The role of neutral and adaptive evolutionary processes on patterns of genetic diversity across small cave‐dwelling populations of Icelandic Arctic charr (<i>Salvelinus alpinus</i>)
Bibliographic record
Abstract
Abstract Understanding the adaptability of small populations in the face of environmental change is a central problem in evolutionary biology. Solving this problem is challenging because neutral evolutionary processes that operate on historical and contemporary timescales can override the effects of selection in small populations. We assessed the effects of isolation by colonization (IBC), isolation by dispersal limitation (IBDL) as reflected by a pattern of isolation by distance (IBD), and isolation by adaptation (IBA) and the roles of genetic drift and gene flow on patterns of genetic differentiation among 19 cave‐dwelling populations of Icelandic Arctic charr (Salvelinus alpinus). We detected evidence of IBC based on the genetic affinity of nearby cave populations and the genetic relationships between the cave populations and the presumed ancestral population in the lake. A pattern of IBD was evident regardless of whether high‐level genetic structuring (IBC) was taken into account. Genetic signatures of bottlenecks and lower genetic diversity in smaller populations indicate the effect of drift. Estimates of gene flow and fish movement suggest that gene flow is limited to nearby populations. In contrast, we found little evidence of IBA as patterns of local ecological and phenotypic variation showed little association with genetic differentiation among populations. Thus, patterns of genetic variation in these small populations likely reflect localized gene flow and genetic drift superimposed onto a larger‐scale structure that is largely a result of colonization history. Our simultaneous assessment of the effects of neutral and adaptive processes in a tractable and replicated system has yielded novel insights into the evolution of small populations on both historical and contemporary timescales and over a smaller spatial scale than is typically studied.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".