Anthropogenic disturbance and evolutionary parameters: a lemon shark population experiencing habitat loss
Bibliographic record
Abstract
The level of genetic variation in natural populations influences evolutionary potential, and may therefore influence responses to selection in the face of future environmental changes. By combining long-term monitoring of marked individuals with genetic pedigree reconstruction, we assessed whether habitat loss influenced genetic variation in a lemon shark (Negaprion brevirostris) population at an isolated nursery lagoon (Bimini, Bahamas). We also tracked changes in the strength and direction of natural selection. Contrary to initial expectations, we found that after the habitat loss neutral genetic variation increased, as did additive genetic variance for juvenile morphological traits (body length and mass). We hypothesize that these effects might result from philopatric behavior in females coupled with a possible influx of male genotypes from other nursery sites. We also found changes in the strength of selection on morphological traits, which weakened considerably after the disturbance; habitat loss therefore changed the phenotypes favored by natural selection. Because such human-induced shifts in the adaptive landscape may be common, we suggest that conservation biologists should not simply focus on neutral genetic variation per se, but also on assessing and preserving evolutionary parameters, such as additive genetic variation and selection.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".