Patterns of fish species distributions replicated across three parallel rivers suggest biotic zonation in response to a longitudinal temperature gradient
Bibliographic record
Abstract
Abstract Environmental gradients determine the distributions of individual species, which, in turn, shape patterns of species assemblage across those gradients. We used species distribution models to study the assemblage of fish species along the three mainstem rivers in the South Saskatchewan River Basin (SSRB) in Alberta, which flow in parallel across an 800‐km longitudinal span and down 1400 m from the Rocky Mountains to the Great Plains of North America. We estimated the similarity of species assemblages along each river to identify general patterns of species assemblage associated with temperature and five other physiochemical variables. Mean July water temperature, which ranged from <11°C at high elevation to >21°C at low elevation, was strongly associated with the presence–absence of most species in the SSRB. We found that high turnover occurred at two locations along the longitudinal gradient: where mean July water temperature was approximately 15°C and where mean July water temperature was approximately 19 or 20°C. There was also an increase in species richness at lower elevations where water temperatures were higher. Models incorporating forecasted changes in water temperature with climate change will likely provide accurate predictions of changes in the diversity and distribution of riverine fish communities across topographically heterogeneous landscapes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".