Species traits influence the genetic consequences of river fragmentation on two co-occurring redhorse (Moxostoma) species
Bibliographic record
Abstract
We used microsatellite DNA markers to test whether fragmentation of the Trent River (Ontario, Canada) has reduced genetic diversity and increased genetic differentiation among populations of river redhorse ( Moxostoma carinatum ) and shorthead redhorse (Moxostoma macrolepidotum). Allelic richness of both species was significantly greater along the free-flowing Muskegon River (Michigan, USA) than along the fragmented Trent River. Contrary to expectations, there was no evidence of a fragment length effect on genetic diversity, recent population bottlenecks, or increased relatedness among individuals in fragmented populations. High levels of linkage disequilibrium indicate extinction–recolonization population dynamics along the Trent River. For both species, pairwise FSTtests identified weak but statistically significant population differentiation. In the Trent River, differentiation was significantly greater for river redhorse than for shorthead redhorse and, for both species, greater than in the Muskegon River. Moderate fragmentation effects likely reflect the permeability of the dam-lock system to redhorse movement. Differences between species indicate that as a result of smaller effective population sizes, habitat specialists and species at the periphery of their geographic range are more sensitive to river fragmentation.
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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 | 0.000 |
| 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) | 0.001 | 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".