Tracking dispersal across a patchy landscape reveals a dynamic interaction between genotype and habitat structure
Bibliographic record
Abstract
Theoretical and empirical studies often show that within populations, individuals vary in their propensity to disperse. We aspired to understand how this behavioural variation is impacted by the distribution and pattern of food patches across a landscape. In a series of experiments we examined how inter‐patch distance and the distribution of food patches influenced dispersal in wild‐type strains of Drosophila melanogaster with natural allelic variants of the foraging ( for ) gene known to influence dispersal in this species. The ‘rover' strain was homozygous for the for R allele (more dispersive) whereas the ‘sitter' strain was homozygous for for s (less dispersive). We also assessed an outbred population of flies with an unknown dispersal propensity. Dispersal was assayed in a multi‐patch lab arena (25 cells, 5 × 5 array). In the inter‐patch distance trials, landscapes of two different sizes (small versus large) were used, both with food in all 25 cells. Dispersal was reduced in the large landscape relative to the small landscape for all three fly strains. Sitter dispersal was lowest relative to both rovers and the outbred flies, whose dispersal tendencies were similar. In the patch distribution trials, flies were assayed in landscapes with varying distribution and number of cells containing food. Dispersal generally increased as the number of patches with food increased, however, rovers and sitters adopted similar dispersal strategies when food was fixed and limited. Conversely, their strategies differed when the total amount of food increased with the number of patches. We find that both the inter‐patch distance and distribution can influence dispersal. However, the effect of inter‐patch distance and distribution on dispersals depends on genotype × environment interaction. Our findings highlight the importance of considering G × E when assessing how dispersal strategies and landscape dynamics influence the distribution of animal communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".