Golden mussel (<i>Limnoperna fortunei</i>) survival during winter at the northern invasion front implies a potential high‐latitude distribution
Bibliographic record
Abstract
Abstract Aim Golden mussel Limnoperna fortunei is an invasive bivalve in many freshwater ecosystems in Asia and South America. Cold winter temperatures are expected to restrict its spread to high‐latitude areas. Cold tolerance and potential distribution of this species remain largely unstudied because the most extensively studied populations occur in tropical and sub‐tropical areas. We sought to investigate cold tolerance of golden mussels and to model their potential distribution at higher latitudes. Location China, Global. Methods We investigated overwintering survival of caged golden mussels in a reservoir located at the northern invasion front in north China. We then determined the lowest water temperature at which mussel filtering occurred in laboratory. Finally, we modelled relative environmental suitability globally based on Maximum Entropy using the species’ most updated occurrence records. Results Golden mussels in a northern invasion front reservoir could survive over a course of 6 days at <1°C, or 41 days at <2°C, or 108 days at <5°C, with 27% survival overall. Caged mussels were inaccessible to local predators and reproduced, with the subsequent population size increasing in early summer by ~280%, representing a potential source population. Laboratory tests demonstrated that the lowest water temperature at which mussels could filter water was 5.5°C, and 50% of individuals became active when temperature rose to 7.5–8.0°C. Species distribution modelling illustrated a potential distribution of golden mussels at higher latitude than presently found. Models that considered updated high‐latitude occurrence records predicted a significantly larger suitable area than currently exists, including near the lower Laurentian Great Lakes. Main conclusions Our findings suggest enhanced cold tolerance of golden mussels and wider potential distribution than currently exists. We emphasize the importance of examining samples from invasion fronts when developing distribution predictions for spreading invasive species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".