Projecting exceedance of juvenile salmonid thermal maxima in streams under climate change: A crosswalk from lab experiments to riparian restoration
Bibliographic record
Abstract
Abstract Concern over rising water temperatures for freshwater ectotherms has led to application of experimentally derived thermal thresholds to stream temperature models for estimation of streams at high risk of exceeding thermal thresholds under current and future climate conditions. We optimised an approach that links field‐relevant thermal maxima experiments to corresponding stream temperature models and identifies opportunities to reduce stream temperatures through riparian tree growth. We conducted a thermal maxima experiment on cold‐water adapted juvenile Chinook (Oncorhynchus tshawytscha) and coho salmon (Oncorhynchus kisutch) that reflected natural temperatures by using incremental temperature ramping with diel fluctuations (IT‐Dmax) and refit a regionally specific stream temperature model for British Columbia, Canada to directly relate to lab‐derived thresholds. Salmon‐bearing streams across British Columbia were categorised by threshold exceedance risk (i.e., low, moderate, high, severe) based on risk tolerance scenarios for management decision making (i.e., considering a range of prediction intervals [PIs] and stream thermal sensitivities to air temperature). We linked these results directly to riparian management and restoration actions by estimating the potential for riparian tree growth to reduce threshold exceedance risk. Lab‐derived IT‐Dmax endpoints were consistently 24°C (based on the median value between the 7‐day average of the daily maximum and the mean weekly average temperature) across acclimation treatments for both species. Under current conditions, most stream reaches (99.6% using an intermediate risk tolerance scenario) were below the thermal threshold; streams with moderate to high risk of threshold exceedance were projected to increase from 0.4% to 1.5% (total linear stream length of 6,929 km) by end of century. The risk‐averse (high sensitivity, 75% PI) and risk‐tolerant (low sensitivity, 95% PI) scenarios differed by 1,107 km of streams predicted to have a moderate to severe risk of exceeding thresholds by end of century. Maximal riparian tree growth was predicted to shift 670 km of streams from moderate to low exceedance risk under end‐of‐century conditions and intermediate risk tolerance, showing the potential for mitigation from thermal impacts. Our integrative approach tackled several key considerations in identifying streams at high temperature risk for freshwater ectotherms that to date have not been addressed. Furthermore, we showcased the delineation of spatially comprehensive estimates that identify where management mitigation and a specific restoration activity may be most needed to reduce impacts of rising temperatures on rearing salmonids across an extensive region.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".