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The global ocean is currently facing a severe challenge of “cryptic ecosystem degradation”, characterized by subtle but progressive declines in ecosystem integrity. Conventional water quality monitoring often indicates regulatory “compliance”, while underlying processes such as food web disruption, loss of keystone species, and functional degradation of ecosystems continue largely undetected. This management paradox arises from traditional assessment paradigms that emphasize physicochemical conditions while overlooking biological structure and ecosystem functionality. This study proposes a target-driven framework for marine ecosystem health assessment, aiming to facilitate a paradigm shift in environmental management from “comprehensive census” approaches to “precision-based diagnostic” strategies. Departing from fixed indicator frameworks, the proposed system introduces a “three-level funnel” generation logic: (i) identification of core ecological issues based on specific management objectives, (ii) selection of key biological components and associated indicators, and (iii) design of an optimal spatiotemporal observation strategy. Guided by the principles of usability, effectiveness and adequacy, the framework is supported by an integrated “space-air-sea-intelligence” technological network and enables the transformation of observational data into actionable decision-making knowledge through an intelligent diagnostic engine. Case studies demonstrate that the framework can dynamically generate customized assessment schemes tailored to diverse management objectives, including “overall health assessment, ” “aquaculture carrying capacity evaluation”, and “disaster early warning”. Among these, the classical triad-based Index of Biotic Integrity (Z/F/B-IBI), encompassing “zooplankton-fish-benthos” represents the optimal scheme generated by the framework for achieving “regional-scale ecosystem health assessment”. Overall, the proposed system aims to maximize management efficiency while minimizing observational costs, thereby providing a dynamically adaptable, “precision medicine-like” solution for ecosystem-based marine management.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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