Ecological and genetic evidence that low-order streams inhibit dispersal by red-backed salamanders (Plethodon cinereus)
Bibliographic record
Abstract
While many studies have examined the barrier effects of large rivers on animal dispersal and gene flow, few studies have considered the barrier effects of small streams. We used displacement experiments and analyses of genetic population structure to examine the effects of first-order and second-order streams on the dispersal of terrestrial red-backed salamanders, Plethodon cinereus (Green, 1818). We marked red-backed salamanders from near the edges of one first-order stream and one second-order stream, and experimentally displaced them either across the stream or an equal distance farther into the forest. A comparison of return rates indicated that both streams were partial barriers to salamander movement, reducing return rates by approximately 50%. Analysis of six microsatellite loci from paired plots on the same side and on opposite sides of the second-order stream suggested that the stream did contribute to genetic differentiation of salamander populations. Collectively, our results imply that low-order streams do influence patterns of movement and gene flow in red-backed salamanders. We suggest that given the high density of first-order and second-order streams in most landscapes, these features may have important effects on species that, like red-backed salamanders, have limited dispersal and large geographic ranges.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".