Predatory impact of the freshwater invader<i>Dikerogammarus villosus</i>(Crustacea: Amphipoda)
Bibliographic record
Abstract
To assess the increasing threats to aquatic ecosystems from invasive species, we need to elucidate the mechanisms of impacts of current and predicted future invaders. Dikerogammarus villosus, a PontoCaspian amphipod crustacean, is invading throughout Europe and predicted to invade the North American Great Lakes. European field studies show that populations of macroinvertebrates decline after D. villosus invasion. The mechanism of such impacts has not been addressed empirically; however, D. villosus is known to prey upon and replace other amphipods. Therefore, in this study, we used microcosm and mesocosm laboratory experiments, with both single and mixed prey species scenarios, to assess any predatory impact of D. villosus on a range of macroinvertebrate taxa, trophic groups, and body sizes. Dikerogammarus villosus predatory behaviour included shredding of prey and infliction of "bite" injuries on multiple victims. Dikerogammarus villosus killed significantly greater numbers of macroinvertebrates than did the native Gammarus duebeni, which is currently being replaced by D. villosus. This invader thus appears to impact on freshwater ecosystems through its exceptional predatory capabilities. We predict that future invasions by D. villosus will have serious direct and indirect effects on freshwaters, with its invasion facilitated in a larger "invasional meltdown" in regions like the North American Great Lakes.
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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 | 0.000 |
| 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 | 0.001 |
| 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".