Z Li, Y Liu, Y Guo, Z Wei, J Chen, Q Wang, T Li, S Li
{"title":"[影响云南大理白族自治州Pomacea分布的因素及适宜分布区预测]。","authors":"Z Li, Y Liu, Y Guo, Z Wei, J Chen, Q Wang, T Li, S Li","doi":"10.16250/j.32.1915.2024201","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To investigate the factors affecting the distribution of <i>Pomacea</i> and project the trends in the spread of suitable distribution areas of <i>Pomacea</i> in 2050 and 2070 in Dali Bai Autonomous Prefecture, so as to provide insights into <i>Pomacea</i> control in the prefecture.</p><p><strong>Methods: </strong>The longitudes and latitudes of <i>Pomacea</i> sampling sites were captured based on <i>Pomacea</i> field survey data in 12 cities (counties) of Dali Bai Autonomous Prefecture from 2023 to 2024. A total of 19 climatic factors (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 month, mean temperature of the coldest month, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, mean temperature of the warmest quarter, and mean temperature of the coldest quarter) and representative concentration pathways (RCPs) were retrieved from the world climate database (www.worldclim.org). All climatic variables were employed to create a maximum entropy (MaxEnt) model. The predictive accuracy of the model was assessed with the area under the receiver operating characteristic (ROC) curve (AUC), and the contributions of these 19 climatic factors to the distribution of <i>Pomacea</i> were analyzed in Dali Bai Autonomous Prefecture using Jackknife test. In addition, the suitable distribution areas of <i>Pomacea</i> were predicted with the MaxEnt model in Dali Bai Autonomous Prefecture in 2024 and in 2050 and 2070 under RCP4.5.</p><p><strong>Results: </strong>Data pertaining to 91 <i>Pomacea</i> sampling sites were captured. ROC analysis revealed the MaxEnt model had an AUC value of 0.885 ± 0.088 for predicting the suitable distribution areas of <i>Pomacea</i> in Dali Bai Autonomous Prefecture. Of the 19 climatic factors, the maximum temperature of the warmest month had the highest contribution to the distribution of <i>Pomacea</i> in Dali Bai Autonomous Prefecture, followed by mean temperature of the driest quarter, mean temperature of the wettest quarter and minimum temperature of the coldest month. The suitable distribution area of <i>Pomacea</i> was predicted to be 14 555.69 km<sup>2</sup> in Dali Bai Autonomous Prefecture in 2024, and would expand gradually to the southeastern part of the prefecture in the future due to climatic factors. The suitable distribution areas of <i>Pomacea</i> were projected to expand to 21 475.61 km<sup>2</sup> in 2050 and 25 782.52 km<sup>2</sup> in 2070 in Dali Bai Autonomous Prefecture, respectively.</p><p><strong>Conclusions: </strong>Temperature is an important contributor to the distribution of <i>Pomacea</i> in Dali Bai Autonomous Prefecture, and the suitable distribution area of <i>Pomacea</i> will gradually expand to the southeastern part of the prefecture in 2050 and 2070.</p>","PeriodicalId":38874,"journal":{"name":"中国血吸虫病防治杂志","volume":"37 1","pages":"69-75"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Factors affecting <i>Pomacea</i> distribution and prediction of suitable distribution areas of <i>Pomacea</i> in Dali Bai Autonomous Prefecture of Yunnan Province].\",\"authors\":\"Z Li, Y Liu, Y Guo, Z Wei, J Chen, Q Wang, T Li, S Li\",\"doi\":\"10.16250/j.32.1915.2024201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To investigate the factors affecting the distribution of <i>Pomacea</i> and project the trends in the spread of suitable distribution areas of <i>Pomacea</i> in 2050 and 2070 in Dali Bai Autonomous Prefecture, so as to provide insights into <i>Pomacea</i> control in the prefecture.</p><p><strong>Methods: </strong>The longitudes and latitudes of <i>Pomacea</i> sampling sites were captured based on <i>Pomacea</i> field survey data in 12 cities (counties) of Dali Bai Autonomous Prefecture from 2023 to 2024. A total of 19 climatic factors (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 month, mean temperature of the coldest month, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, mean temperature of the warmest quarter, and mean temperature of the coldest quarter) and representative concentration pathways (RCPs) were retrieved from the world climate database (www.worldclim.org). All climatic variables were employed to create a maximum entropy (MaxEnt) model. The predictive accuracy of the model was assessed with the area under the receiver operating characteristic (ROC) curve (AUC), and the contributions of these 19 climatic factors to the distribution of <i>Pomacea</i> were analyzed in Dali Bai Autonomous Prefecture using Jackknife test. In addition, the suitable distribution areas of <i>Pomacea</i> were predicted with the MaxEnt model in Dali Bai Autonomous Prefecture in 2024 and in 2050 and 2070 under RCP4.5.