DISENTANGLING THE EFFECTS OF EVOLUTIONARY, DEMOGRAPHIC, AND ENVIRONMENTAL FACTORS INFLUENCING GENETIC STRUCTURE OF NATURAL POPULATIONS: ATLANTIC HERRING AS A CASE STUDY
Bibliographic record
Abstract
The spatial structuring of intraspecific genetic diversity is the result of random genetic drift, natural selection, migration, mutation, and their interaction with historical processes. The contribution of each has been typically difficult to estimate, but recent advances in statistical genetics have provided valuable new investigative tools to tackle such complexity. Using a combination of such methods, we examined the roles of environment (i.e., natural selection), random genetic processes (i.e., drift), and demography and life histories (e.g., feeding migrations) on population structure of a widely distributed and abundant marine pelagic fish of economic importance, Atlantic herring (Clupea harengus). Individuals were collected during peak spawning time from 19 spawning locations spanning the region from the western North Sea to the eastern Baltic Sea (N= 1859, eight microsatellite loci). We carried out separate analyses of neutral and selected genetic variation, which allowed us to establish that the two most important factors affecting population structure were selection due to salinity at spawning sites and feeding migrations. The genetic signal left by the demographic history of herring, on the other hand, seems to have been largely eroded, which is not surprising given the large reproductive potential and presumed enormous local effective population sizes of pelagic fish that constrain the effect of stochastic processes. The approach we used can in principle be applied to any abundant and widely distributed aquatic or terrestrial species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".