Factors influencing thermal variability and fish distribution in small boreal steams / by Lisa McKee.
Bibliographic record
Abstract
The spatial variability of stream temperature is an important component of habitat within streams providing optimal temperatures for foraging and thermal refugia for sensitive species such as brook trout. Riparian shading and lateral contributions of groundwater through the hyporheic zone are the main contributors to spatial variability in stream temperature. The first two objectives of this study were to quantify thermal variability in stream systems through extensive mapping of streambed temperatures and to evaluate the influence of thermal variability on the stream fish distribution and community structure. The third objective was to examine associations between thermal variability and environmental variables at reach, riparian and catchment scales to identify features that may be used to characterize thermally important stream reaches. A total of 55 sample sites were surveyed during the warmest and driest season for Northwestern Ontario (mid-July - September) in streams from 4 catchments size classes; 1, 3, 5 and 10 km [squared]. Streambed thermal variability occurred on a sub-metre scale with temperature fluctuating up to 5.8 ?C across a transect perpendicular to stream flow. The maximum variability found was 10.1 ?C within a 50 m reach and 12.0 ?C within a 300 m survey. Thermal \nvariability was driven by cold streambed temperatures; 44 of 55 reaches had larger deviations below the mean streambed temperature than above the mean, which is an indication of cool groundwater entering the streambed. Fish species diversity and brook trout abundance was significantly higher in reaches with high thermal variability, while \nrainbow trout abundance was significantly lower. Fish species richness within a reach could be predicted as low (<5) or high (>5), with thermal variability as an independent variable using logistic regression. High (>0.10) or low (<0.10) rainbow trout abundance (fish/m[squared]) could also be predicted using thermal variability. Fish size was not found to be \nassociated to thermal variability. Furthermore, thermal variability was correlated with terrestrial variables associated with groundwater movement, including the amount of adjacent land contributing surface and subsurface runoff to the stream, also known as reach contributing area (RCA). Reaches with large RCAs had significantly higher levels of thermal variability compared to reaches with small RCAs. However, the relationship \nbetween thermal variability and RCA was only found for reaches in the two largest stream catchment size classes (5 and 10 km[squared]) due to the dominance of groundwater during base flow of the two smallest catchments (1 and 3 km[squared]). Areas of low and high thermal variability differed in landscape topography, terrestrial surface roughness, landform geology and streambed permeability, which are all related to groundwater flow. \nIn regions such as Northwestern Ontario, where hydrologic pathways are related to topographic features it is possible to use environmental features, such as RCA, to locate lateral groundwater inputs into streams. This predictive ability allows for identification, management and protection of valued ecosystem components important for the maintenance of ecological integrity of streams. \nStudy area : Nipigon Bay Basin, Northwestern Ontario.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".