论文
论文标题: Assessing the conservation status of Chinese freshwater fish using deep learning
作者: Chen, Jinnan; Ding, Chengzhi; He, Dekui; Ding, Liuyong; Ji, Songhao; Du, Tingqi; Sun, Jingrui; Huang, Minrui; Tao, Juan
出版刊物: REVIEWS IN FISH BIOLOGY AND FISHERIES
出版日期: JUL 8
出版年份: 2023
卷/期:
DOI: 10.1007/s11160-023-09792-5
论文摘要: The lack of information on the extinction risk of most species is a fundamental challenge in prioritizing conservation strategies and bending the curve of current biodiversity decline. Machine learning methods have shown promising potential to fill this gap, but their applicability remains to be validated at different taxa (especially aquatic species) and spatial scales. We assessed the extinction risk of 1162 freshwater fish species in China that have not yet been included in the latest IUCN Red List using multiple neural network algorithms based on datasets of species occurrences, biological traits, phylogeny, and relevant environmental layers. The best deep learning models dramatically improved the assessment coverage from 29.9% (496 species) to 93.2-93.9% (1545-1557 species) of the whole fauna with an accuracy of 95.4-99.0%. By combining our prediction results with the IUCN Red List, we found that 23.8-26.5% (394-440 species) of Chinese freshwater fishes were identified as possibly threatened species, which is roughly four times the IUCN assessment. Newly assessed species and threatened species were mainly from the orders Cypriniformes (prediction added: 837-846 species; final threatened: 325-350 species), Siluriformes (113-122; 28-37) and Perciformes (74-76; 18-25). The increase in threatened species richness based on predictions was led by the upper reaches of the Pearl and Yangtze. Overall, our findings suggest that deep learning algorithms can provide robust and time-saving assessments of extinction risk for entire freshwater fish fauna on a large national scale, thereby facilitating relevant conservation prioritization.
== 实验室与学会 ==
  • == 实验室与学会 ==
  • 淡水生态与生物技术国家重点实验室
  • 中国科学院藻类生物学重点实验室
  • 农业部淡水养殖病害防治重点实验室
  • 武汉白暨豚保护基金会
  • 湖北省海洋湖沼学会
  • 中国动物学会原生动物学分会
  • 中国动物学会斑马鱼分会
  • 湖北省暨武汉动物学会
  • 中国水产学会鱼病学专业委员会
  • 中国鱼类学会
== 平台建设 ==
  • == 平台建设 ==
  • “一带一路”海域赤潮数据库
  • 国家水生生物种质资源库
  • 国家斑马鱼资源中心
  • 中国科学院淡水藻种库
  • 中国科学院武汉生命科学大型仪器区域中心
  • 湿地生态系统观测研究野外站联盟
  • 中国科学院水生生物研究所分析测试中心
  • 中国科学院超级计算武汉分中心
  • 水生生物博物馆
== 相关网站推荐 ==
  • == 相关网站推荐 ==
  • 中国科学院
  • 农业农村部
  • 科学技术部
  • 生态环境部
  • 国家自然科学基金委员会
  • 中国科学院武汉分院
  • 湖北省科学技术厅
  • 湖北省生态环境厅
  • 湖北省农业农村厅