中国畜牧兽医 ›› 2024, Vol. 51 ›› Issue (2): 728-735.doi: 10.16431/j.cnki.1671-7236.2024.02.029

• 预防兽医 • 上一篇    下一篇

蛙壶菌潜在分布区及其风险因素预测与分析

别佳, 沙龙倩, 阎建余   

  1. 昆明学院农学与生命科学学院, 昆明 650214
  • 收稿日期:2023-08-15 出版日期:2024-02-05 发布日期:2024-01-29

Prediction and Analysis of Potential Distribution Area and Risk Factors of Bathochytrium dendrobatidis

BIE Jia, SHA Longqian, YAN Jianyu   

  1. College of Agriculture and Life Sciences, Kunming University, Kunming 650214, China
  • Received:2023-08-15 Online:2024-02-05 Published:2024-01-29
  • Contact: 云南省教育厅科学研究基金项目(2022J0643);昆明学院引进人才科研项目(YJL23002) E-mail:biejia0408@163.com

摘要: 【目的】壶菌病是一种由蛙壶菌(Batrachochytrium dendrobatidis)感染导致的疾病,在近半个多世纪的时间里,是导致两栖动物种群数量大量减少甚至灭绝的主要原因之一。目前,壶菌病尚没有行之有效的治疗方法,因此,对世界范围内蛙壶菌的分布与传播进行调查研究,分析蛙壶菌在世界和区域尺度的分布风险及影响因素,可为其预防措施的制定提供基础研究资料。【方法】使用蛙壶菌在世界范围内的分布数据,共考虑了20个气候环境变量,经过对蛙壶菌分布数据和变量进行筛选,建立最大熵模型,比较纳入模型的6个变量与蛙壶菌分布风险的关系,并预测蛙壶菌在世界范围和中国大陆的分布风险。【结果】变量中对蛙壶菌分布概率贡献度最高的前4位依次为年平均气温、年降水量、温度季节性、降水季节性。蛙壶菌分布概率与年平均气温和温度季节性总体均呈先正相关后负相关,与年降水量和降水季节性分别呈正相关和负相关。蛙壶菌全球分布的高风险地区主要在中国大陆南部、澳大利亚东南部、巴布亚新几内亚中部、瑞典南部、德国、波兰、罗马尼亚、英国南部、爱尔兰、法国、马达加斯加东部、苏丹、美国南部、秘鲁东部、埃塞俄比亚和日本等。其在中国大陆分布的高风险区域主要集中在广东北部、广西中部和北部、云南、贵州、湖南、江西、福建、浙江、江苏南部、安徽南部、湖北南部、重庆、四川东部及南部、陕西南部等地区。【结论】蛙壶菌的分布与气温和降水关系密切,其在全球分布的高风险地区主要在欧洲、非洲南部和北美洲南部,在中国大陆分布的高风险区域主要集中在华中、华东、华南地区和西南地区的东南部。

关键词: 蛙壶菌; 最大熵模型; 风险预测; 两栖动物; 气象因素

Abstract: 【Objective】 Chytridiomycosis is a disease caused by the infection of the aquatic fungus Batrachyttrium dentrobatidis (B.dendrobatidis), which has been one of the main reasons for the significant reduction or even extinction of amphibian populations for over half a century.At present, there is no effective treatment method for chytridiomycosis yet.The investigation and research on the distribution and spread of B.dendrobatidis worldwide, as well as the analysis of the distribution risks and influencing factors of B.dendrobatidis at the global and regional scales, can provide basic research data for the formulation of preventive measures.【Method】 The distribution data of B.dendrobatidis in the world were used and 20 climate environment variables were considered.After the B.dendrobatidis distribution data and variables were screened, a MaxEnt model was established to compare the relationship between six variables and B.dendrobatidis distribution risk, and predicted the distribution risk of B.dendrobatidis in the world and mainland China.【Result】 The results showed that the top four variables with the highest contribution to the probability of B.dendrobatidis distribution were in order of annual average temperature, annual precipitation, temperature seasonality and precipitation seasonality.The overall relationship between annual average temperature, temperature seasonality and the distribution probability of the B.dendrobatidis showed a positive correlation first and then a negative correlation.The relationship between annual precipitation, precipitation seasonality and the distribution probability of the B.dendrobatidis were positively and negatively correlated, respectively.The high-risk areas of global distribution of B.dendrobatidis were mainly distributed in Southern mainland China, Southeastern Australia, Central Papua New Guinea, Southern Sweden, Germany, Poland, Romania, Southern United Kingdom, Ireland, France, Eastern Madagascar, Sudan, Southern United States, Eastern Peru, The Federal Democratic Republic of Ethiopia and Japan etc.And the high-risk areas of B.dendrobatidis in mainland China were mainly concentrated in Northern Guangdong, Central and Northern Guangxi, Yunnan, Guizhou, Hunan, Jiangxi, Fujian, Zhejiang, Southern Jiangsu, Southern Anhui, Southern Hubei, Chongqing, Eastern and Southern Sichuan, Southern Shaanxi, etc.【Conclusion】 The distribution of B.dendrobatidis was closely related to temperature and precipitation.The high-risk areas of B.dendrobatidis in the world were mainly in Europe, Southern Africa and Southern North America, and the high-risk areas in mainland China were mainly in Central China, Eastern China, Southern China and the Southeast of Southwest region of China.

Key words: Batrachochytrium dendrobatidis; maximum entropy model; risk prediction; amphibian; climate environment variables

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