Understanding gaps in early detection of and rapid response to invasive species in the United States: A literature review and bibliometric analysis

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY Ecological Informatics Pub Date : 2024-10-24 DOI:10.1016/j.ecoinf.2024.102855
Amy K. Wray , Aimee C. Agnew , Mary E. Brown , E.M. Dean , Nicole D. Hernandez , Audrey Jordon , Cayla R. Morningstar , Sara E. Piccolomini , Harrison A. Pickett , Wesley M. Daniel , Brian E. Reichert
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Abstract

While concepts regarding invasive species establishment patterns and eradication possibilities have long been a topic of invasion biology, the specific terminology referring to early detection of and rapid response to (EDRR) invasive species emerged in scientific literature during the early 2000s. Since then, the EDRR approach has expanded to include a suite of detection, planning, and management tools. By conducting a systematic literature review, we attempt to characterize the field of EDRR in the United States and its territories as reflected by publication records. Specifically, we assessed publication data such as the number of publications per year, the most common journals where papers were published, and the relationship between author's keywords for studies focusing on aquatic and terrestrial habitats. For publications that used invasive species occurrence or abundance data (whether collected for the purposes of the respective publication or acquired from another data source), we manually vetted additional information such as focal taxa, data collection years and locations, sources of other data used, and whether data or code were deposited in open access formats. We also conducted network analyses for the author institutions that coauthored papers together most frequently and for the references most cited by EDRR publications. Overall, we found that silos existed in terms of which author institutions worked together, which existing literature was cited, and which topics were frequently explored. We also found evidence of substantial gaps in data access and use. For example, although a wide variety of data sources for invasive species occurrences are available, these sources were seldom cited by published literature, and newly collected data were not often deposited into invasive species databases or other open-source data repositories. Considering the continued advocation for a centralized national EDRR information system, our study indicates that facilitating access to data, decision support tools, and other informational resources represents a key opportunity for improving EDRR capabilities.
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了解美国在早期发现和快速应对入侵物种方面的差距:文献综述和文献计量分析
虽然有关入侵物种的建立模式和根除可能性的概念早已成为入侵生物学的一个主题,但有关入侵物种的早期检测和快速反应(EDRR)的具体术语却出现在本世纪初的科学文献中。从那时起,EDRR 方法已经扩展到一整套检测、规划和管理工具。通过进行系统的文献综述,我们试图从出版记录中了解美国及其属地的 EDRR 领域的特点。具体来说,我们评估了出版物数据,如每年的出版物数量、最常见的论文发表期刊,以及针对水生和陆生栖息地研究的作者关键词之间的关系。对于使用了入侵物种发生率或丰度数据(无论是为发表论文而收集的数据,还是从其他数据源获取的数据)的论文,我们通过人工方式审核了其他信息,如重点分类群、数据收集年份和地点、所使用的其他数据来源,以及数据或代码是否以开放存取格式存放。我们还对最常合作发表论文的作者机构以及 EDRR 出版物引用最多的参考文献进行了网络分析。总体而言,我们发现在哪些作者机构合作、哪些现有文献被引用以及哪些主题经常被探讨等方面存在孤岛现象。我们还发现在数据获取和使用方面存在巨大差距。例如,虽然入侵物种出现的数据来源多种多样,但这些来源很少被发表的文献引用,新收集的数据也不经常存入入侵物种数据库或其他开放源数据存储库。考虑到建立一个集中的国家环境、经济和社会风险评估与报告信息系统的呼声不绝于耳,我们的研究表明,促进数据、决策支持工具和其他信息资源的获取是提高环境、经济和社会风险评估与报告能力的关键机会。
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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