基于Maxent的两种入侵性鱼类(麦穗鱼和鲫)的全球适生区预测

doi:10.3724/SP.J.1003.2014.13163

生物多样性 ›› 2014, Vol. 22 ›› Issue (2): 182-188. DOI: 10.3724/SP.J.1003.2014.13163 cstr: 32101.14.SP.J.1003.2014.13163

所属专题：生物入侵

基于Maxent的两种入侵性鱼类(麦穗鱼和鲫)的全球适生区预测

张熙骜^1,³, 隋晓云^1,^2,^*(), 吕植², 陈毅峰^1,^*()

1 中国科学院水生生物多样性与保护重点实验室, 中国科学院水生生物研究所, 武汉 430072
2 北京大学生命科学学院, 北京 100871
3 (中国科学院大学, 北京 100049

收稿日期:2013-07-15 接受日期:2013-12-09 出版日期:2014-03-20 发布日期:2014-04-03
通讯作者: 隋晓云,陈毅峰
基金资助:
国家自然科学重点基金(41030208);中国科学院创新方向性项目(KSCX2-YW-Z-1023);973计划(2009CB119200)

A prediction of the global habitat of two invasive fishes (Pseudorasbora parva and Carassius auratus) from East Asia using Maxent

Xi’ao Zhang^1,³, Xiaoyun Sui^1,^2,^*(), Zhi Lü², Yifeng Chen^1,^*()

1 The Key Laboratory of Aquatic Biodiversity and Conservation of Chinese Academy of Sciences, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072
2 School of Life Sciences, Peking University, Beijing 100871
3 University of Chinese Academy of Sciences, Beijing 100049

Received:2013-07-15 Accepted:2013-12-09 Online:2014-03-20 Published:2014-04-03
Contact: Sui Xiaoyun,Chen Yifeng

1. 附录I WorldClim气候数据名称及描述.pdf(122KB)

摘要/Abstract

摘要：

受引种、贸易等人类活动的影响, 麦穗鱼(Pseudorasbora parva)和鲫(Carassius auratus)在全球范围内已经广泛分布并造成巨大的生态危害。根据野外采样和文献记录, 本文系统整理了麦穗鱼和鲫在世界各地的分布情况, 以高达2.5弧分分辨率的环境数据底图, 利用Maxent模型预测了麦穗鱼和鲫在全球的适生区, 以期为防控麦穗鱼和鲫的入侵提供早期预警。结果表明麦穗鱼和鲫在全球范围的分布区非常广泛, 除南极洲外的各个大洲均有其适生区, 因此这两种鱼还有继续扩散的潜力, 并可能在美国、巴西和阿根廷等国家出现由生物入侵导致的生态学问题。麦穗鱼的适生区主要集中在15º–55º N之间, 欧洲是麦穗鱼入侵的重灾区, 尤其是法国、荷兰周边的西欧国家和匈牙利、塞尔维亚周边的东欧国家; 而美国中部的密西西比河流域、东部及南部沿海, 以及西雅图至加拿大的温哥华之间是麦穗鱼潜在入侵风险性极高的区域。鲫自然分布于欧洲至东亚的广大地区, 目前已经在澳大利亚、加拿大、美国、马达加斯加、印度和越南等国家有分布, 未来还可能进一步扩散至大洋洲的新西兰和新喀里多尼亚, 北美洲的墨西哥至南美洲的阿根廷, 以及非洲的塞内加尔、几内亚和南非等国家; 尤其是南美洲的阿根廷和巴西, 非洲西部的几内亚、喀麦隆等国家将是鲫入侵风险极高的区域。

关键词: Maxent, 麦穗鱼, 鲫, 生物入侵, 适生区

Abstract

The topmouth gudgeon (Pseudorasbora parva) and goldfish (Carassius auratus), two small omnivorous freshwater fishes, have been introduced into many countries by human activities during recent years. Wide and severe ecological harm has resulted from the pathogens that these fishes carried and from the extremely large populations of these fishes. Based on data from a large number of distribution points systematically identified by our long-term sampling survey and from the FishBase, and on high-resolution environmental data, we used Maxent to obtain the first prediction of the potential global distribution of the two fishes. The results of the study suggest that the topmouth gudgeon and goldfish have an extremely wide potential range in the world and that this is especially the case for the goldfish. In fact, all continents except Antarctica include many potential habitats for the two fishes. Accordingly, these two fishes may spread more widely and create more serious danger to the aquatic ecosystem in the future as human activities continue to expand. Especially, the Mississippi valley in the middle part, and the eastern and southern coasts of the United States of America, region from Seattle to Vancouver of Canada will be the areas of the topmouth gudgeon with highly potential invasive risk. And those areas for goldfish will be the countries of Argentina and Brazil in South America, Guinea and Cameroon in West Africa. Our results also suggest that the use of Maxent with comprehensive distribution data and high resolution environmental data represents a new and valid method for obtaining early warnings serving to prevent the danger of biological invasion.

