Data from: Environmental estrogens cause predation-induced population decline in a freshwater fish
Bibliographic record
Abstract
Understanding population-level effects of environmental stressors on aquatic biota requires knowledge of the direct adverse effects of pollutants on individuals and species interactions that relate to survival and reproduction. Here, we connect behavioral assays with survival trials and a modeling approach to quantify changes in antipredator escape performance of a larval freshwater fish following exposure to an environmental estrogen, and predict changes in population abundance. We quantified the effects of short-term (21 d) exposure to 17-β estradiol (E2) on the antipredator escape performance of larval fathead minnows (Pimephales promelas) and the probability of predation by a natural predator, the bluegill sunfish (Lepomis macrochirus). Compared with unexposed minnows, minnows exposed to environmentally relevant concentrations of E2 that approach total estrogenic activity of waste-water dominated environments (38 and 103 ng/L) had delayed response times and slower escape speeds, and were more susceptible to predation. Incorporating these data into a stage-structured population model demonstrated that enhanced predation mortality at the larval stage is sufficient to generate marked population declines. These results indicate that subtle, sub-lethal shifts in the behavior of individuals due to human-mediated environmental change can impact species interactions with measurable population-level effects. Such changes have the potential to alter higher-order trophic interactions and disrupt aquatic communities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.040 | 0.007 |
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".