1. Many ecological decisions are slowed by the gap between collecting and analysing biodiversity data. Edge computing moves processing closer to the sensor, with edge artificial intelligence (AI) enabling on-device inference, reducing reliance on data transfer and continuous connectivity. In principle, this shifts biodiversity monitoring from passive logging towards autonomous, responsive sensing systems. In practice, however, adoption remains fragmented, with key architectural trade-offs, performance constraints, and implementation challenges rarely reported systematically. 2. Here, we analyse 82 studies published between 2017 and 2025 that implement edge computing for biodiversity monitoring across acoustic, vision, tracking, and multi-modal systems. We synthesise hardware platforms, AI model optimisation, and wireless communication to critically assess how design choices shape ecological inference, deployment longevity, and operational feasibility. 3. Publications increased from 3 in 2017 to 19 in 2025. We identify four system types: (I) TinyML, low-power microcontrollers (MCUs) for single-taxon or rare-event detection; (II) Edge AI, single-board computers (SBCs) for multi-species classification and real-time alerts; (III) Distributed edge AI; and (IV) Cloud AI for retrospective processing pipelines. Each system type represents context-dependent trade-offs among power consumption, computational capability, and communication requirements. 4. Our analysis reveals the evolution of edge computing systems from proof-of-concept to robust, scalable tools. We argue that edge computing offers opportunities for responsive biodiversity management, but realising this potential requires increased collaboration between ecologists, engineers, and data scientists to align model development and system design with ecological questions, field constraints, and ethical considerations.

翻译：1. 许多生态决策因生物多样性数据收集与分析之间的脱节而进展缓慢。边缘计算将处理过程移至传感器近端，边缘人工智能（AI）支持设备端推理，从而降低了对数据传输和持续连接的依赖。从原理上讲，这使生物多样性监测从被动记录转向自主、响应的感知系统。然而在实践中，其应用仍呈碎片化，关键架构权衡、性能约束和实施挑战鲜有被系统性地报告。2. 本文分析了2017年至2025年间发表的82项研究，这些研究在声学、视觉、追踪及多模态系统中实施了用于生物多样性监测的边缘计算。我们综合评估了硬件平台、AI模型优化和无线通信，以批判性分析设计选择如何影响生态推断、部署寿命和操作可行性。3. 相关出版物从2017年的3篇增至2025年的19篇。我们识别出四种系统类型：（I）用于单一类群或稀有事件检测的TinyML低功耗微控制器（MCU）；（II）用于多物种分类和实时警报的Edge AI单板计算机（SBC）；（III）分布式边缘AI；以及（IV）用于回顾性处理流程的云AI。每种系统类型均体现了功耗、计算能力和通信需求之间依赖于具体情境的权衡。4. 我们的分析揭示了边缘计算系统从概念验证向稳健、可扩展工具的演进。我们认为，边缘计算为响应式生物多样性管理提供了机遇，但实现这一潜力需要生态学家、工程师和数据科学家之间加强协作，以使模型开发和系统设计与生态问题、野外约束及伦理考量相一致。