Dispersal acts as both bane and balm for invaded zooplankton communities
Bibliographic record
Abstract
Dispersal of native species from the regional pool can recover invaded communities to a pre-invaded state by supplementing declining populations or providing resistant species. However, dispersal may also exacerbate the negative effects of an invader. Introduced species can open or create new niche space, which could facilitate the establishment of competitors or predators that previously could not succeed in the uninvaded local community. To investigate the interaction between dispersal and invasion by a non-native consumer, we conducted a field mesocosm experiment that introduced zebra mussels into native zooplankton communities. Regional zooplankton were collected and added to both invaded and uninvaded communities. In uninvaded communities, zooplankton dispersal reduced cladoceran diversity by ∼40%, rotifer abundance by ∼65% and copepod nauplii abundance by ∼80%. In invaded communities, dispersal increased cladoceran diversity by ∼60%, but also further exacerbated the negative effects of zebra mussels on rotifer abundance. This experiment illustrates the potential for dispersal to both positively and negatively affect local communities, and how these effects may change with disturbance and the taxa or community metric of study.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".