Predicted growth of lake trout and Chinook salmon in a warming lake
Bibliographic record
Abstract
Warming water temperatures present challenges for ectotherms in freshwater ecosystems through influence on metabolic rate and bioenergetics. Diet, such as the inclusion of high energy prey, can reduce these influences, but accurate temperature profiles are key to improved predictions. Here, using the Wisconsin bioenergetics approach, we modelled lake trout (Salvelinus namaycush) and Chinook salmon (Oncorhynchus tshawytscha) growth for two time periods (present [2010–2019] and future 30-year (2041–2070) averaged scenarios) in Lake Ontario with in-situ observed year-round temperatures (obtained through acoustic telemetry and pop-off data storage tags deployed between 2016 and 2019) occupied by the species. For the future, we considered two water temperature scenarios (low and high, where experienced temperature increased by a mean of 0.4 °C and 1.0 °C, respectively) and the effects of a hypothetical diet switch with the inclusion of higher energy prey (bloater, Coregonus hoyi) currently being reintroduced. Under all forecasted warming conditions, lake trout performed well and growth was 41.3 to 82.5 % above present when bloater was re-incorporated in the diet up to 40 %. Chinook salmon maximum attainable growth declined between 11.2 and 29.6 % under forecasted warming scenarios without diet change. However, when bloater comprised ∼ 33 % of their diet, Chinook salmon growth increased 3.7 % under the low future scenario compared to present. These results demonstrate that impacts of future lake warming on predatory fish will vary with life-history characteristics of species and composition and abundance of prey base, and highlights the need for effective management that diversifies and conserves forage fish species in the Great Lakes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".