Context-Dependent Responses of Aquatic Insects to Metals and Metal Mixtures: A Quantitative Analysis Summarizing 24 Yr of Stream Mesocosm Experiments
Bibliographic record
Abstract
Modernizing water quality criteria to predict how contaminants affect natural aquatic communities requires that we utilize data obtained across multiple lines of evidence, including laboratory, mesocosm, and field studies. We report the results of 29 mesocosm experiments conducted from 1994 to 2017 at the Colorado State University Stream Research Laboratory (Fort Collins, CO, USA). The primary goal of the present study was to quantify responses of aquatic insect communities collected from 8 different locations to different combinations of cadmium (Cd), copper (Cu), iron (Fe), and zinc (Zn). Treatments that included Cu or Fe, either alone or in combination with other metals, were especially toxic to aquatic insects. The results showed that effects of metals were context dependent and varied significantly among the 8 sites where communities were collected. In particular, effects on communities from smaller streams were significantly greater than those from larger streams. Our analyses also showed that several morphological (body size, shape, gills, degree of sclerotization) and life history (voltinism) traits were significantly correlated with sensitivity to metals. Across all taxa and experiments, aquatic insects broadly classified as small (maximum body length <8 mm) were significantly more sensitive to metals than medium or large individuals. These findings demonstrate the advantages of integrating results of mesocosm experiments with species traits to develop a mechanistic understanding of biotic and abiotic factors that influence community responses to contaminants. Environ Toxicol Chem 2019;38:2486-2496. © 2019 SETAC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".