Experimental mixing of a north-temperate lake: testing the thermal limits of a cold-water invasive fish
Bibliographic record
Abstract
Species’ thermal limits play a key role in determining spatial distributions and understanding their response to changing environments. Manipulation of thermal habitat is a potential avenue of exploration for management of invasive species such as the cold-water rainbow smelt (Osmerus mordax), which has adverse effects on native fish communities in central North American inland lakes. In an effort to test the thermal limits and selectively eradicate rainbow smelt, we experimentally mixed Crystal Lake, Wisconsin, USA, during summer of 2012 and 2013 to warm the hypolimnion and eliminate cold-water habitat. This whole-ecosystem manipulation allowed for field testing of published thermal thresholds reported for rainbow smelt. The rainbow smelt population responded to the thermal manipulation by exhibiting unexpected shifts in behavior, intrapopulation divergence in body condition, and significant population declines. Small individuals of each adult age-class tended to survive the manipulation, and the population persisted despite high mortality rates. Our results indicate a high degree of size-based intrapopulation variation in thermal sensitivity for this species. Our findings also raise questions regarding applicability of lab- and model-derived thermal limits to field scenarios, highlighting a need for further field evaluations of species’ thermal limits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".