Tymoteusz Miller, Grzegorz Michoński, Irmina Durlik, Polina Kozlovska, Paweł Biczak
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引用次数: 0
Abstract
Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative and scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, and conservation planning. This systematic review follows the PRISMA framework to analyze AI applications in freshwater biodiversity studies. Using a structured literature search across Scopus, Web of Science, and Google Scholar, we identified 312 relevant studies published between 2010 and 2024. This review categorizes AI applications into species identification, habitat assessment, ecological risk evaluation, and conservation strategies. A risk of bias assessment was conducted using QUADAS-2 and RoB 2 frameworks, highlighting methodological challenges, such as measurement bias and inconsistencies in the model validation. The citation trends demonstrate exponential growth in AI-driven biodiversity research, with leading contributions from China, the United States, and India. Despite the growing use of AI in this field, this review also reveals several persistent challenges, including limited data availability, regional imbalances, and concerns related to model generalizability and transparency. Our findings underscore AI's potential in revolutionizing biodiversity monitoring but also emphasize the need for standardized methodologies, improved data integration, and interdisciplinary collaboration to enhance ecological insights and conservation efforts.
淡水生态系统日益受到气候变化和人为活动的威胁,需要创新和可扩展的监测解决方案。人工智能(AI)已成为水生生物多样性研究的变革性工具,可实现自动物种识别、预测栖息地建模和保护规划。本系统综述遵循PRISMA框架,分析人工智能在淡水生物多样性研究中的应用。通过对Scopus、Web of Science和b谷歌Scholar的结构化文献检索,我们确定了2010年至2024年间发表的312项相关研究。本文综述了人工智能在物种鉴定、生境评价、生态风险评价和保护策略等方面的应用。使用QUADAS-2和RoB 2框架进行了偏倚风险评估,强调了方法学上的挑战,如模型验证中的测量偏倚和不一致性。引用趋势表明,人工智能驱动的生物多样性研究呈指数级增长,其中中国、美国和印度的贡献最大。尽管人工智能在这一领域的应用越来越多,但本综述也揭示了一些持续存在的挑战,包括有限的数据可用性、区域失衡以及与模型通用性和透明度相关的问题。我们的研究结果强调了人工智能在彻底改变生物多样性监测方面的潜力,但也强调了标准化方法、改进数据整合和跨学科合作的必要性,以增强生态洞察力和保护工作。
期刊介绍:
Biology (ISSN 2079-7737) is an international, peer-reviewed, quick-refereeing open access journal of Biological Science published by MDPI online. It publishes reviews, research papers and communications in all areas of biology and at the interface of related disciplines. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files regarding the full details of the experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.