AI-based discovery of habitats from museum collections.

IF 16.7 1区 生物学 Q1 ECOLOGY Trends in ecology & evolution Pub Date : 2024-04-01 Epub Date: 2024-02-13 DOI:10.1016/j.tree.2024.01.006
Christopher B Jones, Kristin Stock, Sarah E Perkins
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Abstract

Museum collection records are a source of historic data for species occurrence, but little attention is paid to the associated descriptions of habitat at the sample locations. We propose that artificial intelligence methods have potential to use these descriptions for reconstructing past habitat, to address ecological and evolutionary questions.

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基于人工智能的博物馆藏品生境发现。
博物馆收藏记录是物种出现的历史数据来源,但很少有人关注样本地点的相关栖息地描述。我们建议,人工智能方法有可能利用这些描述来重建过去的栖息地,从而解决生态和进化问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Trends in ecology & evolution
Trends in ecology & evolution 生物-进化生物学
CiteScore
26.50
自引率
3.00%
发文量
178
审稿时长
6-12 weeks
期刊介绍: Trends in Ecology & Evolution (TREE) is a comprehensive journal featuring polished, concise, and readable reviews, opinions, and letters in all areas of ecology and evolutionary science. Catering to researchers, lecturers, teachers, field workers, and students, it serves as a valuable source of information. The journal keeps scientists informed about new developments and ideas across the spectrum of ecology and evolutionary biology, spanning from pure to applied and molecular to global perspectives. In the face of global environmental change, Trends in Ecology & Evolution plays a crucial role in covering all significant issues concerning organisms and their environments, making it a major forum for life scientists.
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