月降水量、降水量季节性变化标准差、最湿季度降水量、最干季度降水量、最暖季度降水量、最冷季度降水量等 19 个气候变量, 海拔、年度归一化植被指数、植被类型、地形地貌、土地利用类型等 5 个地理变量和国内生产总值、人口分 布 2 个人口经济变量。采用 R 软件Biomod2 软件包建立 12 个物种分布模型, 包括表面分布区分室模型 (surface range envelope, SRE)、广义线性模型 (generalized linear model, GLM)、广义加性模型 (generalized additive model, GAM)、多元适应 回归样条 (multivariate adaptive regression splines, MARS)、推广回归树 (generalized boosted model, GBM)、分类树分析 (classification tree analysis, CTA)、柔性判别分析 (flexible discriminant analysis, FDA)、最大熵模型 (maximum entropy model, MAXENT)、优化版最大熵 (optimized maximum entropy model, MAXNET)、人工神经网络 (artificial neural network, ANN)、随 机森林 (random forest, RF) 和极限梯度提升 (extreme gradient boosting, XGBOOST)。利用受试者工作特征曲线下面积 (area under curve, AUC)、真实技巧统计量 (true skill statistics, TSS) 和 <i>Kappa</i> 系数对各模型进行性能评价, 选择性能较好的单一模型构建最优组合模型, 基于气候、地形和社会经济因子分析影响中华白蛉生存的因素。根据 1991—2020 年和 2041—2060 年 (2050s)、2081—2100 年 (2090s) 气候数据, 分别对共享社会经济路径 (shared socioeconomic pathways, SSP) 126、SSP370 和 SSP585 气候情景下甘肃省中华白蛉适宜分布区进行预测。 <b>结果</b> 成功建立了 11 种预测甘肃省中华白蛉潜在 分布区的模型。其中随机森林模型预测准确度最高 (AUC = 0.998)。以RF、XGBOOST、GLM 和 MARS 4 种表现较好的单 一模型构建的组合模型 (AUC = 0.999) 较各单一模型准确度有所提升。26 个环境变量中, 最湿季度降水量、最暖月最高 气温和年度归一化植被指数对中华白蛉适宜生境分布影响最大, 贡献率分别为 12.00%、11.58% 和 11.29%。1991—2020 年气候条件下, 甘肃省中华白蛉潜在适生区面积约 5.80 × 10 ⁴ km ², 其中高适生区为 1.42 × 10 ⁴ km ², 主要集中于陇南南部。2050s 甘肃省中华白蛉非适生区与低适生区面积较 1991—2020 年均不同程度降低, 但中、高适生区面积存在扩张迁移趋 势; 在 SSP126 情景下 2090s 中华白蛉适生区面积将大幅增加, 在 SSP585 情景下 2090s 中华白蛉高适生区转变为极高适生 区的面积将大幅增加。未来全球气候变暖有利于中华白蛉的生存繁衍, 2050s 至 2090s 甘肃省中华白蛉高适生区将呈向 北扩展的趋势。在SSP126情景下, 2050s 与 2090s 甘肃省中华白蛉适生区面积分别较 1991—2020 年增加 194.75% 和 204.79%; 在SSP370 情景下, 2050s 与 2090s 甘肃省中华白蛉中、高适生区面积较 1991—2020 年分别增加 164.40% 和 209.03%, 在SSP585情景下分别增加 195.98% 和 211.66%。 <b>结论</b> 甘肃省中华白蛉潜在适生区范围会随气候变化而逐渐 迁移, 未来可有针对性地在陇中和陇东地区对中华白蛉加强监测和防控, 从而为 MT-ZVL 早期预警提供支持。 </p>