Urbanisation Is Associated With Reduced Genetic Diversity in Marine Fish Populations
Bibliographic record
Abstract
The benefits to humans 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, coastal development, and trade and travel routes. While our understanding of the ecological and evolutionary consequences of urbanisation for wildlife in terrestrial systems has received considerable recent attention, the consequences of urbanisation in marine systems are not well known. Using microsatellite datasets associated with published research on marine fish population genetics, we built a global database of genotypic data spanning 75,361 individuals sampled from 73 species at 1085 sample sites throughout the world's oceans. We found that genetic diversity and effective population sizes were significantly lower at marine fish sample sites associated with denser human populations, regardless of species and locality. The loss of genetic diversity near denser human populations indicates habitats near human settlements are less able to support large populations. Small effective population sizes, in turn, dampen the efficiency of natural selection near dense urban settlements. The loss of genetic diversity near cities is concerning for maintaining functioning marine ecosystems and sustainable fisheries. Our work highlights the need to mitigate environmental threats from human activities and focus efforts on sustainable urban planning and resource use to conserve marine biodiversity and sustain 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".