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入侵科学的概念分类方案。

A conceptual classification scheme of invasion science.

作者信息

Musseau Camille L, Bernard-Verdier Maud, Heger Tina, Skopeteas Leonidas H, Strasiewsky David, Mietchen Daniel, Jeschke Jonathan M

机构信息

Department of Evolutionary and Integrative Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB).

Department of Biology, Chemistry, Pharmacy, Institute of Biology, at Freie Universität Berlin (FUB).

出版信息

Bioscience. 2024 Oct 26;74(12):840-850. doi: 10.1093/biosci/biae093. eCollection 2024 Dec.

Abstract

In the era of big data and global biodiversity decline, there is a pressing need to transform data and information into findable and actionable knowledge. We propose a conceptual classification scheme for invasion science that goes beyond hypothesis networks and allows to organize publications and data sets, guide research directions, and identify knowledge gaps. Combining expert knowledge with literature analysis, we identified five major research themes in this field: introduction pathways, invasion success and invasibility, impacts of invasion, managing biological invasions, and meta-invasion science. We divided these themes into 10 broader research questions and linked them to 39 major hypotheses forming the theoretical foundation of invasion science. As artificial intelligence advances, such classification schemes will become important references for organizing scientific information. Our approach can be extended to other research fields, fostering cross-disciplinary connections to leverage the scientific knowledge needed to address Anthropocene challenges.

摘要

在大数据时代以及全球生物多样性下降的背景下,迫切需要将数据和信息转化为可查找且可付诸行动的知识。我们提出了一种入侵科学的概念分类方案,该方案超越了假设网络,能够对出版物和数据集进行组织,指导研究方向,并识别知识空白。通过将专家知识与文献分析相结合,我们确定了该领域的五个主要研究主题:引入途径、入侵成功与可入侵性、入侵的影响、生物入侵管理以及元入侵科学。我们将这些主题细分为10个更广泛的研究问题,并将它们与构成入侵科学理论基础的39个主要假设相联系。随着人工智能的发展,此类分类方案将成为组织科学信息的重要参考。我们的方法可以扩展到其他研究领域,促进跨学科联系,以利用应对人类世挑战所需的科学知识。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abd9/11660931/9ba09e53e2cc/biae093fig1.jpg

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