Natal dispersal in a social landscape: Considering individual behavioral phenotypes and social environment in dispersal ecology
Bibliographic record
Abstract
Abstract Natal dispersal, the movement of an organism from its birthplace to the site of first reproduction, is fundamental to many ecological and evolutionary processes. Mechanistically, individual dispersal decisions can depend on both individual phenotype and environmental cues. In particular, many established evolutionary theories of dispersal highlight the importance of the social environment. More recent research in behavioral ecology has focused on the importance of individual behavioral phenotypes. We reviewed the literature on individual behavioral phenotypes and dispersal and suggest that how individual behavioral phenotypes interact with the immediate social environment experienced by individuals in influencing dispersal is still poorly understood, despite growing interest. We found that very few studies had examined the interaction of individual behavioral phenotypes and social factors, and behavioral phenotypes related to social tendencies were less commonly measured than were behavioral phenotypes related to exploration or response to risk. Further, and unsurprisingly, studies on social behavioral phenotypes and dispersal behaviors during the transience stage of dispersal were underrepresented compared to the departure or settlement stages. Future studies in this area should aim to: a) make explicit links between behavioral traits and their proposed effects on dispersal decisions throughout multiple stages of dispersal, b) integrate more continuous dispersal variables, and c) consider the effects of the spatial distribution and phenotypes of conspecifics (i.e., the social landscape) encountered by individual dispersers.
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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 | 0.003 |
| 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 | 0.002 |
| 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".