Urbanization is associated with reduced genetic diversity in marine fish populations
Bibliographic record
Abstract
Abstract The economic and ecological benefits of living by the ocean have led many coastal settlements to grow into large densely populated cities. Large coastal cities have had considerable environmental effects on marine ecosystems through resource extraction, waste disposal, and use for transportation. Thus, it is important to understand the consequences of urbanization and human activities on evolutionary processes and biodiversity in marine fishes. Using published population genetic datasets for marine fishes amounting to 75,496 individuals sampled from 73 species at 1143 sample sites throughout the world’s oceans, we evaluated how human population density and a composite measure of cumulative human impacts affected genetic diversity and differentiation. We found that genetic diversity was significantly lower in marine fish populations associated with denser human populations regardless of species and locality. The effects of cumulative human impacts on genetic diversity were less prominent, perhaps due to this measure capturing more spatially varying processes. Urbanization in coastal regions has degraded marine biodiversity in a way that erodes adaptive potential for marine fish populations. This highlights the need to mitigate threats from human activities and focus efforts on sustainable urban planning and resource use to conserve marine biodiversity sustaining coastal fisheries and ecosystems.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".