Projecting the Impact of Climatic Change on Coldwater Fish Habitat in Minnesota Lakes
Bibliographic record
Abstract
Climate warming would alter water temperature and dissolved oxygen (DO) characteristics of lakes. These changes can be expected to have an effect on fish populations. A process-oriented, dynamic, one-dimensional year-round lake water quality model was developed and applied to simulate daily temperature and DO profiles in Minnesota lakes over 48 (1961 to 2008) or 18 (1991 to 2008) years, depending on weather data availability. The model was calibrated with data from 28 study lakes, and the average standard error of estimate against measured data was 1.47 °C for water temperature and 1.50 mg/L for DO. Projected future climate scenarios were based on the output of the Coupled Global Climate Model (CGCM), CCCma CGCM 3.1 from the Canadian Climate Centre for Climate Modeling and Analysis (CCCma), and the Model for Interdisciplinary Research on Climate, MIROC 3.2 developed in Japan. Simulated water temperature and DO profiles under past and projected future climate conditions were used to identify coldwater fish habitat in the 28 Minnesota lakes. The purpose of the study was to identify refuge lakes for cisco (tullibee), a coldwater fish species, under climate warming scenarios. Cisco is a food source for predatory sport fish species such as walleye and northern pike. Suitable coldwater fish habitat was identified by two methods: (1) a constant upper (lethal) temperature and a lower DO survival limit over the entire simulation period, or (2) variable temperature and DO survival limits based on observed fish mortality. Results obtained by both fish habitat models indicate that 19 deep, seasonally stratified, mesotrophic or oligotrophic lakes located in north or north-central Minnesota can support coldwater fish habitat under both past and future climate scenarios, and these lakes are candidates of refuge lakes. Once refuge lakes are identified, lake watershed protection efforts can be initiated at refuge lakes to prevent deterioration of water quality in these lakes by anthropogenic activities. The presentation will cover the lake water quality model development and calibration, the fish habitat model development, the application of both models to 28 lakes, the simulation results and the implications for watershed management to prevent other deteriorations of coldwater fish habitat.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".