Accumulation of Current-Use Pesticides in Neotropical Montane Forests
Bibliographic record
Abstract
In Central America, chemical-intensive tropical agriculture takes place in close proximity to highly valued and biologically diverse ecosystems, yet the potential for atmospheric transport of pesticides from plantations to national parks and other reserves is poorly characterized. The specific meteorological conditions of mountain ranges can lead to contaminant convergence at high altitudes, raising particular concern for montane forest ecosystems downwind from pesticide use areas. Here we show, based on a wide-ranging air and soil sampling campaign across Costa Rica, that soils in some neotropical montane forests indeed display much higher concentrations of currently used pesticides than soils elsewhere in the country. Specifically, elevated concentrations of the fungicide chlorothalonil, the herbicide dacthal, and the insecticide metabolite endosulfan sulfate on volcanoes Barva and Poas, lying directly downwind of the extensive banana plantations of the Caribbean lowland, indicate the occurrence of atmospheric transport and wet deposition of pesticides at high altitudes. Calculations with a contaminant fate model, designed for mountain regions and parametrized to the Costa Rican environment, show that chemicals with a log K(AW) between -3 and -5 have a greater potential for accumulation at high altitudes. This enrichment behavior is quantified by the Mountain Contamination Potential and is sensitive to contaminant degradability. The modeling work supports the hypothesis suggested by the field results that it is enhanced precipitation scavenging at high elevations (caused by lower temperatures and governed by K(AW)) that causes pesticides to accumulate in tropical montane areas. By providing for the first time evidence of significant transfer of currently used pesticides to Central American montane cloud forests, this study highlights the need to evaluate the risk that tropical agricultural practices place on the region's ecological reserves.
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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 | 0.000 |
| 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".