Salmonid thermal habitat contraction in a hydrogeologically complex setting
Bibliographic record
Abstract
Abstract Broadening our understanding of river thermal variability is of paramount importance considering the role temperature plays in aquatic ecosystem health. At the catchment scale, spatial statistical river network models (SSN) are popular for analyses of river temperature, as these are less “data hungry” than other modeling methods, and have offered invaluable insights into how thermal habitats of salmonids may change with climate warming. However, recent work has demonstrated that hydrogeological complexity can disrupt river temperature spatial autocorrelation. We test the prediction that the non‐linearity of hydrological processes inherent in a hydrogeologically complex setting, such as the Miramichi River, invalidates the SSN approach, and a Random Forest (RF) model can overcome these complexities. In all instances, RFs outperformed SSNs when predicting average (TwA) and maximum (TwM) August river temperature during 2017, and were quite robust (TwA and TwM: R2 = 0.93; RMSE = 0.6°C; R2 = 0.91; RMSE = 1.0°C, respectively). We conclude that RF models can capture the inherent non‐linearity of hydrological processes in complex hydrogeologic settings. We examined thermal habitat change for adult and 1+/2+ Atlantic salmon—AS—(Salmo salar), and all age classes of brook trout—BKT—(Salvelinus fontinalis), during August 2017, with thresholds of behavioral thermoregulation specific to the catchment. We assumed a baseline = TwA and investigated river network contraction (km) for TwM. During TwA, all habitat was suggested to be thermally suitable for 1+/2+ AS (<23°C), but 4.2% was unsuitable for adult AS and BKT of all ages (>20°C). For TwM, ~80% of the catchment was predicted to be unsuitable for adult AS and BKT. We examined two boundaries for behavorial thermoregulation in 1+/2+ AS: >23°C and >27°C. For the >23°C boundary, ~27.7% of the catchment is thermally unsuitable during TwM, and 4.9% is thermally unsuitable for the >27°C boundary. TwA in August 2017 was identical to long‐term (1970–1999) July–August TwA, as such these thermal maps will be useful for resource managers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".