Benthic Macroinvertebrates as Bioindicators of Stream Health Within the Central African Copperbelt
Bibliographic record
Abstract
Abstract Globally, stream ecosystem health in mining regions, including the Central African Copperbelt (CACB), is threatened by increased aquatic metal pollution. Stream biomonitoring and bioassessment offers an eco-friendly nature-based approach to determine the aquatic ecological conditions and inform management interventions in mining landscapes. This study conducted monthly between May 2022 and April 2023, explored the taxonomic and functional responses of macroinvertebrates as bioindicators of the ecological condition of four streams in the north-western Zambian Copperbelt of the CACB. 252 sediment samples were collected and analysed for metals. 144 macroinvertebrate samples were collected to determine their taxonomic and functional traits. ANOVA (analysis-of-variance), SIMPER (similarity-percentage), ANOSIM (analysis-of-similarities), CCA (canonical correspondence analysis) and multidimensional biotic index analyses were conducted to correlate environmental and biotic variables. Metal concentrations (mg/kg) in sediments followed the order As (5.27) < Pb (5.75) < Zn (44.6) < Ni (89.3) < Cu (187.5). From ANOVA, the macroinvertebrate community was significantly dominated by Insecta families, mostly Diptera (23.3%) and Coleoptera (16.7%). Trophically, predators significantly dominated in the order Odonata (34.7%) > Hemiptera (31.8%) > Diptera (18.9%). SIMPER depicted a strong influence of Cu, EC, velocity, sediment texture and organic matter on macroinvertebrate diversity and spatial distribution. ANOSIM indicated the highest community dissimilarity at the most impacted stream (KYABR). Habitat assessment metrics, including ZISS-1, ASPT, and EPT/Chironomidae ratio, registered a “good ecological status” with the presence of pollution-sensitive taxa (Helodidae and Perlidae), in concordance with environmental variables, confirming the suitability of macroinvertebrates as proxy bioindicators of stream health in mining landscapes. The study confirms the potential of macroinvertebrates, as potential bioindicators of stream health in mining landscapes. Their diversity and distribution is strongly influenced by Cu, EC, velocity, and sediment characteristics while habitat assessment metrics indicate a "good ecological status" despite moderate metal contamination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".