Scaling up: understanding movement from individual differences to population-level dispersal
Bibliographic record
Abstract
Abstract Dispersal is fundamental to life on our planet. Dispersal facilitates colonization of continents and islands. Dispersal mediates gene flow among populations, and influences the rate of spread of invasive species. Theory suggests that individuals consistently differ in dispersal propensity, however determining the relative contributions of environmental factors to individual and population-level dispersal, represent a major challenge to understand the spread of organisms. To address this, we conducted a field experiment using Drosophila melanogaster. As proxies for individuals with different dispersal propensities, we used wildtype strains of flies with natural variants of the foraging gene, known to influence dispersal in laboratory and field experiments. These included flies with for s alleles known to be less dispersive, flies with the for R alleles which are more dispersive flies as well as an outbred population established from field collected flies. We released approximately 6000 flies of each strain in an experimental arena (100 m × 100 m) in the field and our recaptures were used to determine dispersal of flies over time. To estimate environmental effects on dispersal, we measured temperature, wind direction and wind speed. Using partial-differential equations we combined ecological diffusion with advection to estimate dispersal rates and responses to wind. We found that temperature effects elicited a similar response in high and low dispersal lab strains with dispersal rate increasing with temperature most rapidly at temperatures above 18°C. This was in contrast to outbred flies which remained unresponsive to temperature changes. We also detected a response to wind with advection rates increasing linearly with wind speed for all flies in general. Our results suggest that response to temperature and wind can minimize known differences in behavioural predispositions to disperse. Our results also suggest that the direction and magnitude of wind may play a key role in the colonization and distribution of fly populations. Our findings therefore have implications for forecasting the spread of pests and invasive species as well as pathogens and vectors of disease. Our findings further contribute to the understanding of how the environment can modify behavioural predispositions and to influence population-level dispersal in fly populations in particular and insect species in general.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".