Natal habitat conditions have carryover effects on dispersal capacity and behavior
Bibliographic record
Abstract
Abstract Local habitat quality affects regional dynamics, including metapopulation persistence and speciation, by altering dispersal. However, most previous studies have not been able to determine whether dispersal is more strongly affected by habitat quality experienced at the dispersal stage, or carryover effects of habitat quality from previous life stages. Strong carryover effects will cause dispersal to be temporally disconnected from its drivers, altering the impact of dispersal on metapopulation dynamics, and potentially complicating empirical estimates of context‐dependent dispersal. Here, we used a fully factorial mesocosm experiment to assess how both habitat quality experienced during development and at adulthood affected emigration in adult backswimmers ( Notonecta undulata ). We found strong carryover effects of natal habitat quality on dispersal; individuals from high‐quality natal environments had higher emigration rates than individuals from low‐quality natal environments. However, emigration did not depend on adult habitat quality. This suggests that conditions experienced during development can outweigh the effects of habitat quality at later life stages, resulting in time lags between environmental triggers and the onset of dispersal behavior. If there are critical life stages at which dispersal rates are determined, habitat quality at those stages may have outsized impacts on biological dynamics in spatiotemporally variable landscapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".