Impacts of co‐occurring environmental changes on Alaskan stream fishes
Bibliographic record
Abstract
Abstract Freshwater fishes are now facing unprecedented environmental changes across their northern ranges, especially due to rapid warming occurring at higher latitudes. However, empirical research that examines co‐occurring environmental effects on northern fish communities remains limited. We used fish community data from 1587 Alaskan stream sites to examine the potential combined and interacting effects of climate change, current weather, habitat, land use, and fire on two community‐level metrics (species richness, relative abundance), and on the distributions of three Alaskan fish species. Our models were 71–76% accurate in predicting the distribution of Alaskan stream fishes using a combination of climate and habitat variables. In contrast to other freshwater ecosystems that are most threatened by land use pressures, we did not detect any evidence for the potential stress of anthropogenic land use or fire on stream fishes. Warming temperatures increased overall community richness and abundance but produced differing responses at the species level. Juvenile salmon presence was positively associated with several climate variables including warmer spring and autumn temperatures and wetter summers. In comparison, warmer seasonal temperatures contributed to declines for northern‐adapted species such as Arctic grayling and Dolly Varden. This study highlights the overarching role of current and changing climate in regulating northern stream fish biodiversity. Although many fish species may benefit from climate change across their northern ranges, localised declines are likely to occur and may prove detrimental for communities with limited fishing portfolios. Climate change adaptation and mitigation strategies customised for rapidly changing northern ecosystems will play an essential role in preserving ecologically unique northern species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".