Short-term costs of relocating a territory in a Caribbean damselfish, Stegastes diencaeus
Bibliographic record
Abstract
Little is known about the costs of relocating a territory into an established neighbourhood.In this study we investigated short-term costs of relocation in the longfin damselfish, Stegastes diencaeus, on a fringing reef in Barbados.Experimental removals of residents created vacancies, and focal observations over two days examined the intensity and duration of behavioural changes in the newcomers.Newcomers used smaller territories than original residents, and exhibited increased movement, increased agonistic behaviour and decreased foraging.The behavioural changes suggest that energetics are a major cost to relocation, but that opportunity costs, predation risk and injuries are also important.Differences between strangers and expanding neighbours support the concept of 'dear enemy' recognition, but familiarity does not influence the agonistic behaviour initiated by these newcomers.The costs reported here represent important limitations to the mobility of individuals and provide insights into the stability of fish territories.RESUME Peu d'information est connue sur les couts relies a la relocalisation d'un territoire vers un quartier etabli.Dans cette etude, nous enquetons sur les couts a court terme lies a la relocalisation du demoiselle noire Stegastes diencaeus, sur des recifs coralliens aux Barbade.Les deplacements experimentaux des residents ont cree des places vacantes, et des observations visuelles, sur une periode de deux jours, ont examine l'intensite et la duree des changements comportementaux chez les nouveaux venus.Les nouveaux venus ont utilise des territoires plus petits que les residents originaux et ont demontre plus de mouvements, plus de comportements agonistiques et une diminution de recherche alimentaire.Les changements comportementaux sugerent que la perte d'energie est un cout majeur a la relocalisation, mais que les couts d'occasion, les risques de predation et les blessures sont aussi importants.Les differences entre les etrangers et les voisins prenant de l'expansion soutien le concept de reconnaissance du "cher ennemi", mais la familiarite n'influence pas le comportement agonistique initie pas ces nouveaux venus.Les couts rapportes dans cette etude represented des limites importantes a la mobilite et foumissent des perspicacites dans la stabilite de territoires de poisson.
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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 | 0.001 |
| 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 | 0.001 |
| 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".