Is there a role for aggression in round goby invasion fronts?
Bibliographic record
Abstract
The role of aggression as a factor promoting invasiveness remains hotly debated. Increased aggression or a lack of tolerance for conspecifics may promote population spread. Some previous research suggests that more aggressive or bold individuals are increasingly likely to disperse and as such these individuals may be overrepresented at the invasion front. In contrast, it has also been argued that individuals at the invasion front represent the least aggressive or least competitive individuals in the population, as these animals are excluded from established areas. Accordingly, the invasion front should be made up of shy, submissive individuals that exhibit reduced aggression. In this study we explore these alternative predictions by quantifying the levels of intra-specific aggression in the round goby (Neogobius melanostomus), an invasive fish that continues to spread rapidly through the Laurentian Great Lakes region in North America. We collected size matched male round goby from an invasion front as well as from an area with an established population, and we staged resource contests between them. Invasion front fish won 65% of the contests and tended to perform more aggressive acts overall. Invasion front fish were not more active or bold prior to the contest, and used the same types of aggressive displays as fish from established areas. Our results also showed that body size asymmetry was an overriding determinant of competitive outcomes, and that body size rather than individual variation in aggressiveness might be the most important contributing factor determining the composition of round goby invasion fronts throughout the Laurentian Great Lakes and its tributaries.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".