Legacy effects of four decades of insecticide applications on contemporary riverine benthic macroinvertebrates
Bibliographic record
Abstract
Insecticides have known effects on riverine benthic macroinvertebrate (BMI) assemblages. However, there is limited understanding of the legacy effects of insecticides, particularly in watersheds that received decades of historical applications. From 1952 to 1993, over 6.2 million ha in the province of New Brunswick (Canada) was treated with one to twelve different insecticides including dichlorodiphenyltrichloroethane (DDT), aminocarb, fenitrothion, and phosphamidon. Using a contemporary BMI dataset that included 274 sites within watersheds that cover 50 % of New Brunswick, we evaluated the relative importance of historical insecticide applications and contemporary environmental variables in explaining variability in BMI assemblages. We found that historical insecticides explained a significant, but small, amount of variation in contemporary assemblages. The number of insecticide applications showed a stronger association with BMI metrics than the total amount of insecticide(s) applied, though contemporary environmental measures such as urban land use, substrate size, geology, and climate all had more explanatory power than insecticide metrics. Our results suggested that while contemporary environmental variables and historical insecticide applications both affect BMI assemblage composition, the former often had a stronger role in structuring assemblages. Recognizing the influence of legacy stressors provides important context for understanding contemporary bioindicator responses to environmental change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".