Aggression and sociality: conflicting or complementary traits of a successful invader?
Bibliographic record
Abstract
Invasion biology research has identified two juxtaposing behavioural traits, aggressiveness and sociality, that may both increase the success of species invasions. Highly aggressive invaders can out-compete native species for resources, while social gregarious invaders can tolerate high conspecific density. In order to tease apart the effects of aggressive versus social tendencies on the success of invasive species, we studied round goby (Neogobius melanostomus), a highly successful invasive fish species now common in the Laurentian Great Lakes. While round goby are well known for being aggressive, much less is known about their tendency to affiliate with conspecifics, in spite of the fact that they thrive in extremely high densities in many of their invaded habitats. We collected round goby from Hamilton Harbour, ON, Canada and conducted three separate experiments to explore group-forming behaviour by measuring preference for conspecifics. We found that round goby have a strong preference to associate with a single conspecific, and that both males and females showed this preference. No overall preference was detected for large versus small groups of conspecifics. Females chose the safety of a shelter over associating with a conspecific but males were equally attracted to conspecifics as shelter. Our results provide new insight into how interactions between aggressive and social behaviours play a role in the rapid spread of invasive round goby.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".