Mixed evidence for adaptation to environmental pollution
Bibliographic record
Abstract
Adaptation to pollution has been studied since the first observations of heavy metal tolerance in plants decades ago. To document micro-evolutionary responses to pollution, researchers have used phenotypic, molecular genetics, and demographic approaches. We reviewed 258 articles and evaluated the evidence for adaptive responses following exposure to a wide range of pollutants, across multiple taxonomic groups. We also conducted a meta-analysis to calculate the magnitude of phenotypic change in invertebrates in response to metal pollution. The majority of studies that reported differences in responses to pollution were focused on phenotypic responses at the individual level. Most of the studies that used demographic assays in their investigations found that negative effects induced by pollution often worsened over multiple generations. Our meta-analysis did not reveal a significant relationship between metal pollution intensity and changes in the traits studied, and this was probably due to differences in coping responses among different species, the broad array of abiotic and biotic factors, and the weak statistical power of the analysis. We found it difficult to make broad statements about how likely or how common adaptation is in the presence of environmental contamination. Ecological and evolutionary responses to contamination are complex, and difficult to interpret in the context of taxonomic, and methodological biases, and the inconsistent set of approaches that have been used to study adaptation to pollution in the laboratory and in the field. This review emphasizes the need for: (a) long-term monitoring programs on exposed populations that link demography to phenotypic, genetic, and selection assays; (b) the use of standardized protocols across studies especially for similar taxa. Approaches that combine field and laboratory studies offer the greatest opportunity to reveal the complex eco-evolutionary feedback that can occur under selection imposed by pollution.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".