Modelling migratory behaviour and habitat use of fish in a large, uninterrupted river network: A case study of a migratory salmonid
Bibliographic record
Abstract
Abstract Bull trout ( Salvelinus confluentus ) research has historically focused on highly impacted systems or occurred in a reactionary manner following overharvest. Here, we used telemetry and multi‐state capture–recapture modelling to inform management decision‐making for this highly migratory, conservation‐listed species in British Columbia's upper Fraser River watershed (UFW). Our work reduced critical information gaps surrounding the seasonal migration patterns, distribution, and survival rates of fluvial bull trout within the region. By using results of an existing microsatellite DNA analysis, we were able to assign each tagged individual to one of five spawning populations. This assignment allowed us to investigate potential differences in migratory behaviour and seasonal habitat use among the different spawning populations. We identified that the majority of tagged UFW fluvial bull trout made repeated, long‐distance migrations (>300 km one direction) to appropriate spawning, overwintering and foraging habitats. The five populations did not vary in their seasonal transition probabilities at the spatial scale investigated, suggesting key habitats are important to multiple populations. We also detected post‐spawning migration and distribution patterns that suggest UFW bull trout exploit seasonal resource pulses during salmon smolt outmigration. Our results highlight the importance of protecting, restoring and maintaining a diverse assemblage of complex habitats and the natural connections between them (e.g. between tributary spawning habitats and larger rivers) at a spatial scale that supports migratory bull trout life histories. This work provides valuable information for prioritizing conservation actions and identifying restoration opportunities both in the UFW and across the species' range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".