Relationships between water quality and mosquito presence and abundance: a systematic review and meta-analysis
Bibliographic record
Abstract
Mosquito-borne diseases (MBDs) are emerging in response to climate and land use changes. As mosquito (Diptera: Culicidae) habitat selection is often contingent on water availability for egg and larval development, studies have recognized water quality also influences larval habitats. However, underlying species-, genera-, and mosquito level preferences for water quality conditions are varied. This systematic review and meta-analysis aimed to identify, characterize, appraise, and synthesize available global data on the relationships between water quality and mosquito presence and abundance (MPA); with the goal to further our understanding of the geographic expansion of MBD risks. A systematic review was conducted to identify studies investigating the relationships between water quality properties and MPA. Where appropriate, random-effects meta-analyses were conducted to provide pooled estimates for the association between the most reported water quality properties and MPA. The most reported water quality parameters were pH (87%), nitrogen concentrations (56%), turbidity (56%), electrical conductivity (54%), dissolved oxygen (43%), phosphorus concentrations (30%), and alkalinity (10%). Overall, pH (P = 0.05), turbidity (P < 0.0001), electrical conductivity (P = 0.005), dissolved oxygen (P < 0.0001), nitrogen (P < 0.0001), and phosphorus (P < 0.0001) showed significantly positive pooled correlations with MPA, while alkalinity showed a nonsignificant null pooled correlation (P = 0.85). We observed high heterogeneity in most meta-analyses, and climate zonation was shown to influence the pooled estimates. Linkages between MPA and water quality properties will enhance our capacity to predict MBD risks under changing environmental and land use changes.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".