Widespread agrochemicals differentially affect zooplankton biomass and community structure
Bibliographic record
Abstract
Anthropogenic environmental change is causing habitat deterioration at unprecedented rates in freshwater ecosystems. Despite increasing more rapidly than many other agents of global change, synthetic chemical pollution-including agrochemicals such as pesticides-has received relatively little attention in freshwater community and ecosystem ecology. Determining the combined effects of multiple agrochemicals on complex biological systems remains a major challenge, requiring a cross-field integration of ecology and ecotoxicology. Using a large-scale array of experimental ponds, we investigated the response of zooplankton community properties (biomass, composition, and diversity metrics) to the individual and joint presence of three globally widespread agrochemicals: the herbicide glyphosate, the neonicotinoid insecticide imidacloprid, and nutrient fertilizers. We tracked temporal variation in zooplankton biomass and community structure along single and combined pesticide gradients (each spanning eight levels), under low (mesotrophic) and high (eutrophic) nutrient-enriched conditions, and quantified (1) response threshold concentrations, (2) agrochemical interactions, and (3) community resistance and recovery. We found that the biomass of major zooplankton groups differed in their sensitivity to pesticides: ≥0.3 mg/L glyphosate elicited long-lasting declines in rotifer communities, both pesticides impaired copepods (≥3 µg/L imidacloprid and ≥5.5 mg/L glyphosate), whereas some cladocerans were highly tolerant to pesticide contamination. Strong interactive effects of pesticides were only recorded in ponds treated with the combination of the highest doses. Overall, glyphosate was the most influential driver of aggregate community properties of zooplankton, with biomass and community structure responding rapidly but recovering unequally over time. Total community biomass showed little resistance when first exposed to glyphosate, but rapidly recovered and even increased with glyphosate concentration over time; in contrast, taxon richness decreased in more contaminated ponds but failed to recover. Our results indicate that the biomass of tolerant taxa compensated for the loss of sensitive species after the first exposure, conferring greater community resistance upon a subsequent contamination event; a case of pollution-induced community tolerance in freshwater animals. These findings suggest that zooplankton biomass may be more resilient to agrochemical pollution than community structure; yet all community properties measured in this study were affected at glyphosate concentrations below common water quality guidelines in North America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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 teacher head, 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".