Phenotype‐by‐environment interactions influence dispersal
Bibliographic record
Abstract
Numerous studies have demonstrated that dispersal is dependent on both disperser phenotype and the local environment. However, there is substantial variability in the observed strength and direction of phenotype- and environment-dependent dispersal. This has been hypothesized to be the result of interactive effects among the multiple phenotypic and environmental factors that influence dispersal. Here, our goal was to test the hypothesis that these interactions are responsible for generating variation in dispersal behaviour. We achieved these goals by conducting a large, 2-year, mark-release-recapture study of the backswimmer Notonecta undulata in an array of 36 semi-natural ponds. We measured the effects of multiple phenotypic (sex and body size) and environmental (population density and sex ratio) factors, on both dispersal probability and dispersal distance. We found support for the hypothesis that interactive effects influence dispersal and produce variability in phenotype- and environment-dependent dispersal: dispersal probability was dependent on the three-way interaction between sex, body mass and population density. Small males displayed strong, positive density dependence in their dispersal behaviour, while large males and females overall did not respond strongly to density. Small notonectids, regardless of sex, were more likely to disperse, but this effect was strongest at high population densities. Finally, the distance dispersed by backswimmers was a negative function of population density, a pattern which we hypothesize could be related to: (a) individuals from high and low density patches having different dispersal strategies, or (b) the effect of density on dispersal capacity. These results suggest that phenotype-by-environment interactions strongly influence dispersal. Since phenotype- and environment-dependent dispersal has different consequences for ecological and evolutionary dynamics (e.g. metapopulation persistence and local adaptation) than random dispersal, interactive effects may have wide-reaching impacts on populations and communities. We therefore argue that more investment should be made into estimating the effects of multiple, interacting factors on dispersal and determining whether similar interactive effects are acting across systems.
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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 | 0.002 |
| 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 | 0.001 |
| 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".