Key words: Carassius auratus, Pseudorasbora parva, potential distribution area, ecological risk, Maxent, biological invasion

张熙骜, 隋晓云, 吕植, 陈毅峰 (2014) 基于Maxent的两种入侵性鱼类(麦穗鱼和鲫)的全球适生区预测. 生物多样性, 22, 182-188. DOI: 10.3724/SP.J.1003.2014.13163.

Xi’ao Zhang,Xiaoyun Sui,Zhi Lü,Yifeng Chen (2014) A prediction of the global habitat of two invasive fishes (Pseudorasbora parva and Carassius auratus) from East Asia using Maxent. Biodiversity Science, 22, 182-188. DOI: 10.3724/SP.J.1003.2014.13163.

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链接本文: https://www.biodiversity-science.net/CN/10.3724/SP.J.1003.2014.13163

https://www.biodiversity-science.net/CN/Y2014/V22/I2/182

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代码 Code	数据描述	Data description
BIO1	年均温	Annual mean temperature
BIO2	平均温度月较差	Mean diurnal range
	(月最高温与月最低温的平均差)	(Mean of monthly (max temp–min temp))
BIO3	等温线	Isothermality (BIO2/BIO7) (* 100)
BIO4	温度季节变化	Temperature seasonality (standard deviation *100)
BIO5	最暖月最高温度	Max temperature of warmest month
BIO6	最冷月最低温度	Min temperature of coldest month
BIO7	温度全年波动范围	Temperature annual range (BIO5–BIO6)
BIO8	最湿润季节平均温度	Mean temperature of wettest quarter
BIO9	最干燥季节平均温度	Mean temperature of driest quarter
BIO10	最暖月平均温度	Mean temperature of warmest month
BIO11	最冷月平均温度	Mean temperature of coldest month
BIO12	年降水量	Annual precipitation
BIO13	最湿润月降雨量	Precipitation of wettest month
BIO14	最干燥月降雨量	Precipitation of driest month
BIO15	各季节降雨量	Precipitation seasonality (coefficient of variation)
BIO16	最湿润季节降雨量	Precipitation of wettest quarter
BIO17	最干燥季节降雨量	Precipitation of driest quarter
BIO18	最暖季节平均温度	Mean temperature of warmest quarter
BIO19	最冷季节平均温度	Mean temperature of coldest quarter
Tmean1–12	1–12月平均温度	Average monthly mean temperature
Tmin1–12	1–12月最小温度	Average monthly minimum temperature
Tmax1–12	1–12月最大温度	Average monthly maximum temperature
Prec1–12	1–12月平均降雨量	Average monthly precipitation
Alt	高程	Altitude (elevation above sea level)

代码 Code	数据描述	Data description
BIO1	年均温	Annual mean temperature
BIO2	平均温度月较差	Mean diurnal range
	(月最高温与月最低温的平均差)	(Mean of monthly (max temp–min temp))
BIO3	等温线	Isothermality (BIO2/BIO7) (* 100)
BIO4	温度季节变化	Temperature seasonality (standard deviation *100)
BIO5	最暖月最高温度	Max temperature of warmest month
BIO6	最冷月最低温度	Min temperature of coldest month
BIO7	温度全年波动范围	Temperature annual range (BIO5–BIO6)
BIO8	最湿润季节平均温度	Mean temperature of wettest quarter
BIO9	最干燥季节平均温度	Mean temperature of driest quarter
BIO10	最暖月平均温度	Mean temperature of warmest month
BIO11	最冷月平均温度	Mean temperature of coldest month
BIO12	年降水量	Annual precipitation
BIO13	最湿润月降雨量	Precipitation of wettest month
BIO14	最干燥月降雨量	Precipitation of driest month
BIO15	各季节降雨量	Precipitation seasonality (coefficient of variation)
BIO16	最湿润季节降雨量	Precipitation of wettest quarter
BIO17	最干燥季节降雨量	Precipitation of driest quarter
BIO18	最暖季节平均温度	Mean temperature of warmest quarter
BIO19	最冷季节平均温度	Mean temperature of coldest quarter
Tmean1–12	1–12月平均温度	Average monthly mean temperature
Tmin1–12	1–12月最小温度	Average monthly minimum temperature
Tmax1–12	1–12月最大温度	Average monthly maximum temperature
Prec1–12	1–12月平均降雨量	Average monthly precipitation
Alt	高程	Altitude (elevation above sea level)

基于Maxent的两种入侵性鱼类(麦穗鱼和鲫)的全球适生区预测

A prediction of the global habitat of two invasive fishes (Pseudorasbora parva and Carassius auratus) from East Asia using Maxent

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