Spatial Modeling to Project Southern Appalachian Trout Distribution in a Warmer Climate
Bibliographic record
Abstract
Abstract In the southern Appalachian Mountains, the distributions of native brook trout Salvelinus fontinalis and introduced rainbow trout Oncorhynchus mykiss and brown trout Salmo trutta are presently limited by temperature and are expected to be limited further by a warmer climate. To estimate trout habitat in a future, warmer climate, we produced a regional map of wild trout habitat based on information from stream samples, expert knowledge, and suitable land cover. We then developed a quantile regression model of the elevation–latitude boundary for the present distribution of trout; this constitutes a more direct, spatially explicit approach to modeling trout distribution than the use of thermal limits. In combination with a lapse rate model, the boundary model was used to project future wild trout distributions over a range of higher temperatures. If the predictions of the Hadley Centre global circulation model (GCM) are assumed, about 53% of trout habitat would be lost; if the more extreme Canadian Centre GCM is used, 97% would be lost. With increasing temperature, fragmentation would increase, leaving populations in small, isolated patches vulnerable to extirpation because of the decreased likelihood of recolonization. The regional trout habitat map and the models produced here were useful for making these predictions, and the map could be used for assessing the impacts of other regional stressors.
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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 | 0.001 |
| 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".