Linking Spatial Stream Network and Site Occupancy Models to Predict the Distribution of Arctic Grayling in a Mountainous Watershed
Bibliographic record
Abstract
ABSTRACT Temperature is a critical abiotic factor driving the distribution of aquatic ectotherms in thermally heterogenous stream networks. However, comprehensive analyses of co‐located stream temperature and ectotherm telemetry data at ecologically pertinent spatiotemporal scales are not available. Such analyses are essential for predicting shifts in ectotherm distributions in response to climate‐driven changes in the availability of suitable thermal habitats. Our study addresses gaps in the modelling of aquatic species distribution by applying modern analytical techniques to datasets containing co‐located habitat data and animal occurrence data collected over large spatial extents over multiple years. Specifically, our objective was to characterise variation in the availability of thermal habitat and quantify its influence on the distribution of a cold‐water fish, Arctic grayling ( Thymallus arcticus ), during the summer feeding period in the Parsnip River watershed in northern British Columbia. Between 2019 and 2021, we collected air and water temperature data in the Parsnip River and several tributaries to develop a spatial stream network model (SSNM) that was used to characterise the spatiotemporal distribution of water temperatures in the watershed. Over this period, we also tagged and continuously monitored the spatial distribution of adult Arctic grayling using acoustic telemetry. Their detections were analysed with dynamic site occupancy models as a function of co‐located water temperatures predicted by the SSNM, stream magnitude, and stream gradient. Both water temperature and stream magnitude exhibited non‐linear relationships with probability of site occupancy by adult Arctic grayling, whereas no effect of stream gradient was detected. Results suggest a high probability of occupancy (> 0.66 at temperatures ranging from 6.9°C–14.7°C, with a peak at 10.8°C) The predicted distribution of thermal habitat within this range was potentially limiting in only one of the 3 years during the study period. Based on maximum weekly average temperatures, the distribution of thermal habitat with a potential high probability of use during the summer feeding period was reduced from 55% in 2019 and 52% in 2020 to 17% of the accessible watershed length in 2021. Small streams in high elevation tributaries (> 800 m) are important cold‐water habitats, providing Arctic grayling and other cold‐water adapted aquatic ectotherms with thermal refuge under warm conditions. Preservation of high elevation streams and restoration of buffering upslope processes in impacted watersheds should be considered a priority to ensure a future for cold‐water adapted species in watersheds that exceed thermal optima at lower elevations.
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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".