Spatio-seasonal variation in wetland water quality, heavy metal pollution and macroinvertebrate communities in the Waterberg Mountain Complex
Bibliographic record
Abstract
Wetlands in semi-arid southern Africa are increasingly threatened by combined climatic and anthropogenic stressors, yet seasonal data on water quality and biota remain scarce. We assessed 11 wetlands in the Waterberg Mountain Complex, Limpopo Province sampling during early rains and late rains. At each site we measured in-situ physico-chemical variables, quantified water-sediment heavy metals and collected benthic macroinvertebrates. Dissolved oxygen was the only physico-chemical variable showing a significant seasonal decline (11.7 mg/l early rains to 5.8 mg/l in late rains; p < 0.05). Iron exceeded Canadian guidelines in 64% of LR samples (max = 22 mg/l). Cadmium exhibited the greatest seasonal increase in sediments (p < 0.01). Diptera dominated macroinvertebrate assemblages particularly at the most metal-enriched site whereas Ephemeroptera, Trichoptera and Odonata were abundant in wetlands with higher oxygen and lower metal loads. Canonical correspondence analysis linked turbidity, conductivity and temperature with tolerant taxa (Hemiptera, Hydracarina), whereas redundancy analysis indicated zinc and cadmium strongly structured communities at polluted sites. These findings highlight oxygen limitation and localized Fe–Cr–Cd enrichment as key stressors influencing macroinvertebrate diversity. As the first integrated seasonal assessment for Waterberg wetlands, the study provides a baseline for monitoring systems facing intensifying land-use and climate pressures and underscores the need for continued multi-season biomonitoring to guide adaptive management.
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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".