Positive correlation between dispersal and body size in Green Frogs (<i>Rana</i> <i>clamitans</i>) naturally colonizing an experimental landscape
Bibliographic record
Abstract
Dispersers are often assumed to have the mean phenotype observed across the entire metapopulation, despite growing evidence of dispersal–phenotype correlations. We examined three dispersal–phenotype correlations in Green Frogs (Rana clamitans Latreille, 1801 = Lithobates clamitans (Latreille, 1801)). Two were in traits that have been previously tied to fitness (body size and body condition), while a third (relative hindlimb length) has been linked to movement performance. We constructed a spatially dispersed array of experimental ponds in close proximity to source ponds known to support Green Frog breeding populations. Over the course of two breeding seasons (four sampling periods), we measured phenotypes of all Green Frogs that had colonized the experimental ponds and a sample of individuals from the source ponds. After only 1 month, a positive correlation was detected between dispersal and body size within the population of dispersers occupying the experimental ponds. After a 2nd month, this positive dispersal – body size correlation was also present when comparing the population of dispersers to the population of nondispersers remaining at the source ponds. Even if generated solely by plasticity, a positive correlation between dispersal and body size (a trait tightly linked to fitness) has the ability to alter metapopulation capacity and thus the probability of regional species persistence.
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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".