Secondary spread of zebra mussels (<i>Dreissena polymorpha</i>) in coupled lake-stream systems
Bibliographic record
Abstract
:We postulated that dispersal through streams is an important factor in the spread of nonindigenous aquatic species to uninvaded lakes. We tested this hypothesis with zebra mussels (Dreissena polymorpha), whose planktonic larvae are particularly prone to transport through streams. To examine this potential mechanism of spread, we (1) assessed populations of zebra mussels in 2000 and 2003 in coupled lake-stream systems of the St. Joseph River basin (Indiana and Michigan, USA) and (2) examined the interconnectedness of lake-stream systems by evaluating all invaded inland lakes and reservoirs in the United States. We compared observed patterns in zebra mussel populations in 2000 and 2003 to patterns predicted by two proposed models of spread: the static source–sink model and the progressive downstream-march model. Adult zebra mussel densities in lake outflows declined with distance downstream of invaded lakes. Maximum downstream occurrences of adults were variable over the years surveyed, but did not increase through time, suggesting that the source–sink model best fit zebra mussel distributions in these lake-stream couplets. For the conterminous US, we examined the connectedness of inland lakes in close proximity to invaded lakes to determine if stream connections were related to invasions. We also measured the distances between invaded lakes and downstream lakes that were potential recipients of colonists to examine the importance of stream distance in relation to zebra mussel invasions. Lakes connected to invaded lakes were more likely to be invaded than non-connected lakes, and the probability of becoming invaded increased with the proximity between lakes. Our results suggest that a better understanding of the role that streams play as pathways for new biological invasions is crucial for directing management and prevention efforts.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".