| 传统形态学与环境核酸技术在生物多样性监测中的分歧:从技术分化到范式革新 |
| Divergence Between Traditional Morphology and Environmental Nucleic Acids-Based Techniques in Biodiversity Monitoring:From Technical Divergence to Paradigm Innovation |
| 投稿时间：2025-10-27 修订日期：2025-12-01 |
| DOI：10.19316/j.issn.1002-6002.2026.01.03 |
| 中文关键词: 生物多样性 形态学 eDNA eRNA 研究范式 |
| 英文关键词:biodiversity traditional morphology eDNA eRNA paradigm |
| 基金项目:京津冀环境综合治理国家科技重大专项(2025ZD1200800,2025ZD1207600) |
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| 中文摘要: |
| 传统形态学方法与环境核酸技术在生物多样性监测中常产生结果差异,引发对新技术可靠性的质疑。作者系统分析了两类方法在监测对象、时空代表性等方面的差异,指出结果差异源于研究范式的系统性不同,而非技术本身的不可靠性;提出系统性监测应整合eDNA/eRNA的"时空广度""预警能力"与传统形态学的"定量性",以提升多样性监测的全面性与可靠性。在未来多源大数据融合的背景下,生物多样性监测正经历以"大数据驱动与应用需求"为核心的范式转型。这一转型并非依赖两类技术的简单互补即可实现,而需超越技术层面的形式整合,构建以"环境核酸信号流"为核心的生物多样性信息技术体系,将连续的分子信号转译为可量化、可解释的生物多样性与生态学参数。通过融合生态学、模型科学、时空统计与人工智能,实现生物信号从采样到反演的全链条解析,并注入多维度生态系统韧性与可塑性理论,揭示物种互作与环境响应的动态机制,推动生物多样性监测由"数据采集"向"生态认知"、由"形态描述"向"机制解析"转变,最终形成多源数据语义同化与模型耦合的"信号流驱动的生态认知"新研究范式。 |
| 英文摘要: |
| Traditional morphology and environmental nucleic acids-based techniques often yield differed results in biodiversity monitoring,raising concerns on the reliability of molecular approaches.This study systematically discusses the differences between these two approaches in terms of monitoring targets,spatiotemporal representation,and technical workflows,showing that such discrepancies arise from fundamental paradigm differences rather than methodological unreliability.Thus,systematic biodiversity monitoring should integrate the spatiotemporal breadth and early-warning capability of eDNA/eRNA with the precision and quantitative accuracy of morphological methods to enhance the comprehensiveness and reliability of biodiversity monitoring.In the context of future multi-source big data integration,biodiversity monitoring is undergoing a paradigm shift driven by big data and wide application needs.Addressing this transformation requires more than technical complementarity,calling for a biodiversity information technology framework centered on environmental molecular signal flow to translate continuous molecular signals into quantifiable and interpretable biodiversity and ecological parameters.By integrating ecological principles,modeling,spatiotemporal statistics,and artificial intelligence,full-chain analytical tools can be developed to trace molecular signals from sampling through decay to inversion.Furthermore,by incorporating multidimensional theories of ecosystem resilience and plasticity,it helps reveal the dynamic mechanisms of species interactions,functional group dynamics,and environmental responses.Ultimately,this approach advances biodiversity monitoring to evolove from data collection to ecological cognition,and from morphological description to mechanistic innovation,forming a new "signal flow-driven ecological cognition paradigm" that unifies multi-source data through semantic assimilation and model coupling. |
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