Mechanistic Multitrophic Insight on Seasonal Hypoxia Impacts in Urbanized Estuarine Ecosystems
Bibliographic record
Abstract
Seasonal hypoxia is intensifying in urbanized estuaries worldwide, posing escalating threats to biodiversity and ecosystem functioning across food webs. Despite growing concern, how oxygen depletion alters ecological dynamics across trophic levels remains poorly understood, highlighting the urgent need for integrated and multitrophic assessments. Here, we conducted a comprehensive analysis in the Pearl River Estuary, a globally representative urbanized estuary, by integrating multidimensional data sets including environmental DNA (eDNA) and traditional plankton monitoring, species functional traits, biomass and water quality. Our findings reveal pronounced community reassembly under hypoxic conditions, characterized by increases in pollution-tolerant and invasive taxa, and concurrent declines of sensitive taxa. While α -diversity increased significantly across all taxonomic groups (median increased 35%–65%), β -diversity declined, indicating community homogenization to some extent under hypoxia stress. The complexity of organismal networks declined under hypoxia, with decreases of 12%–30% in node number, average degree, and clustering coefficient, and a >70% loss of fish nodes; microbial taxa increasingly occupied central network positions. Structural equation modeling identified that hypoxia disrupted internal regulatory pathways, shifting the system from biodiversity-driven top-down control to bottom-up dominance, effectively decoupling biodiversity from ecosystem functions. Overall, this study provides one of the few multitrophic frameworks to mechanistically elucidate how seasonal hypoxia restructures biodiversity, weakens trophic regulation, and compromises ecosystem resilience in urbanized estuarine systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".