Projecting cold-water fish habitat in lakes of the glacial lakes region under changing land use and climate regimes
Bibliographic record
Abstract
Cold-water habitat in lakes is projected to decrease under future climate scenarios, and existing trends suggest such declines are already impacting cold-water fish populations. Herein, we predict the effects of future climate and land use change on cold-water fish habitat in the glacial lakes of the upper midwestern US. Ecoregion-specific, regional regression models were developed to predict annual phosphorus loading rates to lakes based on land use and hydrology and coupled to a previously developed fish habitat model. Outputs from one land use change model and three global climate models were then used to project future cold-water habitat. Significant decreases in cold-water habitat quality were projected in all four ecoregions of the study region, with increases in air temperature generally having greater impacts on habitat than land use changes. Projected localized increases in urbanization and corn acreage were found to degrade cold-water habitat for a subset of lakes in all ecoregions. For cisco (Coregonus artedi), the most thermally tolerant of the four species considered, it was found that most of the highest quality (tier 1) refuge lakes will shift to lower quality (tier 2) lakes with adequate habitat, and about half of the tier 2 lakes will shift to tier 3 (non-refuge) lakes with marginal 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 | 0.000 |
| 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 | 0.000 | 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".