Linking spatial stream network modeling and telemetry data to investigate thermal habitat use by adult arctic grayling
Bibliographic record
Abstract
River networks have a high amount of thermal habitat heterogeneity which is a critical abiotic factor driving freshwater fish distribution. The fitness repercussions of residing outside a species’ optimal thermal limits, and the resulting behavioural responses to stress, restrict the amount of freshwater habitat available to ectothermic organisms. Changes in ectotherm distribution due to climate-related shifts in thermal habitat availability have been well documented and have been especially pronounced at distributional limits. My objective was to characterize variations in the availability of thermal habitat and quantify its influence on the distribution of a cold-water adapted aquatic ectotherm, Arctic grayling in the Parsnip River watershed in northern British Columbia. The presence of a thermal gradient in the watershed was revealed by a spatial stream network model and its influence on adult Arctic grayling summer distributions was explored with a dynamic site occupancy model using acoustic telemetry data. Results suggest a high probability of occupancy (> 0.75) at temperatures ranging from 8.7-14.2ºC with a peak at 10.9ºC. The distribution of thermal habitat within this range was limiting in only one of the three years during the study period. In 2021 the distribution of thermal habitat with a high probability of use during the study period was reduced to 57% of the accessible watershed length from 89% in 2019 and 87% in 2020. Small streams in high elevation tributaries (e.g., >800 m) are important cold-water sources for Arctic grayling refugia under warm conditions. Increased habitat protections for high elevation streams should be prioritized to ensure a future for cold-water adapted species in the Parsnip River watershed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".