Immigration allows population persistence and maintains genetic diversity despite an attempted experimental extinction
Bibliographic record
Abstract
Widespread fragmentation and degradation of habitats make organisms increasingly vulnerable to declines in population size. Immigration is a key process potentially affecting the rescue and persistence of populations in the face of such pressures. Field research addressing severe demographic declines in the context of immigration among interconnected local populations is limited owing to difficulties in detecting such demographic events and the need for long-term monitoring of populations. In a 17-subpopulation metapopulation of the butterfly, Parnassius smintheus , all adults observed in two adjacent patches were removed over eight consecutive generations. Despite this severe and long-term reduction in survival and reproduction, the targeted populations did not go extinct. Here, we use genetic data to assess the role of immigration versus in situ reproduction in allowing the persistence of these populations. We genotyped 471 samples collected from the targeted populations throughout the removal experiment at 152 single nucleotide polymorphisms. We found no reduction in the genetic diversity of the targeted populations over time, but a decrease in the number of loci in Hardy–Weinberg equilibrium, consistent with a high level of immigration from multiple surrounding populations. Our results highlight the role of connectivity and movement in making metapopulations resilient to even severe and protracted localized population reductions.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".