Spatial and Seasonal Variations in Invertebrate Communities in the Chai River Based on eDNA Biomonitoring
Bibliographic record
Abstract
As environmental indicators, invertebrate communities are closely related to changes in the water environment. Efficient and accurate monitoring of invertebrates is of great significance for providing references for water environment conservation. However, environmental DNA metabarcoding has rarely been used in invertebrate research at the Chai River in Kunming, Yunnan, China, and the current characteristics of invertebrate communities are unclear. Therefore, this study investigated the spatial and seasonal patterns of invertebrates and the environmental stressors of the Chai River. Based on eDNA metabarcoding, 873 ASVs of invertebrates belonging to Annelida, Arthropoda, Cnidaria, Gastrotricha, Mollusca, Nematoda, Platyhelminthes, Protozoa, and Rotifera were identified, with Arthropoda being the absolute dominant phylum. Distinct spatial and seasonal variations in the invertebrate communities (e.g., ASV number, dominant genera, relative abundances) were observed. Macrothrix and Acanthamoeba were the first and second most dominant genera, both in dry and wet periods. A spatial–seasonal heterogeneity of the relation between the invertebrate communities and environmental factors was observed in the Chai River. The water temperature (WT), chemical oxygen demand (COD), conductivity (C), and Chlorophyll-a (Chl-a) levels were deemed to be the crucial environmental factors influencing the distributions of invertebrate communities in the Chai River, which was consistent with the spatial and seasonal differences in pollution characteristics around the Chai River. This study provides insights into conserving the diversity of invertebrate communities and the management of the Chai River and similar agricultural rivers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".