Fouling mussels (<i>Dreissena</i> spp.) colonize soft sediments in Lake Erie and facilitate benthic invertebrates
Bibliographic record
Abstract
Summary We conducted survey and transplant studies to determine whether colonization and residency on soft sediments by introduced, fouling mussels (Dreissena polymorpha and D. bugensis) were affected by physical disturbance, and whether Dreissena presence in turn influenced the diversity and population densities of other benthic invertebrates. Surveys revealed that colony density was typically higher at moderate depths than at shallower and greater ones. However, the largest, midsummer colonies and greatest coverage of sediments by mussels occurred at deeper sites. Disturbance of transplanted colonies varied by depth and colony size, with deeper and larger colonies experiencing the lowest destruction rates. Colony destruction rate was positively correlated with current velocity adjacent to the lakebed. Absence of mussel colonies at shallow sites was not determined by recruitment or substrate limitation, as recruit density was higher and sediment characteristics more suitable for postveliger settlement at shallow than at deeper sites. Rather, seasonal storms have much stronger effects in shallow than in deep water. Mussel residency on soft sediment has profound effects on invertebrate biodiversity. Invertebrate species (taxon) richness and total abundance were positively correlated with mussel colony area. Mussel‐sediment habitat supported between 462 and 703% more taxa, and between 202 and 335% more individuals (exclusive of Dreissena) than adjacent soft‐sediment lacking mussels. Results from this study illustrate that physical disturbance directly limits the distribution of mussels on soft sediments, and the diversity and abundance of other benthic invertebrates in consequence.
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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 | 0.001 | 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".