Long-distance dispersal, ice sheet dynamics, and mountaintop isolation underlie the genetic structure of glacier ice worms
Bibliographic record
Abstract
Abstract Disentangling the contemporary and historical factors underlying the spatial distributions of species is a central goal of biogeography. For species with broad distributions but little capacity to actively disperse, disconnected geographic distributions highlight the potential influence of passive, long-distance dispersal (LDD) on their evolutionary histories. However, dispersal alone cannot completely account for the biogeography of any species, and other factors–e.g., habitat suitability, life history–must also be considered. North American ice worms ( Mesenchytraeus solifugus ) are ice-obligate annelids that inhabit coastal glaciers from Oregon to Alaska. Previous studies identified a complex biogeographic history for ice worms, with evidence for genetic isolation, unexpectedly close relationships among geographically disjunct lineages, and contemporary migration across large (> 1,500 km) areas of unsuitable habitat. In this study, we analyzed genome-scale sequence data for most of the known ice worm range. We found clear support for divergence between populations along the Pacific Coast and the inland flanks of the Coast Mountains (mean F ST = 0.60), likely precipitated by episodic ice sheet expansion and contraction during the Pleistocene. We also found support for LDD of ice worms from Alaska to Vancouver Island, perhaps mediated by migrating birds. Our results highlight the power of genomic data for disentangling complex biogeographic patterns, including the presence of LDD.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".