用AI提升城市生物多样性监测与保护效率
Artificial Intelligence for Sustainable Urban Biodiversity: A Framework for Monitoring and Conservation
- 融合遥感、声学与公众科学数据，实现大规模生态分析
- AI辅助决策使保护规划预测准确率提升18.5%
- 适合城市生态管理、政策制定者及环境科技研究者
城市快速扩张对生物多样性保护构成挑战，亟需创新的生态系统管理手段。本研究探讨人工智能（AI）在城市生物多样性保护中的作用、应用及实施框架。结果显示：(a) AI显著提升物种检测与监测能力，在城市野生动物追踪和外来物种管理中准确率超90%；(b) 融合遥感、声学监测与公民科学数据，支持大尺度生态系统分析；(c) AI决策工具优化保护规划与资源分配，相比传统方法预测准确率最高提升18.5%。研究提出面向城市生物多样性管理的AI驱动框架，强调其在监测、保护策略与生态成效方面的价值。实施建议包括：(a) 标准化数据采集与模型验证，(b) 确保不同城市环境中AI的公平可及性，(c) 制定生物多样性监测伦理准则。研究结论指出，整合AI需平衡技术创新与生态智慧，应对数据质量、社会经济差异与伦理问题。
原文摘要 · Abstract (English)
The rapid expansion of urban areas challenges biodiversity conservation, requiring innovative ecosystem management. This study explores the role of Artificial Intelligence (AI) in urban biodiversity conservation, its applications, and a framework for implementation. Key findings show that: (a) AI enhances species detection and monitoring, achieving over 90% accuracy in urban wildlife tracking and invasive species management; (b) integrating data from remote sensing, acoustic monitoring, and citizen science enables large-scale ecosystem analysis; and (c) AI decision tools improve conservation planning and resource allocation, increasing prediction accuracy by up to 18.5% compared to traditional methods. The research presents an AI-Driven Framework for Urban Biodiversity Management, highlighting AI's impact on monitoring, conservation strategies, and ecological outcomes. Implementation strategies include: (a) standardizing data collection and model validation, (b) ensuring equitable AI access across urban contexts, and (c) developing ethical guidelines for biodiversity monitoring. The study concludes that integrating AI in urban biodiversity conservation requires balancing innovation with ecological wisdom and addressing data quality, socioeconomic disparities, and ethical concerns.
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