</p><p><strong>Results: </strong>Data pertaining to 91 <i>Pomacea</i> sampling sites were captured. ROC analysis revealed the MaxEnt model had an AUC value of 0.885 ± 0.088 for predicting the suitable distribution areas of <i>Pomacea</i> in Dali Bai Autonomous Prefecture. Of the 19 climatic factors, the maximum temperature of the warmest month had the highest contribution to the distribution of <i>Pomacea</i> in Dali Bai Autonomous Prefecture, followed by mean temperature of the driest quarter, mean temperature of the wettest quarter and minimum temperature of the coldest month. The suitable distribution area of <i>Pomacea</i> was predicted to be 14 555.69 km<sup>2</sup> in Dali Bai Autonomous Prefecture in 2024, and would expand gradually to the southeastern part of the prefecture in the future due to climatic factors. The suitable distribution areas of <i>Pomacea</i> were projected to expand to 21 475.61 km<sup>2</sup> in 2050 and 25 782.52 km<sup>2</sup> in 2070 in Dali Bai Autonomous Prefecture, respectively.</p><p><strong>Conclusions: </strong>Temperature is an important contributor to the distribution of <i>Pomacea</i> in Dali Bai Autonomous Prefecture, and the suitable distribution area of <i>Pomacea</i> will gradually expand to the southeastern part of the prefecture in 2050 and 2070.</p>\",\"PeriodicalId\":38874,\"journal\":{\"name\":\"中国血吸虫病防治杂志\",\"volume\":\"37 1\",\"pages\":\"69-75\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国血吸虫病防治杂志\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.16250/j.32.1915.2024201\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国血吸虫病防治杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.16250/j.32.1915.2024201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
[Factors affecting Pomacea distribution and prediction of suitable distribution areas of Pomacea in Dali Bai Autonomous Prefecture of Yunnan Province].
Objective: To investigate the factors affecting the distribution of Pomacea and project the trends in the spread of suitable distribution areas of Pomacea in 2050 and 2070 in Dali Bai Autonomous Prefecture, so as to provide insights into Pomacea control in the prefecture.
Methods: The longitudes and latitudes of Pomacea sampling sites were captured based on Pomacea field survey data in 12 cities (counties) of Dali Bai Autonomous Prefecture from 2023 to 2024. A total of 19 climatic factors (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 month, mean temperature of the coldest month, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, mean temperature of the warmest quarter, and mean temperature of the coldest quarter) and representative concentration pathways (RCPs) were retrieved from the world climate database (www.worldclim.org). All climatic variables were employed to create a maximum entropy (MaxEnt) model. The predictive accuracy of the model was assessed with the area under the receiver operating characteristic (ROC) curve (AUC), and the contributions of these 19 climatic factors to the distribution of Pomacea were analyzed in Dali Bai Autonomous Prefecture using Jackknife test. In addition, the suitable distribution areas of Pomacea were predicted with the MaxEnt model in Dali Bai Autonomous Prefecture in 2024 and in 2050 and 2070 under RCP4.5.
Results: Data pertaining to 91 Pomacea sampling sites were captured. ROC analysis revealed the MaxEnt model had an AUC value of 0.885 ± 0.088 for predicting the suitable distribution areas of Pomacea in Dali Bai Autonomous Prefecture. Of the 19 climatic factors, the maximum temperature of the warmest month had the highest contribution to the distribution of Pomacea in Dali Bai Autonomous Prefecture, followed by mean temperature of the driest quarter, mean temperature of the wettest quarter and minimum temperature of the coldest month. The suitable distribution area of Pomacea was predicted to be 14 555.69 km2 in Dali Bai Autonomous Prefecture in 2024, and would expand gradually to the southeastern part of the prefecture in the future due to climatic factors. The suitable distribution areas of Pomacea were projected to expand to 21 475.61 km2 in 2050 and 25 782.52 km2 in 2070 in Dali Bai Autonomous Prefecture, respectively.
Conclusions: Temperature is an important contributor to the distribution of Pomacea in Dali Bai Autonomous Prefecture, and the suitable distribution area of Pomacea will gradually expand to the southeastern part of the prefecture in 2050 and 2070.
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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