Interactive effects of temperature and velocity on the feeding behavior of competing native and invasive stream fishes
Bibliographic record
Abstract
The integration of invasive species into native communities may result in the destabilization of food webs through predation of native prey and competition with native predators. These negative effects are likely to be exacerbated by climate change, increasing the frequency of heat waves, droughts, and high-flow events. Invasive Round Goby (Neogobius melanostomus) and native White Sucker (Catostomus commersonii) were collected from the wild and acclimated to a range of temperatures expected to capture increases in temperature under climate change. In the laboratory, we measured the effects of temperature (19, 22, 25, and 28°C) and velocity gradients (0, 0.15, 0.3, 0.6 m/s at 22 and 25°C) on the prey consumption and foraging behavior of each species. We found that the prey consumption and number of attacks (lunges at prey) of both species increased with temperature but were consistently higher in Round Goby. Velocity had an interactive effect with temperature for prey consumption and foraging behavior, where generally, velocity tolerance for both species was higher at 22°C than at 25°C. White Sucker tolerance to velocity was higher than Round Goby, suggesting that systems with higher velocities may provide a competitive advantage in high-flow environments. Our results stress the importance of interactive experiments when examining the impact of invasive species on native competitors under shifting climatic conditions.
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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 | 0.000 |
| 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".