Automated biodiversity research critically requires multidisciplinary expertise.

IF 16.7 1区生物学 Q1 ECOLOGY

Trends in ecology & evolution Pub Date : 2025-05-26 DOI:10.1016/j.tree.2025.05.003

Pedro Cardoso, Vasco Branco, Luís Correia, Brent C Emerson, Veronika N Laine, Manuel Lopes

引用次数: 0

Abstract

Data-driven computational methods in taxonomy, ecology, and conservation biology are revolutionising these disciplines, from data collection to analysis and interpretation. However, effective automation requires both critical thinking about the limitations of the data and methods, and thorough training and validation by experts, supported by strong interdisciplinary collaboration.

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自动化生物多样性研究迫切需要多学科专业知识。

分类学、生态学和保护生物学中数据驱动的计算方法正在彻底改变这些学科，从数据收集到分析和解释。然而，有效的自动化既需要对数据和方法的局限性进行批判性思考，也需要专家进行彻底的培训和验证，并得到强大的跨学科合作的支持。

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 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.