Spatial variability of thermal regimes and other environmental determinants of stream fish communities in the Great Lakes Basin, Ontario, Canada
Bibliographic record
Abstract
Abstract Temperature is one of the most important environmental variables in stream ecosystems because it affects the growth, survival and distribution of stream biota. This study examined if the spatial variability of thermal regimes and 18 other environmental variables were associated with fish communities in watersheds throughout the Great Lakes Basin (GLB), Ontario. The thermal regimes were defined as regimes 1, 2 and 3 and had maximum water temperatures of 26.4, 28.4 and 23.5°C, and spring warming rates of 0.20, 0.12 and 0.10 °C d−1, respectively. The spatial variability of the thermal regimes (VTR) within the watersheds was summarized into four VTR groups: S1, S2, M23 and M123. Stream sites in S1 watersheds had temperatures characteristic of regime 1 whereas stream sites in S2 watersheds followed regime 2. M23 watersheds had sites with a mix of regimes 2 and 3 whereas M123 watersheds had all three thermal regimes at sites throughout watersheds. Canonical correspondence analysis (CCA) indicated that 16% of the variation in fish communities was related to the spatial VTR in the watersheds. Forward selection CCA indicated that elevation, the S1 VTR group, sparse forest cover, wetland area, base flow index (groundwater discharge potential), flow and industrial stress explained 42% of the variance in the fish communities. Simplified indicator species analysis (ISA) showed that different species could be used as indicators for each of the VTR groups. Human activities such as industrial development, deforestation, groundwater withdrawal and flow alteration all may affect the environmental variables related to stream fish communities. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".