Sixty years of anthropogenic pressure: a spatio‐temporal genetic analysis of brown trout populations subject to stocking and population declines
Bibliographic record
Abstract
Analyses of historical samples can provide invaluable information on changes to the genetic composition of natural populations resulting from human activities. Here, we analyse 21 microsatellite loci in historical (archived scales from 1927 to 1956) and contemporary samples of brown trout (Salmo trutta) from six neighbouring rivers in Denmark, to compare the genetic structure of wild populations before and after population declines and stocking with nonlocal strains of hatchery trout. We show that all populations have been strongly affected by stocking, with admixture proportions ranging from 14 to 64%. Historical population genetic structure was characterized by isolation by distance and by positive correlations between historical effective population sizes and habitat area within river systems. Contemporary population genetic structure still showed isolation by distance, but also reflected differences among populations in hatchery trout admixture proportions. Despite significant changes to the genetic composition within populations over time, dispersal rates among populations were roughly similar before and after stocking. We also assessed whether population declines or introgression by hatchery strain trout should be the most significant conservation concern in this system. Based on theoretical considerations, we argue that population declines have had limited negative effects for the persistence of adaptive variation, but admixture with hatchery trout may have resulted in reduced local adaptation. Collectively, our study demonstrates the usefulness of analysing historical samples for identifying the most important consequences of human activities on the genetic structure of wild populations.
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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 | 0.001 | 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) | 0.000 | 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".