Data for: Spatial and temporal genetic variation in an exploited reef fish: The effects of exploitation on cohort genetic structure
Bibliographic record
Abstract
Many coral reef fishes are fished, often resulting in detrimental genetic effects; however, reef fishes often show unpredictable patterns of genetic variation, which potentially mask the effects of fishing. Our goals were to characterize spatial and temporal genetic variation and determine the effects of fishing on an exploited reef fish, Plectropomus leopardus, Lacepède (the common coral trout). To determine population structure, we genotyped 417 Great Barrier Reef coral trout from four populations sampled in two years (1996 and 2004) at nine microsatellite loci. To test for exploitation effects, we additionally genotyped 869 individuals from a single cohort (ages 3-5) across eight different reefs, including fished and control populations. Genetic structure differed substantially in the two sampled years, with only one year exhibiting isolation by distance. Thus, genetic drift likely plays a role in shaping population genetic structure in this species. Although we found no loss of genetic diversity associated with exploitation, our relatedness patterns show that pulse fishing likely affects populations. Additionally, genetic structure in the cohort samples likely reflected spatial variation in recruitment contributing to genetic structure at the population level. Overall, we show that fishing does impact coral reef fishes, highlighting the importance of repeated widespread sampling to accurately characterize the genetic structure of reef fishes, as well as the power of analysing cohorts to avoid the impacts of recruitment-related genetic swamping. The high temporal and spatial variability in genetic structure, combined with possible selection effects, will make conservation/management of reef fish species complex.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".