Veliger density and environmental conditions control quagga mussel colonization rates in two perialpine lakes
Bibliographic record
Abstract
Quagga and zebra mussels (Dreissena bugensis and D. polymorpha) are spreading across lakes in Europe and North America. In particular, quagga mussels colonize lakes to great depths (>200 m). To better understand the colonization pattern of quagga mussels in deep lakes, we studied the settlement of quagga mussels along a depth gradient on colonization plates at multiple depths (1–140 m) in the pelagic zone of two recently invaded perialpine lakes, Lake Constance and Lake Geneva. We measured colonization rates every three months over one year on colonization plates deployed in both lakes at defined depths. We also assessed long-term population dynamics from abundance and size distribution using repeated photogrammetry of colonization plates. Highest colonization rates and largest mussel sizes occurred above 8 m depth, and almost no zebra mussels were found. Colonization rates decreased to almost zero below 30 m. Colonization rates on plates were associated with variation in environmental conditions as well as veliger densities in the plankton across season and depth. Temperature was the most important environmental parameter that influenced colonization. Our results will help to better understand the seasonal colonization patterns of invasive quagga mussels in deep 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 | 0.001 |
| 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 | 0.001 |
| 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".