Local climate and habitat continuity interact to alter contemporary dispersal potential
Bibliographic record
Abstract
Abstract Understanding the evolution of dispersal under changing global environments is essential to predicting a species ability to track shifting ecological niches. Two important, but anthropogenically altered, sources of selection on dispersal are climate and habitat continuity. Despite the likelihood these global drivers of selection act simultaneously on plant populations, their combined effects on dispersal are rarely examined. To understand the interactive effect of climate and habitat continuity on dispersal potential, we study Geum triflorum - a perennial grassland species that spans a wide range of environments, including both continuous prairie and isolated alvar habitats. We explore how the local climate of the growing season and habitat continuity (continuous vs isolated) interact to alter dispersal potential. We find a consistent interactive effect of local climate and habitat continuity on dispersal potential. Across continuous prairie populations, an increased number of growing degree days favors traits that increase dispersal potential. However, for isolated alvar populations, dispersal potential tends to decrease as the number of growing degree days increase. Our findings suggest that under continued warming, populations in continuous habitats will benefit from increased gene flow, while isolated populations will become increasingly segregated, with reduced potential to track shifting fitness optima.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".