生物多样性
海洋生物多样性
化石记录
比例(比率)
透视图(图形)
古生物学
无脊椎动物
古生代
无脊椎动物
生态学
地质记录
环境资源管理
数据科学
地质学
地理
计算机科学
环境科学
生物
地图学
人工智能
作者
Junxuan Fan,Shu‐zhong Shen,Douglas H. Erwin,Peter M. Sadler,Norman MacLeod,Qiuming Cheng,Xudong Hou,Jiao Yang,Xiangdong Wang,Yue Wang,Hua Zhang,Xu Chen,Guoxiang Li,Yichun Zhang,Yukun Shi,Dong‐xun Yuan,Qing Chen,Linna Zhang,Chao Li,Yingying Zhao
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2020-01-17
卷期号:367 (6475): 272-277
被引量:431
标识
DOI:10.1126/science.aax4953
摘要
A finer record of biodiversity We have pressing, human-generated reasons to explore the influence of environmental change on biodiversity. Looking into the past can not only inform our understanding of this relationship but also help us to understand current change. Paleontological records depend on fossil availability and predictive modeling, however, and thus tend to give us a picture with large temporal jumps, millions of years wide. Such a scale makes it difficult to truly understand the action of environmental forces on ecological processes. Enabled by a supercomputer, Fan et al. used machine learning to analyze a large marine Paleozoic dataset, creating a record with time intervals of only ∼26,000 years (see the Perspective by Wagner). This fine-scale resolution revealed new events and important details of previously described patterns. Science , this issue p. 272 ; see also p. 249
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