Ecological responses to elevated water temperatures across invasive populations of the round goby (<i>Neogobius melanostomus</i>) in the Great Lakes basin
Bibliographic record
Abstract
Climate warming is expected to alter the distribution, abundance, and impact of non-native species in aquatic ecosystems. In laboratory experiments, we measured the maximum feeding rate and critical thermal maximum (CT max ) of an invasive Eurasian fish, the round goby (Neogobius melanostomus), acclimated to a range of temperatures (18–28 °C) reflecting current and projected future thermal conditions for the nearshore Great Lakes. Fish were collected from four distinct populations along a latitudinal gradient from the western basin of Lake Erie to Hamilton Harbour (Lake Ontario) and the upper St. Lawrence River. Thermal tolerance increased with acclimation temperature for populations in Lakes Erie and Ontario. However, the St. Lawrence River populations had lower acclimation capacity and exhibited an unexpected decline in CT max at the highest acclimation temperature. Maximum feeding rates peaked at 18–24 °C and declined with temperatures above 24 °C. Northern populations in the basin appear poorly adapted to elevated temperatures such that their performance and impact could be reduced by climate warming. Thermal response data from latitudinally distributed populations are needed to inform invasive species risk assessment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".