Habitat use and movement patterns for a fluvial species, the Arctic grayling, in a watershed impacted by a large reservoir: evidence from otolith microchemistry
Bibliographic record
Abstract
1 Fluvial environments are lost following construction of dams and flooding of river valleys. Rivers flowing into newly created reservoirs are still connected, but whether fluvial species of fish use or migrate through the newly formed lacustrine habitat is not known. We determined chemical composition of otoliths using laser ablation inductively coupled plasma mass spectrometry to assess patterns of movement in Arctic grayling (Thymallus arcticus) from a fluvial system flooded by dam construction to assess the reservoir impact on fish populations. 2 Rivers examined within the upper Peace River watershed in British Columbia had considerable differences in water chemistry. Elemental signatures measured in grayling otoliths were highly correlated to the stream chemistries where the fish were captured. Shifts between chemically distinct environments could be detected for grayling; some fish experienced significant changes in water chemistry while others did not. 3 Discriminant function analyses for otolith chemistry suggested that Arctic grayling formed four main populations: Parsnip watershed, Nation watershed, Omineca watershed and Ingenika watershed. There also appeared to be groupings according to smaller watersheds (subpopulations) within these systems. 4 Synthesis and applications. Construction of the WAC Bennett hydroelectric dam and formation of the Williston Reservoir may have created a barrier for migration of fluvial Upper Peace River Arctic grayling. Populations that were thought to have remained healthy into the 1980s were likely impacted immediately but this was not obvious due to the presence of residual ‘prereservoir’ fish. Natural chemical markers in otoliths revealed that Arctic grayling in our study did not move into the reservoir and may now be restricted only to a number of tributary systems with no interconnectivity. Future management must take account of this new, fragmented distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".