Data from: Spatio-temporal dynamics of density-dependent dispersal during a population colonisation
Bibliographic record
Abstract
Predicting population colonisations requires understanding how spatio-temporal changes in density affect dispersal. Density can inform on fitness prospects, acting as a cue for either habitat quality, or competition over resources. However, when escaping competition, high local density should only increase emigration if lower-density patches are available elsewhere. Few empirical studies on dispersal have considered the effects of density at the local and landscape scale simultaneously. To explore this, we analyze 5 years of individual-based data from an experimental introduction of wild guppies Poecilia reticulata. Natal dispersal showed a decrease in local density dependence as density at the landscape level increased. Landscape density did not affect dispersal among adults, but local density-dependent dispersal switched from negative (conspecific attraction) to positive (conspecific avoidance), as the colonisation progressed. This study demonstrates that densities at various scales interact to determine dispersal, and suggests that dispersal trade-offs differ across life stages.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.030 |
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".